nils-herrmann/clean_scirepeval huggingface dataset:
Detection of motorcyclists without helmet in videos using convolutional neural network

In order to ensure the safety measures, the detection of traffic rule violators is a highly desirable but challenging task due to various difficulties such as occlusion, illumination, poor quality of surveillance video, varying whether conditions, etc. In this paper, we present a framework for automatic detection of motorcyclists driving without helmets in surveillance videos. In the proposed approach, first we use adaptive background subtraction on video frames to get moving objects. Later convolutional neural network (CNN) is used to select motorcyclists among the moving objects. Again, we apply CNN on upper one fourth part for further recognition of motorcyclists driving without a helmet. The performance of the proposed approach is evaluated on two datasets, IITH_Helmet_1 contains sparse traffic and IITH_Helmet_2 contains dense traffic, respectively. The experiments on real videos successfully detect 92.87% violators with a low false alarm rate of 0.5% on an average and thus shows the efficacy of the proposed approach.
Security on FPGAs: State-of-the-art implementations and attacks

In the last decade, it has become apparent that embedded systems are integral parts of our every day lives. The wireless nature of many embedded applications as well as their omnipresence has made the need for security and privacy preserving mechanisms particularly important. Thus, as field programmable gate arrays (FPGAs) become integral parts of embedded systems, it is imperative to consider their security as a whole. This contribution provides a state-of-the-art description of security issues on FPGAs, both from the system and implementation perspectives. We discuss the advantages of reconfigurable hardware for cryptographic applications, show potential security problems of FPGAs, and provide a list of open research problems. Moreover, we summarize both public and symmetric-key algorithm implementations on FPGAs.
Efficient movement representation by embedding Dynamic Movement Primitives in deep autoencoders

Predictive modeling of human or humanoid movement becomes increasingly complex as the dimensionality of those movements grows. Dynamic Movement Primitives (DMP) have been shown to be a powerful method of representing such movements, but do not generalize well when used in configuration or task space. To solve this problem we propose a model called autoencoded dynamic movement primitive (AE-DMP) which uses deep autoencoders to find a representation of movement in a latent feature space, in which DMP can optimally generalize. The architecture embeds DMP into such an autoencoder and allows the whole to be trained as a unit. To further improve the model for multiple movements, sparsity is added for the feature layer neurons; therefore, various movements can be observed clearly in the feature space. After training, the model finds a single hidden neuron from the sparsity that can efficiently generate new movements. Our experiments clearly demonstrate the efficiency of missing data imputation using 50-dimensional human movement data.
SemEval-2018 Task 1: Affect in Tweets

We present the SemEval-2018 Task 1: Affect in Tweets, which includes an array of subtasks on inferring the affectual state of a person from their tweet. For each task, we created labeled data from English, Arabic, and Spanish tweets. The individual tasks are: 1. emotion intensity regression, 2. emotion intensity ordinal classification, 3. valence (sentiment) regression, 4. valence ordinal classification, and 5. emotion classification. Seventy-five teams (about 200 team members) participated in the shared task. We summarize the methods, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful. We also analyze systems for consistent bias towards a particular race or gender. The data is made freely available to further improve our understanding of how people convey emotions through language.
Load balancing cloud computing: State of art

The rapid development of Internet has given birth to a new business model: Cloud Computing. This new paradigm has experienced a fantastic rise in recent years. Because of its infancy, it remains a model to be developed. In particular, it must offer the same features of services than traditional systems. The cloud computing is large distributed systems that employ distributed resources to deliver a service to end users by implementing several technologies. Hence providing acceptable response time for end users, presents a major challenge for cloud computing. All components must cooperate to meet this challenge, in particular through load balancing algorithms. This will enhance the availability and will gain the end user confidence. In this paper we try to give an overview of load balancing in the cloud computing by exposing the most important research challenges.
RenderBots-Multi-Agent Systems for Direct Image Generation

The term stroke-based rendering collectively describes techniques where images are generated from elements that are usually larger than a pixel. These techniques lend themselves well for rendering artistic styles such as stippling and hatching. This paper presents a novel approach for stroke-based rendering that exploits multi agent systems. RenderBots are individual agents each of which in general represents one stroke. They form a multi agent system and undergo a simulation to distribute themselves in the environment. The environment consists of a source image and possibly additional G-buffers. The final image is created when the simulation is finished by having each RenderBot execute its painting function.  classes differ in their physical behavior as well as their way of painting so that different styles can be created in a very flexible way.
Saliency from hierarchical adaptation through decorrelation and variance normalization

In most cases authors are permitted to post their version of the article (e.g. in Word or Tex form) to their personal website or institutional repository. Authors requiring further information regarding Elsevier's archiving and manuscript policies are encouraged to visit: a b s t r a c t a r t i c l e i n f o This paper presents a novel approach to visual saliency that relies on a contextually adapted representation produced through adaptive whitening of color and scale features. Unlike previous models, the proposal is grounded on the specific adaptation of the basis of low level features to the statistical structure of the image. Adaptation is achieved through decorrelation and contrast normalization in several steps in a hierarchical approach, in compliance with coarse features described in biological visual systems. Saliency is simply computed as the square of the vector norm in the resulting representation. The performance of the model is compared with several state-of-the-art approaches, in predicting human fixations using three different eye-tracking datasets. Referring this measure to the performance of human priority maps, the model proves to be the only one able to keep the same behavior through different datasets, showing free of biases. Moreover, it is able to predict a wide set of relevant psychophysical observations, to our knowledge, not reproduced together by any other model before. Research on the estimation of visual saliency has experienced an increasing activity in the last years from both computer vision and neuro-science perspectives, giving rise to a number of improved approaches. Furthermore, a wide diversity of applications based on saliency are being proposed that range from image retargeting  to human-like robot surveillance , object learning and recognition [3–5], objectness definition , image processing for retinal implants [7], and many others. Existing approaches to visual saliency have adopted a number of quite different strategies. A first group, including many early models, is very influenced by psychophysical theories supporting a parallel processing of several feature dimensions. Models in this group are particularly concerned with biological plausibility in their formulation, and they resort to the modeling of visual functions. Outstanding examples can be found in  or in . Most recent models are in a second group that broadly aims to estimate the inverse of the probability density of a set of low level features by different procedures. In this kind of models, low level features are usually …
Design of a Near-Infrared Device for the Study of Glucose Concentration Measurements By

Maintaining healthy blood glucose concentration levels is advantageous for the prevention of diabetes and obesity. Present day technologies limit such monitoring to patients who already have diabetes. The purpose of this project is to suggest a non-invasive method for measuring blood glucose concentration levels. Such a method would provide useful for even people without illness, addressing preventive care. This project implements near-infrared light of wavelengths 1450nm and 2050nm through the use of light emitting diodes and measures transmittance through solutions of distilled water and d-glucose of concentrations 50mg/dL, 100mg/dL, 150mg/dL, and 200mg/dL by using an InGaAs photodiode. Regression analysis is done. Transmittance results were observed when using near-infrared light of wavelength 1450nm. As glucose concentration increases, output voltage from the photodiode also increases. The relation observed was linear. No significant transmittance results were obtained with the use of 2050nm infrared light due to high absorbance and low power. The use of 1450nm infrared light provides a means of measuring glucose concentration levels.
All Silicon Marx-bank Topology for High-voltage, High-frequency Rectangular Pulses

This paper discusses the operation of a fully integrated solid-state Marx generator circuit, which has been developed for high-frequency (kHz), high-voltage (kV) applications needing rectangular pulses. The conventional Marx generator, used for high-voltage pulsed applications, uses inductors, or resistors, to supply the charging capacitors voltage, which has the disadvantages of size, power loss and frequency limitation. The proposed circuit takes advantage of the intensive use of power semiconductor switches, replacing the passive elements in the conventional circuit, to increase the performance, strongly reducing losses and increasing the pulse repetition frequency. Also, the proposed topology enables the use of typical half-bridge semiconductor structures, while ensuring that the maximum voltage blocked by the semiconductors is the voltage of each capacitor (i.e. the power supply voltage), even with mismatches in the synchronized switching, and with fault conditions. A laboratory prototype with five stages, 5 kW peak power, of this all silicon Marx generator circuit, was constructed using 1200 V IGBTs and diodes, operating with 1000 V d-c input voltage and 10 kHz frequency, giving 5 kV pulses, with 10 mus width and 50 ns rise time
Capacitively-tuned split microstrip resonators for RFID barcodes

A new method of realising RF barcodes is presented using arrays of identical microstrip dipoles capacitively tuned to be resonant at different frequencies within the desired licensed-free ISM bands. When interrogated, the reader detects each dipole's resonance frequency and with n resonant dipoles, potentially 2/sup n/-1 items in the field can be tagged and identified. Results for RF barcode elements in the 5.8 GHz band are presented. It is shown that with accurate centre frequency prediction and by operating over multiple ISM and other license-exempt bands, a useful number of information bits can be realised. Further increase may be possible using ultra-wideband (UWB) technology. Low cost lithographic printing techniques based on using metal ink on low cost substrates could lead to an economical alternative to current RFID systems in many applications.
Cloud of Things: Integrating Internet of Things and cloud computing and the issues involved

With the trend going on in ubiquitous computing, everything is going to be connected to the Internet and its data will be used for various progressive purposes, creating not only information from it, but also, knowledge and even wisdom. Internet of Things (IoT) becoming so pervasive that it is becoming important to integrate it with cloud computing because of the amount of data IoT's could generate and their requirement to have the privilege of virtual resources utilization and storage capacity, but also, to make it possible to create more usefulness from the data generated by IoT's and develop smart applications for the users. This IoT and cloud computing integration is referred to as Cloud of Things in this paper. IoT's and cloud computing integration is not that simple and bears some key issues. Those key issues along with their respective potential solutions have been highlighted in this paper.
A Metric to Quantify Virtual Scene Movement for the Study of Cybersickness: Definition, Implementation, and Verification

This paper presents a metric to quantify visual scene movement perceived inside a virtual environment (VE) and illustrates how this method could be used in future studies to determine a cybersickness dose value to predict levels of cybersickness in VEs. Sensory conflict theories predict that cybersickness produced by a VE is a kind of visually induced motion sickness. A comprehensive review indicates that there is only one subjective measure to quantify visual stimuli presented inside a VE. A metric, referred to as spatial velocity (SV), is proposed. It combines objective measures of scene complexity and scene movement velocity. The theoretical basis for the proposed SV metric and the algorithms for its implementation are presented. Data from two previous experiments on cybersickness were reanalyzed using the metric. Results showed that increasing SV by either increasing the scene complexity or scene velocity significantly increased the rated level of cybersickness. A strong correlation between SV and the level of cybersickness was found. The use of the spatial velocity metric to predict levels of cybersickness is also discussed.
Bias compensation and tracking with monopulse radars in the presence of multi path

The problem of tracking targets in the presence of reflections from sea or ground is addressed. Both types of reflections (specular and diffuse) are considered. Specular reflection causes large peak errors followed by an approximately constant bias in the monopulse ratio, while diffuse reflection has random variations which on the average generate a bias in the monopulse ratio. Expressions for the average error (bias) in the monopulse ratio due to specular and diffuse reflections and the corresponding variance in the presence of noise in the radar channels are derived. A maximum maneuver-based filter and a multiple model estimator are used for tracking. Simulation results for five scenarios, typical of sea skimmers, with Swerling III fluctuating radar cross sections (RCSs) indicate the significance and efficiency of the technique developed in this paper-a 65% reduction of the rms error in the target height estimate.
1 Bootstrap : A Statistical Method

This paper at tempts to introduce readers w ith the c oncept and methodology of bootstrap in Stat ist ics, which is p l aced under a larger umbrel la of resampl ing. Major port ion of the discu ssions should be accessib le to any one who has had a couple of col le ge level appl ied stat is t ics courses. Towards the end, we at tempt to provide gl impses of the vast l i terature publ ished on the topic, which s hould be helpfu l to someone aspir ing to go into the depth of the method ology. A sect ion is dedicated to i l lustrate real data examples. A tech nical appendix is inc luded, which contains a short proof of “bootst ra p Centra l L imit Theorem” for the means. I t should inspire a mathem at ical minded reader to study further. We th ink the selected set of ref erences cover the greater part of the developments on this subject matter . 1. Introduct ion and the Idea B. Efron introduced a statistical method, which is called Bootstrap, published in 1979 (Efron 1979). It spread like brush fire in statistical sciences within a couple of decades. Now if one conducts a “Google search” for the above title, an astounding 1.86 million records will be mentioned; scanning through even a fraction of these records is a daunting task. We attempt first to explain the idea behind the method and the purpose of it at a rather rudimentary level. The primary task of a statistician is to summarize a sample based study and generalize the finding to the parent population in a scientific manner. A technical term for a sample summary number is (sample) statistic. Some basic sample statistics are, for instance, sample mean, sample median, sample standard deviation etc. Of course, a summary statistic like the sample mean will fluctuate from sample to sample and a statistician would like to know the magnitude of these fluctuations around the corresponding population parameter in an overall sense. This is then used in assessing Margin of Errors. The entire picture of all possible values of a sample statistics presented in the form of a probability distribution is called a sampling distribution. There is a
A Malware Detection Scheme Based on Mining Format Information

Malware has become one of the most serious threats to computer information system and the current malware detection technology still has very significant limitations. In this paper, we proposed a malware detection approach by mining format information of PE (portable executable) files. Based on in-depth analysis of the static format information of the PE files, we extracted 197 features from format information of PE files and applied feature selection methods to reduce the dimensionality of the features and achieve acceptable high performance. When the selected features were trained using classification algorithms, the results of our experiments indicate that the accuracy of the top classification algorithm is 99.1% and the value of the AUC is 0.998. We designed three experiments to evaluate the performance of our detection scheme and the ability of detecting unknown and new malware. Although the experimental results of identifying new malware are not perfect, our method is still able to identify 97.6% of new malware with 1.3% false positive rates.
The sound of sarcasm

The present study was conducted to identify possible acoustic cues of sarcasm. Native English speakers produced a variety of simple utterances to convey four different attitudes: sarcasm, humour, sincerity, and neutrality. Following validation by a separate naı̈ve group of native English speakers, the recorded speech was subjected to acoustic analyses for the following features: mean fundamental frequency (F0), F0 standard deviation, F0 range, mean amplitude, amplitude range, speech rate, harmonics-to-noise ratio (HNR, to probe for voice quality changes), and one-third octave spectral values (to probe resonance changes). The results of analyses indicated that sarcasm was reliably characterized by a number of prosodic cues, although one acoustic feature appeared particularly robust in sarcastic utterances: overall reductions in mean F0 relative to all other target attitudes. Sarcasm was also reliably distinguished from sincerity by overall reductions in HNR and in F0 standard deviation. In certain linguistic contexts, sarcasm could be differentiated from sincerity and humour through changes in resonance and reductions in both speech rate and F0 range. Results also suggested a role of language used by speakers in conveying sarcasm and sincerity. It was concluded that sarcasm in speech can be characterized by a specific pattern of prosodic cues in addition to textual cues, and that these acoustic characteristics can be influenced by language used by the speaker.
3D LIDAR-Camera Extrinsic Calibration Using an Arbitrary Trihedron

This paper presents a novel way to address the extrinsic calibration problem for a system composed of a 3D LIDAR and a camera. The relative transformation between the two sensors is calibrated via a nonlinear least squares (NLS) problem, which is formulated in terms of the geometric constraints associated with a trihedral object. Precise initial estimates of NLS are obtained by dividing it into two sub-problems that are solved individually. With the precise initializations, the calibration parameters are further refined by iteratively optimizing the NLS problem. The algorithm is validated on both simulated and real data, as well as a 3D reconstruction application. Moreover, since the trihedral target used for calibration can be either orthogonal or not, it is very often present in structured environments, making the calibration convenient.
A Survey of Techniques for Event Detection in Twitter

Twitter is among the fastest-growing microblogging and online social networking services. Messages posted on Twitter (tweets) have been reporting everything from daily life stories to the latest local and global news and events. Monitoring and analyzing this rich and continuous user-generated content can yield unprecedentedly valuable information, enabling users and organizations to acquire actionable knowledge. This article provides a survey of techniques for event detection from Twitter streams. These techniques aim at finding real-world occurrences that unfold over space and time. In contrast to conventional media, event detection from Twitter streams poses new challenges. Twitter streams contain large amounts of meaningless messages and polluted content, which negatively affect the detection performance. In addition, traditional text mining techniques are not suitable, because of the short length of tweets, the large number of spelling and grammatical errors, and the frequent use of informal and mixed language. Event detection techniques presented in literature address these issues by adapting techniques from various fields to the uniqueness of Twitter. This article classifies these techniques according to the event type, detection task, and detection method and discusses commonly used features. Finally, it highlights the need for public benchmarks to evaluate the performance of different detection approaches and various features.
Fast and "soft-arm" tactics [robot arm design]

This article considered the problem of designing joint-actuation mechanisms that can allow fast and accurate operation of a robot arm, while guaranteeing a suitably limited level of injury risk. Different approaches to the problem were presented, and a method of performance evaluation was proposed based on minimum-time optimal control with safety constraints. The variable stiffness transmission (VST) scheme was found to be one of a few different possible schemes that allows the most flexibility and potential performance. Some aspects related to the implementation of the mechanics and control of VST actuation were also reported.
DroidBot: A Lightweight UI-Guided Test Input Generator for Android

As many automated test input generation tools for Android need to instrument the system or the app, they cannot be used in some scenarios such as compatibility testing and malware analysis. We introduce DroidBot, a lightweight UI-guided test input generator, which is able to interact with an Android app on almost any device without instrumentation. The key technique behind DroidBot is that it can generate UI-guided test inputs based on a state transition model generated on-the-fly, and allow users to integrate their own strategies or algorithms. DroidBot is lightweight as it does not require app instrumentation, thus users do not need to worry about the inconsistency between the tested version and the original version. It is compatible with most Android apps, and able to run on almost all Android-based systems, including customized sandboxes and commodity devices. Droidbot is released as an open-source tool on GitHub, and the demo video can be found at https://youtu.be/3-aHG_SazMY.