nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of mobile internet speed, signal strength and FMDH antenna design for improved internet speed

Internet speed is important in many applications. Mobile signal strength of − 110 to − 120 dBm provides poor internet speed by mobile phones. Mobile makes less signal strength and less internet speed from mobile towers. Signal strength ranges between − 30 and − 65 dBm must be maintained to achieve high-speed internet through mobile phones. Good antenna design is necessary to maintain good signal strength and to improve high-speed internet from weak internet speed in required applications. In this work, an FM radio receiver antenna and parabolic dish antenna with a winding helical coil is proposed for the design of an antenna (FMDH antenna) in order to improve weak internet signals to enable high-speed internet signal. Cell tower radiation is normally reduced at a distance of 1000 m, with a weak signal resulting in slow internet speeds. The reduced radiation signal density from the cell tower causes slower internet speeds in mobile. The winding coil of a helical antenna receives the weaker internet signal from the cell towers to increase gain and signal strength between − 30 and − 65 dBm, improving internet to high-speed performance. The FM radio receiver antenna length is increased, so the gain in the main lobe radiation pattern improves internet speed. High-speed internet is needed for computers through mobile internet. Most computers work through mobile internet connections. Wi-Fi connection signal strength should be − 30 to − 60 dBm for maintaining high-speed internet. The FMDH antenna design is proposed in this paper improves mobile Wi-Fi internet, increasing internet signal strength and internet speed.
Fast Vanishing-Point Detection in Unstructured Environments

Vision-based road detection in unstructured environments is a challenging problem as there are hardly any discernible and invariant features that can characterize the road or its boundaries in such environments. However, a salient and consistent feature of most roads or tracks regardless of type of the environments is that their edges, boundaries, and even ruts and tire tracks left by previous vehicles on the path appear to converge into a single point known as the vanishing point. Hence, estimating this vanishing point plays a pivotal role in the determination of the direction of the road. In this paper, we propose a novel methodology based on image texture analysis for the fast estimation of the vanishing point in challenging and unstructured roads. The key attributes of the methodology consist of the optimal local dominant orientation method that uses joint activities of only four Gabor filters to precisely estimate the local dominant orientation at each pixel location in the image plane, the weighting of each pixel based on its dominant orientation, and an adaptive distance-based voting scheme for the estimation of the vanishing point. A series of quantitative and qualitative analyses are presented using natural data sets from the Defense Advanced Research Projects Agency Grand Challenge projects to demonstrate the effectiveness and the accuracy of the proposed methodology.
Deep Learning-Based Large-Scale Automatic Satellite Crosswalk Classification

High-resolution satellite imagery has been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Despite the high availability, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this data set is used to train deep-learning-based models in order to accurately classify satellite images that contain or not contain zebra crossings. A novel data set with more than 240000 images from 3 continents, 9 countries, and more than 20 cities was used in the experiments. The experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.
Snooping Keystrokes with mm-level Audio Ranging on a Single Phone

This paper explores the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. Acoustic keystroke snooping is challenging because it requires distinguishing and labeling sounds generated by tens of keys in very close proximity. Existing work on acoustic keystroke recognition relies on training with labeled data, linguistic context, or multiple phones placed around a keyboard --- requirements that limit usefulness in an adversarial context. In this work, we show that mobile audio hardware advances can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate scenarios where our system can recover $94\%$ of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.
Expected Stock Returns and Volatility

This paper examines the relation between stock returns and stock market volatility. We find evidence that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns. There is also evidence that unexpected stock market returns are negatively related to the unexpected change in the volatility of stock returns. This negative relation provides indirect evidence of a positive relation between expected risk premiums and volatility. Disciplines Finance | Finance and Financial Management Comments At the time of publication, author Robert F. Stambaugh was affiliated with the University of Chicago. Currently, he is a faculty member at the Wharton School at the University of Pennsylvania.  http://repository.upenn.edu/fnce_papers/363 Expected Stock Returns and Volatility by Kenneth R. French* G. William Schwert** Robert F. Starnbaugh
The Accuracy Rate of Holt-Winters Model with Particle Swarm Optimization in Forecasting Exchange Rates

Exchange rates forecasting is a crucial and challenging task. Accurate forecasting of the imminent movements of exchange rates is very important in investments, trade and economics. In this paper, an exponential smoothing using the Holt-Winters Model is used for forecasting exchange rates. Parameter search for the smoothing constants is done through computer simulation using Particle Swarm Optimization (PSO). Experiment results show that PSO is able to compute good values for the smoothing constants, producing forecasts with accuracy in determining the direction (rise or fall) of exchange rates. Furthermore, the computed Mean Absolute Deviation (MAD) and the Residual Standard Error (RSE) of the exchange rate forecasts from the actual observed data indicate that PSO can also be used to improve forecasting precision.
Perceptual objects capture attention

A recent study has demonstrated that the mere organization of some elements in the visual field into an object attracts attention automatically [Kimchi, R., Yeshurun, Y., & Cohen-Savransky, A. (2007). Automatic, stimulus-driven attentional capture by objecthood. Psychonomic Bulletin & Review, 14(1), 166-172]. We tested whether similar results will emerge when the target is not a part of the object and with simplified task demands. A matrix of 16 black L elements in various orientations preceded the presentation of a Vernier target. The target was either added to the matrix (Experiment 1), or appeared after its offset (Experiment 2). On some trials four elements formed a square-like object, and on some of these trials the target appeared in the center of the object. No featural uniqueness or abrupt onset was associated with the object and it did not predict the target location or the direction of the target's horizontal offset. Performance was better when the target appeared in the center of the object than in a different location than the object, even when the target appeared after the matrix offset. These findings support the hypothesis that a perceptual object captures attention (Kimchi et al., 2007), and demonstrate that this automatic deployment of attention to the object is robust and involves a spatial component.
Discovering Reader's Emotions Triggered from News Articles

The study of sentiment analysis aims to explore the occurrences of opinion words in a document, and their orientation and strength. Most existing researches focus on writer's emotions, which are the feelings that authors were expressing. In contrast, news articles are usually described in objective terms, in which opinion words are rare. In this paper, we propose to discover the reader's feelings when they read news articles. First, different feature units are extracted such as unigrams, bigrams, and segmented words. We compare several feature selection methods including document frequency variation, information gain, mutual information, and chi-square test, to select the candidate features for sentiment classification. Then, the performance for multi-class classification and multiple binary classification are evaluated. The experimental results on real news articles show the effectiveness of the proposed method for discovering reader's emotions triggered from news articles. Further investigation is needed to validate the performance in larger scales.
Fixing the mirrors: a feasibility study of the effects of dance movement therapy on young adults with autism spectrum disorder.

From the 1970s on, case studies reported the effectiveness of therapeutic mirroring in movement with children with autism spectrum disorder. In this feasibility study, we tested a dance movement therapy intervention based on mirroring in movement in a population of 31 young adults with autism spectrum disorder (mainly high-functioning and Asperger's syndrome) with the aim to increase body awareness, social skills, self-other distinction, empathy, and well-being. We employed a manualized dance movement therapy intervention implemented in hourly sessions once a week for 7 weeks. The treatment group (n = 16) and the no-intervention control group (n = 15) were matched by sex, age, and symptom severity. Participants did not participate in any other therapies for the duration of the study. After the treatment, participants in the intervention group reported improved well-being, improved body awareness, improved self-other distinction, and increased social skills. The dance movement therapy-based mirroring approach seemed to address more primary developmental aspects of autism than the presently prevailing theory-of-mind approach. Results suggest that dance movement therapy can be an effective and feasible therapy approach for autism spectrum disorder, while future randomized control trials with bigger samples are needed.
Bilateral guided upsampling

We present an algorithm to accelerate a large class of image processing operators. Given a low-resolution reference input and output pair, we model the operator by fitting local curves that map the input to the output. We can then produce a full-resolution output by evaluating these low-resolution curves on the full-resolution input. We demonstrate that this faithfully models state-of-the-art operators for tone mapping, style transfer, and recoloring. The curves are computed by lifting the input into a bilateral grid and then solving for the 3D array of affine matrices that best maps input color to output color per x, y, intensity bin. We enforce a smoothness term on the matrices which prevents false edges and noise amplification. We can either globally optimize this energy, or quickly approximate a solution by locally fitting matrices and then enforcing smoothness by blurring in grid space. This latter option reduces to joint bilateral upsampling [Kopf et al. 2007] or the guided filter [He et al. 2013], depending on the choice of parameters. The cost of running the algorithm is reduced to the cost of running the original algorithm at greatly reduced resolution, as fitting the curves takes about 10 ms on mobile devices, and 1--2 ms on desktop CPUs, and evaluating the curves can be done with a simple GPU shader.
LLC series resonant converter based LED lamp driver with ZVS

This paper proposes an LLC resonant converter based LED (Light Emitting Diode) lamp driver with high power factor. The proposed circuit uses a boost converter for PFC (Power Factor Correction) which operates in continuous conduction mode (CCM) and a quasi half bridge resonant converter to drive the LED lamp load. The LLC converter is designed such that solid state switches of quasi half bridge are working under zero voltage switching (ZVS) to reduce switching losses. The analysis, design, modeling and simulation of 50 W LED driver are carried out using MATLAB/Simulink tool for universal AC mains. The power quality indices are calculated such as total harmonic distortion of AC mains current (THDi), power factor (PF) and crest factor (CF) to evaluate the performance of proposed LED lamp driver.
Facebook in higher education promotes social but not academic engagement

Although there is evidence that academically successful students are engaged with their studies, it has proved difficult to define student engagement clearly. Student engagement is commonly construed as having two dimensions, social and academic. The rapid adoption of social media and digital technologies has ensured increasing interest in using them for improving student engagement. This paper examines Facebook usage among a first year psychology student cohort and reports that although the majority of students (94%) had Facebook accounts and spent an average of one hour per day on Facebook, usage was found to be predominantly social. Personality factors influenced usage patterns, with more conscientious students tending to use Facebook less than less conscientious students. This paper argues that, rather than promoting social engagement in a way that might increase academic engagement, it appears that Facebook is more likely to operate as a distracting influence.
High isolation dual-polarized patch antenna using integrated defected ground structure

This letter presents a high isolation dual-frequency orthogonally polarized rectangular patch antenna utilizing microstrip feed line integrated with a defected ground structure (DGS). The demonstrated approach results in a significant improvement in port isolation in comparison to a conventional dual-polarized antenna fed by simple microstrip lines. Measurements show an improvement of 20 dB in port isolation relative to the conventional antenna, operating at 2 and 2.5 GHz. Image impedance of a microstrip line with DGS is controlled by the DGS geometry without modifying the dimension of the line. A 150 /spl Omega/ high impedance line is effectively implemented using a microstrip line with 75 /spl Omega/ line width by incorporating the DGS.
Diffusion of innovations 1 Diffusion of innovations

The diffusion of innovations according to Rogers. With successive groups of consumers adopting the new technology (shown in blue), its market share (yellow) will eventually reach the saturation level. In mathematics the S curve is known as the logistic function. Diffusion of Innovations is a theory that seeks to explain how, why, and at what rate new ideas and technology spread through cultures. The concept was first studied by the French sociologist Gabriel Tarde (1890) and by German and Austrian anthropologists such as Friedrich Ratzel and Leo Frobenius.[1] Its basic epidemiological or internal-influence form was formulated by H. Earl Pemberton,[2] who provided examples of institutional diffusion such as postage stamps and compulsory school laws.
Robust checkerboard recognition for efficient nonplanar geometry registration in projector-camera systems

Projector-camera systems always need complicated geometry calibration to get a correct display result on nonplanar projection surface. Geometry registration of most calibration methods dealing with arbitrary surfaces is done by projecting a set of structure light patterns or by manually 3D modeling, which are both time-consuming. In this paper, we propose a robust checkerboard calibration pattern recognition method to help nonplanar surface geometry registration. By approximating the nonplanar surface to be composite of many planar quad patches, pixels mapping between the calibration camera and a projector can be got by projecting only one checkerboard calibration pattern recognized by our method. Compared with geometry registration with structure light or encoded points, which need project many images, our method can be more efficient. Our recognition method has two steps: corner detection and checkerboard pattern match. Checkerboard internal corners are defined as special conjunction points of four alternating dark and bright regions. A candidate corner's neighbor points within a rectangular or a circular window are treated as in different one-point-width layers. By processing the points layers in corner detection, we transform the 2D points distribution into 1D, which simplifies the regions amount counting and also improves the robustness against noises caused by deformation and complex illumination. After corner detection, the pre-known checkerboard grids rows and columns amounts are used to match and decide the right checkerboard corners from the results that have found. Regions boundary data produced during the corner detection also assist the matching process.
Incorporating Word Reordering Knowledge into Attention-based Neural Machine Translation

This paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism to attend to source words regarding both the semantic requirement and the word reordering penalty. Experiments on Chinese-English translation show that the approaches can improve word alignment quality and achieve significant translation improvements over a basic attention-based NMT by large margins. Compared with previous works on identical corpora, our system achieves the state-of-the-art performance on translation quality.
Kinematic features of unrestrained vertical arm movements.

Unrestrained human arm trajectories between point targets have been investigated using a three-dimensional tracking apparatus, the Selspot system. Movements were executed between different points in a vertical plane under varying conditions of speed and hand-held load. In contrast to past results which emphasized the straightness of hand paths, movement regions were discovered in which the hand paths were curved. All movements, whether curved or straight, showed an invariant tangential velocity profile when normalized for speed and distance. The velocity profile invariance with speed and load is interpreted in terms of simplification of the underlying arm dynamics, extending the results of Hollerbach and Flash (Hollerbach, J. M., and T. Flash (1982)  67-77).
Mesoniviridae: a proposed new family in the order Nidovirales formed by a single species of mosquito-borne viruses

Recently, two independent surveillance studies in Côte d’Ivoire and Vietnam, respectively, led to the discovery of two mosquito-borne viruses, Cavally virus and Nam Dinh virus, with genome and proteome properties typical for viruses of the order Nidovirales. Using a state-of-the-art approach, we show that the two insect nidoviruses are (i) sufficiently different from other nidoviruses to represent a new virus family, and (ii) related to each other closely enough to be placed in the same virus species. We propose to name this new family Mesoniviridae. Meso is derived from the Greek word “mesos” (in English “in the middle”) and refers to the distinctive genome size of these insect nidoviruses, which is intermediate between that of the families Arteriviridae and Coronaviridae, while ni is an abbreviation for “nido”. A taxonomic proposal to establish the new family Mesoniviridae, genus Alphamesonivirus, and species Alphamesonivirus 1 has been approved for consideration by the Executive Committee of the ICTV.
A deep learning framework for modeling structural features of RNA-binding protein targets.

RNA-binding proteins (RBPs) play important roles in the post-transcriptional control of RNAs. Identifying RBP binding sites and characterizing RBP binding preferences are key steps toward understanding the basic mechanisms of the post-transcriptional gene regulation. Though numerous computational methods have been developed for modeling RBP binding preferences, discovering a complete structural representation of the RBP targets by integrating their available structural features in all three dimensions is still a challenging task. In this paper, we develop a general and flexible deep learning framework for modeling structural binding preferences and predicting binding sites of RBPs, which takes (predicted) RNA tertiary structural information into account for the first time. Our framework constructs a unified representation that characterizes the structural specificities of RBP targets in all three dimensions, which can be further used to predict novel candidate binding sites and discover potential binding motifs. Through testing on the real CLIP-seq datasets, we have demonstrated that our deep learning framework can automatically extract effective hidden structural features from the encoded raw sequence and structural profiles, and predict accurate RBP binding sites. In addition, we have conducted the first study to show that integrating the additional RNA tertiary structural features can improve the model performance in predicting RBP binding sites, especially for the polypyrimidine tract-binding protein (PTB), which also provides a new evidence to support the view that RBPs may own specific tertiary structural binding preferences. In particular, the tests on the internal ribosome entry site (IRES) segments yield satisfiable results with experimental support from the literature and further demonstrate the necessity of incorporating RNA tertiary structural information into the prediction model. The source code of our approach can be found in https://github.com/thucombio/deepnet-rbp.