nils-herrmann/clean_scirepeval huggingface dataset:
Traffic Signs Detection Based on Faster R-CNN

In this paper, we use a advanced method called Faster R-CNN to detect traffic signs. This new method represents the highest level in object recognition, which don't need to extract image feature manually anymore and can segment image to get candidate region proposals automatically. Our experiment is based on a traffic sign detection competition in 2016 by CCF and UISEE company. The mAP(mean average precision) value of the result is 0.3449 that means Faster R-CNN can indeed be applied in this field. Even though the experiment did not achieve the best results, we explore a new method in the area of the traffic signs detection. We believe that we can get a better achievement in the future.
The Bi-mode Insulated Gate Transistor (BIGT) a potential technology for higher power applications

In this paper, an advanced Reverse Conducting (RC) IGBT concept is presented. The new technology is referred to as the Bi-mode Insulated Gate Transistor (BIGT) implying that the device can operate at the same current densities in transistor (IGBT) mode and freewheeling diode mode by utilizing the same available silicon volume in both operational modes. The BIGT design concept differs from that of the standard RC-IGBT while targeting to fully replace the state-of-the-art two-chip IGBT/Diode approach with a single chip. The BIGT is also capable of improving the over-all performance especially under hard switching conditions.
Accuracy assessment and interpretation for optical tracking systems

Highly accurate spatial measurement systems are among the enabling technologies that have made image-guided surgery possible in modern operating theaters. Assessing the accuracies of such systems is subject to much ambiguity, though. The underlying mathematical models that convert raw sensor data into position and orientation measurements of sufficient accuracy complicate matters by providing measurements having non-uniform error distributions throughout their measurement volumes. Users are typically unaware of these issues, as they are usually presented with only a few specifications based on some “representative” statistics that were themselves derived using various data reduction methods. As a result, much of the important underlying information is lost. Further, manufacturers of spatial measurement systems often choose protocols and statistical measures that emphasize the strengths of their systems and diminish their limitations. Such protocols often do not reflect the end users’ intended applications very well. Users and integrators thus need to understand many aspects of spatial metrology in choosing spatial measurement systems that are appropriate for their intended applications. We examine the issues by discussing some of the protocols and their statistical measures typically used by manufacturers. The statistical measures for a given protocol can be affected by many factors, including the volume size, region of interest, and the amount and type of data collected. We also discuss how different system configurations can affect the accuracy. Single-marker and rigid body calibration results are presented, along with a discussion of some of the various factors that affect their accuracy. Although the findings presented here were obtained using the NDI Polaris optical tracking systems, many are applicable to spatial measurement systems in general.
Wideband Circularly Polarized Antenna With Gain Improvement

A wideband circularly polarized antenna with coaxial balun feed, using a broadband 90° hybrid feed network, is proposed. By introducing a narrow patch connecting the pair of bowtie dipoles as an entity that increases the effective radiation area, the proposed antenna delivers a higher gain than that of the conventional dual-fed-type circularly polarized antenna. It can achieve a measured impedance bandwidth for VSWR ≤ 1.5 of 57% and a 3-dB axial-ratio bandwidth of 51.8%. Moreover, the antenna has stable peak circularly polarized gain better than 9.0 dBi ranging from 1.1 to 1.6 GHz. For the entire half-power beamwidth, the axial ratio can be kept below 3 dB.
Neural systems for recognizing emotion

Recognition of emotion draws on a distributed set of structures that include the occipitotemporal neocortex, amygdala, orbitofrontal cortex and right frontoparietal cortices. Recognition of fear may draw especially on the amygdala and the detection of disgust may rely on the insula and basal ganglia. Two important mechanisms for recognition of emotions are the construction of a simulation of the observed emotion in the perceiver, and the modulation of sensory cortices via top-down influences.
The role of inter-channel trust transfer in establishing mobile commerce trust

Mobile brokerage services represent an emerging application of mobile commerce in the brokerage industry. Compared with telephone-based trading services and online brokerage services, they have advantages such as ubiquity, convenience, and privacy. However, the number of investors using mobile brokerage services to conduct brokerage transactions is far smaller than those using other trading methods. A plausible reason for this is that investors lack initial trust in mobile brokerage services, which affects their acceptance of them. This research examines trust transfer as a means of establishing initial trust in mobile brokerage services. We analyze how an investor’s trust in the online brokerage services of a brokerage firm affects her cognitive beliefs about the mobile brokerage services of the firm and what other key factors influence the formation of initial trust in mobile brokerage services. We develop and empirically test a theoretical model of trust transfer from the online to the mobile channels. Our results indicate that trust in online brokerage services not only has a direct effect on initial trust but also has an indirect effect through other variables. This study provides useful suggestions and implications for academics and practitioners.
Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities

Three-dimensional computerized characterization of biomedical solid textures is key to large-scale and high-throughput screening of imaging data. Such data increasingly become available in the clinical and research environments with an ever increasing spatial resolution. In this text we exhaustively analyze the state-of-the-art in 3-D biomedical texture analysis to identify the specific needs of the application domains and extract promising trends in image processing algorithms. The geometrical properties of biomedical textures are studied both in their natural space and on digitized lattices. It is found that most of the tissue types have strong multi-scale directional properties, that are well captured by imaging protocols with high resolutions and spherical spatial transfer functions. The information modeled by the various image processing techniques is analyzed and visualized by displaying their 3-D texture primitives. We demonstrate that non-convolutional approaches are expected to provide best results when the size of structures are inferior to five voxels. For larger structures, it is shown that only multi-scale directional convolutional approaches that are non-separable allow for an unbiased modeling of 3-D biomedical textures. With the increase of high-resolution isotropic imaging protocols in clinical routine and research, these models are expected to best leverage the wealth of 3-D biomedical texture analysis in the future. Future research directions and opportunities are proposed to efficiently model personalized image-based phenotypes of normal biomedical tissue and its alterations. The integration of the clinical and genomic context is expected to better explain the intra class variation of healthy biomedical textures. Using texture synthesis, this provides the exciting opportunity to simulate and visualize texture atlases of normal ageing process and disease progression for enhanced treatment planning and clinical care management.
Search Engines - Information Retrieval in Practice

This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Search Engines: Information Retrieval in Practice is ideal for introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. It is also a valuable tool for search engine and information retrieval professionals. В Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice , is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines.В Coverage of the underlying IR and mathematical models reinforce key concepts. The bookвЂTMs numerous programming exercises make extensive use of Galago, a Java-based open source search engine.
A monopulse slot array antenna based on dual-layer substrate integrated waveguide (SIW)

In this paper, a monopulse slot array antenna working at Ka-band based on dual-layer substrate integrated waveguide (SIW) is presented. The 16×16 slot array antenna with feeding network and the monopulse sum-difference comparator is designed on separated layers which takes the advantage of compact size and high aperture efficiency. The honeycomb SIW (HCSIW) is adopted to design the novel right-angle corner and 90 degree 3-dB coupler. The simulated bandwidth (S11 of sum port < −10 dB) of the antenna is about 9.43%. The maximum gain of sum pattern at 35 GHz is around 26.1 dB with sidelobe suppression of −27 dB in both E-plane and H-plane. This proposed antenna presents a good candidate for the directional-finding systems which are restricted by aperture size.
The wire-tap channel

We consider the situation in which digital data is to be reliably transmitted over a discrete, memoryless channel (dmc) that is subjected to a wire-tap at the receiver. We assume that the wire-tapper views the channel output via a second dmc). Encoding by the transmitter and decoding by the receiver are permitted. However, the code books used in these operations are assumed to be known by the wire-tapper. The designer attempts to build the encoder-decoder in such a way as to maximize the transmission rate R, and the equivocation d of the data as seen by the wire-tapper. In this paper, we find the trade-off curve between R and d, assuming essentially perfect (“error-free”) transmission. In particular, if d is equal to Hs, the entropy of the data source, then we consider that the transmission is accomplished in perfect secrecy. Our results imply that there exists a Cs > 0, such that reliable transmission at rates up to Cs is possible in approximately perfect secrecy.
Computational social science and social computing

Computational social science is an emerging research area at the intersection of computer science, statistics, and the social sciences, in which novel computational methods are used to answer questions about society. The field is inherently collaborative: social scientists provide vital context and insight into pertinent research questions, data sources, and acquisition methods, while statisticians and computer scientists contribute expertise in developing mathematical models and computational tools. New, large-scale sources of demographic, behavioral, and network data from the Internet, sensor networks, and crowdsourcing systems augment more traditional data sources to form the heart of this nascent discipline, along with recent advances in machine learning, statistics, social network analysis, and natural language processing. The related research area of social computing deals with the mechanisms through which people interact with computational systems, examining questions such as how and why people contribute user-generated content and how to design systems that better enable them to do so. Examples of social computing systems include prediction markets, crowdsourcing markets, product review sites, and collaboratively edited wikis, all of which encapsulate some notion of aggregating crowd wisdom, beliefs, or ideas—albeit in different ways. Like computational social science, social computing blends techniques from machine learning and statistics with ideas from the social sciences. For example, the economics literature on incentive design has been especially influential.
Localized Deep Extreme Learning Machines for Efficient RGB-D Object Recognition

Existing RGB-D object recognition methods either use channel specific handcrafted features, or learn features with deep networks. The former lack representation ability while the latter require large amounts of training data and learning time. In real-time robotics applications involving RGB-D sensors, we do not have the luxury of both. In this paper, we propose Localized Deep Extreme Learning Machines (LDELM) that efficiently learn features from RGB-D data. By using localized patches, not only is the problem of data sparsity solved, but the learned features are robust to occlusions and viewpoint variations. LDELM learns deep localized features in an unsupervised way from random patches of the training data. Each image is then feed-forwarded, patch-wise, through the LDELM to form a cuboid of features. The cuboid is divided into cells and pooled to get the final compact image representation which is then used to train an ELM classifier. Experiments on the benchmark Washington RGB-D and 2D3D datasets show that the proposed algorithm not only is significantly faster to train but also outperforms state-of-the-art methods in terms of accuracy and classification time.
Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features

Plant diseases have turned into a dilemma as it can cause significant reduction in both quality and quantity of agricultural products. Automatic detection of plant diseases is an essential research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves. The proposed system is a software solution for automatic detection and classification of plant leaf diseases. The developed processing scheme consists of four main steps, first a color transformation structure for the input RGB image is created, then the green pixels are masked and removed using specific threshold value followed by segmentation process, the texture statistics are computed for the useful segments, finally the extracted features are passed through the classifier. The proposed algorithm’s efficiency can successfully detect and classify the examined diseases with an accuracy of 94%. Experimental results on a database of about 500 plant leaves confirm the robustness of the proposed approach.
Hacktivism trends, digital forensic tools and challenges: A survey

Hacktivism is the biggest challenge being faced by the Cyber world. Many digital forensic tools are being developed to deal with this challenge but at the same pace hackers are developing the counter techniques. This paper includes the digital forensics basics along with the recent trends of hacktivism in social networking sites, cloud computing, websites and phishing. The various tools of forensics with the platform supported, the recent versions and licensing details are discussed. The paper extends with the current challenges being faced by digital forensics.
A comparison of static, dynamic, and hybrid analysis for malware detection

In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs) on both static and dynamic feature sets and compare the resulting detection rates over a substantial number of malware families. We also consider hybrid cases, where dynamic analysis is used in the training phase, with static techniques used in the detection phase, and vice versa. In our experiments, a fully dynamic approach generally yields the best detection rates. We discuss the implications of this research for malware detection based on hybrid techniques.
Successful implementation of user-centered game based learning in higher education: An example from civil engineering

The use of an online game for learning in higher education aims to make complex theoretical knowledge more approachable. Permanent repetition will lead to a more in-depth learning.  To gain insight into whether and to what extent, online games have the potential to contribute to student learning in higher education.  The online game was used for the first time during a lecture on Structural Concrete at Master’s level, involving 121 seventh semester students.  Pretest/posttest experimental control group design with questionnaires and an independent online evaluation.  The minimum learning result of playing the game was equal to that achieved with traditional methods. A factor called “joy” was introduced, according to Nielsen (2002), which was amazingly high.  The experimental findings support the efficacy of game playing. Students enjoyed this kind of e-Learning.
Lane Detection Based on the Random Sample Consensus

In order to improve real-time and robustness of the lane detection and get more ideal lane, in the image preprocessing, the filter is used in strengthening lane information of the binary image, reducing the noise and removing irrelevant information. The lane edge detection is by using Canny operator, then the corner detection method is used in getting the Image corners coordinates and finally using the RANSAC to circulation fit for corners, according to the optimal lanes parameters drawing lane. Through experiment of different scenes, this method can not only effectively rule out linear pixel interference of outside the road in multiple complex environments, but also quickly and accurately identify lane. This method improves the stability of the lane detection to a certain extent, which has good robust and real-time.
9.2 A scalable 0.1-to-1.7GHz spatio-spectral-filtering 4-element MIMO receiver array with spatial notch suppression enabling digital beamforming

Multiple-antenna receivers offer numerous advantages over single-antenna receivers, including sensitivity improvement, ability to reject interferers spatially and enhancement of data-rate or link reliability via MIMO. In the recent past, RF/analog phased-array receivers have been investigated [1-4]. On the other hand, digital beamforming offers far greater flexibility, including ability to form multiple simultaneous beams, ease of digital array calibration and support for MIMO. However, ADC dynamic range is challenged due to the absence of spatial interference rejection at RF/analog.
High-Quality Real-Time Video Inpaintingwith PixMix

While image inpainting has recently become widely available in image manipulation tools, existing approaches to video inpainting typically do not even achieve interactive frame rates yet as they are highly computationally expensive. Further, they either apply severe restrictions on the movement of the camera or do not provide a high-quality coherent video stream. In this paper we will present our approach to high-quality real-time capable image and video inpainting. Our PixMix approach even allows for the manipulation of live video streams, providing the basis for real Diminished Reality (DR) applications. We will show how our approach generates coherent video streams dealing with quite heterogeneous background environments and non-trivial camera movements, even applying constraints in real-time.
A Ka-Band Monopulse Microstrip Antenna Array

A 1 times 8 monopulse microstrip patch antenna array in Ka-band has been proposed. The radiating element is a modified circular patch antenna, designed for getting circular polarization. The feed network is composed of six Wilkinson power dividers, a 3 dB hybrid, and a microstrip to waveguide transition. Both the array and the feed network are placed on the same layer.
Technological Resources to Prevent Cyberbullying During Adolescence: The Cyberprogram 2.0 Program and the Cooperative Cybereduca 2.0 Videogame

Bullying and cyberbullying have serious consequences for all those involved, especially the victims, and its prevalence is high throughout all the years of schooling, which emphasizes the importance of prevention. This article describes an intervention proposal, made up of a program (Cyberprogram 2.0 Garaigordobil and Martínez-Valderrey, 2014a) and a videogame (Cooperative Cybereduca 2.0 Garaigordobil and Martínez-Valderrey, 2016b) which aims to prevent and reduce cyberbullying during adolescence and which has been validated experimentally. The proposal has four objectives: (1) To know what bullying and cyberbullying are, to reflect on the people involved in these situations; (2) to become aware of the harm caused by such behaviors and the severe consequences for all involved; (3) to learn guidelines to prevent and deal with these situations: know what to do when one suffers this kind of violence or when observing that someone else is suffering it; and (4) to foster the development of social and emotional factors that inhibit violent behavior (e.g., communication, ethical-moral values, empathy, cooperation…). The proposal is structured around 25 activities to fulfill these goals and it ends with the videogame. The activities are carried out in the classroom, and the online video is the last activity, which represents the end of the intervention program. The videogame (www.cybereduca.com) is a trivial pursuit game with questions and answers related to bullying/cyberbullying. This cybernetic trivial pursuit is organized around a fantasy story, a comic that guides the game. The videogame contains 120 questions about 5 topics: cyberphenomena, computer technology and safety, cybersexuality, consequences of bullying/cyberbullying, and coping with bullying/cyberbullying. To evaluate the effectiveness of the intervention, a quasi-experimental design, with repeated pretest-posttest measures and control groups, was used. During the pretest and posttest stages, 8 assessment instruments were administered. The experimental group randomly received the intervention proposal, which consisted of one weekly 1-h session during the entire school year. The results obtained with the analyses of variance of the data collected before and after the intervention in the experimental and control groups showed that the proposal significantly promoted the following aspects in the experimental group: (1) a decrease in face-to-face bullying and cyberbullying behaviors, in different types of school violence, premeditated and impulsive aggressiveness, and in the use of aggressive conflict-resolution strategies; and (2) an increase of positive social behaviors, self-esteem, cooperative conflict-resolution strategies, and the capacity for empathy. The results provide empirical evidence for the proposal. The importance of implementing programs to prevent bullying in all its forms, from the beginning of schooling and throughout formal education, is discussed.
Permanent-magnets linear actuators applicability in automobile active suspensions

Significant improvements in automobile suspension performance are achieved by active systems. However, current active suspension systems are too expensive and complex. Developments occurring in power electronics, permanent magnet materials, and microelectronic systems justifies analysis of the possibility of implementing electromagnetic actuators in order to improve the performance of automobile suspension systems without excessively increasing complexity and cost. In this paper, the layouts of hydraulic and electromagnetic active suspensions are compared. The actuator requirements are calculated, and some experimental results proving that electromagnetic suspension could become a reality in the future are shown.