nils-herrmann/clean_scirepeval huggingface dataset:
A quantitative approach for evaluating software maintenance services

The ever-increasing representativeness of software maintenance in the daily effort of software team requires initiatives for enhancing the activities accomplished to provide a good service for users who request a software improvement. This article presents a quantitative approach for evaluating software maintenance services based on cluster analysis techniques. The proposed approach provides a compact characterization of the services delivered by a maintenance organization, including characteristics such as service, waiting, and queue time. The ultimate goal is to help organizations to better understand, manage, and improve their current software maintenance process. We also report in this paper the usage of the proposed approach in a medium-sized organization throughout 2010. This case study shows that 72 software maintenance requests can be grouped in seven distinct clusters containing requests with similar characteristics. The in-depth analysis of the clusters found with our approach can foster the understanding of the nature of the requests and, consequently, it may improve the process followed by the software maintenance team.
Analysis of Windows 8 Registry Artifacts

Microsoft's series of Windows operating systems represents some of the most commonly encountered technologies in the field of digital forensics. It is then fair to say that Microsoft's design decisions greatly affect forensic efforts. Because of this, it is exceptionally important for the forensics community to keep abreast of new developments in the Windows product line. With each new release, the Windows operating system may present investigators with significant new artifacts to explore. Described by some as the heart of the Windows operating system, the Windows registry has been proven to contain many of these forensically interesting artifacts. Given the weight of Microsoft's influence on digital forensics and the role of the registry within Windows operating systems, this thesis delves into the Windows 8 registry in the hopes of developing new Windows forensics utilities.
Demonstration of advanced reconnaissance techniques with the airborne SAR/GMTI sensor PAMIR

PAMIR (Phased Array Multifunctional Imaging Radar) is an experimental airborne radar system that has been designed and built by the Research Institute for High Frequency Physics and Radar Techniques (FHR) of Forschungsgesellschaft für Angewandte Naturwissenschaften (FGAN). The goal is to meet the growing demands for future reconnaissance systems with respect to flexibility and multi-mode operation by the use of an electronically steerable phased array antenna. The X-band system with a bandwidth of 1.8 GHz serves as a platform for different tasks. One of the main objectives is to demonstrate synthetic aperture radar (SAR) imaging at a very high resolution and for a long range. The fine resolution will also be applied for inverse SAR (ISAR) imaging of ground moving targets. Moreover, five parallel receiving channels allow array processing techniques like ground moving target indication (GMTI) via space–time adaptive processing, electronic counter-counter-measures and interferometric SAR with a very high 3D-resolution. A multi-channel scan-MTI mode with a range resolution adapted to the target size allows for a wide area GMTI operation that can be complemented by target tracking. Together with the predecessor system AER-II, operating at a frequency band contained in that of PAMIR, the possibility of experimental investigation of bistatic SAR is given. SAR images of large urban areas and ISAR images of moving objects, both with finest resolution down to the sub-decimetre scale, are presented. Results of GMTI in a wide area scanning mode and broadband bistatic experiments including true bistatic SAR processing are shown as well.
Drug discovery: new models for industry-academic partnerships.

The re-focusing of pharmaceutical industry research away from early discovery activities is stimulating the development of novel models of drug discovery, notably involving academia as a 'front end'. In this article the authors explore the drivers of change, the role of new entrants (universities with specialised core facilities) and novel partnership models. If they are to be sustainable and deliver, these new models must be flexible and properly funded by industry or public funding, rewarding all partners for contributions. The introduction of an industry-like process and experienced management teams signals a revolution in discovery that benefits society by improving the value gained from publicly funded research.
High voltage pulse power supply using Marx generator & solid-state switches

High voltage pulse power supply using Marx generator and solid-state switches is proposed in this study. The Marx generator is composed of 12 stages and each stage is made of IGBT stack, two diode stacks, and capacitor. To charge the capacitors of each stage in parallel, inductive charging method is used and this method results in high efficiency and high repetition rates. It can generate the pulse voltage with the following parameters: voltage: up to 120 kV, rising time: sub /spl mu/S, pulse width: up to 10 /spl mu/S, pulse repetition rate: 1000 pps. The proposed pulsed power generator uses IGBT stack with a simple driver and has modular design. So this system structure gives compactness and easiness to implement total system. Some experimental results are included to verify the system performances in this paper.
A fully integrated 24 GHz 8-path phased-array receiver in silicon

A fully integrated 8-channel phased-array receiver at 24 GHz is demonstrated. Each channel achieves a gain of 43 dB, noise figure of 8 dB, and an IIP3 of -11dBm, consuming 29 mA of current from a 2.5 V supply. The 8-channel array has a beam-forming resolution of 22.5/spl deg/, a peak-to-null ratio of 20 dB (4-bits), a total array gain of 61 dB, and improves the signal-to-noise ratio by 9 dB.
Adaptive Recommender Systems for Travel Planning

Conversational recommender systems have been introduced in Travel and Tourism applications in order to support interactive dialogues which assist users in acquiring their goals, e.g., travel planning in a dynamic packaging system. Notwithstanding this increased interactivity, these systems employ an interaction strategy that is specified a priori (at design time) and is followed quite rigidly during the interaction. In this paper we illustrate a new type of conversational recommender system which uses Reinforcement Learning techniques in order to autonomously learn an adaptive interaction strategy. After a successful validation in an off-line experiment (with simulated users), the approach is now applied within an online recommender system which is supported by the Austrian Tourism portal (Austria.info). In this paper, we present the methodology behind the adaptive conversational recommender system and a summarization of the most important issues which have been addressed in order to validate the approach in an online context with real users.
Mobile Assisted Language Learning: A Literature Review

Mobile assisted language learning (MALL) is a subarea of the growing field of mobile learning (mLearning) research which increasingly attracts the attention of scholars. This study provides a systematic review of MALL research within the specific area of second language acquisition during the period 2007 2012 in terms of research approaches, methods, theories and models, as well as results in the form of linguistic knowledge and skills. The findings show that studies of mobile technology use in different aspects of language learning support the hypothesis that mobile technology can enhance learners’ second language acquisition. However, most of the reviewed studies are experimental, small-scale, and conducted within a short period of time. There is also a lack of cumulative research; most theories and concepts are used only in one or a few papers. This raises the issue of the reliability of findings over time, across changing technologies, and in terms of scalability. In terms of gained linguistic knowledge and skills, attention is primarily on learners’ vocabulary acquisition, listening and speaking skills, and language acquisition in more general terms.
RETSIM : A shoe store agent-based simulation for fraud detection

RetSim is an agent-based simulator of a shoe store based on the transactional data of one of the largest retail shoe sellers in Sweden. The aim of RetSim is the generation of synthetic data that can be used for fraud detection research. Statistical and a Social Network Analysis (SNA) of relations between staff and customers was used to develop and calibrate the model. Our ultimate goal is for RetSim to be usable to model relevant scenarios to generate realistic data sets that can be used by academia, and others, to develop and reason about fraud detection methods without leaking any sensitive information about the underlying data. Synthetic data has the added benefit of being easier to acquire, faster and at less cost, for experimentation even for those that have access to their own data. We argue that RetSim generates data that usefully approximates the relevant aspects of the real data.
Temperament and personality: origins and outcomes.

This article reviews how a temperament approach emphasizing biological and developmental processes can integrate constructs from subdisciplines of psychology to further the study of personality. Basic measurement strategies and findings in the investigation of temperament in infancy and childhood are reviewed. These include linkage of temperament dimensions with basic affective-motivational and attentional systems, including positive affect/approach, fear, frustration/anger, and effortful control. Contributions of biological models that may support these processes are then reviewed. Research indicating how a temperament approach can lead researchers of social and personality development to investigate important person-environment interactions is also discussed. Lastly, adult research suggesting links between temperament dispositions and the Big Five personality factors is described.
Hybrid Full-Bridge Three-Level LLC Resonant Converter- A Novel DC-DC Converter Suitable for Fuel Cell Power System

This paper proposes a novel hybrid full-bridge three-level LLC resonant converter. It integrates the advantages of the hybrid full-bridge three-level converter and the LLC resonant converter. It can operate not only under three-level mode but also under two-level mode, so it is very suitable for wide input voltage range application, such as fuel cell power system. The input current ripple and output filter can also be reduced. Three-level leg switches just sustain only half of the input voltage. ZCS is achieved for the rectifier diodes, and the voltage stress across the rectifier diodes can be minimized to the output voltage. The main switches can realize ZVS from zero to full load. A 200-400 V input, 360 V/4 A output prototype converter is built in our lab to verify the operation principle of the proposed converter
An Empirical Investigation of Net-Enabled Business Value

acknowledges support by the National Science Foundation CAREER award under contract number IIS-9875746. The authors are grateful to the senior editor, associate editor and three anonymous reviewers for constructive critique and suggestions for improvements. Austin. His publications have appeared in several top tier journals such as American Economic Review,  Many traditional organizations have undertaken major initiatives to leverage the Internet to transform how they coordinate value activities with customers, suppliers and other business partners with the objective of improving firm performance. This paper addresses processes through which business value is created through such Internet-enabled value chain activities. Relying on the resource-based view (RBV) of the firm, we propose a model positing that a firm's abilities to coordinate and exploit firm resources – processes, IT and readiness of customers and suppliers – create online informational capabilities – a higher order resource – which then leads to improved operational and financial performance. The outcome of a firm's online informational capabilities is reflected in superior operational performance through customer and supplier-side digitization efforts, which reflect the extent to which transactions and external interactions occur electronically. We also hypothesize that increased customer and supplier-side digitization leads to better financial performance. The model is tested with data from over 1000 firms in manufacturing, retail and wholesale sectors. The analysis suggests that while most firms are lagging in their supplier-side initiatives relative to the customer-side, supplier-side digitization has a strong positive impact on customer-side digitization, which, in turn, leads to better financial performance. Further, both customer and supplier readiness to engage in digital interactions are shown to be as important as a firm's internal digitization initiatives, implying that a firm's transformation-related decisions should include its customers' and suppliers' resources and incentives.
A Dictionary Learning Approach for Classification: Separating the Particularity and the Commonality

Empirically, we find that, despite the class-specific features owned by the objects appearing in the images, the objects from different categories usually share some common patterns, which do not contribute to the discrimination of them. Concentrating on this observation and under the general dictionary learning (DL) framework, we propose a novel method to explicitly learn a common pattern pool (the commonality) and class-specific dictionaries (the particularity) for classification. We call our method DL-COPAR, which can learn the most compact and most discriminative class-specific dictionaries used for classification. The proposed DL-COPAR is extensively evaluated both on synthetic data and on benchmark image databases in comparison with existing DL-based classification methods. The experimental results demonstrate that DL-COPAR achieves very promising performances in various applications, such as face recognition, handwritten digit recognition, scene classification and object recognition.
Detecting Nastiness in Social Media

Although social media has made it easy for people to connect on a virtually unlimited basis, it has also opened doors to people who misuse it to undermine, harass, humiliate, threaten and bully others. There is a lack of adequate resources to detect and hinder its occurrence. In this paper, we present our initial NLP approach to detect invective posts as a first step to eventually detect and deter cyberbullying. We crawl data containing profanities and then determine whether or not it contains invective. Annotations on this data are improved iteratively by in-lab annotations and crowdsourcing. We pursue different NLP approaches containing various typical and some newer techniques to distinguish the use of swear words in a neutral way from those instances in which they are used in an insulting way. We also show that this model not only works for our data set, but also can be successfully applied to different data sets.
Innate immune mechanisms of colitis and colitis-associated colorectal cancer

The innate immune system provides first-line defences in response to invading microorganisms and endogenous danger signals by triggering robust inflammatory and antimicrobial responses. However, innate immune sensing of commensal microorganisms in the intestinal tract does not lead to chronic intestinal inflammation in healthy individuals, reflecting the intricacy of the regulatory mechanisms that tame the inflammatory response in the gut. Recent findings suggest that innate immune responses to commensal microorganisms, although once considered to be harmful, are necessary for intestinal homeostasis and immune tolerance. This Review discusses recent findings that identify a crucial role for innate immune effector molecules in protection against colitis and colitis-associated colorectal cancer and the therapeutic implications that ensue.
Electronic transport properties of individual chemically reduced graphene oxide sheets.

Individual graphene oxide sheets subjected to chemical reduction were electrically characterized as a function of temperature and external electric fields. The fully reduced monolayers exhibited conductivities ranging between 0.05 and 2 S/cm and field effect mobilities of 2-200 cm2/Vs at room temperature. Temperature-dependent electrical measurements and Raman spectroscopic investigations suggest that charge transport occurs via variable range hopping between intact graphene islands with sizes on the order of several nanometers. Furthermore, the comparative study of multilayered sheets revealed that the conductivity of the undermost layer is reduced by a factor of more than 2 as a consequence of the interaction with the Si/SiO2 substrate.
RSSI based indoor and outdoor distance estimation for localization in WSN

Research has revealed that the correlation between distance and RSSI (Received Signal Strength Indication) values is the key of ranging and localization technologies in wireless sensor networks (WSNs). In this paper, an RSSI model that estimates the distance between sensor nodes in WSNs is presented. The performance of this model is evaluated and analyzed in a real system deployment in an indoor and outdoor environment by performing an empirical measurement using Crossbow IRIS wireless sensor motes. Our result shows that there is less error in distance estimation in an outdoor environment compared to indoor environment. The results of these evaluations would contribute towards obtaining accurate locations of wireless sensor nodes.
Improved and Simplified Inapproximability for k-means

The k-means problem consists of finding k centers in R that minimize the sum of the squared distances of all points in an input set P from R to their closest respective center. Awasthi et. al. recently showed that there exists a constant ε′ > 0 such that it is NP-hard to approximate the k-means objective within a factor of 1 + ε′. We establish that the constant ε′ is at least 0.0013. For a given set of points P ⊂ R, the k-means problem consists of finding a partition of P into k clusters (C1, . . . , Ck) with corresponding centers (c1, . . . , ck) that minimize the sum of the squared distances of all points in P to their corresponding center, i.e. the quantity arg min (C1,...,Ck),(c1,...,ck) k ∑
Characterization and performance comparison of ripple-based control for voltage regulator modules

Three recently developed control methods for voltage regulator modules, namely, V/sup 2/ control, enhanced V/sup 2/ control, and enhanced V/sup 2/ control without output voltage dynamic feedback, are analyzed and compared in this paper. All three methods utilize the output voltage switching ripple for pulse-width modulation (PWM), hence, are collectively referred to as ripple-based control. A general modeling method based on the Krylov-Bogoliubov-Mitropolsky ripple estimation technique is applied to develop averaged models for single-channel as well as multichannel buck converters employing each of the control methods. Unlike existing models that are limited to small-signal operation, the proposed models are valid for large-signal operation and are capable of predicting subharmonic instability without including any sample-and-hold block as used in previous models. The paper also shows that adding parallel, high-quality ceramic capacitors at the output, which are ignored in previous models, can lead to pulse skipping and ripple instability, and a solution based on proper selection of the ceramic capacitors and/or ramp compensation at the PWM is presented. The models are further applied to analyze and compare the performance of the three control methods in terms of ripple stability, effective load current feedforward gain, and output impedance.
A survey of MRI-based medical image analysis for brain tumor studies.

MRI-based medical image analysis for brain tumor studies is gaining attention in recent times due to an increased need for efficient and objective evaluation of large amounts of data. While the pioneering approaches applying automated methods for the analysis of brain tumor images date back almost two decades, the current methods are becoming more mature and coming closer to routine clinical application. This review aims to provide a comprehensive overview by giving a brief introduction to brain tumors and imaging of brain tumors first. Then, we review the state of the art in segmentation, registration and modeling related to tumor-bearing brain images with a focus on gliomas. The objective in the segmentation is outlining the tumor including its sub-compartments and surrounding tissues, while the main challenge in registration and modeling is the handling of morphological changes caused by the tumor. The qualities of different approaches are discussed with a focus on methods that can be applied on standard clinical imaging protocols. Finally, a critical assessment of the current state is performed and future developments and trends are addressed, giving special attention to recent developments in radiological tumor assessment guidelines.
Impact of the needle winding technology on the operational behavior of an asynchronous machine

Distributed round-wire windings are usually manufactured using the insertion technology. If the needle winding technology is applied instead the end windings have to be conducted in a three-layer axial arrangement. This leads to differing coil lengths and thus to a phase asymmetry which is much more distinct than the one resulting from the insertion technology. In addition, it is possible that the first phase exhibits a higher end winding leakage inductance than the other phases if the distance between the first phase and the front end side of the stator core is too short. In this case the magnetic flux lines of the end windings partially close across the stator core producing an increase of the end winding leakage inductance. Therefore, in this paper the impact of the needle winding technology on the operational behavior of an asynchronous machine is investigated. For this purpose a needle wound electrical machine with three-layer end windings is compared to an electrical machine with very symmetric windings built up using manual insertion. By the use of the no load and blocked rotor test as well as a static stator measurement the machine parameters are determined and the impact of the phase asymmetry is investigated. In addition, load measurements are conducted in order to quantify the impact of the production related differences.