Reviewerly/exHarmony huggingface dataset:
Improved EfficientNet Network for Efficient Manifold Ranking-Based Image Retrieval

The Efficient Manifold Ranking (EMR) is a scalable graph-based ranking algorithm that is applied widely in Content-based Image Retrieval (CBIR). However, the effectiveness of an EMR algorithm depends on (1) the feature extraction technique applied to images to extract feature vectors and (2) the relational graph architecture of anchor points built inside the EMR. To address the first problem, EfficientNet-B7 + is proposed in this article which is fine-tuned from a pre-trained model of EfficientNet and is used to extract deep feature vectors of images. Regarding the second problem, we adopt the relational graph architecture of lvdc-EMR, in which the anchor points of the graph are generated by a variant of the Fuzzy C-Mean (FCM) clustering algorithm that was developed by our research team. The experiments conducted on three benchmark datasets Logo2K +, VGGFACE2-S, and Corel30K bring the mean image retrieval accuracy to 88%, demonstrating the effectiveness of our proposed method. Comparing the average values while retrieving using lvdc-EMR, the proposed EfficientNet-B7 + obtains from 4% to 6% better than the original EfficientNet-B7.
Detection of Fence Climbing Behavior in Surveillance Videos Using YOLO V4

Nowadays, with the development of technology and the Internet, most households have surveillance cameras to observe everything around the house. Therefore, detecting abnormal human behaviors using videos generated by surveillance cameras has attracted much recent research. This paper focuses on applying the YOLO v4 to build the model detecting abnormal human behaviors, especially detecting fence climbing behaviors. Experimental results on the dataset, including 5340 images extracted from videos, showed that the model obtained the IoU measure of 71% and F1-score measure of 87%.
Development of a Deep Learning-Based Object Detection and Localization Model for Controlling a Robotic Pick-and-Place System

A new model for the multi-task of object detection and localization is developed in this paper. This model is used to control a robotic pick-and-place system for real-time applications. The model is developed from a deep neural network, namely RetinaFace with MobileNet as its backbone, with modifications to the output to allow simultaneous detection and localization of objects. A self-generated dataset was prepared for the training and testing processes. The error calibration of the camera is implemented. The robot's overall control algorithm is created. The results show that our model has a high accuracy of 97.4% for object detection and an error of less than 2.41 mm for object localization. The computation is also efficient, reaching 25 FPS with the very lightweight hardware of the Jetson Nano. Experiments with the pick-and-place method have a success rate of 100%.
Interactive Control Between Human and Omnidirectional Mobile Robot: A Vision-Based Deep Learning Approach

Nowadays, mobile robots have been popular not only in industrial applications such as materials transportation but also in non-industrial applications, e.g., human assistance. Among developed configurations, omnidirectional mobile robots have attracted great attention in recent times due to their superior maneuverability over their conventional counterparts. In this research, an application of a four mecanum-wheeled omnidirectional mobile robot (4-MWMR) in human assistance has been developed. By using image processing, the 4-MWMR is capable of following an authorized person, thereby assisting users in transporting large-size or heavy-weight materials. Good experimental results show the ability of the developed system to be used in practice.
Analyzing Information Security Among Nonmalicious Employees

Insider threats pose a significant risk to organizational data security, and many organizations implement information security policies (ISPs) to reduce insider threats. This study used the unified theory of acceptance and use of technology 2 (UTAUT2) to examine factors that predict compliance among nonmalicious employees. A partial least squares structural equation modeling approach was used to examine survey data collected from N = 158 nonmalicious employees. The analysis indicated that social influence and facilitating conditions were the only UTAUT2 factors significantly predicting nonmalicious employees’ compliance. The study’s findings suggest that organizations should focus on building workplace cultures emphasizing ISP compliance’s social importance.
Fault Analysis Approach of Physical Machines in Cloud Infrastructure

The large-scale cloud computing environment has raised great challenges for fault analysis in infrastructure. The openness of cloud computing makes it challenging to assess the state of the infrastructure, which affects the data center's continuing availability. In addition, the fault detection in which prior-knowledge of faults has not been defined yet can make mistakes since using supervise learning. To address this issues, the fault analysis model of physical machines in cloud infrastructure is proposed by three components, i.e., abnormal score, fault detection, and ranking suspicious metrics. The proposed model is validated by using a current Google cluster trace dataset.
Improving Availability of Enterprise Blockchain Using Real-Time Supervisor

Blockchain is one of the key technologies today and is increasingly being applied in many different fields. Blockchain in the enterprise is emphasized due to its ability to overcome the inherent limitations of centralized applications widely used in enterprises. The modern workplace trend after the COVID-19 pandemic has paved the way for the deployment of enterprise blockchain networks on end devices (nodes) provided by enterprises to employees. However, according to the above approach, ensuring the availability of nodes is very difficult because it heavily depends on employees’ behaviors. To increase enterprise blockchain availability, we propose a novel approach that integrates a real-time supervisor with enterprise blockchain to monitor and ensure established policies.
Proposing a Semantic Tagging Model on Bilingual English Vietnamese Corpus

In this article, we report on the results of the study on building a semantic tagging system using the English-Vietnamese bilingual corpus to create a lexical resource with lexical notes based on translation similarities, transfer of vocabulary, and classification schemes through bilingual connections. This tool plays an important role in building and developing natural languages processing systems such as automatic translation, text summary, text extraction, information retrieval, and question-answer automatic. In this context, our goal to see English as the source language is roughly translated into Vietnamese, we use an English-Vietnamese bilingual concept dictionary for the purpose of annotating the semantics of words. In our experiments, we used hand-annotated vocabulary sets, compared with the proposed model results, and the results were achieved based on an average vocabulary coverage of 83.05%, with accuracy reaching up to 82.40%. This test can be applied to build lexical la-bels for other languages than bilingual corpus whose source language is English.
Development of a Human Daily Action Recognition System for Smart-Building Applications

In this work, a daily action recognition system for smart-building applications is developed. The system consists of a processing pipeline to perform tasks of human detection, pose estimation, and action class classification. The Yolov7-Pose was used for the human detection and pose estimation task, while a trained model based on the CRT-GC method was used for the action classification. The prediction of the start-to-finish duration of an action in a sequence video is performed via the sliding window method. For the trained model and the evaluation, a self-generated dataset of six classes of daily actions with challenging conditions was created. The evaluation results show that the Yolov7-Pose outperforms others in terms of accuracy, robustness, and computational efficiency. The pose estimation reaches an AP50 of 89.1%, and the action recognition has an mAP50 of 85.6%, in which the highest accuracy reaches 95.7%. The total computing time for the overall processing pipeline is 14ms. The obtained results indicate that there is a high potential for practical applications.
On the Principles of Microservice-NoSQL-Based Design for Very Large Scale Software: A Cassandra Case Study

Developing very large scale distributed software systems is challenging from both functional and data management perspectives. Methods based on Microservices Architecture (MSA) have gained popularity for addressing the functional challenges. On the other hand, cloud-aware, very large scale NoSQL data management systems have also proved their effectiveness in tackling data management’s scalability challenges. Recent work have studied the combined approach for specific methods and systems. However, there has been no work that propose a complete method or study the underlying design principles. In this paper, we present the result of our initial research on this subject. We choose Cassandra as a case study as it is a popular system that supports cloud-aware, very-large-scale NoSQL data management. We propose the CaMSAndra software development method that combines the MSA and Cassandra methods. We define a UML metamodel for CaMSAndra and uses it as the basis for discussing the design principles. We analyse the relationship between bounded context and application workflow and, based on this, define a hierarchical service design that builds a service hierarchy by transforming an application workflow. We also discuss a data-driven cluster design in connection to the microservices. We demonstrate CaMSAndra with a well-known software domain called Hotel Reservation. We contend that our method is promising for developing very large microservice-based, NoSQL-based systems in general.
Securing Network Address in PingER and IOS Application Development

The main objective of this research is to study the Internet network performance across many countries including India in last few years. Comparisons are drawn between the various conditions that existed in the nation at that period and how the network in those regions was most affected. Ping end-to-end reporting is known by the acronym PingER. This study, which began in 1995, set out to gauge Internet performance for a number of different reasons. To enhance the monitoring experience, I have designed an IOS application for researchers to help them remotely monitor and encryption of the server’s geolocation to protect it from various malicious attacks. This paper discusses the same in detail. PingER (ping end-to-end reporting) is the name given to the Internet end-to-end performance measurement (IEPM) project to monitor the end-to-end performance of Internet links. Hence to secure the data that is being transferred around the world, there is an urgent need to maintain its integrity as well, without doing so the credibility of the data can be tampered with or worse re-routed and manipulated. Not only this since it is a project to improve and identify the bad links between the networks, but the users also provide a direct link to access and review them, thus creating a need to build an application that is readily available to the audience to use and monitor. Since PingER measurements are conducted for more than 700 targets in over 160 countries, which indeed constitutes 99% of the world’s population. This paper discusses the necessity for and creation of an IOS application that will attempt to give capabilities similar to a PingER MA to aid researchers in real-time pinging and monitoring. Also, to avoid information hijacking and manipulation, enable encryption and IP spoofing on the SLAC PingER server.
Development of a High-Speed and Accurate Face Recognition System Based on FPGAs

In this work, a high-speed and accurate face recognition system based on Field Programmable Gate Arrays (FPGAs) was completely developed. A complete pipeline that contains a sequence of processing steps, including preprocessing, face feature extraction, and matching, is proposed. For processing steps, lightweight deep neural models were developed and optimized so that they could be computationally accelerated on an FPGA. Besides the core processing pipeline, a database as well as a user application server were also developed to fully meet the requirements of readily commercialized applications. The experimental evaluation results show that our system has a very high accuracy based on the BLUFR benchmark, with a precision of 99.336%. Also, the system is very computationally efficient, as the computing time to recognize an ID in a dataset of 1000 IDs with 4000 images on the FPGA ZCU-104 is only 30.3 ms. For the critical case, the system can process 8 camera streams and simultaneously recognize a maximum of 80 IDs within a computing time of 342.3 ms for each ID. With its high-speed and accuracy characteristics, the developed system has a high potential for practical applications.
Integrating Security and Privacy Mechanisms with Fast Health Interoperability Resources (FHIR), a Scoping Review

The scoping review reported by this article aimed to analyse how security and privacy mechanism are being integrated with Fast Health Interoperability Resources (FHIR). An electronic search was conducted, and 37 studies were included in the review. The results show that 19 studies (i.e., more than half of the included studies) reported on the use of blockchain technology to (i) assure secure data sharing, (ii) provide secure Personal Health Records, (iii) support authentication and auditing mechanisms, (iv) support smart legal contracts, and (v) monitor the access to clinical data. The remainder 18 articles reported on the implementation of security and privacy mechanisms related to (i) data security at transmission, (ii) data security at storage, (iii) access control; (iv) data anonymization, and (v) management of informed consents.
Quantum Computing and Lawful Interception Applications

This paper proposes a secure turnkey framework for lawful interception (LI) solutions addressing the challenges that the new technologies of 5G and quantum computing impose to the work of police authorities on prevention and detection of illegal activities. Current LI technologies need to be extended to comply with the 5G architecture and a fully distributed LI platform is proposed, including virtualized components, dynamically instantiated within the 5G network, in the vicinity of the appropriate 5G network functions, across the whole chain of legal interception. Zero touch management system can hide the network and operational complexity from the operator, while Quantum Computing (QC) and Artificial Intelligence (AI) tools can enable easier extraction of intelligent information from large volumes of intercepted data and IoT devices.
Server-side Adaptive Federated Learning over Wireless Mesh Network

In federated learning, distributed nodes train a local machine learning model and exchange it through a central aggregator. In real environments, these training nodes are heterogeneous in computing capacity and bandwidth, thus their specific characteristics influence the performance of the federated learning process. We propose for such situations the design of a federated learning server that is able to adapt dynamically to the heterogeneity of the training nodes. In experiments with real devices deployed in a wireless mesh network, we observed that the designed adaptive federated learning server successfully exploited the idle times of the fast nodes by assigning them larger training workloads, which led to a higher global model performance without increasing the training time.
Topic Modelling for Automatically Identification of Relevant Concepts Discussed in Academic Documents

A combination of natural language processing and topic modeling identifies topic terms a collection of documents. Latent Dirichlet Allocation (LDA) is an algorithm widely used to infer topics that the document belongs to, on the basis of words contains in it. This research applied LDA to identify topics automatically from academic documents as a way of validating the relevant concepts discussed in the curriculum literature. The experiment involved academic documents about Knowledge Building. We apply some techniques to prepare the data, after data training and validation. Topic modeling was used to identify the topic terms that can be used in the knowledge-building dialogue. Those concepts are meaningful to both the teacher or students because they provide a visualization of the content coherence.
Mobile Development with Xamarin: Brief Literature, Visualizations and Important Issues

Mobile applications and devices play a very important role in everyone’s life, due to their easy mobility, small size and interaction with people. But at the time of developing these applications it is a problem to identify the right platform that allows an adequate development. In this context, this paper proposes a literature review of the Xamarin mobile development platform, identifying the current state of development, the visualizations presented by the platform and certain important considerations. It can be stated that there is a great need for research on everything involved in mobile development, so that developers and users can benefit from the use of multiplatform tools. As a result, it is identified that Xamarin is a very robust platform for mobile development, and is one of the most used and popular worldwide.
Software Application to Assist the Publishing Sector: A Tool in MS Excel Environment

The rapid change in technology influences any sector of economics including the media industry. The new circumstances imposed by the coronavirus crisis require to take adequate response to adjust to the new reality. In response to these challenges, the business model print-on-demand seems to be a promising solution, especially in the production of digital/print books. In this context, the current article describes the developed software application to assist the publishing sector realized as a tool in MS Excel environment. Developed application can be used as a decision support tool considering different customer requirements, different circulation, digital publishing, or print on demand. Implementing it in Google Sheets allows other manuscripts to be easily added, as well as to change the printing price under different circulation. The developed application was used in a small publishing company to meet the requirements of various clients. The obtained results are encouraging and demonstrate the applicability of the described application.
Performance Comparison of Cryptographic Algorithms Used in Cloud Computing

Cloud computing is a rapidly growing technology, but because of its distributed nature, it is also susceptible to attacks. To ensure security, data transferred uses cryptographic algorithms to encrypt the data. These algorithms’ performance is vital as data needs to be available quickly and accessible to users. A reliable way to compare performance that is normalized is through throughput. This paper compares some of the state-of-the-art used algorithms in cryptography to newer algorithms depending on the file size based on their throughput (kilobyte per millisecond). The algorithms were divided depending on their file size into three different groups. The conclusion was that different algorithms perform best for different file sizes. Algorithms, on average, work best on data between 1 and 10 MB in size, according to this research.
Enhancement of the Machine Safety Using OpenCV

Hand pose detection is the system of recognizing where the joint of the hand is detected which is used to communicate the machine with the outside environment. In industries, 22.9% accidents are caused due to human faults. In order to avoid this accident, the human posture can be used to detect a human error in the industrial area, and when the human limbs enter the operation area, the machine can stop, thus preventing the accident. This can be implemented with the use of TensorFlow, MediaPipe, and OpenCV. This article focuses on the hand pose detection model used to prevent accidents at work. The study shows that the media pipeline hand gesture recognition model has the highest accuracy for recognition with OpenCV. The media pipeline recognizes 21 different locations on the palm that accurately detect the presence of the hand in the environment.
Impact of Fourth Industrial Revolution-Related Technologies on Higher Education

The advent of fourth industrial revolution (4IR) has brought a variety of opportunities and changed the ways things operate in various spheres of life. This has not left the education sector behind. This study aims at assessing the impact of the 4IR related technologies on higher education with engineering education in South African Universities as case study. This chapter considers the 4IR holistically and attempts to highlight the impact of its associated technologies on engineering education. In this study, data were obtained through qualitative (survey with open-ended questions) and quantitative (surveys with closed-ended questions) research methods. The study shows that the engineering curriculum needs to be overhauled, the mode of teaching engineering education needs to be adapted to take advantage of the 4IR, and that mode of assessments must be reviewed to align with the 4IR era. Consequently, there is urgent need for more proactive response from the educational sector than the more gradual societal evolution and subsequent response from educational institutions observed in earlier industrial revolutions. Efforts need to be geared towards the formulation of education systems that equip graduates with the skills that are in demand.
On Challenging Aspects of Reproducibility in Deep Anomaly Detection

This companion paper focuses on challenging aspects of reproducibility that emerge in anomaly detection with Deep Neural Networks. We provide motivating examples based on our work and present mitigation strategies. Furthermore, we document a trade-off between the complexity of experiments and the strength of the empirical evidence obtained through them, both of which impact different types of reproducibility. Ultimately, we argue that the reproducibility of inferences should be prioritized over the reproducibility of exact numerical results.
A Comprehensive Survey on Visualization of Human Action Recognition: By Hidden Markov Model and Convolution Neural Network

Humanity’s recognition action from a visual standpoint content is a difficult task as different types of problems arise in the recognition of human action. In the realm of computer vision, human action recognition (HAR) has reached a significant milestone. The advancement of technology allows us to address this issue and makes it a viable topic of research. A lot of research has already been done on HAR, and still, a lot is left. In this context, the focus of this survey is on the various types of HAR approaches that have been developed in the recent ten years. This paper uses a hidden Markov model based on several algorithms to solve the problem of human action recognition. We are comparing two techniques of HAR to find the best out of them. The hidden Markov model and the convolution neural network are two types of neural network. A convolutional neural network (CNN) capable of recognizing local patterns in input data is trained to recognize human actions from the local patterns in the feature representation. We found out that CNN is a better algorithm for recognizing human actions as it shows the body’s movement and body joints in two different aspects of the CNN graph.