nils-herrmann/clean_scirepeval huggingface dataset:
Where do developers log? an empirical study on logging practices in industry

System logs are widely used in various tasks of software system management. It is crucial to avoid logging too little or too much. To achieve so, developers need to make informed decisions on where to log and what to log in their logging practices during development. However, there exists no work on studying such logging practices in industry or helping developers make informed decisions. To fill this significant gap, in this paper, we systematically study the logging practices of developers in industry, with focus on where developers log. We obtain six valuable findings by conducting source code analysis on two large industrial systems (2.5M and 10.4M LOC, respectively) at Microsoft. We further validate these findings via a questionnaire survey with 54 experienced developers in Microsoft. In addition, our study demonstrates the high accuracy of up to 90% F-Score in predicting where to log.
Apps to promote physical activity among adults: a review and content analysis

In May 2013, the iTunes and Google Play stores contained 23,490 and 17,756 smartphone applications (apps) categorized as Health and Fitness, respectively. The quality of these apps, in terms of applying established health behavior change techniques, remains unclear.



The study sample was identified through systematic searches in iTunes and Google Play. Search terms were based on Boolean logic and included AND combinations for physical activity, healthy lifestyle, exercise, fitness, coach, assistant, motivation, and support. Sixty-four apps were downloaded, reviewed, and rated based on the taxonomy of behavior change techniques used in the interventions. Mean and ranges were calculated for the number of observed behavior change techniques. Using nonparametric tests, we compared the number of techniques observed in free and paid apps and in iTunes and Google Play.



On average, the reviewed apps included 5 behavior change techniques (range 2-8). Techniques such as self-monitoring, providing feedback on performance, and goal-setting were used most frequently, whereas some techniques such as motivational interviewing, stress management, relapse prevention, self-talk, role models, and prompted barrier identification were not. No differences in the number of behavior change techniques between free and paid apps, or between the app stores were found.



The present study demonstrated that apps promoting physical activity applied an average of 5 out of 23 possible behavior change techniques. This number was not different for paid and free apps or between app stores. The most frequently used behavior change techniques in apps were similar to those most frequently used in other types of physical activity promotion interventions.
DeepTrend: A Deep Hierarchical Neural Network for Traffic Flow Prediction

In this paper, we consider the temporal pattern in traffic flow time series, and implement a deep learning model for traffic flow prediction. Detrending based methods decompose original flow series into trend and residual series, in which trend describes the fixed temporal pattern in traffic flow and residual series is used for prediction. Inspired by the detrending method, we propose DeepTrend, a deep hierarchical neural network used for traffic flow prediction which considers and extracts the time-variant trend. DeepTrend has two stacked layers: extraction layer and prediction layer. Extraction layer, a fully connected layer, is used to extract the time-variant trend in traffic flow by feeding the original flow series concatenated with corresponding simple average trend series. Prediction layer, an LSTM layer, is used to make flow prediction by feeding the obtained trend from the output of extraction layer and calculated residual series. To make the model more effective, DeepTrend needs first pre-trained layer-by-layer and then fine-tuned in the entire network. Experiments show that DeepTrend can noticeably boost the prediction performance compared with some traditional prediction models and LSTM with detrending based methods.
Image Classification using Support Vector Machine and Artificial Neural Network

Image classification is one of classical problems of concern in image processing. There are various approaches for solving this problem. The aim of this paper is bring together two areas in which are Artificial Neural Network (ANN) and Support Vector Machine (SVM) applying for image classification. Firstly, we separate the image into many sub-images based on the features of images. Each sub-image is classified into the responsive class by an ANN. Finally, SVM has been compiled all the classify result of ANN. Our proposal classification model has brought together many ANN and one SVM. Let it denote ANN_SVM. ANN_SVM has been applied for Roman numerals recognition application and the precision rate is 86%. The experimental results show the feasibility of our proposal model.
Supporting the analytical reasoning process in information visualization

This paper presents a new information visualization framework that supports the analytical reasoning process. It consists of three views - a data view, a knowledge view and a navigation view. The data view offers interactive information visualization tools. The knowledge view enables the analyst to record analysis artifacts such as findings, hypotheses and so on. The navigation view provides an overview of the exploration process by capturing the visualization states automatically. An analysis artifact recorded in the knowledge view can be linked to a visualization state in the navigation view. The analyst can revisit a visualization state from both the navigation and knowledge views to review the analysis and reuse it to look for alternate views. The whole analysis process can be saved along with the synthesized information. We present a user study and discuss the perceived usefulness of a prototype based on this framework that we have developed.
ZooBP: Belief Propagation for Heterogeneous Networks

Given a heterogeneous network, with nodes of di↵erent types – e.g., products, users and sellers from an online recommendation site like Amazon – and labels for a few nodes (‘honest’, ‘suspicious’, etc), can we find a closed formula for Belief Propagation (BP), exact or approximate? Can we say whether it will converge? BP, traditionally an inference algorithm for graphical models, exploits so-called “network e↵ects” to perform graph classification tasks when labels for a subset of nodes are provided; and it has been successful in numerous settings like fraudulent entity detection in online retailers and classification in social networks. However, it does not have a closed-form nor does it provide convergence guarantees in general. We propose ZooBP, a method to perform fast BP on undirected heterogeneous graphs with provable convergence guarantees. ZooBP has the following advantages: (1) Generality: It works on heterogeneous graphs with multiple types of nodes and edges; (2) Closed-form solution: ZooBP gives a closed-form solution as well as convergence guarantees; (3) Scalability: ZooBP is linear on the graph size and is up to 600⇥ faster than BP, running on graphs with 3.3 million edges in a few seconds. (4) E↵ectiveness: Applied on real data (a Flipkart e-commerce network with users, products and sellers), ZooBP identifies fraudulent users with a near-perfect precision of 92.3 % over the top 300 results.
Hierarchical structure-and-motion recovery from uncalibrated images

This paper addresses the structure-and-motion problem, that requires to find camera motion and 3D structure from point matches. A new pipeline, dubbed Samantha, is presented, that departs from the prevailing sequential paradigm and embraces instead a hierarchical approach. This method has several advantages, like a provably lower computational complexity, which is necessary to achieve true scalability, and better error containment, leading to more stability and less drift. Moreover, a practical autocalibration procedure allows to process images without ancillary information. Experiments with real data assess the accuracy and the computational efficiency of the method.
An Ensemble Method to Produce High-Quality Word Embeddings

A currently successful approach to computational semantics is to represent words as embeddings in a machine-learned vector space. We present an ensemble method that combines embeddings produced by GloVe (Pennington et al., 2014) and word2vec (Mikolov et al., 2013) with structured knowledge from the semantic networks ConceptNet (Speer and Havasi, 2012) and PPDB (Ganitkevitch et al., 2013), merging their information into a common representation with a large, multilingual vocabulary. The embeddings it produces achieve state-of-the-art performance on many word-similarity evaluations. Its score of ρ = .596 on an evaluation of rare words (Luong et al., 2013) is 16% higher than the previous best known system.
EXAMINING HEALTHCARE PROFESSIONALS' ACCEPTANCE OF ELECTRONIC MEDICAL RECORDS USING UTAUT

With the growing demand for digital information in health care, the electronic medical record (EMR) represents the foundation of health information technology. It is essential, however, in an industry still largely dominated by paper-based records, that such systems be accepted and used. This research evaluates registered nurses’, certified nurse practitioners and physician assistants’ acceptance of EMR’s as a means to predict, define and enhance use. The research utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) as the theoretical model, along with the Partial Least Square (PLS) analysis to estimate the variance. Overall, the findings indicate that UTAUT is able to provide a reasonable assessment of health care professionals’ acceptance of EMR’s with social influence a significant determinant of intention and use.
Segmentation and Clustering of Car-Following Behavior: Recognition of Driving Patterns

Driving behavior can be influenced by many factors that are not feasible to collect in driving behavior studies. The research presented in this paper investigates the characteristics of a wide range of driving behaviors linking driving states to the drivers' actions. The proposed methodology is structured such that a known state can be linked to multiple actions, thus accounting for the effects of unknown driving state factors. A two-step algorithm was developed and used for the segmentation and clustering of driving behaviors. The algorithm segments and clusters car-following behaviors based on eight state-action variables: the longitudinal acceleration, the lateral acceleration, the yaw rate, the vehicle speed, the lane offset, the yaw angle, the range, and the range rate. The results of this methodology are state-action clusters that define the driving pattern of drivers. The sample used in this paper included 20 different drivers, i.e., ten car and ten truck drivers. The results revealed that behavior patterns were different between car drivers but not between truck drivers. The results also show that car drivers exhibit behaviors that are unique to each driver, whereas truck drivers show a common driving pattern. The characteristics and frequency of recognized driving patterns are provided in this paper, along with the corresponding modeling parameters of each pattern.
Unexpected artefacts in PRNU-based camera identification: a 'Dresden Image Database' case-study

Although sensor noise is generally known as a very reliable means to uniquely identify digital cameras, care has to be taken with respect to camera model characteristics that may cause false accusations. While earlier reports focused on so-called linear patterns with a regular grid structure, also distortions due to geometric corrections of radial lens distortion have recently gained interest. Here, we report observations from a case study with the 'Dresden Image Database' that revealed further artefacts. We found diagonal line artefacts in Nikon CoolPix S710 sensor noise, as well as non-trivial dependencies between sensor noise, exposure time (FujiFilm J50) and focal length (Casio EX-Z150). At slower shutter speeds, original J50 images exhibit a slight horizontal shift, whereas EX-Z150 images exhibit irregular geometric distortions, which depend on the focal length and which become visible in the p-map of state-of-the-art resampling detectors. The observed artefacts may provide valuable clues for camera model identification, but also call for particular attention when creating reference noise patterns for applications that require low false negative rates.
Mitigating routing misbehavior in mobile ad hoc networks

This paper describes two techniques that improve throughput in an ad hoc network in the presence of nodes that agree to forward packets but fail to do so. To mitigate this problem, we propose categorizing nodes based upon their dynamically measured behavior. We use a watchdog that identifies misbehaving nodes and a pathrater that helps routing protocols avoid these nodes. Through simulation we evaluate watchdog and pathrater using packet throughput, percentage of overhead (routing) transmissions, and the accuracy of misbehaving node detection. When used together in a network with moderate mobility, the two techniques increase throughput by 17% in the presence of 40% misbehaving nodes, while increasing the percentage of overhead transmissions from the standard routing protocol's 9% to 17%. During extreme mobility, watchdog and pathrater can increase network throughput by 27%, while increasing the overhead transmissions from the standard routing protocol's 12% to 24%.
Handoff Rate and Coverage Analysis in Multi-Tier Heterogeneous Networks

This paper analyzes the impact of user mobility in multi-tier heterogeneous networks. We begin by obtaining the handoff rate for a mobile user in an irregular cellular network with the access point locations modeled as a homogeneous Poisson point process. The received signal-to-interference-ratio (SIR) distribution along with a chosen SIR threshold is then used to obtain the probability of coverage. To capture potential connection failures due to mobility, we assume that a fraction of handoffs result in such failures. Considering a multi-tier network with orthogonal spectrum allocation among tiers and the maximum biased average received power as the tier association metric, we derive the probability of coverage for two cases: 1) the user is stationary (i.e., handoffs do not occur, or the system is not sensitive to handoffs); 2) the user is mobile, and the system is sensitive to handoffs. We derive the optimal bias factors to maximize the coverage. We show that when the user is mobile, and the network is sensitive to handoffs, both the optimum tier association and the probability of coverage depend on the user's speed; a speed-dependent bias factor can then adjust the tier association to effectively improve the coverage, and hence system performance, in a fully-loaded network.
The Evolution of MAC Protocols in Wireless Sensor Networks: A Survey

Wireless Sensor Networks (WSNs) have become a leading solution in many important applications such as intrusion detection, target tracking, industrial automation, smart building and so on. Typically, a WSN consists of a large number of small, low-cost sensor nodes that are distributed in the target area for collecting data of interest. For a WSN to provide high throughput in an energy-efficient way, designing an efficient Medium Access Control (MAC) protocol is of paramount importance because the MAC layer coordinates nodes' access to the shared wireless medium. To show the evolution of WSN MAC protocols, this article surveys the latest progresses in WSN MAC protocol designs over the period 2002-2011. In the early development stages, designers were mostly concerned with energy efficiency because sensor nodes are usually limited in power supply. Recently, new protocols are being developed to provide multi-task support and efficient delivery of bursty traffic. Therefore, research attention has turned back to throughput and delay. This article details the evolution of WSN MAC protocols in four categories: asynchronous, synchronous, frame-slotted, and multichannel. These designs are evaluated in terms of energy efficiency, data delivery performance, and overhead needed to maintain a protocol's mechanisms. With extensive analysis of the protocols many future directions are stated at the end of this survey. The performance of different classes of protocols could be substantially improved in future designs by taking into consideration the recent advances in technologies and application demands.
Two's company, three's a crowd: a case study of crowdsourcing software development

Crowdsourcing is an emerging and promising approach which involves delegating a variety of tasks to an unknown workforce - the crowd. Crowdsourcing has been applied quite successfully in various contexts from basic tasks on Amazon Mechanical Turk to solving complex industry problems, e.g. InnoCentive. Companies are increasingly using crowdsourcing to accomplish specific software development tasks. However, very little research exists on this specific topic. This paper presents an in-depth industry case study of crowdsourcing software development at a multinational corporation. Our case study highlights a number of challenges that arise when crowdsourcing software development. For example, the crowdsourcing development process is essentially a waterfall model and this must eventually be integrated with the agile approach used by the company. Crowdsourcing works better for specific software development tasks that are less complex and stand-alone without interdependencies. The development cost was much greater than originally expected, overhead in terms of company effort to prepare specifications and answer crowdsourcing community queries was much greater, and the time-scale to complete contests, review submissions and resolve quality issues was significant. Finally, quality issues were pushed later in the lifecycle given the lengthy process necessary to identify and resolve quality issues. Given the emphasis in software engineering on identifying bugs as early as possible, this is quite problematic.
New results on recurrent network training: unifying the algorithms and accelerating convergence

How to efficiently train recurrent networks remains a challenging and active research topic. Most of the proposed training approaches are based on computational ways to efficiently obtain the gradient of the error function, and can be generally grouped into five major groups. In this study we present a derivation that unifies these approaches. We demonstrate that the approaches are only five different ways of solving a particular matrix equation. The second goal of this paper is develop a new algorithm based on the insights gained from the novel formulation. The new algorithm, which is based on approximating the error gradient, has lower computational complexity in computing the weight update than the competing techniques for most typical problems. In addition, it reaches the error minimum in a much smaller number of iterations. A desirable characteristic of recurrent network training algorithms is to be able to update the weights in an on-line fashion. We have also developed an on-line version of the proposed algorithm, that is based on updating the error gradient approximation in a recursive manner.
Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples

Many machine learning models are vulnerable to adversarial examples: inputs that are specially crafted to cause a machine learning model to produce an incorrect output. Adversarial examples that affect one model often affect another model, even if the two models have different architectures or were trained on different training sets, so long as both models were trained to perform the same task. An attacker may therefore train their own substitute model, craft adversarial examples against the substitute, and transfer them to a victim model, with very little information about the victim. Recent work has further developed a technique that uses the victim model as an oracle to label a synthetic training set for the substitute, so the attacker need not even collect a training set to mount the attack. We extend these recent techniques using reservoir sampling to greatly enhance the efficiency of the training procedure for the substitute model. We introduce new transferability attacks between previously unexplored (substitute, victim) pairs of machine learning model classes, most notably SVMs and decision trees. We demonstrate our attacks on two commercial machine learning classification systems from Amazon (96.19% misclassification rate) and Google (88.94%) using only 800 queries of the victim model, thereby showing that existing machine learning approaches are in general vulnerable to systematic black-box attacks regardless of their structure.
High performance SiC trench devices with ultra-low ron

We have developed SiC trench structure Schottoky diodes and SiC double-trench MOSFETs. We succeeded in improving device performance by the reduction of the electric field through the introduction of the aforementioned trench structures. The threshold voltage of the trench structure Schottky diode is 0.48V smaller than the planar. Also, the lowest on-resistance in SiC MOSFETs was achieved.
Automatic Extraction of Domain Name Generation Algorithms from Current Malware

Many botnets employ a method called domain fluxing for resilience. This technique strengthens the addressing layer of a botnet and allows a bot herder to dynamically provide command and control servers. For the calculation of new domains, a domain name generation algorithm (DGA) is used. In order to take actions against a domain fluxing botnet, the domain name generation algorithm has to be known.
A Slim RFID Tag Antenna Design for Metallic Object Applications

A slim radio frequency identification (RFID) tag antenna design for metallic objects application is proposed in this letter. It is designed based on a high-impedance surface (HIS) unit cell structure directly rather than adopting a large HIS ground plane. The antenna structure consists of metallic rectangular patches electrically connected through vias to the ground plane to form an RFID tag antenna that is suitable for mounting on metallic objects. The experimental tests show that the maximum read range of RFID tag placed on a metallic object is about 3.1 m and the overall size is 65 times 20 times 1.5 mm3. It is thinner than inverted-F antenna (IFA), planar inverted-F antenna (PIFA), or patch-type antennas for metallic objects. Simulation and measurement results of the proposed RFID tag antenna are also presented in this letter.
AdReveal: improving transparency into online targeted advertising

To address the pressing need to provide transparency into the online targeted advertising ecosystem, we present AdReveal, a practical measurement and analysis framework, that provides a first look at the prevalence of different ad targeting mechanisms. We design and implement a browser based tool that provides detailed measurements of online display ads, and develop analysis techniques to characterize the contextual, behavioral and re-marketing based targeting mechanisms used by advertisers. Our analysis is based on a large dataset consisting of measurements from 103K webpages and 139K display ads. Our results show that advertisers frequently target users based on their online interests; almost half of the ad categories employ behavioral targeting. Ads related to Insurance, Real Estate and Travel and Tourism make extensive use of behavioral targeting. Furthermore, up to 65% of ad categories received by users are behaviorally targeted. Finally, our analysis of re-marketing shows that it is adopted by a wide range of websites and the most commonly targeted re-marketing based ads are from the Travel and Tourism and Shopping categories.