Reviewerly/exHarmony huggingface dataset:
Real-Time Object Detection using Deep Learning and Open CV

The object detection is used in almost every realworld application such as autonomous traversal, visual system, face detection and even more. This paper aims at applying object detection technique to assist visually impaired people. It helps visually impaired people to know about the objects around them to enable them to walk free. A prototype has been implemented on a Raspberry PI 3 using OpenCV libraries, and satisfactory performance is achieved. In this paper, detailed review has been carried out on object detection using region – conventionaal neural network (RCNN) based learning systems for a real-world application. This paper explores the various process of detecting objects using various object detections methods and walks through detection including a deep neural network for SSD implemented using Caffe model.
Thai undergraduate digital citizenship skills education: A second-order confirmatory factor analysis

From the study’s initial qualitative analysis, 10 latent variables and 22 indicators concerning digital citizenship skills (DCS) were used in a questionnaire on a sample of 506 Thai Business Computer Programme undergraduate students attending one of six regional Thai Rajabhat Universities. Goodness-of-fit and descriptive statistical analysis [mean, standard deviation, Kaiser–Meyer–Olkin index analysis and Bartlett’s test of sphericity were analysed by using SPSS® for Windows Version 21 and LISREL 9.10 for the second-order confirmatory factor analysis CFA. The results revealed that each student’s opinion on their DCSs had a ‘high agreement’ level. Moreover, importance ranking from high to low of the 10 primary indicators was each student’s footprint (1.00), cyberbullying (1.00), law (0.97), access (0.88), security (0.84), literacy (0.83), empathy (0.81), etiquette (0.80), collaboration (0.70) and thinking (0.65). Therefore, it is suggested that this study’s results be used to assist various agencies and ministries in building programmes for student digital citizenship skills.&#x0D; &#x0D; Keywords: Digital skills education; student cyberbullying; student digital footprints; teaching digital literacy; Thailand
Load Balancing in Cloud Computing with Enhanced Genetic Algorithm

Cloud computing has a decentralized architecture in which virtual machine migration is one of the major challenges which affects the network performance. To balance the network load, various techniques are designed for the virtual machine migration. In the previous research work, genetic algorithm was proposed for Virtual Machine (VM) migration which can balance the network load. The genetic algorithm is complex in nature which increases the execution time. In this research work, genetic algorithm is improved for VM migration which reduces the execution time and also space and bandwidth utilization.
Social Spider Optimization Algorithm: Theory and its Applications

An extensive variety of optimization problems are solved by swarm intelligence algorithms that are modelled based on the animal or insect behaviour while living in groups. One such recent swarm intelligence algorithm is Social Spider Optimization (SSO). This paper thoroughly reviews and analyses the characteristics of this meta-heuristic algorithm. Since the existing literature of this algorithm is comparatively limited, the paper discusses the research ideas presented in such existing works and classifies the literature on basis of the application areas like image processing, optical flow, electric circuits, neural networks and basic sciences. It also sets a basis for research applications of the algorithm in order to tap the complete potential of the algorithm in other areas to achieve desired results.
Security Issues in Blockchain based Applications

Data security is the key to the development of modern internet technology. By allowing digital information to be distributed but not copied, blockchain technology created the backbone of a new type of internet. It allows storage of all transactions into immutable records and every record distributed across many participant’s nodes. As there is huge increase in the use cases for blockchain technology, nowadays, the primary issue is to enable secure data sharing, data integrity and authentication. The goal of this study paper is to provide a systematic literature survey exploring the use of blockchain as a base technology for securing financial as well as non-financial applications. The purpose is to identify the whether blockchain technology is capable to provide the desired security solutions in various application. The advantages, challenges and the solution provided by previous research is discussed. The survey was focused on various security issues like Confidentiality, Integrity, Availability, Authenticity, Accountability, and Reliability. The study shows that the Blockchain technology is prospective for financial and non-financial services since it can provide solutions for majority of security issues. Future research needs to provide the implementations of desired solutions discussed in security challenges of Blockchain technology.
Exploring the Axiom of Excluded Middle and Axiom of Ontradiction in Fuzzy Sets

Fuzzy sets are considered as a fine extension of classical sets (crisp) in which the elements possess diverse degrees of membership functions. Zadeh is the initiator of fuzzy sets that predominantly deal with imprecision and vagueness. In this paper, the Law of Excluded middle and the Law of Contradiction were discussed in an exemplary mode. In addition to that the definitions of fuzzy sets, crisp sets and the various operations on them were presented in a consecutive manner.
Energy Efficient VM Migration in Cloud Datacenter using Dolphin Echolocation Optimization with Tchebycheff Algorithm

The workload in cloud computing surroundings changes progressively delivering unwanted circumstances, for example, load unbalancing and minor usage. Virtual machine migration is an impressive plan in such circumstances inorder to improve system performance. With a specific end goal to give productive energy virtual machine migration is essential that migrates a running virtual machine without disconnecting the client or application. In any case, an algorithm in view of a single objective is generally familiar with to coordinate the migration process. Unexpectedly, there stay alive unconsidered variables affecting the migration process, for example, burden capacity, power utilization and resource wastage. We offer a multi-objective algorithm for obtaining VM migration by evaluating the multi objectives that are responsible for migration overhead. In this manner, we suggest a narrative relocation approach united by a Multi objective Dolphin Echolocation Optimization Algorithm (MO-DEOA) to assess several objectives. The aim is to efficiently obtain improved migration that concurrently diminishes power consumption by guaranteeing the performance of the system.
Providing Enhanced Resource Management Framework for Cloud Storage

Data centers are progressively being re-intended for workload combination with a specific end goal to receive the rewards of better resource usage, control cost, and physical space investment cost. Among the strengths driving costs are server and storage virtualization innovations. A key understanding is that there is a more noteworthy cooperative energy between the two layers of storage and server virtualization to be application piece sharing data than was beforehand thought conceivable. In this segment, we display ERMF, a platform that is intended to have MapReduce applications in virtualized cost. ERMF gives a bunch file framework that backings a uniform record framework name space over the group by coordinating the discrete nearby storage of the individual hubs. Our paper proposes ERMF accommodates the two data and VM resource assignment with contending requirements, for example, storage usage, changing CPU load and system connect limits. ERMF utilizes a stream arrange based calculation that can improve MapReduce performance under the predetermined limitations by starting situation, as well as by straightening out through VM and data relocation also. Moreover, ERMF uncovered, generally shrouded, bring down level topology data to the MapReduce work scheduler with the goal that it makes close ideal task scheduling.
Design and Prototype Development of Portable Trash Collector Boat for Small Stream Application

Commercially available trash collector boats are often designed in large size to cater to the high trash loading for surface water cleaning purpose. On the other hand, for small streams and drainage, the manual cleaning method is often used. This situation is undesirable since it can lead to health problem and diseases to the operator due to the number of impurities present in the water. This paper highlights a proposed design of a trash collecting boat for cleaning purposes in small area applications such as lakes, small streams and drainage. The trash collector boat is designed to be a small and automated boat capable of collecting floating solids off the water surface and store them temporarily. The trash will be manually collected at the end of the operation. The design methodology has been accomplished using engineering design method to propose a suitable design of portable trash collector boat based on commercially available design. With the aid of Autodesk Inventor 2009, a 3D representation of the trash collector boat was generated to visualize all the details regarding the trash collector boat. System fabrication will be conducted using appropriate material to ensure the efficiency of trash collector boat. Testing and analysis were conducted to evaluate the system performance, monitoring unit performance and loading capacity of the portable trash collector boat. The results found that the collector boat is capable of handling a maximum trash load of 6 kg in a single operation.
Development of Mobile Learning Media (MLM) to Enchance Students’ Understanding of CNC Programming Subjects

This study aims to develop Mobile Learning Media (MLM) technology-based learning in learning programming of Computer Numerical Control (CNC) in vocational high schools.This development is important to improve student competence to be ready for work.Product development design uses the ADDIE technique which includes the stages of analysis, design, development, implementation and evaluation.The application of product development was carried out by using an experimental trial method with a random control group pretest-postest design.The number of research respondents are 120 students with 60 students in the experimental class and 60 students in the control class.Research data includes data on students' learning outcomes and interview data with students after the application of the use of mobile learning which has been developed.Analysis of research data used descriptive statistical techniques and independent sample T-test by using SPSS 2.0 software.The results showed that the use of MLM can facilitate students to learn anywhere and anytime.MLM in CNC learning can provide real learning on how the CNC program is run, so that it can increase students' motivation and learning achievement.MLM supports students' independent learning and makes students become more confident in mastering their competencies, so that teachers can do a variety of jobs easily.
Feature-Based Opinion Mining for Amazon Product’s using MLT

analysis of sentiment’s or opinion mining is one of the major challenge of NLP (natural language processing) .Business Analytics plays a major role in the present scenario with a view to improve their business. These human beings especially relies upon on reviews about their product to resist in the marketplace and information analytics which can give us an excellent insight on what to expect in the future. Opinions can be referred to, with which futures opinions can be expected. Few words or terms can determine outcomes or results. As maximum of these business people try to improve their business to get maximum profit by selling quality products .So, in this regard sentiment analysis has gain a whole lot attention in current years.SA is an area of study within NLP which is used in identifying the view or opinion of a particular feature inside a content i.e., text. This paper is based on the different techniques used to classify a specified text according to the views expressed in it, i.e. whether a person's overall mentality is negative or positive or neutral. We also examine the two-advance methods (feature classification followed by polarity classification) followed along with the experimental results. Finally in this paper we compared 3 ML classification techniques 1) SVM, 2) Naïve Bayes (NB) 3) Logistic Regression with Hybrid Algorithm in which hybrid algorithm gives more accuracy when compared with the other 3 ML algorithms.
An Enhanced K-Nearest Neighbor Predictive Model through Metaheuristic Optimization

The k-nearest neighbor (KNN) algorithm is vulnerable to noise, which is rooted in the dataset and has negative effects on its accuracy. Hence, various researchers employ variable minimization techniques before predicting the KNN in the quest so as to improve its predictive capability. The genetic algorithm (GA) is the most widely used metaheuristics for such purpose; however, the GA suffers a problem that its mating scheme is bounded on its crossover operator. Thus, the use of the novel inversed bi-segmented average crossover (IBAX) is observed. In the present work, the crossover improved genetic algorithm (CIGAL) is instrumental in the enhancement of KNN’s prediction accuracy. The use of the unmodified genetic algorithm has removed 13 variables, while the CIGAL then further removes 20 variables from the 30 total variables in the faculty evaluation dataset. Consequently, the integration of the CIGAL to the KNN (CIGAL-KNN) prediction model improves the KNN prediction accuracy to 95.53%. In contrast to the model of having the unmodified genetic algorithm (GA-KNN), the use of the lone KNN algorithmand the prediction accuracy is only at 89.94% and 87.15%, respectively. To validate the accuracy of the models, the use of the 10-folds cross-validation technique reveals 93.13%, 89.27%, and 87.77% prediction accuracy of the CIGAL-KNN, GA-KNN, and KNN prediction models, respectively. As the result, the CIGAL carried out an optimized GA performance and increased the accuracy of the KNN algorithm as a prediction model.
A Multi-Faceted Approach for Trustworthy AI in Cybersecurity

Artificial intelligence (AI) is an increasingly used technology in today’s society due to its seemingly limitless use cases, from automation to augmentation and beyond. However, some questions remain unanswered, including what the consequences of these implementations are, how such implementations impact humans, and how trustworthy and secure they are. This article focuses on the implications of AI for the field of cybersecurity: new applications to secure, new attack vectors for malicious actors, and new ways to protect our systems with AI. The authors argue that it is imperative to incorporate ethics and related responsibility principles as central elements in the design and operation of AI systems for effective cyber defense. This paper proposes a multi-faceted approach to respond to the emerging challenges associated with AI and emphasizes what is needed now to expose students to AI in their curriculum. Due to the exploratory nature of this study and the newness of the field, our goal is to invite further discussion and investigation of this important subject, and to begin developing a curriculum to introduce trustworthy AI throughout the curriculum.
Enhancing Supply Chain Management Using Blockchain Technology

Blockchain technology is emerging in every field. Blockchain is a constantly growing ledger that keeps a permanent record of all the transactions that have taken place in a distributed, decentralized and immutable way. Supply Chain Management is a broad range of activities that involve the management of the flow of goods and services, from the point of inception to the point of consumption. Blockchain technology can i) assure provenance tracking and traceability across the supply chain management ii) improves the quantity, quality and access to information at every step of the way across the chain iii) provides a more transparent way to provide data or information at a given instance of time. Blockchain technology can also be adapted in the supply chain management to overcome its disadvantages. The paper shows different case studies where the benefits of applying blockchain technology in supply chain management are appealing that has shown many benefits. It highlights the cases that have reduced fraud and errors; mitigate risks and provide data transparency and privacy, etc. The paper shows the creation of a general blockchain network and the information passing mechanism which is used to pass the information among the participants across the supply chain. With information passing mechanism we can provide data transparency to the consumers across the supply chain.
Research On Enterprise Public and Private Cloud Service

In this paper, a generic comparison between the Enterprise Public and Private Cloud is analyzed based on its features and service provided. Cloud computing technology is widely adopted in today's industry where large database applications are deployed. Every organization moving towards cloud will have to consider many factors such as security, portability, database size, platform independence, multi-tenancy, customer requirements and other related resources. The detailed study focuses on essential cloud characteristics, service models, and cloud operation models such as Integrated (Hybrid), Public, Internal (Private) and Community clouds. The outcome is an effective assessment of leading two Enterprise cloud technology service providers – ownCloud© and HP Public Cloud® and its implications on the organizations which used them for cloud based applications. ownCloud© is the preferred open source file system for organizations to access for solutions across the world. HP Cloud offered by Hewlett-Packard (HP) provides public, private, hybrid and community cloud based services for various enterprise and other large-scale business organizations. Both the Private Cloud and Public Cloud can be enhanced to protect the data from intrusion with various security features where multiple users share the same cloud for storing and retrieving data
Blockchain: Technological Revolution in Business and Administration

Blockchain is the Technology which allows assets to be transferred from one party to another simultaneously and without needing a third party, guaranteeing utmost security. In addition, it is a multiple global ledgers, shared by all people around the world, which enables them to do all kinds of transactions (Sutardja Center for Entrepreneurship &amp; Technology Technical Report, 2015) in real time and without falling prey to fraud or manipulation. Besides, it is the biggest data bases, accessible to all people (BigchainDB, 2016) and containing verified assets which can be safely exchanged. Blockchain will have a huge effect on deferent levels, it will change the way all people adopt to deal through decades and threat traditional intermediary parties, on the other side it faces many obstacles stumbling blocks to be fully operated in different fields. This paper try to address the mechanism of blockchain technology, and how it works in simple words, then determine the possible applications of blockchain, and how to make best use of this technology, and finally the threats and challenges of blockchain.
Automatic Synthesis of Virtual Wheelchair Training Scenarios

In this paper, we propose an optimization-based approach for automatically generating virtual scenarios for wheelchair training in virtual reality. To generate a virtual training scenario, our approach automatically generates a realistic furniture layout for a scene as well as a training path that the user needs to go through by controlling a simulated wheelchair. The training properties of the path, namely, its desired length, the extent of rotation, and narrowness, are optimized so as to deliver the desired training effects. We conducted an evaluation to validate the efficacy of the proposed virtual reality training approach. Users showed improvement in wheelchair control skills in terms of proficiency and precision after receiving the proposed virtual reality training.
AN APPLIED STUDY OF RNN MODELS FOR PREDICTING CRYPTOCURRENCY PRICES

In recent years, cryptocurrency, and its foundational technology, blockchain, have become increasingly popular.As the value of some cryptocurrencies has cataclysmically risen and fallen, many people are curious about what the future of cryptocurrency prices look like, and what means are available to predict that future.We conducted experiments on three RNN models to determine whether one performed better than the others when predicting the price of Bitcoin, Ripple, and Litecoin.We also collected Google trends data for these three cryptocurrencies and ran additional experiments to explore whether this additional data significantly boosted the prediction accuracy of these models.After performing all our experiments, we found that among the three RNN models we tested, none performed significantly better than the others.Additionally, we concluded that supplementing the three models with Google trends data did not significantly boost the prediction accuracy of the models.We discuss implications of this research in the paper.
The nexus between digitisation and knowledge-based economy in low-income countries: the case of post-conflict Syria

In the era of global economic structural transitions, evaluating the trade-off costs of prioritising digitisation over improving a sustainable socio-economic development in low-income economies should be placed at the core of policymaking. This study endeavours to examine the impact of digitisation on the transition to knowledge-based economy in low-income economies, post-conflict Syria as a case study. We use the documentary research method (DSM) to analyse documents related to digitisation and ICT related activities in Syria. Then, we run autoregressive distributed lag model (ARDL) to investigate the short and long-run relationship between digitisation process and the transition process towards knowledge-based economy in post-conflict Syria. Our results indicate that the digitisation process in Syria is not a priority at the short and long-run and cannot solely support the transition process towards knowledge-based economy in post-conflict Syria. On the contrary, digitisation, in the absence of certain conditions, may harm the transition towards knowledge-based economy.
Neural Network Programming in Python

In this paper a basic introduction to neural networks is made. An emphasis is given on a two layer perceptron used extensively for function approximation. The backpropagation learning rule is than briefly introduced. A short introduction into Python programming language is made and a program for the perceptron design is written and discussed in some detail. The “neurolab” library is used for this purpose.