nils-herrmann/clean_scirepeval huggingface dataset:
Reducing consistency in human realism increases the uncanny valley effect; increasing category uncertainty does not

Human replicas may elicit unintended cold, eerie feelings in viewers, an effect known as the uncanny valley. Masahiro Mori, who proposed the effect in 1970, attributed it to inconsistencies in the replica's realism with some of its features perceived as human and others as nonhuman. This study aims to determine whether reducing realism consistency in visual features increases the uncanny valley effect. In three rounds of experiments, 548 participants categorized and rated humans, animals, and objects that varied from computer animated to real. Two sets of features were manipulated to reduce realism consistency. (For humans, the sets were eyes-eyelashes-mouth and skin-nose-eyebrows.) Reducing realism consistency caused humans and animals, but not objects, to appear eerier and colder. However, the predictions of a competing theory, proposed by Ernst Jentsch in 1906, were not supported: The most ambiguous representations-those eliciting the greatest category uncertainty-were neither the eeriest nor the coldest.
Real-Time Anomaly Detection for Streaming Analytics

Much of the worlds data is streaming, time-series data, where anomalies give significant information in critical situations. Yet detecting anomalies in streaming data is a difficult task, requiring detectors to process data in real-time, and learn while simultaneously making predictions. We present a novel anomaly detection technique based on an on-line sequence memory algorithm called Hierarchical Temporal Memory (HTM). We show results from a live application that detects anomalies in financial metrics in realtime. We also test the algorithm on NAB, a published benchmark for real-time anomaly detection, where our algorithm achieves best-in-class results.
jMWE: A Java Toolkit for Detecting Multi-Word Expressions

jMWE is a Java library for implementing and testing algorithms that detect Multi-Word Expression (MWE) tokens in text. It provides (1) a detector API, including implementations of several detectors, (2) facilities for constructing indices of MWE types that may be used by the detectors, and (3) a testing framework for measuring the performance of a MWE detector. The software is available for free download. jMWE is a Java library for constructing and testing Multi-Word Expression (MWE) token detectors. The original goal of the library was to detect tokens (instances) of MWE types in a token stream, given a list of types such as those that can be extracted from an electronic dictionary such as WordNet (Fellbaum, 1998). The purpose of the library is not to discover new MWE types, but rather find instances of a set of given types in a given text. The library also supports MWE detectors that are not list-based. The functionality of the library is basic, but it is a necessary foundation for any system that wishes to use MWEs in later stages of language processing. It is a natural complement to software for discovering MWE types, such as mwetoolkit (Ramisch et al., 2010) or the NSP package (Banerjee and Pedersen, 2003). jMWE is available online for free download (Finlayson and Kulkarni, 2011a). 1 Library Facilities Detector API The core of the library is the detector API. The library defines a detector interface which provides a single method for detecting MWE tokens in a list of individual tokens; anyone interested in taking advantage of jMWE’s testing infrastructure or writing their own MWE token detection algorithm need only implement this interface. jMWE provides several baseline MWE token detection strategies. Also provided are detector filters, which apply a specific constraint to, or resolve conflicts in, the output another detector. MWE Index jMWE also provides classes for constructing, storing, and accessing indices of valid MWE types. An MWE index allows an algorithm to retrieve a list of MWE types given a single word token and part of speech. The index also lists how frequently, in a particular concordance, a set of tokens appears as a particular MWE type rather than as independent words. To facilitate construction of indices, jMWE provides bindings to the MIT Java Wordnet Interface (JWI) (Finlayson, 2008b) and JSemcor (Finlayson, 2008a), as well as classes which extract all MWE types from those resources and write them to disk. Test Harness The linchpin of jMWE’s testing infrastructure is a test harness that runs an MWE detector over a given corpus and measures its precision and recall. The library comes with default bindings for running detectors over the Semcor corpus or any other corpus that can be mounted with the JSemcor library. Nevertheless, jMWE is not restricted to running tests over Semcor, or even restricted to using JSemcor for interfacing with a corpus: a detector can be run over any corpus whose MWE instances have been marked can be analyzed, merely by implementing four interfaces. Also included in the testing in-
System Analysis via Integral Quadratic Constraints

This paper introduces a unified approach to robustness analysis with respect to nonlinearities, time variations, and uncertain parameters. From an original idea by Yakubovich, the approach has been developed under a combination of influences from the Western and Russian traditions of control theory. It is shown how a complex system can be described, using integral quadratic constraints (IQC’s) for its elementary components. A stability theorem for systems described by IQC’s is presented that covers classical passivity/dissipativity arguments but simplifies the use of multipliers and the treatment of causality. A systematic computational approach is described, and relations to other methods of stability analysis are discussed. Last, but not least, the paper contains a summarizing list of IQC’s for important types of system components.
A green vehicle routing problem with customer satisfaction criteria

This paper develops an MILP model, named Satisfactory-Green Vehicle Routing Problem. It consists of routing a heterogeneous fleet of vehicles in order to serve a set of customers within predefined time windows. In this model in addition to the traditional objective of the VRP, both the pollution and customers’ satisfaction have been taken into account. Meanwhile, the introduced model prepares an effective dashboard for decision-makers that determines appropriate routes, the best mixed fleet, speed and idle time of vehicles. Additionally, some new factors evaluate the greening of each decision based on three criteria. This model applies piecewise linear functions (PLFs) to linearize a nonlinear fuzzy interval for incorporating customers’ satisfaction into other linear objectives. We have presented a mixed integer linear programming formulation for the S-GVRP. This model enriches managerial insights by providing trade-offs between customers’ satisfaction, total costs and emission levels. Finally, we have provided a numerical study for showing the applicability of the model.
Improving Automated Bug Triaging with Specialized Topic Model

Bug triaging refers to the process of assigning a bug to the most appropriate developer to fix. It becomes more and more difficult and complicated as the size of software and the number of developers increase. In this paper, we propose a new framework for bug triaging, which maps the words in the bug reports (i.e., the term space) to their corresponding topics (i.e., the topic space). We propose a specialized topic modeling algorithm named <italic> multi-feature topic model (MTM)</italic> which extends Latent Dirichlet Allocation (LDA) for bug triaging. <italic>MTM </italic> considers product and component information of bug reports to map the term space to the topic space. Finally, we propose an incremental learning method named <italic>TopicMiner</italic> which considers the topic distribution of a new bug report to assign an appropriate fixer based on the affinity of the fixer to the topics. We pair <italic> TopicMiner</italic> with MTM (<italic>TopicMiner<inline-formula><tex-math notation="LaTeX">$^{MTM}$</tex-math> <alternatives><inline-graphic xlink:href="xia-ieq1-2576454.gif"/></alternatives></inline-formula></italic>). We have evaluated our solution on 5 large bug report datasets including GCC, OpenOffice, Mozilla, Netbeans, and Eclipse containing a total of 227,278 bug reports. We show that <italic>TopicMiner<inline-formula><tex-math notation="LaTeX"> $^{MTM}$</tex-math><alternatives><inline-graphic xlink:href="xia-ieq2-2576454.gif"/></alternatives></inline-formula> </italic> can achieve top-1 and top-5 prediction accuracies of 0.4831-0.6868, and 0.7686-0.9084, respectively. We also compare <italic>TopicMiner<inline-formula><tex-math notation="LaTeX">$^{MTM}$</tex-math><alternatives> <inline-graphic xlink:href="xia-ieq3-2576454.gif"/></alternatives></inline-formula></italic> with Bugzie, LDA-KL, SVM-LDA, LDA-Activity, and Yang et al.'s approach. The results show that <italic>TopicMiner<inline-formula> <tex-math notation="LaTeX">$^{MTM}$</tex-math><alternatives><inline-graphic xlink:href="xia-ieq4-2576454.gif"/> </alternatives></inline-formula></italic> on average improves top-1 and top-5 prediction accuracies of Bugzie by 128.48 and 53.22 percent, LDA-KL by 262.91 and 105.97 percent, SVM-LDA by 205.89 and 110.48 percent, LDA-Activity by 377.60 and 176.32 percent, and Yang et al.'s approach by 59.88 and 13.70 percent, respectively.
BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research

The problem of estimating the electricity consumption of individual appliances in a building from a limited number of voltage and/or current measurements in the distribution system has received renewed interest from the research community in recent years. In this paper, we present a Building-Level fUlly-labeled dataset for Electricity Disaggregation (BLUED). The dataset consists of voltage and current measurements for a single-family residence in the United States, sampled at 12 kHz for a whole week. Every state transition of each appliance in the home during this time was labeled and time-stamped, providing the necessary ground truth for the evaluation of event-based algorithms. With this dataset, we aim to motivate algorithm development and testing. The paper describes the hardware and software configuration, as well as the dataset’s benefits and limitations. We also present some of our detection results as a preliminary benchmark.
ANALYZING TABLES OF STATISTICAL TESTS.

Pollen selection in breeding glasshouse tomatoes for low energy conditions, pp. 125-130. In D. Mulcahy, G. Bergamini Mulcahy, and E. Ottaviano (eds.), Biotechnology and Ecology of Pollen. Springer-Verlag, N.Y. SARI-GORLA, M. C. FROVA, AND R. REDAELLI. 1986. Extent ofgene expression at the gametophytic phase in maize, pp. 27-32. In D. Mulcahy, G. Bergamini Mulcahy, and E. Ottaviano (eds.), Biotechnology and Ecology of Pollen. Springer-Verlag, N.Y. SEARCY, K., AND D. MULCAHY. 1986. Gametophytic expression of heavy metal tolerance, pp. 159-164. In D. Mulcahy, G. Bergamini Mulcahy, and E. Ottaviano (eds.), Biotechnology and Ecology of Pollen. Springer-Verlag, N.Y. SHfYANNA, K. R., AND J. HESLOP-HARRISON. 1981. Membrane state and pollen viability.  SIMON, J., AND J. C. SANFORD. 1986. Induction of gametic selection in situ by stylar application of selective agents, pp. 107-112. In D. Mulcahy, G. Bergamini Mulcahy, and E. Ottaviano (eds.), Biotechnology and Ecology ofPollen. Springer-Verlag, N.Y. SNOW, A. A. 1986. Pollination dynamics in Epilobium canum (Onagraceae): Consequences for gametophytic selection.
Forecasting Exchange Rates Using Feedforward and Recurrent Neural Networks

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.  In this paper we investigate the out-of-sample forecasting ability of feedforward and recurrent neural networks based on empirical foreign exchange rate data. A two-step procedure is proposed to construct suitable networks, in which networks are selected based on the predictive stochastic complexity (PSC) criterion, and the selected networks are estimated using both recursive Newton algorithms and the method of nonlinear least squares. Our results show that PSC is a sensible criterion for selecting networks and for certain exchange rate series, some selected network models have significant market timing ability and/or significantly lower out-of-sample mean squared prediction error relative to the random walk model.
Safer Classification by Synthesis

The discriminative approach to classification using deep neural networks has become the de-facto standard in various fields. Complementing recent reservations about safety against adversarial examples, we show that conventional discriminative methods can easily be fooled to provide incorrect labels with very high confidence to out of distribution examples. We posit that a generative approach is the natural remedy for this problem, and propose a method for classification using generative models. At training time, we learn a generative model for each class, while at test time, given an example to classify, we query each generator for its most similar generation, and select the class corresponding to the most similar one. Our approach is general and can be used with expressive models such as GANs and VAEs. At test time, our method accurately “knows when it does not know,” and provides resilience to out of distribution examples while maintaining competitive performance for standard examples.
Fault Diagnostic System for a Multilevel Inverter Using a Neural Network

In this paper, a fault diagnostic system in a multilevel-inverter using a neural network is developed. It is difficult to diagnose a multilevel-inverter drive (MLID) system using a mathematical model because MLID systems consist of many switching devices and their system complexity has a nonlinear factor. Therefore, a neural network classification is applied to the fault diagnosis of a MLID system. Five multilayer perceptron (MLP) networks are used to identify the type and location of occurring faults from inverter output voltage measurement. The neural network design process is clearly described. The classification performance of the proposed network between normal and abnormal condition is about 90%, and the classification performance among fault features is about 85%. Thus, by utilizing the proposed neural network fault diagnostic system, a better understanding about fault behaviors, diagnostics, and detections of a multilevel inverter drive system can be accomplished. The results of this analysis are identified in percentage tabular form of faults and switch locations
CARDAP: A Scalable Energy-Efficient Context Aware Distributed Mobile Data Analytics Platform for the Fog

Distributed online data analytics has attracted significant research interest in recent years with the advent of Fog and Cloud computing. The popularity of novel distributed applications such as crowdsourcing and crowdsensing have fostered the need for scalable energy-efficient platforms that can enable distributed data analytics. In this paper, we propose CARDAP, a (C)ontext (A)ware (R)eal-time (D)ata (A)nalytics (P)latform. CARDAP is a generic, flexible and extensible, component-based platform that can be deployed in complex distributed mobile analytics applications e.g. sensing activity of citizens in smart cities. CARDAP incorporates a number of energy efficient data delivery strategies using real-time mobile data stream mining for data reduction and thus less data transmission. Extensive experimental evaluations indicate the CARDAP platform can deliver significant benefits in energy efficiency over naive approaches. Lessons learnt and future work
CarcinoPred-EL: Novel models for predicting the carcinogenicity of chemicals using molecular fingerprints and ensemble learning methods

Carcinogenicity refers to a highly toxic end point of certain chemicals, and has become an important issue in the drug development process. In this study, three novel ensemble classification models, namely Ensemble SVM, Ensemble RF, and Ensemble XGBoost, were developed to predict carcinogenicity of chemicals using seven types of molecular fingerprints and three machine learning methods based on a dataset containing 1003 diverse compounds with rat carcinogenicity. Among these three models, Ensemble XGBoost is found to be the best, giving an average accuracy of 70.1 ± 2.9%, sensitivity of 67.0 ± 5.0%, and specificity of 73.1 ± 4.4% in five-fold cross-validation and an accuracy of 70.0%, sensitivity of 65.2%, and specificity of 76.5% in external validation. In comparison with some recent methods, the ensemble models outperform some machine learning-based approaches and yield equal accuracy and higher specificity but lower sensitivity than rule-based expert systems. It is also found that the ensemble models could be further improved if more data were available. As an application, the ensemble models are employed to discover potential carcinogens in the DrugBank database. The results indicate that the proposed models are helpful in predicting the carcinogenicity of chemicals. A web server called CarcinoPred-EL has been built for these models ( http://ccsipb.lnu.edu.cn/toxicity/CarcinoPred-EL/ ).
The Emerging Neuroscience of Social Media

Social media use is a global phenomenon, with almost two billion people worldwide regularly using these websites. As Internet access around the world increases, so will the number of social media users. Neuroscientists can capitalize on the ubiquity of social media use to gain novel insights about social cognitive processes and the neural systems that support them. This review outlines social motives that drive people to use social media, proposes neural systems supporting social media use, and describes approaches neuroscientists can use to conduct research with social media. We close by noting important directions and ethical considerations of future research with social media.
A Survey on Data-Flow Testing

Data-flow testing (DFT) is a family of testing strategies designed to verify the interactions between each program variable’s definition and its uses. Such a test objective of interest is referred to as a def-use pair. DFT selects test data with respect to various test adequacy criteria (i.e., data-flow coverage criteria) to exercise each pair. The original conception of DFT was introduced by Herman in 1976. Since then, a number of studies have been conducted, both theoretically and empirically, to analyze DFT’s complexity and effectiveness. In the past four decades, DFT has been continuously concerned, and various approaches from different aspects are proposed to pursue automatic and efficient data-flow testing. This survey presents a detailed overview of data-flow testing, including challenges and approaches in enforcing and automating it: (1) it introduces the data-flow analysis techniques that are used to identify def-use pairs; (2) it classifies and discusses techniques for data-flow-based test data generation, such as search-based testing, random testing, collateral-coverage-based testing, symbolic-execution-based testing, and model-checking-based testing; (3) it discusses techniques for tracking data-flow coverage; (4) it presents several DFT applications, including software fault localization, web security testing, and specification consistency checking; and (5) it summarizes recent advances and discusses future research directions toward more practical data-flow testing.
Deep Learning for Information Retrieval

Recent years have observed a significant progress in information retrieval and natural language processing with deep learning technologies being successfully applied into almost all of their major tasks. The key to the success of deep learning is its capability of accurately learning distributed representations (vector representations or structured arrangement of them) of natural language expressions such as sentences, and effectively utilizing the representations in the tasks. This tutorial aims at summarizing and introducing the results of recent research on deep learning for information retrieval, in order to stimulate and foster more significant research and development work on the topic in the future.
 The tutorial mainly consists of three parts. In the first part, we introduce the fundamental techniques of deep learning for natural language processing and information retrieval, such as word embedding, recurrent neural networks, and convolutional neural networks. In the second part, we explain how deep learning, particularly representation learning techniques, can be utilized in fundamental NLP and IR problems, including matching, translation, classification, and structured prediction. In the third part, we describe how deep learning can be used in specific application tasks in details. The tasks are search, question answering (from either documents, database, or knowledge base), and image retrieval.
Stability Analysis for Neural Networks With Time-Varying Interval Delay

This letter is concerned with the stability analysis of neural networks (NNs) with time-varying interval delay. The relationship between the time-varying delay and its lower and upper bounds is taken into account when estimating the upper bound of the derivative of Lyapunov functional. As a result, some improved delay/interval-dependent stability criteria for NNs with time-varying interval delay are proposed. Numerical examples are given to demonstrate the effectiveness and the merits of the proposed method.
Digitally Controlled Low-Dropout Regulator with Fast-Transient and Autotuning Algorithms

A digitally controlled low-dropout voltage regulator (LDO) that can perform fast-transient and autotuned voltage is introduced in this paper. Because there are still several arguments regarding the digital implementation on the LDOs, pros and cons of the digital control are first discussed in this paper to illustrate its opportunity in the LDO applications. Following that, the architecture and configuration of the digital scheme are demonstrated. The working principles and design flows of the functional algorithms are also illustrated and then verified by the simulation before the circuit implementation. The proposed LDO was implemented by the 0.18-μm manufacturing process for the performance test. Experimental results show that the LDO's output voltage Vout can accurately perform the dynamic voltage scaling function at various Vout levels (1/2, 5/9, 2/3, and 5/6 of the input voltage VDD) from a wide VDD range (from 1.8 to 0.9 V). The transient time is within 2 μs and the voltage spikes are within 50 mV when a 1-μF output capacitor is used. Test of the autotuning algorithm shows that the proposed LDO is able to work at its optimal performance under various uncertain conditions.
A Forensic Analysis of Images on Online Social Networks

The Web 3.0 is approaching fast and the Online Social Networks (OSNs) are becoming more and more pervasive in today daily activities. A subsequent consequence is that criminals are running at the same speed as technology and most of the time highly sophisticated technological machineries are used by them. Images are often involved in illicit or illegal activities, with it now being fundamental to try to ascertain as much as information on a given image as possible. Today, most of the images coming from the Internet flow through OSNs. The paper analyzes the characteristics of images published on some OSNs. The analysis mainly focuses on how the OSN processes the uploaded images and what changes are made to some of the characteristics, such as JPEG quantization table, pixel resolution and related metadata. The experimental analysis was carried out in June-July 2011 on Facebook, Badoo and Google+. It also has a forensic value: it can be used to establish whether an image has been downloaded from an OSN or not.