nils-herrmann/clean_scirepeval huggingface dataset:
Double-Hypergraph Based Sentence Ranking for Query-Focused Multi-document Summarizaton

Traditional graph based sentence ranking approaches modeled the documents as a text graph where vertices represent sentences and edges represent pairwise similarity relationships between two sentences. Such modeling cannot capture complex group relationships shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose two different group relationships (sentence-topic relationship and document-topic relationship) shared among sentences, and construct a double-hypergraph integrating these relationships into a unified framework. Then, a double-hypergraph based sentence ranking algorithm is developed for query-focused multi-document summarization, in which Markov random walk is defined on each hypergraph and the mixture Markov chains are formed so as to perform transductive learning in the double-hypergraph. When evaluated on DUC datasets, performance of the proposed approach is remarkable.
Deep Voice 3: Scaling Text-to-Speech with Convolutional Sequence Learning

We present Deep Voice 3, a fully-convolutional attention-based neural textto-speech (TTS) system. Deep Voice 3 matches state-of-the-art neural speech synthesis systems in naturalness while training an order of magnitude faster. We scale Deep Voice 3 to dataset sizes unprecedented for TTS, training on more than eight hundred hours of audio from over two thousand speakers. In addition, we identify common error modes of attention-based speech synthesis networks, demonstrate how to mitigate them, and compare several different waveform synthesis methods. We also describe how to scale inference to ten million queries per day on a single GPU server.
Self-esteem and perceived regard: how I see myself affects my relationship satisfaction.

The authors explored the relations among self-esteem, perceived regard, and satisfaction in dating relationships. On the basis of the dependency regulation model (T. DeHart, B. Pelham, & S. Murray, 2004), the authors hypothesized that self-esteem would influence individuals' self-perceptions and views of how their partners perceive them (metaperception). They also hypothesized that perceived regard (self-perception minus metaperception) would predict relationship satisfaction. Regression analyses indicated that for moderate relationship-relevant traits (i.e., caring, loving), high self-esteem was associated with self-enhancement (idealization), whereas low self-esteem was associated with self-deprecation. For low relationship-relevant traits (i.e., quiet, reserved), both low and high self-esteem individuals self-enhanced. Hierarchical regression analyses indicated that self-esteem and perceived regard for moderate relationship-relevant traits predicted satisfaction. The authors discuss the implications of idealization, self-verification, and self-deprecation for the perceivers and their relationships.
Quiet Neighborhoods: Key to Protect Job Performance Predictability

Interference of nearby jobs has been recently identified as the dominant reason for the high performance variability of parallel applications running on High Performance Computing (HPC) systems. Typically, HPC systems are dynamic with multiple jobs coming and leaving in an unpredictable fashion, sharing simultaneously the system interconnection network. In such environment contention for network resources is causing random stalls in the progress of application execution degrading application and system performance overall. Eliminating job interactions in their neighbourhoods is key for guaranteeing performance predictability of applications. In this paper we are proposing the concept of quiet neighbourhoods that significantly reduce job interactions. Quiet neighbourhoods are created by the system resource manager in two phases. First, multiple virtual network blocks are defined on the top of the physical network resources based on typical workload distributions. Second, newly arriving jobs are allocated in these virtual blocks based on their size.
Fuzzy Keyword Search Over Encrypted Data in Cloud Computing

As Cloud Computing becomes prevalent, more and more sensitive information are being centralized into the cloud. For the protection of data privacy, sensitive data usually have to be encrypted before outsourcing, which makes effective data utilization a very challenging task. Although traditional searchable encryption schemes allow a user to securely search over encrypted data through keywords and selectively retrieve files of interest, these techniques support only exact keyword search. This significant drawback makes existing techniques unsuitable in cloud computing as it is greatly affect system usability, rendering user searching experiences very frustrating and system efficiency very low. In this paper, for the first time we formalize and solve the problem of effective fuzzy keyword search over encrypted cloud while maintaining keyword privacy. In our solution, we exploit edit distance to quantify keyword similarity and develop new advanced technique on constructing fuzzy keyword sets which greatly reduces the storage and representation overheads. In this way, we show that our proposed solution is secure and privacy preserving, while realizing the goal of fuzzy keyword search.
Support vector machine-based classification of Alzheimer’s disease from whole-brain anatomical MRI

We present and evaluate a new automated method based on support vector machine (SVM) classification of whole-brain anatomical magnetic resonance imaging to discriminate between patients with Alzheimer’s disease (AD) and elderly control subjects. We studied 16 patients with AD [mean age ± standard deviation (SD) = 74.1 ± 5.2 years, mini-mental score examination (MMSE) = 23.1 ± 2.9] and 22 elderly controls (72.3 ± 5.0 years, MMSE = 28.5 ± 1.3). Three-dimensional T1-weighted MR images of each subject were automatically parcellated into regions of interest (ROIs). Based upon the characteristics of gray matter extracted from each ROI, we used an SVM algorithm to classify the subjects and statistical procedures based on bootstrap resampling to ensure the robustness of the results. We obtained 94.5% mean correct classification for AD and control subjects (mean specificity, 96.6%; mean sensitivity, 91.5%). Our method has the potential in distinguishing patients with AD from elderly controls and therefore may help in the early diagnosis of AD.
An Annotated Corpus and Method for Analysis of Ad-Hoc Structures Embedded in Text

We describe a method for identifying and performing functional analysis of structured regions that are embedded in natural language documents, such as tables or key-value lists. Such regions often encode information according to ad hoc schemas and avail themselves of visual cues in place of natural language grammar, presenting problems for standard information extraction algorithms. Unlike previous work in table extraction, which assumes a relatively noiseless two-dimensional layout, our aim is to accommodate a wide variety of naturally occurring structure types. Our approach has three main parts. First, we collect and annotate a a diverse sample of “naturally” occurring structures from several sources. Second, we use probabilistic text segmentation techniques, featurized by skip bigrams over spatial and token category cues, to automatically identify contiguous regions of structured text that share a common schema. Finally, we identify the records and fields within each structured region using a combination of distributional similarity and sequence alignment methods, guided by minimal supervision in the form of a single annotated record. We evaluate the last two components individually, and conclude with a discussion of further work.
RAVEN: Perception-aware Optimization of Power Consumption for Mobile Games

High-end mobile GPUs are now becoming an integral part of mobile devices. However, a mobile GPU constitutes a major portion of power consumption on the devices, and mobile games top as the most popular class of graphics applications. This paper presents the design and implementation of RAVEN, a novel, on-the-fly frame rate scaling system for mobile gaming applications. RAVEN utilizes human visual perception of graphics change to opportunistically achieve power saving without degrading user experiences. The system develops a light-weight frame comparison technique to measure and predict perception-aware frame similarity. It also builds a low resolution virtual display which clones the device screen for performing similarity measurement at a low-power cost. It is able to work on an existing commercial smartphone and support applications from app stores without any modifications. It has been implemented on Nexus 5X, and its performance has been measured with 13 games. The system effectively reduces the overall power consumption of mobile devices while maintaining satisfactory user experiences. The power consumption is reduced by 21.78% on aver-age and up to 34.74%.
Event-based Camera Pose Tracking using a Generative Event Model

Event-based vision sensors mimic the operation of biological retina and they represent a major paradigm shift from traditional cameras. Instead of providing frames of intensity measurements synchronously, at artificially chosen rates, event-based cameras provide information on brightness changes asynchronously, when they occur. Such non-redundant pieces of information are called “events”. These sensors overcome some of the limitations of traditional cameras (response time, bandwidth and dynamic range) but require new methods to deal with the data they output. We tackle the problem of event-based camera localization in a known environment, without additional sensing, using a probabilistic generative event model in a Bayesian filtering framework. Our main contribution is the design of the likelihood function used in the filter to process the observed events. Based on the physical characteristics of the sensor and on empirical evidence of the Gaussian-like distribution of spiked events with respect to the brightness change, we propose to use the contrast residual as a measure of how well the estimated pose of the event-based camera and the environment explain the observed events. The filter allows for localization in the general case of six degrees-of-freedom motions.
Web Ontologies to Categorialy Structure Reality: Representations of Human Emotional, Cognitive, and Motivational Processes

This work presents a Web ontology for modeling and representation of the emotional, cognitive and motivational state of online learners, interacting with university systems for distance or blended education. The ontology is understood as a way to provide the required mechanisms to model reality and associate it to emotional responses, but without committing to a particular way of organizing these emotional responses. Knowledge representation for the contributed ontology is performed by using Web Ontology Language (OWL), a semantic web language designed to represent rich and complex knowledge about things, groups of things, and relations between things. OWL is a computational logic-based language such that computer programs can exploit knowledge expressed in OWL and also facilitates sharing and reusing knowledge using the global infrastructure of the Web. The proposed ontology has been tested in the field of Massive Open Online Courses (MOOCs) to check if it is capable of representing emotions and motivation of the students in this context of use.
Performance of PMASynRM With Ferrite Magnets for EV/HEV Applications Considering Productivity

Although motors that use rare-earth permanent magnets typically exhibit high performance, their cost is high, and there are concerns about the stability of the raw material supply. This paper proposes a permanent-magnet-assisted synchronous reluctance motor (PMASynRM) with a ferrite magnet that does not use rare-earth materials considering productivity. The performance of the proposed PMASynRM is evaluated based on the finite-element method and an experiment using a prototype machine. The analysis results reveal that the proposed PMASynRM has the same power density and an equivalent efficiency as rare-earth permanent-magnet synchronous motors for hybrid electric vehicles (2003 Toyota Prius). Furthermore, some experimental results are presented in order to validate the analytical results. As a result, the proposed PMASynRM was found to achieve high-power-density and high-efficiency performance.
Influence of Rectifiers on High-Speed Permanent Magnet Generator Electromagnetic and Temperature Fields in Distributed Power Generation Systems

A power converter is very necessary in the system of high-speed permanent magnet generators (HSPMG), since the frequency of the output electrical energy is generally hundreds of Hertz or even more than 1000 Hz. However, the converter with switches in fast on-off transitions will have some effects on the generator and, therewith, some current harmonics will be also induced in the device. Taking a 117 kW 60 000 r/min HSPMG as an example, the harmonics in this generator were analyzed. And then, the voltage harmonic content, the current harmonic content, and the current total harmonic distortion could be obtained. In addition, stator core losses and rotor eddy current losses were analyzed comparatively, and the influence of the converter on the generator power factor was further studied. Based on the 3-D coupling field between fluid and temperature, the temperature distributions were obtained when the generator was connected to different loads, and the influence of the rectifier on the generator temperature field could be obtained, which was also compared with the test data.
The fragment assembly string graph

We present a concept and formalism, the string graph, which represents all that is inferable about a DNA sequence from a collection of shotgun sequencing reads collected from it. We give time and space efficient algorithms for constructing a string graph given the collection of overlaps between the reads and, in particular, present a novel linear expected time algorithm for transitive reduction in this context. The result demonstrates that the decomposition of reads into kmers employed in the de Bruijn graph approach described earlier is not essential, and exposes its close connection to the unitig approach we developed at Celera. This paper is a preliminary piece giving the basic algorithm and results that demonstrate the efficiency and scalability of the method. These ideas are being used to build a next-generation whole genome assembler called BOA (Berkeley Open Assembler) that will easily scale to mammalian genomes.
LLC resonant converter burst mode control with constant burst time and optimal switching pattern

In this paper, a novel burst mode control solution for LLC resonant converters is proposed for the highest efficiency and small output voltage ripple. The burst time is set to be constant, the off time is modulated by load conditions. During the burst time, three pulse switching pattern is implemented to keep output voltage low frequency ripple minimal. The switch first turned on is controlled to be last turned off to reduce the initial hard switching turn on loss when burst starts. The pulse width of first switch is optimized to settle LLC to the steady-state under the highest efficiency load condition in one pulse. Zero current detection (ZCD) of the resonant current is implemented to achieve first switch partial ZVS, further increasing light load efficiency.
An empirical evaluation of internet latency expansion

The Internet's latency expansion determines the asymptotic performance of large-scale distributed systems (such as Peer-to-Peer systems), but previous studies on the Internet have defined expansion in terms of router-level hops. In this paper, we empirically determine the Internet's latency expansion characteristics using measurements from two different Internet topology datasets. Our results show that the Internet router-level topology exhibits a power-law latency expansion, in contrast to its exponential expansion rate in terms of hops.
Cloud Computing Adoption by Higher Education Institutions in Saudi Arabia: Analysis Based on TOE

Academic study of Cloud Computing within Saudi Arabia is an emerging research field. Saudi Arabia represents the largest economy in the Arabian Gulf region. This positions it as a potential market of cloud computing technologies. Adoption of new innovations should be preceded by analysis of the added value, challenges and adequacy from technological, organizational and environmental perspectives. (2) Statement of Contribution/ This cross-sectional exploratory empirical research is based on Technology, Organization and Environment model targeting higher education institutions. In this study, the factors that influence the adoption by higher education institutions were analyzed and tested using Partial Least Square. (3) Results,  Three factors were found significant in this context. Relative Advantage, Data Privacy and Complexity are the most significant factors. The model explained 43% of the total adoption measure variation. Significant differences in the areas of cloud computing compatibility, complexity, vendor lock-in and peer pressure between large and small institutions were revealed. Items for future cloud computing research were explored through open-ended questions. Adoption of cloud services by higher education institutions has been started. It was found that the adoption rate among large universities is higher than small higher education institutions. Improving the network and Internet Infrastructure in Saudi Arabia at an affordable cost is a pre-requisite for cloud computing adoption. Cloud service provider should address the privacy and complexity concerns raised by non-adopters. Future information systems that are potential for hosting in cloud were prioritized.
SEISMIC BEHAVIOR OF INTERMEDIATE BEAMS IN STEEL PLATE SHEAR WALLS

This paper presents some preliminary results of an ongoing research whose objective is to investigate the seismic behavior of intermediate beams (i.e., the beams other than those at the roof and foundation levels) in multi-story steel plate shear walls (SPSWs). Of primary interest is the determination of the strength level needed to avoid the formation of in-span plastic hinges, a relevant practical issue that has not been considered in past investigations. To attain this objective, the seismic response of different SPSW models was analyzed by performing linear and nonlinear analysis. The intermediate beams of the SPSW models were designed to resist: (I) forces imposed by gravity loads only; (II) forces determined by the ASCE 7 load combinations; and (III) forces imposed by fully yielded plates. For comparison purposes, SPSW models designed according to the Canadian Standard CAN/CSA S16-01 were considered as well. It is concluded that intermediate beams designed according to criteria I and II are prone to inspan plastic hinges and to excessive plastic deformations. It was also found that, while in-span plastic hinges do not appear in the intermediate beams of the CAN/CSA S16-01 models, they do appear in the roof and foundation beams of these models when a collapse mechanism develops.
Risks and risk mitigation in global software development: A tertiary study

There is extensive interest in global software development (GSD) which has led to a large number of papers reporting on GSD. A number of systematic literature reviews (SLRs) have attempted to aggregate information from individual studies.  We wish to investigate GSD SLR research with a focus on discovering what research has been conducted in the area and to determine if the SLRs furnish appropriate risk and risk mitigation advice to provide guidance to organizations involved with GSD.  We performed a broad automated search to identify GSD SLRs. Data extracted from each study included: (1) authors, their affiliation and publishing venue, (2) SLR quality, (3) research focus, (4) GSD risks, (5) risk mitigation strategies and, (6) for each SLR the number of primary studies reporting each risk and risk mitigation strategy.  We found a total of 37 papers reporting 24 unique GSD SLR studies. Major GSD topics covered include: (1) organizational environment, (2) project execution, (3) project planning and control and (4) project scope and requirements. We extracted 85 risks and 77 risk mitigation advice items and categorized them under four major headings: outsourcing rationale, software development, human resources, and project management. The largest group of risks was related to project management. GSD outsourcing rationale risks ranked highest in terms of primary study support but in many cases these risks were only identified by a single SLR.  The focus of the GSD SLRs we identified is mapping the research rather than providing evidence-based guidance to industry. Empirical support for the majority of risks identified is moderate to low, both in terms of the number of SLRs identifying the risk, and in the number of primary studies providing empirical support. Risk mitigation advice is also limited, and empirical support for these items is low.
Spatial reconstruction of single-cell gene expression data

Spatial localization is a key determinant of cellular fate and behavior, but methods for spatially resolved, transcriptome-wide gene expression profiling across complex tissues are lacking. RNA staining methods assay only a small number of transcripts, whereas single-cell RNA-seq, which measures global gene expression, separates cells from their native spatial context. Here we present Seurat, a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns. We applied Seurat to spatially map 851 single cells from dissociated zebrafish (Danio rerio) embryos and generated a transcriptome-wide map of spatial patterning. We confirmed Seurat's accuracy using several experimental approaches, then used the strategy to identify a set of archetypal expression patterns and spatial markers. Seurat correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups. Seurat will be applicable to mapping cellular localization within complex patterned tissues in diverse systems.
Antecedents and Consequences of Organizational Commitment Among Pakistani University Teachers

The purpose of this study was to determine if selected personal characteristics, facets of job satisfaction, and the two dimensions of organizational justice (distributive justice & procedural justice) significantly explained variance in the organizational commitment of Pakistani university teachers. In addition, the present study examined the influence of organizational commitment on two organizational outcomes—job performance and turnover intentions. Data were gathered from 125 full-time teachers from 33 universities in the three major cities of Pakistan: Lahore, Islamabad/Rawalpindi, and Peshawar. The results of the study indicate that the personal characteristics, facets of job satisfaction and two dimensions of organizational justice as a group were significantly related to organizational commitment of teachers. Individually, distributive justice and trust in management were found to be the strongest correlates of commitment. Moreover, commitment was found to be negatively related to turnover intentions (.40) and positively related to a self-report measure of job performance (.32). __________________________________________________________________________
A Machine Learning Approach for Cryptanalysis

The paper introduces a novel approach of using neural networks to perform cryptanalysis on a lightweight cipher like the Simon cipher. The neural network takes in plaintexts and their corresponding ciphertexts to predict the key that was used to encrypt the plaintext. The neural network is tested with various rounds of the cipher system to see how it fares, and is also tested with various configurations of the neural network to determine which configuration could give us the highest accuracy at determining the key correctly.