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The scale and diversity of Internet censorship practices makes it difficult to precisely monitor where, when, and how censorship occurs, as well as what is censored. The potential risks in performing the measurements make this problem even more challenging. Software deobfuscation is a crucial activity in security analysis and especially in malware analysis. We present Catena, an efficiently-verifiable Bitcoin witnessing scheme. Catena implements a log as an OP_RETURN transaction chain and prevents forks in the log by leveraging Bitcoin’s security against double spends.
The effectiveness of the Android permission system fundamentally hinges on the user’s correct understanding of the capabilities of the permissions being granted. The system consists of an arbitrary number of communicating nodes, deployable at different locations over the Internet. Its registered users can post content and establish intra-node and inter-node friendships, used to regulate access control over the posts. Tor is vulnerable to network-level adversaries who can observe both ends of the communication to deanonymize users. Recent work has shown that Tor is susceptible to the previously unknown active BGP routing attacks, called RAPTOR attacks, which expose Tor users to more network-level adversaries. In this paper, we aim to mitigate and detect such active routing attacks against Tor.
Cryptographic functions have been commonly abused by malware developers to hide malicious behaviors, disguise destructive payloads, and bypass network-based firewalls. Now-infamous crypto-ransomware even encrypts victim’s computer documents until a ransom is paid. This paper studies information flows via tuning channels in the presence of automatic memory management. TLS is the most commonly deployed family of protocols for seeming network communications. TLS are critically dependent on the correct validation of the X.
As recent studies show, the majority of all exploited Java vulnerabilities comprise incorrect or insufficient implementations of access-control checks. This paper for the first time studies the problem in depth. As the most successful cryptocurrency to date, Bitcoin constitutes a target of choice for attackers. While many attack vectors have already been uncovered, one important vector has been left out though: attacking the currency via the Internet routing infrastructure itself.
Modern vehicles are required to comply with a range of environmental regulations limiting the level of emissions for various greenhouse gases, toxins and particulate matter. To ensure compliance, regulators test vehicles in controlled settings and empirically measure their emissions at the tailpipe. Online underground economy is an important channel that connects the merchants of illegal products and their buyers, which is also constantly monitored by legal authorities. Despite a great deal of work to improve the TLS PKI, CA misbehavior continues to occur, resulting in unauthorized certificates that can be used to mount man-in-the-middle attacks against HTTPS sites. CAs lack the incentives to invest in higher security, and the manual effort required to report a rogue certificate deters many from contributing to the security of the TLS PKI.
Authorization bugs, when present in online social networks, are usually caused by missing or incorrect authorization checks and can allow attackers to bypass the online social network’s protections. Since the first whole-genome sequencing, the biomedical research community has made significant steps towards a more precise, predictive and personalized medicine. Genomic data is nowadays widely considered privacy-sensitive and consequently protected by strict regulations and released only after careful consideration. Implementing and Proving the TLS 1. The record layer is the main bridge between TLS applications and internal sub-protocols. Within the next few years, billions of IoT devices will densely populate our cities. In this paper we describe a new type of threat in which adjacent IoT devices will infect each other with a worm that will rapidly spread over large areas, provided that the density of compatible IoT devices exceeds a certain critical mass.
Is Interaction Necessary for Distributed Private Learning? But OPE and ORE ciphertexts necessarily leak information about plaintexts, and what level of security they provide in practice has been unclear. We target the popular Helios family of voting protocols, for which we identify appropriate levels of abstractions to allow the simplification and convenient reuse of proof steps across many variations of the voting scheme. We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained. We focus on the basic membership inference attack: given a data record and black-box access to a model, determine if the record was in the model’s training dataset.
In this paper we present a simple and reliable authentication method for mobile devices equipped with multi-touch screens such as smart phones, tablets and laptops. Users are authenticated by performing specially designed multi-touch gestures with one swipe on the touchscreen. Differential testing uses similar programs as cross-referencing oracles to find semantic bugs that do not exhibit explicit erroneous behaviors like crashes or assertion failures. Unfortunately, existing differential testing tools are domain-specific and inefficient, requiring large numbers of test inputs to find a single bug.
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Code reuse attacks exploiting memory disclosure vulnerabilities can bypass all deployed mitigations. The computer security community has advocated widespread adoption of secure communication tools to counter mass surveillance. One TPM to Bind Them All: Fixing TPM2. The specification of the most recent TPM 2. 0 interfaces for direct anonymous attestation unfortunately has a number of severe shortcomings. Secure multiparty computation enables a set of parties to securely carry out a joint computation of their private inputs without revealing anything but the output. In the past few years, the efficiency of secure computation protocols has increased in leaps and bounds.
Embedded systems are ubiquitous in every aspect of modern life. As the Internet of Thing expands, our dependence on these systems increases. Many of these interconnected systems are and will be low cost bare-metal systems, executing without an operating system. Protecting vast quantities of data poses a daunting challenge for the growing number of organizations that collect, stockpile, and monetize it. Generating public randomness is hard, however, because active adversaries may behave dishonestly to bias public random choices toward their advantage.
Existing solutions do not scale to hundreds or thousands of participants, as is needed in many decentralized systems. Machine learning is widely used in practice to produce predictive models for applications such as image processing, speech and text recognition. These models are more accurate when trained on large amount of data collected from different sources. However, the massive data collection raises privacy concerns. AR applications provide users with immersive virtual experiences by capturing input from a user’s surroundings and overlaying virtual output on the user’s perception of the real world.
Full-text search systems, such as Elasticsearch and Apache Solr, enable document retrieval based on keyword queries. In many deployments these systems are multi-tenant, meaning distinct users’ documents reside in, and their queries are answered by, one or more shared search indexes. Large deployments may use hundreds of indexes across which user documents are randomly assigned. Programs that take highly-structured files as inputs normally process inputs in stages: syntax parsing, semantic checking, and application execution. Deep bugs are often hidden in the application execution stage, and it is non-trivial to automatically generate test inputs to trigger them.
Permission systems are the main defense that mobile platforms, such as Android and iOS, offer to users to protect their private data from prying apps. However, due to the tension between usability and control, such systems have several limitations that often force users to overshare sensitive data. Protected database search systems cryptographically isolate the roles of reading from, writing to, and administering the database. This separation limits unnecessary administrator access and protects data in the case of system breaches. The idea of a paperless office has been dreamed of for more than three decades. However, nowadays printers are still one of the most essential devices for daily work and common Internet users.
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Malware sandboxes, widely used by antivirus companies, mobile application marketplaces, threat detection appliances, and security researchers, face the challenge of environment-aware malware that alters its behavior once it detects that it is being executed on an analysis environment. Online programming discussion platforms such as Stack Overflow serve as a rich source of information for software developers. Available information include vibrant discussions and oftentimes ready-to-use code snippets. Previous research identified Stack Overflow as one of the most important information sources developers rely on. TLS libraries for secure communication has further propelled its prominence.
The security guarantees provided by X. 509 hinge on the assumption that the underlying implementation rigorously scrutinizes X. Users are given the freedom to use those mandated points at any position. Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access.
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Unlike large-scale malware such as botnets, a RAT is controlled individually by a human operator interacting with the compromised machine remotely. Neural networks provide state-of-the-art results for most machine learning tasks. Unfortunately, neural networks are vulnerable to adversarial examples: given an input x and any target classification t, it is possible to find a new input x’ that is similar to x but classified as t. This makes it difficult to apply neural networks in security-critical areas. In addition, code clones – code fragments that are copied and pasted within or between software systems – are also proliferating.
Verified Models and Reference Implementations for the TLS 1. Its clean-slate design is a reaction both to the increasing demand for low-latency HTTPS connections and to a series of recent high-profile attacks on TLS. Side channel attacks have been used to extract critical data such as encryption keys and confidential user data in a variety of adversarial settings. In practice, this threat is addressed by adhering to a constant-time programming discipline, which imposes strict constraints on the way in which programs are written. In recent years, researchers have shown that unwanted web tracking is on the rise, as advertisers are trying to capitalize on users’ online activity, using increasingly intrusive and sophisticated techniques.
Developing a remote exploit is not easy. It requires a comprehensive understanding of a vulnerability and delicate techniques to bypass defense mechanisms. As a result, attackers may prefer to reuse an existing exploit and make necessary changes over developing a new exploit from scratch. In this paper we present vSQL, a novel cryptographic protocol for publicly verifiable SQL queries on dynamic databases.
This article needs to be updated. Please update this article to reflect recent events or newly available information. Google Translate is a free multilingual machine translation service developed by Google, to translate text. Launched in April 2006 as a statistical machine translation service, it used United Nations and European Parliament transcripts to gather linguistic data.
Rather than translating languages directly, it first translates text to English and then to the target language. Google Translate can translate multiple forms of text and media, including text, speech, images, sites, or real-time video, from one language to another. The Google Translate app for Android and iOS supports more than 100 languages and can translate 37 languages via photo, 32 via voice in “conversation mode”, and 27 via real-time video in “augmented reality mode”. The Android app was released in January 2010, and for iOS on February 8, 2011.
A January 2011 Android version experimented with a “Conversation Mode” that allowed users to communicate fluidly with a nearby person in another language. Originally limited to English and Spanish, the feature received support for 12 new languages, still in testing, the following October. In January 2015, the apps gained the ability to translate physical signs in real time using the device’s camera, as a result of Google’s acquisition of the Word Lens app. In May 2011, Google announced that the Google Translate API for software developers had been deprecated and would cease functioning. Because the API was used in numerous third-party websites and apps, the original decision to deprecate it led some developers to criticize Google and question the viability of using Google APIs in their products. Google Translate also provides translations for Google Assistant and the devices that Google Assistant runs on such as Google Home and Google Pixel Buds.
The following languages are supported in Google Translate. Users can now choose to have the romanization written for Chinese, Japanese, Korean, Russian, Ukrainian, Belarusian, Bulgarian, Greek, Hindi and Thai. For translations from Arabic, Persian and Hindi, the user can enter a Latin transliteration of the text and the text will be transliterated to the native script for these languages as the user is typing. Speech program launched in Hindi and Spanish.
Allows phonetic typing for Arabic, Greek, Hindi, Persian, Russian, Serbian and Urdu. Also the old versions of French, German, Italian and Spanish. Latin uses the same synthesizer as Italian. Speech program launched in Arabic, Japanese and Korean. Choice of different translations for a word. Dutch male voice synthesizer replaced with female. New speech program launched in English.
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New speech program in French, Spanish, Italian and German. 16 additional languages can be used with camera-input: Bulgarian, Catalan, Croatian, Danish, Estonian, Finnish, Hungarian, Indonesian, Icelandic, Latvian, Lithuanian, Norwegian, Romanian, Slovak, Slovenian and Swedish. Speech program launched in Khmer and Sinhala. In April 2006, Google Translate launched with a statistical machine translation engine. Google Translate does not apply grammatical rules, since its algorithms are based on statistical analysis rather than traditional rule-based analysis. 150-200 million words, and two monolingual corpora each of more than a billion words.
To acquire this huge amount of linguistic data, Google used United Nations and European Parliament transcripts. When Google Translate generates a translation, it looks for patterns in hundreds of millions of documents to help decide on the best translation. By detecting patterns in documents that have already been translated by human translators, Google Translate makes intelligent guesses as to what an appropriate translation should be. In 2014, Google launched “Translate Community”, a platform aimed at improving the translation service by seeking help from volunteers. In August 2016, a Google Crowdsource app was released for Android users, in which translation tasks are offered.
Some languages produce better results than others. Google Translate performs well especially when English is the target language and the source language is from the European Union due to the prominence of translated EU parliament notes. A 2010 analysis indicated that French to English translation is relatively accurate. In 2017, Google Translate was used during a court hearing when court officials at Teesside Magistrates’ Court failed to book an interpreter for a Chinese defendant.
Found in translation: More accurate, fluent sentences in Google Translate”. See which features work with each language”. Google Translate now serves 200 million people daily”. Translate text messages, webpages, or documents”.
Translate with handwriting or virtual keyboard”. Google introduces Google Translate Chrome Extension for inline translations of text”. Rant: Google Translate Toolbar In Chrome 5 Needs An ‘Off’ Button”. Google Translate on the App Store”. Google Translate adds 20 new languages to video text translation”.
Google Translate’s New ‘Conversation Mode’: Hands On”. Google Translate Now Does Real-Time Voice And Sign Translations On Mobile”. Google Translate just got smarter: Word Lens and instant voice translations in latest update”. Google Translate adds video translation support for 25 more languages”. Google Translate’s App Now Instantly Translates Printed Text In 27 Languages”. Spring cleaning for some of our APIs”. Google pulls the rug out from under web service API developers, nixes Google Translate and 17 others”.
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Google gets rid of APIs for Translate and other services”. Giving a voice to more languages on Google Translate”. Five more languages on Google Translate”. SVOX Archived December 26, 2010, at the Wayback Machine. Google Translate welcomes you to the Indic web”.
Google Adds its 65th Language to Google Translate with Lao”. Google adds Sindhi to its translate language options”. Google Translate now includes Sindhi and Pashto”. Google can now translate text into Sindhi, Pashto and vice versa”. Expert in Machine Learning and Machine Translation, Joins Human Longevity, Inc. La Jolla, CA: Human Longevity, Inc.
Can Google break the computer language barrier? Google seeks world of instant translations”. Archived from the original on August 22, 2010. Google Switches to Its Own Translation System”. Google Translate Drops Systran For Home Brewed Translation”.
A Neural Network for Machine Translation, at Production Scale”. Zero-Shot Translation with Google’s Multilingual Neural Machine Translation System”. Learning to Forget: Continual Prediction with LSTM”. Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. An Infusion of AI Makes Google Translate More Powerful Than Ever. Cade Metz, WIRED, Date of Publication: 09.
Google Translate now uses neural machine translation for some languages”. Google adds Indonesian and eight new Indian languages to its neural machine translation”. Google Wants To Improve Its Translations Through Crowdsourcing”. Google sets up a community site to help improve Google Translate”. New Google Crowdsource app asks you to help with translation and text transcription a few seconds at a time”.
New Crowdsource app lets you work for Google for free”. Reinventing discovery: the new era of networked science. Google Translate Tangles With Computer Learning”. Archived from the original on September 12, 2007. Translation Party: Tapping Into Google Translate’s Untold Creative Genius”.
A British court was forced to rely on Google Translate because it had no interpreter”. Desktop computer clipart – Yellow theme. Jump to navigation Jump to search This article is about the Google Android device. Dick’s fictional Nexus-6 androids, see Replicant. Sound Dual Front Facing Stereo Speakers, 3. Connectivity Micro USB, NFC, Bluetooth 4.
Google and Motorola Mobility that runs the Android operating system. The Nexus 6’s design and hardware is very similar to that of the second-generation Moto X, which was released around the same time, with the Nexus 6 being larger and having higher specs while running stock Android. The Nexus 6 was unveiled on October 15, 2014, with pre-order availability since October 29, 2014, and a delivery date in early November. T version is SIM-locked, with tethering disabled until a fee is paid to enable it, and comes with custom ringtones. Google services and applications, but still runs a similarly stock version of Android. On April 22, 2015, it was announced that the Nexus 6 would be the only phone then supported by Google’s new Project Fi venture. Following the September 2015 release of the Nexus 6’s successor, the Nexus 6P, Google stopped selling the Nexus 6 on the Play Store in December 2015.
The Nexus 6 is powered by a 2. 7 GHz quad-core Snapdragon 805 processor with 3 GB of RAM, and either 32 or 64 GB of internal storage. The Nexus 6’s hardware supports USB On-The-Go for storage devices, the functionality was originally disabled by software but was enabled in later Android updates. The Nexus 6 was released with Android 5.