Reviewerly/exHarmony huggingface dataset:
Near Field Communication (NFC) Applications in the Tourism Industry

The use of near field communication (NFC), a type of wireless technology that allows data transfer by bringing two devices closer together, has been increasing very rapidly. NFC offers great ease to its users through its contactless operation and provides the optimum environment for wireless communication with other technologies. Users with NFC-enabled mobile phones can share websites, contact information, phone numbers, music files, videos, and photos and can also unlock doors and can use their phones as mobile wallets to make purchases safely and easily. In recent years, with the increasing use of technology, it is clear that NFC technology is promising for the tourism industry. This chapter provides background on NFC technology. The features of NFC technology and its advantages and disadvantages for the tourism industry are evaluated. NFC technology applications (data sharing, payments, tickets, keys, etc.) for the tourism industry are explained. Finally, examples of NFC applications in the tourism industry are discussed for both tourists and sector stakeholders.
Perceptions and acceptance levels of a web-based nutrition educational application (Digipedia Edu_Makugiz) of West Java traditional food for teenagers in Bandung, Indonesia

Abstract Traditional cuisine is being forgotten particularly by young generation. One of the biggest triggers is the rapid distribution of information on modern food such as junk or fast food supported by the fast development of digital technology. In addition, low knowledge and awareness of traditional food, especially among teenagers, also contribute to this phenomenon. In Indonesia, one of the areas well known with its traditional cuisine in West Java province, which is dominated by Sundanese food. The purpose of this study was to find out the perception and acceptance level of teenagers towards a web-based application namely Digipedia Edu-Makugiz consisting of Sundanese traditional food and beverages as nutrition learning media for teenagers. There were 55 teenagers from Bandung, Indonesia, aged between 12 to 21 years old having smartphones and access to the internet selected to be the subjects of this study. The data comprised subject characteristics (age and gender), their perception, and their use of the application which had been published on the domain www.digipedia-edumakugiz.id. The data were analyzed using Microsoft Excel and SPSS 22 program and interpretation of the perception and acceptance level of the application. Based on the data analysis, this study found that most of the respondents found it quite easy to operate the application. This led to their interest to further use the application based on their specific needs. In relation to the development of the application, there was an additional feature of language (bilingual) as a strategy to attract more users. It was also proven that development on contents and attributes of the application made the application even more interesting for teenagers to find more information on West Java traditional food and beverages.
A Lightweight Vulnerability Scanning and Security Enhanced System For Oracle Database

Databases store the important information of a company, and once it is leaked, it will bring immeasurable losses. Determining how to find vulnerabilities and effectively repair them were the main objectives, we design and implement a modular Oracle Vulnerability scanning and security enhanced system, which can effectively scan and repair known vulnerabilities of Oracle database. Users can customize and extend related functions for specific vulnerabilities. The experimental results showed that the system can effectively detect a variety of vulnerabilities and supplied easy to use automatic repair functions, which provided solution to the serious security problems of the Oracle database for SMEs (small to medium-sized enterprises).
Comparison of Face Recognition Loss Functions

Significant progresses have been made to face recognition algorithms in recent years. The progresses include the improvements of the solutions and the availability of more challenging databases. As the performance on previous benchmark databases, such as MPIE and LFW, saturates, more challenging databases are emerging and keep driving the advancements of face recognition solutions. The loss function considered in the solution usually plays the most critical role. We compare a few latest loss functions with the same feature embedding network by evaluating their performance on two recently-released databases, the IARPA Janus BenchmarkB (IJB-B) [1], and IARPA Janus BenchmarkC (IJB-C) [2], and highlight the directions for the next phase research.
Long Period Re-identification Approach to Improving the Quality of Education: A Preliminary Study

Early school leaving is one of the most frequently mentioned reasons to social exclusion later in life. In order to reduce the risk of early school leaving, it is necessary to automate the process of entering unjustified lessons’ delays in school management system. A person’s re-identifying (Re-ID) is a complex automated process, where most studies use an approach to analyze the descriptors of clothing and appearance that are intended for the use of short-period Re-ID. In contrast, there is not much research in the real-time long-term Re-ID process, when images or videos are taken at intervals of several days or months in an uncontrolled environment. In this case descriptors characterizing a person’s biometric identity based on unique features, such as a facial digital image, are required. The objective of this research is to develop a real-time person’s long-term re-identification approach for accounting of non-attended lessons in educational institutions. The proposed Re-ID mechanism includes face identification and new method of using multiple face etalon versions and multiple versions of descriptors for a single person. This allows Re-ID of a person in different clothing and appearance from different camera angles in a long term.
Opinion Mining: Applications Trends

With current rapid technology acceleration, and an enormous quantity of generated data. From, Clouds, networks, social media, and statistics. a novel technology appears to facilitate and come out with valuable knowledge that can be useful In all life levels, tasks and profitability returns. Massive quantities of data consider as assets for the government, enterprises, and even individuals. This paper aimed to presents a literature review regarding sentiment analysis and opinion mining recent applications area.
Relation Classification via CNNs with Attention Mechanism for Multi-Window-Sized Kernels

Relation classification is an important ingredient task in the construction of knowledge graph, question answering system and numerous other natural language processing (NLP) tasks. With the application of deep neural networks (DNNs) such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs), relation classification task has achieved satisfactory results. However, many proposed models can not take well advantages of multiple window sizes for filters in CNNs and finally hurt the performance of this task. Moreover, unlike public and general dataset that has a large quantity of instances from natural languages or daily conversations, the performances of many deep neural networks with high complexity are not well enough for a small corpus in specific fields. To work out these problems, we propose a novel CNN model with attention mechanism for multi-window-sized kernels to capture the most important information and test our system not only on a general dataset of SemEval 2010 but also on a small dataset built from Chinese fundamentals of electric circuits textbook artificially. The experimental results show that our system outperforms the baseline systems for the SemEval 2010 relation classification task and validate the effectiveness of CNN on the specific Chinese small corpus relation classification task.
Motion Parallax Estimation for Ultra Low Altitude Obstacle Avoidance

By making full use of the curvature of the earth and ground clutter, the ultra-low altitude flight technique can significantly increase the difficulty of detecting and intercepting unmanned aerial vehicles (UAVs), and improve their penetration probabilities. However, unpredicted static/dynamic obstacles seriously threaten the flight safety of the ultra-low altitude UAVs. Therefore, fine autonomous spatial perception capability is essential for ultra-low altitude UAVs with limited loads and computing resources to avoid obstacles. To solve the difficult problem of spatial perception for ultra-low altitude obstacle avoidance, a motion parallax estimation algorithm is proposed by combing the deep learning and the epipolar geometry in this paper. By utilizing the deep neural network, the end-to-end mapping relationship from sequence images to depth values of pixels is constructed for spatial perception. At the same time, to overcome the interference caused by the image consistency hypothesis, the loss function is constructed based on the 3D geometric information of the scene rather than the re-projection error between images. Finally, the proposed algorithm is evaluated on the KITTI dataset and infrared image dataset to verify its effectiveness and generalization.
An Interference-Aware Strategy for Co-locating High Performance Computing Applications in Clouds

Cross-interference may happen when applications share a common physical machine, affecting negatively their performances. This problem occurs frequently when high performance applications are executed in clouds. Some papers of the related literature have considered this problem when proposing strategies for Virtual Machine Placement. However, they neither have employed a suitable method for predicting interference nor have considered the minimization of the number of used physical machines and interference at the same time. In this paper, we present a solution based on the Iterated Local Search framework to solve the Interference-aware Virtual Machine Placement Problem for HPC applications in Clouds (IVMP). This problem aims to minimize, at the same time, the interference suffered by HPC applications which share common physical machines and the number of physical machines used to allocate them. Experiments were conducted in a real scenario by using applications from oil and gas industry and applications from the HPCC benchmark. They showed that our method reduced interference in more than 40%, using the same number of physical machines of the most widely employed heuristics to solve the problem.
Research on KRT Monitoring Instrument Alarm Threshold in Main Control Room of Nuclear Power Plant

In this paper, the intake air gamma radioactive monitoring alarm threshold of nuclear plant main control room was studied for both under normal operation condition and accident conditions. According to two different layout schemes, gamma radiation dose rate of measuring point was calculated by the MCNP code under three typical accidents (including S3, DBA-LOCA and LOCA conditions). The results show that it is proper that to keep original value of the detector alarm threshold of improved main control room, it could provide effective radiation protection for indoor staff.
Shallow Deep Learning: Embedding Verbatim K-Means in Deep Neural Networks

In this paper we show how to implement a deep neural network that is strictly equivalent (sans floating-point errors) to the verbatim (batch) k-means algorithm or Lloyd's algorithm, when trained with gradient descent. Most interestingly, doing so shows that the k-means algorithm, a staple of "conventional'" or "shallow'" machine learning, can actually be seen as a special case of deep learning, contrary to the general perception that deep learning is a subset of machine learning. Doing so also automatically introduces yet another unsupervised learning technique into the arsenal of deep learning, which happens to be an example of interpretable deep neural networks as well. Finally, we also show how to utilize the powerful deep learning infrastructures with very little extra effort for adaptation.
Multi-objective Optimization Research and Applied in Cloud Computing

In many real-life applications, a decision maker often needs to handle different conflicting objectives. Problems with more than one conflicting objective are called multi-objective optimization problems (MOPs). Multi-objective evolutionary algorithms (MOEAs) have been developed for solving MOPs. MOEAs have been shown to perform well on some MOPs with two or three objectives; however, MOEAs have substantial difficulties for tackling MOPs with more than three objectives, often referred to as many-objective problems (MaOPs) nowadays. In my thesis, first, I plan to propose an efficient multi-objective artificial bee colony algorithm based on decomposition for solving MOPs. Then, another effective adaptive many-objective evolutionary algorithm is designed to deal with MaOPs. What's more, based on defining a multi-objective optimization model of task scheduling in cloud computing, I use an improved particle swarm optimization algorithm to solve the model. Finally, I try to establish a many-objective optimization model of offloading in mobile edge computing, and find a suitable many-objective evolutionary algorithm for solving it. The proposed algorithms are compared to several state-of-the-art algorithms on these models. The experimental results will show the efficiency and effectiveness of the proposed algorithms.
Case Study Applying GQM+Strategies with SysML for IoT Application System Development

Developing an Internet of Things(IoT) application system has some challenges. For example, distinguishing the problem and solution domains is non-trivial in the developments and there are wide variations in the solution space to satisfy the given needs due to the nature of IoT systems. To deal with these problems, we propose an approach that integrates the GQM+Strategies method and the modeling language SysML for systems engineering. We also present a case study of the proposed approach and discuss future directions.
Recommending Relevant Research Articles based on Citation Graph and Textual Information

With the ever increasing number of published research articles, it is important to develop new solutions for getting better recommendations than a naïve keyword based search. A plethora of methods are proposed for citation recommendation by using either the citation graph that exists between the research articles or the textual information embedded in the research articles. Taking inspiration from recent developments, we propose a novel method by combining the feature vectors obtained from doc2vec and node2vec where the former extracts the text features and the latter extracts the citation network features using Canonical Correlation Analysis. We have elaborated the proposed hybrid approach on the realworld Cora citation network. We observed that the ensemble method outperforms the state-of-the art distributed method representations.
Effects of Integrated Instance-Random-Sampling and Feature Reduction on Classifiers Performance and Training Speed

This paper addresses the effect of data reduction on speeding up training while keeping or improving the accuracy performance of classification. Since many studies have focused on feature selection, but did not adequately consider instance selection, our work focuses on both instance reduction and feature reduction, integrated into a whole reduction method. We examined in prior work Simple Random Sample Selection without Replacement, integrated with the Information Gain-based Feature Selection method, and compared its performance with the unintegrated instance selection and feature selection alone, using a single reduction rate. Our results proved that the integration of instance and feature selection performed much better than instance or feature selection alone. In this paper, we examine our approach in more depth, trying different reduction rates and different distributions of reduction rates between instance reduction and feature reduction. Our results show that for nearly all common classifiers, our integrated data reduction speeds up training significantly while keeping the accuracy unchanged (and sometimes even improved) at even high reduction rates. We also present the optimal feature-instance reduction-rates tradeoff.
Galois Connections for Recursive Types

Building a static analyser for a real language involves modeling of large domains capturing the many available data types. To scale domain design and support efficient development of project-specific analyzers, it is desirable to be able to build, extend, and change abstractions in a systematic and modular fashion. We present a framework for modular design of abstract domains for recursive types and higher-order functions, based on the theory of solving recursive domain equations. We show how to relate computable abstract domains to our framework, and illustrate the potential of the construction by modularizing a monolithic domain for regular tree grammars. A prototype implementation in the dependently typed functional language Agda shows how the theoretical solution can be used in practice to construct static analysers.
Readability Analysis of Kannada Language

This paper proposes a neural network based approach to predict a readability score for a given sentence in Kannada language without the use of any predefined word list. We have used textbooks of Grade-1 to Grade-10 as input to train the model, and were able to achieve a correlations well above all the state-of-the-art methods. We also show and prove the general behavior of readability scores of w.r.t the number of sentences considered in each class.
Swarm Intelligent Route Planning of UAV with Elastic Track Correction

Main purpose of UAV path planning are shorten the distance, avoid flight threats and obstacles. Planning feasible path intervals is the basis of path planning. In long-distance, multi-obstacle path planning, it is more important to plan a reasonable distance and sufficient safety path. Therefore, aiming at the problem of complex track constraints in long-distance and multi-obstacle path planning, a new method of track initialization and adjustment named elastic track correction is proposed to make up for the limitations of current track initialization and solution space. Using wolf pack intelligence algorithm as validation algorithm, this paper compares the effectiveness of elastic track correction mechanism with that of general planning method, which proves that adding elastic correction mechanism to track planning algorithm can effectively improve the reliability of track planning, and can get a feasible solution of lower cost function.
Digital Forensic Analysis of Facebook App in Virtual Environment

Facebook is the most popular social media app which is used worldwide to connect people, groups, societies and friends. While the innumerable benefits justify its popularity, it is also used by cyber criminals to harass women and children and spread social disharmony. The app stores lot of information which can help in reconstructing the sequence of crime. Towards this, the research does an in-depth analysis of the Facebook app on the latest android version of a virtual phone. The concept of virtual android phone using genymotion emulator is used to bring out the various artifacts stored by Facebook app which can be produced as crucial evidence in the Court of Law.
Learning Based SLIC Superpixel Generation and Image Segmentation

Superpixel generation is to cluster the pixels with similar features and plays an important role for image segmentation. Conventional superpixel generation methods are more meaningful, however, the learning based method can generate the superpixels directly from the segments in the ground truth and achieve even better performance. In this work, an advanced superpixel generation algorithm that combines the advantages of conventional methods and modern neural network techniques is proposed. In addition to colors and locations, we find that the feature generated by neural networks also provide useful information for superpixel assignment. Simulations show that, with the proposed superpixels, a much more precise segmentation result can be achieved.
Application of Multi-Stage Filtering and Multi-Layer Model in the Context of Dark and Non uniformly Illuminated Images

The increasing relevance of photography leads to the development of image processing techniques. Among the entire image processing steps., image enhancement is one of the most important one. Nowadays image enhancement is used for various applications like object recognition., remote sensing., surveillance., computer vision., etc. In this paper an image enhancement algorithm which enhances dark and non-uniformly illuminated images affected by bad weather conditions like rain., fog and haze are presented. This method is a combination of multi-stage filtering of rainy image and multi-layer model based image enhancement algorithms. The whole process involves three steps. They are rain streak removal., fog or haze removal and image enhancement. Experimental results show that the presented method improves the visual quality of images very well. PSNR and SSIM values are calculated and compared with existing methods.
Entity Synonym Discovery via Multiple Attentions

Entity synonym discovery is an important task, and it can benefit many downstream applications, such as web search, question answering and knowledge graph construction. Two types of approaches are widely exploited to discover synonyms from a raw text corpus, including the distributional based approaches and pattern based approaches. However, they suffered from either low precision or low recall. In this paper, we propose a novel framework SynMine to extract synonyms from massive raw text corpora. The framework can integrate corpus-level statistics and local contexts in a unified way via a multi-attention mechanism. Extensive experiments on a real-world dataset show the effectiveness of our approach.
Hans Lundberg: Canada’s (and possibly the World’s) first minerals geophysicist is inducted into the Canadian Mining Hall of Fame

Hans Lundberg was a geoscientist whose career spanned the first half of the 20th century. He was part Indiana Jones, part Howard Hughes and part Jules Verne. He was born in 1893 in Malmo, Sweden an...
Fully Homomorphic Encryption Without Noise

In this paper, the authors present a novel fully homomorphic encryption scheme operating between and capable of arbitrarily performing additions and multiplications. The new scheme is compact and each operation (addition or multiplication) performed on any two ciphertexts produces a fresh ciphertext without any associated noise. Thus, the scheme does not need any bootstrapping procedure or noise reduction technique to refresh ciphertexts. In the absence, to the best of the knowledge of the authors, of any existing fully, partially or leveled homomorphic encryption scheme using as the set of plaintexts, the new cryptosystem has been implemented and has had its performance compared to the identity encoding.