nils-herrmann/clean_scirepeval huggingface dataset:
Software-defined batteries

Different battery chemistries perform better on different axes, such as energy density, cost, peak power, recharge time, longevity, and efficiency. Mobile system designers are constrained by existing technology, and are forced to select a single chemistry that best meets their diverse needs, thereby compromising other desirable features. In this paper, we present a new hardware--software system, called Software Defined Battery (SDB), which allows system designers to integrate batteries of different chemistries. SDB exposes application programming interfaces (APIs) to the operating system, which controls the amount of charge flowing in and out of each battery, enabling it to dynamically trade one battery property for another depending on application and/or user needs. Using micro-benchmarks from our prototype SDB implementation, and through detailed simulations, we demonstrate that it is possible to combine batteries which individually excel along different axes to deliver an enhanced collective performance when compared to traditional battery packs.
Strategies for Evaluating Software Usability

This paper presents a usage analysis and taxonomy of methods which are used to evaluate the usability of computer systems. To accommodate the analysis and taxonomy, a matrix of strategies which can be used for effective usability evaluation is presented. Such an analysis, taxonomy and strategies support human-computer interaction (HCI) professionals who have the responsibility for ensuring computer system usability. The strategies outlined are named Virtual Engineering, Soft Modelling, Hard Review and Real World. This paper also uses a composite set of existing popular generic evaluation methods which can be used as part of these strategies. The methods used are observation, questionnaire, interview, empirical methods, user groups, cognitive walkthroughs, heuristic methods, review methods and model methods. The paper continues by presenting a Usage Analysis Table of these methods and concludes by grouping them into a Taxonomy of Usability Evaluation  A key emphasis of this paper is the appropriateness of individual methods to lifecycle timing.
Reversible data hiding in encrypted images using interpolation-based distributed space reservation

Reversible data hiding (RDH) in encrypted images has attained more attention recently in research community. Privacy protection of additional data as well as cover media makes it attractive for applications in medical imaging, cloud storage, forensics etc. In this paper, a new method for reversible data hiding in encrypted images (RDH-EI), is proposed. Our method adopts the approach of reserving sufficient space for the additional data before encrypting the cover image. First we identify suitable blocks for hiding data from various parts of the image. Before encrypting the image, one or more LSB-planes of these blocks are backed-up into remaining parts of the image using a high-performing traditional RDH method that works on unencrypted images. After encrypting the image, those LSB-planes are used to hide additional data. Recovery of original cover image and error-free extraction of additional data is guaranteed always. Moreover, the proposed method is simple and intuitive. Experimentally results show that our method outperforms the state-of-the-art methods for reversible data hiding in encrypted images.
Congested traffic states in empirical observations and microscopic simulations

We present data from several German freeways showing different kinds of congested traffic forming near road inhomogeneities, specifically lane closings, intersections, or uphill gradients. The states are localized or extended, homogeneous or oscillating. Combined states are observed as well, like the coexistence of moving localized clusters and clusters pinned at road inhomogeneities, or regions of oscillating congested traffic upstream of nearly homogeneous congested traffic. The experimental findings are consistent with a recently proposed theoretical phase diagram for traffic near on-ramps [D. Helbing, A. Hennecke, and M. Treiber,  (1999)]. We simulate these situations with a continuous microscopic single-lane model, the "intelligent driver model," using empirical boundary conditions. All observations, including the coexistence of states, are qualitatively reproduced by describing inhomogeneities with local variations of one model parameter. We show that the results of the microscopic model can be understood by formulating the theoretical phase diagram for bottlenecks in a more general way. In particular, a local drop of the road capacity induced by parameter variations has essentially the same effect as an on-ramp.
User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop

Collaborative Filtering(CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.
A Survey of Radial Methods for Information Visualization

Radial visualization, or the practice of displaying data in a circular or elliptical pattern, is an increasingly common technique in information visualization research. In spite of its prevalence, little work has been done to study this visualization paradigm as a methodology in its own right. We provide a historical review of radial visualization, tracing it to its roots in centuries-old statistical graphics. We then identify the types of problem domains to which modern radial visualization techniques have been applied. A taxonomy for radial visualization is proposed in the form of seven design patterns encompassing nearly all recent works in this area. From an analysis of these patterns, we distill a series of design considerations that system builders can use to create new visualizations that address aspects of the design space that have not yet been explored. It is hoped that our taxonomy will provide a framework for facilitating discourse among researchers and stimulate the development of additional theories and systems involving radial visualization as a distinct design metaphor.
Game Theory in Wireless Networks: A Tutorial

The behavior of a given wireless device may affect the communication capabilities of a neighboring device, notably because the radio communication channel is usually shared in wireless networks. In this tutorial, we carefully explain how situations of this kind can be modelled by making use of game theory. By leveraging on four simple running examples, we introduce the most fundamental concepts of non-cooperative game theory. This approach should help students and scholars to quickly master this fascinating analytical tool without having to read the existing lengthy, economics-oriented books. It should also assist them in modelling problems of their own.
Dual-band dual polarized D-shaped microstrip antenna

Design and analysis of novel dual-band dual polarized D-shaped microstrip antenna is proposed. The D-shaped microstrip antenna excites TM10 and TM01 modes which are orthogonal to each other, thereby realizing dual polarized antenna characteristics. Further, another novel configuration, a vertical slot cut D-shaped patch is proposed. The slot cut D-shaped patch demonstrates triple band dual polarized antenna response, with bandwidth of 1 to 2% at each of the frequencies in 1000 MHz frequency range. The proposed dual-polarized configuration yields broadside radiation pattern across all the resonant modes.
Adaptive web caching: towards a new global caching architecture

An adaptive, highly scalable, and robust web caching system is needed to effectively handle the exponential growth and extreme dynamic environment of the World Wide Web. Our work presented last year sketched out the basic design of such a system . This sequel paper reports our progress over the past year. To assist caches making Web query forwarding decisions, we sketch out the basic design of a URL routing framework. To assist fast searching within each cache group, we let neighbor caches share content information (borrowing work elsewhere on efficient hashing functions). Equipped with the URL routing table and neighbor cache contents, a cache in the revised design can now search the local group, and forward all missing queries quickly and efficiently, thus eliminating both the waiting delay and the overhead associated with multicasted queries. The paper also presents a proposal for incremental deployment that provides a smooth transition from the currently deployed cache infrastructure to the new design.
Compression of soft-body animation sequences

We describe a compression scheme for the geometry component of 3D animation sequences. This scheme is based on the principle component analysis (PCA) method, which represents the animation sequence using a small number of basis functions. Second-order linear prediction coding (LPC) is applied to the PCA coefficients in order to further reduce the code size by exploiting the temporal coherence present in the sequence. Our results show that applying LPC to the PCA scheme results in significant performance improvements relative to other coding methods. The use of these codes will make animated 3D data more accessible for graphics and visualization applications. r 2003
Female waist-to-hip ratio, body mass index and sexual attractiveness in China.

Men and women at Northwest University (n=751), Xi’an, China were asked to judge the attractiveness of photographs of female patients who had undergone micrograft surgery to reduce their waist-to-hip ratios (WHR). Micrograft surgery involves harvesting adipose tissue from the waist and reshaping the buttocks to produce a low WHR and an ‘hourglass’ female figure. This gynoid distribution of female body fat has been shown to correlate with measures of fertility and health. Significantly larger numbers of subjects, of both sexes, chose post-operative photographs, with lower WHRs, as more attractive than pre-operative photographs of the same women. Some patients had gained, and some had lost weight, post-operatively, with resultant changes in body mass index (BMI). However, these changes in BMI were not related to judgments of attractiveness. These results show that the hourglass female figure is rated as attractive in China, and that WHR, rather than BMI, plays a crucial role in such attractiveness judgments [Current Zoology 56 (2): 175–181, 2010].
Bayesian Policy Gradient and Actor-Critic Algorithms

Policy gradient methods are reinforcement learning algorithms that adapt a parameterized policy by following a performance gradient estimate. Many conventional policy gradient methods use Monte-Carlo techniques to estimate this gradient. The policy is improved by adjusting the parameters in the direction of the gradient estimate. Since Monte-Carlo methods tend to have high variance, a large number of samples is required to attain accurate estimates, resulting in slow convergence. In this paper, we first propose a Bayesian framework for policy gradient, based on modeling the policy gradient as a Gaussian process. This reduces the number of samples needed to obtain accurate gradient estimates. Moreover, estimates of the natural gradient as well as a measure of the uncertainty in the gradient estimates, namely, the gradient covariance, are provided at little extra cost. Since the proposed Bayesian framework considers system trajectories as its basic observable unit, it does not require the dynamics within trajectories to be of any particular form, and thus, can be easily extended to partially observable problems. On the downside, it cannot take advantage of the Markov property when the system is Markovian. To address this issue, we proceed to supplement our Bayesian policy gradient framework with a new actor-critic learning model in which a Bayesian class of non-parametric critics, based on Gaussian process temporal difference learning, is used. Such critics model the action-value function as a Gaussian process, allowing Bayes’ rule to be used in computing the posterior distribution over action-value functions, conditioned on the observed data. Appropriate choices of the policy parameterization and of the prior covariance (kernel) between action-values allow us to obtain closed-form expressions for the posterior distribution of the gradient of the expected return with respect to the policy parameters. We perform detailed experimental comparisons of the proposed Bayesian policy gradient and actor-critic algorithms with classic Monte-Carlo based policy gradient methods, as well as with each other, on a number of reinforcement learning problems.
An emerging trend in functional foods for the prevention of cardiovascular disease and diabetes: Marine algal polyphenols.

Marine macroalgae are gaining recognition among the scientific community as a significant source of functional food ingredients. Due to the harsh environments in which macroalgae survive, they produce unique bioactive compounds that are not found in terrestrial plants. Polyphenols are the predominant bioactive compound in brown algae and are accountable for the majority of its biological activity. Phlorotannins are a type of polyphenol that are unique to marine sources and have exhibited protective effects against hyperglycemia, hyperlipidemia, inflammation and oxidative stress, known risk factors for cardiovascular disease and diabetic complications, in cell culture, animal studies and some human studies. This review updates the information on marine polyphenols, with a particular focus on phlorotannins and their potential health benefits in relation to the prevention and treatment of risk factors for type 2 diabetes and cardiovascular diseases.
Understanding Jordanian Consumers ’ Online Purchase Intentions : Integrating Trust to the UTAUT 2 Framework

This study examines the consumers’ intention to purchase products online using the extended unified theory of acceptance and use of technology (UTAUT2) as a basic model. The proposed model is integrated with perceived trust as a mediating variable to understand the influence of the core constructs of the UTAUT2 model such as performance expectancy, social influence and facilitating conditions on it. Moreover, the constructs such as effort expectancy, perceived trust and price value were hypothesized to understand their direct impact on consumers’ online purchase intention. A valid data sample of 284 respondents was gathered from the students and faculty members from six different private and public universities in Jordan. The findings indicated that all six hypotheses between seven constructs were found significant. The paper also discusses the theoretical contributions and implications of this research for practice toward the end.
Controllable text generation

In last few years generative models advanced greatly in the visual domain. Proposed solutions to problems like image generation and interpretable image representation learning achieve very impressive results and advance quickly. Unlike the tasks of text generation. Text generation is a challenging task. Test samples are discrete and as a result are non-differentiable. This doesnt allow the use of global discriminator, which are common in the visual domain. For example in generative adversarial networks used for image generation. An alternative is a variational autoencoder with element-wise reconstruction loss, but this approach loses the ability to assess generated sentence as a whole. For controllable text generation, one more challenge is learning disentangled latent representation. Varying individual elements of latent representation can cause unpredictable results in the generated sample. In my project, I work on model described in paper ”Toward Controllable Text Generation”. The paper proposes a model that addresses issues stated above. The model is variational autoencoder with an extended wake-sleep procedure and structured latent representation, consisting of vector sampled from prior distribution and attributes used for imposing desired semantic properties. Each attribute has dedicated discriminator, which makes learning model, which produces text samples with desired semantic properties possible.
A virtual metrology system for semiconductor manufacturing

Nowadays, the semiconductor manufacturing becomes very complex, consisting of hundreds of individual processes. If a faulty wafer is produced in an early stage but detected at the last moment, unnecessary resource consumption is unavoidable. Measuring every wafer’s quality after each process can save resources, but it is unrealistic and impractical because additional measuring processes put in between each pair of contiguous processes significantly increase the total production time. Metrology, as is employed for product quality monitoring tool today, covers only a small fraction of sampled wafers. Virtual metrology (VM), on the other hand, enables to predict every wafer’s metrology measurements based on production equipment data and preceding metrology results. A well established VM system, therefore, can help improve product quality and reduce production cost and cycle time. In this paper, we develop a VM system for an etching process in semiconductor manufacturing based on various data mining techniques. The experimental results show that our VM system can not only predict the metrology measurement accurately, but also detect possible faulty wafers with a reasonable confidence.
A method for Automatic Text Summarization using Consensus of Multiple Similarity Measures and Ranking Techniques

In the era of information overload, text summarization can be defined as the process of extracting useful information from a large space of available content using traditional filtering methods. One of the major challenges in the domain of extraction based summarization is that a single statistical measure is not sufficient to produce efficient summaries which would be close to human-made ‘gold standard’, since each measure suffers from individual weaknesses. We deal with this problem by proposing a text summarization model that combines various statistical measures so that the pitfalls of an individual technique could be compensated by the strengths of others. Experimental results are presented to demonstrate the effectiveness of the proposed method using the TAC 2011 Multiling pilot dataset for English language and ROUGE summary evaluation tool.
Effects of haptic guidance and disturbance on motor learning: Potential advantage of haptic disturbance

One of the primary goals of haptic guidance is to facilitate the learning of complex human motor skills by providing haptic cues that are helpful to induce desired movements. Nevertheless, a majority of previous studies have found that haptic guidance is ineffective, or sometimes even detrimental, to motor skill learning. In this paper, we propose the opposite concept, haptic disturbance, and evaluate its efficacy. In haptic disturbance, haptic cues that interfere with the movements of a learner are presented during training. We designed two methods of haptic disturbance using repulsive and noise-like forces, respectively. The effects of these methods were experimentally assessed, comparatively with the conventional methods of visual learning only and progressive haptic guidance. The motor task was to track a dot moving on a 2D plane with a haptic interface operated with one arm. We found that during training, the progressive haptic guidance showed the best tracking accuracy, but in immediate and delayed retention tests, the noise-like haptic disturbance led to the best performance. The results suggest high potentials for haptic disturbance to be a general strategy for expediting the motor learning process.
Camera phone based motion sensing: interaction techniques, applications and performance study

This paper presents TinyMotion, a pure software approach for detecting a mobile phone user's hand movement in real time by analyzing image sequences captured by the built-in camera. We present the design and implementation of TinyMotion and several interactive applications based on TinyMotion. Through both an informal evaluation and a formal 17-participant user study, we found that 1. TinyMotion can detect camera movement reliably under most background and illumination conditions. 2. Target acquisition tasks based on TinyMotion follow Fitts' law and Fitts law parameters can be used for TinyMotion based pointing performance measurement. 3. The users can use Vision TiltText, a TinyMotion enabled input method, to enter sentences faster than MultiTap with a few minutes of practicing. 4. Using camera phone as a handwriting capture device and performing large vocabulary, multilingual real time handwriting recognition on the cell phone are feasible. 5. TinyMotion based gaming is enjoyable and immediately available for the current generation camera phones. We also report user experiences and problems with TinyMotion based interaction as resources for future design and development of mobile interfaces.
Intracranial EEG and human brain mapping

This review is an attempt to highlight the value of human intracranial recordings (intracranial electro-encephalography, iEEG) for human brain mapping, based on their technical characteristics and based on the corpus of results they have already yielded. The advantages and limitations of iEEG recordings are introduced in detail, with an estimation of their spatial and temporal resolution for both monopolar and bipolar recordings. The contribution of iEEG studies to the general field of human brain mapping is discussed through a review of the effects observed in the iEEG while patients perform cognitive tasks. Those effects range from the generation of well-localized evoked potentials to the formation of large-scale interactions between distributed brain structures, via long-range synchrony in particular. A framework is introduced to organize those iEEG studies according to the level of complexity of the spatio-temporal patterns of neural activity found to correlate with cognition. This review emphasizes the value of iEEG for the study of large-scale interactions, and describes in detail the few studies that have already addressed this point.
Direct Torque and Indirect Flux Control of Brushless DC Motor

In this paper, the position-sensorless direct torque and indirect flux control of brushless dc (BLDC) motor with nonsinusoidal back electromotive force (EMF) has been extensively investigated. In the literature, several methods have been proposed for BLDC motor drives to obtain optimum current and torque control with minimum torque pulsations. Most methods are complicated and do not consider the stator flux linkage control, therefore, possible high-speed operations are not feasible. In this study, a novel and simple approach to achieve a low-frequency torque ripple-free direct torque control (DTC) with maximum efficiency based on dq reference frame is presented. The proposed sensorless method closely resembles the conventional DTC scheme used for sinusoidal ac motors such that it controls the torque directly and stator flux amplitude indirectly using d-axis current. This method does not require pulsewidth modulation and proportional plus integral regulators and also permits the regulation of varying signals. Furthermore, to eliminate the low-frequency torque oscillations, two actual and easily available line-to-line back EMF constants ( kba and kca) according to electrical rotor position are obtained offline and converted to the dq frame equivalents using the new line-to-line park transformation. Then, they are set up in the look-up table for torque estimation. The validity and practical applications of the proposed sensorless three-phase conduction DTC of BLDC motor drive scheme are verified through simulations and experimental results.