nils-herrmann/clean_scirepeval huggingface dataset:
Large Scale Crowdsourcing and Characterization of Twitter Abusive Behavior

In recent years online social networks have suffered an increase in sexism, racism, and other types of aggressive and cyberbullying behavior, often manifesting itself through offensive, abusive, or hateful language. Past scientific work focused on studying these forms of abusive activity in popular online social networks, such as Facebook and Twitter. Building on such work, we present an eight month study of the various forms of abusive behavior on Twitter, in a holistic fashion. Departing from past work, we examine a wide variety of labeling schemes, which cover different forms of abusive behavior. We propose an incremental and iterative methodology that leverages the power of crowdsourcing to annotate a large collection of tweets with a set of abuse-related labels. By applying our methodology and performing statistical analysis for label merging or elimination, we identify a reduced but robust set of labels to characterize abuse-related tweets. Finally, we offer a characterization of our annotated dataset of 80 thousand tweets, which we make publicly available for further scientific exploration.
Group Equivariant Convolutional Networks

We introduce Group equivariant Convolutional Neural Networks (G-CNNs), a natural generalization of convolutional neural networks that reduces sample complexity by exploiting symmetries. By convolving over groups larger than the translation group, G-CNNs build representations that are equivariant to these groups, which makes it possible to greatly increase the degree of parameter sharing. We show how G-CNNs can be implemented with negligible computational overhead for discrete groups such as the group of translations, reflections and rotations by multiples of90 degrees. G-CNNs achieve state of the art results on rotated MNIST and significantly improve over a competitive baseline on augmented and non-augmented CIFAR-10.
Query optimizers: time to rethink the contract?

Query Optimization is expected to produce good execution plans for complex queries while taking relatively small optimization time. Moreover, it is expected to pick the execution plans with rather limited knowledge of data and without any additional input from the application. We argue that it is worth rethinking this prevalent model of the optimizer. Specifically, we discuss how the optimizer may benefit from leveraging rich usage data and from application input. We conclude with a call to action to further advance query optimization technology.
Threat analysis of IoT networks using artificial neural network intrusion detection system

The Internet of things (IoT) is still in its infancy and has attracted much interest in many industrial sectors including medical fields, logistics tracking, smart cities and automobiles. However as a paradigm, it is susceptible to a range of significant intrusion threats. This paper presents a threat analysis of the IoT and uses an Artificial Neural Network (ANN) to combat these threats. A multi-level perceptron, a type of supervised ANN, is trained using internet packet traces, then is assessed on its ability to thwart Distributed Denial of Service (DDoS/DoS) attacks. This paper focuses on the classification of normal and threat patterns on an IoT Network. The ANN procedure is validated against a simulated IoT network. The experimental results demonstrate 99.4% accuracy and can successfully detect various DDoS/DoS attacks.
Virtual reality-augmented rehabilitation for patients in subacute phase post stroke : a feasibility study

Upper extremity (UE) rehabilitation is of utmost importance to the achievement of full inclusion and functional independence. Traditionally presented as well as technology-based therapeutic interventions have produced measurable changes in motor function and motor control but fall short of major reductions in disability. Animal models of stroke suggest that the first two weeks to one month post stroke may be a critical time period of increased brain plasticity. This study shows the feasibility of adding one hour of intensive robotic/virtual reality (VR) therapy to on-going rehabilitation in the acute phase of recovery post-stroke. All five of the subjects made substantial improvements in Upper Extremity Fugl-Meyer Assessment (UEFMA) scores (mean improvement = 6 points (SD=2)) as well as improvements in Wolf Motor Function Test (WMFT) time (average decrease = 41% (SD=35) after training with more consistent changes in the proximal arm portions of the WMFT and the UEFMA as well as in upper arm kinematics. Maps of cortical excitability indicate an increase in both the area of activation and the volume of activation of the first dorsal interosseous (FDI) muscle after a two-week training period.
Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015) , on the eight datasets with relatively large training data that were used for testing the very deep characterlevel CNN in Conneau et al. (2016) . Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in  though the results should be interpreted with some consideration due to the unique pre-processing of . The shallow word-level CNN uses more parameters and therefore requires more storage than the deep character-level CNN; however, the shallow word-level CNN computes much faster.
Corpus-Based Approaches to Semantic Interpretation in Natural Language Processing

into empirical, corpus-based learning approaches to natural language processing (NLP). Most empirical NLP work to date has focused on relatively low-level language processing such as part-ofspeech tagging, text segmentation, and syntactic parsing. The success of these approaches has stimulated research in using empirical learning techniques in other facets of NLP, including semantic analysis—uncovering the meaning of an utterance. This article is an introduction to some of the emerging research in the application of corpusbased learning techniques to problems in semantic interpretation. In particular, we focus on two important problems in semantic interpretation, namely, word-sense disambiguation and semantic parsing.
Iris Center Corneal Reflection Method for Gaze Tracking Using Visible Light

Very few attempts, if any, have been made to use visible light in corneal reflection approaches to the problem of gaze tracking. The reasons usually given to justify the limited application of this type of illumination are that the required image features are less accurately depicted, and that visible light may disturb the user. The aim of this paper is to show that it is possible to overcome these difficulties and build an accurate and robust gaze tracker under these circumstances. For this purpose, visible light is used to obtain the corneal reflection or glint in a way analogous to the well-known pupil center corneal reflection technique. Due to the lack of contrast, the center of the iris is tracked instead of the center of the pupil. The experiments performed in our laboratory have shown very satisfactory results, allowing free-head movement and no need of recalibration.
Language Understanding With Ontological Semantics

Ontological Semantics is an approach to automatically extracting the meaning of natural language texts. The OntoSem text analysis system, developed according to this approach, generates ontologically grounded, disambiguated text meaning representations that can serve as input to intelligent agent reasoning. This article focuses on two core subtasks of overall semantic analysis: lexical disambiguation and the establishment of the semantic dependency structure. In addition to describing the knowledge bases and processors used to carry out these tasks, we introduce a novel evaluation suite suited specifically to knowledge-based systems. To situate this contribution in the field, we critically compare the goals, methods and tasks of Ontological Semantics with those of the currently dominant paradigm of natural language processing, which relies on machine learning.
Distracted and confused?: Selective attention under load

The ability to remain focused on goal-relevant stimuli in the presence of potentially interfering distractors is crucial for any coherent cognitive function. However, simply instructing people to ignore goal-irrelevant stimuli is not sufficient for preventing their processing. Recent research reveals that distractor processing depends critically on the level and type of load involved in the processing of goal-relevant information. Whereas high perceptual load can eliminate distractor processing, high load on "frontal" cognitive control processes increases distractor processing. These findings provide a resolution to the long-standing early and late selection debate within a load theory of attention that accommodates behavioural and neuroimaging data within a framework that integrates attention research with executive function.
Covering models and optimization techniques for emergency response facility location and planning: a review

With emergencies being, unfortunately, part of our lives, it is crucial to efficiently plan and allocate emergency response facilities that deliver effective and timely relief to people most in need. Emergency Medical Services (EMS) allocation problems deal with locating EMS facilities among potential sites to provide efficient and effective services over a wide area with spatially distributed demands. It is often problematic due to the intrinsic complexity of these problems. This paper reviews covering models and optimization techniques for emergency response facility location and planning in the literature from the past few decades, while emphasizing recent developments. We introduce several typical covering models and their extensions ordered from simple to complex, including Location Set Covering Problem (LSCP), Maximal Covering Location Problem (MCLP), Double Standard Model (DSM), Maximum Expected Covering Location Problem (MEXCLP), and Maximum Availability Location Problem (MALP) models. In addition, recent developments on hypercube queuing models, dynamic allocation models, gradual covering models, and cooperative covering models are also presented in this paper. The corresponding optimization X. Li (B) · Z. Zhao · X. Zhu Department of Industrial and Information Engineering, University of Tennessee, 416 East Stadium Hall, Knoxville, TN 37919, USA e-mail: Xueping.Li@utk.edu Z. Zhao e-mail: zzhao8@utk.edu X. Zhu , USA e-mail:
Three-dimensional optical holography using a plasmonic metasurface

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A survey of maximum PPT techniques of PV systems

This paper introduces a survey of different maximum peak power tracking (MPPT) techniques used in the implementation of photovoltaic power systems. It will discuss different 30 techniques used in tracking maximum power in photovoltaic arrays. This paper can be considered as a completion, updating, and declaration of the good efforts made in , that discussed 19 MPPT techniques in PV systems, while summarizes additional 11 MPPT methods.
Min Max Normalization Based Data Perturbation Method for Privacy Protection

Data mining system contain large amount of private and sensitive data such as healthcare, financial and criminal records. These private and sensitive data can not be share to every one, so privacy protection of data is required in data mining system for avoiding privacy leakage of data. Data perturbation is one of the best methods for privacy preserving. We used data perturbation method for preserving privacy as well as accuracy. In this method individual data value are distorted before data mining application. In this paper we present min max normalization transformation based data perturbation. The privacy parameters are used for measurement of privacy protection and the utility measure shows the performance of data mining technique after data distortion. We performed experiment on real life dataset and the result show that min max normalization transformation based data perturbation method is effective to protect confidential information and also maintain the performance of data mining technique after data distortion.
RETracer: Triaging Crashes by Reverse Execution from Partial Memory Dumps

Many software providers operate crash reporting services to automatically collect crashes from millions of customers and file bug reports. Precisely triaging crashes is necessary and important for software providers because the millions of crashes that may be reported every day are critical in identifying high impact bugs. However, the triaging accuracy of existing systems is limited, as they rely only on the syntactic information of the stack trace at the moment of a crash without analyzing program semantics.
 In this paper, we present RETracer, the first system to triage software crashes based on program semantics reconstructed from memory dumps. RETracer was designed to meet the requirements of large-scale crash reporting services. RETracer performs binary-level backward taint analysis without a recorded execution trace to understand how functions on the stack contribute to the crash. The main challenge is that the machine state at an earlier time cannot be recovered completely from a memory dump, since most instructions are information destroying.
 We have implemented RETracer for x86 and x86-64 native code, and compared it with the existing crash triaging tool used by Microsoft. We found that RETracer eliminates two thirds of triage errors based on a manual analysis of 140 bugs fixed in Microsoft Windows and Office. RETracer has been deployed as the main crash triaging system on Microsoft's crash reporting service.
On the capacitated vehicle routing problem

We consider the Vehicle Routing Problem, in which a fixed fleet of delivery vehicles of uniform capacity must service known customer demands for a single commodity from a common depot at minimum transit cost. This difficult combinatorial problem contains both the Bin Packing Problem and the Traveling Salesman Problem (TSP) as special cases and conceptually lies at the intersection of these two well-studied problems. The capacity constraints of the integer programming formulation of this routing model provide the link between the underlying routing and packing structures. We describe a decomposition-based separation methodology for the capacity constraints that takes advantage of our ability to solve small instances of the TSP efficiently. Specifically, when standard procedures fail to separate a candidate point, we attempt to decompose it into a convex combination of TSP tours; if successful, the tours present in this decomposition are examined for violated capacity constraints; if not, the Farkas Theorem provides a hyperplane separating the point from the TSP polytope. We present some extensions of this basic concept and a general framework within which it can be applied to other combinatorial models. Computational results are given for an implementation within the parallel branch, cut, and price framework SYMPHONY.
Keratosis pilaris and ulerythema ophryogenes associated with an 18p deletion caused by a Y/18 translocation.

We present a patient with partial monosomy of the short arm of chromosome 18 caused by de novo translocation t(Y;18) and a generalized form of keratosis pilaris (keratosis pilaris affecting the skin follicles of the trunk, limbs and face-ulerythema ophryogenes). Two-color FISH with centromere-specific Y and 18 DNA probes identified the derivative chromosome 18 as a dicentric with breakpoints in p11.2 on both involved chromosomes. The patient had another normal Y chromosome. This is a third report the presence of a chromosome 18p deletion (and first case of a translocation involving 18p and a sex chromosome) with this genodermatosis. Our data suggest that the short arm of chromosome 18 is a candidate region for a gene causing keratosis pilaris. Unmasking of a recessive mutation at the disease locus by deletion of the wild type allele could be the cause of the recessive genodermatosis.
Alert correlation survey: framework and techniques

Managing raw alerts generated by various sensors are becoming of more significance to intrusion detection systems as more sensors with different capabilities are distributed spatially in the network. Alert Correlation addresses this issue by reducing, fusing and correlating raw alerts to provide a condensed, yet more meaningful view of the network from the intrusion standpoint. Techniques from a divers range of disciplines have been used by researchers for different aspects of correlation. This paper provides a survey of the state of the art in alert correlation techniques. Our main contribution is a two-fold classification of literature based on correlation framework and applied techniques. The previous works in each category have been described alongside with their strengths and weaknesses from our viewpoint.
Cassandra: structured storage system on a P2P network

Cassandra is a distributed storage system for managing structured data that is designed to scale to a very large size across many commodity servers, with no single point of failure. Reliability at massive scale is a very big challenge. Outages in the service can have significant negative impact. Hence Cassandra aims to run on top of an infrastructure of hundreds of nodes (possibly spread across different datacenters). At this scale, small and large components fail continuously; the way Cassandra manages the persistent state in the face of these failures drives the reliability and scalability of the software systems relying on this service. Cassandra has achieved several goals--scalability, high performance, high availability and applicability. In many ways Cassandra resembles a database and shares many design and implementation strategies with databases. Cassandra does not support a full relational data model; instead, it provides clients with a simple data model that supports dynamic control over data layout and format.
Auditory feedback in learning and maintenance of vocal behaviour

Songbirds are one of the best-studied examples of vocal learners. Learning of both human speech and birdsong depends on hearing. Once learned, adult song in many species remains unchanging, suggesting a reduced influence of sensory experience. Recent studies have revealed, however, that adult song is not always stable, extending our understanding of the mechanisms involved in song maintenance, and their similarity to those active during song learning. Here we review some of the processes that contribute to song learning and production, with an emphasis on the role of auditory feedback. We then consider some of the possible neural substrates involved in these processes, particularly basal ganglia circuitry. Although a thorough treatment of human speech is beyond the scope of this article, we point out similarities between speech and song learning, and ways in which studies of these disparate behaviours complement each other in developing an understanding of general principles that contribute to learning and maintenance of vocal behaviour.
A Manufacturing Big Data Solution for Active Preventive Maintenance

Industry 4.0 has become more popular due to recent developments in cyber-physical systems, big data, cloud computing, and industrial wireless networks. Intelligent manufacturing has produced a revolutionary change, and evolving applications, such as product lifecycle management, are becoming a reality. In this paper, we propose and implement a manufacturing big data solution for active preventive maintenance in manufacturing environments. First, we provide the system architecture that is used for active preventive maintenance. Then, we analyze the method used for collection of manufacturing big data according to the data characteristics. Subsequently, we perform data processing in the cloud, including the cloud layer architecture, the real-time active maintenance mechanism, and the offline prediction and analysis method. Finally, we analyze a prototype platform and implement experiments to compare the traditionally used method with the proposed active preventive maintenance method. The manufacturing big data method used for active preventive maintenance has the potential to accelerate implementation of Industry 4.0.
Comparison of Lexicon based and Naïve Bayes Classifier in Sentiment Analysis

1 M.Tech. Student 2 Assistant Professor 1,2 Department of Computer Science and Engineering 1,2 Don Bosco Institute of Technology, Affiliated by VTU Abstract— In the recent years Sentiment analysis (SA) has gained momentum by the increase of social networking sites. Sentiment analysis has been an important topic for data mining, social media for classifying reviews and thereby rating the entities such as products, movies etc. This paper represents a comparative study of sentiment classification of lexicon based approach and naive bayes classifier of machine learning in sentiment analysis.
Face Verification via Learned Representation on Feature-Rich Video Frames

Abundance and availability of video capture devices, such as mobile phones and surveillance cameras, have instigated research in video face recognition, which is highly pertinent in law enforcement applications. While the current approaches have reported high accuracies at equal error rates, performance at lower false accept rates requires significant improvement. In this paper, we propose a novel face verification algorithm, which starts with selecting feature-rich frames from a video sequence using discrete wavelet transform and entropy computation. Frame selection is followed by representation learning-based feature extraction, where three contributions are presented: 1) deep learning architecture, which is a combination of stacked denoising sparse autoencoder (SDAE) and deep Boltzmann machine (DBM); 2) formulation for joint representation in an autoencoder; and 3) updating the loss function of DBM by including sparse and low rank regularization. Finally, a multilayer neural network is used as the classifier to obtain the verification decision. The results are demonstrated on two publicly available databases, YouTube Faces and Point and Shoot Challenge. Experimental analysis suggests that: 1) the proposed feature-richness-based frame selection offers noticeable and consistent performance improvement compared with frontal only frames, random frames, or frame selection using perceptual no-reference image quality measures and 2) joint feature learning in SDAE and sparse and low rank regularization in DBM helps in improving face verification performance. On the benchmark Point and Shoot Challenge database, the algorithm yields the verification accuracy of over 97% at 1% false accept rate whereas, on the YouTube Faces database, over 95% verification accuracy is observed at equal error rate.