nils-herrmann/clean_scirepeval huggingface dataset:
EduWear: smart textiles as ways of relating computing technology to everyday life

In this paper, we report on the outcomes of the European project EduWear. The aim of the project was to develop a construction kit with smart textiles and to examine its impact on young people. The construction kit, including a suitable programming environment and a workshop concept, was adopted by children in a number of workshops.
 The evaluation of the workshops showed that designing, creating, and programming wearables with a smart textile construction kit allows for creating personal meaningful projects which relate strongly to aspects of young people's life worlds. Through their construction activities, participants became more self-confident in dealing with technology and were able to draw relations between their own creations and technologies present in their environment. We argue that incorporating such constructionist processes into an appropriate workshop concept is essential for triggering thought processes about the character of digital media beyond the construction process itself.
Blockchain-based trusted computing in social network

MIT Media Lab employed blockchain to describe a decentralized personal data management system (i.e. Decentralizing Privacy) that ensures users own and control their data without authentication from a third party. In this paper, we employ a better encryption algorithm from NTT Service Evolution Laboratory to enforce the “Decentralizing Privacy”. Instead of using Proof-of-Work (PoW) for protection, we employ Proof-of-Credibility Score to improve the pervious system and analyzed attack situations.
Tools for dynamics simulation of robots: a survey based on user feedback

The number of tools for dynamics simulation has grown in the last years. It is necessary for the robotics community to have elements to ponder which of the available tools is the best for their research. As a complement to an objective and quantitative comparison, difficult to obtain since not all the tools are open-source, an element of evaluation is user feedback. With this goal in mind, we created an online survey about the use of dynamical simulation in robotics. This paper reports the analysis of the participants’ answers and a descriptive information fiche for the most relevant tools. We believe this report will be helpful for roboticists to choose the best simulation tool for their researches.
GPS-denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

GPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INS) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INS can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPSdenied environments with two rotorcraft UAS. The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16 minute flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, ∗girish.chowdhary@gatech.edu, dmagree@gatech.edu, eric.johnson@ae.gatech.edu. G. Chowdhary, A. Wu, E. Johnson, D. Magree, are affiliated with Georgia Institute of Technology, Atlanta, GA. A. Shein is affiliated with CyPhy Works Inc., Danvers MA. and gusty indoor environment.
Learning to Learn without Gradient Descent by Gradient Descent

We learn recurrent neural network optimizers trained on simple synthetic functions by gradient descent. We show that these learned optimizers exhibit a remarkable degree of transfer in that they can be used to efficiently optimize a broad range of derivative-free black-box functions, including Gaussian process bandits, simple control objectives, global optimization benchmarks and hyper-parameter tuning tasks. Up to the training horizon, the learned optimizers learn to tradeoff exploration and exploitation, and compare favourably with heavily engineered Bayesian optimization packages for hyper-parameter tuning.
Autonomous Multisensor System Powered by a Solar Thermoelectric Energy Harvester With Ultralow-Power Management Circuit

An autonomous multisensor system powered by an energy harvester fabricated with a flat-panel solar thermoelectric generator with an ultralow-power management circuit is presented. The multisensor system was tested in an agricultural application, where every 15 min the values of the temperature, air humidity, and solar radiation have to be measured and stored in a mass memory device (a Secure Digital card), with their respective time stamp. The energy-harvesting switching dc-dc converter is based on a low-input-voltage commercial integrated circuit (LTC3108), which charges a 1.65-F supercapacitor up to 5.0 V. A novel ultralow-power management circuit was developed to replace the internal power management circuitry of the LTC3108, and using this circuit, the operation of the system when no energy can be harvested from the environment is extended from 136 h to more than 266 h. The solar thermoelectric generator used for the energy harvesting is composed of a bismuth telluride thermoelectric generator with a 110-mV/°C Seebeck coefficient sandwiched between a 40 cm × 40 cm anodized aluminum flat panel and an aluminum heatsink. On a sunny winter day in the southern hemisphere (12 August 2014, at Campinas, SP-Brazil, Latitude: 22° 54'), the energy supplied by the harvesting system to the supercapacitor was 7
Character Motion Synthesis by Topology Coordinates

In this paper, we propose a new method to efficiently synthesi ze character motions that involve close contacts such as wearing a T-shirt, passing the arms through the strin gs of a knapsack, or piggy-back carrying an injured person. We introduce the concept of topology coordinates, i n which the topological relationships of the segments are embedded into the attributes. As a result, the computati on for collision avoidance can be greatly reduced for complex motions that require tangling the segments of the bo dy. Our method can be combinedly used with other prevalent frame-based optimization techniques such as inv erse kinematics.
How does online social networking enhance life satisfaction? The relationships among online supportive interaction, affect, perceived social support, sense of community, and life satisfaction

The purpose of this study is to examine whether supportive interactions on social networking sites mediate the influence of SNS use and the number of SNS friends on perceived social support, affect, sense of community, and life satisfaction. Employing momentary sampling, the current study also looked at the relationship between supportive interaction and immediate affect after the interaction over a period of 5 days. An analysis of 339 adult participants revealed a positive relationship between supportive interaction and positive affect after the interaction. A path model revealed positive associations among the number of SNS friends, supportive interactions, affect, perceived social support, sense of community, and life satisfaction. Implications for the research of online social networking and social support are discussed.
Reinforcement Learning Neural Network to the Problem of Autonomous Mobile Robot Obstacle Avoidance

An approach to the problem of autonomous mobile robot obstacle avoidance using reinforcement learning neural network is proposed in this paper. Q-learning is one kind of reinforcement learning method that is similar to dynamic programming and the neural network has a powerful ability to store the values. We integrate these two methods with the aim to ensure autonomous robot behavior in complicated unpredictable environment. The simulation results show that the simulated robot using the reinforcement learning neural network can enhance its learning ability obviously and can finish the given task in a complex environment.
A Survey on Human Motion Analysis from Depth Data

Human pose estimation has been actively studied for decades. While traditional approaches rely on 2d data like images or videos, the development of Time-of-Flight cameras and other depth sensors created new opportunities to advance the field. We give an overview of recent approaches that perform human motion analysis which includes depthbased and skeleton-based activity recognition, head pose estimation, facial feature detection, facial performance capture, hand pose estimation and hand gesture recognition. While the focus is on approaches using depth data, we also discuss traditional image based methods to provide a broad overview of recent developments in these areas.
Pre-processing of Mammogram Images

Digital mammogram has become the most effective technique for early breast cancer detection modality. Digital mammogram takes an electronic image of the breast and stores it directly in a computer. High quality mammogram images are high resolution and large size images. Processing these images require high computational capabilities. The transmission of these images over the net is sometimes critical especially if the diagnosis of remote radiologists is required. The aim of this study is to develop an automated system for assisting the analysis of digital mammograms. Computer image processing techniques will be applied to enhance images and this is followed by segmentation of the region of interest (ROI). Subsequently, the textural features will be extracted from the ROI. The texture features will be used to classify the ROIs as either masses or non-masses.
What's in a Name? Using First Names as Features for Gender Inference in Twitter

Despite significant work on the problem of inferring a Twitter user’s gender from her online content, no systematic investigation has been made into leveraging the most obvious signal of a user’s gender: first name. In this paper, we perform a thorough investigation of the link between gender and first name in English tweets. Our work makes several important contributions. The first and most central contribution is two different strategies for incorporating the user’s self-reported name into a gender classifier. We find that this yields a 20% increase in accuracy over a standard baseline classifier. These classifiers are the most accurate gender inference methods for Twitter data developed to date. In order to evaluate our classifiers, we developed a novel way of obtaining gender-labels for Twitter users that does not require analysis of the user’s profile or textual content. This is our second contribution. Our approach eliminates the troubling issue of a label being somehow derived from the same text that a classifier will use to
Mime: compact, low power 3D gesture sensing for interaction with head mounted displays

We present Mime, a compact, low-power 3D sensor for unencumbered free-form, single-handed gestural interaction with head-mounted displays (HMDs). Mime introduces a real-time signal processing framework that combines a novel three-pixel time-of-flight (TOF) module with a standard RGB camera. The TOF module achieves accurate 3D hand localization and tracking, and it thus enables motion-controlled gestures. The joint processing of 3D information with RGB image data enables finer, shape-based gestural interaction.
 Our Mime hardware prototype achieves fast and precise 3D gestural control. Compared with state-of-the-art 3D sensors like TOF cameras, the Microsoft Kinect and the Leap Motion Controller, Mime offers several key advantages for mobile applications and HMD use cases: very small size, daylight insensitivity, and low power consumption. Mime is built using standard, low-cost optoelectronic components and promises to be an inexpensive technology that can either be a peripheral component or be embedded within the HMD unit. We demonstrate the utility of the Mime sensor for HMD interaction with a variety of application scenarios, including 3D spatial input using close-range gestures, gaming, on-the-move interaction, and operation in cluttered environments and in broad daylight conditions.
Medium voltage power converter design and demonstration using 15 kV SiC N-IGBTs

This paper summarizes the different steps that have been undertaken to design medium voltage power converters using the state-of-the-art 15 kV SiC N-IGBTs. The 11 kV switching characterization results, 11 kV high dv/dt gate driver validation, and the heat-run test results of the SiC IGBT at 10 kV, 550 W/cm2 (active area) have been recently reported as individual topics. In this paper, it is attempted to link all these individual topics and present them as a complete subject from the double pulse tests to the converter design, for evaluating these novel high voltage power semiconductor devices. In addition, the demonstration results of two-level H-Bridge and three-level NPC converters, both at 10 kV dc input, are being presented for the first-time. Lastly, the performance of two-chip IGBT modules for increased current capability and demonstration of three-level poles, built using these modules, at 10 kV dc input with sine-PWM and square-PWM modulation for rectifier and dc-dc stages of a three-phase solid state transformer are presented.
Bursty Feature Representation for Clustering Text Streams

Text representation plays a crucial role in classical text mining, where the primary focus was on static text. Nevertheless, well-studied static text representations including TFIDF are not optimized for non-stationary streams of information such as news, discussion board messages, and blogs. We therefore introduce a new temporal representation for text streams based on bursty features. Our bursty text representation differs significantly from traditional schemes in that it 1) dynamically represents documents over time, 2) amplifies a feature in proportional to its burstiness at any point in time, and 3) is topic independent. Our bursty text representation model was evaluated against a classical bagof-words text representation on the task of clustering TDT3 topical text streams. It was shown to consistently yield more cohesive clusters in terms of cluster purity and cluster/class entropies. This new temporal bursty text representation can be extended to most text mining tasks involving a temporal dimension, such as modeling of online blog pages.
A 0.8-V 0.25-mW Current-Mirror OTA With 160-MHz GBW in 0.18-μm CMOS

A low-voltage low-power CMOS operational transconductance amplifier (OTA) with near rail-to-rail output swing is presented in this brief. The proposed circuit is based on the current-mirror OTA topology. In addition, several circuit techniques are adopted to enhance the voltage gain. Simulated from a 0.8-V supply voltage, the proposed OTA achieves a 62-dB dc gain and a gain–bandwidth product of 160 MHz while driving a 2-pF load. The OTA is designed in a 0.18m CMOS process. The power consumption is 0.25 mW including the common-mode feedback circuit.
Spatio-temporal Address Mutation for Proactive Cyber Agility against Sophisticated Attackers

The static one-to-one binding of hosts to IP addresses allows adversaries to conduct thorough reconnaissance in order to discover and enumerate network assets. Specifically, this fixed address mapping allows distributed network scanners to aggregate information gathered at multiple locations over different times in order to construct an accurate and persistent view of the network. The unvarying nature of this view enables adversaries to collaboratively share and reuse their collected reconnaissance information in various stages of attack planning and execution. This paper presents a novel moving target defense (MTD) technique which enables host-to-IP binding of each destination host to vary randomly across the network based on the source identity (spatial randomization) as well as time (temporal randomization). This spatio-temporal randomization will distort attackers' view of the network by causing the collected reconnaissance information to expire as adversaries transition from one host to another or if they stay long enough in one location. Consequently, adversaries are forced to re-scan the network frequently at each location or over different time intervals. These recurring probings significantly raises the bar for the adversaries by slowing down the attack progress, while improving its detectability. We introduce three novel metrics for quantifying the effectiveness of MTD defense techniques: deterrence, deception, and detectability. Using these metrics, we perform rigorous theoretical and experimental analysis to evaluate the efficacy of this approach. These analyses show that our approach is effective in countering a significant number of sophisticated threat models including collaborative reconnaissance, worm propagation, and advanced persistent threat (APT), in an evasion-free manner.
Real-time vehicle detection using parts at intersections

In this study, we propose a novel, lightweight approach to real-time detection of vehicles using parts at intersections. Intersections feature oncoming, preceding, and cross traffic, which presents challenges for vision-based vehicle detection. Ubiquitous partial occlusions further complicate the vehicle detection task, and occur when vehicles enter and leave the camera's field of view. To confront these issues, we independently detect vehicle parts using strong classifiers trained with active learning. We match part responses using a learned matching classification. The learning process for part configurations leverages user input regarding full vehicle configurations. Part configurations are evaluated using Support Vector Machine classification. We present a comparison of detection results using geometric image features and appearance-based features. The full vehicle detection by parts has been evaluated on real-world data, runs in real time, and shows promise for future work in urban driver assistance.
Moringa oleifera oil: a possible source of biodiesel.

Biodiesel is an alternative to petroleum-based conventional diesel fuel and is defined as the mono-alkyl esters of vegetable oils and animal fats. Biodiesel has been prepared from numerous vegetable oils, such as canola (rapeseed), cottonseed, palm, peanut, soybean and sunflower oils as well as a variety of less common oils. In this work, Moringa oleifera oil is evaluated for the first time as potential feedstock for biodiesel. After acid pre-treatment to reduce the acid value of the M. oleifera oil, biodiesel was obtained by a standard transesterification procedure with methanol and an alkali catalyst at 60 degrees C and alcohol/oil ratio of 6:1. M. oleifera oil has a high content of oleic acid (>70%) with saturated fatty acids comprising most of the remaining fatty acid profile. As a result, the methyl esters (biodiesel) obtained from this oil exhibit a high cetane number of approximately 67, one of the highest found for a biodiesel fuel. Other fuel properties of biodiesel derived from M. oleifera such as cloud point, kinematic viscosity and oxidative stability were also determined and are discussed in light of biodiesel standards such as ASTM D6751 and EN 14214. The 1H NMR spectrum of M. oleifera methyl esters is reported. Overall, M. oleifera oil appears to be an acceptable feedstock for biodiesel.
Understanding the Digital Economy: Data, Tools, and Research

If you really want to be smarter, reading can be one of the lots ways to evoke and realize. Many people who like reading will have more knowledge and experiences. Reading can be a way to gain information from economics, politics, science, fiction, literature, religion, and many others. As one of the part of book categories, understanding the digital economy data tools and research always becomes the most wanted book. Many people are absolutely searching for this book. It means that many love to read this kind of book.
Trustrace: Mining Software Repositories to Improve the Accuracy of Requirement Traceability Links

Traceability is the only means to ensure that the source code of a system is consistent with its requirements and that all and only the specified requirements have been implemented by developers. During software maintenance and evolution, requirement traceability links become obsolete because developers do not/cannot devote effort to updating them. Yet, recovering these traceability links later is a daunting and costly task for developers. Consequently, the literature has proposed methods, techniques, and tools to recover these traceability links semi-automatically or automatically. Among the proposed techniques, the literature showed that information retrieval (IR) techniques can automatically recover traceability links between free-text requirements and source code. However, IR techniques lack accuracy (precision and recall). In this paper, we show that mining software repositories and combining mined results with IR techniques can improve the accuracy (precision and recall) of IR techniques and we propose Trustrace, a trust--based traceability recovery approach. We apply Trustrace on four medium-size open-source systems to compare the accuracy of its traceability links with those recovered using state-of-the-art IR techniques from the literature, based on the Vector Space Model and Jensen-Shannon model. The results of Trustrace are up to 22.7 percent more precise and have 7.66 percent better recall values than those of the other techniques, on average. We thus show that mining software repositories and combining the mined data with existing results from IR techniques improves the precision and recall of requirement traceability links.