nils-herrmann/clean_scirepeval huggingface dataset:
A11y Attacks: Exploiting Accessibility in Operating Systems

Driven in part by federal law, accessibility (a11y) support for disabled users is becoming ubiquitous in commodity OSs. Some assistive technologies such as natural language user interfaces in mobile devices are welcomed by the general user population. Unfortunately, adding new features in modern, complex OSs usually introduces new security vulnerabilities. Accessibility support is no exception. Assistive technologies can be defined as computing subsystems that either transform user input into interaction requests for other applications and the underlying OS, or transform application and OS output for display on alternative devices. Inadequate security checks on these new I/O paths make it possible to launch attacks from accessibility interfaces. In this paper, we present the first security evaluation of accessibility support for four of the most popular computing platforms: Microsoft Windows, Ubuntu Linux, iOS, and Android. We identify twelve attacks that can bypass state-of-the-art defense mechanisms deployed on these OSs, including UAC, the Yama security module, the iOS sandbox, and the Android sandbox. Further analysis of the identified vulnerabilities shows that their root cause is that the design and implementation of accessibility support involves inevitable trade-offs among compatibility, usability, security, and (economic) cost. These trade-offs make it difficult to secure a system against misuse of accessibility support. Based on our findings, we propose a number of recommendations to either make the implementation of all necessary security checks easier and more intuitive, or to alleviate the impact of missing/incorrect checks. We also point out open problems and challenges in automatically analyzing accessibility support and identifying security vulnerabilities.
Probabilistic Inference Using Markov Chain Monte Carlo Methods

Probabilistic inference is an attractive approach to uncertain reasoning and em pirical learning in arti cial intelligence Computational di culties arise however because probabilistic models with the necessary realism and exibility lead to com plex distributions over high dimensional spaces Related problems in other elds have been tackled using Monte Carlo methods based on sampling using Markov chains providing a rich array of techniques that can be applied to problems in arti cial intelligence The Metropolis algorithm has been used to solve di cult problems in statistical physics for over forty years and in the last few years the related method of Gibbs sampling has been applied to problems of statistical inference Concurrently an alternative method for solving problems in statistical physics by means of dynamical simulation has been developed as well and has recently been uni ed with the Metropolis algorithm to produce the hybrid Monte Carlo method In computer science Markov chain sampling is the basis of the heuristic optimization technique of simulated annealing and has recently been used in randomized algorithms for approximate counting of large sets In this review I outline the role of probabilistic inference in arti cial intelligence present the theory of Markov chains and describe various Markov chain Monte Carlo algorithms along with a number of supporting techniques I try to present a comprehensive picture of the range of methods that have been developed including techniques from the varied literature that have not yet seen wide application in arti cial intelligence but which appear relevant As illustrative examples I use the problems of probabilistic inference in expert systems discovery of latent classes from data and Bayesian learning for neural networks
Agile Software Development: The Business of Innovation

T he rise and fall of the dot-com-driven Internet economy shouldn't distract us from seeing that the business environment continues to change at a dramatically increasing pace. To thrive in this turbulent environment, we must confront the business need for relentless innovation and forge the future workforce culture. Agile software development approaches such as Extreme Programming , Crystal methods, Lean Development, Scrum, Adaptive Software Development (ASD), and others view change from a perspective that mirrors today's turbulent business and technology environment. In a recent study of more than 200 software development projects, QSM Associates' Michael Mah reported that the researchers couldn't find nearly half of the projects' original plans to measure against. Why? Conforming to plan was no longer the primary goal; instead, satisfying customers—at the time of delivery , not at project initiation—took precedence. In many projects we review, major changes in the requirements, scope, and technology that are outside the development team's control often occur within a project's life span. Accepting that Barry Boehm's life cycle cost differentials theory—the cost of change grows through the software's development life cycle—remains valid, the question today is not how to stop change early in a project but how to better handle inevitable changes throughout its life cycle. Traditional approaches assumed that if we just tried hard enough, we could anticipate the complete set of requirements early and reduce cost by eliminating change. Today, eliminating change early means being unresponsive to business con-ditions—in other words, business failure. Similarly, traditional process manage-ment—by continuous measurement, error identification, and process refine-ments—strove to drive variations out of processes. This approach assumes that variations are the result of errors. Today, while process problems certainly cause some errors, external environmental changes cause critical variations. Because we cannot eliminate these changes, driving down the cost of responding to them is the only viable strategy. Rather than eliminating rework, the new strategy is to reduce its cost. However, in not just accommodating change, but embracing it, we also must be careful to retain quality. Expectations have grown over the years. The market demands and expects innovative, high-quality software that meets its needs— and soon. Agile methods are a response to this expectation. Their strategy is to reduce the cost of change throughout a project. Extreme Programming (XP), for example, calls for the software development team to • produce the first delivery in weeks, to achieve an early win and rapid …
Bioinspired locomotion and grasping in water: the soft eight-arm OCTOPUS robot.

The octopus is an interesting model for the development of soft robotics, due to its high deformability, dexterity and rich behavioural repertoire. To investigate the principles of octopus dexterity, we designed an eight-arm soft robot and evaluated its performance with focused experiments. The OCTOPUS robot presented here is a completely soft robot, which integrates eight arms extending in radial direction and a central body which contains the main processing units. The front arms are mainly used for elongation and grasping, while the others are mainly used for locomotion. The robotic octopus works in water and its buoyancy is close to neutral. The experimental results show that the octopus-inspired robot can walk in water using the same strategy as the animal model, with good performance over different surfaces, including walking through physical constraints. It can grasp objects of different sizes and shapes, thanks to its soft arm materials and conical shape.
Modeling direct and indirect influence across heterogeneous social networks

Real-world diffusion phenomena are governed by collective behaviors of individuals, and their underlying connections are not limited to single social networks but are extended to globally interconnected heterogeneous social networks. Different levels of heterogeneity of networks in such global diffusion may also reflect different diffusion processes. In this regard, we focus on uncovering mechanisms of information diffusion across different types of social networks by considering hidden interaction patterns between them. For this study, we propose dual representations of heterogeneous social networks in terms of direct and indirect influence at a macro level. Accordingly, we propose two macro-level diffusion models by extending the Bass model with a probabilistic approach. By conducting experiments on both synthetic and real datasets, we show the feasibility of the proposed models. We find that real-world news diffusion in social media can be better explained by direct than indirect diffusion between different types of social media, such as News, social networking sites (SNS), and Blog media. In addition, we investigate different diffusion patterns across topics. The topics of Politics and Disasters tend to exhibit concurrent and synchronous diffusion by direct influence across social media, leading to high relative entropy of diverse media participation. The Arts and Sports topics show strong interactions within homogeneous networks, while interactions with other social networks are unbalanced and relatively weak, which likely drives lower relative entropy. We expect that the proposed models can provide a way of interpreting strength, directionality, and direct/indirectness of influence between heterogeneous social networks at a macro level.
High Order Regularization for Semi-Supervised Learning of Structured Output Problems

Semi-supervised learning, which uses unlabeled data to help learn a discriminative model, is especially important for structured output problems, as considerably more effort is needed to label its multi-dimensional outputs versus standard single output problems. We propose a new max-margin framework for semi-supervised structured output learning, that allows the use of powerful discrete optimization algorithms and high order regularizers defined directly on model predictions for the unlabeled examples. We show that our framework is closely related to Posterior Regularization, and the two frameworks optimize special cases of the same objective. The new framework is instantiated on two image segmentation tasks, using both a graph regularizer and a cardinality regularizer. Experiments also demonstrate that this framework can utilize unlabeled data from a different source than the labeled data to significantly improve performance while saving labeling effort.
Can testedness be effectively measured?

Among the major questions that a practicing tester faces are deciding where to focus additional testing effort, and deciding when to stop testing. Test the least-tested code, and stop when all code is well-tested, is a reasonable answer. Many measures of "testedness" have been proposed; unfortunately, we do not know whether these are truly effective. In this paper we propose a novel evaluation of two of the most important and widely-used measures of test suite quality. The first measure is statement coverage, the simplest and best-known code coverage measure. The second measure is mutation score, a supposedly more powerful, though expensive, measure. 
 We evaluate these measures using the actual criteria of interest: if a program element is (by these measures) well tested at a given point in time, it should require fewer future bug-fixes than a "poorly tested" element. If not, then it seems likely that we are not effectively measuring testedness. Using a large number of open source Java programs from Github and Apache, we show that both statement coverage and mutation score have only a weak negative correlation with bug-fixes. Despite the lack of strong correlation, there are statistically and practically significant differences between program elements for various binary criteria. Program elements (other than classes) covered by any test case see about half as many bug-fixes as those not covered, and a similar line can be drawn for mutation score thresholds. Our results have important implications for both software engineering practice and research evaluation.
Study of S-box properties in block cipher

In the field of cryptography, the substitution box (S-box) becomes the most widely used ciphers. The process of creating new and powerful S-boxes never end. Various methods are proposed to make the S-box becomes strongest and hard to attack. The strength or weakness of S-box will be determined through the analysis of S-box properties. However, the analysis of the properties of the S-box in block ciphers is still lacking because there is no specific guidelines and technique based on S-box properties. Hence, the cipher is easier to attack by an adversary if the S-box properties are not robust. The purpose of this paper is to describe and review of the S-box properties in block ciphers. As a result, for future work, a new model for analysis S-box properties will be proposed. The model can be used to analysis the properties to determine the strength and weakness of any S-boxes.
On-Line New Event Detection and Tracking

We define and describe the related problems of new event detection and event tracking within a stream of broadcast news stories. We focus on a strict on-line setting-i.e., the system must make decisions about one story before looking at any subsequent stories. Our approach to detection uses a single pass clustering algorithm and a novel thresholding model that incorporates the properties of events as a major component. Our approach to tracking is similar to typical information filtering methods. We discuss the value of “surprising” features that have unusual occurrence characteristics, and briefly explore on-line adaptive filtering to handle evolving events in the news. New event detection and event tracking are part of the Topic Detection and Tracking (TDT) initiative.
Realization of a composite right/left-handed leaky-wave antenna with circular polarization

A leaky-wave antenna (LWA) with circular polarization based on the composite right/left-handed (CRLH) substrate integrated waveguide (SIW) is investigated and presented. Series interdigital capacitors have been introduced into the circuit by etching the slots on the waveguide surface achieving a CRLH functionality. Two symmetrical leaky traveling-wave transmission lines with orthogonal polarizations are placed side-by-side and excited with 90° phase difference generating a pure circular polarization mode. The main beam of this antenna can be steered continuously by varying the frequency while maintaining a low axial ratio (below 3 dB) within the main beam direction. The performance of this LWA is verified through both full-wave simulation and measurement of a fabricated prototype showing a good agreement.
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b

The Common Spatial Pattern (CSP) algorithm is an effective and popular method for classifying 2-class motor imagery electroencephalogram (EEG) data, but its effectiveness depends on the subject-specific frequency band. This paper presents the Filter Bank Common Spatial Pattern (FBCSP) algorithm to optimize the subject-specific frequency band for CSP on Datasets 2a and 2b of the Brain-Computer Interface (BCI) Competition IV. Dataset 2a comprised 4 classes of 22 channels EEG data from 9 subjects, and Dataset 2b comprised 2 classes of 3 bipolar channels EEG data from 9 subjects. Multi-class extensions to FBCSP are also presented to handle the 4-class EEG data in Dataset 2a, namely, Divide-and-Conquer (DC), Pair-Wise (PW), and One-Versus-Rest (OVR) approaches. Two feature selection algorithms are also presented to select discriminative CSP features on Dataset 2b, namely, the Mutual Information-based Best Individual Feature (MIBIF) algorithm, and the Mutual Information-based Rough Set Reduction (MIRSR) algorithm. The single-trial classification accuracies were presented using 10 × 10-fold cross-validations on the training data and session-to-session transfer on the evaluation data from both datasets. Disclosure of the test data labels after the BCI Competition IV showed that the FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b respectively.
HW/SW Codesign and FPGA Acceleration of Visual Odometry Algorithms for Rover Navigation on Mars

Future Mars exploration missions rely heavily on high-mobility autonomous rovers equipped with sophisticated scientific instruments and possessing advanced navigational capabilities. Increasing their navigation velocity and localization accuracy is essential for enabling these rovers to explore large areas on Mars.  Mars rovers move slowly, partially due to the long execution time of complex computer vision algorithms running on their slow space-grade CPUs. This paper exploits the advent of high-performance space-grade field-programmable gate arrays (FPGAs) to accelerate the navigation of future rovers. Specifically, it focuses on visual odometry (VO) and performs HW/SW codesign to achieve one order of magnitude faster execution and improved accuracy. Conforming to the specifications of the European Space Agency, we build a proof-of-concept system on an HW/SW platform with processing power resembling that to be available onboard future rovers. We develop a codesign methodology adapted to the rover's specifications, design parallel architectures, and customize several feature extraction, matching, and motion estimation algorithms. We implement and evaluate five distinct HW/SW pipelines on a Virtex6 FPGA and a 150 MIPS CPU. We provide a detailed analysis of their cost-time-accuracy tradeoffs and quantify the benefits of employing FPGAs for implementing VO. Our solution achieves a speedup factor of 16× over a CPU-only implementation, handling a stereo image pair in less than 1 s, with a 1.25% mean positional error after a 100 m traverse and an FPGA cost of 54 K LUTs and 1.46-MB RAM.
Mobile Money: Understanding and Predicting its Adoption and Use in a Developing Economy

Access to financial institutions is difficult in developing economies and especially for the poor. However, the widespread adoption of mobile phones has enabled the development of mobile money systems that deliver financial services through the mobile phone network. Despite the success of mobile money, there is a lack of quantitative studies that unveil which factors contribute to the adoption and sustained usage of such services. In this paper, we describe the results of a quantitative study that analyzes data from the world's leading mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone communications and M-Pesa transactions in an African country. Our contributions are threefold: (1) we analyze the customers' usage of M-Pesa and report large-scale patterns of behavior; (2) we present the results of applying machine learning models to predict mobile money adoption (AUC=0.691), and mobile money spending (AUC=0.619) using multiple data sources: mobile phone data, M-Pesa agent information, the number of M-Pesa friends in the user's social network, and the characterization of the user's geographic location; (3) we discuss the most predictive features in both models and draw key implications for the design of mobile money services in a developing country. We find that the most predictive features are related to mobile phone activity, to the presence of M-Pesa users in a customer's ego-network and to mobility. We believe that our work will contribute to the understanding of the factors playing a role in the adoption and sustained usage of mobile money services in developing economies.
Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment

Twitter is a microblogging website where users read and write millions of short messages on a variety of topics every day. This study uses the context of the German federal election to investigate whether Twitter is used as a forum for political deliberation and whether online messages on Twitter validly mirror offline political sentiment. Using LIWC text analysis software, we conducted a content analysis of over 100,000 messages containing a reference to either a political party or a politician. Our results show that Twitter is indeed used extensively for political deliberation. We find that the mere number of messages mentioning a party reflects the election result. Moreover, joint mentions of two parties are in line with real world political ties and coalitions. An analysis of the tweets’ political sentiment demonstrates close correspondence to the parties' and politicians’ political positions indicating that the content of Twitter messages plausibly reflects the offline political landscape. We discuss the use of microblogging message content as a valid indicator of political sentiment and derive suggestions for further research.
Feature Mining for Image Classification

The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).
Inferring Algorithmic Patterns with Stack-Augmented Recurrent Nets

Despite the recent achievements in machine learning, we are still very far from achieving real artificial intelligence. In this paper, we discuss the limitations of standard deep learning approaches and show that some of these limitations can be overcome by learning how to grow the complexity of a model in a structured way. Specifically, we study the simplest sequence prediction problems that are beyond the scope of what is learnable with standard recurrent networks, algorithmically generated sequences which can only be learned by models which have the capacity to count and to memorize sequences. We show that some basic algorithms can be learned from sequential data using a recurrent network associated with a trainable memory.
AlGaN/GaN-Based HEMTs Failure Physics and Reliability: Mechanisms Affecting Gate Edge and Schottky Junction

This paper presents a comprehensive review of AlGaN/GaN high electron mobility transistor failure physics and reliability, focusing on mechanisms affecting the gate-drain edge, where maximum electric field and peak temperatures are reached. Physical effects at the origin of device degradation (inverse piezoelectric effect, time-dependent trap formation and percolative conductive paths formation, and electrochemical AlGaN and GaN degradation) are discussed on the basis of literature data and unpublished results. Thermally activated mechanisms involving metal-metal and metal-semiconductor interdiffusion at the gate Schottky junction are also discussed.