nils-herrmann/clean_scirepeval huggingface dataset:
Cosine Siamese Models for Stance Detection

Fake news detection has received much attention since the 2016 United States Presidential election, where election outcomes are thought to have been influenced by the unregulated abundance of fake news articles. The recently released Fake News Challenge (FNC) aims to address the fake news problem by decomposing the problem into distinct NLP tasks, the first of which is stance detection. We employ a neural architecture consisting of two homologous subnetworks for headline and body processing and a subsequent node for headline/body comparison and stance prediction. Headlines and bodies are represented with a weighted bag-of-words combination of word vectors passed through a ReLU, where the weights are learned. Stance is quantified by computing the cosine similarity of these weighted bag-of-words representations, and the score is regressed to a relaxed, continuous label space in which the true discrete labels are posited to lie. Our model, which outperforms other recurrent methods, achieves an FNC score of 0.891 out of 1.00, a 0.10 increase from the published 0.79 FNC baseline. The cosine similarity function induces a natural geometry among the learned headline and body representations, with unrelated inputs generally orthogonal to each other and agreeing inputs nearly collinear. Our findings implicate the importance of the optimization objective, as opposed to the architecture of the subnetwork models, to success in stance detection, echoing recent work demonstrating the competitiveness of weighted bag-of-words models for textual similarity tasks.
SEM-PPA: A semantical pattern and preference-aware service mining method for personalized point of interest recommendation

Point-of-Interest (POI) recommendation has received increasing attention in Location-based Social Networks (LBSNs). It involves user behavior analysis, movement pattern model and trajectory sequence prediction, in order to recommend personalized services to target user. Existing POI recommendation methods are confronted with three problems: (1) they only consider the location information of users' check-ins, which causes data sparsity; (2) they fail to consider the order of users' visited locations, which is valuable to reflect the interest or preference of users; (3) users cannot be recommended the suitable services when they move into the new place. To address the above issues, we propose a semantical pattern and preference-aware service mining method called SEM-PPA to make full use of the semantic information of locations for personalized POI recommendation. In SEM-PPA, we firstly propose a novel algorithm to classify the locations into different types for location identification; then we construct the user model for each user from four aspects, which are location trajectory, semantic trajectory, location popularity and user familiarity; in addition, a potential friends discovery algorithm based on movement pattern is proposed. Finally, we conduct extensive experiments to evaluate the recommendation accuracy and recommendation effectiveness on two real-life datasets from GeoLife and Beijing POI. Experimental results show that SEM-PPA can achieve better recommendation performance in particular for sparse data and recommendation accuracy in comparison with other methods.
Paraphrase-Supervised Models of Compositionality

Compositional vector space models of meaning promise new solutions to stubborn language understanding problems. This paper makes two contributions toward this end: (i) it uses automaticallyextracted paraphrase examples as a source of supervision for training compositional models, replacing previous work which relied on manual annotations used for the same purpose, and (ii) develops a contextaware model for scoring phrasal compositionality. Experimental results indicate that these multiple sources of information can be used to learn partial semantic supervision that matches previous techniques in intrinsic evaluation tasks. Our approaches are also evaluated for their impact on a machine translation system where we show improvements in translation quality, demonstrating that compositionality in interpretation correlates with compositionality in translation.
Comparative Analysis of Discontinuous and Continuous PWM Techniques in VSI-Fed Five-Phase Induction Motor

This paper reports on flux harmonic distortion factor and current ripple analysis for various discontinuous pulse-width-modulation (PWM) techniques, which are applicable in conjunction with five-phase two-level inverter-fed induction motors. The analysis is based on the application of the complex space vector approach. The results are compared throughout with the corresponding continuous PWM techniques. Different space vector PWM and carrier-based PWM methods are encompassed by the analysis. This paper is supported by extensive simulation results. Verification of theoretical findings is provided through experimental measurements on a five-phase induction motor.
Spectral Clustering Based on Local PCA

We propose a spectral clustering method based on local principal components analysis (PCA). After performing local PCA in selected neighborhoods, the algorithm builds a nearest neighbor graph weighted according to a discrepancy between the principal subspaces in the neighborhoods, and then applies spectral clustering. As opposed to standard spectral methods based solely on pairwise distances between points, our algorithm is able to resolve intersections. We establish theoretical guarantees for simpler variants within a prototypical mathematical framework for multi-manifold clustering, and evaluate our algorithm on various simulated data sets.
Tripartite model of anxiety and depression: psychometric evidence and taxonomic implications.

We review psychometric and other evidence relevant to mixed anxiety-depression. Properties of anxiety and depression measures, including the convergent and discriminant validity of self- and clinical ratings, and interrater reliability, are examined in patient and normal samples. Results suggest that anxiety and depression can be reliably and validly assessed; moreover, although these disorders share a substantial component of general affective distress, they can be differentiated on the basis of factors specific to each syndrome. We also review evidence for these specific factors, examining the influence of context and scale content on ratings, factor analytic studies, and the role of low positive affect in depression. With these data, we argue for a tripartite structure consisting of general distress, physiological hyperarousal (specific anxiety), and anhedonia (specific depression), and we propose a diagnosis of mixed anxiety-depression.
Optimization tool for direct water cooling system of high power IGBT modules

Thermal management of power electronic devices is essential for reliable system performance especially at high power levels. Since even the most efficient electronic circuit becomes hot because of ohmic losses, it is clear that cooling is needed in electronics and even more as the power increases. One of the most important activities in the thermal management and reliability improvement is the cooling system design. As industries are developing smaller power devices with higher power densities, optimized design of cooling systems with minimum thermal resistance and pressure drop become important issue for thermal design engineers. This paper aims to present a user friendly optimization tool for direct water cooling system of a high power module which enables the cooling system designer to identify the optimized solution depending on customer load profiles and available pump power. CFD simulations are implemented to find best solution for each scenario.
Water Nonintrusive Load Monitoring

Resource conservation decisions require detailed consumption information. This paper presents sensors and signal processing techniques that use pipe vibration signatures to non-intrusively identify water consumption at the appliance level. The method requires as little as one easily installed vibration sensor. This method provides a no-fuss retrofit solution for detecting the operation of a building's water consuming appliances. In addition, flow rate is nonintrusively obtained from a conventional water meter via a new, high sensitivity strap-on magnetic sensor. Together, these two sensors track load operating schedule and water consumption in a building, demonstrated here at three different field test sites.
Mobile Services Acceptance Model

Along with the rapid development of information and communication technology, more and more mobile commerce applications are available. However, what is the user's perception and reaction of those new applications? It is critical to study the factors that influence the user adoption of mobile applications. In this research work, we propose an extended technology acceptance model, which is called mobile services acceptance model, with a consideration of trust, context, and personal initiatives and characteristics factors in addition to perceived usefulness and perceived ease of use. Furthermore, in order to demonstrate the applicability of the mobile services acceptance model, one case study with respect to the user adoption of the mobile application---FindMyFriends is presented.
Bridging Weighted Rules and Graph Random Walks for Statistical Relational Models

2 The aim of statistical relational learning is to learn statistical models from relational or graph3 structured data. Three main statistical relational learning paradigms include weighted rule 4 learning, random walks on graphs, and tensor factorization. These paradigms have been mostly 5 developed and studied in isolation for many years, with few works attempting at understanding 6 the relationship among them or combining them. In this paper, we study the relationship between 7 the path ranking algorithm (PRA), one of the most well-known relational learning methods in 8 the graph random walk paradigm, and relational logistic regression (RLR), one of the recent 9 developments in weighted rule learning. We provide a simple way to normalize relations and 10 prove that relational logistic regression using normalized relations generalizes the path ranking 11 algorithm. This result provides a better understanding of relational learning, especially for the 12 weighted rule learning and graph random walk paradigms. It opens up the possibility of using the 13 more flexible RLR rules within PRA models and even generalizing both by including normalized 14 and unnormalized relations in the same model.
Are Generative Classifiers More Robust to Adversarial Attacks?

There is a rising interest in studying the robustness of deep neural network classifiers against adversaries, with both advanced attack and defence techniques being actively developed. However, most recent work focuses on discriminative classifiers, which only model the conditional distribution of the labels given the inputs. In this paper we propose the deep Bayes classifier, which improves classical naive Bayes with conditional deep generative models. We further develop detection methods for adversarial examples, which reject inputs that have negative log-likelihood under the generative model exceeding a threshold pre-specified using training data. Experimental results suggest that deep Bayes classifiers are more robust than deep discriminative classifiers, and the proposed detection methods achieve high detection rates against many recently proposed attacks.
“Exascale Computing and Big Data: The Next Frontier,”

For scientific and engineering computing, exascale (10 operations per second) is the next proxy in the long trajectory of exponential performance increases that has continued for over half a century. Similarly, large-scale data preservation and sustainability within and across disciplines, metadata creation and multidisciplinary fusion, and digital privacy and security define the frontiers of big data. Solving the myriad technical, political and economic challenges of exascale computing will require coordinated planning across government, industry and academia, commitment to data sharing and sustainability, collaborative research and development, and recognition that both international competition and collaboration will be necessary.
Tamperproof transmission of fingerprints using visual cryptography schemes

Visual Cryptography and biometrics have been identified as the two most important aspects of digital security. In this paper, we propose a method for the preparation and secure transmission of fingerprint images using visual cryptography scheme. Visual cryptography (VC) is a kind of secret image sharing scheme that uses the human visual system to perform the decryption computations. A visual cryptography scheme (VCS) allows confidential messages to be encrypted into k-out-of-n secret sharing schemes. Whenever the number of participants from the group (n) is larger than or equal to the predetermined threshold value (k), the confidential message can be obtained by these participants. VCS is interesting because decryption can be done with no prior knowledge of cryptography and can be performed without any cryptographic computations. In this paper, the fingerprint image is broken up into n pieces, which individually yield no information about the image. These pieces of the images, called shares or shadows, may then be distributed among a group of n participants/dealers. By combining any k (k ≤ n) of these shares, the original fingerprint image can be recovered, but combining less than k of them will not reveal any information about the image. The scheme is perfectly secure and very easy to implement. To decode the encrypted information, i.e., to get the original information back, the shares are stacked and the secret image pops out. The only drawback of the VCS is the loss in contrast of reconstructed image. The proposed method for the reconstruction of fingerprint image is based on XNOR operation. This will enable one to obtain back perfect fingerprint image.
Session-Based Recommendations Using Item Embedding

Recent methods for learning vector space representations of words, word embedding, such as GloVe and Word2Vec have succeeded in capturing fine-grained semantic and syntactic regularities. We analyzed the effectiveness of these methods for e-commerce recommender systems by transferring the sequence of items generated by users' browsing journey in an e-commerce website into a sentence of words. We examined the prediction of fine-grained item similarity (such as item most similar to iPhone 6 64GB smart phone) and item analogy (such as iPhone 5 is to iPhone 6 as Samsung S5 is to Samsung S6) using real life users' browsing history of an online European department store. Our results reveal that such methods outperform related models such as singular value decomposition (SVD) with respect to item similarity and analogy tasks across different product categories. Furthermore, these methods produce a highly condensed item vector space representation, item embedding, with behavioral meaning sub-structure. These vectors can be used as features in a variety of recommender system applications. In particular, we used these vectors as features in a neural network based models for anonymous user recommendation based on session's first few clicks. It is found that recurrent neural network that preserves the order of user's clicks outperforms standard neural network, item-to-item similarity and SVD (recall@10 value of 42% based on first three clicks) for this task.
C-RAN simulator: A tool for evaluating 5G cloud-based networks system-level performance

Cloud radio-access network (C-RAN) has the most promising network architecture to support the new fifth generation (5G) mobile network. The C-RAN witnesses a revolutionary base station functionality split, where it is composed of simplified remote radio heads (RRH) and several baseband units (BBUs) pools in network backbone. The C-RAN assists in employing several functionalities more efficiently. It has privileges over the existing radio-access networks (RANs) in case of global resource management, interference management, and radio-access technology (RAT) selection to guarantee higher system throughput and lower energy consumption. In this paper, we demonstrate a C-RAN system-level simulator that performs centralized user scheduling, edge-user joint transmission (JT), and global per-antenna carrier aggregation (CA). In addition, we demonstrate the capability of our system-level simulator to investigate the bottleneck effect of the fronthaul-link capacity as the system expands or C-RAN coordinated facilities increase. Our analysis shows throughput enhancement compared to the traditional RAN; however, with high fronthaul links utilization rates.
Size matters: word count as a measure of quality on wikipedia

Wikipedia, "the free encyclopedia", now contains over two million English articles, and is widely regarded as a high-quality, authoritative encyclopedia. Some Wikipedia articles, however, are of questionable quality, and it is not always apparent to the visitor which articles are good and which are bad. We propose a simple metric -- word count -- for measuring article quality. In spite of its striking simplicity, we show that this metric significantly outperforms the more complex methods described in related work.
A GaN MMIC chipset suitable for integration in future X-band spaceborne radar T/R module Frontends

A set of monolithic microwave integrated circuits (MMICs) has been successfully developed by using a qualified European GaN HEMT technology. In particular a high power amplifier (HPA), a low noise amplifier (LNA) and a single pole double throw (SPDT) switch have been designed, manufactured and tested. The presented chipset is very suitable for integration in future GaN-based T/R module Frontend for spaceborne X-band SAR applications. In particular, the MMIC HPA integrates two stages of gain in 5.5 × 4.0 mm2 of area. Its measured performance is an output power of 27 W, a PAE of 36% with a linear gain greater than 24 dB from 8.8 GHz to 10.2 GHz. The MMIC LNA integrates three stages of gain in 3.0 × 2.02 mm2 of area. Its measured performance is a small signal gain greater than 23 dB with an associated noise figure of 1.6 dB in the frequency range from 7.4 to 11.4 GHz. Besides, its output power at 1 dB of gain compression is greater than 22 dBm. The MMIC SPDT switch exploits a robust asymmetrical absorptive/reflective topology in 3.0 × 1.0 mm2 of area. The chosen topology allows obtaining different functionalities of each switched branch. In the frequency range from 8.4 GHz to 10.8 GHz its measured performance is an insertion loss lower than 1 dB for both tx and rx path, and tx-mode rx-mode isolations better than 20 dB and 28 dB respectively. Besides, the tx path 1 dB insertion loss compression occurs at nearly 20 W of input power.
Planar Array Antennas with Travelling-Wave Excitation in Millimeter-Wave Band

あらまし ミリ波の周波数帯において指向性走査可能な平面アンテナが望まれている.本論文では,ミリ波の 代表的な二つの平面アンテナである導波管アンテナとマイクロストリップアンテナについて,開発した平面アン テナを紹介する.ペンシルビームが必要な機械走査型アンテナに適した二次元導波管アレーアンテナと,複数の サブアレーの配列からなる電子走査方式のサブアレー平面アンテナ用に,導波管アレーアンテナとマイクロスト リップコムラインアンテナについて開発した例を示す.これらについて,各放射素子で反射特性を低減すること が,特性の向上・設計自由度の拡大に有効であることを示す.また,これらのアンテナについて低損失に給電す る構造を示すと同時に,片端から給電するよりも,中央から給電する方が,周波数帯域幅が広くなることを示す. 更に,これらのアンテナ実現のためのキーとなる進行波励振アレー設計技術について解説する. キーワード ミリ波,アレーアンテナ,平面アンテナ,導波管アンテナ,マイクロストリップアンテナ
Transfer Learning : The Impact of Test Set Word Vectors , with Applications to Political Tweets

A major difficulty in applying deep learning in novel domains is the expense associated with acquiring sufficient training data. In this work, we extend literature in deep transfer learning by studying the role of initializing the embedding matrix with word vectors from GLoVe on a target dataset before training models with data from another domain. We study transfer learning on variants of four models (2 RNNs, a CNN, and an LSTM) and three datasets. We conclude that 1) the simple idea of initializing word vectors significantly and robustly improves transfer learning performance, 2) cross-domain learning occurs in fewer iterations than in-domain learning, considerably reduces train time, and 3) blending various out-of-domain datasets before training improves transfer learning. We then apply our models to a dataset of over 400k tweets by politicians, classifying sentiment and subjectivity vs. objectivity. This dataset was provided unlabelled, motivating an unsupervised and transfer learning approach. With transfer learning, we achieve reasonable performance on sentiment classification, but fail in classifying subjectivity vs. objectivity.
Hierarchical Complementary Attention Network for Predicting Stock Price Movements with News

It has been shown that stock price movements are influenced by news. To predict stock movements with news, many existing works rely only on the news title since the news content may contain irrelevancies which seriously degrade the prediction accuracy. However, we observe that there is still useful information in the content which is not reflected in the title, and simply ignoring the content will result in poor performance. In this paper, taking advantage of neural representation learning, we propose a hierarchical complementary attention network (HCAN) to capture valuable complementary information in news title and content for stock movement prediction. In HCAN, we adopt a two-level attention mechanism to quantify the importances of the words and sentences in a given news. Moreover, we design a novel measurement for calculating the attention weights to avoid capturing redundant information in the news title and content. Experimental results on news datasets show that our proposed model outperforms the state-of-the-art techniques.
Automatic Eye Type Detection in Retinal Fundus Image Using Fusion of Transfer Learning and Anatomical Features

Retinal fundus images are mainly used by ophthalmologists to diagnose and monitor the development of retinal and systemic diseases. A number of computer-aided diagnosis (CAD) systems have been developed aimed at automation of mass screening and diagnosis of retinal diseases. Eye type (left or right eye) of a given retinal image is an important meta data information for a CAD. At present, eye type is graded manually which is time consuming and error prone. This article presents an automatic method for eye type detection, which can be integrated into existing retinal CAD systems to make them more faster and accurate. Our method combines transfer learning and anatomical prior knowledge based features to maximize the classification accuracy. We evaluate the proposed method on a retinal image set containing 5000 images. Our method shows a classification accuracy of 94% (area under the receiver operating characteristics curve (AUC) = 0.990).
VHDL procedure for combinational divider

In the paper, a synthesizable combinational integer number divider VHDL model is described that is suitable for implementation in the FPGA devices. The algorithm the divider is based on is briefly introduced. Along the model, testbench for its functional verification is presented. Results of implementation in Xilinx Spartan-3 and Spartan-6 devices — amount of FPGA resources used and maximum delay, are given in tables.