nils-herrmann/clean_scirepeval huggingface dataset:
Interleaved Converter With Voltage Multiplier Cell for High Step-Up and High-Efficiency Conversion

A novel interleaved high step-up converter with voltage multiplier cell is proposed in this paper to avoid the extremely narrow turn-off period and to reduce the current ripple, which flows through the power devices compared with the conventional interleaved boost converter in high step-up applications. Interleaved structure is employed in the input side to distribute the input current, and the voltage multiplier cell is adopted in the output side to achieve a high step-up gain. The voltage multiplier cell is composed of the secondary windings of the coupled inductors, a series capacitor, and two diodes. Furthermore, the switch voltage stress is reduced due to the transformer function of the coupled inductors, which makes low-voltage-rated MOSFETs available to reduce the conduction losses. Moreover, zero-current-switching turn- on soft-switching performance is realized to reduce the switching losses. In addition, the output diode turn-off current falling rate is controlled by the leakage inductance of the coupled inductors, which alleviates the diode reverse recovery problem. Additional active device is not required in the proposed converter, which makes the presented circuit easy to design and control. Finally, a 1-kW 40-V-input 380-V-output prototype operating at 100 kHz switching frequency is built and tested to verify the effectiveness of the presented converter.
Analyzing Bitcoin Price Volatility Julio

In this work we do an analysis of Bitcoin’s price and volatility. Particularly, we look at Granger-causation relationships among the pairs of time series: Bitcoin price and the S&P 500, Bitcoin price and the VIX, Bitcoin realized volatility and the S&P 500, and Bitcoin realized volatility and the VIX. Additionally, we explored the relationship between Bitcoin weekly price and public enthusiasm for Blockchain, the technology behind Bitcoin, as measured by Google Trends data. we explore the Granger-causality relationships between Bitcoin weekly price and Blockchain Google Trend time series. We conclude that there exists a bidirectional Granger-causality relationship between Bitcoin realized volatility and the VIX at the 5% significance level, that we cannot reject the hypothesis that Bitcoin weekly price do not Granger-causes Blockchain trends and that we cannot reject the hypothesis that Bitcoin realized volatility do not Granger-causes S&P 500.
Interactive Metro Map Editing

Manual editing of a metro map is essential because many aesthetic and readability demands in map generation cannot be achieved by using a fully automatic method. In addition, a metro map should be updated when new metro lines are developed in a city. Considering that manually designing a metro map is time-consuming and requires expert skills, we present an interactive editing system that considers human knowledge and adjusts the layout to make it consistent with user expectations. In other words, only a few stations are controlled and the remaining stations are relocated by our system. Our system supports both curvilinear and octilinear layouts when creating metro maps. It solves an optimization problem, in which even spaces, route straightness, and maximum included angles at junctions are considered to obtain a curvilinear result. The system then rotates each edge to extend either vertically, horizontally, or diagonally while approximating the station positions provided by users to generate an octilinear layout. Experimental results, quantitative and qualitative evaluations, and user studies show that our editing system is easy to use and allows even non-professionals to design a metro map.
A note on the least squares fitting of ellipses

Rosin, P.L., A note on the least squares fitting of ellipses, Pattern Recognition Letters 14 (1993) 799-808. The characteristics of two normalisations for the general conic equation are investigated for use in least squares fitting: either setting F = 1 or A + C= 1. The normalisations vary in three main areas: curvature bias, singularities, transformational invariance. It is shown that setting F = 1 is the more appropriate for ellipse fitting since it is less heavily curvature biased.  A + C= 1 produces more eccentric conics, resulting either in over-elongated ellipses or hyperbolae. Although the F = 1 normalisation is less well suited than the A + C= 1 normalisation with respect to singularities and transformational invariance both these problems are solved by normalising the data, shifting it so that it is centred on the origin before fitting, and then re-expressing the fit in the original frame of reference.
The bilingual brain. Proficiency and age of acquisition of the second language.

Functional imaging methods show differences in the pattern of cerebral activation associated with the subject's native language (L1) compared with a second language (L2). In a recent PET investigation on bilingualism we showed that auditory processing of stories in L1 (Italian) engages the temporal lobes and temporoparietal cortex more extensively than L2 (English). However, in that study the Italian subjects learned L2 late and attained a fair, but not an excellent command of this language (low proficiency, late acquisition bilinguals). Thus, the different patterns of activation could be ascribed either to age of acquisition or to proficiency level. In the current study we use a similar paradigm to evaluate the effect of early and late acquisition of L2 in highly proficient bilinguals. We studied a group of Italian-English bilinguals who acquired L2 after the age of 10 years (high proficiency, late acquisition bilinguals) and a group of Spanish-Catalan bilinguals who acquired L2 before the age of 4 years (high proficiency, early acquisition bilinguals). The differing cortical responses we had observed when low proficiency volunteers listened to stories in L1 and L2 were not found in either of the high proficiency groups in this study. Several brain areas, similar to those observed for L1 in low proficiency bilinguals, were activated by L2. These findings suggest that, at least for pairs of L1 and L2 languages that are fairly close, attained proficiency is more important than age of acquisition as a determinant of the cortical representation of L2.
The Adaptive Ontology-Based Personalized Recommender System

Recommender systems provide strategies that help users search or make decisions within the overwhelming information spaces nowadays. They have played an important role in various areas such as e-commerce and e-learning. In this paper, we propose a hybrid recommendation strategy of content-based and knowledge-based methods that are flexible for any field to apply. By analyzing the past rating records of every user, the system learns the user’s preferences. After acquiring users’ preferences, the semantic search-and-discovery procedure takes place starting from a highly rated item. For every found item, the system evaluates the Interest Intensity indicating to what degree the user might like it. Recommender systems train a personalized estimating module using a genetic algorithm for each user, and the personalized estimating model helps improve the precision of the estimated scores. With the recommendation strategies and personalization strategies, users may have better recommendations that are closer to their preferences. In the latter part of this paper, a realworld case, a movie-recommender system adopting proposed recommendation strategies, is implemented.
A Review and Future Perspectives of Arabic Question Answering Systems

Question Answering Systems (QASs) have emerged as a good alternative for information seekers to retrieve precise information over the Internet. A good amount of research has been done to improve the performance of QASs across several languages, including European and Asian languages. However, Arabic, a morphologically rich Semitic language spoken by over 422 million people, has not seen similar development in the field of question answering. This article reviews the developments taking place in Arabic QASs as well as the challenges faced by researchers in developing Arabic QASs. After conducting an extensive literature survey of a number of English and Arabic QASs, this article classifies them according to several criteria. The most commonly used architecture for the development of an Arabic QAS, known as pipeline architecture, has been presented. In order to encourage and support the new researchers and scholars in conducting research in Arabic QASs, a list of techniques, tools, and computational linguistic resources, required to implement the components of the presented pipelined architecture, are described in this article in a simple and persuasive manner. Finally, the gap analysis between the research in Arabic and English QASs has been performed and accordingly, some future directions for research in Arabic QASs have been proposed.
Beyond Sentential Semantic Parsing: Tackling the Math SAT with a Cascade of Tree Transducers

We present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions – the math portion of the Scholastic Aptitude Test (SAT). By using a tree transducer cascade as its basic architecture, our system (called EUCLID) propagates uncertainty from multiple sources (e.g. coreference resolution or verb interpretation) until it can be confidently resolved. Experiments show the first-ever results (43% recall and 91% precision) on SAT algebra word problems. We also apply EUCLID to the public Dolphin algebra question set, and improve the state-of-the-art F1-score from 73.9% to 77.0%.
How stigma interferes with mental health care.

Many people who would benefit from mental health services opt not to pursue them or fail to fully participate once they have begun. One of the reasons for this disconnect is stigma; namely, to avoid the label of mental illness and the harm it brings, people decide not to seek or fully participate in care. Stigma yields 2 kinds of harm that may impede treatment participation: It diminishes self-esteem and robs people of social opportunities. Given the existing literature in this area, recommendations are reviewed for ongoing research that will more comprehensively expand understanding of the stigma-care seeking link. Implications for the development of antistigma programs that might promote care seeking and participation are also reviewed.
Harassment detection: a benchmark on the #HackHarassment dataset

Online harassment has been a problem to a greater or lesser extent since the early days of the internet. Previous work has applied anti-spam techniques like machine-learning based text classification (Reynolds, 2011) to detecting harassing messages. However, existing public datasets are limited in size, with labels of varying quality. The #HackHarassment initiative (an alliance of 1 tech companies and NGOs devoted to fighting bullying on the internet) has begun to address this issue by creating a new dataset superior to its predecssors in terms of both size and quality. As we (#HackHarassment) complete further rounds of labelling, later iterations of this dataset will increase the available samples by at least an order of magnitude, enabling corresponding improvements in the quality of machine learning models for harassment detection. In this paper, we introduce the first models built on the #HackHarassment dataset v1.0 (a new open dataset, which we are delighted to share with any interested researcherss) as a benchmark for future research.
Multi-View Data Generation Without View Supervision

The development of high-dimensional generative models has recently gained a great surge of interest with the introduction of variational auto-encoders and generative adversarial neural networks. Different variants have been proposed where the underlying latent space is structured, for example, based on attributes describing the data to generate. We focus on a particular problem where one aims at generating samples corresponding to a number of objects under various views. We assume that the distribution of the data is driven by two independent latent factors: the content, which represents the intrinsic features of an object, and the view, which stands for the settings of a particular observation of that object. Therefore, we propose a generative model and a conditional variant built on such a disentangled latent space. This approach allows us to generate realistic samples corresponding to various objects in a high variety of views. Unlike many multiview approaches, our model doesn’t need any supervision on the views but only on the content. Compared to other conditional generation approaches that are mostly based on binary or categorical attributes, we make no such assumption about the factors of variations. Our model can be used on problems with a huge, potentially infinite, number of categories. We experiment it on four image datasets on which we demonstrate the effectiveness of the model and its ability to generalize.
TEEN: a routing protocol for enhanced efficiency in wireless sensor networks

Wireless sensor networks are expected to find wide applicability and increasing deployment in the near future. In this paper, we propose a formal classification of sensor networks, based on their mode of functioning, as proactive and reactive networks. Reactive networks, as opposed to passive data collecting proactive networks, respond immediately to changes in the relevant parameters of interest. We also introduce a new energy efficient protocol, TEEN (Threshold sensitive Energy Efficient sensor Network protocol) for reactive networks. We evaluate the performance of our protocol for a simple temperature sensing application. In terms of energy efficiency, our protocol has been observed to outperform existing conventional sensor network protocols.
A 250kW transverse-laminated Synchronous Reluctance motor

Throughout the last 15 years, Synchronous Reluctance (SyR) motor drives have been presented as a competitive alternative to Induction Motor (IM) drives in many variable speed applications, at least in the small power range. Very few examples of SyR motors above the 100Nm torque size are present in the literature. The main advantage of the SyR motor lays in the absence of rotor Joule losses that permits to obtain a continuous torque that is higher than the one of an IM of the same size. The paper presents a 250kW, 1000rpm Synchronous Reluctance motor for industrial applications and its performance comparison with an Induction Motor with identical stator. The rotor of the SyR motor has been purposely designed to fit the stator and housing of the IM competitor. The experimental comparison of the two motors is presented, on a regenerative test bench where one the two motors under test can load the competitor and vice-versa. The results of the experimental tests confirm the assumption that the SyR motor gives more torque and then more power in continuous operation at rated windings temperature. However, the IM maintains a certain advantage in terms of flux-weakening capability that means a larger constant-power speed range.
Frequency-Scalable Nonlinear-Transmission-Line-Based Vector Network Analyzers

An overview of the high-frequency reflectometer technology deployed in Anritsu’s VectorStar Vector Network Analyzer (VNA) family is given, leading to a detailed description of the architecture used to extend the frequency range of VectorStar into the high millimeter waves. It is shown that this technology results in miniature frequency-extension modules that provide unique capabilities such as direct connection to wafer probes, dense multi-port measurements, test-port power leveling, enhanced raw directivity, and reduced measurement complexity when compared with existing solutions. These capabilities, combined with the frequency-scalable nature of the reflectometers provide users with a unique and compelling solution for their current and future high-frequency measurement needs.
An Improved BLE Indoor Localization with Kalman-Based Fusion: An Experimental Study

Indoor positioning has grasped great attention in recent years. A number of efforts have been exerted to achieve high positioning accuracy. However, there exists no technology that proves its efficacy in various situations. In this paper, we propose a novel positioning method based on fusing trilateration and dead reckoning. We employ Kalman filtering as a position fusion algorithm. Moreover, we adopt an Android device with Bluetooth Low Energy modules as the communication platform to avoid excessive energy consumption and to improve the stability of the received signal strength. To further improve the positioning accuracy, we take the environmental context information into account while generating the position fixes. Extensive experiments in a testbed are conducted to examine the performance of three approaches: trilateration, dead reckoning and the fusion method. Additionally, the influence of the knowledge of the environmental context is also examined. Finally, our proposed fusion method outperforms both trilateration and dead reckoning in terms of accuracy: experimental results show that the Kalman-based fusion, for our settings, achieves a positioning accuracy of less than one meter.
FRASAD: A framework for model-driven IoT Application Development

This paper presents FRASAD, an effective model-driven software development framework to manage the complexity of Internet of Things (IoT) applications. We propose a node-centric software architecture and a rule-based programming model that allow designers to describe their application using only sensor node domain concepts. The final application code is successively generated from the initial models through an automatic model transformation process. The evaluation results show that our framework enables a fast way to develop IoT applications by reducing the cost of dealing with the heterogeneity and complexity exhibited by sensor nodes and their operating systems.
Rule-based Approach for Personality Prediction Through Handwriting Analysis

Handwriting Analysis or Graphology is a scientific method of identifying, evaluating and understanding personality through the strokes and patterns revealed by handwriting. Handwriting reveals the true personality including emotional outlay, fears, honesty, defenses and over many other individual personality traits. It is not document examination, which involves the examination of a sample of handwriting to determine the author. Handwriting is often referred to as brain writing. Each personality trait is represented by a neurological brain pattern. Each neurological brain pattern produces a unique neuromuscular movement that is the same for every person who has that particular personality trait. When writing, these tiny movements occur unconsciously. Each written movement or stroke reveals a specific personality trait. Graphology is the science of identifying these strokes as they appear in handwriting and describe the corresponding personality trait. Handwriting has long been considered individualistic. Thus handwriting can be used effectively as a biometric. In this paper an attempt is made towards personality prediction of the writer through rule-based approach. The personality traits revealed by the baseline and the pen pressure, as found in an individual’s handwriting are explored in this paper. Two parameters, the baseline and the pen pressure, are the inputs to a rule-base which outputs the personality trait of the writer. The evaluation of the baseline is using the polygonalization method and the evaluation of the pen pressure utilizes the grey-level threshold value. The baseline and the pen pressure in one’s handwriting reveals a lot of accurate information about the writer. Hence this paper focuses on personality prediction using the baseline and the pen pressure. The authenticity of the methodology is validated by examination of multiple samples.
A Compact Quadband CPW-Fed Slot Antenna for M-WiMAX/WLAN Applications

A novel compact CPW-fed slot antenna for quadband operation is proposed. The antenna is printed on a 25 × 20 mm2 FR4 substrate with thickness of 1.6 mm and relative permittivity of 4.6. Three L-shaped slots and a rectangular slot are fabricated on the substrate to achieve quadband operation of 2.07-2.77, 3.3-3.8, 5.15-5.35, and 5.7-5.89 GHz. As the four resonant frequencies of antenna are excited by certain parts of the patch respectively, one resonant frequency can be flexibly tuned with little effect on the other. Experimental results demonstrate the good broadside radiation patterns for the four bands and suggest that the proposed antenna is suitable for the M-WiMAX and WLAN applications.
Sparse Binary Compression: Towards Distributed Deep Learning with minimal Communication

Currently, progressively larger deep neural networks are trained on ever growing data corpora. As this trend is only going to increase in the future, distributed training schemes are becoming increasingly relevant. A major issue in distributed training is the limited communication bandwidth between contributing nodes or prohibitive communication cost in general. These challenges become even more pressing, as the number of computation nodes increases. To counteract this development we propose sparse binary compression (SBC), a compression framework that allows for a drastic reduction of communication cost for distributed training. SBC combines existing techniques of communication delay and gradient sparsification with a novel binarization method and optimal weight update encoding to push compression gains to new limits. By doing so, our method also allows us to smoothly trade-off gradient sparsity and temporal sparsity to adapt to the requirements of the learning task. Our experiments show, that SBC can reduce the upstream communication on a variety of convolutional and recurrent neural network architectures by more than four orders of magnitude without significantly harming the convergence speed in terms of forward-backward passes. For instance, we can train ResNet50 on ImageNet in the same number of iterations to the baseline accuracy, using ×3531 less bits or train it to a 1% lower accuracy using ×37208 less bits. In the latter case, the total upstream communication required is cut from 125 terabytes to 3.35 gigabytes for every participating client.