nils-herrmann/clean_scirepeval huggingface dataset:
Dead-time elimination method and current polarity detection circuit for three-phase PWM-controlled inverter

This paper will present a dead-time elimination scheme and the related current polarity detection circuit without separate power sources for three-phase inverters. The presented scheme includes the freewheeling current polarity detection circuit and the PWM control generator without dead time. It will be shown that the presented scheme eliminates the dead time of PWM control for inverter and therefore dramatically improving the output voltage loss and current distortion. Experimental results derived from an FPGA-based PWM-controlled inverter are shown for confirmation.
Blue-Collar Discourses of Workplace Dignity : Using Outgroup Comparisons to Construct Positive Identities

People generally possess a strong desire to construct positive, dignified work identities. However, this goal may be more challenging for some people, such as blue-collar workers, whose occupations may not offer qualities typically associated with workplace dignity. Interviews with 37 people from a blue-collar mining community reveal three central identity discourses about workplace dignity: All jobs are important and valuable; dignity is located in the quality of the job performed; and dignity emerges from the way people treat and are treated by others. Participants communicated these themes by backgrounding their own occupations and drawing comparisons between two outgroups, low-status, lowpaid dirty workers and high-prestige, white-collar professionals. Implications for understanding how identity work is negotiated and for managing a blue-collar workforce are explained.
Batch Identification Game Model for Invalid Signatures in Wireless Mobile Networks

Secure access is one of the fundamental problems in wireless mobile networks. Digital signature is a widely used technique to protect messages’ authenticity and nodes’ identities. From the practical perspective, to ensure the quality of services in wireless mobile networks, ideally the process of signature verification should introduce minimum delay. Batch cryptography technique is a powerful tool to reduce verification time. However, most of the existing works focus on designing batch verification algorithms for wireless mobile networks without sufficiently considering the impact of invalid signatures, which can lead to verification failures and performance degradation. In this paper, we propose a Batch Identification Game Model (BIGM) in wireless mobile networks, enabling nodes to find invalid signatures with reasonable delay no matter whether the game scenario is complete information or incomplete information. Specifically, we analyze and prove the existence of Nash Equilibriums (NEs) in both scenarios, to select the dominant algorithm for identifying invalid signatures. To optimize the identification algorithm selection, we propose a self-adaptive auto-match protocol which estimates the strategies and states of attackers based on historical information. Comprehensive simulation results in terms of NE reasonability, algorithm selection accuracy, and identification delay are provided to demonstrate that BIGM can identify invalid signatures more efficiently than existing algorithms.
Transfer knowledge for high sparsity in deep neural networks

Deep neural networks have achieve great success in various applications recently. Lots of efforts have been made to reduce their parameters to make these models accessible to portable devices. However, the conflict between model size and performance loss is always crucial when we compress a deep neural network. In this work, we propose a new method to keep the performance of networks when pruning them. For a model to be pruned, we train a teacher model to boost its inference accuracy, so that higher compression ratio can be reached with the same or better performance.
Open-Domain Neural Dialogue Systems

Until recently, the goal of developing opendomain dialogue systems that not only emulate human conversation but fulfill complex tasks, such as travel planning, seemed elusive. However, we start to observe promising results in the last few years as the large amount of conversation data is available for training and the breakthroughs in deep learning and reinforcement learning are applied to dialogue. In this tutorial, we start with a brief introduction to the history of dialogue research. Then, we describe in detail the deep learning and reinforcement learning technologies that have been developed for two types of dialogue systems. First is a task-oriented dialogue system that can help users accomplish tasks, ranging from meeting scheduling to vacation planning. Second is a social bot that can converse seamlessly and appropriately with humans. In the final part of the tutorial, we review attempts to developing opendomain neural dialogue systems by combining the strengths of task-oriented dialogue systems and social bots. The tutorial material is available at http://opendialogue.miulab.tw.
An "AI readability" Formula for French as a Foreign Language

This paper present a new readability formula for French as a foreign language (FFL), which relies on 46 textual features representative of the lexical, syntactic, and semantic levels as well as some of the specificities of the FFL context. We report comparisons between several techniques for feature selection and various learning algorithms. Our best model, based on support vector machines (SVM), significantly outperforms previous FFL formulas. We also found that semantic features behave poorly in our case, in contrast with some previous readability studies on English as a first language.
Concept of standardization, extraction and pre phytochemical screening strategies for herbal drug

Standardization of drugs means confirmation of its identity and determination of its quality and purity. At present due to advancement in the chemical knowledge of crude drugs various methods like botanical, chemical, spectroscopic and biological methods are used for estimating active constituents present in the crude drugs in addition to its physical constants. Plants have been known to relieve various diseases in Ayurveda. Therefore, the researchers today are emphasizing on evaluation and characterization of various plants and plant constituents against a number of diseases based on their traditional claims of the plants given in Ayurveda. Extraction of the bioactive plant constituents has always been a challenging task for the researchers. In this present review, an attempt has been made to give an overview of certain extractants and extraction processes with their advantages and disadvantages.
Full-reference image quality assessment-based B-mode ultrasound image similarity measure

Kele Xu, Xi Liu, Chang Liu, Hengxing Cai, Zhifeng Gao Pierre and Marie Curie University, 75005 Paris, France ESPCI Paris, PSL Research University, CNRS, 1 rue Jussieu, Paris, 75005, France College of Electronic Science and Engineering, National University of Defense Technology, Changsha 410073, China Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China School of Software and Microelectronics, Peking University, 100871 Beijing, China School of Engineering, Sun Yat-Sen University, 510006 Guangzhou, China School of Software and Microelectronics, Peking University, 100871 Beijing, China
Discovering affective regions in deep convolutional neural networks for visual sentiment prediction

In this paper, we address the problem of automatically recognizing emotions in still images. While most of current work focus on improving whole-image representations using CNNs, we argue that discovering affective regions and supplementing local features will boost the performance, which is inspired by the observation that both global distributions and salient objects carry massive sentiments. We propose an algorithm to discover affective regions via deep framework, in which we use an off-the-shelf tool to generate N object proposals from a query image and rank these proposals with their objectness scores. Then, each proposal's sentiment score is computed using a pre-trained and fine-tuned CNN model. We combine both scores and select top K regions from the N candidates. These K regions are regarded as the most affective ones of the input image. Finally, we extract deep features from the whole-image and the selected regions, respectively, and sentiment label is predicted. The experiments show that our method is able to detect the affective local regions and achieve state-of-the-art performances on several popular datasets.
Cluster Definition by the Optimization of Simple Measures

We adopt the following measures of clustering based on simple edge counts in an undirected loop-free graph. Let S be a subset of the points of the graph. The compactness of S is measured by the number of edges connecting points in S to other points in S. The isolation or separation of S is measured by the negative of the number of edges connecting points in S to points not in S. The subset S is a cluster if it is compact and isolated. We study the cluster search problem: find a subset S which maximizes a linear combination of the compactness and (negative) isolation edge counts. We show that a closely related decision problem is NP-complete. We develop a pruned search tree algorithm which is much faster than complete search, especially for graphs which are derived from points embedded in a space of low dimensionality.
The Robust Estimation of Multiple Motions: Parametric and Piecewise-Smooth Flow Fields

reflections, shadows, or fragmented occlusion (for example, looking through the branches of a tree). The assumpMost approaches for estimating optical flow assume that, within a finite image region, only a single motion is present. tion may also be violated when the region spans a depth This single motion assumption is violated in common situations discontinuity in the scene. The standard spatial coherence involving transparency, depth discontinuities, independently constraint suffers from the same problem. It assumes that moving objects, shadows, and specular reflections. To robustly the flow within a neighborhood changes gradually since it estimate optical flow, the single motion assumption must be is caused by a single motion. Optical flow, however, is relaxed. This paper presents a framework based on robust estitypically only piecewise smooth since depth boundaries in mation that addresses violations of the brightness constancy the scene may give rise to discontinuities in the flow field and spatial smoothness assumptions caused by multiple moas a result of motion parallax or the independent motion tions. We show how the robust estimation framework can be of objects. In these common situations, most current apapplied to standard formulations of the optical flow problem proaches cannot recover the multiple motions, give erronethus reducing their sensitivity to violations of their underlying ous motion estimates for the region, and fail to signal that assumptions. The approach has been applied to three standard the underlying assumption of a single motion has been viotechniques for recovering optical flow: area-based regression, lated. correlation, and regularization with motion discontinuities. The fundamental problem is that the estimation of optiThis paper focuses on the recovery of multiple parametric motion models within a region, as well as the recovery of piecewisecal flow involves the pooling of constraints over some spasmooth flow fields, and provides examples with natural and tial neighborhood. To gain accurate estimates this region synthetic image sequences.  must be sufficiently large to constrain the solution (the standard aperture problem), yet the larger the region of integration the more likely it is to contain multiple motions
General Sales Forecast Models for Automobile Markets and their Analysis

In this paper, various enhanced sales forecast methodologies and models for the automobile market are presented. The methods used deliver highly accurate predictions while maintaining the ability to explain the underlying model at the same time. The representation of the economic training data is discussed, as well as its effects on the newly registered automobiles to be predicted. The methodology mainly consists of time series analysis and classical Data Mining algorithms, whereas the data is composed of absolute and/or relative market-specific exogenous parameters on a yearly, quarterly, or monthly base. It can be concluded that the monthly forecasts were especially improved by this enhanced methodology using absolute, normalized exogenous parameters. Decision Trees are considered as the most suitable method in this case, being both accurate and explicable. The German and the US-American automobile market are presented for the evaluation of the forecast models.
Change in perceived psychosocial status following a 12-week Tai Chi exercise programme.

This paper reports a study to examine change in psychosocial status following a 12-week Tai Chi exercise intervention among ethnic Chinese people with cardiovascular disease risk factors living in the United States of America.



Regular participation in physical activity is associated with protection against cardioavascular disease, and improvements in physical and psychological health. Increasing amounts of scientific evidence suggests that mind-body exercise, such as Tai Chi, are related to improvements in mental health, emotional well-being, and stress reduction. No prior study has examined the effect of a Tai Chi exercise intervention on psychosocial status among people with cardiovascular disease risk factors.



This was a quasi-experimental study. Participants attended a 60-minute Tai Chi exercise class three times per week for 12 weeks. Data were collected at baseline, 6 and 12 weeks following the intervention. Psychosocial status was assessed using Chinese versions of Cohen's Perceived Stress Scale, Profile of Mood States, Multidimensional Scale of Perceived Social Support, and Tai Chi exercise self-efficacy.



A total of 39 participants, on average 66-year-old (+/-8.3), married (85%), Cantonese-speaking (97%), immigrants participated. The majority were women (69%), with < or =12 years education (87%). Statistically significant improvements in all measures of psychosocial status were found (P < or = 0.05) following the intervention. Improvement in mood state (eta2 = 0.12), and reduction in perceived stress (eta2 = 0.13) were found. In addition, Tai Chi exercise statistically significantly increased self-efficacy to overcome barriers to Tai Chi (eta2 = 0.19), confidence to perform Tai Chi (eta2 = 0.27), and perceived social support (eta2 = 0.12).



Tai Chi was a culturally appropriate mind-body exercise for these older adults, with statistically significant psychosocial benefits observed over 12-weeks. Further research examining Tai Chi exercise using a randomized clinical trial design with an attention-control group may reduce potential confounding effects, while exploring potential mechanisms underlying the relaxation response associated with mind-body exercise. In addition, future studies with people with other chronic illnesses in all ethnic groups are recommended to determine if similar benefits can be achieved.
CLaaS: Cybersecurity Lab as a Service

The explosive growth of IT infrastructures, cloud systems, and Internet of Things (IoT) have resulted in complex systems that are extremely difficult to secure and protect against cyberattacks which are growing exponentially in complexity and also in number. Overcoming the cybersecurity challenges is even more complicated due to the lack of training and widely available cybersecurity environments to experiment with and evaluate new cybersecurity detection and protection methods. Therefore, the goal of our research is to address the education, training, and experimentation challenges of the cybersecurity by exploiting cloud services. In this paper, we present the design, analysis, and evaluation of a cloud service, that we refer to as Cybersecurity Lab as a Service (CLaaS), which offers virtual cybersecurity experiments that can be accessed from anywhere and from any device (desktop, laptop, tablet, or mobile device) with Internet connectivity. In CLaaS, we exploit cloud computing systems and virtualization technologies to provide virtual cybersecurity experiments and hands-on experiences on how vulnerabilities are exploited to launch cyberattacks, how they can be removed, and how cyber resources and services can be hardened or better protected. We also present our experimental results and evaluation of CLaaS virtual cybersecurity experiments that have been used by graduate students taking our cybersecurity class as well as by high school students participating in GenCyber camps.
CDN brokering

Content distribution networks (CDNs) increase the capacity of individual Web sites and attempt to deliver content from caches that are located “closer” to end-users than the origin servers that provide the content. CDN brokering provides CDNs a way to interoperate by allowing one CDN to intelligently redirect clients dynamically to other CDNs. This paper describes the goals, architecture, and performance of a CDN brokerage system. Our system has been deployed on the Internet on a provisional basis, and our architectural ideas have helped advance the evolution of Internet standards for interoperating CDNs.
Instruments and Methods for Calibration of Oscillometric Blood Pressure Measurement Devices

Most of automatic blood pressure measurement devices on the market implement an oscillometric method. This paper highlights the need for standardized calibration procedures for these devices in order to reduce logistical difficulties, time-consuming processes, and high costs involved in clinical validations. A critical overview of researchers' and manufacturers' proposals to answer this need is presented as a contribution to the IEEE P1721 project for a new standard on Objective Measurement of Systemic Arterial Blood Pressure in Humans.
Knowledge management strategy and its link to knowledge creation process

Knowledge has become to be considered as valuable strategic assets that can provide proprietary competitive advantages. It is more important for companies to distinguish themselves through knowledge management strategies. Without a constant creation of knowledge, a business is condemned to poor performance. However, it is still unclear how these strategies affect knowledge creation. Knowledge management strategies can be categorized as being either human or system oriented. This paper proposes a model to illustrate the link between the strategies and its creating process. The model is derived on the basis of samples from 58 Korean firms. The model depicts how companies should align the strategies with four knowledge creation modes such as socialization, externalization, combination, and internalization. It is found that human strategy is more likely to be effective for socialization while system strategy is more likely to be effective for combination. Furthermore, the survey result suggests that managers should adjust knowledge management strategies in view of the characteristics of their departments. q 2002 Elsevier
JM: An R Package for the Joint Modelling of Longitudinal and Time-to-Event Data

In longitudinal studies measurements are often collected on different types of outcomes for each subject. These may include several longitudinally measured responses (such as blood values relevant to the medical condition under study) and the time at which an event of particular interest occurs (e.g., death, development of a disease or dropout from the study). These outcomes are often separately analyzed; however, in many instances, a joint modeling approach is either required or may produce a better insight into the mechanisms that underlie the phenomenon under study. In this paper we present the R package JM that fits joint models for longitudinal and time-to-event data.
A Distributed Hybrid Recommendation Framework to Address the New-User Cold-Start Problem

With the development of electronic commerce, recommender system is becoming an important research topic. Existing methods of recommender systems such as collaborative filtering and content analysis are suitable for different cases. Not every recommender system is capable of handling all kinds of situations. In this paper, we bring forward a distributed hybrid recommendation framework to address the new-user cold-start problem based on user classification. First, current user characteristics, user context and operating records are used to classify the user type. Then, suitable recommendation algorithms are dynamically selected based on the current user type, and executed in parallel. Finally, the recommendation results are merged into a consolidated list. In the experiment on movie ratings dataset Movie Lens, the proposed framework can enhance the accuracy of recommendation system. Also, a 100% coverage can be achieved in the case of new users. This shows the potential of the proposed framework in addressing new-user cold-start problem.
Obfuscation of executable code to improve resistance to static disassembly

A great deal of software is distributed in the form of executable code. The ability to reverse engineer such executables can create opportunities for theft of intellectual property via software piracy, as well as security breaches by allowing attackers to discover vulnerabilities in an application. The process of reverse engineering an executable program typically begins with disassembly, which translates machine code to assembly code. This is then followed by various decompilation steps that aim to recover higher-level abstractions from the assembly code. Most of the work to date on code obfuscation has focused on disrupting or confusing the decompilation phase. This paper, by contrast, focuses on the initial disassembly phase. Our goal is to disrupt the static disassembly process so as to make programs harder to disassemble correctly. We describe two widely used static disassembly algorithms, and discuss techniques to thwart each of them. Experimental results indicate that significant portions of executables that have been obfuscated using our techniques are disassembled incorrectly, thereby showing the efficacy of our methods.
Secure Border Gateway Protocol (Secure-BGP)

The Border Gateway Protocol (BGP), which is used to distribute routing information between autonomous systems (ASes), is a critical component of the Internet’s routing infrastructure. It is highly vulnerable to a variety of malicious attacks, due to the lack of a secure means of verifying the authenticity and legitimacy of BGP control traffic. This document describes a secure, scalable, deployable architecture (S-BGP) for an authorization and authentication system that addresses most of the security problems associated with BGP. The paper discusses the vulnerabilities and security requirements associated with BGP, describes the S-BGP countermeasures, and explains how they address these vulnerabilities and requirements. In addition, this paper provides a comparison of this architecture with other approaches that have been proposed, analyzes the performance implications of the proposed countermeasures, and addresses operational issues.