Google to Pay only $13 Million for sniffing passwords and emails over your wifi using Street View cars between 2007 – 2010

Posted on by

After nearly a decade in court, Google has agreed to pay $13 million in a class-action lawsuit alleging its Street View program collected people’s private data over wifi from 2007 to 2010. In addition to the moolah, the settlement—filed Friday in San Francisco—also calls for Google to destroy all the collected data and teach people how to encrypt their wifi networks.

A quick refresher. Back when Google started deploying its little Street View cars around our neighborhoods, the company also ended up collecting about 600 GB of emails, passwords, and other payload data from unencrypted wifi networks in over 30 countries. In a 2010 blog, Google said the data collection was a “mistake” after a German data protection group asked to audit the data collected by the cars.

[…]

The basis for the class-action lawsuit was that Google was basically infringing on federal wiretapping laws. Google had argued in a separate case on the same issue, Joffe vs Google, that its “mistake” was legal, as unencrypted wifi are a form of radio communication and thereby, readily accessible by the general public. The courts did not agree, and in 2013 ruled Google’s defense was bunk. And despite Google claiming the collection was a “mistake,” according to CNN, in this particular class-action lawsuit, investigators found that Google engineers created the software and embedded them into Street View cars intentionally.

[…]

If you thought Google would pay out the nose for this particular brand of evil, you’d be mistaken. The class-action netted $13 million, with punitive payments only going to the original 22 plaintiffs—additional class members won’t get anything. The remaining money will be then distributed to eight data privacy and consumer protection organizations. Similarly, another case brought by 38 states on yet again, the same issue, only netted a $7 million settlement.

Source: Google Set to Pay $13 Million in Street View Class-Action Suit

Big Tech faces broad U.S. Justice Department antitrust probe

Posted on by

The U.S. Justice Department said on Tuesday it was opening a broad investigation of major digital technology firms into whether they engage in anticompetitive practices, the strongest sign the Trump administration is stepping up its scrutiny of Big Tech.

The review will look into “whether and how market-leading online platforms have achieved market power and are engaging in practices that have reduced competition, stifled innovation, or otherwise harmed consumers,” the Justice Department said in a statement.

The Justice Department did not identify specific companies but said the review would consider concerns raised about “search, social media, and some retail services online” — an apparent reference to Alphabet Inc, Amazon.com Inc and Facebook Inc, and potentially Apple Inc.

[…]

Senator Richard Blumenthal, a Democrat, said the Justice Department “must now be bold and fearless in stopping Big Tech’s misuse of its monopolistic power. Too long absent and apathetic, enforcers now must prevent privacy abuse, anticompetitive tactics, innovation roadblocks, and other hallmarks of excessive market power.”

In June, Reuters reported the Trump administration was gearing up to investigate whether Amazon, Apple, Facebook and Alphabet’s Google misuse their massive market power, setting up what could be an unprecedented, wide-ranging probe of some of the world’s largest companies.

[…]

The Justice Department said the review “is to assess the competitive conditions in the online marketplace in an objective and fair-minded manner and to ensure Americans have access to free markets in which companies compete on the merits to provide services that users want.”

[…]

“There is growing consensus among venture capitalists and startups that there is a kill zone around Google, Amazon, Facebook and Apple that prevents new startups from entering the market with innovative products and services to challenge these incumbents,” said Representative David Cicilline, a Democrat who heads the subcommittee.

[…]

Senator Marsha Blackburn, a Republican, praised the investigation and said a Senate tech task force she chairs would be looking at how to “foster free markets and competition.”

Source: Big Tech faces broad U.S. Justice Department antitrust probe – Reuters

It’s good to hear that the arguments are not only founded on product pricing but are much more wider ranging and address what exactly makes a monopoly.

Tinder Bypasses Google Play, Revolt Against App Store “Fee” (30% monopolistic arm wrench)

Posted on by

Tinder joined a growing backlash against app store taxes by bypassing Google Play in a move that could shake up the billion-dollar industry dominated by Google and Apple Inc.

The online dating site launched a new default payment process that skips Google Play and forces users to enter their credit card details straight into Tinder’s app, according to new research by Macquarie analyst Ben Schachter. Once a user has entered their payment information, the app not only remembers it, but also removes the choice to swap back to Google Play for future purchases, he wrote.

“This is a huge difference,” Schachter said in an interview. “It’s an incredibly high-margin business for Google bringing in billions of dollars,” he said

The shares of Tinder’s parent company, Match Group Inc., spiked 5% when Schachter’s note was published on Thursday. Shares of Google parent Alphabet Inc. were little changed.

Apple and Google launched their app stores in 2008, and they soon grew into powerful marketplaces that matched the creations of millions of independent developers with billions of smartphone users. In exchange, the companies take as much as 30% of revenue. The app economy is expected to grow to $157 billion in 2022, according to App Annie projections.

As the market expands, a growing revolt has been gaining steam over the past year. Spotify Technology SA filed an antitrust complaint with the European Commission earlier this year, claiming the cut Apple takes amounts to a tax on competitors. Netflix Inc. has recently stopped letting Apple users subscribe via the App Store and Epic Games Inc. said last year it wouldn’t distribute Fortnite, one of the world’s most popular video games, through Google Play.

Source: Tinder (MTCH) Bypasses Google Play, Revolt Against App Store Fee – Bloomberg

Microsoft Bribes U.S. gov with $25 Million to End U.S. Probe Into Bribery Overseas

Posted on by

Microsoft Corp. agreed to pay $25 million to settle U.S. government investigations into alleged bribery by former employees in Hungary.

The software maker’s Hungarian subsidiary entered into a non-prosecution agreement with the U.S. Department of Justice and a cease-and-desist order with the Securities and Exchange Commission, Microsoft said in an email to employees from Chief Legal Officer Brad Smith that was posted Monday on the company’s web site. The case concerned violations of the Foreign Corrupt Practices Act, according to an SEC filing

The Justice Department concluded that between 2013 and June 2015 “a senior executive and some other employees at Microsoft Hungary participated in a scheme to inflate margins in the Microsoft sales channel, which were used to fund improper payments under the FCPA,” Smith wrote in the email.

Microsoft sold software to partners at a discount and the partners then resold the products to the Hungarian government at a higher price. The difference went to fund kickbacks to government officials, the Wall Street Journal reported in 2018. The company fired the employees involved, Smith noted.

[…]

The SEC noted that some Microsoft employees violated the law by engaging in unscrupulous sales practices in Saudi Arabia, Turkey and Thailand.

[…]

The U.S. uses the FCPA to police bribe-paying around the world, in what officials have said is an effort to even the playing field. Since 2005, the government has collected billions of dollars in fines from foreign companies and U.S. firms found to be in violation of the law.

Source: Microsoft Pays $25 Million to End U.S. Probe Into Bribery Overseas – Bloomberg

UK cops want years of data from victims phones for no real reason, but it is being misused

Posted on by

A report (PDF), released today by Big Brother Watch and eight other civil rights groups, has argued that complainants are being subjected to “suspicion-less, far-reaching digital interrogations when they report crimes to police”.

It added: “Our research shows that these digital interrogations have been used almost exclusively for complainants of rape and serious sexual offences so far. But since police chiefs formalised this new approach to victims’ data through a national policy in April 2019, they claim they can also be used for victims and witnesses of potentially any crime.”

The policy referred to relates to the Digital Processing Notices instituted by forces earlier this year, which victims of crime are asked to sign, allowing police to download large amounts of data, potentially spanning years, from their phones. You can see what one of the forms looks like here (PDF).

[…]

The form is 9 pages long and states ‘if you refused permission… it may not be possible for the investigation or prosecution to continue’. Someone in a vulnerable position is unlikely to feel that they have any real choice. This does not constitute informed consent either.

Rape cases dropped over cops’ demands for search

The report described how “Kent Police gave the entire contents of a victim’s phone to the alleged perpetrator’s solicitor, which was then handed to the defendant”. It also outlined a situation where a 12-year-old rape survivor’s phone was trawled, despite a confession from the perpetrator. The child’s case was delayed for months while the Crown Prosecution Service “insisted on an extensive digital review of his personal mobile phone data”.

Another case mentioned related to a complainant who reported being attacked by a group of strangers. “Despite being willing to hand over relevant information, police asked for seven years’ worth of phone data, and her case was then dropped after she refused.”

Yet another individual said police had demanded her mobile phone after she was raped by a stranger eight years ago, even after they had identified the attacker using DNA evidence.

Source: UK cops blasted over ‘disproportionate’ slurp of years of data from crime victims’ phones • The Register

Researchers Reveal That Anonymized Data Is Easy To Reverse Engineer

Posted on by

Researchers at Imperial College London published a paper in Nature Communications on Tuesday that explored how inadequate current techniques to anonymize datasets are. Before a company shares a dataset, they will remove identifying information such as names and email addresses, but the researchers were able to game this system.

Using a machine learning model and datasets that included up to 15 identifiable characteristics—such as age, gender, and marital status—the researchers were able to accurately reidentify 99.98 percent of Americans in an anonymized dataset, according to the study. For their analyses, the researchers used 210 different data sets that were gathered from five sources including the U.S. government that featured information on more than 11 million individuals. Specifically, the researchers define their findings as a successful effort to propose and validate “a statistical model to quantify the likelihood for a re-identification attempt to be successful, even if the disclosed dataset is heavily incomplete.”

[…]Even the hypothetical illustrated by the researchers in the study isn’t a distant fiction. In June of this year, a patient at the University of Chicago Medical Center filed a class-action lawsuit against both the private research university and Google for the former sharing his data with the latter without his consent. The medical center allegedly de-identified the dataset, but still gave Google records with the patient’s height, weight, vital signs, information on diseases they have, medical procedures they’ve undergone, medications they are on, and date stamps. The complaint pointed out that aside from the breach of privacy in sharing intimate data without a patient’s consent, that even if it was in some way anonymized, the tools available to a powerful tech corporation make it pretty easy for them to reverse engineer that information and identify a patient.

“Companies and governments have downplayed the risk of re-identication by arguing that the datasets they sell are always incomplete,” de Montjoye said in a statement. “Our findings contradict this and demonstrate that an attacker could easily and accurately estimate the likelihood that the record they found belongs to the person they are looking for.”

Source: Researchers Reveal That Anonymized Data Is Easy To Reverse Engineer

IBM gives cancer-killing drug AI projects to the open source community

Posted on by

Researchers from IBM’s Computational Systems Biology group in Zurich are working on AI and machine learning (ML) approaches to “help to accelerate our understanding of the leading drivers and molecular mechanisms of these complex diseases,” as well as methods to improve our knowledge of tumor composition.

“Our goal is to deepen our understanding of cancer to equip industries and academia with the knowledge that could potentially one day help fuel new treatments and therapies,” IBM says.

The first project, dubbed PaccMann — not to be confused with the popular Pac-Man computer game — is described as the “Prediction of anticancer compound sensitivity with Multi-modal attention-based neural networks.”

[…]

The ML algorithm exploits data on gene expression as well as the molecular structures of chemical compounds. IBM says that by identifying potential anti-cancer compounds earlier, this can cut the costs associated with drug development.

[…]

The second project is called “Interaction Network infErence from vectoR representATions of words,” otherwise known as INtERAcT. This tool is a particularly interesting one given its automatic extraction of data from valuable scientific papers related to our understanding of cancer.

With roughly 17,000 papers published every year in the field of cancer research, it can be difficult — if not impossible — for researchers to keep up with every small step we make in our understanding.

[…]

INtERAcT aims to make the academic side of research less of a burden by automatically extracting information from these papers. At the moment, the tool is being tested on extracting data related to protein-protein interactions — an area of study which has been marked as a potential cause of the disruption of biological processes in diseases including cancer.

[…]

The third and final project is “pathway-induced multiple kernel learning,” or PIMKL. This algorithm utilizes datasets describing what we currently know when it comes to molecular interactions in order to predict the progression of cancer and potential relapses in patients.

PIMKL uses what is known as multiple kernel learning to identify molecular pathways crucial for categorizing patients, giving healthcare professionals an opportunity to individualize and tailor treatment plans.

PaccMann and INtERAcT‘s code has been released and are available on the projects’ websites. PIMKL has been deployed on the IBM Cloud and the source code has also been released.

Source: IBM gives cancer-killing drug AI project to the open source community | ZDNet

But  now the big question: will they maintain it?

Quantum interference allows huge data sets to be sifted through much more quickly

Posted on by

Contemporary science, medicine, engineering and information technology demand efficient processing of data—still images, sound and radio signals, as well as information coming from different sensors and cameras. Since the 1970s, this has been achieved by means of the Fast Fourier Transform algorithm (FFT). The FFT makes it possible to efficiently compress and transmit data, store pictures, broadcast digital TV, and talk over a mobile phone. Without this algorithm, medical imaging systems based on magnetic resonance or ultrasound would not have been developed. However, it is still too slow for many demanding applications.

To meet this goal, scientists have been trying for years to harness quantum mechanics. This resulted in the development of a quantum counterpart of the FFT, the Quantum Fourier Transform (QFT), which can be realized with a quantum computer. As the quantum computer simultaneously processes all possible values (so-called “superpositions”) of input data, the number of operations decreases considerably.

[…]

Mathematics describes many transforms. One of them is a Kravchuk transform. It is very similar to the FFT, as it allows processing of discrete (e.g. digital) data, but uses Kravchuk functions to decompose the input sequence into the spectrum. At the end of the 1990s, the Kravchuk transform was “rediscovered” in computer science. It turned out to be excellent for image and sound processing. It allowed scientists to develop new and much more precise algorithms for the recognition of printed and handwritten text (including even the Chinese language), gestures, sign language, people, and faces. A dozen years ago, it was shown that this transform is ideal for processing low-quality, noisy and distorted data, and thus it could be used for computer vision in robotics and autonomous vehicles. There is no fast algorithm to compute this transform, but it turns out that quantum mechanics allows one to circumvent this limitation.

“Holy Grail” of computer science

In their article published in Science Advances, scientists from the University of Warsaw—Dr. Magdalena Stobinska and Dr. Adam Buraczewski, scientists from the University of Oxford, and NIST, have shown that the simplest quantum gate, which interferes between two quantum states, essentially computes the Kravchuk transform. Such a gate could be a well-known optical device—a beam splitter, which divides photons between two outputs. When two states of quantum light enter its input ports from two sides, they interfere. For example, two identical photons, which simultaneously enter this device, bunch into pairs and come out together by the same exit port. This is the well-known Hong-Ou-Mandel effect, which can also be extended to states made of many particles. By interfering “packets” consisting of many indistinguishable photons (indistinguishability is very important, as its absence destroys the quantum effect), which encode the information, one obtains a specialized quantum computer that computes the Kravchuk transform.

The experiment was performed in a quantum optical laboratory at the Department of Physics at the University of Oxford, where a special setup was built to produce multiphoton quantum states, so-called Fock states. This laboratory is equipped with TES (Transmission Edge Sensors), developed by NIST, which operate at near-absolute zero temperatures. These detectors possess a unique feature: they can actually count photons. This allows one to precisely read the quantum state leaving the beam splitter and thus, the result of the computation. Most importantly, such a computation of the quantum Kravchuk transform always takes the same time, regardless of the size of the input data set. It is the “Holy Grail” of computer science: an algorithm consisting of just one operation, implemented with a single gate. Of course, in order to obtain the result in practice, one needs to perform the experiment several hundred times to get the statistics. This is how every quantum computer works. However, it does not take long, because the laser produces dozens of millions of multiphoton “packets” per second.

Source: Quantum interference in the service of information technology

The Constellations | IAU

Posted on by

Over half of the 88 constellations the IAU recognizes today are attributed to ancient Greek, which consolidated the earlier works by the ancient Babylonian, Egyptian and Assyrian. Forty eight of the constellations we know were recorded in the seventh and eighth books of Claudius Ptolemy’s Almagest, although the exact origin of these constellations still remains uncertain. Ptolemy’s descriptions are probably strongly influenced by the work of Eudoxus of Knidos in around 350 BC. Between the 16th and 17th century AD, European astronomers and celestial cartographers added new constellations to the 48 previously described by Ptolemy; these new constellations were mainly “new discoveries” made by the Europeans who first explored the southern hemisphere. Those who made particular contributions to the “new” constellations include the Polish-born, German astronomer Johannes Hevelius; three Dutch cartographers, Frederick de Houtman, Pieter Dirksz Keyser and Gerard Mercator; the French astronomer Nicolas Louis de Lacaille; the Flemish mapmaker Petrus Plancius and the Italian navigator Amerigo Vespucci.

IAU and the 88 Constellations

Originally the constellations were defined informally by the shapes made by their star patterns, but, as the pace of celestial discoveries quickened in the early 20th century, astronomers decided it would be helpful to have an official set of constellation boundaries. One reason was to aid in the naming of new variable stars, which brighten and fade rather than shine steadily. Such stars are named for the constellation in which they reside, so it is important to agree where one constellation ends and the next begins.

Eugène Delporte originally listed the 88 “modern” constellations on behalf of the IAU Commission 3 (Astronomical Notations), in Délimitation scientifique des constellations. (Delporte, 1930)

Constellation Figures

In star maps it is common to mark line “patterns” that represent the shapes that give the name to the constellations. However, the IAU defines a constellation by its boundary (indicated by sky coordinates) and not by its pattern and the same constellation may have several variants in its representation.

The constellations should be differentiated from asterisms. Asterisms are patterns or shapes of stars that are not related to the known constellations, but nonetheless are widely recognised by laypeople or in the amateur astronomy community. Examples of asterisms include the seven bright stars in Ursa Major known as “the Plough” in Europe or “the Big Dipper” in America, as well as “the Summer Triangle”, a large triangle, seen in the summer night sky in the northern hemisphere and composed of the bright stars Altair, Deneb and Vega. Whilst a grouping of stars may be officially designated a constellation by the IAU, this does not mean that the stars in that constellation are necessarily grouped together in space. Sometimes stars will be physically close to each other, like the Pleiades, but constellations are generally really a matter of perspective. They are simply our Earth-based interpretation of two dimensional star patterns on the sky made up of stars of many differing brightnesses and distances from Earth.

 

Constellation Names

Each Latin constellation name has two forms: the nominative, for use when talking about the constellation itself, and the genitive, or possessive, which is used in star names. For instance, Hamal, the brightest star in the constellation Aries (nominative form), is also called Alpha Arietis (genitive form), meaning literally “the alpha of Aries”.

The Latin names of all the constellations, their abbreviated names and boundaries can be found in the table below. They are a mix of the ancient Greek patterns recorded by Ptolemy as well as some more “modern” patterns observed later by more modern astronomers.

The IAU adopted three-letter abbreviations of the constellation names at its inaugural General Assembly in Rome in 1922. So, for instance, Andromeda is abbreviated to And whilst Draco is abbreviated to Dra.

Charts and tables

The charts below were produced in collaboration with Sky & Telescope magazine (Roger Sinnott & Rick Fienberg). Alan MacRobert’s constellation patterns, drawn in green in the charts, were influenced by those of H. A. Rey but in many cases were adjusted to preserve earlier traditions. The images are released under the Creative Commons Attribution 3.0 Unported license.

Quick links : And , Ant, Aps, Aqr, Aql, Ara, Ari, Aur, Boo, Cae, Cam, Cnc, CVn, CMa, CMi, Cap, Car, Cas, Cen, Cep, Cet, Cha, Cir, Col, Com, CrA, CrB, Crv, Crt, Cru, Cyg, Del, Dor, Dra, Equ, Eri, For, Gem, Gru, Her, Hor, Hya, Hyi, Ind, Lac, Leo, LMi, Lep, Lib, Lup, Lyn, Lyr, Men, Mic, Mon, Mus, Nor, Oct, Oph, Ori, Pav, Peg, Per, Phe, Pic, Psc, PsA, Pup, Pyx, Ret, Sge, Sgr, Sco, Scl, Sct, Ser, Sex, Tau, Tel, Tri, TrA, Tuc, UMa, UMi, Vel, Vir, Vol, Vul , Chart text legend

Charts Graphical Legend

Charts

Name /
Pronunciation		 Abbr. English Name Genitive /
Pronunciation		 Downloads
Andromeda

an-DRAH-mih-duh

 And the Chained Maiden Andromedae
an-DRAH-mih-dee		 Constellation charts
GIF (117 KB)
PDF (829 KB)
TIF

Constellation boundary
TXT (2 KB)
Antlia

ANT-lee-uh

 Ant the Air Pump Antliae
ANT-lee-ee		 Constellation charts
GIF (111 KB)
PDF (815 KB)
TIF

Constellation boundary
TXT (1 KB)
Apus
APE-us, APP-us		 Aps the Bird of Paradise Apodis

APP-oh-diss

 Constellation charts
GIF (155 KB)
PDF (836 KB)
TIF

Constellation boundary
TXT (1 KB)
Aquarius

uh-QUAIR-ee-us

 Aqr the Water Bearer Aquarii

uh-QUAIR-ee-eye

 Constellation charts
GIF (124 KB)
PDF (879 KB)
TIF

Constellation boundary
TXT (1 KB)
Aquila

ACK-will-uh, uh-QUILL-uh

 Aql the Eagle Aquilae

ACK-will-ee, uh-QUILL-ee

 Constellation charts
GIF (108 KB)
PDF (820KB)
TIF

Constellation boundary
TXT (1 KB)
Ara

AIR-uh, AR-uh

 Ara the Altar Arae

AIR-ee, AR-ee

 Constellation charts
GIF (114 KB)
PDF (807 KB)
TIF

Constellation boundary
TXT (1 KB)
Aries

AIR-eez, AIR-ee-yeez

 Ari the Ram Arietis

uh-RYE-ih-tiss

 Constellation charts
GIF (118 KB)
PDF (805 KB)
TIF

Constellation boundary
TXT (1 KB)
Auriga

aw-RYE-guh

 Aur the Charioteer Aurigae

aw-RYE-ghee

 Constellation charts
GIF (122 KB)
PDF (381 KB)
TIF

Constellation boundary
TXT (1 KB)
Boötes

bo-OH-teez

 Boo the Herdsman Boötis

bo-OH-tiss

 Constellation charts
GIF (147 KB)
PDF (823KB)
TIF

Constellation boundary
TXT (1 KB)
Caelum

SEE-lum

 Cae the Engraving Tool Caeli

SEE-lye

 Constellation charts
GIF (97 KB)
PDF (780 KB)
TIF

Constellation boundary
TXT (1 KB)
Camelopardalis

cuh-MEL-oh- PAR-duh-liss

 Cam the Giraffe Camelopardalis

cuh-MEL-oh- PAR-duh-liss

 Constellation charts
GIF (156 KB)
PDF (888 KB)
TIF

Constellation boundary
TXT (2 KB)
Cancer

CAN-ser

 Cnc the Crab Cancri

CANG-cry

 Constellation charts
GIF (108 KB)
PDF (814 KB)
TIF

Constellation boundary
TXT (1 KB)
Canes Venatici

CANE-eez (CAN-eez) ve-NAT-iss-eye

 CVn the Hunting Dogs Canum Venaticorum

CANE-um (CAN-um) ve-nat-ih-COR-um

 Constellation charts
GIF (106 KB)
PDF (790 KB)
TIF

Constellation boundary
TXT (1 KB)
Canis Major

CANE-iss (CAN-iss) MAY-jer

 CMa the Great Dog Canis Majoris

CANE-iss (CAN-iss) muh-JOR-iss

 Constellation charts
GIF (134 KB)
PDF (849 KB)
TIF

Constellation boundary
TXT (1 KB)
Canis Minor

CANE-iss (CAN-iss) MY-ner

 CMi the Lesser Dog Canis Minoris

CANE-iss (CAN-iss) mih-NOR-iss

 Constellation charts
GIF (83 KB)
PDF (766 KB)
TIF

Constellation boundary
TXT (1 KB)
Capricornus

CAP-rih-CORN-us

 Cap the Sea Goat Capricorni

CAP-rih-CORN-eye

 Constellation charts
GIF (98 KB)
PDF (818 KB)
TIF

Constellation boundary
TXT (1 KB)
Carina

cuh-RYE-nuh, cuh-REE-nuh

 Car the Keel Carinae

cuh-RYE-nee, cuh-REE-nee

 Constellation charts
GIF (143 KB)
PDF (882 KB)
TIF

Constellation boundary
TXT (1 KB)
Cassiopeia

CASS-ee-uh-PEE-uh

 Cas the Seated Queen Cassiopeiae

CASS-ee-uh-PEE-ye

 Constellation charts
GIF (139 KB)
PDF (846 KB)
TIF

Constellation boundary
TXT (1 KB)
Centaurus Cen the Centaur Centauri Constellation charts
GIF (178 KB)
PDF (549 KB)
TIF

Constellation boundary
TXT (1 KB)
Cepheus Cep the King Cephei Constellation charts
GIF (200 KB)
PDF (873 KB)
TIF

Constellation boundary
TXT (2 KB)
Cetus

SEE-tus

 Cet the Sea Monster Ceti

SEE-tie

 Constellation charts
GIF (122 KB)
PDF (873 KB)
TIF

Constellation boundary
TXT (1 KB)
Chamaeleon

cuh-MEAL-yun, cuh-MEAL-ee-un

 Cha the Chameleon Chamaeleontis

cuh-MEAL-ee-ON-tiss

 Constellation charts
GIF (183 KB)
PDF (834 KB)
TIF

Constellation boundary
TXT (1 KB)
Circinus

SER-sin-us

 Cir the Compass Circini

SER-sin-eye

 Constellation charts
GIF (131 KB)
PDF (818 KB)
TIF

Constellation boundary
TXT (1 KB)
Columba

cuh-LUM-buh

 Col the Dove Columbae

cuh-LUM-bee

 Constellation charts
GIF (99 KB)
PDF (797 KB)
TIF

Constellation boundary
TXT (1 KB)
Coma Berenices

COE-muh BER-uh-NICE-eez

 Com the Bernice’s Hair Comae Berenices

COE-mee BER-uh-NICE-eez

 Constellation charts
GIF (101 KB)
PDF (788 KB)
TIF

Constellation boundary
TXT (1 KB)
Corona Australis

cuh-ROE-nuh aw-STRAL-iss3

 CrA the Southern Crown Coronae Australis

cuh-ROE-nee aw-STRAL-iss3

 Constellation charts
GIF (107 KB)
PDF (787 KB)
TIF

Constellation boundary
TXT (1 KB)
Corona Borealis

cuh-ROE-nuh bor-ee-AL-iss3

 CrB the Northern Crown cuh-ROE-nee bor-ee-AL-iss3 Constellation charts
GIF (89 KB)
PDF (771 KB)
TIF

Constellation boundary
TXT (1 KB)
Corvus

COR-vus

 Crv the Crow Corvi

COR-vye

 Constellation charts
GIF (74 KB)
PDF (763 KB)
TIF

Constellation boundary
TXT (1 KB)
Crater

CRAY-ter

 Crt the Cup Crateris

cruh-TEE-riss

 Constellation charts
GIF (75 KB)
PDF (787 KB)
TIF

Constellation boundary
TXT (1 KB)
Crux

CRUCKS, CROOKS

 Cru the Southern Cross Crucis

CROO-siss

 Constellation charts
GIF (119 KB)
PDF (811 KB)
TIF

Constellation boundary
TXT (1 KB)
Cygnus

SIG- SIG-nu

 Cyg the Swan Cygni

SIG-nye

 Constellation charts
GIF (174 KB)
PDF (866 KB)
TIF

Constellation boundary
TXT (1 KB)
Delphinus

del-FINE-us, del-FIN-us

 Del the Dolphin Delphini

del-FINE-eye, del-FIN-eye

 Constellation charts
GIF (81 KB)
PDF (767 KB)
TIF

Constellation boundary
TXT (1 KB)
Dorado

duh-RAH-do

 Dor the Swordfish Doradus

duh-RAH-dus

 Constellation charts
GIF (108 KB)
PDF (795 KB)
TIF

Constellation boundary
TXT (1 KB)
Draco

DRAY-co

 Dra the Dragon Draconis

druh-CONE-iss

 Constellation charts
GIF (153 KB)
PDF (898 KB)
TIF

Constellation boundary
TXT (2 KB)
Equuleus

eh-QUOO-lee-us

 Equ the Little Horse Equulei

eh-QUOO-lee-eye

 Constellation charts
GIF (69 KB)
PDF (749 KB)
TIF

Constellation boundary
TXT (1 KB)
Eridanus

ih-RID-un-us

 Eri the River Eridani

ih-RID-un-eye

 Constellation charts
GIF (167 KB)
PDF (941 KB)
TIF

Constellation boundary
TXT (2 KB)
Fornax

FOR-naks

 For the Furnace Fornacis

for-NAY-siss

 Constellation charts
GIF (108 KB)
PDF (811 KB)
TIF

Constellation boundary
TXT (1 KB)
Gemini

JEM-uh-nye, JEM-uh-nee

 Gem the Twins Geminorum

JEM-uh-NOR-um

 Constellation charts
GIF (122 KB)
PDF (832 KB)
TIF

Constellation boundary
TXT (1 KB)
Grus

GRUSS, GROOS

 Gru the Crane Gruis

GROO-iss

 Constellation charts
GIF (127 KB)
PDF (829 KB)
TIF

Constellation boundary
TXT (1 KB)
Hercules

HER-kyuh-leez

 Her the Hercules Herculis

HER-kyuh-liss

 Constellation charts
GIF (156 KB)
PDF (829 KB)
TIF

Constellation boundary
TXT (1 KB)
Horologium

hor-uh-LOE-jee-um

 Hor the Clock Horologii

hor-uh-LOE-jee-eye

 Constellation charts
GIF (107 KB)
PDF (788 KB)
TIF

Constellation boundary
TXT (1 KB)
Hydra

HIGH-druh

 Hya the Female Water Snake Hydrae

HIGH-dree

 Constellation charts
GIF (127 KB)
PDF (929 KB)
TIF

Constellation boundary
TXT (2 KB)
Hydrus

HIGH-drus

 Hyi the Male Water Snake Hydri

HIGH-dry

 Constellation charts
GIF (143 KB)
PDF (821 KB)
TIF

Constellation boundary
TXT (1 KB)
Indus

IN-dus

 Ind the Indian Indi

IN-dye

 Constellation charts
GIF (131 KB)
PDF (834 KB)
TIF

Constellation boundary
TXT (1 KB)
Lacerta

luh-SER-tuh

 Lac the Lizard Lacertae

luh-SER-tee

 Constellation charts
GIF (124 KB)
PDF (812 KB)
TIF

Constellation boundary
TXT (1 KB)
Leo

LEE-oh

 Leo the Lion Leonis

lee-OH-niss

 Constellation charts
GIF (142 KB)
PDF (820 KB)
TIF

Constellation boundary
TXT (1 KB)
Leo Minor

LEE-oh MY-ner

 LMi the Lesser Lion Leonis Minoris

lee-OH-niss mih-NOR-iss

 Constellation charts
GIF (103 KB)
PDF (799 KB)
TIF

Constellation boundary
TXT (1 KB)
Lepus

LEEP-us, LEP-us

 Lep the Hare Leporis

LEP-or-iss

 Constellation charts
GIF (94 KB)
PDF (787 KB)
TIF

Constellation boundary
TXT (1 KB)
Libra

LEE-bruh, LYE-bruh

 Lib the Scales Librae

LEE-bree, LYE-bree

 Constellation charts
GIF (111 KB)
PDF (819 KB)
TIF

Constellation boundary
TXT (1 KB)
Lupus

LOOP-us

 Lup the Wolf Lupi

LOOP-eye

 Constellation charts
GIF (137 KB)
PDF (857 KB)
TIF

Constellation boundary
TXT (1 KB)
Lynx

LINKS

 Lyn the Lynx Lyncis

LIN-siss

 Constellation charts
GIF (111 KB)
PDF (796 KB)
TIF

Constellation boundary
TXT (1 KB)
Lyra

LYE-ruh

 Lyr the Lyre Lyrae

LYE-ree

 Constellation charts
GIF (91 KB)
PDF (776 KB)
TIF

Constellation boundary
TXT (1 KB)
Mensa

MEN-suh

 Men the Table Mountain Mensae

MEN-see

 Constellation charts
GIF (161 KB)
PDF (827 KB)
TIF

Constellation boundary
TXT (1 KB)
Microscopium

my-cruh-SCOPE-ee-um

 Mic the Microscope Microscopii

my-cruh-SCOPE-ee-eye

 Constellation charts
GIF (87 KB)
PDF (776 KB)
TIF

Constellation boundary
TXT (1 KB)
Monoceros

muh-NAH-ser-us

 Mon the Unicorn Monocerotis

muh-NAH-ser-OH-tiss

 Constellation charts
GIF (110 KB)
PDF (821 KB)
TIF

Constellation boundary
TXT (1 KB)
Musca

MUSS-cuh

 Mus the Fly Muscae

MUSS-see, MUSS-kee

 Constellation charts
GIF (134 KB)
PDF (828 KB)
TIF

Constellation boundary
TXT (1 KB)
Norma

NOR-muh

 Nor the Carpenter’s Square Normae

NOR-mee

 Constellation charts
GIF (118 KB)
PDF (803 KB)
TIF

Constellation boundary
TXT (1 KB)
Octans

OCK-tanz

 Oct the Octant Octantis

ock-TAN-tiss

 Constellation charts
GIF (140 KB)
PDF (821 KB)
TIF

Constellation boundary
TXT (1 KB)
Ophiuchus

OFF-ee-YOO-kus, OAF-ee-YOO-kus

 Oph the Serpent Bearer Ophiuchi

OFF-ee-YOO-kye, OAF-ee-YOO-kye

 Constellation charts
GIF (175 KB)
PDF (854 KB)
TIF

Constellation boundary
TXT (2 KB)
Orion

oh-RYE-un, uh-RYE-un

 Ori the Hunter Orionis

or-eye-OH-niss

 Constellation charts
GIF (181 KB)
PDF (873 KB)
TIF

Constellation boundary
TXT (1 KB)
Pavo

PAY-vo

 Pav the Peacock Pavonis

puh-VOE-niss

 Constellation charts
GIF (143 KB)
PDF (859 KB)
TIF

Constellation boundary
TXT (1 KB)
Pegasus

PEG-us-us

 Peg the Winged Horse Pegasi

PEG-us-eye

 Constellation charts
GIF (136 KB)
PDF (868 KB)
TIF

Constellation boundary
TXT (2 KB)
Perseus

PER-see-us, PER-syoos

 Per the Hero Persei

PER-see-eye

 Constellation charts
GIF (127 KB)
PDF (836 KB)
TIF

Constellation boundary
TXT (1 KB)
Phoenix

FEE-nix

 Phe the Phoenix

 

 Phoenicis

fuh-NICE-iss

 Constellation charts
GIF (119 KB)
PDF (828 KB)
TIF

Constellation boundary
TXT (1 KB)
Pictor

PICK-ter

 Pic the Painter’s Easel Pictoris

pick-TOR-iss

 Constellation charts
GIF (108 KB)
PDF (794 KB)
TIF

Constellation boundary
TXT (1 KB)
Pisces

PICE-eez, PISS-eez

 Psc the Fishes Piscium

PICE-ee-um, PISH-ee-um

 Constellation charts
GIF (87 KB)
PDF (859 KB)
TIF

Constellation boundary
TXT (1 KB)
Piscis Austrinus

PICE-iss (PISS-iss) aw-STRY-nus

 PsA the Southern Fish Piscis Austrini

PICE-iss (PISS-iss) aw-STRY-nye

 Constellation charts
GIF (87 KB)
PDF (778 KB)
TIF

Constellation boundary
TXT (1 KB)
Puppis

PUP-iss

 Pup the Stern Puppis

PUP-iss

 Constellation charts
GIF (185 KB)
PDF (868 KB)
TIF

Constellation boundary
TXT (1 KB)
Pyxis

PIX-iss

 Pyx the Compass Pyxidis

PIX-ih-diss

 Constellation charts
GIF (84 KB)
PDF (775 KB)
TIF

Constellation boundary
TXT (1 KB)
Reticulum

rih-TICK-yuh-lum

 Ret the Reticle Reticuli

rih-TICK-yuh-lye

 Constellation charts
GIF (107 KB)
PDF (786 KB)
TIF

Constellation boundary
TXT (1 KB)
Sagitta

suh-JIT-uh

 Sge the Arrow Sagittae

suh-JIT-ee

 Constellation charts
GIF (90 KB)
PDF (773 KB)
TIF

Constellation boundary
TXT (1 KB)
Sagittarius

SAJ-ih-TARE-ee-us

 Sgr the Archer Sagittarii

SAJ-ih-TARE-ee-eye

 Constellation charts
GIF (163 KB)
PDF (878 KB)
TIF

Constellation boundary
TXT (1 KB)
Scorpius

SCOR-pee-us

 Sco the Scorpion Scorpii

SCOR-pee-eye

 Constellation charts
GIF (194 KB)
PDF (874 KB)
TIF

Constellation boundary
TXT (1 KB)
Sculptor

SCULP-ter

 Scl the Sculptor Sculptoris

sculp-TOR-iss

 Constellation charts
GIF (119 KB)
PDF (810 KB)
TIF

Constellation boundary
TXT (1 KB)
Scutum

SCOOT-um, SCYOOT-um

 Sct the Shield Scuti

SCOOT-eye, SCYOOT-eye

 Constellation charts
GIF (120 KB)
PDF (784 KB)
TIF

Constellation boundary
TXT (1 KB)
Serpens

SER-punz

 Ser the Serpent Serpentis

ser-PEN-tiss

 Constellation charts (Serpens Caput)
GIF (112 KB)
PDF (780 KB)
TIF

Constellation boundary (Serpens Caput)
TXT (1 KB)

Constellation charts (Serpens Cauda)
GIF (126 KB)
PDF (791 KB)
TIF

Constellation boundary (Serpens Cauda)
TXT (1 KB)
Sextans

SEX-tunz

 Sex the Sextant Sextantis

sex-TAN-tiss

 Constellation charts
GIF (83 KB)
PDF (782 KB)
TIF

Constellation boundary
TXT (1 KB)
Taurus

TOR-us

 Tau the Bull Tauri

TOR-eye

 Constellation charts
GIF (115 KB)
PDF (832 KB)
TIF

Constellation boundary
TXT (1 KB)
Telescopium Tel the Telescope Telescopii Constellation charts
GIF (148 KB)
PDF (834 KB)
TIF

Constellation boundary
TXT (1 KB)
Triangulum

try-ANG-gyuh-lum

 Tri the Triangle Trianguli

try-ANG-gyuh-lye

 Constellation charts
GIF (89 KB)
PDF (764 KB)
TIF

Constellation boundary
TXT (1 KB)
Triangulum Australe

try-ANG-gyuh-lum aw-STRAL-ee

 TrA the Southern Triangle Trianguli Australis

try-ANG-gyuh-lye aw-STRAL-iss

 Constellation charts
GIF (124 KB)
PDF (815 KB)
TIF

Constellation boundary
TXT (1 KB)
Tucana

too-KAY-nuh, too-KAH-nuh

 Tuc the Toucan Tucanae

too-KAY-nee, too-KAH-nee

 Constellation charts
GIF (127 KB)
PDF (806 KB)
TIF

Constellation boundary
TXT (1 KB)
Ursa Major

ER-suh MAY-jur

 UMa the Great Bear Ursae Majoris

ER-suh muh-JOR-iss

 Constellation charts
GIF (174 KB)
PDF (885 KB)
TIF

Constellation boundary
TXT (1 KB)
Ursa Minor

ER-suh MY-ner

 UMi the Little Bear Ursae Minoris

ER-suh mih-NOR-iss

 Constellation charts
GIF (135 KB)
PDF (800 KB)
TIF

Constellation boundary
TXT (1 KB)
Vela

VEE-luh, VAY-luh

 Vel the Sails Velorum

vee-LOR-um, vuh-LOR-um

 Constellation charts
GIF (131 KB)
PDF (850 KB)
TIF

Constellation boundary
TXT (1 KB)
Virgo

VER-go

 Vir the Maiden Virginis

VER-jin-iss

 Constellation charts
GIF (98 KB)
PDF (831 KB)
TIF

Constellation boundary
TXT (1 KB)
Volans

VOH-lanz

 Vol the Flying Fish Volantis

vo-LAN-tiss

 Constellation charts
GIF (123 KB)
PDF (812 KB)
TIF

Constellation boundary
TXT (1 KB)
Vulpecula

vul-PECK-yuh-luh

 Vul the Fox Vulpeculae

vul-PECK-yuh-lee

 Constellation charts
GIF (124 KB)
PDF (805 KB)
TIF

Constellation boundary
TXT (1 KB)


Charts Text Legend

Each constellation comes with the following basic information:

  1. Name
  2. Pronunciation of the name
  3. Abbreviation
  4. English Name
  5. Genitive
  6. Pronunciation of the genitive
  7. Chart for screen view (GIF)
  8. Chart for printing (PDF in A4 format)
  9. Boundary coordinates (TXT)

Explanation of the fields:

  1. The name is the Latin name adopted by the International Astronomical Union in 1930.
  2. Pronunciation as described above.
  3. Abbreviation, the standard three-letter form of the Latin name.
  4. The popular name in English.
  5. The genitive is the possessive form of the constellation’s name in Latin. For example, alpha Orionis is the alpha star in the constellation of Orion.
  6. Pronunciation as described above.
  7. Chart in GIF format, 1000 pixels wide.
  8. Chart in PDF format, to be printed in A4 format.
  9. A text file containing a set of coordinates that defines the boundaries of the constellations in the sky. The format is:
    HH MM SS.SSSS| DD.DDDDDDD|XXX

    Where:
    HH MM SS.SSSS defines the right ascension hour, minute and second with J2000 coordinates
    DD.DDDDDDD defines the declination with J2000 coordinates
    XXX is the abbreviation of the constellation name
    | is the separator of the fields

    Example:
    22 57 51.6729| 35.1682358|AND

Source: The Constellations | IAU

Every Visible Star in the Night Sky, in One Giant Map

Posted on by
Visible Stars in the Night Sky Map

Stars have served as a basis for navigation for thousands of years. Polaris, also dubbed the North Star in the Ursa Minor constellation, is arguably one of the most influential, even though it sits 434 light years away.

[…]

n the star map above, the orange lines denote the twelve signs of the Zodiac, each found roughly along the same band from 10° to -30° longitude. These Zodiac alignments, along with planetary movements, form the basis of astrology, which has been practiced across cultures to predict significant events. While the scientific method has widely demonstrated that astrology doesn’t hold much validity, many people still believe in it today.

The red lines on the visualization signify the constellations officially recognized by the International Astronomical Union (IAU) in 1922. Its ancient Greek origins are recorded on the same map as the blue lines, from which the modern constellation boundaries are based. Here’s a deeper dive into all 88 IAU constellations:

(Source: International Astronomical Union)

[…]

We now know that the night sky isn’t as static as people used to believe. Although it’s Earth’s major pole star today, Polaris was in fact off-kilter by roughly 8° a few thousand years ago. Our ancestors saw the twin northern pole stars, Kochab and Pherkad, where Polaris is now.

This difference is due to the Earth’s natural axial tilt. Eight degrees may not seem like much, but because of this angle, the constellations we gaze at today are the same, yet completely different from the ones our ancestors looked up at.

If you liked exploring this star map, be sure to check out the geology of Mars from the same designer.

Source: Every Visible Star in the Night Sky, in One Giant Map

Google and Facebook might be tracking your porn history, researchers warn

Posted on by

Being able to access porn on the internet might be convenient, but according to researchers it’s not without its security risks. And they’re not just talking about viruses.

Researchers at Microsoft, Carnegie Mellon University and the University of Pennsylvania analyzed 22,484 porn sites and found that 93% leak user data to a third party. Normally, for extra protection when surfing the web, a user might turn to incognito mode. But, the researchers said, incognito mode only ensures that your browsing history is not stored on your computer.

According to a study released Monday, Google was the No. 1 third-party company. The research found that Google, or one of its subsidiaries like the advertising platform DoubleClick, had trackers on 74% of the pornography sites examined. Facebook had trackers on 10% of the sites.

“In the US, many advertising and video hosting platforms forbid ‘adult’ content. For example, Google’s YouTube is the largest video host in the world, but does not allow pornography,” the researchers wrote. “However, Google has no policies forbidding websites from using their code hosting (Google APIs) or audience measurement tools (Google Analytics). Thus, Google refuses to host porn, but has no limits on observing the porn consumption of users, often without their knowledge.”

Google didn’t immediately respond to requests for comment.

“We don’t want adult websites using our business tools since that type of content is a violation of our Community Standards. When we learn that these types of sites or apps use our tools, we enforce against them,” Facebook spokesperson Joe Osborne said in an email Thursday.

Elena Maris, a Microsoft researcher who worked on the study, told The New York Times the “fact that the mechanism for adult site tracking” is so similar to online retail should be “a huge red flag.”

“This isn’t picking out a sweater and seeing it follow you across the web,” Maris said. “This is so much more specific and deeply personal.”

Source: Google and Facebook might be tracking your porn history, researchers warn – CNET

Permission-greedy apps delayed Android 6 upgrade so they could harvest more user data

Posted on by

Android app developers intentionally delayed updating their applications to work on top of Android 6.0, so they could continue to have access to an older permission-requesting mechanism that granted them easy access to large quantities of user data, research published by the University of Maryland last month has revealed.

The central focus of this research was the release of Android (Marshmallow) 6.0 in October 2015. The main innovation added in Android 6.0 was the ability for users to approve app permissions on a per-permission basis, selecting which permissions they wanted to allow an app to have.

[…]

Google gave app makers three years to update

As the Android ecosystem grew, app developers made a habit of releasing apps that requested a large number of permissions, many of which their apps never used, and which many developers were using to collect user data and later re-selling it to analytics and data tracking firms.

This changed with the release of Android 6.0; however, fearing a major disruption in its app ecosystem, Google gave developers three years to update their apps to work on the newer OS version.

This meant that despite users running a modern Android OS version — like Android 6, 7, or 8 — apps could declare themselves as legacy apps (by declaring an older Android Software Development Kit [SDK]) and work with the older permission-requesting mechanism that was still allowing them to request blanket permissions.

Two-year-long experiment

In research published in June, two University of Maryland academics say they conducted tests between April 2016 and March 2018 to see how many apps initially coded to work on older Android SDKs were updated to work on the newer Android 6.0 SDK.

The research duo says they installed 13,599 of the most popular Android apps on test devices. Each month, the research team would update the apps and scan the apps’ code to see if they were updated for the newer Android 6.0 release.

“We find that an app’s likelihood of delaying upgrade to the latest platform version increases with an increase in the ratio of dangerous permissions sought by the apps, indicating that apps prefer to retain control over access to the users’ private information,” said Raveesh K. Mayya and Siva Viswanathan, the two academics behind the research.

[…]

Additional details about this research can be found in a white paper named “Delaying Informed Consent: An Empirical Investigation of Mobile Apps’ Upgrade Decisions” that was presented in June at the 2019 Workshop on the Economics of Information Security in Boston.

Source: Permission-greedy apps delayed Android 6 upgrade so they could harvest more user data | ZDNet

Humans may be able to live on Mars within walls of aerogel – a wonder material that can trap heat and block radiation

Posted on by

We may be able to survive and live on Mars in regions protected by thin ceilings of silica aerogel, a strong lightweight material that insulates heat and blocks harmful ultraviolet radiation while weighing almost nothing.

Researchers at Harvard University in the US, NASA, and the University of Edinburgh in Scotland envision areas of Mars enclosed by two to three-centimetre-thick walls of silica aerogel. The strange material is ghost-like in appearance, and although it’s up to 99.98 per cent air, it’s actually a solid.

Aerogels come in various shapes and forms with their own mix of properties. Typically, they are made from sucking out the liquid in a gel using something called a supercritical dryer device. The resulting aerogel consists of pockets of air, and is therefore ultralight and can be capable of trapping heat. It can also be made hydrophobic or semi-porous as needed.

The semitransparent solid, therefore, has odd properties that may just help humans colonize the Red Planet. The solid silica can be manufactured to block out, say, dangerous UV rays while allowing visible light through.

However, it’s the trapping of heat that is most interesting here. When the boffins shone a lamp onto a thin block of silica aerogel, measuring less than 3cm thick, they found that the surface beneath the material warmed up to 65 degrees Celsius (that’s 150 degrees Fahrenheit for you Americans), high enough, of course, to melt ice into water. The results were published in Nature Astronomy on Monday.

Welcome to the Hotel Aerogel

The academics reckon if a region of ice near the higher latitudes of Mars was covered with a layer of aerogel, then the frosty ground would melt to produce liquid water as the environment heats up. It’d also be warm enough for humans to live and farm food in order to survive in the otherwise harsh, acrid conditions elsewhere the planet.

“The ideal place for a Martian outpost would have plentiful water and moderate temperatures,” said Laura Kerber, co-author of the paper and a geologist at NASA’s Jet Propulsion Laboratory. “Mars is warmer around the equator, but most of the water ice is located at higher latitudes. Building with silica aerogel would allow us to artificially create warm environments where there is already water ice available.”

Source: Humans may be able to live on Mars within walls of aerogel – a wonder material that can trap heat and block radiation • The Register

Machine learning has been used to automatically translate long-lost languages

Posted on by

Jiaming Luo and Regina Barzilay from MIT and Yuan Cao from Google’s AI lab in Mountain View, California. This team has developed a machine-learning system capable of deciphering lost languages, and they’ve demonstrated it by having it decipher Linear B—the first time this has been done automatically. The approach they used was very different from the standard machine translation techniques.

First some background. The big idea behind machine translation is the understanding that words are related to each other in similar ways, regardless of the language involved.

So the process begins by mapping out these relations for a specific language. This requires huge databases of text. A machine then searches this text to see how often each word appears next to every other word. This pattern of appearances is a unique signature that defines the word in a multidimensional parameter space. Indeed, the word can be thought of as a vector within this space. And this vector acts as a powerful constraint on how the word can appear in any translation the machine comes up with.

These vectors obey some simple mathematical rules. For example: king – man + woman = queen. And a sentence can be thought of as a set of vectors that follow one after the other to form a kind of trajectory through this space.

The key insight enabling machine translation is that words in different languages occupy the same points in their respective parameter spaces. That makes it possible to map an entire language onto another language with a one-to-one correspondence.

In this way, the process of translating sentences becomes the process of finding similar trajectories through these spaces. The machine never even needs to “know” what the sentences mean.

This process relies crucially on the large data sets. But a couple of years ago, a German team of researchers showed how a similar approach with much smaller databases could help translate much rarer languages that lack the big databases of text. The trick is to find a different way to constrain the machine approach that doesn’t rely on the database.

Now Luo and co have gone further to show how machine translation can decipher languages that have been lost entirely. The constraint they use has to do with the way languages are known to evolve over time.

The idea is that any language can change in only certain ways—for example, the symbols in related languages appear with similar distributions, related words have the same order of characters, and so on. With these rules constraining the machine, it becomes much easier to decipher a language, provided the progenitor language is known.  

Luo and co put the technique to the test with two lost languages, Linear B and Ugaritic. Linguists know that Linear B encodes an early version of ancient Greek and that Ugaritic, which was discovered  in 1929, is an early form of Hebrew.

Given that information and the constraints imposed by linguistic evolution, Luo and co’s machine is able to translate both languages with remarkable accuracy. “We were able to correctly translate 67.3% of Linear B cognates into their Greek equivalents in the decipherment scenario,” they say. “To the best of our knowledge, our experiment is the first attempt of deciphering Linear B automatically.”

That’s impressive work that takes machine translation to a new level. But it also raises the interesting question of other lost languages—particularly those that have never been deciphered, such as Linear A.

In this paper, Linear A is conspicuous by its absence. Luo and co do not even mention it, but it must loom large in their thinking, as it does for all linguists. Yet significant breakthroughs are still needed before this script becomes amenable to machine translation.

For example, nobody knows what language Linear A encodes. Attempts to decipher it into ancient Greek have all failed. And without the progenitor language, the new technique does not work.

But the big advantage of machine-based approaches is that they can test one language after another quickly without becoming fatigued. So it’s quite possible that Luo and co might tackle Linear A with a brute-force approach—simply attempt to decipher it into every language for which machine translation already operates.

 

Source: Machine learning has been used to automatically translate long-lost languages – MIT Technology Review

Bulb smart meters in England wake up from comas miraculously speaking fluent Welsh

Posted on by

Smart meters in England are suddenly switching to Welsh language displays, much to the confusion of owners.

Several people report that the meters, made by energy provider Bulb, are spontaneously opting for Welsh instead of English, sometimes after freezing and being restarted. This would be unhelpful even for many residents of Wales, but the problem has been seen as far east as West Sussex.

The issue is fixable, although choosing the right options is easier if you speak a bit of Welsh. Anyone remember the fun of switching your mate’s Nokia to Finnish language menus?

This seems to be the latest in a string of issues suffered by Bulb, although to be fair the firm is not the first to be stumped by the stupidity of smart meters.

Last month it updated customers who were having problems with the meters’ “In-Home Display” – a small screen connected to the meter that is meant to show electricity usage and costs. Bulb now reckons 85 per cent of these devices will link to the meter immediately: “And the majority of those that don’t connect first time can now be fixed remotely.”

It is also dealing with a problem of automatic, monthly readings not appearing on accounts by taking daily readings, which apparently have a different process.

Source: Bulb smart meters in England wake up from comas miraculously speaking fluent Welsh • The Register

Evite Invites Over 100 Million People to Their Data Breach – with cleartext passwords

Posted on by

“In April 2019, the social planning website for managing online invitations Evite identified a data breach of their systems. Upon investigation, they found unauthorised access to a database archive dating back to 2013. The exposed data included a total of 101 million unique email addresses, most belonging to recipients of invitations. Members of the service also had names, phone numbers, physical addresses, dates of birth, genders and passwords stored in plain text exposed. The data was provided to HIBP by a source who requested it be attributed to “JimScott.Sec@protonmail.com”.”

Source: Evite Invites Over 100 Million People to Their Data Breach

It’s 2019 and people still store personal information in plain text?!

Search for them in your emailbox – you may have received evites from others instead of having made an account, in which case you are also in the data breach

Good luck deleting someone’s private info from a trained neural network – it’s likely to bork the whole thing

Posted on by

AI systems have weird memories. The machines desperately cling onto the data they’ve been trained on, making it difficult to delete bits of it. In fact, they often have to be completely retrained from scratch with the newer, smaller dataset.

That’s no good in an age where individuals can request their personal data be removed from company databases under the EU GDPR rules. How do you remove a person’s data from a machine learning that has already been trained? A 2017 research paper by law and policy academics hinted that it may even be impossible.

“Deletion is difficult because most machine learning models are complex black boxes so it is not clear how a data point or a set of data point is really being used,” James Zou, an assistant professor of biomedical data science at Stanford University, told The Register.

In order to leave out specific data, models will often have to be retrained with the newer, smaller dataset. That’s a pain as it costs money and time.

The research, led by Antonio Ginart, a PhD student at Stanford University, studied the problem of trying to delete data in machine learning models and managed to craft two “provably deletion efficient algorithms” to remove data across six different datasets for k-means clustering models, a machine learning method to develop classifiers. The results have been released in a paper in arXiv this week.

The trick is to assess the impacts of deleting data from a trained model. In some cases, it can lead to a decrease in the system’s performance.

“First, quickly check to see if deleting a data point would have any effect on the machine learning model at all – there are settings where there’s no effect and so we can perform this check very efficiently. Second, see if the data to be deleted only affects some local component of the learning system and just update locally,” Zou explained.

It seems to work okay for k-means clustering models under certain circumstances, when the data can be more easily separated. But when it comes to systems that aren’t deterministic like modern deep learning models, it’s incredibly difficult to delete data.

Zou said it isn’t entirely impossible, however. “We don’t have tools just yet but we are hoping to develop these deletion tools in the next few months.” ®

Source: Good luck deleting someone’s private info from a trained neural network – it’s likely to bork the whole thing • The Register

Galileo Satellite Positioning Service Outage

Posted on by

The Galileo satellite positioning service is currently unavailable, with all satellites marked as in outage . Galileo is the European-built and operated alternative to GPS. The outage is being attributed to problems at the Precise Timing Facility in Italy. The availability of multiple Global Navigation Satellite Systems (GNSS) and the relative newness of Galileo (the system is still under construction and only the newest GNSS receivers will track it) means that it is likely that few users will see an impact but the problem highlights our potential vulnerability to the loss of positioning and timing services available through GNSS.

Source: Galileo Satellite Positioning Service Outage – Slashdot

Microsoft Office 365: Banned in German schools over privacy fears

Posted on by

Schools in the central German state of Hesse have been have been told it’s now illegal to use Microsoft Office 365.

The state’s data-protection commissioner has ruled that using the popular cloud platform’s standard configuration exposes personal information about students and teachers “to possible access by US officials”.

That might sound like just another instance of European concerns about data privacy or worries about the current US administration’s foreign policy.

But in fact the ruling by the Hesse Office for Data Protection and Information Freedom is the result of several years of domestic debate about whether German schools and other state institutions should be using Microsoft software at all.

Besides the details that German users provide when they’re working with the platform, Microsoft Office 365 also transmits telemetry data back to the US.

Last year, investigators in the Netherlands discovered that that data could include anything from standard software diagnostics to user content from inside applications, such as sentences from documents and email subject lines. All of which contravenes the EU’s General Data Protection Regulation, or GDPR, the Dutch said.

Germany’s own Federal Office for Information Security also recently expressed concerns about telemetry data that the Windows operating system sends.

To allay privacy fears in Germany, Microsoft invested millions in a German cloud service, and in 2017 Hesse authorities said local schools could use Office 365. If German data remained in the country, that was fine, Hesse’s data privacy commissioner, Michael Ronellenfitsch, said.

But in August 2018 Microsoft decided to shut down the German service. So once again, data from local Office 365 users would be data transmitted over the Atlantic. Several US laws, including 2018’s CLOUD Act and 2015’s USA Freedom Act, give the US government more rights to ask for data from tech companies.

It’s actually simple, Austrian digital-rights advocate Max Schrems, who took a case on data transfers between the EU and US to the highest European court this week, tells ZDNet.

School pupils are usually not able to give consent, he points out. “And if data is sent to Microsoft in the US, it is subject to US mass-surveillance laws. This is illegal under EU law.”

Source: Microsoft Office 365: Banned in German schools over privacy fears | ZDNet

Microsoft tells resellers: ‘We listened to you, and we have acted’ (PS: Plz keep making us money)

Posted on by

Faced with continued rumbles of discontent from its reseller network on the eve of its Inspire conference, Microsoft has climbed down from plans to pull free software licences from its channel chums.

Doubtless fearful of a keynote sabotaged by a baying mob of angry resellers, Microsoft corporate veep for commercial partners Gavriella Schuster was tasked with the job of backing down.

Thanking its besuited middlemen and woman for “sharing your feedback with us”, Schuster confirmed the kindly corporation had “made the decision to roll back all planned changes related to internal use rights and competency timelines”.

So that 1 July 2020 retirement of the internal use rights? Not going to happen. For now.

Schuster blustered that “a thorough review” had taken place over the, er, days since the company dispensed the bad news and said: “We listened to you, and we have acted.”

The veep sadly missed out the words: “We looked at what annoying those who sell our stuff would do to our bottom line” in the latter comment. Fixed it for you.

Source: Microsoft tells resellers: ‘We listened to you, and we have acted’ (PS: Plz keep making us money) • The Register

Bitpoint cryptocurrency exchange hacked for $32 million

Posted on by

Japan-based cryptocurrency exchange Bitpoint announced it lost 3.5 billion yen (roughly $32 million) worth of cryptocurrency assets after a hack that happened late yesterday, July 11.

The exchange suspended all deposits and withdrawals this morning to investigate the hack, it said in a press release.

Thoroughly compromised

In a more detailed document released by RemixPoint, the legal entity behind Bitpoint, the company said that hackers stole funds from both of its “hot” and “cold” wallets. This suggests the exchange’s network was thoroughly compromised.

Hot wallets are used to store funds for current transactions, while the cold wallets are offline devices storing emergency and long-term funds.

Bitpoint reported the attackers stole funds in five cryptocurrencies, including Bitcoin, Bitcoin Cash, Litecoin, Ripple, and Ethereal.

The exchange said it detected the hack because of errors related to the remittance of Ripple funds to customers. Twenty-seven minutes after detecting the errors, Bitpoint admins realized they had been hacked, and three hours later, they discovered thefts from other cryptocurrency assets.

Another three and a half hours later, after a meeting with management, the exchange shut down, and law enforcement notified.

Two-third of stolen funds belonged to customers

The exchange also said that 2.5 billion yen ($23 million) of the total 3.5 billion yen ($32 million) that were stolen were customer funds, while the rest were funds owned by the exchange itself, as reserve funds and profits from past activity.

Source: Bitpoint cryptocurrency exchange hacked for $32 million | ZDNet

FTC Fines Facebook $5 Billion for Cambridge Analytica – not  very much considering earnings – and does not curtail future breaches

Posted on by

The Federal Trade Commission, which has been investigating Facebook in the wake of its massive Cambridge Analytica scandal, has voted to approve levying a massive $5 billion fine against the social media giant, according to reporting in both the Wall Street Journal and the Washington Post. It’s the single largest fine against a tech company by the FTC to date, but its inadequacy to curtail future breaches of this sort already has progressive lawmakers furious

Facebook was aware of a fine of this magnitude potentially coming down the pike for some time, and braced for a hit between $3 billion and $5 billion. The approval vote—which reportedly split down party lines, with three Republicans voting in favor and two Democrats against—was on the higher end of the expected spectrum.

This is expected to cap the agency’s investigation into the data-mining scandal that compromised up to 87 million Facebook users’ personal data. The data was originally harvested using a seemingly benign quiz app on the platform but was later potentially used by Cambridge Analytica, a political consultancy, for the unrelated purpose of political ad targeting.

[…]

While massive by the standards of tech companies, which too frequently get off with a slap on the wrist of lax data privacy practices which endanger users, the FTC’s fine still represents less than a third of the company’s $15.08 billion earnings from just the first quarter of this year.

Source: FTC Fines Facebook $5 Billion, Democrats Call It a Failure

Palantir’s Top-Secret User Manual for Cops shows how easily they can find scary amounts of information on you and your friends

Posted on by

Through a public record request, Motherboard has obtained a user manual that gives unprecedented insight into Palantir Gotham (Palantir’s other services, Palantir Foundry, is an enterprise data platform), which is used by law enforcement agencies like the Northern California Regional Intelligence Center. The NCRIC serves around 300 communities in northern California and is what is known as a “fusion center,” a Department of Homeland Security intelligence center that aggregates and investigates information from state, local, and federal agencies, as well as some private entities, into large databases that can be searched using software like Palantir.

Fusion centers have become a target of civil liberties groups in part because they collect and aggregate data from so many different public and private entities. The US Department of Justice’s Fusion Center Guidelines list the following as collection targets:

1562941666896-Screen-Shot-2019-07-12-at-102230-AM
Data via US Department of Justice. Chart via Electronic Information Privacy Center.
1562940862696-Screen-Shot-2019-07-12-at-101110-AM
A flow chart that explains how cops can begin to search for records relating to a single person.

The guide doesn’t just show how Gotham works. It also shows how police are instructed to use the software. This guide seems to be specifically made by Palantir for the California law enforcement because it includes examples specific to California. We don’t know exactly what information is excluded, or what changes have been made since the document was first created. The first eight pages that we received in response to our request is undated, but the remaining twenty-one pages were copyrighted in 2016. (Palantir did not respond to multiple requests for comment.)

The Palantir user guide shows that police can start with almost no information about a person of interest and instantly know extremely intimate details about their lives. The capabilities are staggering, according to the guide:

  • If police have a name that’s associated with a license plate, they can use automatic license plate reader data to find out where they’ve been, and when they’ve been there. This can give a complete account of where someone has driven over any time period.
  • With a name, police can also find a person’s email address, phone numbers, current and previous addresses, bank accounts, social security number(s), business relationships, family relationships, and license information like height, weight, and eye color, as long as it’s in the agency’s database.
  • The software can map out a person’s family members and business associates of a suspect, and theoretically, find the above information about them, too.

All of this information is aggregated and synthesized in a way that gives law enforcement nearly omniscient knowledge over any suspect they decide to surveil.

[…]

In order for Palantir to work, it has to be fed data. This can mean public records like business registries, birth certificates, and marriage records, or police records like warrants and parole sheets. Palantir would need other data sources to give police access to information like emails and bank account numbers.

“Palantir Law Enforcement supports existing case management systems, evidence management systems, arrest records, warrant data, subpoenaed data, RMS or other crime-reporting data, Computer Aided Dispatch (CAD) data, federal repositories, gang intelligence, suspicious activity reports, Automated License Plate Reader (ALPR) data, and unstructured data such as document repositories and emails,” Palantir’s website says.

Some data sources—like marriage, divorce, birth, and business records—also implicate other people that are associated with a person personally or through family. So when police are investigating a person, they’re not just collecting a dragnet of emails, phone numbers, business relationships, travel histories, etc. about one suspect. They’re also collecting information for people who are associated with this suspect.

Source: Revealed: This Is Palantir’s Top-Secret User Manual for Cops – VICE

It turns out Bystanders do Help Strangers in Need

Posted on by
Research dating back to the late 1960s documents how the great majority of people who witness crimes or violent behavior refuse to intervene.

Psychologists dubbed this non-response as the “bystander effect”—a phenomenon which has been replicated in scores of subsequent psychological studies. The “bystander effect” holds that the reason people don’t intervene is because we look to one another. The presence of many bystanders diffuses our own sense of personal responsibility, leading people to essentially do nothing and wait for someone else to jump in.

Past studies have used police reports to estimate the effect, but results ranged from 11 percent to 74 percent of incidents being interventions. Now, widespread surveillance cameras allow for a new method to assess real-life human interactions. A new study published this year in the American Psychologist finds that this well-established bystander effect may largely be a myth. The study uses footage of more than 200 incidents from surveillance cameras in Amsterdam; Cape Town; and Lancaster, England.

Researchers watched footage and coded the nature of the conflict, the number of direct participants in it, and the number of bystanders. Bystanders were defined as intervening if they attempted a variety of acts, including pacifying gestures, calming touches, blocking contact between parties, consoling victims of aggression, providing practical help to a physical harmed victim, or holding, pushing, or pulling an aggressor away. Each event had an average of 16 bystanders and lasted slightly more than three minutes.

The study finds that in nine out of 10 incidents, at least one bystander intervened, with an average of 3.8 interveners. There was also no significant difference across the three countries and cities, even though they differ greatly in levels of crime and violence.

Instead of more bystanders creating an immobilizing “bystander effect,” the study actually found the more bystanders there were, the more likely it was that at least someone would intervene to help. This is a powerful corrective to the common perception of “stranger danger” and the “unknown other.” It suggests that people are willing to self-police to protect their communities and others. That’s in line with the research of urban criminologist Patrick Sharkey, who finds that stronger neighborhood organizations, not a higher quantity of policing, have fueled the Great Crime Decline.

Source: How Often Will Bystanders Help Strangers in Need? – CityLab

Carbon nanotube device channels heat into light, could increase solar panel efficiency

Posted on by

The ever-more-humble carbon nanotube may be just the device to make solar panels—and anything else that loses energy through heat—far more efficient.

Rice University scientists are designing arrays of aligned single-wall carbon to channel mid- (aka heat) and greatly raise the efficiency of solar energy systems.

Gururaj Naik and Junichiro Kono of Rice’s Brown School of Engineering introduced their technology in ACS Photonics.

Their invention is a hyperbolic thermal emitter that can absorb intense heat that would otherwise be spewed into the atmosphere, squeeze it into a narrow bandwidth and emit it as light that can be turned into electricity.

The discovery rests on another by Kono’s group in 2016 when it found a simple method to make highly aligned, wafer-scale films of closely packed nanotubes.

[…]

The aligned nanotube films are conduits that absorb and turn it into narrow-bandwidth photons. Because electrons in nanotubes can only travel in one direction, the aligned films are metallic in that direction while insulating in the perpendicular direction, an effect Naik called hyperbolic dispersion. Thermal photons can strike the film from any direction, but can only leave via one.

“Instead of going from heat directly to electricity, we go from to light to electricity,” Naik said. “It seems like two stages would be more efficient than three, but here, that’s not the case.”

[…]

Naik said adding the emitters to standard solar cells could boost their efficiency from the current peak of about 22%. “By squeezing all the wasted thermal energy into a small spectral region, we can turn it into electricity very efficiently,” he said. “The theoretical prediction is that we can get 80% efficiency.”

Nanotube films suit the task because they stand up to temperatures as high as 1,700 degrees Celsius (3,092 degrees Fahrenheit). Naik’s team built proof-of-concept devices that allowed them to operate at up to 700 C (1,292 F) and confirm their narrow-band output. To make them, the team patterned arrays of submicron-scale cavities into the chip-sized films.

Source: Carbon nanotube device channels heat into light