Reviewerly/exHarmony huggingface dataset:
Tracking of Moving Target in Sports Video Sequence Based on Interactive Multiple Model

Detection and tracking of moving targets is a crucial subject in the field of applied vision. In this paper, the tracking technology for moving objects in sports video is studied, and an optimized interactive multiple model algorithm is proposed. The optimized motion model is used in the kernel-based mean transfer algorithm to obtain the precise position of the moving target. After verification, the interactive multiple model algorithm has improved the prediction accuracy of the moving target and ensured that the mean transfer algorithm can converge to the local optimal solution.
An ISAR Image Segmentaion Method Based on MMARP Model

This paper presents a method of unsupervised segmentation for Inverse synthetic aperture radar (ISAR) images. Firstly, a generalized multiresolution likelihood ratio (GMLR) is defined, which classifies different kinds of signals more accurately than classical likelihood ratio by fusing more and different signal features. For our ISAR image segmentation application, multiresolution stochastic structure inherent in ISAR imagery is well captured by a set of multiscale autoregressive (MAR) models. Secondly, good parameter estimates of GMLR can be obtained by estimating several mixture multiscale autoregressive prediction (MMARP) models using EM algorithm. Thirdly, considering the independence assumption of maximum likelihood estimation of parameter by EM algorithm and reduction of the segmentation time, we present the bootstrap sampling techniques applied above algorithm. Finally, Experimental results demonstrate that our algorithm performs fairly well.
Achieving Privacy-Utility Trade-off in existing Software Systems

Privacy and utility of data are two aspects of a system that are often diagonally opposite to each other. Privacy concerns drive design decisions that can reduce the ability to make deductions or correlations from a given dataset (e.g. reducing the probability that an individual could be recognised from a given set of health records). Utility, on the other hand, tries to maximise the chances of finding essential relationships in the real world, that can then be used for making smarter systems (e.g. the ability to predict that an individual is at higher risk of being affected by a terminal disease). A term that is often used to explain this paradox is called the Privacy-Utility trade-off. Software practitioners have often ignored the privacy aspects due to lack of legal obligations, and have generally concentrated on achieving functionality. But with a renewed interest in Artificial Intelligence, privacy concerns are going to become more critical in the near future. This will force the software providers to reevaluate their existing products and services from a privacy perspective. In this work, we analyse some of the challenges that a typical software provider would face while doing so. We present a privacy model that can be applied to existing systems, which in turn can suggest first-cut privacy solutions requiring minimal alterations in deployed applications. To the best of our knowledge, no open-source initiative has been started until now to cater to these requirements. We briefly introduce the prototype of an open-source tool that we are developing, which is aimed at facilitating this analysis. The initial results were obtained over some standard datasets, as well as a real-world credit card fraud dataset, which seemed to collate with our intuitions.
Singing Voice Conversion Based on Non-Parallel Corpus

With the continuous development of deep learning, research on the conversion of singing voice has gradually enriched. The study of singing voice conversion comes from voice conversion. The singing voice conversion is to sing the voice of the target singer without changing the sound content of the source singer. In this paper, we use WORLD vocoder and speech signal processing toolkit (SPTK) to extract the acoustic characteristics of songs and use two mirrored generative adversarial Nets complete the conversion of acoustic features. The experiment realizes the singing conversion of non-parallel corpus. Subjective evaluations show that the songs after the conversion have a good performance in the quality and similarity of the songs.
The development of an instruments diagnostic two-tier multiple choice assisted smartphone on salt hydrolysis

Abstract Research has been conducted instruments diagnostic two-tier multiple choice assisted smartphone on salt hydrolysis, or called Smartphone Tes Diagnostik Miskonsepsi Hidrolisis Garam (S-TDM-HG). In this study using descriptive methods that are part of research and development (R &amp; D). The procedure of research is carried out through several stages of work, namely: (1) determination of subject matter; (2) study of core competencies, basic competencies, syllabus and material to determine concept labels, (3) concept map making, (4) identification of student misconceptions based on concept labels made in concept maps, (5) designing questions and answers (6) collection of existing multimedia, (7) making scripts and storyboards, (8) making S-TDM-HG. The development of the items adapted the stages developed by Treagust and McIntire. The concept map developed is used to identify what concepts will be developed in the process of making S-TDM-HG questions. Based on the essay test data given to 34 students in one of the high schools in Sumedang, the initial identification of misconceptions from students was generated to develop the S-TDM-HG problem in the first tier (tier 1) and second tier (tier 2). Based on the 16 response patterns obtained there are six possible answers to misconceptions, one possible answer understands the concept and nine possible answers that do not understand. 22 items developed, 20 questions are said to be valid with a CVI value of 0.9385. Based on 20 valid questions, which meet the content validation criteria as many as 18 items.
Automated Chinese Language Proficiency Scoring by utilizing Siamese Convolutional Neural Network and fusion based approach

The previous approaches have failed to effectually score the language proficiency of a non-native speakers especially in case of non- English languages which are complex and a slight change of pronunciation can alter the nature of the word. In this study, we proposed an automated language scoring system to test the proficiency of Chinese language. We have employed a novel fusion approach of a 38-feature based model and a Siamese convolutional neural network (Siamese CNN) which can accuracy identify the difference between the native speech and the test taker's speech. The results show that out model have achieved comparable performance to the state of the art and solved the pronunciation problems as well. Furthermore, we have provided a fusion based approach and provided extensive amount of experiments which shows that our method is state of the art and can be utilized in real time Chinese language proficiency scoring.
Perspectives of Integration QR Codes and RFID readers in large-scale events controlled by HRM systems

There is an increasing trend in the application of integrated Quick Response(QR) and RFID technologies to large-scale events. QR codes and RFID readers containing logs data were introduced into an international computer project competition organized for students in the university. This paper aims to discover the perspectives of Integrated Technologies, which has been created and show how this integration affects to optimization and efficiency of the processes. The presented study is based on the application of the Human Resource Management System for the education sector(e-HRMS), a centralized platform that powers and manages the structure of international competitions. The study findings reveal that the integration of ICT technologies positively affects optimization and process management in the education sector. Moreover, the most important parameter that affects both technologies has been found and checked clearly.
Projected Weight Regularization to Improve Neural Network Generalization

Generalization of a deep neural network (DNN) is one major concern when employing the deep learning approach for solving practical problems. In this paper we propose a new technique, named projected weight regularization (PWR), to improve the generalization capacity of a DNN model. Consider a weight matrix W from a particular neural layer in the model. Our objective is to make the eigenvalues of the matrix product WW <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> have comparable or roughly the same magnitudes while allowing the DNN model to fit the training data sufficiently accurate. Intuitively speaking, by doing so, it would prevent the W matrix from matching the training data too well. Specifically, at each iteration, we first project the W matrix to a number of vectors along randomly generated directions. After that, we build an objective function of the projected vectors to regularize their behaviours towards comparable eigenvalue magnitudes of WW <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</sup> . Experimental results on training VGG16 for CIFAR10 show that PWR combined with centered weight normalization (CWN) yields promising validation performance compared to orthonormal regularisation combined with CWN.
Improvement on the Representation and Fusion Method of Fragmented Knowledge Structure

Abstract With the rapid development of Internet, cloud computing, Internet of things and other new technologies, information is growing exponentially, and human society has entered a new stage of fragmented knowledge. Fragmentation is flexible, random, and contains rich information, but fragmented information is fragmented, disordered, incomplete, large in size and fast in updating, so it is difficult to form effective knowledge. To resolve the problem of extracting and mining fragmented knowledge, firstly, this paper studies the fragmented knowledge map and he integration method of fragmented knowledge. secondly, we study the integration method of fragmented knowledge, from the aspects of incomplete and redundant fragmented information; Finally, we put forward several points of fragmented knowledge processing methods Key scientific issues and challenges.
Analyzing End-users Requirements of Al Mustaqbal University College Website: An Emperical Case Study

Websites play a significant role in the success of many organizations. The navigational data of website visitors are utilized to maintain successful relationship with customers. Web Usage Mining (WUM) is the branch of Data Mining (DM) which focus on the study of end-users navigation behaviors through the analysis of log files. In this paper, Al Mustaqbal University College (MUC) website has been studied. Association Rule mining algorithm is used to analyze the website log file. As a result, frequent webpages are identified. The obtained rules show some strong association between disconnected pages. Hence, website developer could incorporate these results to develop an interactive websites and provide more enjoyable navigation experience.
Next-Generation IoT Education Program of KOSEN Using a Virtual Workshop

IoT has been getting a lot of attention in the global industry. Recent education programs also adopt information technologies such as IoT and ICT to improve the teaching method. However, education of KOSEN is desired to teach how to realize IoT and ICT, not only to utilize information technology. In this education program, we gave lectures about social needs of IoT and the management method of a project first. Based on the lecture, students investigated real information applications and then proposed their new technical seeds of information applications independently. Some projects were launched to realize the technical seeds. To move their project forward, they were able to use a virtual workshop in National Institute of Technology, Gunma College. Finally, students discussed their final products within the projects, and then we evaluated deliverables and processes of their projects. The virtual workshop includes visualization of existing equipment, which is placed in laboratories and facilities of our college, by network management. We connected the hardware and software resources to our college network and rebuilt the resources to the virtual workshop which was shared across our college. Moreover, we introduced new network-compatible IoT products other than existing equipment. This paper describes our education program to nurture IoT engineers in Gunma College.
Arabic Text Extraction by Convolutional Neural Network Through Robust Dataset Design

Text extraction from images plays a vital role in modern-day applications like verifications of checks, vehicle’s number plate scanning, finding evidences of crime cases, etc. we proposed a methodology of extracting Arabic characters from a given image. The process has been divided into three phases. In the first phase, the given image containing Arabic text to be extracted has been subjected to various image molding subprocesses like cleaning, blurring, binarization, and segmentation to make the image compatible for recognition phase. Available Dataset for Arabic has limitation of one form and hence we created dataset which fits real-world scenario (second phase). The final phase describes the Convolutional Neural Network (CNN) model used for recognizing the Arabic characters. The achieved accuracy of the trained model was about 95% for extraction of Arabic characters.
Malicious Message Detection on Twitter via Dissemination Paths

Malicious message detection, also known as spam detection, on social networks has long been a critical but challenging problem to be addressed. It has attracted more and more attention from government, industry, and academia, due to an increasing number of recent massive cybercrimes that exploit the dissemination of faked or harmful messages on social networks. Previous work relies on the content of messages or the behavior of user accounts to differentiate and classify malicious and benign messages, which becomes ineffective under evasion tactics from advanced attackers. Different from those solutions, in this work, we leverage the differences in dissemination between benign and malicious messages on social networks and derive features from their dissemination paths among users and social communities to detect malicious messages. Experimental results obtained from real-world datasets demonstrate that our new approach is robust and effective in detecting malicious messages on social networks, which achieves 95% precision and 95% recall.
Improving the Result of Personalized Questionnaire Towards Solving Cold User Problem

Collaborative filtering techniques is among the popular approaches used in addressing product recommender systems, which uses ratings and predictions to make new suggestions. However the major weakness of collaborative filtering approaches is cold user problem. Literature investigation has shown that cold user problem could be effectively addressed using active learning technique of administering personalized questionnaire. Unfortunately, the result of personalized questionnaire technique could contain some user preference uncertainties where the product database is too large (as in Amazon.com). This research work tends to address the weakness of personalized questionnaire technique by applying the active learning technique of uncertainty reduction over the result obtained from administering personalized questionnaire. This strategy has the tendency of resolving user preference uncertainties that could be associated with the result of personalized questionnaire. This research work is in progress. Preliminary result is encouraging.
Dynamic User Interests Profiling Using Fuzzy Logic Application

The user profile contains different user information, such as personal information and interests. Research on profiling user interests can be divided into two groups. The first group builds the user interests based on the text extracted from browsing history (could generate a lot of false interests). The second group uses both user behavior and browsing history to determine his interests. The latter solution does not use enough factors (one or two factors only) and calculates the weight of each factor via predefined ranges, which generate a false factor weight and false user interests. In this paper, we propose an approach that employs Fuzzy Logic with several factors (scrolling speed, time spent, and the number of visits). This approach adapts the weight of each factor to the user habits, build and update the user profile from his browsing history. The results show that our approach significantly decreases the error rate.
Improving Efficiency in Attack Detection through Real-time Processing Monitoring and Machine Learning

Modern networks can benefit immensely from the flexibility and the improvements in delivery of services that Software Defined Networking (SDN) provides. Creating new services for users can be streamlined through some sort of software component, such as a cloud orchestrator. The network configuration and the resource allocation can happen very efficiently using virtual nodes while isolating customer environments.However, in order to ensure that the virtual nodes are truly isolated and that they are not misbehaving, it is essential to build security guards within the SDN infrastructure. Despite the flexibility and programmability benefits associated with cloud, SDN and Network Function Virtualization environments, these virtual paradigms face many security challenges. Such systems are very complex, which open up more attack vectors than traditional IP networks.In this paper, we propose the design and implementation of an SDN security framework that helps in the detection of failures and malicious activities that target virtual network nodes. The framework we are proposing leverages the network-wide visibility of SDN in order to gather operational characteristics of virtual nodes, feed them into machine learning classifiers and determine unusual behavior or counterfeit nodes with high level of confidence.
A Multimedia Information Retrieval Method Based on Cross-Modal Hashing

How to retrieve multimedia information effectively and efficiently is of great importance in information retrieval and computer vision. Therefore, in this paper, we aim to fully utilize the cross-modal hashing technology to solve the multimedia information retrieval problem. The main idea of this paper is to learn two different categories of hash functions for two different data modality, such as image modality and text modality. Then, the goal of this work is to learn several hashing functions, and then represent the samples to binary codes, and the proposed method generates hashing codes via connecting various modalities. In particular, we exploit the non-negative matrix factorization to learn a shared semantic space for different data modalities. Experimental results demonstrate that the proposed method can achieve higher accuracy of cross-domain multimedia information retrieval than other methods.
The IoT Security Economy

This chapter explores the Internet of Things (IoT) economy and the impact the criminal pursuit of money has on the current and future state of IoT security. A different class of cybercriminal seeks the destabilization of another entity via the destruction of their assets. But just like IoT, the draw of a new technology and prospects for opening new business ventures abound, creating counter-stabilizing pressures. Cryptocurrencies have fluctuated wildly, especially in the last couple of years. This is common with new currencies, even when they are national currencies. The chapter also explores the challenges facing the greenest defenders—builders of consumer IoT products such as smart toys—whose inexperience with technology makes them a prime target for the bad guys. Crypto mining usually requires expensive, specialized hardware that consumes large amounts of electricity, which can offset the gains in cryptocurrency rewards for the average miner.
POLYCiNN: Multiclass Binary Inference Engine using Convolutional Decision Forests

Convolutional Neural Networks (CNNs) have achieved significant success in image classification. One of the main reasons that CNNs achieve state-of-the-art accuracy is using many multi-scale learnable windowed feature detectors called kernels. Fetching of kernel feature weights from memory and performing the associated multiply and accumulate computations consume massive amount of energy. This hinders the widespread usage of CNNs, especially in embedded devices. In comparison with CNNs, decision forests are computationally efficient since they are composed of decision trees, which are binary classifiers by nature and can be implemented using AND-OR gates instead of costly multiply and accumulate units. In this paper, we investigate the migration of CNNs to decision forests as one of the promising approaches for reducing both execution time and power consumption while achieving acceptable accuracy. We introduce POLYCiNN, an architecture composed of a stack of decision forests. Each decision forest classifies one of the overlapped sub-images of the original image. Then, all decision forest classifications are fused together to classify the input image. In POLYCiNN, each decision tree is implemented in a single 6-input Look-Up Table and requires no memory access. Therefore, POLYCiNN can be efficiently mapped to simple and densely parallel hardware designs. We validate the performance of POLYCiNN on the benchmark image classification tasks of the MNIST, CIFAR-10 and SVHN datasets.
Kazakh Language Open Vocabulary Language Model with Deep Neural Networks

Natural Language models are a crucial tool in computational linguistics. They are specially difficult to build in agglutinative languages, which require attention since the words are formed by attaching sequences of different morphemes, where each morpheme can change the meaning of the word. For the mentioned type of language fixed and limited vocabulary itself can pose restrictions. The character-based solution may help to overcome the problem. However, it triggers the disambiguation of a word according to the context. The present work aims to build a character-based language model for the Kazakh Language, with the use of Deep Neural Networks, namely a Long Short-Term Memory model. The Language Model in the present research is generative and aims to produce all possible correct words within the context given. A word can be treated as a morpheme generated by characters where any possible word type could be generated. In order to understand the language model correctly, it is necessary to use data which was initially written in Kazakh and not translated from other sources. Therefore, the model will be trained using books written in Kazakh.
Trained Model Fusion for Object Detection Using Gating Network

The major approaches of transfer learning in computer vision have tried to adapt the source domain to the target domain one-to-one. However, this scenario is difficult to apply to real applications such as video surveillance systems. As those systems have many cameras installed at each location regarded as source domains, it is difficult to identify the proper source domain. In this paper, we introduce a new transfer learning scenario that has various source domains and one target domain, assuming video surveillance system integration. Also, we propose a novel method for automatically producing a high accuracy model by fusing models trained at various source domains. In particular, we show how to apply a gating network to fuse source domains for object detection tasks, which is a new approach. We demonstrate the effectiveness of our method through experiments on traffic surveillance datasets.