nils-herrmann/clean_scirepeval huggingface dataset:
The MSR Cookbook: Mining a decade of research

The Mining Software Repositories (MSR) research community has grown significantly since the first MSR workshop was held in 2004. As the community continues to broaden its scope and deepens its expertise, it is worthwhile to reflect on the best practices that our community has developed over the past decade of research. We identify these best practices by surveying past MSR conferences and workshops. To that end, we review all 117 full papers published in the MSR proceedings between 2004 and 2012. We extract 268 comments from these papers, and categorize them using a grounded theory methodology. From this evaluation, four high-level themes were identified: data acquisition and preparation, synthesis, analysis, and sharing/replication. Within each theme we identify several common recommendations, and also examine how these recommendations have evolved over the past decade. In an effort to make this survey a living artifact, we also provide a public forum that contains the extracted recommendations in the hopes that the MSR community can engage in a continuing discussion on our evolving best practices.
Steps Toward Deep Kernel Methods from Infinite Neural Networks

Contemporary deep neural networks exhibit impressive results on practical problems. These networks generalize well although their inherent capacity may extend significantly beyond the number of training examples. We analyze this behavior in the context of deep, infinite neural networks. We show that deep infinite layers are naturally aligned with Gaussian processes and kernel methods, and devise stochastic kernels that encode the information of these networks. We show that stability results apply despite the size, offering an explanation for their empir-
Guaranteed safe online learning of a bounded system

For some time now machine learning methods have been widely used in perception for autonomous robots. While there have been many results describing the performance of machine learning techniques with regards to their accuracy or convergence rates, relatively little work has been done on developing theoretical performance guarantees about their stability and robustness. As a result, many machine learning techniques are still limited to being used in situations where safety and robustness are not critical for success. One way to overcome this difficulty is by using reachability analysis, which can be used to compute regions of the state space, known as reachable sets, from which the system can be guaranteed to remain safe over some time horizon regardless of the disturbances. In this paper we show how reachability analysis can be combined with machine learning in a scenario in which an aerial robot is attempting to learn the dynamics of a ground vehicle using a camera with a limited field of view. The resulting simulation data shows that by combining these two paradigms, one can create robotic systems that feature the best qualities of each, namely high performance and guaranteed safety.
mRMR-ABC: A Hybrid Gene Selection Algorithm for Cancer Classification Using Microarray Gene Expression Profiling

An artificial bee colony (ABC) is a relatively recent swarm intelligence optimization approach. In this paper, we propose the first attempt at applying ABC algorithm in analyzing a microarray gene expression profile. In addition, we propose an innovative feature selection algorithm, minimum redundancy maximum relevance (mRMR), and combine it with an ABC algorithm, mRMR-ABC, to select informative genes from microarray profile. The new approach is based on a support vector machine (SVM) algorithm to measure the classification accuracy for selected genes. We evaluate the performance of the proposed mRMR-ABC algorithm by conducting extensive experiments on six binary and multiclass gene expression microarray datasets. Furthermore, we compare our proposed mRMR-ABC algorithm with previously known techniques. We reimplemented two of these techniques for the sake of a fair comparison using the same parameters. These two techniques are mRMR when combined with a genetic algorithm (mRMR-GA) and mRMR when combined with a particle swarm optimization algorithm (mRMR-PSO). The experimental results prove that the proposed mRMR-ABC algorithm achieves accurate classification performance using small number of predictive genes when tested using both datasets and compared to previously suggested methods. This shows that mRMR-ABC is a promising approach for solving gene selection and cancer classification problems.
A 16b 250MS/s IF-sampling pipelined A/D converter with background calibration

Wireless communication applications have driven the development of high-resolution A/D converters (ADCs) with high sample rates, good AC performance and IF sampling capability to enable wider cellular coverage, more carriers, and to simplify the system design. We describe a 16b ADC with a sample rate up to 250MS/s that employs background calibration of the residue amplifier (RA) gain errors. The ADC has an integrated input buffer and is fabricated on a 0.18µm BiCMOS process. When the input buffer is bypassed, the SNR is 77.5dB and the SFDR is 90dB at 10MHz input frequency. With the input buffer, the SNR is 76dB and the SFDR is 95dB. The ADC consumes 850mW from a 1.8V supply, and the input buffer consumes 150mW from a 3V supply. The input span is 2.6Vp-p and the jitter is 60fs.
Multilayer Antenna Package for IEEE 802.11ad Employing Ultralow-Cost FR4

This paper demonstrates the possibility and feasibility of an ultralow-cost antenna-in-package (AiP) solution for the upcoming generation of wireless local area networks (WLANs) denoted as IEEE802.11ad. The iterative design procedure focuses on maximally alleviating the inherent disadvantages of high-volume FR4 process at 60 GHz such as its relatively high material loss and fabrication restrictions. Within the planar antenna package, the antenna element, vertical transition, antenna feedline, and low- and high-speed interfaces are allocated in a vertical schematic. A circular stacked patch antenna renders the antenna package to exhibit 10-dB return loss bandwidth from 57-66 GHz. An embedded coplanar waveguide (CPW) topology is adopted for the antenna feedline and features less than 0.24 dB/mm in unit loss, which is extracted from measured parametric studies. The fabricated single antenna package is 9 mm × 6 mm × 0.404 mm in dimension. A multiple-element antenna package is fabricated, and its feasibility for future phase array applications is studied. Far-field radiation measurement using an inhouse radio-frequency (RF) probe station validates the single-antenna package to exhibit more than 4.1-dBi gain and 76% radiation efficiency.
Proactive Insider Threat Detection through Graph Learning and Psychological Context

The annual incidence of insider attacks continues to grow, and there are indications this trend will continue. While there are a number of existing tools that can accurately identify known attacks, these are reactive (as opposed to proactive) in their enforcement, and may be eluded by previously unseen, adversarial behaviors. This paper proposes an approach that combines Structural Anomaly Detection (SA) from social and information networks and Psychological Profiling (PP) of individuals. SA uses technologies including graph analysis, dynamic tracking, and machine learning to detect structural anomalies in large-scale information network data, while PP constructs dynamic psychological profiles from behavioral patterns. Threats are finally identified through a fusion and ranking of outcomes from SA and PP. The proposed approach is illustrated by applying it to a large data set from a massively multi-player online game, World of War craft (WoW). The data set contains behavior traces from over 350,000 characters observed over a period of 6 months. SA is used to predict if and when characters quit their guild (a player association with similarities to a club or workgroup in non-gaming contexts), possibly causing damage to these social groups. PP serves to estimate the five-factor personality model for all characters. Both threads show good results on the gaming data set and thus validate the proposed approach.
Agent-based virtual organization architecture

The purpose of this paper is to present the applicability of THOMAS, an architecture specially designed to model agent-based virtual organizations, in the development of a multiagent system for managing and planning routes for clients in a mall. In order to build virtual organizations, THOMAS offers mechanisms to take into account their structure, behaviour, dynamic, norms and environment. Moreover, one of the primary characteristics of the THOMAS architecture is the use of agents with reasoning and planning capabilities. These agents can perform a dynamic reorganization when they detect changes in the environment. The proposed architecture is composed of a set of related modules that are appropriate for developing systems in highly volatile environments similar to the one presented in this study. This paper presents THOMAS as well as the results obtained after having applied the system to a case study. & 2011 Elsevier Ltd. All rights reserved.
Side channel analysis of AVR XMEGA crypto engine

AVR XMEGA is the recent general-purpose 8-bit microcontroller from Atmel featuring symmetric crypto engines. We analyze the resistance of XMEGA crypto engines to side channel attacks. We reveal the relatively strong side channel leakage of the AES engine that enables full 128-bit AES secret key recovery in a matter of several minutes with a measurement setup cost about 1000 USD. 3000 power consumption traces are sufficient for the successful attack. Our analysis was performed without knowing the details of the crypto engine internals; quite the contrary, it reveals some details about the implementation. We sketch other feasible side channel attacks on XMEGA and suggest the counter-measures that can raise the complexity of the attacks but not fully prevent them.
Active Learning for Black-Box Semantic Role Labeling with Neural Factors

Active learning is a useful technique for tasks for which unlabeled data is abundant but manual labeling is expensive. One example of such a task is semantic role labeling (SRL), which relies heavily on labels from trained linguistic experts. One challenge in applying active learning algorithms for SRL is that the complete knowledge of the SRL model is often unavailable, against the common assumption that active learning methods are aware of the details of the underlying models. In this paper, we present an active learning framework for blackbox SRL models (i.e., models whose details are unknown). In lieu of a query strategy based on model details, we propose a neural query strategy model that embeds both language and semantic information to automatically learn the query strategy from predictions of an SRL model alone. Our experimental results demonstrate the effectiveness of both this new active learning framework and the neural query strategy model.
Practical and lightweight domain isolation on Android

In this paper, we introduce a security framework for practical and lightweight domain isolation on Android to mitigate unauthorized data access and communication among applications of different trust levels (e.g., private and corporate). We present the design and implementation of our framework, TrustDroid, which in contrast to existing solutions enables isolation at different layers of the Android software stack: (1) at the middleware layer to prevent inter-domain application communication and data access, (2) at the kernel layer to enforce mandatory access control on the file system and on Inter-Process Communication (IPC) channels, and (3) at the network layer to mediate network traffic. For instance, (3) allows network data to be only read by a particular domain, or enables basic context-based policies such as preventing Internet access by untrusted applications while an employee is connected to the company's network.
 Our approach accurately addresses the demands of the business world, namely to isolate data and applications of different trust levels in a practical and lightweight way. Moreover, our solution is the first leveraging mandatory access control with TOMOYO Linux on a real Android device (Nexus One). Our evaluation demonstrates that TrustDroid only adds a negligible overhead, and in contrast to contemporary full virtualization, only minimally affects the battery's life-time.
Biometrics and Face Recognition Techniques

Biometrics is a growing technology, which has been widely used in forensics, secured access and prison security. A biometric system is fundamentally a pattern recognition system that recognizes a person by determining the authentication by using his different biological features i.e. Fingerprint, retina-scan, iris scan, hand geometry, and face recognition are leading physiological biometrics and behavioral characteristic are Voice recognition, keystroke-scan, and signature-scan. In this paper different biometrics techniques such as Iris scan, retina scan and face recognition techniques are discussed.  Biometric, Biometric techniques, Eigenface, Face recognition.
Self-localization and control of an omni-directional mobile robot based on an omni-directional camera

This paper presents a design of simultaneous localization and mapping (SLAM) for an omni-directional mobile robot using an omni-directional camera. A method is proposed to realize visual SLAM of the omni-directional mobile robot based on extended Kalman filter (EKF). Taking advantage of the 360° view of omni-directional images, visual reference scan approach is adopted in the SLAM design. Features of previously visited places can be used repeatedly to reduce the complexity of EKF calculation. Practical experiments of the proposed self-localization and control algorithms have been carried out by using a self-constructed omni-directional mobile robot. The localization error between the start point and target point is less than 0.15m and 1° after traveling more than 40 meters in an indoor environment.
Time on the Internet at home, loneliness, and life satisfaction: Evidence from panel time-diary data

This study uses data collected from adult U.S. residents in 2004 and 2005 to examine whether loneliness and life satisfaction are associated with time spent at home on various Internet activities. Cross-sectional models reveal that time spent browsing the web is positively related to loneliness and negatively related to life satisfaction. Some of the relationships revealed by cross-sectional models persist even when considering the same individuals over time in fixed-effects models that account for time-invariant, individual-level characteristics. Our results vary according to how the time use data were collected, indicating that survey design can have important consequences for research in this area.
Towards Optimal One Pass Large Scale Learning with Averaged Stochastic Gradient Descent

For large scale learning problems, it is desirable if we can obtain the optimal model parameters by going through the data in only one pass. Polyak and Juditsky (1992) showed that asymptotically the test performance of the simple average of the parameters obtained by stochastic gradient descent (SGD) is as good as that of the parameters which minimize the empirical cost. However, to our knowledge, despite its optimal asymptotic convergence rate, averaged SGD (ASGD) received little attention in recent research on large scale learning. One possible reason is that it may take a prohibitively large number of training samples for ASGD to reach its asymptotic region for most real problems. In this paper, we present a finite sample analysis for the method of Polyak and Juditsky (1992). Our analysis shows that it indeed usually takes a huge number of samples for ASGD to reach its asymptotic region for improperly chosen learning rate. More importantly, based on our analysis, we propose a simple way to properly set learning rate so that it takes a reasonable amount of data for ASGD to reach its asymptotic region. We compare ASGD using our proposed learning rate with other well known algorithms for training large scale linear classifiers. The experiments clearly show the superiority of ASGD.
On the Comprehension of Program Comprehension

Research in program comprehension has evolved considerably over the past decades. However, only little is known about how developers practice program comprehension in their daily work. This article reports on qualitative and quantitative research to comprehend the strategies, tools, and knowledge used for program comprehension. We observed 28 professional developers, focusing on their comprehension behavior, strategies followed, and tools used. In an online survey with 1,477 respondents, we analyzed the importance of certain types of knowledge for comprehension and where developers typically access and share this knowledge.
 We found that developers follow pragmatic comprehension strategies depending on context. They try to avoid comprehension whenever possible and often put themselves in the role of users by inspecting graphical interfaces. Participants confirmed that standards, experience, and personal communication facilitate comprehension. The team size, its distribution, and open-source experience influence their knowledge sharing and access behavior. While face-to-face communication is preferred for accessing knowledge, knowledge is frequently shared in informal comments.
 Our results reveal a gap between research and practice, as we did not observe any use of comprehension tools and developers seem to be unaware of them. Overall, our findings call for reconsidering the research agendas towards context-aware tool support.
Improving Elevator Performance Using Reinforcement Learning

This paper describes the application of reinforcement learning (RL) to the di cult real world problem of elevator dispatching. The elevator domain poses a combination of challenges not seen in most RL research to date. Elevator systems operate in continuous state spaces and in continuous time as discrete event dynamic systems. Their states are not fully observable and they are nonstationary due to changing passenger arrival rates. In addition, we use a team of RL agents, each of which is responsible for controlling one elevator car. The team receives a global reinforcement signal which appears noisy to each agent due to the e ects of the actions of the other agents, the random nature of the arrivals and the incomplete observation of the state. In spite of these complications, we show results that in simulation surpass the best of the heuristic elevator control algorithms of which we are aware. These results demonstrate the power of RL on a very large scale stochastic dynamic optimization problem of practical utility.
Optimal randomized classification in adversarial settings

The problem of learning to distinguish good inputs from malicious has come to be known as adversarial classification emphasizing the fact that, unlike traditional classification, the adversary can manipulate input instances to avoid being so classified. We offer the first general theoretical analysis of the problem of adversarial classification, resolving several important open questions in the process. First, we significantly generalize previous results on adversarial classifier reverse engineering (ACRE), showing that if a classifier can be efficiently learned, it can subsequently be efficiently reverse engineered with arbitrary precision. We extend this result to randomized classification schemes, but now observe that reverse engineering is imperfect, and its efficacy depends on the defender’s randomization scheme. Armed with this insight, we proceed to characterize optimal randomization schemes in the face of adversarial reverse engineering and classifier manipulation. What we find is quite surprising: in all the model variations we consider, the defender’s optimal policy tends to be either to randomize uniformly (ignoring baseline classification accuracy), which is the case for targeted attacks, or not to randomize at all, which is typically optimal when attacks are indiscriminate.
Comparison of Self organizing maps and Sammon's mapping on agricultural datasets for precision agriculture

Over the ages technology has been occupying every field including the agriculture. Precision agriculture and Visual data mining uses technology to apply specific principles of data to interpret details like when and how much fertilizers to be used in a particular area (land). Data mining is the process of detecting patterns in a clustering form (certain chunk of information) to get more precise and accurate information. Not only it provides facilitating results but also improves the efficiency of farmer's productivity, it has also helped in qualitative improvement in the overall quality of life by providing timely and data inputs for decision making. Raw data collected from the statistics analysis has helped in determining the data to its full extent. This paper shows the techniques applied to real and till date agriculture data to reduce the high dimensional input data to much smaller size. We are using Self-organizing maps and multi-dimensional scaling techniques (Sammon's mapping) to reduce the data.
A survey of research and practices of Network-on-chip

The scaling of microchip technologies has enabled large scale systems-on-chip (SoC). Network-on-chip (NoC) research addresses global communication in SoC, involving (i) a move from computation-centric to communication-centric design and (ii) the implementation of scalable communication structures. This survey presents a perspective on existing NoC research. We define the following abstractions: system, network adapter, network, and link to explain and structure the fundamental concepts. First, research relating to the actual network design is reviewed. Then system level design and modeling are discussed. We also evaluate performance analysis techniques. The research shows that NoC constitutes a unification of current trends of intrachip communication rather than an explicit new alternative.
The Myths and Facts behind Cyber Security Risks for Industrial Control Systems

Process control and SCADA systems, with their reliance on proprietary networks and hardware, have long been considered immune to the network attacks that have wreaked so much havoc on corporate information systems. Unfortunately, new research indicates this complacency is misplaced – the move to open standards such as Ethernet, TCP/IP and web technologies is letting hackers take advantage of the control industry’s ignorance. This paper summarizes the incident information collected in the BCIT Industrial Security Incident Database (ISID), describes a number of events that directly impacted process control systems and identifies the lessons that can be learned from these security events.
Modeling and simulation of robot based on Matlab/SimMechanics

In recent years, studies about control methods for complex machines and robots have been developed rapidly. Biped robots are often treated as inverted pendulums for its simple structure. But modeling of robot and other complex machines is a time-consuming procedure. A new method of modeling and simulation of robot based on SimMechanics is proposed in this paper. Physical modeling, parameter setting and simulation are presented in detail. The SimMechanics block model is first used in modeling and simulation of inverted pendulums. Simulation results of the SimMechanics block model and mathematical model for single inverted pendulum are compared. Furthermore, a full state feedback controller is designed to satisfy the performance requirement. It indicates that SimMechanics can be used for unstable nonlinear system and robots.