nils-herrmann/clean_scirepeval huggingface dataset:
Unit testing concurrent software

There are many difficulties associated with developing correct multithreaded software, and many of the activities that are simple for single threaded software are exceptionally hard for multithreaded software. One such example is constructing unit tests involving multiple threads. Given, for example, a blocking queue implementation, writing a test case to show that it blocks and unblocks appropriately using existing testing frameworks is exceptionally hard. In this paper, we describe the MultithreadedTC framework which allows the construction of deterministic and repeatable unit tests for concurrent abstractions. This framework is not designed to test for synchronization errors that lead to rare probabilistic faults under concurrent stress. Rather, this framework allows us to demonstrate that code does provide specific concurrent functionality (e.g., a thread attempting to acquire a lock is blocked if another thread has the lock).
 We describe the framework and provide empirical comparisons against hand-coded tests designed for Sun's Java concurrency utilities library and against previous frameworks that addressed this same issue. The source code for this framework is available under an open source license.
A Contribution to Phased Array Ultrasonic Inspection of Welds Part 1 : Data Plotting for Sand B-Scan Displays

Part 1 of the paper presents the detection and sizing capability based on image display of sectorial scan. Examples are given for different types of weld defects: toe cracks, internal porosity, side-wall lack of fusion, underbead crack, inner-surface breaking cracks, slag inclusions, incomplete root penetration and internal cracks. Based on combination of S-scan and B-scan plotted into 3-D isometric part, the defect features could be reconstructed and measured into a draft package. Comparison between plotted data and actual defect sizes are also presented.
Tracelet-based code search in executables

We address the problem of code search in executables. Given a function in binary form and a large code base, our goal is to statically find similar functions in the code base. Towards this end, we present a novel technique for computing similarity between functions. Our notion of similarity is based on decomposition of functions into tracelets: continuous, short, partial traces of an execution. To establish tracelet similarity in the face of low-level compiler transformations, we employ a simple rewriting engine. This engine uses constraint solving over alignment constraints and data dependencies to match registers and memory addresses between tracelets, bridging the gap between tracelets that are otherwise similar. We have implemented our approach and applied it to find matches in over a million binary functions. We compare tracelet matching to approaches based on n-grams and graphlets and show that tracelet matching obtains dramatically better precision and recall.
A Review on the Kinematic Simulation and Analyses of Parallel Manipulators

Generally, the robot manipulators are classified into serial type open loop chains and parallel type closed loop chains. Since decades, robot manipulators were designed and operated using the open loop serial chains. In general, Robots used in industries are all general purpose manipulators, consisting of multiple rigid links and are connected in series by means of joints such as revolute, prismatic and etc. A robot consists of a fixed support base at one end and a gripper to hold a tool at the other end for manipulating and performing various operations. Human arm is considered as an example, for Open loop manipulators. As the structure is cantilever supported, the load carrying capacity is poor but besides it has the advantage of reaching large area of workspaces and dexterous maneuverability.
Hierarchical Game-Theoretic Planning for Autonomous Vehicles

The actions of an autonomous vehicle on the road affect and are affected by those of other drivers, whether overtaking, negotiating a merge, or avoiding an accident. This mutual dependence, best captured by dynamic game theory, creates a strong coupling between the vehicle’s planning and its predictions of other drivers’ behavior, and constitutes an open problem with direct implications on the safety and viability of autonomous driving technology. Unfortunately, dynamic games are too computationally demanding to meet the real-time constraints of autonomous driving in its continuous state and action space. In this paper, we introduce a novel game-theoretic trajectory planning algorithm for autonomous driving, that enables real-time performance by hierarchically decomposing the underlying dynamic game into a long-horizon “strategic” game with simplified dynamics and full information structure, and a short-horizon “tactical” game with full dynamics and a simplified information structure. The value of the strategic game is used to guide the tactical planning, implicitly extending the planning horizon, pushing the local trajectory optimization closer to global solutions, and, most importantly, quantitatively accounting for the autonomous vehicle and the human driver’s ability and incentives to influence each other. In addition, our approach admits non-deterministic models of human decisionmaking, rather than relying on perfectly rational predictions. Our results showcase richer, safer, and more effective autonomous behavior in comparison to existing techniques.
On the regularization of WGANs

Since their invention, generative adversarial networks (GANs) have become a popular approach for learning to model a distribution of real (unlabeled) data. Convergence problems during training are overcome by Wasserstein GANs which minimize the distance between the model and the empirical distribution in terms of a different metric, but thereby introduce a Lipschitz constraint into the optimization problem. A simple way to enforce the Lipschitz constraint on the class of functions, which can be modeled by the neural network, is weight clipping. Augmenting the loss by a regularization term that penalizes the deviation of the gradient norm of the critic (as a function of the network’s input) from one, was proposed as an alternative that improves training. We present theoretical arguments why using a weaker regularization term enforcing the Lipschitz constraint is preferable. These arguments are supported by experimental results on several data sets.
An unusual case of thorax compression.

A 33-year-old man was found 20 Cm upper of the floor, compressed by rubbish container in the elevator in an unusually awkward position. The scene investigation corresponded exactly with the localization of the injuries found in the victim. This is a case of death by thorax compression without other fatal factors in which the force causing the chest compression was distinctly determined by the autopsy and scene investigation as accidental traumatic asphyxia.
Mylar: a degree-of-interest model for IDEs

Even when working on a well-modularized software system, programmers tend to spend more time navigating the code than working with it. This phenomenon arises because it is impossible to modularize the code for all tasks that occur over the lifetime of a system. We describe the use of a degree-of-interest (DOI) model to capture the task context of program elements scattered across a code base. The Mylar tool that we built encodes the DOI of program elements by monitoring the programmer's activity, and displays the encoded DOI model in views of Java and AspectJ programs. We also present the results of a preliminary diary study in which professional programmers used Mylar for their daily work on enterprise-scale Java systems.
Traffic Priority and Load Adaptive MAC Protocol for QoS Provisioning in Body Sensor Networks

Body sensor networks (BSNs) carry heterogeneous traffic types having diverse QoS requirements, such as delay, reliability and throughput. In this paper, we design a priority-based traffic load adaptivemedium access control (MAC) protocol for BSNs, namely, PLA-MAC, which addresses the aforementioned requirements and maintains efficiency in power consumption. In PLA-MAC, we classify sensed data packets according to their QoS requirements and accordingly calculate their priorities. The transmission schedules of the packets are determined based on their priorities. Also, the superframe structure of the proposed protocol varies depending on the amount of traffic load and thereby ensures minimal power consumption. Our performance evaluation shows that the PLA-MAC achieves significant improvements over the state-of-the-art protocols.
ALPHA-DIVERGENCE BRIDGES MAXIMUM LIKELI- HOOD AND REINFORCEMENT LEARNING IN NEURAL SEQUENCE GENERATION

Neural sequence generation is commonly approached by using maximumlikelihood (ML) estimation or reinforcement learning (RL). However, it is known that they have their own shortcomings; ML presents training/testing discrepancy, whereas RL suffers from sample inefficiency. We point out that it is difficult to resolve all of the shortcomings simultaneously because of a tradeoff between ML and RL. In order to counteract these problems, we propose an objective function for sequence generation using α-divergence, which leads to an ML-RL integrated method that exploits better parts of ML and RL. We demonstrate that the proposed objective function generalizes ML and RL objective functions because it includes both as its special cases (ML corresponds to α→ 0 and RL to α→ 1). We provide a proposition stating that the difference between the RL objective function and the proposed one monotonically decreases with increasing α. Experimental results on machine translation tasks show that minimizing the proposed objective function achieves better sequence generation performance than ML-based methods.
Implementation of LabVIEW®-based joint-linear motion blending on a lab-manufactured 6-axis articulated robot (RS2)

For fast and accurate motion of 6-axis articulated robot, more noble motion control strategy is needed. In general, the movement strategy of industrial robots can be divided into two kinds, PTP (Point to Point) and CP (Continuous Path). In recent, industrial robots which should be co-worked with machine tools are increasingly needed for performing various jobs, as well as simple handling or welding. Therefore, in order to cope with high-speed handling of the cooperation of industrial robots with machine tools or other devices, CP should be implemented so as to reduce vibration and noise, as well as decreasing operation time. This paper will realize CP motion (especially joint-linear) blending in 3-dimensional space for a 6-axis articulated (lab-manufactured) robot (called as “RS2”) by using LabVIEW®  programming, based on a parametric interpolation. Another small contribution of this paper is the proposal of motion blending simulation technique based on Recurdyn® V7, in order to figure out whether the joint-linear blending motion can generate the stable motion of robot in the sense of velocity magnitude at the end-effector of robot or not. In order to evaluate the performance of joint-linear motion blending, simple PTP (i.e., linear-linear) is also physically implemented on RS2. The implementation results of joint-linear motion blending and PTP are compared in terms of vibration magnitude and travel time by using the vibration testing equipment of Medallion of Zonic®. It can be confirmed verified that the vibration peak of joint-linear motion blending has been reduced to 1/10, compared to that of PTP.
End-to-End Deep Learning of Optical Fiber Communications

In this paper, we implement an optical fiber communication system as an end-to-end deep neural network, including the complete chain of transmitter, channel model, and receiver. This approach enables the optimization of the transceiver in a single end-to-end process. We illustrate the benefits of this method by applying it to intensity modulation/direct detection (IM/DD) systems and show that we can achieve bit error rates below the 6.7% hard-decision forward error correction (HD-FEC) threshold. We model all componentry of the transmitter and receiver, as well as the fiber channel, and apply deep learning to find transmitter and receiver configurations minimizing the symbol error rate. We propose and verify in simulations a training method that yields robust and flexible transceivers that allow—without reconfiguration—reliable transmission over a large range of link dispersions. The results from end-to-end deep learning are successfully verified for the first time in an experiment. In particular, we achieve information rates of 42 Gb/s below the HD-FEC threshold at distances beyond 40 km. We find that our results outperform conventional IM/DD solutions based on two- and four-level pulse amplitude modulation with feedforward equalization at the receiver. Our study is the first step toward end-to-end deep learning based optimization of optical fiber communication systems.
MRI of the hand and fingers.

Injuries of the hand and fingers occur commonly in professional athletes as well as weekend warriors. Magnetic resonance imaging plays a vital role in the evaluation of these injuries for accurate diagnosis, preoperative planning, potential complication, and follow-up during rehabilitation. A detailed analysis of these smaller structures necessitates optimal imaging quality coupled with comprehensive knowledge of the imaging anatomy. In this article, we discuss technical aspects and normal anatomy of hand and fingers imaging on magnetic resonance imaging. This section is followed by discussion of soft tissue and osseous injuries including mechanism of injury, clinical presentation, and imaging findings.
A longitudinal Study of Trust and Perceived Usefulness in Consumer Acceptance of an eService: the Case of Online Health Services

Despite the proliferation of e-services, they are still characterized by uncertainties. As result, consumer trust beliefs are considered an important determinant of e-service adoption. Past work has not however considered the potentially dynamic nature of these trust beliefs, and how early-stage trust might influence later-stage adoption and use. To address this gap, this study draws on the theory of reasoned action and expectation-confirmation theory to carry out a longitudinal study of trust in eservices. Specifically, we examine how trust interacts with other consumer beliefs, such as perceived usefulness, and how together these beliefs influence consumer intentions and actual behaviours toward e-services at both initial and later stages of use. The empirical context is online health information services. Data collection was carried out at two time periods, approximately 7 weeks apart using a student population. The results show that perceived usefulness and trust are important at both initial and later stages in consumer acceptance of online health services. Consumers’ actual usage experiences modify perceptions of usefulness and influence the confirmation of their initial expectations. These results have implications for our understanding of the dynamic nature of trust and perceived usefulness, and their roles in long term success of e-services.
A new PWM dimmer using two active switches for AC LED lamp

This paper presents a new dimmer using only two active switches for AC LED lamp. The control method of the proposed dimmer is pulse width control (PWM) method. Compared with the conventional phase- controlled dimmer, the proposed PWM dimmer can produce sine wave and it does not cause harmonics problem. Furthermore, the proposed control method does not amplify the light flicker due to independence of the input voltage. Therefore, the proposed PWM dimmer can be used as the AC LED lamp's dimmer instead of the conventional phase-controlled dimmer. The experimental result shows that the proposed PWM dimmer has good performances.
Forensic analysis of Telegram Messenger on Android smartphones

In this paper we present a methodology for the forensic analysis of the artifacts generated on Android smartphones by Telegram Messenger, the official client for the Telegram instant messaging platform, which provides various forms of secure individual and group communication, by means of which both textual and non-textual messages can be exchanged among users, as well as voice calls. Our methodology is based on the design of a set of experiments suitable to elicit the generation of artifacts and their retention on the device storage, and on the use of virtualized smartphones to ensure the generality of the results and the full repeatability of the experiments, so that our findings can be reproduced and validated by a third-party. In this paper we show that, by using the proposed methodology, we are able (a) to identify all the artifacts generated by Telegram Messenger, (b) to decode and interpret each one of them, and (c) to correlate them in order to infer various types of information that cannot be obtained by considering each one of them in isolation. As a result, in this paper we show how to reconstruct the list of contacts, the chronology and contents of the messages that have been exchanged by users, as well as the contents of files that have been sent or received. Furthermore, we show how to determine significant properties of the various chats, ∗Corresponding author. Address: viale T. Michel 11, 15121 Alessandria (Italy).  Email addresses: cosimo.anglano@uniupo.it (Cosimo Anglano), massimo.canonico@uniupo.it (Massimo Canonico), marco.guazzone@uniupo.it (Marco Guazzone)  2017 groups, and channels in which the user has been involved (e.g., the identifier of the creator, the date of creation, the date of joining, etc.). Finally, we show how to reconstruct the log of the voice calls made or received by the user. Although in this paper we focus on Telegram Messenger, our methodology can be applied to the forensic analysis of any application running on the Android platform.
Changes in Gut and Plasma Microbiome following Exercise Challenge in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS)

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a disease characterized by intense and debilitating fatigue not due to physical activity that has persisted for at least 6 months, post-exertional malaise, unrefreshing sleep, and accompanied by a number of secondary symptoms, including sore throat, memory and concentration impairment, headache, and muscle/joint pain. In patients with post-exertional malaise, significant worsening of symptoms occurs following physical exertion and exercise challenge serves as a useful method for identifying biomarkers for exertion intolerance. Evidence suggests that intestinal dysbiosis and systemic responses to gut microorganisms may play a role in the symptomology of ME/CFS. As such, we hypothesized that post-exertion worsening of ME/CFS symptoms could be due to increased bacterial translocation from the intestine into the systemic circulation. To test this hypothesis, we collected symptom reports and blood and stool samples from ten clinically characterized ME/CFS patients and ten matched healthy controls before and 15 minutes, 48 hours, and 72 hours after a maximal exercise challenge. Microbiomes of blood and stool samples were examined. Stool sample microbiomes differed between ME/CFS patients and healthy controls in the abundance of several major bacterial phyla. Following maximal exercise challenge, there was an increase in relative abundance of 6 of the 9 major bacterial phyla/genera in ME/CFS patients from baseline to 72 hours post-exercise compared to only 2 of the 9 phyla/genera in controls (p = 0.005). There was also a significant difference in clearance of specific bacterial phyla from blood following exercise with high levels of bacterial sequences maintained at 72 hours post-exercise in ME/CFS patients versus clearance in the controls. These results provide evidence for a systemic effect of an altered gut microbiome in ME/CFS patients compared to controls. Upon exercise challenge, there were significant changes in the abundance of major bacterial phyla in the gut in ME/CFS patients not observed in healthy controls. In addition, compared to controls clearance of bacteria from the blood was delayed in ME/CFS patients following exercise. These findings suggest a role for an altered gut microbiome and increased bacterial translocation following exercise in ME/CFS patients that may account for the profound post-exertional malaise experienced by ME/CFS patients.
DataSynthesizer: Privacy-Preserving Synthetic Datasets

To facilitate collaboration over sensitive data, we present DataSynthesizer, a tool that takes a sensitive dataset as input and generates a structurally and statistically similar synthetic dataset with strong privacy guarantees. The data owners need not release their data, while potential collaborators can begin developing models and methods with some confidence that their results will work similarly on the real dataset. The distinguishing feature of DataSynthesizer is its usability --- the data owner does not have to specify any parameters to start generating and sharing data safely and effectively.
 DataSynthesizer consists of three high-level modules --- DataDescriber, DataGenerator and ModelInspector. The first, DataDescriber, investigates the data types, correlations and distributions of the attributes in the private dataset, and produces a data summary, adding noise to the distributions to preserve privacy. DataGenerator samples from the summary computed by DataDescriber and outputs synthetic data. ModelInspector shows an intuitive description of the data summary that was computed by DataDescriber, allowing the data owner to evaluate the accuracy of the summarization process and adjust any parameters, if desired.
 We describe DataSynthesizer and illustrate its use in an urban science context, where sharing sensitive, legally encumbered data between agencies and with outside collaborators is reported as the primary obstacle to data-driven governance.
 The code implementing all parts of this work is publicly available at https://github.com/DataResponsibly/DataSynthesizer.
Global Norm-Aware Pooling for Pose-Robust Face Recognition at Low False Positive Rate

In this paper, we propose a novel Global Norm-Aware Pooling (GNAP) block, which reweights local features in a convolutional neural network (CNN) adaptively according to their L2 norms and outputs a global feature vector with a global average pooling layer. Our GNAP block is designed to give dynamic weights to local features in different spatial positions without losing spatial symmetry. We use a GNAP block in a face feature embedding CNN to produce discriminative face feature vectors for poserobust face recognition. The GNAP block is of very cheap computational cost, but it is very powerful for frontal-profile face recognition. Under the CFP frontal-profile protocol, the GNAP block can not only reduce EER dramatically but also boost TPR@FPR=0.1% (TPR i.e. True Positive Rate, FPR i.e. False Positive Rate) substantially. Our experiments show that the GNAP block greatly promotes pose-robust face recognition over the base model especially at low false positive rate.