nils-herrmann/clean_scirepeval huggingface dataset:
A Web Service Discovery Approach Based on Common Topic Groups Extraction

Web services have attracted much attention from distributed application designers and developers because of their roles in abstraction and interoperability among heterogeneous software systems, and a growing number of distributed software applications have been published as Web services on the Internet. Faced with the increasing numbers of Web services and service users, researchers in the services computing field have attempted to address a challenging issue, i.e., how to quickly find the suitable ones according to user queries. Many previous studies have been reported towards this direction. In this paper, a novel Web service discovery approach based on topic models is presented. The proposed approach mines common topic groups from the service-topic distribution matrix generated by topic modeling, and the extracted common topic groups can then be leveraged to match user queries to relevant Web services, so as to make a better trade-off between the accuracy of service discovery and the number of candidate Web services. Experiment results conducted on two publicly-available data sets demonstrate that, compared with several widely used approaches, the proposed approach can maintain the performance of service discovery at an elevated level by greatly decreasing the number of candidate Web services, thus leading to faster response time.
Analysis of Torque Capability and Quality in Vernier Permanent-Magnet Machines

Vernier permanent-magnet (VPM) machines have been obtaining a lot of attention over the past few years due to several advantages, such as their high torque density and simple mechanical structures. Moreover, it is found that the torque ripple of VPM machines is ultralow, even without specific design measures such as a short pitch, skewing slots/poles, magnet shaping technology, etc. This paper presents theoretical analysis and comprehensive simulations on the torque ripple of VPM machines. First, a general instantaneous torque equation of VPM machines is proposed to analyze torque features and the effect of parameters on the torque performance of VPM machines. Subsequently, based on the general torque equation and a finite-element algorithm, it is verified that torque smoothness is the inherent characteristic of VPM machines, and the torque ripple of VPM machines can be below 0.2%. Furthermore, it is demonstrated that the torque density of a VPM machine is 40% larger than that of a regular permanent-magnet machine. All these advantages demonstrate that VPM machines can obtain much better steady and dynamic drive performance. Finally, all the theoretical analyses are verified by experiments on a VPM prototype.
Mobile PHRs Compliance with Android and iOS Usability Guidelines

Mobile Personal Health Records (PHRs) have achieved a particularly strong market share since the appearance of more powerful mobile devices and popular worldwide mobile application markets such as Apple’s App Store and Android’s Google Play. However, Android and Apple have a set of recommendations on design and usability targeted towards developers who wish to publish apps in their stores: Android Design Guidelines and iOS Human Interface Guidelines. This paper aims to evaluate compliance with these guidelines by assessing the usability recommendations of a set of 24 selected mobile PHR applications. An analysis process based on a well-known Systematic Literature Review (SLR) protocol was used. The results show that the 24 mobile PHR applications studied are not suitably structured. 46 % of these applications do not use any of the recommended patterns, using instead lists or springboards, which are deprecated patterns for top-level menus. 70 % of the PHRs require a registration to be able to test the application when these interactions should be delayed. Our study will help both PHR users to select user-friendly mobile PHRs and PHR providers and developers to identify the good usability practices implemented by the applications with the highest scores.
Video retargeting: automating pan and scan

When a video is displayed on a smaller display than originally intended, some of the information in the video is necessarily lost. In this paper, we introduce Video Retargeting that adapts video to better suit the target display, minimizing the important information lost. We define a framework that measures the preservation of the source material, and methods for estimating the important information in the video. Video retargeting crops each frame and scales it to fit the target display. An optimization process minimizes information loss by balancing the loss of detail due to scaling with the loss of content and composition due to cropping. The cropping window can be moved during a shot to introduce virtual pans and cuts, subject to constraints that ensure cinematic plausibility. We demonstrate results of adapting a variety of source videos to small display sizes.
Volterra models and three-layer perceptrons

This paper proposes the use of a class of feedforward artificial neural networks with polynomial activation functions (distinct for each hidden unit) for practical modeling of high-order Volterra systems. Discrete-time Volterra models (DVMs) are often used in the study of nonlinear physical and physiological systems using stimulus-response data. However, their practical use has been hindered by computational limitations that confine them to low-order nonlinearities (i.e., only estimation of low-order kernels is practically feasible). Since three-layer perceptrons (TLPs) can be used to represent input-output nonlinear mappings of arbitrary order, this paper explores the basic relations between DVMs and TLPs with tapped-delay inputs in the context of nonlinear system modeling. A variant of TLP with polynomial activation functions-termed "separable Volterra networks" (SVNs)-is found particularly useful in deriving explicit relations with DVM and in obtaining practicable models of highly nonlinear systems from stimulus-response data. The conditions under which the two approaches yield equivalent representations of the input-output relation are explored, and the feasibility of DVM estimation via equivalent SVN training using backpropagation is demonstrated by computer-simulated examples and compared with results from the Laguerre expansion technique (LET). The use of SVN models allows practicable modeling of high-order nonlinear systems, thus removing the main practical limitation of the DVM approach.
Multi-modal Mean-Fields via Cardinality-Based Clamping

Mean Field inference is central to statistical physics. It has attracted much interest in the Computer Vision community to efficiently solve problems expressible in terms of large Conditional Random Fields. However, since it models the posterior probability distribution as a product of marginal probabilities, it may fail to properly account for important dependencies between variables. We therefore replace the fully factorized distribution of Mean Field by a weighted mixture of such distributions, that similarly minimizes the KL-Divergence to the true posterior. By introducing two new ideas, namely, conditioning on groups of variables instead of single ones and using a parameter of the conditional random field potentials, that we identify to the temperature in the sense of statistical physics to select such groups, we can perform this minimization efficiently. Our extension of the clamping method proposed in previous works allows us to both produce a more descriptive approximation of the true posterior and, inspired by the diverse MAP paradigms, fit a mixture of Mean Field approximations. We demonstrate that this positively impacts real-world algorithms that initially relied on mean fields.
Automatic Face Replacement in Video Based on 2D Morphable Model

This paper presents an automatic face replacement approach in video based on 2D morphable model. Our approach includes three main modules: face alignment, face morph, and face fusion. Given a source image and target video, the Active Shape Models (ASM) is adopted to source image and target frames for face alignment. Then the source face shape is warped to match the target face shape by a 2D morphable model. The color and lighting of source face are adjusted to keep consistent with those of target face, and seamlessly blended in the target face. Our approach is fully automatic without user interference, and generates natural and realistic results.
A comparison of radial and axial flux structures in electrical machines

This paper explores the comparative advantages and disadvantages of the simplest form of axial flux geometry, the single sided version, compared to the most common form of radial flux geometry with an inside rotor, when considering permanent magnet synchronous machines. A detailed literature review of comparisons is presented. New material is presented highlighting the benefits of single sided axial flux geometry. The constraints and assumptions made when comparing possibilities are discussed in detail, including a study of the biases these can introduce. The basis of this comparison is founded on constant electromagnetic airgap shear stress, being the product of electric loading and magnetic loading, and indeed the constancy of both of those factors. The metric used for comparison is simply that of the masses of the active materials, steel, copper, and magnet material. Examples are presented, including a machine which recently went into production in quantity. A range of lesser issues which are relevant when one is making a choice, are presented and discussed.
PTL-and clock-pulse circuit driven novel shift register architecture

In this paper, low power and area efficient 16-bit SISO(Serial In Serial Out) shift register is proposed. In this proposed design, the master-slave flip-flops are replaced by pulsed latches called SSASPL(Static differential Sense-Amp Shared Pulsed Latch)in order to reduce the area and power consumption. The timing issue of the pulsed latch is overcome by generating multiple non-overlap delayed short pulsed clock signals instead of using single pulsed clock signal. The shift register is divided into sub shift registers to reduce the number of clock buffers. Two circuits of256-bitand a 16-bitshift register(driven by CMOS-AND based clock pulse generator) are also implemented for study and comparison with proposed design. The proposed 16-bit shift register is driven by PTL-AND based clock pulse generator and is implemented using CMOS 0.18μm technology in Cadence Virtuoso. The proposed 16-bit shift register using PTL-AND clock pulse circuit consumes 14% less power and 4% less area compared to CMOS-AND pulse generation circuit.
3D-Printed Biosensor Arrays for Medical Diagnostics

While the technology is relatively new, low-cost 3D printing has impacted many aspects of human life. 3D printers are being used as manufacturing tools for a wide variety of devices in a spectrum of applications ranging from diagnosis to implants to external prostheses. The ease of use, availability of 3D-design software and low cost has made 3D printing an accessible manufacturing and fabrication tool in many bioanalytical research laboratories. 3D printers can print materials with varying density, optical character, strength and chemical properties that provide the user with a vast array of strategic options. In this review, we focus on applications in biomedical diagnostics and how this revolutionary technique is facilitating the development of low-cost, sensitive, and often geometrically complex tools. 3D printing in the fabrication of microfluidics, supporting equipment, and optical and electronic components of diagnostic devices is presented. Emerging diagnostics systems using 3D bioprinting as a tool to incorporate living cells or biomaterials into 3D printing is also reviewed.
Health benefits of fruit and vegetables are from additive and synergistic combinations of phytochemicals.

Cardiovascular disease and cancer are ranked as the first and second leading causes of death in the United States and in most industrialized countries. Regular consumption of fruit and vegetables is associated with reduced risks of cancer, cardiovascular disease, stroke, Alzheimer disease, cataracts, and some of the functional declines associated with aging. Prevention is a more effective strategy than is treatment of chronic diseases. Functional foods that contain significant amounts of bioactive components may provide desirable health benefits beyond basic nutrition and play important roles in the prevention of chronic diseases. The key question is whether a purified phytochemical has the same health benefit as does the whole food or mixture of foods in which the phytochemical is present. Our group found, for example, that the vitamin C in apples with skin accounts for only 0.4% of the total antioxidant activity, suggesting that most of the antioxidant activity of fruit and vegetables may come from phenolics and flavonoids in apples. We propose that the additive and synergistic effects of phytochemicals in fruit and vegetables are responsible for their potent antioxidant and anticancer activities, and that the benefit of a diet rich in fruit and vegetables is attributed to the complex mixture of phytochemicals present in whole foods.
WebRTC technology overview and signaling solution design and implementation

This paper describes the WebRTC technology and implementation of WebRTC client, server and signaling. Main parts of the WebRTC API are described and explained. Signaling methods and protocols are not specified by the WebRTC standards, therefore in this study we design and implement a novel signaling mechanism. The corresponding message sequence chart of the WebRTC communication behavior describes a communication flow between peers and the server. The server application is implemented as a WebSocket server. The client application demonstrates the use of the WebRTC API for achieving real-time communication. Benefits and future development of the WebRTC technology are mentioned.
NAT2TEST Tool: From Natural Language Requirements to Test Cases Based on CSP

Formal models are increasingly being used as input for automated test-generation strategies. However, typically the requirements are captured as English text, and these formal models are not readily available. With this in mind, we have devised a strategy (NAT2TEST) to obtain formal models from natural language requirements automatically, particularly to generate sound test cases. Our strategy is extensible, since we consider an intermediate and hidden formal characterisation of the system behaviour from which other formal notations can be derived. Here, we present the NAT2TEST tool, which implements our strategy.
Complementary and alternative therapies for treating multiple sclerosis symptoms: a systematic review.

Multiple sclerosis (MS) is a chronic disease of the central nervous system without a known cure. Thus the role of complementary and alternative therapies (CATs) for the management of symptoms lies in palliative care and this is borne out by the popularity of these treatments amongst MS sufferers. This review is aimed at determining whether this use is supported by evidence of effectiveness from rigorous clinical trials. Database literature searches were performed and papers were extracted in a pre-defined manner. Twelve randomized controlled trials were located that investigated a CAT for MS: nutritional therapy (4), massage (1), Feldenkrais bodywork (1), reflexology (1), magnetic field therapy (2), neural therapy (1) and psychological counselling (2). The evidence is not compelling for any of these therapies, with many trials suffering from significant methodological flaws. There is evidence to suggest some benefit of nutritional therapy for the physical symptoms of MS. Magnetic field therapy and neural therapy appear to have a short-term beneficial effect on the physical symptoms of MS. Massage/bodywork and psychological counselling seem to improve depression, anxiety and self-esteem. The effectiveness for other CATs is unproven at this time. In all the CATs examined further investigations are needed in the form of rigorous large-scale trials.
TunePad: Computational Thinking Through Sound Composition

Computational thinking skills will be important for the next generation of students. However, there is a disparity in the populations joining the field. Integrating computational thinking into artistic fields has shown to increase participation in computer science. In this paper, we present our initial design prototype for TunePad, a sound composition tablet application controlled by a block-based programming environment. TunePad is designed to introduce learners to computational thinking and to prepare them for text-based coding environments. From our preliminary testing, with children ages 7-14, we observed that our design actively engages learners and communicates how the programming blocks control the sounds being played. This testing is a prelude to more formal studies as we continue to improve the design and interface of TunePad. With this work, we intend to engage students in computational thinking who may not have otherwise been exposed, giving the opportunity to more people to enter the computer science field.
2018 Low-Power Image Recognition Challenge

The Low-Power Image Recognition Challenge (LPIRC, ​https://rebootingcomputing.ieee.org/lpirc​) is an annual competition started in 2015. The competition identifies the best technologies that can classify and detect objects in images efficiently (short execution time and low energy consumption) and accurately (high precision). Over the four years, the winners’ scores have improved more than 24 times. As computer vision is widely used in many battery-powered systems (such as drones and mobile phones), the need for low-power computer vision will become increasingly important. This paper summarizes LPIRC 2018 by describing the three different tracks and the winners’ solutions.
StickEar: making everyday objects respond to sound

encapsulates wireless sensor network technology into a form factor that is intuitive to reuse and redeploy. Each StickEar sensor node consists of a miniature sized microphone and speaker to provide sound-based input/output capabilities. We provide a discussion of interaction design space and hardware design space of StickEar that cuts across domains such as remote sound monitoring, remote triggering of sound, autonomous response to sound events, and controlling of digital devices using sound. We implemented three applications to demonstrate the unique interaction capabilities of StickEar.
Defense Methods Against Adversarial Examples for Recurrent Neural Networks

Adversarial examples are known to mislead deep learning models to incorrectly classify them, even in domains where such models achieve state of the art performance. Until recently, research on both attack and defense methods focused on image recognition, mostly using convolutional neural networks. In recent years, adversarial example generation methods for recurrent neural networks (RNNs) have been published, demonstrating that RNN classifiers are vulnerable as well. In this paper, we present five novel defense methods to make RNN classifiers more robust against such attacks, as opposed to previous defense methods, which where designed only for non-sequence based models. We evaluate our methods against state of the art attacks in the cyber security domain, where real adversaries (malware developers) exist, but our methods can be applied against any sequence based adversarial attack, e.g., in the NLP domain. Using our methods we decrease such attack effectiveness from 99.9% to about 15%.
Longitudinal relations between children's exposure to TV violence and their aggressive and violent behavior in young adulthood: 1977-1992.

Although the relation between TV-violence viewing and aggression in childhood has been clearly demonstrated, only a few studies have examined this relation from childhood to adulthood, and these studies of children growing up in the 1960s reported significant relations only for boys. The current study examines the longitudinal relations between TV-violence viewing at ages 6 to 10 and adult aggressive behavior about 15 years later for a sample growing up in the 1970s and 1980s. Follow-up archival data (N = 450) and interview data (N = 329) reveal that childhood exposure to media violence predicts young adult aggressive behavior for both males and females. Identification with aggressive TV characters and perceived realism of TV violence also predict later aggression. These relations persist even when the effects of socioeconomic status, intellectual ability, and a variety of parenting factors are controlled.
Implementation of Brain Breaks® in the Classroom and Effects on Attitudes toward Physical Activity in a Macedonian School Setting

The purpose of this study was to explore the effects of Brain Break® activities on interest and motivation for physical activity among schoolchildren and the contribution of such activities on learning for health and holistic development. The study sample was comprised of 283 participants, primary school students from 3rd to 5th grades from two public schools in the Republic of Macedonia. Six experimental and six control groups were included in the study. Interventions in classroom settings-based Brain Break® video exercises were introduced in the experimental group during a period of three months. Students' attitudes toward physical activity were tested using a self-report survey instrument entitled "Attitudes toward Physical Activity Scale (APAS)" before and after intervention. Applied factor analyses were completed and the results of these analysis support APAS validity and the successful use of this application in the measurement of the learning experience, self-awareness, self-efficacy, and self-confidence in developing physical fitness. Learning was enhanced by using video exercises. Information presented in this paper is meaningful for the promotion of better exercise habits and the holistic approach to better health by using personal motivation and motivation provided by others. The results from repeated ANCOVA suggest positive effects of the applied Brain Break® video exercises as an interventional program. The study confirms the effect of application of Brain Break® video exercises on children's attitudes for physical activity, motivation for PA, internalization of movement habits as personal good.
When sex is more than just sex: attachment orientations, sexual experience, and relationship quality.

The authors explored the contribution of individual differences in attachment orientations to the experience of sexual intercourse and its association with relationship quality. In Study 1, 500 participants completed self-report scales of attachment orientations and sexual experience. The findings indicated that whereas attachment anxiety was associated with an ambivalent construal of sexual experience, attachment avoidance was associated with more aversive sexual feelings and cognitions. In Study 2, 41 couples reported on their attachment orientations and provided daily diary measures of sexual experiences and relationship interactions for a period of 42 days. Results showed that attachment anxiety amplified the effects of positive and negative sexual experiences on relationship interactions. In contrast, attachment avoidance inhibited the positive relational effect of having sex and the detrimental relational effects of negative sexual interactions. The authors discuss the possibility that attachment orientations are associated with different sex-related strategies and goals within romantic relationships.