nils-herrmann/clean_scirepeval huggingface dataset:
AVIO: Detecting Atomicity Violations via Access-Interleaving Invariants

Concurrency bugs are among the most difficult to test and diagnose of all software bugs. The multicore technology trend worsens this problem. Most previous concurrency bug detection work focuses on one bug subclass, data races, and neglects many other important ones such as atomicity violations, which will soon become increasingly important due to the emerging trend of transactional memory models.This paper proposes an innovative, comprehensive, invariantbased approach called AVIO to detect atomicity violations. Our idea is based on a novel observation called access interleaving invariant, which is a good indication of programmers' assumptions about the atomicity of certain code regions. By automatically extracting such invariants and detecting violations of these invariants at run time, AVIO can detect a variety of atomicity violations.Based on this idea, we have designed and built two implementations of AVIO and evaluated the trade-offs between them. The first implementation, AVIO-S, is purely in software, while the second, AVIO-H, requires some simple extensions to the cache coherence hardware. AVIO-S is cheaper and more accurate but incurs much higher overhead and thus more run-time perturbation than AVIOH. Therefore, AVIO-S is more suitable for in-house bug detection and postmortem bug diagnosis, while AVIO-H can be used for bug detection during production runs.We evaluate both implementations of AVIO using large realworld server applications (Apache and MySQL) with six representative real atomicity violation bugs, and SPLASH-2 benchmarks. Our results show that AVIO detects more tested atomicity violations of various types and has 25 times fewer false positives than previous solutions on average.
Industrial Wireless Sensor Networks: Challenges, Design Principles, and Technical Approaches

In today's competitive industry marketplace, the companies face growing demands to improve process efficiencies, comply with environmental regulations, and meet corporate financial objectives. Given the increasing age of many industrial systems and the dynamic industrial manufacturing market, intelligent and low-cost industrial automation systems are required to improve the productivity and efficiency of such systems. The collaborative nature of industrial wireless sensor networks (IWSNs) brings several advantages over traditional wired industrial monitoring and control systems, including self-organization, rapid deployment, flexibility, and inherent intelligent-processing capability. In this regard, IWSN plays a vital role in creating a highly reliable and self-healing industrial system that rapidly responds to real-time events with appropriate actions. In this paper, first, technical challenges and design principles are introduced in terms of hardware development, system architectures and protocols, and software development. Specifically, radio technologies, energy-harvesting techniques, and cross-layer design for IWSNs have been discussed. In addition, IWSN standards are presented for the system owners, who plan to utilize new IWSN technologies for industrial automation applications. In this paper, our aim is to provide a contemporary look at the current state of the art in IWSNs and discuss the still-open research issues in this field and, hence, to make the decision-making process more effective and direct.
Optimal estimation of the mean function based on discretely sampled functional data: Phase transition

The problem of estimating the mean of random functions based on discretely sampled data arises naturally in functional data analysis. In this paper, we study optimal estimation of the mean function under both common and independent designs. Minimax rates of convergence are established and easily implementable rateoptimal estimators are introduced. The analysis reveals interesting and different phase transition phenomena in the two cases. Under the common design, the sampling frequency solely determines the optimal rate of convergence when it is relatively small and the sampling frequency has no effect on the optimal rate when it is large. On the other hand, under the independent design, the optimal rate of convergence is determined jointly by the sampling frequency and the number of curves when the sampling frequency is relatively small. When it is large, the sampling frequency has no effect on the optimal rate. Another interesting contrast between the two settings is that smoothing is necessary under the independent design, while, somewhat surprisingly, it is not essential under the common design.
Person Re-identification with Deep Similarity-Guided Graph Neural Network

The person re-identification task requires to robustly estimate visual similarities between person images. However, existing person re-identification models mostly estimate the similarities of different image pairs of probe and gallery images independently while ignores the relationship information between different probe-gallery pairs. As a result, the similarity estimation of some hard samples might not be accurate. In this paper, we propose a novel deep learning framework, named Similarity-Guided Graph Neural Network (SGGNN) to overcome such limitations. Given a probe image and several gallery images, SGGNN creates a graph to represent the pairwise relationships between probegallery pairs (nodes) and utilizes such relationships to update the probegallery relation features in an end-to-end manner. Accurate similarity estimation can be achieved by using such updated probe-gallery relation features for prediction. The input features for nodes on the graph are the relation features of different probe-gallery image pairs. The probe-gallery relation feature updating is then performed by the messages passing in SGGNN, which takes other nodes’ information into account for similarity estimation. Different from conventional GNN approaches, SGGNN learns the edge weights with rich labels of gallery instance pairs directly, which provides relation fusion more precise information. The effectiveness of our proposed method is validated on three public person re-identification datasets.
Crowdroid: behavior-based malware detection system for Android

The sharp increase in the number of smartphones on the market, with the Android platform posed to becoming a market leader makes the need for malware analysis on this platform an urgent issue.
 In this paper we capitalize on earlier approaches for dynamic analysis of application behavior as a means for detecting malware in the Android platform. The detector is embedded in a overall framework for collection of traces from an unlimited number of real users based on crowdsourcing. Our framework has been demonstrated by analyzing the data collected in the central server using two types of data sets: those from artificial malware created for test purposes, and those from real malware found in the wild. The method is shown to be an effective means of isolating the malware and alerting the users of a downloaded malware. This shows the potential for avoiding the spreading of a detected malware to a larger community.
Human Behaviour and the Principle of Least Effort: an Introduction to Human Ecology

Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd human behavior and the principle of least effort an introduction to human ecology as the choice of reading, you can find here.
Beat them or ban them: the characteristics and social functions of anger and contempt.

This article reports 3 studies in which the authors examined (a) the distinctive characteristics of anger and contempt responses and (b) the interpersonal causes and effects of both emotions. In the 1st study, the authors examined the distinction between the 2 emotions; in the 2nd study, the authors tested whether contempt could be predicted from previous anger incidents with the same person; and in the 3rd study, the authors examined the effects of type of relationship on anger and contempt reactions. The results of the 3 studies show that anger and contempt often occur together but that there are clear distinctions between the 2 emotions: Anger is characterized more by short-term attack responses but long-term reconciliation, whereas contempt is characterized by rejection and social exclusion of the other person, both in the short-term and in the long-term. The authors also found that contempt may develop out of previously experienced anger and that a lack of intimacy with and perceived control over the behavior of the other person, as well as negative dispositional attributions about the other person, predicted the emergence of contempt.
Classification of brain disease in magnetic resonance images using two-stage local feature fusion

Many classification methods have been proposed based on magnetic resonance images. Most methods rely on measures such as volume, the cerebral cortical thickness and grey matter density. These measures are susceptible to the performance of registration and limited in representation of anatomical structure. This paper proposes a two-stage local feature fusion method, in which deformable registration is not desired and anatomical information is represented from moderate scale.



Keypoints are firstly extracted from scale-space to represent anatomical structure. Then, two kinds of local features are calculated around the keypoints, one for correspondence and the other for representation. Scores are assigned for keypoints to quantify their effect in classification. The sum of scores for all effective keypoints is used to determine which group the test subject belongs to.



We apply this method to magnetic resonance images of Alzheimer's disease and Parkinson's disease. The advantage of local feature in correspondence and representation contributes to the final classification. With the help of local feature (Scale Invariant Feature Transform, SIFT) in correspondence, the performance becomes better. Local feature (Histogram of Oriented Gradient, HOG) extracted from 16×16 cell block obtains better results compared with 4×4 and 8×8 cell block.



This paper presents a method which combines the effect of SIFT descriptor in correspondence and the representation ability of HOG descriptor in anatomical structure. This method has the potential in distinguishing patients with brain disease from controls.
Review on Printed Log Periodic and Yagi MSA

The log periodic antenna and Yagi-Uda antenna are used in the applications where very high directivity is required. They also give very high gain in the range of 17-20dBi. This paper presents a review on various configurations of log periodic and Yagi antennas, their advantages and problems. One problem encountered with Yagi-Uda antenna is relatively less bandwidth. This problem is solved by log periodic antenna which can operate over high bandwidth and providing high gain at the same time. In this paper, a review of various techniques to realize printed Yagi-Uda and log periodic antenna is discussed. They are realized by using different feeding techniques like microstrip feeding, co-axial feeding etc. They are also realized by using modifying the shape of directors and reflectors. The high bandwidth (log periodic antenna) has also been realized by increasing the number of reflectors.
Active behavioral fingerprinting of wireless devices

We propose a simple active method for discovering facts about the chipset, the firmware or the driver of an 802.11 wireless device by observing its responses (or lack thereof) to a series of crafted non-standard or malformed 802.11 frames. We demonstrate that such responses can differ significantly enough to distinguish between a number of popular chipsets and drivers. We expect to significantly expand the number of recognized device types through community contributions of signature data for the proposed open fingerprinting framework. Our method complements known fingerprinting approaches, and can be used to interrogate and spot devices that may be spoofing their MAC addresses in order to conceal their true architecture from other stations, such as a fake AP seeking to engage clients in complex protocol frame exchange (e.g., in order to exploit a driver vulnerability). In particular, it can be used to distinguish rogue APs from legitimate APs before association.
New cache designs for thwarting software cache-based side channel attacks

Software cache-based side channel attacks are a serious new class of threats for computers. Unlike physical side channel attacks that mostly target embedded cryptographic devices, cache-based side channel attacks can also undermine general purpose systems. The attacks are easy to perform, effective on most platforms, and do not require special instruments or excessive computation power. In recently demonstrated attacks on software implementations of ciphers like AES and RSA, the full key can be recovered by an unprivileged user program performing simple timing measurements based on cache misses.
 We first analyze these attacks, identifying cache interference as the root cause of these attacks. We identify two basic mitigation approaches: the partition-based approach eliminates cache interference whereas the randomization-based approach randomizes cache interference so that zero information can be inferred. We present new security-aware cache designs, the Partition-Locked cache (PLcache) and Random Permutation cache (RPcache), analyze and prove their security, and evaluate their performance. Our results show that our new cache designs with built-in security can defend against cache-based side channel attacks in general-rather than only specific attacks on a given cryptographic algorithm-with very little performance degradation and hardware cost.
Tractable Inference for Complex Stochastic Processes

The monitoring and control of any dynamic system depends crucially on the ability to reason about its current status and its future trajectory. In the case of a stochastic system, these tasks typically involve the use of abelief state—a probability distribution over the state of the process at a given point in time. Unfortunately, the state spaces of complex processes are very large, making an explicit representation of a belief state intractable. Even in dynamic Bayesian networks (DBNs), where the process itself can be represented compactly, the representation of the belief state is intractable. We investigate the idea of maintaining a compact approximation to the true belief state, and analyze the conditions under which the errors due to the approximations taken over the lifetime of the process do not accumulate to make our answers completely irrelevant. We show that the error in a belief statecontractsexponentially as the process evolves. Thus, even with multiple approximations, the error in our process remains bounded indefinitely. We show how the additional structure of a DBN can be used to design our approximation scheme, improving its performance significantly. We demonstrate the applicability of our ideas in the context of a monitoring task, showing that orders of magnitude faster inference can be achieved with only a small degradation in accuracy.
Iseult/INUMAC Whole Body 11.7 T MRI Magnet

A new innovative whole body 11.7 T MRI magnet is currently being manufactured at Belfort as part of the Iseult/Inumac project, a French-German initiative focused on very high magnetic-field molecular imaging. It will be installed in a neuroscience research center with other very high field MRI equipment, operating in France at CEA Saclay since November 2006. This actively shielded magnet system, manufactured from an NbTi superconductor, will generate a homogeneous field level of 11.75 T within a 90-cm warm bore. The design was cross checked and verified by Siemens Healthcare MR. After six years of activities, the fabrication is nearly completed at Belfort. The Iseult magnet will be delivered to CEA in the coming months and the commissioning phase will start in the beginning of 2017. The manufacturing steps recently completed are presented. This especially includes intermediate qualification tests, superconducting coils integration in the cold mass structure, and closure up of the helium vessel. Finally, an overall status of the commissioning and tests of the ancillary equipment at the site is presented.
A Fully Integrated Digital LDO With Coarse–Fine-Tuning and Burst-Mode Operation

The digital low dropout regulator (D-LDO) has drawn significant attention recently for its low-voltage operation and process scalability. However, the tradeoff between current efficiency and transient response speed has limited its applications. In this brief, a coarse-fine-tuning technique with burst-mode operation is proposed to the D-LDO. Once the voltage undershoot/ overshoot is detected, the coarse tuning quickly finds out the coarse control word in which the load current should be located, with large power MOS strength and high sampling frequency for a fixed time. Then, the fine-tuning, with reduced power MOS strength and sampling frequency, regulates the D-LDO to the desired output voltage and takes over the steady-state operation for high accuracy and current efficiency. The proposed D-LDO is verified in a 65-nm CMOS process with a 0.01-mm2 active area. The measured voltage undershoot and overshoot are 55 and 47 mV, respectively, with load steps of 2 to 100 mA with a 20-ns edge time. The quiescent current is 82 μA, with a 0.43-ps figure of merit achieved. Moreover, the reference tracking speed is 1.5 V/μs.
Sex differences in intrinsic aptitude for mathematics and science?: a critical review.

This article considers 3 claims that cognitive sex differences account for the differential representation of men and women in high-level careers in mathematics and science: (a) males are more focused on objects from the beginning of life and therefore are predisposed to better learning about mechanical systems; (b) males have a profile of spatial and numerical abilities producing greater aptitude for mathematics; and (c) males are more variable in their cognitive abilities and therefore predominate at the upper reaches of mathematical talent. Research on cognitive development in human infants, preschool children, and students at all levels fails to support these claims. Instead, it provides evidence that mathematical and scientific reasoning develop from a set of biologically based cognitive capacities that males and females share. These capacities lead men and women to develop equal talent for mathematics and science.
Muscular and postural synergies of the human hand.

Because humans have limited ability to independently control the many joints of the hand, a wide variety of hand shapes can be characterized as a weighted combination of just two or three main patterns of covariation in joint rotations, or "postural synergies." The present study sought to align muscle synergies with these main postural synergies and to describe the form of membership of motor units in these postural/muscle synergies. Seventeen joint angles and the electromyographic (EMG) activities of several hand muscles (both intrinsic and extrinsic muscles) were recorded while human subjects held the hand statically in 52 specific shapes (i.e., shaping the hand around 26 commonly grasped objects or forming the 26 letter shapes of a manual alphabet). Principal-components analysis revealed several patterns of muscle synergy, some of which represented either coactivation of all hand muscles, or reciprocal patterns of activity (above and below average levels) in the intrinsic index finger and thumb muscles or (to a lesser extent) in the extrinsic four-tendoned extensor and flexor muscles. Single- and multiunit activity was generally a multimodal function of whole hand shape. This implies that motor-unit activation does not align with a single synergy; instead, motor units participate in multiple muscle synergies. Thus it appears that the organization of the global pattern of hand muscle activation is highly distributed. This organization mirrors the highly fractured somatotopy of cortical hand representations and may provide an ideal substrate for motor learning and recovery from injury.
Knowledge Extraction by Using an Ontology Based Annotation Tool

This paperdescribesa SemanticAnnotationTool for extractionof knowledgestructuresfrom web pagesthroughthe useof simple user-definedknowledgeextractionpatterns.Thesemanticannotation tool contains:an ontology-basedmark-upcomponentwhich allows the user to browse and to mark-uprelevant piecesof information; a learningcomponent(Crystal from the University of Massachusetts at Amherst)which learnsrulesfrom examplesand aninformationextractioncomponent whichextractstheobjectsand relationbetweentheseobjects.Our final aim is to provide support for ontologypopulationby usingthe informationextractioncomponent.Oursystemusesasdomainof study“KMi Planet”,aWebbasednews server thathelpsto communicaterelevant information betweenmembersin our institute.
Opportunities and Limits of Remote Timing Attacks

Many algorithms can take a variable amount of time to complete depending on the data being processed. These timing differences can sometimes disclose confidential information. Indeed, researchers have been able to reconstruct an RSA private key purely by querying an SSL Web server and timing the results. Our work analyzes the limits of attacks based on accurately measuring network response times and jitter over a local network and across the Internet. We present the design of filters to significantly reduce the effects of jitter, allowing an attacker to measure events with 15-100μs accuracy across the Internet, and as good as 100ns over a local network. Notably, security-related algorithms on Web servers and other network servers need to be carefully engineered to avoid timing channel leaks at the accuracy demonstrated in this article.
Disease Trajectory Maps

Medical researchers are coming to appreciate that many diseases are in fact complex, heterogeneous syndromes composed of subpopulations that express different variants of a related complication. Longitudinal data extracted from individual electronic health records (EHR) offer an exciting new way to study subtle differences in the way these diseases progress over time. In this paper, we focus on answering two questions that can be asked using these databases of longitudinal EHR data. First, we want to understand whether there are individuals with similar disease trajectories and whether there are a small number of degrees of freedom that account for differences in trajectories across the population. Second, we want to understand how important clinical outcomes are associated with disease trajectories. To answer these questions, we propose the Disease Trajectory Map (DTM), a novel probabilistic model that learns low-dimensional representations of sparse and irregularly sampled longitudinal data. We propose a stochastic variational inference algorithm for learning the DTM that allows the model to scale to large modern medical datasets. To demonstrate the DTM, we analyze data collected on patients with the complex autoimmune disease, scleroderma. We find that DTM learns meaningful representations of disease trajectories and that the representations are significantly associated with important clinical outcomes.