nils-herrmann/clean_scirepeval huggingface dataset:
Analysis of Three-Level ZVS PWM Inverter for Induction Heating Applications

This paper presents an operation analysis of a high frequency three-level (TL) PWM inverter applied for an induction heating applications. The feature of TL inverter is to achieve zero-voltage switching (ZVS) at above the resonant frequency. The circuit has been modified from the full-bridge inverter to reach high-voltage with low-harmonic output. The device voltage stresses are controlled in a half of the DC input voltage. The prototype operated between 70 and 78 kHz at the DC voltage rating of 580 V can supply the output power rating up to 3000 W. The iron has been heated and hardened at the temperature up to 800degC. In addition, the experiments have been successfully tested and compared with the simulations
Introducing blockchains for healthcare

Blockchains as a technology emerged to facilitate money exchange transactions and eliminate the need for a trusted third party to notarize and verify such transactions as well as protect data security and privacy. New structures of Blockchains have been designed to accommodate the need for this technology in other fields such as e-health, tourism and energy. This paper is concerned with the use of Blockchains in managing and sharing electronic health and medical records to allow patients, hospitals, clinics, and other medical stakeholder to share data amongst themselves, and increase interoperability. The selection of the Blockchains used architecture depends on the entities participating in the constructed chain network. Although the use of Blockchains may reduce redundancy and provide caregivers with consistent records about their patients, it still comes with few challenges which could infringe patients' privacy, or potentially compromise the whole network of stakeholders. In this paper, we investigate different Blockchains structures, look at existing challenges and provide possible solutions. We focus on challenges that may expose patients' privacy and the resiliency of Blockchains to possible attacks.
Large vocabulary Russian speech recognition using syntactico-statistical language modeling

Speech is the most natural way of human communication and in order to achieve convenient and efficient human–computer interaction implementation of state-of-the-art spoken language technology is necessary. Research in this area has been traditionally focused on several main languages, such as English, French, Spanish, Chinese or Japanese, but some other languages, particularly Eastern European languages, have received much less attention. However, recently, research activities on speech technologies for Czech, Polish, Serbo-Croatian, Russian languages have been steadily increasing. In this paper, we describe our efforts to build an automatic speech recognition (ASR) system for the Russian language with a large vocabulary. Russian is a synthetic and highly inflected language with lots of roots and affixes. This greatly reduces the performance of the ASR systems designed using traditional approaches. In our work, we have taken special attention to the specifics of the Russian language when developing the acoustic, lexical and language models. A special software tool for pronunciation lexicon creation was developed. For the acoustic model, we investigated a combination of knowledge-based and statistical approaches to create several different phoneme sets, the best of which was determined experimentally. For the language model (LM), we introduced a new method that combines syntactical and statistical analysis of the training text data in order to build better n-gram models. Evaluation experiments were performed using two different Russian speech databases and an internally collected text corpus. Among the several phoneme sets we created, the one which achieved the fewest word level recognition errors was the set with 47 phonemes and thus we used it in the following language modeling evaluations. Experiments with 204 thousand words vocabulary ASR were performed to compare the standard statistical n-gram LMs and the language models created using our syntactico-statistical method. The results demonstrated that the proposed language modeling approach is capable of reducing the word recognition errors.
Social Interactions Across Media: Interpersonal Communication on the Internet, Telephone and Face-to-Face

Two studies compared college students’ interpersonal interaction online, face-to-face, and on the telephone. A communication diary assessed the relative amount of social interactions college students conducted online compared to face-to-face conversation and telephone calls. Results indicated that while the internet was integrated into college students’ social lives, face-to-face communication remained the dominant mode of interaction. Participants reported using the internet as often as the telephone. A survey compared reported use of the internet within local and long distance social circles to the use of other media within those circles, and examined participants’ most recent significant social interactions conducted across media in terms of purposes, contexts, and quality. Internet interaction was perceived as high in quality, but slightly lower than other media. Results were compared to previous conceptualizations of the roles of internet in one’s social life. new media & society
Unpacking, repacking, and anchoring: advances in support theory.

Support theory represents probability judgment in terms of the support, or strength of evidence, of the focal relative to the alternative hypothesis. It assumes that the judged probability of an event generally increases when its description is unpacked into disjoint components (implicit subadditivity). This article presents a significant extension of the theory in which the judged probability of an explicit disjunction is less than or equal to the sum of the judged probabilities of its disjoint components (explicit subadditivity). Several studies of probability and frequency judgment demonstrate both implicit and explicit subadditivity. The former is attributed to enhanced availability, whereas the latter is attributed to repacking and anchoring.
Extracting Cylinders in Full 3D Data Using a Random Sampling Method and the Gaussian Image

This paper presents a new method for extracting cylinders from an unorganized set of 3D points. The originality of this approach is to separate the extraction problem into two distinct steps. The first step consists in extracting a constrained plane in the Gaussian image. This yields a subset of 3D points along with a direction. In the second step, cylinders of known direction are extracted in the corresponding subset of points. Robustness is achieved by the use of a random sampling method in both steps. Experimental results showing the extraction of pipes in digitized industrial environments are presented.
A deep learning based approach for traffic data imputation

Traffic data is a fundamental component for applications and researches in transportation systems. However, real traffic data collected from loop detectors or other channels often include missing data which affects the relative applications and researches. This paper proposes an approach based on deep learning to impute the missing traffic data. The proposed approach treats the traffic data including observed data and missing data as a whole data item and restores the complete data with the deep structural network. The deep learning approach can discover the correlations contained in the data structure by a layer-wise pre-training and improve the imputation accuracy by conducting a fine-tuning afterwards. We analyze the imputation patterns that can be realized with the proposed approach and conduct a series of experiments. The results show that the proposed approach can keep a stable error under different traffic data missing rate. Deep learning is promising in the field of traffic data imputation.
Convolutional Clustering for Unsupervised Learning

The task of labeling data for training deep neural networks is daunting and tedious, requiring millions of labels to achieve the current state-of-the-art results. Such reliance on large amounts of labeled data can be relaxed by exploiting hierarchical features via unsupervised learning techniques. In this work, we propose to train a deep convolutional network based on an enhanced version of the k-means clustering algorithm, which reduces the number of correlated parameters in the form of similar filters, and thus increases test categorization accuracy. We call our algorithm convolutional k-means clustering. We further show that learning the connection between the layers of a deep convolutional neural network improves its ability to be trained on a smaller amount of labeled data. Our experiments show that the proposed algorithm outperforms other techniques that learn filters unsupervised. Specifically, we obtained a test accuracy of 74.1% on STL-10 and a test error of 0.5% on MNIST.
RPAN: An End-to-End Recurrent Pose-Attention Network for Action Recognition in Videos

Recent studies demonstrate the effectiveness of Recurrent Neural Networks (RNNs) for action recognition in videos. However, previous works mainly utilize video-level category as supervision to train RNNs, which may prohibit RNNs to learn complex motion structures along time. In this paper, we propose a recurrent pose-attention network (RPAN) to address this challenge, where we introduce a novel pose-attention mechanism to adaptively learn pose-related features at every time-step action prediction of RNNs. More specifically, we make three main contributions in this paper. Firstly, unlike previous works on pose-related action recognition, our RPAN is an end-toend recurrent network which can exploit important spatialtemporal evolutions of human pose to assist action recognition in a unified framework. Secondly, instead of learning individual human-joint features separately, our poseattention mechanism learns robust human-part features by sharing attention parameters partially on the semanticallyrelated human joints. These human-part features are then fed into the human-part pooling layer to construct a highlydiscriminative pose-related representation for temporal action modeling. Thirdly, one important byproduct of our RPAN is pose estimation in videos, which can be used for coarse pose annotation in action videos. We evaluate the proposed RPAN quantitatively and qualitatively on two popular benchmarks, i.e., Sub-JHMDB and PennAction. Experimental results show that RPAN outperforms the recent state-of-the-art methods on these challenging datasets.
The Adaptive Hough Transform

We introduce the Adaptive Hough Transform, AHT, as an efficient way of implementing the Hough Transform, HT, method for the detection of 2-D shapes. The AHT uses a small accumulator array and the idea of a flexible iterative "coarse to fine" accumulation and search strategy to identify significant peaks in the Hough parameter spaces. The method is substantially superior to the standard HT implementation in both storage and computational requirements. In this correspondence we illustrate the ideas of the AHT by tackling the problem of identifying linear and circular segments in images by searching for clusters of evidence in 2-D parameter spaces. We show that the method is robust to the addition of extraneous noise and can be used to analyze complex images containing more than one shape.
A framework for classifying and comparing software architecture evaluation methods

Software architecture evaluation has been proposed as a means to achieve quality attributes such as maintainability and reliability in a system. The objective of the evaluation is to assess whether or not the architecture lead to the desired quality attributes. Recently, there have been a number of evaluation methods proposed. There is, however, little consensus on the technical and nontechnical issues that a method should comprehensively address and which of the existing methods is most suitable for a particular issue. We present a set of commonly known but informally described features of an evaluation method and organizes them within a framework that should offer guidance on the choice of the most appropriate method for an evaluation exercise. We use this framework to characterise eight SA evaluation methods.
Analyzing Stock Market Movements Using Twitter Sentiment Analysis

In this paper we investigate the complex relationship between tweet board literature (like bullishness, volume, agreement etc) with the financial market instruments (like volatility, trading volume and stock prices). We have analyzed sentiments for more than 4 million tweets between June 2010 to July 2011 for DJIA, NASDAQ-100 and 13 other big cap technological stocks. Our results show high correlation (upto 0.88 for returns) between stock prices and twitter sentiments. Further, using Granger’s Causality Analysis, we have validated that the movement of stock prices and indices are greatly affected in the short term by Twitter discussions. Finally, we have implemented Expert Model Mining System (EMMS) to demonstrate that our forecasted returns give a high value of Rsquare (0.952) with low Maximum Absolute Percentage Error (MaxAPE) of 1.76% for Dow Jones Industrial Average (DJIA). Stock market ; sentiment analysis ; Twitter ; microblogging ; social network analysis
The vulnerability of UAVs to cyber attacks - An approach to the risk assessment

By 2012 the U.S. military had increased its investment in research and production of unmanned aerial vehicles (UAVs) from $2.3 billion in 2008 to $4.2 billion . Currently UAVs are used for a wide range of missions such as border surveillance, reconnaissance, transportation and armed attacks. UAVs are presumed to provide their services at any time, be reliable, automated and autonomous. Based on these presumptions, governmental and military leaders expect UAVs to improve national security through surveillance or combat missions. To fulfill their missions, UAVs need to collect and process data. Therefore, UAVs may store a wide range of information from troop movements to environmental data and strategic operations. The amount and kind of information enclosed make UAVs an extremely interesting target for espionage and endangers UAVs of theft, manipulation and attacks. Events such as the loss of an RQ-170 Sentinel to Iranian military forces on 4th December 2011  or the “keylogging” virus that infected an U.S. UAV fleet at Creech Air Force Base in Nevada in September 2011  show that the efforts of the past to identify risks and harden UAVs are insufficient. Due to the increasing governmental and military reliance on UAVs to protect national security, the necessity of a methodical and reliable analysis of the technical vulnerabilities becomes apparent. We investigated recent attacks and developed a scheme for the risk assessment of UAVs based on the provided services and communication infrastructures. We provide a first approach to an UAV specific risk assessment and take into account the factors exposure, communication systems, storage media, sensor systems and fault handling mechanisms. We used this approach to assess the risk of some currently used UAVs: The “MQ-9 Reaper” and the “AR Drone”. A risk analysis of the “RQ-170 Sentinel” is discussed.
Secret and public key image watermarking schemes for image authentication and ownership verification

We describe a watermarking scheme for ownership verification and authentication. Depending on the desire of the user, the watermark can be either visible or invisible. The scheme can detect any modification made to the image and indicate the specific locations that have been modified. If the correct key is specified in the watermark extraction procedure, then an output image is returned showing a proper watermark, indicating the image is authentic and has not been changed since the insertion of the watermark. Any modification would be reflected in a corresponding error in the watermark. If the key is incorrect, or if the image was not watermarked, or if the watermarked image is cropped, the watermark extraction algorithm will return an image that resembles random noise. Since it requires a user key during both the insertion and the extraction procedures, it is not possible for an unauthorized user to insert a new watermark or alter the existing watermark so that the resulting image will pass the test. We present secret key and public key versions of the technique.
Designing Business Models for the Internet of Things

According to Gershenfeld and Vasseur (2014) the impressive growth of the Internet in the past two decades is about to be overshadowed as the "things" that surround us start going online. The "Internet of Things" (IOT), a term coined by Kevin Ashton of Procter & Gamble in 1998, has become a new paradigm that views all objects around us connected to the network, providing anyone with “anytime, anywhere” access to information (ITU, 2005; Gomez et al., 2013). The IOT describes the interconnection of objects or “things” for various purposes including identification, communication, sensing, and data collection (Oriwoh et al., 2013). “Things” range from mobile devices to general household objects embedded with capabilities for sensing or communication through the use of technologies such as radio frequency identification (RFID) (Oriwoh et al., 2013; Gomez et al., 2013). The IOT represents the future of computing and communications, and its developThis article investigates challenges pertaining to business model design in the emerging context of the Internet of Things (IOT). The evolution of business perspectives to the IOT is driven by two underlying trends: i) the change of focus from viewing the IOT primarily as a technology platform to viewing it as a business ecosystem; and ii) the shift from focusing on the business model of a firm to designing ecosystem business models. An ecosystem business model is a business model composed of value pillars anchored in ecosystems and focuses on both the firm's method of creating and capturing value as well as any part of the ecosystem's method of creating and capturing value. The article highlights three major challenges of designing ecosystem business models for the IOT, including the diversity of objects, the immaturity of innovation, and the unstructured ecosystems. Diversity refers to the difficulty of designing business models for the IOT due to a multitude of different types of connected objects combined with only modest standardization of interfaces. Immaturity suggests that quintessential IOT technologies and innovations are not yet products and services but a "mess that runs deep". The unstructured ecosystems mean that it is too early to tell who the participants will be and which roles they will have in the evolving ecosystems. The study argues that managers can overcome these challenges by using a business model design tool that takes into account the ecosystemic nature of the IOT. The study concludes by proposing the grounds for a new design tool for ecosystem business models and suggesting that "value design" might be a more appropriate term when talking about business models in ecosystems. New web-based business models being hatched for the Internet of Things are bringing together market players who previously had no business dealings with each other. Through partnerships and acquisitions, [...] they have to sort out how they will coordinate their business development efforts with customers and interfaces with other stakeholders.
EvenTweet: Online Localized Event Detection from Twitter

Microblogging services such as Twitter, Facebook, and Foursquare have become major sources for information about real-world events. Most approaches that aim at extracting event information from such sources typically use the temporal context of messages. However, exploiting the location information of georeferenced messages, too, is important to detect localized events, such as public events or emergency situations. Users posting messages that are close to the location of an event serve as human sensors to describe an event. In this demonstration, we present a novel framework to detect localized events in real-time from a Twitter stream and to track the evolution of such events over time. For this, spatio-temporal characteristics of keywords are continuously extracted to identify meaningful candidates for event descriptions. Then, localized event information is extracted by clustering keywords according to their spatial similarity. To determine the most important events in a (recent) time frame, we introduce a scoring scheme for events. We demonstrate the functionality of our system, called EvenTweet, using a stream of tweets from Europe during the 2012 UEFA European Football Championship.
The History of Information Retrieval Research

This paper describes a brief history of the research and development of information retrieval systems starting with the creation of electromechanical searching devices, through to the early adoption of computers to search for items that are relevant to a user's query. The advances achieved by information retrieval researchers from the 1950s through to the present day are detailed next, focusing on the process of locating relevant information. The paper closes with speculation on where the future of information retrieval lies.
Using Object Affordances to Improve Object Recognition

The problem of object recognition has not yet been solved in its general form. The most successful approach to it so far relies on object models obtained by training a statistical method on visual features obtained from camera images. The images must necessarily come from huge visual datasets, in order to circumvent all problems related to changing illumination, point of view, etc. We hereby propose to also consider, in an object model, a simple model of how a human being would grasp that object (its affordance). This knowledge is represented as a function mapping visual features of an object to the kinematic features of a hand while grasping it. The function is practically enforced via regression on a human grasping database. After describing the database (which is publicly available) and the proposed method, we experimentally evaluate it, showing that a standard object classifier working on both sets of features (visual and motor) has a significantly better recognition rate than that of a visual-only classifier.
Fully digital, vector-controlled PWM VSI-fed AC drives with an inverter dead-time compensation strategy

A dead-time compensation method in voltage-source inverters (VSIs) is proposed. The method is based on a feedforward approach which produces compensating signals obtained from those of the I/sub d/-I/sub q/ current and primary angular frequency references in a rotating reference (d-q) frame. The method features excellent inverter output voltage distortion correction for both fundamental and harmonic components. The correction is not affected by the magnitude of the inverter output voltage or current distortions. Since this dead-time compensation method allows current loop calculations in the d-q frame at a slower sampling rate with a conventional microprocessor than calculations in a stationary reference frame, a fully digital, vector-controlled speed regulator with just a current component loop is realized for PWM (pulsewidth modulation) VSIs. Test results obtained for the compression method are described.<<ETX>>