nils-herrmann/clean_scirepeval huggingface dataset:
WYSIWYG (What You See is What You Get) Volume Visualization

In this paper, we propose a volume visualization system that accepts direct manipulation through a sketch-based What  Get (WYSIWYG) approach. Similar to the operations in painting applications for 2D images, in our system, a full set of tools have been developed to enable direct volume rendering manipulation of color, transparency, contrast, brightness, and other optical properties by brushing a few strokes on top of the rendered volume image. To be able to smartly identify the targeted features of the volume, our system matches the sparse sketching input with the clustered features both in image space and volume space. To achieve interactivity, both special algorithms to accelerate the input identification and feature matching have been developed and implemented in our system. Without resorting to tuning transfer function parameters, our proposed system accepts sparse stroke inputs and provides users with intuitive, flexible and effective interaction during volume data exploration and visualization.
Towards Linear Time Overlapping Community Detection in Social Networks

Membership diversity is a characteristic aspect of social networks in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a fast algorithm, called SLPA, for overlapping community detection in large-scale networks. SLPA spreads labels according to dynamic interaction rules. It can be applied to both unipartite and bipartite networks. It is also able to uncover overlapping nested hierarchy. The time complexity of SLPA scales linearly with the number of edges in the network. Experiments in both synthetic and realworld networks show that SLPA has an excellent performance in identifying both node and community level overlapping structures.
MAC-Layer Concurrent Beamforming Protocol for Indoor Millimeter-Wave Networks

In this paper, we study concurrent beamforming issue for achieving high capacity in indoor millimeter-wave (mmWave) networks. The general concurrent beamforming issue is first formulated as an optimization problem to maximize the sum rates of concurrent transmissions, considering the mutual interference. To reduce the complexity of beamforming and the total setup time, concurrent beamforming is decomposed into multiple single-link beamforming, and an iterative searching algorithm is proposed to quickly achieve the suboptimal transmission/reception beam sets. A codebook-based beamforming protocol at medium access control (MAC) layer is then introduced in a distributive manner to determine the beam sets. Both analytical and simulation results demonstrate that the proposed protocol can drastically reduce total setup time, increase system throughput, and improve energy efficiency.
Detection of Tumor in MRI Images UsingArtificial Neural Networks

Automatic defects detection in MR images is very important in many diagnostic and therapeutic applications. Because of high quantity data in MR images and blurred boundaries, tumour segmentation and classification is very hard. This work has introduced one automatic brain tumour detection method to increase the accuracy and yield and decrease the diagnosis time. The goal is classifying the tissues to two classes of normal and abnormal. MR images that have been used here are MR images from normal and abnormal brain tissues. Here, it is tried to give clear description from brain tissues using Multi-Layer Perceptron Network,energy, entropy, contrast and some other statistic features such as mean, median, variance and correlation. It is used from a feature selection method to reduce the feature space too. This method uses from neural network to do this classification. The purpose of this project is to classify the brain tissues to normal and abnormal classes automatically, that saves the radiologist time, increases accuracy and yield of diagnosis.
A Two-Factor Theory for Website Design

This study was designed to verify whether an analogy to Herzberg’s hygiene-motivational theory about workplace could be used in the web environment. According to this analogy, the presence of hygiene factors would provide the basic functionality of a website, while their absence would create user dissatisfaction. Motivating factors are those that contribute to user satisfaction. They add additional value and may entice users to keep returning to a website. In Phase I, we identified 44 core features in the web environment that were classified into 12 categories by subjects. In Phase II, a different group of subjects in a pilot study were asked to distinguish between the hygiene and motivational characteristics of these features and categories. The preliminary results show that 4 categories and 14 features were judged to be primarily motivational, while 3 categories and 13 features were perceived to be primarily hygiene in nature. The remaining 5 categories and 17 features were perceived to be both.
The cylindrical omnidirectional patch antenna

The need for omnidirectional antennas for wireless applications has increased considerably. The antennas are used in a variety of bands anywhere from 1.7 to 2.5 GHz, in different configurations which mainly differ in gain. The omnidirectionality is mostly obtained using back-to-back elements or simply using dipoles in different collinear-array configurations. The antenna proposed in this paper is a patch which was built in a cylindrical geometry rather than a planar one, and which generates an omnidirectional pattern in the H-plane.
Privacy leakage vs . Protection measures : the growing disconnect

Numerous research papers have listed different vectors of personally identifiable information leaking via tradition al and mobile Online Social Networks (OSNs) and highlighted the ongoing aggregation of data about users visiting popular We b sites. We argue that the landscape is worsening and existing proposals (including the recent U.S. Federal Trade Commission’s report) do not address several key issues. We examined over 100 popular non-OSN Web sites across a number of categories where tens of millions of users representing d iverse demographics have accounts, to see if these sites leak private information to prominent aggregators. Our results raise considerable concerns: we see leakage in sites for every category we examined; fully 56% of the sites directly leak pieces of private information with this result growing to 75% if we also include leakage of a site userid. Sensitive search strings sent to healthcare Web sites and travel itineraries on flight reservation sites are leaked in 9 of the top 10 sites studied for each category. The community needs a clear understanding of the shortcomings of existing privac y protection measures and the new proposals. The growing disconnect between the protection measures and increasing leakage and linkage suggests that we need to move beyond the losing battle with aggregators and examine what roles first-party sites can play in protecting privacy of their use rs.
Compressing Neural Networks using the Variational Information Bottleneck

Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we focus on pruning individual neurons, which can simultaneously trim model size, FLOPs, and run-time memory. To improve upon the performance of existing compression algorithms we utilize the information bottleneck principle instantiated via a tractable variational bound. Minimization of this information theoretic bound reduces the redundancy between adjacent layers by aggregating useful information into a subset of neurons that can be preserved. In contrast, the activations of disposable neurons are shut off via an attractive form of sparse regularization that emerges naturally from this framework, providing tangible advantages over traditional sparsity penalties without contributing additional tuning parameters to the energy landscape. We demonstrate state-of-theart compression rates across an array of datasets and network architectures.
Robot arm control exploiting natural dynamics

This thesis presents an approach to robot arm control exploiting natural dynamics. The approach consists of using a compliant arm whose joints are controlled with simple non-linear oscillators. The arm has special actuators which makes it robust to collisions and gives it a smooth compliant, motion. The oscillators produce rhythmic commands of the joints of the arm, and feedback of the joint motions is used to modify the oscillator behavior. The oscillators enable the resonant properties of the arm to be exploited to perform a variety of rhythmic and discrete tasks. These tasks include tuning into the resonant frequencies of the arm itself, juggling, turning cranks, playing with a Slinky toy, sawing wood, throwing balls, hammering nails and drumming. For most of these tasks, the controllers at each joint are completely independent, being coupled by mechanical coupling through the physical arm of the robot. The thesis shows that this mechanical coupling allows the oscillators to automatically adjust their commands to be appropriate for the arm dynamics and the task. This coordination is robust to large changes in the oscillator parameters, and large changes in the dynamic properties of the arm. As well as providing a wealth of experimental data to support this approach, the thesis also provides a range of analysis tools, both approximate and exact. These can be used to understand and predict the behavior of current implementations, and design new ones. These analysis techniques improve the value of oscillator solutions. The results in the thesis suggest that the general approach of exploiting natural dynamics is a powerful method for obtaining coordinated dynamic behavior of robot arms.  Rodney A. Brooks Title: Professor of Electrical Engineering and Computer Science, MIT 5.4.
Model predictive control of coordinated multi-vehicle formations

A generalized model predictive control (MPC) formulation is derived that extends the existing theory to a multi-vehicle formation stabilization problem. The vehicles are individually governed by nonlinear and constrained dynamics. The extension considers formation stabilization to a set of permissible equilibria, rather than a unique equilibrium. Simulations for three vehicle formations with input constrained dynamics on configuration space SE(2) are performed using a nonlinear trajectory generation (NTG) software package developed at Caltech. Preliminary results and an outline of future work for scaling/decentralizing the MPC approach and applying it to an emerging experimental testbed are given.
Graphical Models with Structured Factors, Neural Factors, and Approximation-aware Training

This thesis broadens the space of rich yet practical models for structured prediction. We introduce a general framework for modeling with four ingredients: (1) latent variables, (2) structural constraints, (3) learned (neural) feature representations of the inputs, and (4) training that takes the approximations made during inference into account. The thesis builds up to this framework through an empirical study of three NLP tasks: semantic role labeling, relation extraction, and dependency parsing—obtaining state-of-the-art results on the former two. We apply the resulting graphical models with structured and neural factors, and approximation-aware learning to jointly model part-of-speech tags, a syntactic dependency parse, and semantic roles in a low-resource setting where the syntax is unobserved. We present an alternative view of these models as neural networks with a topology inspired by inference on graphical models that encode our intuitions about the data.
Engagement in digital entertainment games: A systematic review

a School of Social Sciences, Faculty of Health, Education and Social Sciences, University of the West of Scotland, High St., Paisley Campus, Paisley PA1 2BE, Scotland, United Kingdom b School of Computing, Faculty of Science and Technology, University of the West of Scotland, Paisley Campus, Paisley PA1 2BE, Scotland, United Kingdom c School of Psychological Sciences and Health, Faculty of Humanities and Social Science, University of Strathclyde, Glasgow, Scotland, United Kingdom
Fine-grained DVFS using on-chip regulators

Limit studies on Dynamic Voltage and Frequency Scaling (DVFS) provide apparently contradictory conclusions. On the one hand early limit studies report that DVFS is effective at large timescales (on the order of million(s) of cycles) with large scaling overheads (on the order of tens of microseconds), and they conclude that there is no need for small overhead DVFS at small timescales. Recent work on the other hand—motivated by the surge of on-chip voltage regulator research—explores the potential of fine-grained DVFS and reports substantial energy savings at timescales of hundreds of cycles (while assuming no scaling overhead).
 This article unifies these apparently contradictory conclusions through a DVFS limit study that simultaneously explores timescale and scaling speed. We find that coarse-grained DVFS is unaffected by timescale and scaling speed, however, fine-grained DVFS may lead to substantial energy savings for memory-intensive workloads. Inspired by these insights, we subsequently propose a fine-grained microarchitecture-driven DVFS mechanism that scales down voltage and frequency upon individual off-chip memory accesses using on-chip regulators. Fine-grained DVFS reduces energy consumption by 12% on average and up to 23% over a collection of memory-intensive workloads for an aggressively clock-gated processor, while incurring an average 0.08% performance degradation (and at most 0.14%). We also demonstrate that the proposed fine-grained DVFS mechanism is orthogonal to existing coarse-grained DVFS policies, and further reduces energy by 6% on average and up to 11% for memory-intensive applications with limited performance impact (at most 0.7%).
High gain 2.4GHz patch antenna array for rural area application

The design and implementation of high gain 2.4GHz patch antenna array for wireless communications application in rural area is presented. The patch antenna array with high gain is expected to minimize the need of tower that almost requires high cost of construction. In order to achieve high gain, the proposed antenna is constructed by 4×4 rectangular patches fed by microstrip line corporate feeding network which is developed using a λ/4 transformer impedance matching technique. The antenna structure is then deployed on a Flame Retardant (FR) 4 Epoxy dielectric substrate which the thickness and dielectric constant of 1.6mm, and 4.4, respectively. Prior hardware realization, some antenna parameters including return loss, voltage standing wave ratio (VSWR), radiation pattern, and gain are characterized through simulation to obtain an optimum design of antenna. While from the measurement, it shows that the characteristics of realized patch antenna array have good agreements with the design results in which the realized antenna has the measured gain of 15.59dB at the center frequency with the return loss of 19.52dB which corresponds to VSWR of 1.24 and the bandwidth response of 130MHz ranges from the frequency of 2.31GHz-2.44GHz.
The Necessity of Syntactic Parsing for Semantic Role Labeling

We provide an experimental study of the role of syntactic parsing in semantic role labeling. Our conclusions demonstrate that syntactic parse information is clearly most relevant in the very first stage – the pruning stage. In addition, the quality of the pruning stage cannot be determined solely based on its recall and precision. Instead it depends on the characteristics of the output candidates that make downstream problems easier or harder. Motivated by this observation, we suggest an effective and simple approach of combining different semantic role labeling systems through joint inference, which significantly improves the performance.
Trust, Satisfaction, and Online Repurchase Intention: The Moderating Role of Perceived Effectiveness of E-Commerce Institutional Mechanisms

The effects of e-commerce institutional mechanisms on trust and online purchase have traditionally been understood in the initial online purchase context. This study extends this literature by exploring the role of ecommerce institutional mechanisms in the online repurchase context. In doing so, it responds to the emerging call for understanding the institutional context under which customer trust operates in an e-commerce environment. Specifically, this study introduces a key moderator, perceived effectiveness of e-commerce institutional mechanisms (PEEIM), to the relationships between trust, satisfaction, and repurchase intention. Drawing on the theory of organizational trust, and based on a survey of 362 returning online customers, we find that PEEIM negatively moderates the relationship between trust in an online vendor and online customer repurchase intention, as it decreases the importance of trust to promoting repurchase behavior. We also find that
Artificial Neural Networks : The next intelligence

This paper is divided in two parts. Part one examines the relevance of Artificial Neural Networks (ANNs) for various business applications. The first section sets the stage for ANNs in the context of modern day business by discussing the evolution of businesses from Industrial Revolution to current Information Age to outline why business today are in critical need of technology that sifts through massive data. Next section introduces Artificial Neural Network technology as a favorable alternative to traditional analytics and informs the reader of the basic concept underlying the technology. Finally, third section screens through four different applications of ANNs to gain an insight into potential business opportunities that lie abound.
Unveiling the complexity of human mobility by querying and mining massive trajectory data

The technologies of mobile communications pervade our society and wireless networks sense the movement of people, generating large volumes of mobility data, such as mobile phone call records and Global Positioning System (GPS) tracks. In this work, we illustrate the striking analytical power of massive collections of trajectory data in unveiling the complexity of human mobility. We present the results of a large-scale experiment, based on the detailed trajectories of tens of thousands private cars with on-board GPS receivers, tracked during weeks of ordinary mobile activity. We illustrate the knowledge discovery process that, based on these data, addresses some fundamental questions of mobility analysts: what are the frequent patterns of people’s travels? How big attractors and extraordinary events influence mobility? How to predict areas of dense traffic in the near future? How to characterize traffic jams and congestions? We also describe M-Atlas, the querying and mining language and system that makes this analytical process possible, providing the mechanisms to master the complexity of transforming raw GPS tracks into mobility knowledge. M-Atlas is centered onto the concept of a trajectory, and the mobility knowledge discovery process can be specified by M-Atlas queries that realize data transformations, data-driven estimation of the parameters of the mining methods, the quality assessment of the obtained results, the quantitative and visual exploration of the discovered behavioral patterns and models, the composition of mined patterns, models and data with further analyses and mining, and the incremental mining strategies to address scalability.
CULZSS: LZSS Lossless Data Compression on CUDA

Increasing needs in efficient storage management and better utilization of network bandwidth with less data transfer have led the computing community to consider data compression as a solution. However, compression introduces extra overhead and performance can suffer. The key elements in making the decision to use compression are execution time and compression ratio. Due to negative performance impact, compression is often neglected. General purpose computing on graphic processing units (GPUs) introduces new opportunities where parallelism is available. Our work targets the use of opportunities in GPU based systems by exploiting parallelism in compression algorithms. In this paper we present an implementation of the Lempel-Ziv-Storer-Szymanski (LZSS) loss less data compression algorithm by using NVIDIA GPUs Compute Unified Device Architecture (CUDA) Framework. Our implementation of the LZSS algorithm on GPUs significantly improves the performance of the compression process compared to CPU based implementation without any loss in compression ratio. This can support GPU based clusters in solving application bandwidth problems. Our system outperforms the serial CPU LZSS implementation by up to 18x, the parallel threaded version up to 3x and the BZIP2 program by up to 6x in terms of compression time, showing the promise of CUDA systems in loss less data compression. To give the programmers an easy to use tool, our work also provides an API for in memory compression without the need for reading from and writing to files, in addition to the version involving I/O.
Improving Air Temperature Prediction with Artificial Neural Networks

The mitigation of crop loss due to damaging freezes requires accurate air temperature prediction models. Previous work established that the Ward-style artificial neural network (ANN) is a suitable tool for developing such models. The current research focused on developing ANN models with reduced average prediction error by increasing the number of distinct observations used in training, adding additional input terms that describe the date of an observation, increasing the duration of prior weather data included in each observation, and reexamining the number of hidden nodes used in the network. Models were created to predict air temperature at hourly intervals from one to 12 hours ahead. Each ANN model, consisting of a network architecture and set of associated parameters, was evaluated by instantiating and training 30 networks and calculating the mean absolute error (MAE) of the resulting networks for some set of input patterns. The inclusion of seasonal input terms, up to 24 hours of prior weather information, and a larger number of processing nodes were some of the improvements that reduced average prediction error compared to previous research across all horizons. For example, the four-hour MAE of 1.40°C was 0.20°C, or 12.5%, less than the previous model. Prediction MAEs eight and 12 hours ahead improved by 0.17°C and 0.16°C, respectively, improvements of 7.4% and 5.9% over the existing model at these horizons. Networks instantiating the same model but with different initial random weights often led to different prediction errors. These results strongly suggest that ANN model developers should consider instantiating and training multiple networks with different initial weights to establish preferred model parameters. Decision support systems, frost protection, fruit, time-series prediction, weather modeling
Spectral ranking

This note tries to attempt a sketch of the history of spectral ranking—a general umbrella name for techniques that apply the theory of linear maps (in particular, eigenvalues and eigenvectors) to matrices that do not represent geometric transformations, but rather some kind of relationship between entities. Albeit recently made famous by the ample press coverage of Google’s PageRank algorithm, spectral ranking was devised more than fifty years ago, almost exactly in the same terms, and has been studied in psychology, social sciences, and choice theory. I will try to describe it in precise and modern mathematical terms, highlighting along the way the contributions given by previous scholars.