Reviewerly/exHarmony huggingface dataset:
Towards a Hybrid Approach for Evolving Bayesian Networks Using Genetic Algorithms

Learning the structure of a Bayesian network from data is complex because the number of possible structures increases super-exponentially with the increase in the number of nodes. To address this problem, we propose a hybrid approach comprised of two phases: the constraint-based phase that identifies dependencies among variables to minimize the search space, followed by a score-and-search phase which employs a genetic algorithm to evolve the Bayesian network from the reduced search space. We evaluate the performance of our approach by comparing it with existing algorithms on a limited amount of data sets generated from three benchmark networks. The results illustrate that the proposed algorithm achieves good performance in learning the structure particularly for medium to large networks. Next, we apply our method to a new data set generated from a hand-designed network - the RoRSS (Rules of the Road Ship Simulator). The preliminary results indicate that our method is also satisfactory for small networks with a limited amount of data. The work presented here is a proof-of-concept for our proposed approach aimed at discovering knowledge from data samples of varying sizes and in the presence of small to high number of nodes. Based on the results obtained so far, we are confident that our method is suitable to efficiently learn the structure of the RoRSS network from a large data set.
Research on Improving SMOTE Algorithms for Unbalanced Data Set Classification

Aiming at the problem that the classification effect of unbalanced data sets is not ideal, a new method of improving SMOTE algorithm, TDSMOTE algorithm, is proposed in this paper. A small number of samples are divided into three regions, dense region, boundary region and sparse region. Different sampling methods are used for samples from different regions. Finally, random forest algorithm is used to classify the samples. Experiments were carried out on six UCI unbalanced datasets and compared with traditional algorithms. Experiments show that the TDSMOTE algorithm achieves higher G-means, F-value and AUC values, and effectively improves the classification performance of unbalanced data sets.
e-Canteen for the Smart Campus Application

Nowadays in 4.0 industrial and digital economic era, all of the manual serving should be changed by the serving service which use a technology as the helping tools and applications to support the smart canteen in solving the problem of serving food and the queue for paying. Therefore, e-canteen web system with the Raspberry Pi as the sever is created, which can ordering directly through the table of customer without waiting for the waiter to bring the menu, and even for payment as well because the system is compactly designed to make it easier for cashier, waiters and even for chef in the kitchen in coordinating each other. The test is conducted by the support of the Apache JMeter software to do the testing of server ability in handling many clients based on the response time from server. The results show that Raspberry Pi 3 Model B with Ram 1 GB capacity able to handle numbers of threads (users) more than 150 users at the same time with response time value around 8 ms–17.6 ms and throughput value around 132.23 request/s. In parameter QoS testing, when using WiFi network in Raspberry and Wireless Network Adapter ARGtek ARG-1209 type, it is obtained that the index value is 3 with 87,5% percentage which means the QoS value in web access analysis when using the Raspberry WiFi network as well as the Wireless Network Adapter is in the good category.
A Novel Approach of Image Interpolation using DST

A novel interpolation technique using a Discrete Sine Transform (DST) is presented here. For fractional pixel prediction, DST is used but DST for image interpolation fails to produce pleasant output. The newly developed method implements a solution by further sub-sampling the Low Resolution (LR) image and finds out error by up-sampling it using DST. The error image which contains high frequency variation is responsible for sharpening the interpolated image. So it is added to available LR images. Then, the resultant image is up-sampled by using DST again to get back to the original size. Interpolated image is of high visual quality in the edge and texture part of the image. From the experimental results, it can be demonstrated that this algorithm performs better both objectively and subjectively. Experimental results reveal that it performs Peak Signal to Noise Ratio (PSNR) 2.3436 dB better upon Discrete Cosine Transform (DCT) and 2.3098 dB upon Bi-cubic interpolation.
Minimum error entropy based multiple model estimation for multisensor hybrid uncertain target tracking systems

In the multisensor target tracking system, the key of the target tracking performance depends on the state estimation accuracy to a great extent. However, the system uncertainties will seriously affect the performance of the state estimation. Up to now, little research focuses on the state estimation for the multi-sensor hybrid target tracking systems with multiple uncertainties including the multiple models, the unknown inputs and the systematic biases. In this study, the minimum error entropy based on the multiple model estimation for the multisensor hybrid uncertain target tracking systems with the multiple system uncertainties is presented. The minimum variance unbiased filter based on the general systematic bias evolution model decoupled with the unknown state is designed to estimate the optimal systematic biases and compensate the system measurements. Taking full advantage of the compensated measurement information in time and space, the multiple model observer based on the minimum error entropy is designed to obtain the optimal state estimation. The simulation results of the target tracking scenario illustrate the effectiveness of the proposed method, and the indoor target tracking and positioning experiment based on the ultrawideband further verifies that the proposed method is satisfying.
Cost-Efficient Adaboost-based Face Detection with FPGA Hardware Accelerator

In this paper, a cost-efficient Adaboost-based hardware accelerator design is developed for face detection. For the realization, a lot of operations and fast calculations of the rectangular Haar features of partial image regions are required. To speed up the processing time for integral image and face detection functions, the hardware accelerator implementation is an important issue for real-time application. In experimental results, by Xilinx XC7a200t device at 205MHz operational frequency, the applied integral image accelerator performs up to 1161 frames/sec with the image size of 240×360 pixels. When the input ROI sizes are 80×80 and 64×64 pixels, the proposed Adaboost-based classifier operates up to 574 and 821 face ROIs/sec, respectively.
Interpreting deep convolutional neural network classification results indirectly through the preprocessing feature fusion method in ship image classification

In spite of widespread adoption, deep convolutional neural networks (DCNNs) have a reputation for remaining “black boxes.” Due to the feature extraction ability of DCNN itself, good predicted results can be achieved without any processing of the raw input images, but it is hard to explain what features of the raw images the results are based on. We propose a preprocessing feature fusion method, based on the Sobel and Canny edge operators, which we treat as the interpretation of the raw images. This method obtains the interpretable edge feature by fusing features in raw ship images, which helps to explain that the DCNN ship classification result is based on the ship edge feature. That is to say, through the interpretation of the raw ship images, we indirectly interpret the classification results of the DCNN to some degree. In subsequent experiments, the highest average accuracy of 85.31% was achieved from the fusion-1 dataset, whereas the highest average accuracy of 74.4% was achieved from the balanced fusion-1 dataset, and the best mean average accuracy of 74.3% is achieved from the cross-validated experiments, which helps to verify the validity of our preprocessing method.
Corpus Usage for Sentiment Analysis of a Hashtag Twitter

Social media (Facebook, Instagram, Twitter, etc.) nowadays can be used for analyzing the objects, e.g. political views, products, services, etc. To understand the performance of an object, the sentiment analysis has been widely used to get the review from the consumers or users (positive or negative responses). Today, in big date era, which is a component of industry 4.0, many corpora are available and can be accessed freely. A corpus can be utilized to train the model through some methods. In this paper a Naïve-Bayes classifier was used to train a corpus from natural language toolkits (NLTK) corpora. As a case study, sentiment analysis for the sample movie “Avengers” was done from the twitter hashtag #avengersendgame. The paper also proposed the usage of a particular corpus to other different language implementations, e.g. for Indonesian language. Through the use of Tweepy and Pandas some Twitter tweets were retrieved and classified after pre-processing. The results showed the capability of the Naïve-Bayes classifier both for English and Indonesian language.
On the Role of the Institute of Headmen of Academic Groups in Russian Universities

The institute of headmen of academic groups is an auxiliary tool for improving the educational process in higher education. This tool is traditional for Russian Universities, but it practically is not used abroad. Based on empirical data, the article highlights its current state, formulates key problems and justifies measures to overcome them. The goal is the formation of a set of sound scientific and practical measures to improve the educational process in Russian higher education by developing the institute of headmen of academic groups. Methodology and methods . On the basis of the questionnaire developed by the author, a student survey was conducted to explore how the institute of headmen of academic groups is functioning in one of the Russian economic universities. The total number of students who were invited to participate in the survey was 238 people (12 academic groups); the actual number of respondents was 193 (81% of students). The questionnaire was anonymous, remote, electronic, free of charge. The results and scientific novelty . The author has formulated a number of key problems characterizing functioning of the institute of headmen of academic groups at the present stage: a) the lack of proper interaction between deans and headmen; b) the lack of real authority among the headmen; c) the lack of candidates for the role of headmen; d) the lack of systems in domestic universities for: d1) attracting students with high organizational skills and leadership to perform the functions of headmen; d2) the formation of a personnel reserve of headmen; d3) supporting headmen’s activities including the development of their personal and business qualities. To overcome them, a number of recommendations have been proposed: a) to formalize the status of the institute of headmen of academic groups as an auxiliary tool for improving the educational process in order to specify the conceptual task of its functioning; b) to intensify the interaction between the organizational and educational units of deans headmen of academic groups; c) to empower councils of headmen so that they can take part in consideration of certain issues related to the implementation of the educational process; d) to create a working system for the selection, preparation and support of the activities of headmen of academic groups; e) to increase the motivation of headmen. Practical relevance . The given recommendations can be used in Russia and abroad at the state and local levels in order to improve the quality of the educational process in higher education institutions.
Scheduled Virtual Machine Placement in IaaS Cloud: A MPSO Approach

Infrastructure-as-a-service (IaaS) is most popular cloud service in Information and Communication Technology. The cloud services are virtual resource pool connected with the network and delivers resources to cloud user via the Internet. In this work, we design a new virtual machine (VM) placement algorithm in IaaS cloud with semi-scheduled context for scheduled VMs. The semi-scheduled context allow to collect the scheduled VMs request in a short-time window before decides VM placement and satisfies resource demands of VMs on daily basis in real time. We followed a modified particle swarm optimization (MPSO) technique with semi-scheduled context for scheduled VM placement in IaaS cloud called SOPSO algorithm. The performance of proposed algorithm shows better than the existing ones in terms of various performance metrics such as active time of the server, power consumption, resource wastage, number of active servers in IaaS cloud. Finally, we show the advantages of semi-scheduled context in performance evaluation, in which small increases size of time window allow to achieve desired objectives with more improvement.
Multicloud- auto scale with prediction and delta correction algorithm

IT enabled enterprise organizations are embracing multi-cloud to handle their peak loads in addition to their private cloud (own data centers) and the market prediction indicates that by year 2020, about 80% of enterprise will have or working towards multi-cloud enablement for their IT needs. There are multiple products from third-party supporting multi-cloud and also some of the cloud service providers have already enabled the service using proprietary cloud management tools with custom built plug-ins. One of the major challenges in multi-cloud is efficient resource utilization and cost savings without vendor/broker lock-in. At present, multi-cloud interface standardization is still in draft. In this paper we propose handling of cloud burst at peak load with our OpenStack [9] Middleware Framework, which uses default OpenStack services. It also has built-in support for prediction, resource allocation and proactive workload migration techniques which can be integrated easily with cloud provider solutions.
5G Service and Discourses on Hyper-connected Society in South Korea: Text Mining of Online News

This study explored social discourses on hyper-connected society by using text mining of online news articles in South Korea. Online news data was collected from a database of news articles provided by the Korea Press Promotion Foundation using the R3.5.3 program, and the data cleaning and tokenization was conducted. Then, a Topic Modeling (Latent Dirichlet Allocation) and Network Analysis (N-gram Language Model) was conducted. The number of topics was set to the best 10 topics based on the results of several LDA results. Many words related to 5G next-generation communications and innovative technologies are mentioned in several topics. In addition, the results showed that various social discourses, such as education, human life, societal changes, governmental support, and industry, are currently being discussed as major topics of a hyper-connected society. The results showed that hyper-connected society not only signifies the emergence and application of innovative communication technology, but also includes extensive changes to human life, social relations, education, and industry.
Electricity anomalies diagnose method based on the electricity information acquisition system

This paper introduces several common electrical anomalies and their characteristics, and gives the relevant characteristic parameters. And then, establishes the relationship between various abnormal events and data in the electricity information acquisition system. On this basis, the electricity anomaly structural model based on the electricity information acquisition system is designed, which including data preprocessing and electricity anomaly detection model. The validity of the structured model is proved by experimental data, which provides an effective method for solving the problem of abnormal power diagnosis under big data conditions.
Moving objects Path Tracking based on Entropy Background Subtraction and CAMShift

For multiple object path tracking we integrate entropy background subtraction method and CAMShift algorithm. Initially claussius entropy theory is used to transform each pixel in the image domain into entropy domain and obtain its energy level. Later we use entropy background subtraction algorithm to detect moving object region in each frame. To improve robustness in the condition where objects are of different color, object colors are same as to background's colors. localization of object is obtained by choosing each objective region. This approach can automatically calibrate moving vehicles in traffic video and achieve multi-target tracking by using a multi-tracker of CAMShift algorithm. The proposed system is also capable of tracing paths of moving vehicles. The results and analysis demonstrates that the methods used in the paper finds solution for automatic multi-object tracking problems in video sequence efficiently.
Progress and Development of Virtual Reality and Augmented Reality Technologies in Tourism

Progress and development of tourism technologies and applications available through smart devices provide an increasing diversity in tourism marketing. Augmented reality, virtual reality applications with the smart technologies, which are considered as new marketing communication instruments, increase awareness and help in terms of getting information about tourists and giving information to the tourist. Thus, this chapter provides an assessment regarding the progress of AR and VR researches published in hospitality and tourism journals. In particular, the chapter determines the current fields, topics, and research methods of AR and VR articles published in 32 hospitality and tourism research journals between 2000-2018, comparing the subjects and research methods of 32 journals and discussing the changes in topic areas throughout the years, to offer suggestions for further research on AR and VR for hospitality and tourism journals.
Quality Model for Testing Augmented Reality Applications

Augmented Reality applications have the capability of merging virtual objects into physical setting, or alternatively they can wrap physical objects within a virtual scene. Augmented reality applications are similar to virtual reality applications in that aspects of the visualizations are computer generated, but augmented reality apps also must contain a view of the physical world. Augmented reality applications are being utilized in service, manufacturing, product areas, as well as gaming. Mobile devices are becoming common runtime environments for augmented reality applications and the mobile device proliferation is enabling a wave of AR applications. Due to the combined nature of digital and physical objects, as well as the environmental and contextual constraints, a traditional test plan is not sufficient. A new quality model is proposed that takes these issues into account, and an example of how machine learning can assist with aspects of the model is discussed.
Investigation of 3-D Relational Geometric Features for Kernel-Based 3-D Sign Language Recognition

Extraction and recognition of human gestures in 3D sign language is a challenging task. 3D sign language gestures are a set of hand and finger movements with respect to face, head and body. 3D motion capture technology involves capturing 3D sign gesture videos that are often affected by background, self-occlusions and lighting. This paper investigates the relation between joints on 3D skeleton. Kernel based methods can be remarkably effective for recognizing 2D and 3D signs. This work explores the potential of global alignment kernels, we use 3D motion capture data for 3D sign language recognition. Accordingly, this paper encodes five 3D relational geometric features (distance, angle, surface, area and perimeter) into global alignment kernel based on similarities between query and database sequence. The proposed framework has been tested on 800 gestures captured by five subject (i.e., 5 × 800 = 4000) sign language dataset and three other publicly available action datasets, namely, CMU, HDM05 and NTU RGB-D. The proposed method outperforms when compared to other state-of-the-art methods on the above datasets.
Text-Enhanced Knowledge Representation Learning Based on Gated Convolutional Networks

Knowledge representation learning (KRL), which transforms both the entities and relations into continuous low dimensional continuous vector space, has attracted considerable research. Most of existing knowledge graph (KG) completion models only considers the structural representation of triples, but do not consider the important text information about entity descriptions in the knowledge base. We propose a text-enhanced KG model based on gated convolution network (GConvTE), which can learn entity descriptions and symbol triples jointly by feature fusion. Specifically, each triple (head entity, relation, tail entity) is represented as a 3-column structural embedding matrix, a 3-column textual embedding matrix and a 3-column joint embedding matrix where each column vector represents a triple element. Textual embeddings are obtained by bidirectional gated recurrent unit with attention (A-BGRU) encoding entity descriptions and joint embeddings are obtained by the combination of textual embeddings and structural embeddings. Extending feature dimension in embedding layer, these three matrixs are concatenated into 3-channel feature block to be fed into convolution layer, where the gated unit is added to selectively output the joint features maps. These feature maps are concatenated and then multiplied with a weight vector via a dot product to return a score. The experimental results show that our model GConvTE achieves better link performance than previous state-of-art embedding models on two benchmark datasets.
A New Efficient Sbox for Strengthening PRESENT Like Block Ciphers Against Linear Cryptanalysis

Substitution box(sbox) is the most crucial element in block ciphers like AES, PRESENT etc., in providing good security. Any weakness of sbox will lead to easier retrieval of secret keys. Cryptologists proved that PRESENT block cipher is vulnerable to linear cryptanalysis and main cause for the same is higher linear cardinality. In this paper we propose a new sbox in which Linear cardinality L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Car</sub> equal to 2. The security strength of PRESENT block cipher (in which the original sbox is replaced by the proposed one) is evaluated through linear cryptanalysis. Also, the proposed sbox obtains a very good avelanche effect for the cipher. Minimum number of active sboxes and total bias of the cipher with proposed sbox are indications of higher security and performance. This sbox can be used for PRESENT like light weight block ciphers. Even though this paper is focused on security against Linear cryptanalysis, the proposed sbox shows excellent robustness against differential cryptanalysis also. The differential cardinality for the proposed sbox, D <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Car</sub> is also 2, which is a very good measure of security.