nils-herrmann/clean_scirepeval huggingface dataset:
Design and development of MATLAB Simulink based Selective Harmonic Elimination technique for three phase voltage source inverter

This proposal investigates Selective Harmonic Elimination (SHE) to eliminate harmonics brought by Pulse Width Modulation (PWM) inverter. The selective harmonic elimination method for three phase voltage source inverter is generally based on ideas of opposite harmonic injection. In this proposed scheme, the lower order harmonics 3rd, 5th, 7th and 9th are eliminated. The dominant harmonics of same order generated in opposite phase by sine PWM inverter and by using this scheme the Total Harmonic Distortion (THD) is reduced. The analysis of Sinusoidal PWM technique (SPWM) and selective harmonic elimination is simulated using MATLAB/SIMULINK model.
COMMON FIXED POINT THEOREMS SATISFYING INTEGRAL TYPE RATIONAL CONTRACTIVE CONDITIONS AND APPLICATIONS

Inspired by the concept of integral type (Branciari type) contractive condition and respective fixed point results, we derive some new common fixed point theorems for quadruple self mappings satisfying a generalized W -contractive condition or a generalized -contractive condition of integral type, also using the notions of compatibility and subsequential continuity (alternately subcompatibility and reciprocal continuity) in the frame of metric spaces. We furnish some examples to illustrate the useability of our main results. Several results existing in the literature are obtained as consequences of the main results. At the end of this article the existence and uniqueness of solutions for certain systems of integral equations and functional equations arising in dynamic programming are demonstrated with the help of the obtained results.
Self-Supervised Intrinsic Image Decomposition

Intrinsic decomposition from a single image is a highly challenging task, due to its inherent ambiguity and the scarcity of training data. In contrast to traditional fully supervised learning approaches, in this paper we propose learning intrinsic image decomposition by explaining the input image. Our model, the Rendered Intrinsics Network (RIN), joins together an image decomposition pipeline, which predicts reflectance, shape, and lighting conditions given a single image, with a recombination function, a learned shading model used to recompose the original input based off of intrinsic image predictions. Our network can then use unsupervised reconstruction error as an additional signal to improve its intermediate representations. This allows large-scale unlabeled data to be useful during training, and also enables transferring learned knowledge to images of unseen object categories, lighting conditions, and shapes. Extensive experiments demonstrate that our method performs well on both intrinsic image decomposition and knowledge transfer.
On Tree-Based Neural Sentence Modeling

Neural networks with tree-based sentence encoders have shown better results on many downstream tasks. Most of existing tree-based encoders adopt syntactic parsing trees as the explicit structure prior. To study the effectiveness of different tree structures, we replace the parsing trees with trivial trees (i.e., binary balanced tree, left-branching tree and right-branching tree) in the encoders. Though trivial trees contain no syntactic information, those encoders get competitive or even better results on all of the ten downstream tasks we investigated. This surprising result indicates that explicit syntax guidance may not be the main contributor to the superior performances of tree-based neural sentence modeling. Further analysis show that tree modeling gives better results when crucial words are closer to the final representation. Additional experiments give more clues on how to design an effective tree-based encoder. Our code is opensource and available at https://github. com/ExplorerFreda/TreeEnc.
New Bridgeless DCM Sepic and Cuk PFC Rectifiers With Low Conduction and Switching Losses

New bridgeless single-phase ac-dc power factor correction (PFC) rectifiers based on Sepic and Cuk topologies are proposed. The absence of an input diode bridge and the presence of only two semiconductor switches in the current flowing path during each switching cycle result in less conduction losses and improved thermal management compared to the conventional Sepic and Cuk PFC converters. The proposed topologies are designed to work in discontinuous conduction mode (DCM) to achieve almost unity power factor in a simple and effective manner. The DCM operation gives additional advantages such as zero-current turn-on in the power switches, zero-current turn-off in the output diode and reduces the complexity of the control circuitry. The proposed rectifiers are theoretically investigated. Performance comparisons between the proposed and conventional Sepic PFC rectifiers are performed. Simulation and experimental results are provided for a design example of a 65-W/48-V at 100- Vrms line voltage to evaluate the performance of the proposed PFC rectifier.
The use of the area under the ROC curve in the evaluation of machine learning algorithms

-In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six "real world" medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invafiant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for "single number" evaluation of machine learning algorithms.  The ROC curve Cross-validation The area under the ROC curve (AUC) Wilcoxon statistic Standard error Accuracy measures
Cyberbullying among Turkish Adolescents

Cyberbullying, harassment through the use of information and communication technology such as cell phones and the Internet, is an emerging phenomenon all around the world. Extensive research on aggression and bullying is guiding educators' understanding of cyberbullying. Yet the gap between the advancement in technology and the dearth of study on cyberbullying suggests that more research is needed to understand the scope of this form of bullying. In order to fill the gap in literature, 269 secondary school Turkish students were surveyed on their engagement in and coping strategies for cyberbullying. The results show that 35.7% of the students displayed bully behaviors, and 23.8% of the students displayed bully-victim behaviors. Only 5.9% of the students were victims. More boys displayed bully, victim, and bully-victim behaviors than girls. When faced with cyberbullying, 25% of the students reported telling their peers and parents about the cyberbullying incident, and 30.6% of the students reported finding active solutions such as blocking the harasser. The implication of the study for future research is discussed.
Relevance Theory and the Saying/Implicating Distinction

A distinction between saying and implicating has held a central place in pragmatic s since Grice, with ‘what is said’ usually equated with the (context-relative) semantic content of an utterance. In relevance theory, a distinction is made between two kinds of communicated assumptions, explicatures and implicatures, with explicatures defined as pragmatic developments of encoded linguistic meaning. It is argued here that, given a context-free semantics for linguistic expression types, together with the explicature/implicature distinction, there is no role for any minimally propositional notion of ‘what is said’.
LSTM vs. BM25 for Open-domain QA: A Hands-on Comparison of Effectiveness and Efficiency

Recent advances in neural networks, along with the growth of rich and diverse community question answering (cQA) data, have enabled researchers to construct robust open-domain question answering (QA) systems. It is often claimed that such state-of-the-art QA systems far outperform traditional IR baselines such as BM25. However, most such studies rely on relatively small data sets, e.g., those extracted from the old TREC QA tracks. Given massive training data plus a separate corpus of Q&A pairs as the target knowledge source, how well would such a system really perform? How fast would it respond? In this demonstration, we provide the attendees of SIGIR 2017 an opportunity to experience a live comparison of two open-domain QA systems, one based on a long short-term memory (LSTM) architecture with over 11 million Yahoo! Chiebukuro (i.e., Japanese Yahoo! Answers) questions and over 27.4 million answers for training, and the other based on BM25. Both systems use the same Q&A knowledge source for answer retrieval. Our core demonstration system is a pair of Japanese monolingual QA systems, but we leverage machine translation for letting the SIGIR attendees enter English questions and compare the Japanese responses from the two systems after translating them into English.
An intelligent emergency response system: preliminary development and testing of automated fall detection.

We have designed an intelligent emergency response system to detect falls in the home. It uses image-based sensors. A pilot study was conducted using 21 subjects to evaluate the efficacy and performance of the fall-detection component of the system. Trials were conducted in a mock-up bedroom setting, with a bed, a chair and other typical bedroom furnishings. A small digital videocamera was installed in the ceiling at a height of approximately 2.6 m. The digital camera covered an area of approximately 5.0 m x 3.8 m. The subjects were asked to assume a series of postures, namely walking/standing, sitting/lying down in an inactive zone, stooping, lying down in a 'stretched' position, and lying down in a 'tucked' position. These five scenarios were repeated three times by each subject in a random order. These test positions totalled 315 tasks with 126 fall-simulated tasks and 189 non-fall-simulated tasks. The system detected a fall on 77% of occasions and missed a fall on 23%. False alarms occurred on only 5% of occasions. The results encourage the potential use of a vision-based system to provide safety and security in the homes of the elderly.
Analysis of Privacy and Security Affecting the Intention of Use in Personal Data Collection in an IoT Environment

In the current IoT (Internet of Things) environment, more and more Things: devices, objects, sensors, and everyday items not usually considered computers, are connected to the Internet, and these Things affect and change our social life and economic activities. By using IoTs, service providers can collect and store personal information in the real world, and such providers can gain access to detailed behaviors of the user. Although service providers offer users new services and numerous benefits using their detailed information, most users have concerns about the privacy and security of their personal data. Thus, service providers need to take countermeasures to eliminate those concerns. To help eliminate those concerns, first we conduct a survey regarding users’ privacy and security concerns about IoT services, and then we analyze data collected from the survey using structural equation modeling (SEM). Analysis of the results provide answers to issues of privacy and security concerns to service providers and their users. And we also analyze the effectiveness and effects of personal information management and protection functions in IoT services. key words: IoT (Internet of Things), privacy, security, SEM (Structural Equation Modeling)
Probabilistic Frame-Semantic Parsing

This paper contributes a formalization of frame-semantic parsing as a structure prediction problem and describes an implemented parser that transforms an English sentence into a frame-semantic representation. It finds words that evoke FrameNet frames, selects frames for them, and locates the arguments for each frame. The system uses two featurebased, discriminative probabilistic (log-linear) models, one with latent variables to permit disambiguation of new predicate words. The parser is demonstrated to significantly outperform previously published results.
Topic Sentiment Joint Model with Word Embeddings

Topic sentiment joint model is an extended model which aims to deal with the problem of detecting sentiments and topics simultaneously from online reviews. Most of existing topic sentiment joint modeling algorithms infer resulting distributions from the co-occurrence of words. But when the training corpus is short and small, the resulting distributions might be not very satisfying. In this paper, we propose a novel topic sentiment joint model with word embeddings (TSWE), which introduces word embeddings trained on external large corpus. Furthermore, we implement TSWE with Gibbs sampling algorithms. The experiment results on Chinese and English data sets show that TSWE achieves significant performance in the task of detecting sentiments and topics simultaneously.
Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks

Learning a similarity function between pairs of objects is at the core of learning to rank approaches. In information retrieval tasks we typically deal with query-document pairs, in question answering -- question-answer pairs. However, before learning can take place, such pairs needs to be mapped from the original space of symbolic words into some feature space encoding various aspects of their relatedness, e.g. lexical, syntactic and semantic. Feature engineering is often a laborious task and may require external knowledge sources that are not always available or difficult to obtain. Recently, deep learning approaches have gained a lot of attention from the research community and industry for their ability to automatically learn optimal feature representation for a given task, while claiming state-of-the-art performance in many tasks in computer vision, speech recognition and natural language processing. In this paper, we present a convolutional neural network architecture for reranking pairs of short texts, where we learn the optimal representation of text pairs and a similarity function to relate them in a supervised way from the available training data. Our network takes only words in the input, thus requiring minimal preprocessing. In particular, we consider the task of reranking short text pairs where elements of the pair are sentences. We test our deep learning system on two popular retrieval tasks from TREC: Question Answering and Microblog Retrieval. Our model demonstrates strong performance on the first task beating previous state-of-the-art systems by about 3\% absolute points in both MAP and MRR and shows comparable results on tweet reranking, while enjoying the benefits of no manual feature engineering and no additional syntactic parsers.
Machine learning for healthcare technologies - an introduction

Much has been written concerning the manner in which healthcare is changing, with a particular emphasis on how very large quantities of data are now being routinely collected during the routine care of patients. The use of machine learning methods to turn these ever-growing quantities of data into interventions that can improve patient outcomes seems as if it should be an obvious path to take. However, the field of machine learning in healthcare is still in its infancy. This book, kindly supported by the Institution of Engineering and Technology, aims to provide a “snapshot” of the state of current research at the interface between machine learning and healthcare. Necessarily, this is a partial and biased sampling of the state of current research, and yet we have aimed to provide a wide-ranging introduction to the depth and scale of work that is being undertaken worldwide. In selecting material for this edited volume, we have placed special emphasis on machine learning projects that are (or are close to) achieving improvement in patient outcomes. For many reasons, uncovered variously in some of the chapters that follow, it is a truism that “healthcare is hard”; there are unique constraints that exist, and considerations that must be taken, when working with healthcare data. However, for all its difficulties, working with healthcare data is exceptionally rewarding, both in terms of the computational challenges that exist and in terms of the outputs of research being able to affect the way in which healthcare is delivered. There are few application areas of machine learning that have such promise to benefit society as does that of healthcare.
Transformation-Based Models of Video Sequences

In this work we propose a simple unsupervised approach for next frame prediction in video. Instead of directly predicting the pixels in a frame given past frames, we predict the transformations needed for generating the next frame in a sequence, given the transformations of the past frames. This leads to sharper results, while using a smaller prediction model. In order to enable a fair comparison between different video frame prediction models, we also propose a new evaluation protocol. We use generated frames as input to a classifier trained with ground truth sequences. This criterion guarantees that models scoring high are those producing sequences which preserve discriminative features, as opposed to merely penalizing any deviation, plausible or not, from the ground truth. Our proposed approach compares favourably against more sophisticated ones on the UCF-101 data set, while also being more efficient in terms of the number of parameters and computational cost.
Combining Monocular SLAM with Shape Priors for Reconstruction of Dynamic Vehicles

Reasoning about objects in images and videos using 3D representations is re-emerging as a popular paradigm in computer vision. Specifically, in the context of scene understanding for roads, 3D vehicle detection and tracking from monocular videos still needs a lot of attention to enable practical applications. Current approaches leverage two kinds of information to deal with the vehicle detection and tracking problem: (1) 3D representations (eg. wireframe models or voxel based or CAD models) for diverse vehicle skeletal structures learnt from data, and (2) classifiers trained to detect vehicles or vehicle parts in single images built on top of a basic feature extraction step. In this paper, we propose to extend current approaches in two ways. First, we extend detection to a multiple view setting. We show that leveraging information given by feature or part detectors in multiple images can lead to more accurate detection results than single image detection. Secondly, we show that given multiple images of a vehicle, we can also leverage 3D information from the scene generated using a unique structure from motion algorithm. This helps us localize the vehicle in 3D, and constrain the parameters of optimization for fitting the 3D model to image data. We show results on the KITTI dataset, and demonstrate superior results compared with recent state-of-the-art methods, with upto 14.64 % improvement in localization error.
Fabrication of Tissue Engineering Scaffolds Using Rapid Prototyping Techniques

Rapid prototyping (RP) techniques are a group of advanced manufacturing processes that can produce custom made objects directly from computer data such as Computer Aided Design (CAD), Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) data. Using RP fabrication techniques, constructs with controllable and complex internal architecture with appropriate mechanical properties can be achieved. One of the attractive and promising utilization of RP techniques is related to tissue engineering (TE) scaffold fabrication. Tissue engineering scaffold is a 3D construction that acts as a template for tissue regeneration. Although several conventional techniques such as solvent casting and gas forming are utilized in scaffold fabrication; these processes show poor interconnectivity and uncontrollable porosity of the produced scaffolds. So, RP techniques become the best alternative fabri methods of TE scaffolds. This paper reviews the current state of the art in the area of tissue engineering scaffolds fabrication using advanced RP processes, as well as the current limitations and future trends in scaffold fabrication RP techniques. Biomanufacturing, Rapid prototyping, Solid Free Form Fabrication, Scaffold Fabrication, Tissue Engineering
Data analytics in smart distribution networks: Applications and challenges

The large volumes of data that will be produced by ubiquitous sensors and meters in future smart distribution networks represent an opportunity for the use of data analytics to extract valuable knowledge and, thus, improve Distribution Network Operator (DNO) planning and operation tasks. Indeed, applications ranging from outage management to detection of non-technical losses to asset management can potentially benefit from data analytics. However, despite all the benefits, each application presents DNOs with diverse data requirements and the need to define an adequate approach. Consequently, it is critical to understand the different interactions among applications, monitoring infrastructure and approaches involved in the use of data analytics in distribution networks. To assist DNOs in the decision making process, this work presents some of the potential applications where data analytics are likely to improve distribution network performance and the corresponding challenges involved in its implementation.
Big Data-Driven Marketing: How Machine Learning Outperforms Marketers' Gut-Feeling

This paper shows how big data can be experimentally used at large scale for marketing purposes at a mobile network operator. We present results from a large-scale experiment in a MNO in Asia where we use machine learning to segment customers for text-based marketing. This leads to conversion rates far superior to the current best marketing practices within MNOs. Using metadata and social network analysis, we created new metrics to identify customers that are the most likely to convert into mobile internet users. These metrics falls into three categories: discretionary income, timing, and social learning. Using historical data, a machine learning prediction model is then trained, validated, and used to select a treatment group. Experimental results with 250 000 customers show a 13 times better conversion-rate compared to the control group. The control group is selected using the current best practice marketing. The model also shows very good properties in the longer term, as 98% of the converted customers in the treatment group renew their mobile internet packages after the campaign, compared to 37% in the control group. These results show that data-driven marketing can significantly improve conversion rates over current best-practice marketing strategies.
A Neural Architecture for Generating Natural Language Descriptions from Source Code Changes

We propose a model to automatically describe changes introduced in the source code of a program using natural language. Our method receives as input a set of code commits, which contains both the modifications and message introduced by an user. These two modalities are used to train an encoder-decoder architecture. We evaluated our approach on twelve real world open source projects from four different programming languages. Quantitative and qualitative results showed that the proposed approach can generate feasible and semantically sound descriptions not only in standard in-project settings, but also in a cross-project setting.