nils-herrmann/clean_scirepeval huggingface dataset:
Fast switching of drift step recovery diodes based on all epi-Si growth

DSRDs are fast HV opening switching devices. Traditionally, these deep junction devices are fabricated on silicon wafers by deep diffusion. We present DSRD results based on silicon epitaxial layers with as-grown junctions. Static measurements showed a rectifying behavior with leakage currents proportional to device dimension. Pulsed power measurements showed that the switching rate was dependant on the DSRD current density.
WebGazer: Scalable Webcam Eye Tracking Using User Interactions

We introduce WebGazer, an online eye tracker that uses common webcams already present in laptops and mobile devices to infer the eye-gaze locations of web visitors on a page in real time. The eye tracking model self-calibrates by watching web visitors interact with the web page and trains a mapping between features of the eye and positions on the screen. This approach aims to provide a natural experience to everyday users that is not restricted to laboratories and highly controlled user studies. WebGazer has two key components: a pupil detector that can be combined with any eye detection library, and a gaze estimator using regression analysis informed by user interactions. We perform a large remote online study and a small in-person study to evaluate WebGazer. The findings show that WebGazer can learn from user interactions and that its accuracy is sufficient for approximating the user’s gaze. As part of this paper, we release the first eye tracking library that can be easily integrated in any website for real-time gaze interactions, usability studies, or web research.
A theoretical framework for the role of fascia in manual therapy.

A theoretical framework for the role that fascia may play in apparently diverse passive manual therapies is presented. The relevant anatomy of fascia is briefly reviewed. Therapies are divided into myofascial ('soft tissue') and manipulative ('joint-based') and comparisons are made between them on a qualitative basis using measures of pain, function and 'autonomic activation'. When these three outcomes are evaluated between therapies it is observed that they are usually comparable in the quality, if not the quantity of the measures. Viewed from a patients' perspective alone the therapeutic benefits are hard to distinguish. It is proposed that a biologically plausible mechanism which may generate a significant component of the observed effects of manual therapies of all descriptions, is the therapeutic stimulation of fascia in its various forms within the body. Such considerations may help explain why diverse therapies apparently give comparable results.
Semi-Supervised Learning on Riemannian Manifolds

We consider the general problem of utilizing both labeled and unlabeled data to improve classification accuracy. Under the assumption that the data lie on a submanifold in a high dimensional space, we develop an algorithmic framework to classify a partially labeled data set in a principled manner. The central idea of our approach is that classification functions are naturally defined only on the submanifold in question rather than the total ambient space. Using the Laplace-Beltrami operator one produces a basis (the Laplacian Eigenmaps) for a Hilbert space of square integrable functions on the submanifold. To recover such a basis, only unlabeled examples are required. Once such a basis is obtained, training can be performed using the labeled data set. Our algorithm models the manifold using the adjacency graph for the data and approximates the Laplace-Beltrami operator by the graph Laplacian. We provide details of the algorithm, its theoretical justification, and several practical applications for image, speech, and text classification.
An ahead pipelined alloyed perceptron with single cycle access time

The increasing pipeline depth, aggressive clock rates and execution width of modern processors require ever more accurate dynamic branch predictors to fully exploit their potential. Recent research on ahead pipelined branch predictors [11, 19] and branch predictors based on perceptrons [10, 11] have offered either increased accuracy or effective single cycle access times, at the cost of large hardware budgets and additional complexity in the branch predictor recovery mechanism. Here we show that a pipelined perceptron predictor can be constructed so that it has an effective latency of one cycle with a minimal loss of accuracy. We then introduce the concept of a precomputed local perceptron, which allows the use of both local and global history in an ahead pipelined perceptron. Both of these two techniques together allow this new perceptron predictor to match or exceed the accuracy of previous designs except at very small hardware budgets, and allow the elimination of most of the complexity in the rest of the pipeline associated with overriding predictors.
Home network configuring scheme for all electric appliances using ZigBee-based integrated remote controller

Controlling the electric appliances is the essential technique in the home automation, and wireless communication between the residence gateway and electric appliances is one of the most important parts in the home network system. In these days, most of the electric appliances are controlled by infrared remote controllers. However, it is very difficult to connect most of the electric appliances to a home network, since the communication protocols are different. In this paper, we propose an integrated remote controller to control electric appliances in the home network with no extra attachment of communication device to the appliances using ZigBee protocol and infrared remote controller technology. The integrated remote controller system for home automation is composed of integrated remote controller, ZigBee to infrared converter, and ZigBee power adapter. ZigBee power adapter is introduced for some appliances which do not have even infrared remote device to be connected in home network. This paper presents a prototype of the proposed system and shows a scheme for the implementation. It provides high flexibility for the users to configure and manage a home network in order to control electric appliances.
An Empirical Study of Online Consumer Review Spam: A Design Science Approach

Because of the sheer volume of consumer reviews posted to the Internet, a manual approach for the detection and analysis of fake reviews is not practical. However, automated detection of fake reviews is a very challenging research problem given the fact that fake reviews could just look like legitimate reviews. Guided by the design science research methodology, one of the main contributions of our research work is the development of a novel methodology and an instantiation which can effectively detect untruthful consumer reviews. The results of our experiment confirm that the proposed methodology outperforms other well-known baseline methods for detecting untruthful reviews collected from amazon.com. Above all, the designed artifacts enable us to conduct an econometric analysis to examine the impact of fake reviews on product sales. To the best of our knowledge, this is the first empirical study conducted to analyze the economic impact of fake consumer reviews.
A new hybrid Modified Firefly Algorithm and Support Vector Regression model for accurate Short Term Load Forecasting

Precise forecast of the electrical load plays a highly significant role in the electricity industry and market. It provides economic operations and effective future plans for the utilities and power system operators. Due to the intermittent and uncertain characteristic of the electrical load, many research studies have been directed to nonlinear prediction methods. In this paper, a hybrid prediction algorithm comprised of Support Vector Regression (SVR) and Modified Firefly Algorithm (MFA) is proposed to provide the short term electrical load forecast. The SVR models utilize the nonlinear mapping feature to deal with nonlinear regressions. However, such models suffer from a methodical algorithm for obtaining the appropriate model parameters. Therefore, in the proposed method the MFA is employed to obtain the SVR parameters accurately and effectively. In order to evaluate the efficiency of the proposed methodology, it is applied to the electrical load demand in Fars, Iran. The obtained results are compared with those obtained from the ARMA model, ANN, SVR-GA, SVR-HBMO, SVR-PSO and SVR-FA. The experimental results affirm that the proposed algorithm outperforms other techniques.
A 32Gb/s wireline receiver with a low-frequency equalizer, CTLE and 2-tap DFE in 28nm CMOS

Standards such as OIF CEI-25G, CEI-28G and 32G-FC require transceivers operating at high data rates over imperfect channels. Equalizers are used to cancel the inter-symbol interference (ISI) caused by frequency-dependent channel losses such as skin effect and dielectric loss. The primary objective of an equalizer is to compensate for high-frequency loss, which often exceeds 30dB at fs/2. However, due to the skin effect in a PCB stripline, which starts at 10MHz or lower, we also need to compensate for a small amount of loss at low frequency (e.g., 500MHz). Figure 2.1.1 shows simulated responses of a backplane channel (42.6dB loss at fs/2 for 32Gb/s) with conventional high-frequency equalizers only (4-tap feed-forward equalizer (FFE), 1st-order continuous-time linear equalizer (CTLE) with a dominant pole at fs/4, and 1-tap DFE) and with additional low-frequency equalization. Conventional equalizers cannot compensate for the small amount of low-frequency loss because the slope of the low-frequency loss is too gentle (<;3dB/dec). The FFE and CTLE do not have a pole in the low frequency region and hence have only a steep slope of 20dB/dec above their zero. The DFE cancels only short-term ISI. Effects of such low-frequency loss have often been overlooked or neglected, because 1) the loss is small (2 to 3dB), 2) when plotted using the linear frequency axis which is commonly used to show frequency dependence of skin effect and dielectric loss, the low-frequency loss is degenerated at DC and hardly visible , and 3) the long ISI tail of the channel pulse response seems well cancelled at first glance by conventional equalizers only . However, the uncompensated low-frequency loss causes non-negligible long-term residual ISI, because the integral of the residual ISI magnitude keeps going up for several hundred UI. As shown by the eye diagrams in the inset of Fig. 2.1.1(b), the residual long-term ISI results in 0.42UI data-dependent Jitter (DDJ) that is difficult to reduce further by enhancing FFE/CTLE/DFE, but can be reduced to 0.21UI by adding a low-frequency equalizer (LFEQ). Savoj et al. also recently reported long-tail cancellation .
Towards Principles of Large-Scale Agile Development - A Summary of the Workshop at XP2014 and a Revised Research Agenda

Large projects are increasingly adopting agile development practices, and this raises new challenges for research. The workshop on principles of large-scale agile development focused on central topics in large-scale: the role of architecture, inter-team coordination, portfolio management and scaling agile practices. We propose eight principles for large-scale agile development, and present a revised research agenda.
AutoVis: Automatic visualization

AutoVis is a data viewer that responds to content – text, relational tables, hierarchies, streams, images – and displays the information appropriately (that is, as an expert would). Its design rests on the grammar of graphics, scagnostics and a modeler based on the logic of statistical analysis. We distinguish an automatic visualization system (AVS) from an automated visualization system. The former automatically makes decisions about what is to be visualized. The latter is a programming system for automating the production of charts, graphs and visualizations. An AVS is designed to provide a first glance at data before modeling and analysis are done. AVS is designed to protect researchers from ignoring missing data, outliers, miscodes and other anomalies that can violate statistical assumptions or otherwise jeopardize the validity of models. The design of this system incorporates several unique features: (1) a spare interface – analysts simply drag a data source into an empty window, (2) a graphics generator that requires no user definitions to produce graphs, (3) a statistical analyzer that protects users from false conclusions, and (4) a pattern recognizer that responds to the aspects (density, shape, trend, and so on) that professional statisticians notice when investigating data sets.
A DC/DC Boosting Technique and Power Management for Ultralow-Voltage Energy Harvesting Applications

With the increasing use of low-voltage portable devices and growing requirements of functionalities embedded into such devices, efficient dc/dc conversion and power management techniques are needed. In this paper, an architecture for boosting extremely low voltages (about 100 mV) to the typical supply voltages of current integrated circuits is presented which is suitable for power harvesting applications too. Starting from a 120-mV supply voltage, the converter reaches an output voltage of 1.2 V, providing an output current of 220 μA and exhibiting a maximum power efficiency of about 30%. Along with the dc/dc converter, a power management circuit is presented, which can regulate the output voltage and improve the overall efficiency. A test chip was fabricated using a United Microelectronics Corporation 180-nm low-threshold CMOS process.
Why do bad moods increase self-defeating behavior? Emotion, risk taking, and self-regulation.

Increased risk taking may explain the link between bad moods and self-defeating behavior. In Study 1, personal recollections of self-defeating actions implicated bad moods and resultant risky decisions. In Study 2, embarrassment increased the preference for a long-shot (high-risk, high-payoff) lottery over a low-risk, low-payoff one. Anger had a similar effect in Study 3. Study 4 replicated this and showed that the effect could be eliminated by making participants analyze the lotteries rationally, suggesting that bad moods foster risk taking by impairing self-regulation instead of by altering subjective utilities. Studies 5 and 6 showed that the risky tendencies are limited to unpleasant moods accompanied by high arousal; neither sadness nor neutral arousal resulted in destructive risk taking.
Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform

In free-viewpoint video, there is a recent trend to represent scene objects as solids rather than using multiple depth maps. Point clouds have been used in computer graphics for a long time, and with the recent possibility of real-time capturing and rendering, point clouds have been favored over meshes in order to save computation. Each point in the cloud is associated with its 3D position and its color. We devise a method to compress the colors in point clouds, which is based on a hierarchical transform and arithmetic coding. The transform is a hierarchical sub-band transform that resembles an adaptive variation of a Haar wavelet. The arithmetic encoding of the coefficients assumes Laplace distributions, one per sub-band. The Laplace parameter for each distribution is transmitted to the decoder using a custom method. The geometry of the point cloud is encoded using the well-established octtree scanning. Results show that the proposed solution performs comparably with the current state-of-the-art, in many occasions outperforming it, while being much more computationally efficient. We believe this paper represents the state of the art in intra-frame compression of point clouds for real-time 3D video.
Learning Maximal Marginal Relevance Model via Directly Optimizing Diversity Evaluation Measures

In this paper we address the issue of learning a ranking model for search result diversification. In the task, a model concerns with both query-document relevance and document diversity is automatically created with training data. Ideally a diverse ranking model would be designed to meet the criterion of maximal marginal relevance, for selecting documents that have the least similarity to previously selected documents. Also, an ideal learning algorithm for diverse ranking would train a ranking model that could directly optimize the diversity evaluation measures with respect to the training data. Existing methods, however, either fail to model the marginal relevance, or train ranking models by minimizing loss functions that loosely related to the evaluation measures. To deal with the problem, we propose a novel learning algorithm under the framework of Perceptron, which adopts the ranking model that \emph{maximizes marginal relevance at ranking and can optimize any diversity evaluation measure in training}. The algorithm, referred to as PAMM (Perceptron Algorithm using Measures as Margins), first constructs positive and negative diverse rankings for each training query, and then repeatedly adjusts the model parameters so that the margins between the positive and negative rankings are maximized. Experimental results on three benchmark datasets show that PAMM significantly outperforms the state-of-the-art baseline methods.
Human Activity Recognition from Accelerometer Data Using a Wearable Device

Activity Recognition is an emerging field of research, born from the larger fields of ubiquitous computing, context-aware computing and multimedia. Recently, recognizing everyday life activities becomes one of the challenges for pervasive computing. In our work, we developed a novel wearable system easy to use and comfortable to bring. Our wearable system is based on a new set of 20 computationally efficient features and the Random Forest classifier. We obtain very encouraging results with classification accuracy of human activities recognition of up
A Search-Based Approach for Cost-Effective Software Test Automation Decision Support and an Industrial Case Study

Test automation is a widely-used approach to reduce the cost of manual software testing. However, if it is not planned or conducted properly, automated testing would not necessarily be more cost effective than manual testing. Deciding what parts of a given System Under Test (SUT) should be tested in an automated fashion and what parts should remain manual is a frequently-asked and challenging question for practitioner testers. In this study, we propose a search-based approach for deciding what parts of a given SUT should be tested automatically to gain the highest Return On Investment (ROI). This work is the first systematic approach for this problem, and significance of our approach is that it considers automation in the entire testing process (i.e., from test-case design, to test scripting, to test execution, and test-result evaluation). The proposed approach has been applied in an industrial setting in the context of a software product used in the oil and gas industry in Canada. Among the results of the case study is that, when planned and conducted properly using our decision-support approach, test automation provides the highest ROI. In this study, we show that if automation decision is taken effectively, test-case design, test execution, and test evaluation can result in about 307%, 675%, and 41% ROI in 10 rounds of using automated test suites.
Time-dependent land uplift and subsidence in the Santa Clara valley , California , from a large interferometric synthetic aperture radar data set

We invert 115 differential interferograms derived from 47 synthetic aperture radar (SAR) scenes for a time-dependent deformation signal in the Santa Clara valley, California. The time-dependent deformation is calculated by performing a linear inversion that solves for the incremental range change between SAR scene acquisitions. A nonlinear range change signal is extracted from the ERS InSAR data without imposing a model of the expected deformation. In the Santa Clara valley, cumulative land uplift is observed during the period from 1992 to 2000 with a maximum uplift of 41 ± 18 mm centered north of Sunnyvale. Uplift is also observed east of San Jose. Seasonal uplift and subsidence dominate west of the Silver Creek fault near San Jose with a maximum peak-to-trough amplitude of 35 mm. The pattern of seasonal versus long-term uplift provides constraints on the spatial and temporal characteristics of water-bearing units within the aquifer. The Silver Creek fault partitions the uplift behavior of the basin, suggesting that it acts as a hydrologic barrier to groundwater flow. While no tectonic creep is observed along the fault, the development of a low-permeability barrier that bisects the alluvium suggests that the fault has been active since the deposition of Quaternary units.
Structured Bayesian Pruning via Log-Normal Multiplicative Noise

Dropout-based regularization methods can be regarded as injecting random noise with pre-defined magnitude to different parts of the neural network during training. It was recently shown that Bayesian dropout procedure not only improves generalization but also leads to extremely sparse neural architectures by automatically setting the individual noise magnitude per weight. However, this sparsity can hardly be used for acceleration since it is unstructured. In the paper, we propose a new Bayesian model that takes into account the computational structure of neural networks and provides structured sparsity, e.g. removes neurons and/or convolutional channels in CNNs. To do this we inject noise to the neurons outputs while keeping the weights unregularized. We establish the probabilistic model with a proper truncated log-uniform prior over the noise and truncated log-normal variational approximation that ensures that the KL-term in the evidence lower bound is computed in closed-form. The model leads to structured sparsity by removing elements with a low SNR from the computation graph and provides significant acceleration on a number of deep neural architectures. The model is easy to implement as it can be formulated as a separate dropout-like layer.
A Magneto-Electric Dipole Antenna With Low-Profile and Simple Structure

By combining a horizontal planar dipole and a vertically oriented folded shorted patch antenna, a new low-profile magneto-electric dipole antenna is presented. The antenna is simply excited by a coaxial feed that works as a balun. A prototype was fabricated and measured. Simulated and measured results agree well. An impedance bandwidth of 45.6% for <formula formulatype="inline"><tex Notation="TeX">${\rm SWR}\leq 1.5$</tex></formula> from 1.86 to 2.96 GHz was achieved. Stable radiation pattern with low cross polarization, low back radiation, and an antenna gain of 8.1 <formula formulatype="inline"><tex Notation="TeX">$\pm$</tex></formula> 0.8 dBi was found over the operating frequencies. The height of the antenna is only <formula formulatype="inline"><tex Notation="TeX">$0.169\lambda$</tex> </formula> (where <formula formulatype="inline"><tex Notation="TeX">$\lambda$</tex> </formula> is the free-space wavelength at the center frequency). In addition, the antenna is dc grounded, which satisfies the requirement of many outdoor antennas.