nils-herrmann/clean_scirepeval huggingface dataset:
A Multibeam Antenna Based on Substrate Integrated Waveguide Technology for MIMO Wireless Communications

A 24-beams slot array antenna, which achieves 360-degrees coverage over azimuth direction, based on substrate integrated waveguide (SIW) technology is designed, fabricated and measured for supporting the concept of triangle cell, beam diversity and multiple input multiple output (MIMO) wireless communications. Constructed by six 4-beams single layer SIW antennas which integrate the 4times8 modified Butler matrix with a low side lobe level (SLL) amplitude distribution, measured SLLs are reduced as low as -14.5 dB for all beams in E-plane. The size of the SIW single layer multibeam antenna is 385 mm times 110 mm including feeding network. The usable bandwidth of the proposed antenna is 3.75% at 16 GHz.
The Relationship between Emotional Intelligence and the Academic Performance among Final Year under Graduates

Emotional Intelligence (EI) has been a popular topic of debate in the field of education in general and management in particular. It is considered as a successful predictor of academic success/performance/achie vement. Researchers have claimed that EI predicts success at schools, and colleges/ universities. However, little empirical research has been conducted to test this assertion. In this study of survey research, the relationship of EI as measured by S.K. Mangal and Shubra Mangal’s EI Inventory (MEII) and academic performance were examined in a sample of final year under graduate students (N=300). EI in its four specified domains namely Intrapersonal Awareness, Interpersonal Awareness, Intrapersonal Management and Interpersonal Management separately as well as totally was found to be positively associated with academic performance of the selected respondents. Also the total EI score showed a percentage of 7.5 per cent of the sample being emotionally intelligent. The significant ‘f’ value has evidently showed that EI could predict academic performance of college students. The findings provide a further need on how to improve upon the academics of students. Also, the study has shown that emotional well being could be emphasized on academic success. Implications and recommendations for developing EI among students are discussed.
Electrically programmable fuse (eFUSE) using electromigration in silicides

For the first time we describe a positive application of electromigration, as an electrically programmable fuse device (eFUSE). Upon programming, eFUSE's show a large increase in resistance that enable easy sensing. The transient device characteristics show that the eFUSE stays in a low resistance state during programming due to the local heating of the fuse link. The programming is enhanced by a device design that uses a large cathode which increases the temperature gradient and minimizes the effect of microstructural variations.
Dual-Resonance NFC Antenna System Based on NFC Chip Antenna

In this letter, to enhance the performance of the near-field communication (NFC) antenna, an antenna system that has dual resonance is proposed. The antenna is based on NFC chip antenna, and the dual resonance comes from the chip antenna itself, the eddy current on printed circuit board, and a nearby loop that has strong coupling with the chip antenna. The performance of the proposed antenna system is confirmed by measurement.
Class-based Prediction Errors to Detect Hate Speech with Out-of-vocabulary Words

Common approaches to text categorization essentially rely either on n-gram counts or on word embeddings. This presents important difficulties in highly dynamic or quickly-interacting environments, where the appearance of new words and/or varied misspellings is the norm. A paradigmatic example of this situation is abusive online behavior, with social networks and media platforms struggling to effectively combat uncommon or nonblacklisted hate words. To better deal with these issues in those fast-paced environments, we propose using the error signal of class-based language models as input to text classification algorithms. In particular, we train a next-character prediction model for any given class, and then exploit the error of such class-based models to inform a neural network classifier. This way, we shift from the ability to describe seen documents to the ability to predict unseen content. Preliminary studies using out-of-vocabulary splits from abusive tweet data show promising results, outperforming competitive text categorization strategies by 4–11%.
MATLAB based code for 3 D joint inversion of Magnetotelluric and Direct Current resistivity imaging data

24 th EM Induction Workshop, Helsingør, Denmark, August 12-19, 2018 1 /4 MATLAB based code for 3D joint inversion of Magnetotelluric and Direct Current resistivity imaging data M. Israil 1 , A. Singh 1 Anita Devi 1 , and Pravin K.Gupta 1 1 Indian Institute of Technology, Roorkee, 247667, India, mohammad.israil@gmail.com
Economic dispatch for a microgrid considering renewable energy cost functions

Microgrids are operated by a customer or a group of customers for having a reliable, clean and economic mode of power supply to meet their demand. Understanding the economics of system is a prime factor which really depends on the cost/kWh of electricity supplied. This paper presents an easy and simple method for analyzing the dispatch rate of power. An isolated microgrid with solar and wind is considered in this paper. Generation cost functions are modeled with the inclusion of investment cost and maintenance cost of resources. Economic dispatch problem is solved using the reduced gradient method. The effects on total generation cost, with the inclusion of wind energy and solar energy into a microgrid is studied and found the most profitable solution by considering different practical scenarios. The paper gives a detailed correlation between the cost function, investment cost, lifetime and the fluctuant energy forecasting of wind and solar resources. It also discusses the advantages of including the renewable energy credits for the solar panel.
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks

In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input. CLEAR facilitates the visualization of attentive regions and levels of interest of DNNs during the decision-making process. It also enables the visualization of the most dominant classes associated with these attentive regions of interest. As such, CLEAR can mitigate some of the shortcomings of heatmap-based methods associated with decision ambiguity, and allows for better insights into the decision-making process of DNNs. Quantitative and qualitative experiments across three different datasets demonstrate the efficacy of CLEAR for gaining a better understanding of the inner workings of DNNs during the decision-making process.
Optimized, Direct Sale of Privacy in Personal-Data Marketplaces

Very recently, we are witnessing the emergence of a number of start-ups that enables individuals to sell their private data directly to brokers and businesses. While this new paradigm may shift the balance of power between individuals and companies that harvest data, it raises some practical, fundamental questions for users of these services: how they should decide which data must be vended and which data protected, and what a good deal is. In this work, we investigate a mechanism that aims at helping users address these questions. The investigated mechanism relies on a hard-privacy model and allows users to share partial or complete profile data with broker companies in exchange for an economic reward. The theoretical analysis of the trade-off between privacy and money posed by such mechanism is the object of this work. We adopt a generic measure of privacy although part of our analysis focuses on some important examples of Bregman divergences. We find a parametric solution to the problem of optimal exchange of privacy for money, and obtain a closed-form expression and characterize the trade-off between profile-disclosure risk and economic reward for several interesting cases.
Mitigation of Continuous and Pulsed Radio Interference with GNSS Antenna Arrays

Dr. Andriy Konovaltsev received his engineer diploma and the Ph.D. degree in electrical engineering from Kharkov State Technical University of Radio Electronics, Ukraine in 1993 and 1996, correspondingly. He joined the Institute of Communications and Navigation of DLR in 2001. His research interests are in array processing for satellite navigation systems, signal processing algorithms for navigation receivers including synchronisation, multipath and radio interference mitigation.
Task assignment with energy efficiency considerations for non-DVS heterogeneous multiprocessor systems

Currently, the multiprocessor architecture platform becomes more and more popular to the applications with higher computational capacity for meeting the fast-grown demand of applications and increasing the performance of whole system. Managing the consumption of resources has become an important issue in many different settings. The energyefficient scheduling for multiprocessors becomes one of important research issues. In this paper we study the scheduling problem of a heterogeneous non-DVSmultiprocessor platform with a task set. The processors have different characteristics of power consumption. We propose an off-line taskto- processor assignment algorithm, the Best-Fit Decreasing Physical Power Consumption (BDPC) algorithm to derive a feasible task assignment with the minimal energy consumption and has the time complexity of O(N(logN + M)), where N and M are the numbers of tasks and processor types, respectively. A series of experiments were conducted to evaluate the proposed algorithm. The experimental results demonstrate that the performance of the proposed BDPC algorithm is better than the compared algorithms.
Automatic Generation of Personas Using YouTube Social Media Data

We develop and implement an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation. From the organization's YouTube channel, we gather demographic data, customer interactions, and topical interests, leveraging more than 188,000 subscriber profiles and more than 30 million user interactions. We demonstrate that online user data can be used to develop personas in real time using social media analytics. To get diverse aspects of personas, we collect user data from other social media channels as well and match them with the corresponding user data to generate richer personas. Our results provide corporate insights into competitive marketing, topical interests, and preferred product features for the users of the online news medium. Research implications are that reasonably rich personas can be generated in real time, instead of being the result of a laborious and time-consuming manual development process.
CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension

We present a new dataset for machine comprehension in the medical domain. Our dataset uses clinical case reports with around 100,000 gap-filling queries about these cases. We apply several baselines and state-of-the-art neural readers to the dataset, and observe a considerable gap in performance (20% F1) between the best human and machine readers. We analyze the skills required for successful answering and show how reader performance varies depending on the applicable skills. We find that inferences using domain knowledge and object tracking are the most frequently required skills, and that recognizing omitted information and spatio-temporal reasoning are the most difficult for the machines.
From information security to ... business security?

This short opinion paper argues that information security, the discipline responsible for protecting a company's information assets against business risks, has now become such a crucial component of good Corporate Governance, that it should rather be called Business Security instead of Information Security. During the last year or two, driven by developments in the field of Corporate Governance, including IT Governance, it became apparent that the scope of Information Security is much wider than just (directly) protecting the data, information and software of a business. Such data, information and software had become invaluable assets of the business as a whole, and not properly protecting this information could have profound business and legal implications. Basically, the data and information of the business became its 'life blood', and compromising this life blood, could kill the business. Executive Management and Boards started realizing that Information Security Governance was becoming their direct responsibility, and that serious personal consequences, specifically legally, could flow from ignoring information security. Information security governance had become an
Time-of-Flight Sensors in Computer Graphics

A growing number of applications depend on accurate and fast 3D scene analysis. Examples are model and lightfield acquisition, collision prevention, mixed reality, and gesture recognition. The estimation of a range map by image analysis or laser scan techniques is still a time-consuming and expensive part of such systems. A lower-priced, fast and robust alternative for distance measurements are Time-of-Flight (ToF) cameras. Recently, significant advances have been made in producing low-cost and compact ToF-devices, which have the potential to revolutionize many fields of research, including Computer Graphics, Computer Vision and Human Machine Interaction (HMI). These technologies are starting to have an impact on research and commercial applications. The upcoming generation of ToF sensors, however, will be even more powerful and will have the potential to become “ubiquitous real-time geometry devices” for gaming, web-conferencing, and numerous other applications. This STAR gives an account of recent developments in ToF-technology and discusses the current state of the integration of this technology into various graphics-related applications.
LG-Trader: Stock trading decision support based on feature selection by weighted localized generalization error model

Stock trading is an important financial activity of human society. Machine learning techniques are adopted to provide trading decision support by predicting the stock price or trading signals of the next day. Decisions are made by analyzing technical indices and fundamental analysis of companies. There are two major machine learning research problems for stock trading decision support: classifier architecture selection and feature selection. In this work, we propose the LG-Trader which will deal with these two problems simultaneously using a genetic algorithm minimizing a new Weighted Localized Generalization Error (wL-GEM). An issue being ignored in current machine learning based stock trading researches is the imbalance among buy, hold and sell decisions. Usually hold decision is the majority in comparison to both buy and sell decisions. So, the wL-GEM is proposed to balance classes by penalizing heavier for generalization error being made in minority classes. The feature selection based on wL-GEM helps to select most useful technical indices among choices for each stock. Experimental results demonstrate that the LG-Trader yields higher profits and rates of return in both stock and index trading. & 2014 Elsevier B.V. All rights reserved.
Looking under the bonnet : factors affecting student adoption of e-learning systems in

The primary questions addressed in this paper are the following: what are the factors that affect students‟ adoption of an e-learning system and what are the relationships among these factors? This paper investigates and identifies some of the major factors affecting students‟ adoption of an e-learning system in a university in Jordan. E-learning adoption is approached from the information systems acceptance point of view. This suggests that a prior condition for learning effectively using e-learning systems is that students must actually use them. Thus, a greater knowledge of the factors that affect IT adoption and their interrelationships is a pre-cursor to a better understanding of student acceptance of e-learning systems. In turn, this will help and guide those who develop, implement, and deliver e-learning systems. In this study, an extended version of the Technology Acceptance Model (TAM) was developed to investigate the underlying factors that influence students‟ decisions to use an e-learning system. The TAM was populated using data gathered from a survey of 486 undergraduate students, who were using the Moodle based e-learning system at the Arab Open University. The model was estimated using Structural Equation Modelling (SEM). A path model was developed to analyze the relationships between the factors to explain students‟ adoption of the e-learning system. Whilst findings support existing literature about prior experience affecting perceptions, they also point to surprising group effects, which may merit future exploration.
Detection of Plagiarism in University Projects Using Metrics-based Spectral Similarity

An original method of spectral similarity analysis for plagiarism detection in university project is presented. The approach is based on a clone detection tool called CLAN that performs metrics based similarity analysis of source code fragments. Definitions and algorithms for spectral similarity analysis are presented and discussed. Experiments performed on university projects are presented. Experimental results include the distribution of similarity in C and C++ projects. Analysis of spectral similarity distribution identifies the most similar pairs of projects that can be considered as candidates for plagiarism.
A Survey of Enabling Technologies of Low Power and Long Range Machine-to-Machine Communications

Low power and long range machine-to-machine (M2M) communication techniques are expected to provide ubiquitous connections for the wireless devices. In this paper, three major low power and long range M2M solutions are surveyed. The first type of solutions is referred to as the low power wide area (LPWA) network. The design of the LPWA techniques features low cost, low data rate, long communication range, and low power consumption. The second type of solutions is the IEEE 802.11ah which features higher data rates using a wider bandwidth than the LPWA-based solutions. The third type of solutions is operated under the cellular network infrastructure. Based on the analysis of the pros and cons of the enabling technologies of the surveyed M2M solutions, as well as the corresponding deployment strategies, the gaps in knowledge are identified. The paper also presents a summary of the research directions for improving the performance of the surveyed low power and long range M2M communication technologies.
Application of Data Mining Techniques in IoT: A Short Review

Internet of Things (IoT) has been growing rapidly due to recent advancements in communications and sensor technologies. Interfacing an every object together through internet looks very difficult, but within a frame of time Internet of Things will drastically change our life. The enormous data captured by the Internet of Things (IoT) are considered of high business as well as social values and extracting hidden information from raw data, various data mining algorithm can be applied to IoT data. In this paper, We survey systematic review of various data mining models as well as its application in Internet of Thing (IoT) field along with its merits and demerits. At last, we discussed challenges in IoT.
Plant-growth-promoting compounds produced by two agronomically important strains of Azospirillum brasilense, and implications for inoculant formulation

We evaluated phytohormone and polyamine biosynthesis, siderophore production, and phosphate solubilization in two strains (Cd and Az39) of Azospirillum brasilense used for inoculant formulation in Argentina during the last 20 years. Siderophore production and phosphate solubilization were evaluated in a chemically defined medium, with negative results. Indole 3-acetic acid (IAA), gibberellic acid (GA3), and abscisic acid (ABA) production were analyzed by gas chromatography-mass spectrometry. Ethylene, polyamine, and zeatin (Z) biosynthesis were determined by gas chromatography-flame ionization detector and high performance liquid chromatography (HPLC-fluorescence and -UV), respectively. Phytohormones IAA, Z, GA3, ABA, ethylene, and growth regulators putrescine, spermine, spermidine, and cadaverine (CAD) were found in culture supernatant of both strains. IAA, Z, and GA3 were found in all two strains; however, their levels were significantly higher (p < 0.01) in Cd (10.8, 2.32, 0.66 μg ml−1). ABA biosynthesis was significantly higher (p < 0.01) in Az39 (0.077 μg ml−1). Ethylene and polyamine CAD were found in all two strains, with highest production in Cd cultured in NFb plus l-methionine (3.94 ng ml−1 h−1) and Az39 cultured in NFb plus l-lysine (36.55 ng ml−1 h−1). This is the first report on the evaluation of important bioactive molecules in strains of A. brasilense as potentially capable of direct plant growth promotion or agronomic yield increase. Az39 and Cd showed differential capability to produce the five major phytohormones and CAD in chemically defined medium. This fact has important technological implications for inoculant formulation as different concentrations of growth regulators are produced by different strains or culture conditions.
LED driver circuit with inherent PFC

A buck-boost topology operating in discontinuous conduction mode (DCM) is used as an off line LED driver for lighting applications. Operating from a full wave rectified ac-voltage with minimum input capacitance, with a constant switching frequency and constant on-time, the utility current has near unity power factor. This LED driver is suitable for cost sensitive applications because this circuit has a minimal parts count and a very simple control circuit. Disadvantages are that the LED drive current is modulated at twice the utility frequency and DCM operation increases component stress levels.
Swing-up and stabilization of a cart-pendulum system under restricted cart track length

This paper describes the swing-up and stabilization of a cart–pendulum system with a restricted cart track length and restricted control force using generalized energy control methods. Starting from a pendant position, the pendulum is swung up to the upright unstable equilibrium con5guration using energy control principles. An “energy well” is built within the cart track to prevent the cart from going outside the limited length. When su9cient energy is acquired by the pendulum, it goes into a “cruise” mode when the acquired energy is maintained. Finally, when the pendulum is close to the upright con5guration, a stabilizing controller is activated around a linear zone about the upright con5guration. The proposed scheme has worked well both in simulation and a practical setup and the conditions for stability have been derived using the multiple Lyapunov functions approach.
Cavity Resonators Do the Trick: A Packaged Substrate Integrated Waveguide, Dual-Band Filter

The 2015 Microwave Theory and Techniques Society (MTT-S) International Microwave Symposium in Phoenix, Arizona, played host to a total of 16 student design competitions. One of these, sponsored jointly by the MTT-12 Technical Committee on Microwave and Millimeter-Wave Packaging and Manufacturing and the MTT-8 Technical Committee on Filters and Passive Components, was based on the design of a substrate integrated waveguide (SIW) dual-band filter.