nils-herrmann/clean_scirepeval huggingface dataset:
Firewall Security: Policies, Testing and Performance Evaluation

This paper explores the firewall security and performance relationship for distributed systems. Experiments are conducted to set firewall security into seven different levels and to quantify their performance impacts. These firewall security levels are formulated, designed, implemented, and tested phase by phase under an experimental environment in which all performed tests are evaluated and compared. Based on the test results, the impacts of the various firewall security levels on system performance with respect to transaction time and latency are measured and analyzed. It is interesting to note that the intuitive belief about security to performance, i.e. the more security would result in less performance, does not always hold in the firewall testing. The results reveal that the significant impact from enhanced security on performance could only be observed under some particular scenarios and thus their relationships are not necessarily inversely related. We also discuss the tradeoff between security and performance.
Of, for, and by the people: the legal lacuna of synthetic persons

Conferring legal personhood on purely synthetic entities is a very real legal possibility, one under consideration presently by the European Union. We show here that such legislative action would be morally unnecessary and legally troublesome. While AI legal personhood may have some emotional or economic appeal, so do many superficially desirable hazards against which the law protects us. We review the utility and history of legal fictions of personhood, discussing salient precedents where such fictions resulted in abuse or incoherence. We conclude that difficulties in holding “electronic persons” accountable when they violate the rights of others outweigh the highly precarious moral interests that AI legal personhood might protect.
Motivation and job satisfaction

The movement of workers to act in a desired manner has always consumed the thoughts of managers. In many ways, this goal has been reached through incentive programs, corporate pep talks, and other types of conditional administrative policy. However, as the workers adjust their behaviour in response to one of the aforementioned stimuli, is job satisfaction actualized? The instilling of satisfaction within workers is a crucial task of management. Satisfaction creates confidence, loyalty and ultimately improved quality in the output of the employed. Satisfaction, though, is not the simple result of an incentive program. Employees will most likely not take any more pride in their work even if they win the weekend getaway for having the highest sales. This paper reviews the literature of motivational theorists and draws from their approaches to job satisfaction and the role of motivation within job satisfaction. The theories of Frederick Herzberg and Edwin Locke are presented chronologically to show how Locke’s theory was a response to Herzberg’s theory. By understanding these theories, managers can focus on strategies of creating job satisfaction. This is followed by a brief examination of Kenneth Blanchard and Paul Hersey’s theory on leadership within management and how this art is changing through time. Herzberg and job satisfaction
Lecture 7 : Physiological Signals from Wireless Scribe :

In order to increase the resolution, we first utilize FMCW to separate signals reflected off from different distances into voxels, so each voxel stands for signal reflected off from a specific distance from the transmitter. Once we have a voxel of interest, we can zoom-in the specific voxel, and analyze the phase shift in that voxel to determine relative movements within that voxel.  1 illustrates the above process.
Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

In modern industries, machine health monitoring systems (MHMS) have been applied wildly with the goal of realizing predictive maintenance including failures tracking, downtime reduction, and assets preservation. In the era of big machinery data, data-driven MHMS have achieved remarkable results in the detection of faults after the occurrence of certain failures (diagnosis) and prediction of the future working conditions and the remaining useful life (prognosis). The numerical representation for raw sensory data is the key stone for various successful MHMS. Conventional methods are the labor-extensive as they usually depend on handcrafted features, which require expert knowledge. Inspired by the success of deep learning methods that redefine representation learning from raw data, we propose local feature-based gated recurrent unit (LFGRU) networks. It is a hybrid approach that combines handcrafted feature design with automatic feature learning for machine health monitoring. First, features from windows of input time series are extracted. Then, an enhanced bidirectional GRU network is designed and applied on the generated sequence of local features to learn the representation. A supervised learning layer is finally trained to predict machine condition. Experiments on three machine health monitoring tasks: tool wear prediction, gearbox fault diagnosis, and incipient bearing fault detection verify the effectiveness and generalization of the proposed LFGRU.
Factors Influencing Business Intelligence Systems Implementation Success in the Enterprises

Reducing the risk of having a failure in Business Intelligence system (BIS) implementation and finding the critical affecting factors have become urgent tasks for the researchers who would like to bridge the knowledge gap in this study area. To help understand the factors influencing success of BIS implementation, this study took theory of TOE framework and empirically tested using data collected from 148 users of BIS. Analytical results suggested that technical characteristic (i.e., relative advantage), organizational characteristic (i.e., organizational size, Top management support, knowledge integrate) and environment characteristic (i.e., consultant ability and training) were significant in affecting user satisfaction. On the other hand, technical characteristic (i.e., compatibility) and environment characteristic (i.e., consultant ability) were significant in affecting overall system effectiveness. The implications of this study and future directions are discussed.
Higher Order Output Filter Design for Grid Connected Power Converters Parikshith .

Design of higher order LCL filters that are used in grid-connected inverter applications involves multiple constraints. The filter requirements are driven by tight filtering tolerances of standards such as IEEE 519-1992 and IEEE P1547.22003. Higher order LCL filters are essential to achieve these regulatory standard requirements at compact size and weight. This paper discusses the design procedure for these higher order LCL filters. The design procedure is analysed in per-unit basis so results can be generalized for different applications and power levels. This paper also gives guidelines for actual construction of the inductor based on a proposed fringing flux model. Experimental results of filtering characteristics show a good match with analysis in the frequency range of interconnected inverter applications.
Realistic image composite with best-buddy prior of natural image patches

Realistic image composite requires the appearance of foreground and background layers to be consistent. This is difficult to achieve because the foreground and the background may be taken from very different environments. This paper proposes a novel composite adjustment method that can harmonize appearance of different composite layers. We introduce the Best-Buddy Prior (BBP), which is a novel compact representations of the joint co-occurrence distribution of natural image patches. BBP can be learned from unlabelled images given only the unsupervised regional segmentation. The most-probable adjustment of foreground can be estimated efficiently in the BBP space as the shift vector to the local maximum of density function. Both qualitative and quantitative evaluations show that our method outperforms previous composite adjustment methods.
A Social Identity Approach to Identify Familiar Strangers in a Social Network

We present a novel problem of searching for ‘familiar strangers’ in a social network. Familiar strangers are individuals who are not directly connected but exhibit some similarity. The power-law nature of social networks determines that majority of individuals are directly connected with a small number of fellow individuals, and similar individuals can be largely unknown to each other. Moreover, the individuals of a social network have only a local view of the network, which makes the problem of aggregating these familiar strangers a challenge. In this work, we formulate the problem, show why it is significant to address the challenge, and present an approach that innovatively employs the social identities of the individuals with competitive approaches. A blogger and citation network are used to showcase technical details and empirical results with related issues and future work.
Animal-assisted therapy with children suffering from insecure attachment due to abuse and neglect: a method to lower the risk of intergenerational transmission of abuse?

Children suffering from insecure attachment due to severe abuse and/or neglect are often characterized by internal working models which, although perhaps adaptive within the original family situation, are inappropriate and maladaptive in other relationships and situations. Such children have a higher probability than the general population of becoming abusing or neglecting parents. Besides the usual goals of psychotherapy, an overall goal is to stop the cycle of abuse in which abused children may grow up to be abusing parents. Therapy with these children is complicated by their distrust in adults as well as difficulties in symbolization due to trauma during the preverbal stage. Animal-Assisted Therapy (AAT) provides avenues for circumventing these difficulties, as well as providing additional tools for reaching the inner world of the client. This article gives a brief background of the connection between insecure attachment and intergenerational transmission of abuse and neglect as well as a brief overview of the principles of AAT in a play therapy setting. A rationale for the use of AAT as a unique therapy technique for children having suffered from abuse and neglect is followed by a number of clinical examples illustrating AAT.
THE IMPACT OF CHANGE MANAGEMENT IN ERP SYSTEM : A CASE STUDY OF MADAR

This paper discusses Enterprise Resource Planning (ERP) change management. A review of the research literature has been presented that focuses on the ERP change management factors. The literature is further classified and the major outcomes of each study have been addressed in this paper. The discussion is supported by a practical ERP project called Madar. The paper is targeted to investigate and identify the reasons for resistance to diffusion and why individuals within an organization resist the changes. This paper also suggests strategies to minimize the resistance if not overcome completely.
A Review of the Design Issues and Techniques for Radial-Flux Brushless Surface and Internal Rare-Earth Permanent-Magnet Motors

This paper reviews many design issues and analysis techniques for the brushless permanent-magnet machine. It reviews the basic requirements for the use of both ac and dc machines and issues concerning the selection of pole number, winding layout, rotor topology, drive strategy, field weakening, and cooling. These are key issues in the design of a motor. Leading-edge design techniques are illustrated. This paper is aimed as a tutor for motor designers who may be unfamiliar with this particular type of machine.
A Framework for Video-Driven Crowd Synthesis

We present a framework for video-driven crowd synthesis. Motion vectors extracted from input crowd video are processed to compute global motion paths. These paths encode the dominant motions observed in the input video. These paths are then fed into a behavior-based crowd simulation framework, which is responsible for synthesizing crowd animations that respect the motion patterns observed in the video. Our system synthesizes 3D virtual crowds by animating virtual humans along the trajectories returned by the crowd simulation framework. We also propose a new metric for comparing the “visual similarity” between the synthesized crowd and exemplar crowd. We demonstrate the proposed approach on crowd videos collected under different settings. ; crowd sysnthesis; motion analysis
Planar distributed structures with negative refractive index

Planar distributed periodic structures of microstrip-line and stripline types, which support left-handed (LH) waves are presented and their negative refractive index (NRI) properties are shown theoretically, numerically, and experimentally. The supported LH wave is fully characterized based on the composite right/left-handed transmission-line theory and the dispersion characteristics, refractive indexes, and Bloch impedance are derived theoretically. In addition, formulas to extract equivalent-circuit parameters from full-wave simulation are given. Open (microstrip) and closed (stripline) structures with a 5/spl times/5 mm/sup 2/ unit cell operating at approximately 4 GHz are designed and characterized by full-wave finite-element-method simulations. A 20 /spl times/ 6 unit-cell NRI lens structure interfaced with two parallel-plate waveguides is designed. The focusing/refocusing effect of the lens is observed by both circuit theory and full-wave simulations. Focusing in the NRI lens is also observed experimentally in excellent agreement with circuit theory and numerical predictions. This result represents the first experimental demonstration of NRI property using a purely distributed planar structure.
Real time heart rate detection from PPG signal in noisy environment

In this paper a Photoplethysmography (PPG) based noise robust real time heart rate measurement technique is proposed. It has been developed using Arduino Uno board based on 8-bit AVR core microcontroller and having 16 MHz clock frequency. The basic idea of the proposed work is to extract the periodic PPG signal contaminated by non-periodic noise and atrifact. The algorithm is based on short term autocorrelation technique over the time shifted PPG signal. The algorithm and the developed system is validated against the heart rates derived from the signals acquired in BIOPAC MP150 data acquisition system. The designed system is highly noise robust and it can detect heart rate with almost 0% error considering BIOPAC MP150 as a standard.
Deep Convolutional Neural Models for Picture Quality Prediction

Convolutional neural networks (CNNs) have been shown to deliver standout performance in a wide variety of visual information processing applications. However, this rapidly developing technology has not yet been applied with systematic energy to the problem of picture quality prediction, primarily because of limitations imposed by a lack of adequate ground truth human subjective data. This situation has begun to change with the development of promising data-gathering methods that are driving new approaches to deep learning-based perceptual picture quality prediction. Here we assay progress in this rapidly evolving field, focussing in particular on new ways to collect large quantities of ground truth data, and on recent CNN-based picture quality prediction models that deliver excellent results on a large real-world picture quality database.
Further defining the language impairment of autism: is there a specific language impairment subtype?

Some children with autism demonstrate poor nonword repetition--a deficit considered to be a psycholinguistic marker of specific language impairment (SLI). The present study examined whether there is an SLI subtype among children with autism. We compared the language abilities of children with SLI (n=34, M age=11;10 S.D.=2;3), and children with autism with (Apoor, n=18, M age=10;11 S.D.=3;1) and without (Aapp, n=16, M age=10;8 S.D.=2;7) structural language difficulties. Participants were administered battery of standardized language and memory tests. Although there were some similarities in the language profile of the SLI and Apoor groups, the two groups differed on the tests of oromotor ability and verbal short-term memory and showed a different pattern of errors on the nonword repetition task. These findings providing evidence against the idea of an SLI subtype in autism. Further analyses suggested that the nonword repetition deficits experienced by some children with autism may arise when there is substantial impairment in multiple autistic domains.



Readers will be introduced to (a) the current state of behavioral, cognitive and genetic research that has investigated the relation between SLI and autism, and (b) three hypotheses of why there exists similarity in the language characteristics of children with SLI and autism. Readers will then be taken through a detailed comparison of the language and memory abilities of group of children with each diagnosis. A theoretical model that seeks to explain the relation between these two disorders will be discussed.
Ultra low power capless LDO with dynamic biasing of derivative feedback

In this paper, a low power, output-capacitor-free, low-dropout regulator (LDO) is proposed with a new dynamic biased, multiloop feedback strategy. Initially, a theoretical macromodel is presented based on the analogy between the capless LDO and the mechanical non-linear harmonic oscillator, enabling to shed new light on the non-linear amplification, dynamic and adaptive biasing techniques used in the multiloop feedback LDO control in the large signal context. To implement some of the unexplored abilities of this model the new capless LDO topology is proposed. The output class AB stage of the error amplifier and the non-linear derivative current amplifier of the LDO feedback loop ensure dynamical extended close-loop bandwidth gain and dynamical damping enhancement for fast load and line LDO transients. Using the new dynamic biasing of the derivative loop, an improvement is obtained in the derivative sensing of the fast output voltage variations, enabling a significant enhancement in the transient response of the capless LDO. The proposed LDO, designed for a maximum current of 50 mA in UMC RF 1P8M 0.13 mm, requires a quiescent current of only 4.1 mA and presents excellent transient response when compared to the state-of-the-art. & 2012 Elsevier Ltd. All rights reserved.
Snakeboard motion planning with viscous friction and skidding

The snakeboard is a well-studied example for mechanical systems analysis, largely because of its simultaneous richness in behavior and simplicity in design. However, few snakeboard models incorporate dissipative friction in the traveling direction and skidding as a violation of the rigid nonholonomic constraints. In this paper we investigate these effects on trajectory planning by evaluating a previously proposed friction model as well as a novel skidding model based on the addition of Rayleigh dissipation functions. We show how these additions change the usual behavior of gaits in the forward planning problem, and incorporate the changes into the solutions of the inverse planning problem by utilizing body coordinates along with a curvature parameterization for trajectories.
AMPds: A public dataset for load disaggregation and eco-feedback research

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.
Boosting Response Aware Model-Based Collaborative Filtering

Recommender systems are promising for providing personalized favorite services. Collaborative filtering (CF) technologies, making prediction of users' preference based on users' previous behaviors, have become one of the most successful techniques to build modern recommender systems. Several challenging issues occur in previously proposed CF methods: (1) most CF methods ignore users' response patterns and may yield biased parameter estimation and suboptimal performance; (2) some CF methods adopt heuristic weight settings, which lacks a systematical implementation; and (3) the multinomial mixture models may weaken the computational ability of matrix factorization for generating the data matrix, thus increasing the computational cost of training. To resolve these issues, we incorporate users' response models into the probabilistic matrix factorization (PMF), a popular matrix factorization CF model, to establish the response aware probabilistic matrix factorization (RAPMF) framework. More specifically, we make the assumption on the user response as a Bernoulli distribution which is parameterized by the rating scores for the observed ratings while as a step function for the unobserved ratings. Moreover, we speed up the algorithm by a mini-batch implementation and a crafting scheduling policy. Finally, we design different experimental protocols and conduct systematical empirical evaluation on both synthetic and real-world datasets to demonstrate the merits of the proposed RAPMF and its mini-batch implementation.
Nanotechnology-Based Cosmeceuticals

Cosmeceuticals are the fastest growing segment of the personal care industry, and a number of topical cosmeceutical treatments for conditions such as photoaging, hyperpigmentation, wrinkles, and hair damage have come into widespread use. In the cosmeceutical arena nanotechnology has played an important role. Using new techniques to manipulate matter at an atomic or molecular level, they have been at the root of numerous innovations, opening up new perspectives for the future of cosmeceutical industry. Nanotechnology-based cosmeceuticals offer the advantage of diversity in products, and increased bioavailability of active ingredients and increase the aesthetic appeal of cosmeceutical products with prolonged effects. However increased use of nanotechnology in cosmeceuticals has raised concern about the possible penetration of nanoparticles through the skin and potential hazards to the human health. This review outlines the different nanoparticles used in various classes of cosmeceuticals, nanotechnology-based cosmeceutical products present in the market, and the potential risk caused by nanoparticles on exposure and recent regulatory steps taken to overcome them.
Automatic detection of discontinuities from 3D point clouds for the stability analysis of jointed rock masses

We describe an strategy for automatic detection of discontinuities in jointed rock masses from a 3D point cloud. The method consists on a sequence of processes for feature extraction and characterization of the discontinuities that use voxelization, robust detection of planes by sequential RANSAC and clustering. The results obtained by applying this methodology to the 3D point cloud are a set of discontinuity families, characterized by their dips and dip directions, and surface wedges. This geometric information is essential for a later stability analysis of the jointed rock mass. The ultimate aim of our investigation is the automatic extraction of discontinuity families for stability analysis of tunnels excavated in jointed rock masses.