nils-herrmann/clean_scirepeval huggingface dataset:
Building TensorFlow Applications in Smart City Scenarios

Nowadays, visual object recognition is one of the key applications for computer vision and deep learning techniques. With the recent development in mobile computing technology, many deep learning framework software support Personal Digital Assistant systems, i.e., smart phones or tablets, allowing developers to conceive innovative applications. In this work, we intend to employ such ICT strategies with the aim of supporting the tourism in an art city: for these reasons, we propose to provide tourists with a mobile application in order to better explore artistic heritage within an urban environment by using just their smartphone's camera. The software solution is based on Google TensorFlow, an innovative deep learning framework mainly designed for pattern recognition tasks. The paper presents our design choices and an early performance evaluation.
Integration of Content and Context Modalities for Multimedia Big Data Retrieval

of a dissertation at the University of Miami. Dissertation supervised by Professor Mei-Ling Shyu. No. of pages in text. (160) With the proliferation of digital photo-capture devices and the development of web technologies, the era of big data has arrived, which poses challenges to process and retrieve vast amounts of data with heterogeneous and diverse dimensionality. In the field of multimedia information retrieval, traditional keyword-based approaches perform well on text data, but it can hardly adapt to image and video due to the fact that a large proportion of this data nowadays is unorganized. This means the textual descriptions of images or videos, also known as metadata, could be unavailable, incomplete or even incorrect. Therefore, Content-Based Multimedia Information Retrieval (CBMIR) has emerged, which retrieves relevant images or videos by analyzing their visual content. Various data mining techniques such as feature selection, classification, clustering and filtering, have been utilized in CBMIR to solve issues involving data imbalance, data quality and size, limited ground truth, user subjectivity, etc. However, as an intrinsic problem of CBMIR, the semantic gap between low-level visual features and high-level semantics is still difficult to conquer. Now, with the rapid popularization of social media repositories, which allows users to upload images and videos, and assign tags to describe them, it has brought new directions as well as new challenges to the area of multimedia information retrieval. As suggested by the name, multimedia is a combination of different content forms that include text, audio, images, videos, etc. A series of research studies have been conducted to take advantage of one modality to compensate the other for
Graph-to-Sequence Learning using Gated Graph Neural Networks

Many NLP applications can be framed as a graph-to-sequence learning problem. Previous work proposing neural architectures on this setting obtained promising results compared to grammar-based approaches but still rely on linearisation heuristics and/or standard recurrent networks to achieve the best performance. In this work, we propose a new model that encodes the full structural information contained in the graph. Our architecture couples the recently proposed Gated Graph Neural Networks with an input transformation that allows nodes and edges to have their own hidden representations, while tackling the parameter explosion problem present in previous work. Experimental results show that our model outperforms strong baselines in generation from AMR graphs and syntax-based neural machine translation.
Modeling Satire in English Text for Automatic Detection

According to the Merriam-Webster dictionary, satire is a trenchant wit, irony, or sarcasm used to expose and discredit vice or folly. Though it is an important language aspect used in everyday communication, the study of satire detection in natural text is often ignored. In this paper, we identify key value components and features for automatic satire detection. Our experiments have been carried out on three datasets, namely, tweets, product reviews and newswire articles. We examine the impact of a number of state-of-the-art features as well as new generalized textual features. By using these features, we outperform the state of the art by a significant 6% margin.
Customer Relationship Management for SMEs

Customer Relationship Management (CRM) is getting more and more a key strategy for companies big and small. Customer care strategy and CRM software go hand in hand. In particular SME’s need a CRM software that easily adapts to their customer care needs while still being low cost. In this paper I discuss the benefits of CRM for SME’s and their special requirements wrt. CRM software. Further, I introduce the IST-project CARUSO whose objective is to provide SME’s with a software framework to implement low cost, customized CRM applications. This paper presents the rational behind the CARUSO project, the architecture of the framework, and the software development process used to build the framework.
A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patient's health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.
GNA: Reconfigurable and Efficient Architecture for Generative Network Acceleration

Generative networks have become ubiquitous in image generation applications like image super-resolution, image to image translation, and text to image synthesis. They are usually composed of convolutional (CONV) layers, convolution-based residual blocks, and deconvolutional (DeCONV) layers. Previous works on neural network acceleration focus too much on optimizing CONV layers computation such as data-reuse or parallel computation, but have low processing element (PE) utilization in computing residual blocks and DeCONV layers: residual blocks require very high memory bandwidth when performing elementwise additions on residual paths; DeCONV layers have imbalanced operation counts for different outputs. In this paper, we propose a dual convolution mapping method for CONV and DeCONV layers to make full use of the available PE resources. A cross-layer scheduling method is also proposed to avoid extra off-chip memory access in residual block processing. Precision-adaptive PEs and buffer bandwidth reconfiguration are used to support flexible bitwidths for both inputs and weights in deep neural networks. We implement a generative network accelerator (GNA) based on intra-PE processing, inter-PE processing, and cross-layer scheduling techniques. Owing to the proposed optimization techniques, GNA achieves energy efficiency of 2.05 TOPS/W with 61% higher PE utilization than traditional methods in generative network acceleration.
A Look at Motion Planning for Autonomous Vehicles at an Intersection

Autonomous Vehicles are currently being tested in a variety of scenarios. As we move towards Autonomous Vehicles, how should intersections look? To answer that question, we break down an intersection management into the different conundrums and scenarios involved in the trajectory planning and current approaches to solve them. Then, a brief analysis of current works in autonomous intersection is conducted. With a critical eye, we try to delve into the discrepancies of existing solutions while presenting some critical and important factors that have been addressed. Furthermore, open issues that have to be addressed are also emphasized. We also try to answer the question of how to benchmark intersection management algorithms by providing some factors that impact autonomous navigation at intersection.
Optimizing Sentence Modeling and Selection for Document Summarization

Extractive document summarization aims to conclude given documents by extracting some salient sentences. Often, it faces two challenges: 1) how to model the information redundancy among candidate sentences; 2) how to select the most appropriate sentences. This paper attempts to build a strong summarizer DivSelect+CNNLM by presenting new algorithms to optimize each of them. Concretely, it proposes CNNLM, a novel neural network language model (NNLM) based on convolutional neural network (CNN), to project sentences into dense distributed representations, then models sentence redundancy by cosine similarity. Afterwards, it formulates the selection process as an optimization problem, constructing a diversified selection process (DivSelect) with the aim of selecting some sentences which have high prestige, meantime, are dis-similar with each other. Experimental results on DUC2002 and DUC2004 benchmark data sets demonstrate the effectiveness of our approach.
Principles of model checking

The solution to get this book is that we don't over you the free book. But, we offer you the free information about principles of model checking. Why should be this book to read and where is the place to get it, even the soft file forms are common questions to utter. In this website, we don't only provide this book. We have still lots of books to read. Yeah, we are on-line library that is always full of recommended books.
ASSESSING BENEFITS OF BUSINESS INTELLIGENCE SYSTEMS – A CASE STUDY

Several arguments can be found in business intelligence literature that the use of business intelligence systems can bring multiple benefits, for example, via faster and easier access to information, savings in information technology (‘IT’) and greater customer satisfaction all the way through to the improved competitiveness of enterprises. Yet, most of these benefits are often very difficult to measure because of their indirect and delayed effects on business success. On top of the difficulties in justifying investments in information technology (‘IT’), particularly business intelligence (‘BI’), business executives generally want to know whether the investment is worth the money and if it can be economically justified. In looking for an answer to this question, various methods of evaluating investments can be employed. We can use the classic return on investment (‘ROI’) calculation, cost-benefit analysis, the net present value (‘NPV’) method, the internal rate of return (‘IRR’) and others. However, it often appears in business practice that the use of these methods alone is inappropriate, insufficient or unfeasible for evaluating an investment in business intelligence systems. Therefore, for this purpose, more appropriate methods are those based mainly on a qualitative approach, such as case studies, empirical analyses, user satisfaction analyses, and others that can be employed independently or can help us complete the whole picture in conjunction with the previously mentioned methods. Since there is no universal approach to the evaluation of an investment in information technology and business intelligence, it is necessary to approach each case in a different way based on the specific circumstances and purpose of the evaluation. This paper presents a case study in which the evaluation of an investment in on-line analytical processing (‘OLAP’) technology in the company Melamin was made through an
Two factor authentication using mobile phones

This paper describes a method of implementing two factor authentication using mobile phones. The proposed method guarantees that authenticating to services, such as online banking or ATM machines, is done in a very secure manner. The proposed system involves using a mobile phone as a software token for One Time Password generation. The generated One Time Password is valid for only a short user-defined period of time and is generated by factors that are unique to both, the user and the mobile device itself. Additionally, an SMS-based mechanism is implemented as both a backup mechanism for retrieving the password and as a possible mean of synchronization. The proposed method has been implemented and tested. Initial results show the success of the proposed method.
Blockchain-Based Database to Ensure Data Integrity in Cloud Computing Environments

Data is nowadays an invaluable resource, indeed it guides all business decisions in most of the computer-aided human activities. Threats to data integrity are thus of paramount relevance, as tampering with data may maliciously affect crucial business decisions. This issue is especially true in cloud computing environments, where data owners cannot control fundamental data aspects, like the physical storage of data and the control of its accesses. Blockchain has recently emerged as a fascinating technology which, among others, provides compelling properties about data integrity. Using the blockchain to face data integrity threats seems to be a natural choice, but its current limitations of low throughput, high latency, and weak stability hinder the practical feasibility of any blockchain-based solutions. In this paper, by focusing on a case study from the European SUNFISH project, which concerns the design of a secure by-design cloud federation platform for the public sector, we precisely delineate the actual data integrity needs of cloud computing environments and the research questions to be tackled to adopt blockchain-based databases. First, we detail the open research questions and the difficulties inherent in addressing them. Then, we outline a preliminary design of an effective blockchain-based database for cloud computing environments.
A Dataset of Hindi-English Code-Mixed Social Media Text for Hate Speech Detection

Hate speech detection in social media texts is an important Natural language Processing task, which has several crucial applications like sentiment analysis, investigating cyber bullying and examining socio-political controversies. While relevant research has been done independently on code-mixed social media texts and hate speech detection, our work is the first attempt in detecting hate speech in HindiEnglish code-mixed social media text. In this paper, we analyze the problem of hate speech detection in code-mixed texts and present a Hindi-English code-mixed dataset consisting of tweets posted online on Twitter. The tweets are annotated with the language at word level and the class they belong to (Hate Speech or Normal Speech). We also propose a supervised classification system for detecting hate speech in the text using various character level, word level, and lexicon based features.
Fingerprint Minutiae: A Constructive Definition

The flow pattern of ridges in a fingerprint is unique to the person in that no two people with the same fingerprints have yet been found. Fingerprints have been in use in forensic applications for many years and, more recently, in computer-automated identification and authentication. For automated fingerprint image matching, a machine representation of a fingerprint image is often a set of minutiae in the print; a minimal, but fundamental, representation is just a set of ridge endings and bifurcations. Oddly, however, after all the years of using minutiae, a precise definition of minutiae has never been formulated. We provide a formal definition of a minutia based on the gray scale image. This definition is constructive, in that, given a minutia image, the minutia location and orientation can be uniquely determined.
Applying a multimedia storytelling website in foreign language learning

Storytelling is a practical and powerful teaching tool, especially for language learning. Teachers in language classrooms, however, may hesitate to incorporate storytelling into language instruction because of an already overloaded curriculum. English foreign language (EFL) teachers in Taiwan report additional problems such as having little prior experience with integrating storytelling into language teaching, locating appropriate stories, and lacking the cultural and language abilities to handle storytelling in English. On the other hand, researchers have demonstrated successful usages of computer and network-assisted English learning. The researchers in this study have developed a multimedia Storytelling Website to study how webbased technology can assist overcoming the obstacles mentioned above. The website contains an accounts administration module, multimedia story composing module, and story re-playing module. In order to demonstrate the effectiveness of this Website in significantly facilitating teacher s storytelling and children s story recall processes in EFL classrooms, it was implemented in one elementary school to test its effectiveness in instruction and in resultant student learning. The results of the study support the significance and the education value of the multimedia Storytelling Website on EFL teaching and learning. If such a Website 0360-1315/$ see front matter 2004  Corresponding author. E-mail address: wtsou@ipx.ntntc.edu.tw (W. Tsou). 18 W. Tsou et al. / Computers & Education 47 (2006) 17–28 can be applied within elementary EFL classrooms, the quality of teaching and learning can be improved and students enjoyment and success in EFL learning may increase.
Fast and Robust Vanishing Point Detection for Unstructured Road Following

Vision-based unstructured road following is a challenging task due to the nature of the scene. This paper describes a novel algorithm to improve the accuracy and robustness of vanishing point estimation with very low computational cost. The novelties of this paper are three aspects: 1) We use joint activities of four Gabor filters and confidence measure for speeding up the process of texture orientation estimation. 2) Misidentification chances and computational complexity of the algorithm are reduced by using a particle filter. It limits vanishing point search range and reduces the number of pixels to be voted. The algorithm combines the peakedness measure of vote accumulator space with the displacements of moving average of observations to regulate the distribution of vanishing point candidates. 3) Attributed to the design of a noise-insensitive observation model, the proposed system still has high detection accuracy even when less than 60 sparsely distributed vanishing point candidates are used for voting as the influence introduced by the stochastic measurement noise of vote function and the sparsity of the vanishing point candidates is reduced. The method has been implemented and tested over 20 000 video frames. Experimental results demonstrate that the algorithm achieves better performance than some state-of-the-art texture-based vanishing point detection methods in terms of detection accuracy and speed.
Ontology Engineering in a Networked World

Change your habit to hang or waste the time to only chat with your friends. It is done by your everyday, don't you feel bored? Now, we will show you the new habit that, actually it's a very old habit to do that can make your life more qualified. When feeling bored of always chatting with your friends all free time, you can find the book enPDF ontology engineering in a networked world and then read it.
Strategies in Learning and Using a Second Language

This new book by Andrew Cohen comes in the well-established series, Applied Linguistics and Language Study, which explores key issues in language acquisition and language use. Cohen’s book focuses on learner strategies and is written primarily for teachers, administrators, and researchers of second and foreign language programmes. It is hard to think of a more suitable author of a book on how to go about the complex endeavour of learning a second or foreign language than Cohen, himself a learner of twelve languages and a continuous user of seven! Of course, Cohen is also an experienced conductor of research focusing on learner strategies and the author and co-author of numerous articles on the topic. Except for a research report on strategies-based instruction which appears in print for the first time in Chapter 5, all of the chapters in the present volume consist of previously published material, either by Cohen alone or co-authored with Cohen, which has been revised and updated. After a short introduction, Cohen starts out with a discussion of terminology in Chapter 2, suggesting the broad working definition of second language learner strategies to encompass both second language learning and second language use strategies. According to Cohen, second language learner strategies can be defined:
Analysis of blood supply in the hard palate and maxillary tuberosity—clinical implications for flap design and soft tissue graft harvesting (a human cadaver study)

The aim of the present study is to provide a detailed macroscopic mapping of the palatal and tuberal blood supply applying anatomical methods and studying specific anastomoses to bridge the gap between basic structural and empirical clinical knowledge. Ten cadavers (three dentate, seven edentulous) have been prepared for this study in the Department of Anatomy, Semmelweis University, Budapest, Hungary, and in the Department of Anatomy of the Medical University of Graz. All cadavers were fixed with Thiel’s solution. For the macroscopic analysis of the blood vessels supplying the palatal mucosa, corrosion casting in four cadavers and latex milk injection in other six cadavers were performed. We recorded major- and secondary branches of the greater palatine artery (GPA) and its relation to the palatine spine, different anastomoses with the nasopalatine artery (NPA), and lesser palatal artery (LPA) as well as with contralateral branches of the GPA. Penetrating intraosseous branches at the premolar-canine area were also detected. In edentulous patients, the GPA developed a curvy pathway in the premolar area. The blood supply around the maxillary tuberosity was also presented. The combination of different staining methods has shed light to findings with relevance to palatal blood supply, offering a powerful tool for the design and execution of surgical interventions involving the hard palate. The present study provides clinicians with a good basis to understand the anatomical background of palatal and tuberal blood supply. This might enable clinicians to design optimized incision- and flap designs. As a result, the risk of intraoperative bleeding and postoperative wound healing complications related to impaired blood supply can be minimized.