Reviewerly/exHarmony huggingface dataset:
Security Against Subversion in a Multi-surveillant Setting

Mass surveillance attracts much of attentions nowadays. Evidences showed that some intelligence agencies try to monitor public's communication by unconventional methods, for example, providing users subverted cryptographic algorithms and compelling them to use. To address this new situation, researchers proposed a series of formal analyses and security definitions. However, current researches are restrictive as they only considered a single surveillant setting. In reality, there may exist multiple surveillants for different governments or manufacturers. This paper initializes the analysis of security against subversion in a multi-surveillant setting. We consider the case where users could only use subverted algorithms from different sources to achieve a subliminal communication. We introduce a new security notion that the transmission of a real message is "undetectable", which means all surveillants either think the users execute the subverted algorithms honestly to transmit an innocuous message, or consider users are using non-subverted algorithms. We present a concrete design and prove that it satisfies our security definition.
Risk of Asynchronous Protocol Update: Attacks to Monero Protocols

In a cryptocurrency system, the protocol incorporated in the node application runs without human intervention. Cryptographic techniques are implemented to determine the ownership of the coins; they enable the owners to transfer the ownership of the coins to other users. Consensus protocols are employed to determine the source of the truth of the information contained in the public ledger called blockchain. When the protocol needs to be updated, all nodes need to replace the application with the newest release. We explore an event where an asynchronous protocol update opens a vulnerability in Monero nodes which have not yet updated to the newest software version. We show that a Denial of Service attack can be launched against the nodes running the outdated protocol, where the attack significantly reduces the system' performance. We also show that an attacker, given a sufficient access to cryptocurrency services, is able to utilise the Denial of Service attack to launch a traceability attack.
Revisiting Location Privacy from a Side-Channel Analysis Viewpoint

Inspired by the literature on side-channel attacks against cryptographic implementations, we describe a framework for the analysis of location privacy. It allows us to revisit (continuous) re-identification attacks with a combination of information theoretic and security metrics. Our results highlight conceptual differences between re-identification attacks exploiting leakages that are internal or external to a pseudonymised database. They put forward the amount of data to collect in order to estimate a predictive model as an important – yet less discussed – dimension of privacy assessments. They finally leverage recent results on the security evaluations/certification of cryptographic implementations to connect information theoretic and security metrics, and to formally bound the risk of re-identification with external leakages.
Effects in the Algorithm Performance from Problem Structure, Searching Behavior and Temperature: A Causal Study Case for Threshold Accepting and Bin-Packing

A review of state of art reveals that the characterization and analysis of the relation between problem-algorithm has been focused only on problem features or on algorithm features; or in some situations on both, but the algorithm logical is not considered in the analysis. The above for selecting an algorithm will give the best solution. However there is more knowledge for discovering from this relation. In this paper, significant features are proposed for describing problem structure and algorithm searching fluctuation; other known metrics were considered (Autocorrelation Coefficient and Length) but were not significant. A causal study case is performed for analyzing causes and effects from: Bin-Packing problem structure, Temperature, searching behavior of Threshold Accepting algorithm and final performance to solving problem instances. The proposed features permitted in the causal study to find relations cause-effect; which gave guidelines for designing a Threshold Accepting self-adaptive algorithm. Its performance outperforms to original algorithm in 74% out of 324 problem cases. The causal analysis on relevant information from problem, algorithm (both) and algorithm logical could be an important guideline to discover rules or principles over several problem domains, which permit the design of self-adaptive algorithms to give the best solution to complex problems.
Block-distributed Gradient Boosted Trees

The Gradient Boosted Tree (GBT) algorithm is one of the most popular machine learning algorithms used in production, for tasks that include Click-Through Rate (CTR) prediction and learning-to-rank. To deal with the massive datasets available today, many distributed GBT methods have been proposed. However, they all assume a row-distributed dataset, addressing scalability only with respect to the number of data points and not the number of features, and increasing communication cost for high-dimensional data. In order to allow for scalability across both the data point and feature dimensions, and reduce communication cost, we propose block-distributed GBTs. We achieve communication efficiency by making full use of the data sparsity and adapting the Quickscorer algorithm to the block-distributed setting. We evaluate our approach using datasets with millions of features, and demonstrate that we are able to achieve multiple orders of magnitude reduction in communication cost for sparse data, with no loss in accuracy, while providing a more scalable design. As a result, we are able to reduce the training time for high-dimensional data, and allow more cost-effective scale-out without the need for expensive network communication.
Natural Language Processing and Automatic Knowledge Extraction for Lexicography

As in many other fields, different indicators from recent years show that in the last decade lexicography also entered a game-changing period due to both the rapid development of advanced computational methods, extending as far as to the field of Artificial Intelligence, and to the availability of previously unseen abundance of language data available in different modalities, from written to spoken, combined with images, video etc., and also in real time. Furthermore, with the general change to permanent (mobile) life online, this decade brought about the rather clear realization that ‘printed lexicography’ is — in general terms — a thing of the past, which also turned its business side upside down. On the other hand, persistent online presence of language learners and other previous users of reference books opened up the possibility for the community itself to create or contribute to compilation of lexicographic works through crowdsourcing or citizen science, activity previously mainly reserved for closed teams working on a single language resource for decades.
Digital Shearing Speckle Pattern Interferometry Based on Rochon Prism and Its Application

This paper presents improved digital shearing speckle pattern interferometry based on Rochon Prism. The advantage of this method is that it avoids the shadow noise caused by transmission light, which improves the shearography accuracy. By reducing the angle of divergence and coordinating the measuring distance and the precision coordinate, the beam-splitting path of the interference optical path is improved. Experimental results prove that the improved method contributes to the practical and instrumental application of shearography.
Deep video-to-video transformations for accessibility with an application to photosensitivity

We demonstrate how to construct a new class of visual assistive technologies that, rather than extract symbolic information, learn to transform the visual environment to make it more accessible. We do so without engineering which transformations are useful allowing for arbitrary modifications of the visual input. As an instantiation of this idea we tackle a problem that affects and hurts millions worldwide: photosensitivity. Any time an affected person opens a website, video, or some other medium that contains an adverse visual stimulus, either intended or unintended, they might experience a seizure with potentially significant consequences. We show how a deep network can learn a video-to-video transformation rendering such stimuli harmless while otherwise preserving the video. This approach uses a specification of the adverse phenomena, the forward transformation, to learn the inverse transformation. We show how such a network generalizes to real-world videos that have triggered numerous seizures, both by mistake and in politically-motivated attacks. A number of complimentary approaches are demonstrated including using a hand-crafted generator and a GAN using a differentiable perceptual metric. Such technology can be deployed offline to protect videos before they are shown or online with assistive glasses or real-time post processing. Other applications of this general technique include helping those with limited vision, attention deficit hyperactivity disorder, and autism.
Analysis and Research on Influencing Factors of Haze Weather

Abstract In recent years, the causes, prevention and control methods and prediction system of continuous haze weather have become the focus of social research.Taking the meteorological observation data of Hebei province as an example, this paper studies the influencing factors of continuous haze weather.Firstly, the improved TOPSIS algorithm is used to preprocess the data. Then, it combines the data mining methods such as hierarchical analysis and grey correlation analysis to carry out modeling analysis, and it is concluded that the particulate matter in the pollution source has the greatest impact on the haze weather. Moreover, as the aerodynamics factor affects the diffusion and aggregation of the haze pollution source, it is found that it has a linear influence on the formation of haze weather.Finally, through the current meteorological observation data, BP neural network is used to predict haze weather changes.A large number of experimental results show that, on the premise of allowing a certain error rate, the prediction effect of the BP neural network model is relatively accurate, and it also indicates that the formation of haze weather is closely related to the air quality index factor and meteorological index factor.
Unsupervised Deep Novelty Detection: Application To Muscle Ultrasound And Myositis Screening

This study investigates unsupervised novelty detection (ND) for screening of rare myopathies and specifically myositis. To support this study we developed from the ground up a novel and fully annotated dataset consisting of 3586 images taken of eighty nine individuals obtained under informed consent during 2016-2017. We developed and compared performance for several ND methods leveraging deep feature embeddings, utilizing generative as well as discriminative deep learning approaches for embeddings, and using various novelty scores. We carried out several performance comparisons including with a clinician, supervised binary classification approaches, and a generative method, demonstrating that our best performing approach is competitive with human performance and other best of breed algorithms.
Precise and Robust Detection of Advertising Fraud

As the online advertising market has grown, advertising frauds (ad frauds) have become a serious problem. Countermeasures against ad frauds are evaded since they rely on noticeable features (e.g., burstiness of ad requests) that attackers can easily change. We propose an ad-fraud-detection method that leverages robust features against attacker evasion. We designed novel features on the basis of the statistics observed in an ad network calculated from a large amount of ad requests from legitimate users, such as the popularity of publisher websites and the tendencies of client environments. We assume that attackers cannot know of or manipulate these statistics and that features extracted from fraudulent ad requests tend to be outliers. These features are used to construct a machine-learning model for detecting fraudulent ad requests. We evaluated our proposed method by using ad-request logs observed within an actual ad network. The results revealed that our designed features improved the recall rate by 10% and had about 100,000 - 160,000 fewer false negatives per day than conventional features based on the burstiness of ad requests. In addition, by evaluating detection performance with long-term dataset, we confirmed that the proposed method is robust against performance degradation over time. Finally, we applied our proposed method to a large dataset constructed on an ad network and found several characteristics of the latest ad frauds in the wild, for example, a large amount of fraudulent ad requests is sent from cloud servers.
Porthos: Macroprogramming Blockchain Systems

The rise of blockchain technology has paved the way for an increasing number of blockchain systems, each having different characteristics. The need for distributed applications that span across multiple blockchain systems is increasing. However, it is currently not possible to write a single-description smart contract which can be compiled to span across multiple blockchain systems. In this paper we present PORTHOS, a macroprogramming framework and domain specific language for writing commitment-based smart contracts that span multiple blockchain systems. The language allows programmers to write smart contracts at a higher level of abstraction by composing together contract blocks, without the need to specify how logic should be split across different blockchain instances. A runtime framework, including both on-chain and off-chain functionality, harmonises the features of different blockchain systems as well as enables communication across the smart contracts. A proof of concept, built on the Ethereum and Hyperledger Fabric blockchain systems and extendible to other systems, illustrates the technique and framework. We also show how the PORTHOS language is expressive enough to define a variety of applications.
SoK

Recently, the NIST launched a competition for lightweight cryptography and a large number of ciphers are expected to be studied and analyzed under this competition. Apart from the classical security, the candidates are desired to be analyzed against physical attacks. Differential Fault Analysis (DFA) is an invasive physical attack method for recovering key information from cipher implementations. Up to date, almost all the block ciphers have been shown to be vulnerable against DFA, while following similar attack patterns. However, so far researchers mostly focused on particular ciphers rather than cipher families, resulting in works that reuse the same idea for different ciphers. In this article, we aim at bridging this gap, by providing a generic DFA attack method targeting Substitution-Permutation Network (SPN) based families of symmetric block ciphers. We provide the overview of the state-of-the-art of the fault attacks on SPNs, followed by generalized conditions that hold on all the ciphers of this design family. We show that for any SPN, as long as the fault mask injected before a non-linear layer in the last round follows a non-uniform distribution, the key search space can always be reduced. This shows that it is not possible to design an SPN-based cipher that is completely secure against DFA, without randomization. Furthermore, we propose a novel approach to find good fault masks that can leak the key with a small number of instances. We then developed a tool, called Joint Difference Distribution Table (JDDT) for pre-computing the solutions for the fault equations, which allows us to recover the last round key with a very small number of pairs of faulty and non-faulty ciphertexts. We evaluate our methodology on various block ciphers, including PRESENT-80, PRESENT-128, GIFT-64, GIFT-128, AES-128, LED-64, LED-128, Skinny, Pride and Prince. The developed technique would allow automated DFA analysis of several candidates in the NIST competitio
Towards a Set of Design Guidelines for Multi-device Experience

Thanks to the hyperconnected world in which we live, we are surrounded by devices. Many of these devices can communicate with each other, and even they can support the same application so that the user can have multiple forms of interaction. However, developers should be careful when considering the control level left to users, since the applications may become unusable. Whether the system or user decides the distribution of the available devices, Graphical User Interface (GUI) consistency must always be preserved. Consistency not only provides users with a robust framework in similar contexts but is an essential learning element and a lever to ensure the GUI efficient usage. This paper proposes a set of consistency guidelines that serve as a means for the construction of multi-device applications. As a case study, DistroPaint was evaluated by experts who identified consistency violations and assessed their severity.
Serial product evaluations online: A three-factor model of leadership, fluency and tedium during product search

In any product search process, consumers encounter multiple products. In today's online retail environment, such product encounters are increasingly serial, such that consumers review products back-to-back (e.g., through swiping in mobile apps). The present research shows that for the serial evaluation of products, the serial position matters through an interaction of a leadership bias, conceptual fluency and tedium. During a product search, evaluations follow an S-shaped pattern consisting of (1) a primacy phase of declining evaluations (caused by a leadership bias towards the first product), (2) a wear-in phase of increasing evaluations (caused by increasing conceptual fluency after a recognition of the shared attributes of the product category), and (3) a wear-out period of declining evaluations (caused by tedium). Ten empirical investigations in an e-commerce setting granularly trace these three phases, establishing conceptually related overall boundary conditions (matching vs. searching products) and moderators (conditional vs. unconditional searching, product similarity, conceptual category knowledge, product image complexity) of the S-shaped pattern of serial product evaluations.
The role of geoscience breakthroughs in gold exploration success: Yilgarn Craton, Australia

Australia experienced a major increase in gold production during the 1980s, and this is reflected in the production of ~250 tonnes of gold per annum since then. Much of the production has come from the Archean Yilgarn Craton of Western Australia, and mostly from gold-only deposits. Discovery is the factor best accounting for the outperformance of Yilgarn gold production in a global context, and both greenfield and brownfield exploration success have been essential. Scientific input to gold exploration evolved rapidly from 1980 and is implicated in the exploration success in the Yilgarn. Six scientific breakthroughs had changed the nature of almost all Yilgarn gold exploration thinking by 1990 and appear to have been important components of the exploration success. Breakthroughs relate to primary gold geology (timing and structural control, favourable host rocks, gold-related alteration), and gold in the regolith environment (landscape evolution, gold geochemistry, targeted sampling). These six breakthroughs changed paradigms from which the Western Australian industry has not reverted. These breakthroughs were not necessarily a key part of the original aims of the researchers. Common features in the development of the breakthroughs were small, but persistent, research groups with an understanding of industry, a willingness to challenge dogma, development of informal industry linkages and the ability to formulate and tackle key questions. Breakthroughs led to applications not considered when the research was conceived. The breakthrough ideas were eventually available globally, but they were not equally applicable and not equally accepted and adopted. An openness to new ideas may correlate with the massive and ongoing exploration success witnessed in the Yilgarn goldfields.
Facial Expression Recognition Based on Regularized Semi-supervised Deep Learning

In the field of facial expression recognition, deep learning has attracted more and more researchers’ attention as a powerful tool. The method can effectively train and test data by using a neural network. This paper mainly uses the semi-supervised deep learning model for feature extraction and adds a regularized sparse representation model as a classifier. The combination of deep learning features and sparse representations fully exploits the advantages of deep learning in feature learning and the advantages of sparse representation in recognition. Experiments show that the features obtained by deep learning have certain subspace features, which accord with the subspace hypothesis of face recognition based on sparse representation. The method of this paper has a good recognition accuracy in facial expression recognition and has certain advantages in small sample problems.
A Set of Usability Heuristics for Mobile Applications

The innovations proposed by the cell phone market have grown steadily in recent years, as well as the evolution of the complexity of operating systems, hardware and applications available. With these changes and changes, new challenges and usability-related quirks emerge and need to be considered during the development process of these applications, which incorporate new user-application interactions, increasingly changing the behavior of smartphone users. It is known that usability is an important factor when choosing the use of these technologies. Usability depends on factors such as the User, their characteristics and abilities, the Task which the user intends to achieve and also the Context of the application's use. This work will lead to a Systematic Review of Literature with the objective of identifying the heuristics and usability metrics used in the literature and/or industry, and based on the results obtained, it is intended to propose a set of usability heuristics focused for the context of mobile applications on smartphone, considering the User, Task and Context, as usability factors and Cognitive Load as an important attribute of usability. Furthermore, an empirical validation of the proposal will be performed with usability specialists and improvements can be incorporated into the proposed model after this validation.
SOA Approach - Identity and Access Management for the Risk Management Platform

The Risk Management system should help customs to more easily and effectively detect irregularities in import, export or transit of goods. Customs administrations today are required to provide extensive facilitation while maintaining control over the international movement of goods, means of transport and persons. The level of risk is determined in the context of the priorities of the Customs administrations e.g. whether the priority is collection of duties and taxes or checking prohibitions and restrictions or any other specific area that has been identified. The aim of the proposed platform in this paper is to achieve a high-quality, multi-layered approach to risk management that is effective and efficient, i.e. the platform is built on decoupled microservices, the different components are working together and an interruption in one segment does not have major effect on the overall system. The main motivation behind this case study is the hands-on experience we have and the close proximity to the project, i.e. information exchange and team discussions as the main available resources.