Reviewerly/exHarmony huggingface dataset:
Detection and localization of multi-scale and oriented objects using an enhanced feature refinement algorithm

&lt;abstract&gt;&lt;p&gt;Object detection is a fundamental aspect of computer vision, with numerous generic object detectors proposed by various researchers. The proposed work presents a novel single-stage rotation detector that can detect oriented and multi-scale objects accurately from diverse scenarios. This detector addresses the challenges faced by current rotation detectors, such as the detection of arbitrary orientations, objects that are densely arranged, and the issue of loss discontinuity. First, the detector also adopts a progressive regression form (coarse-to-fine-grained approach) that uses both horizontal anchors (speed and higher recall) and rotating anchors (oriented objects) in cluttered backgrounds. Second, the proposed detector includes a feature refinement module that helps minimize the problems related to feature angulation and reduces the number of bounding boxes generated. Finally, to address the issue of loss discontinuity, the proposed detector utilizes a newly formulated adjustable loss function that can be extended to both single-stage and two-stage detectors. The proposed detector shows outstanding performance on benchmark datasets and significantly outperforms other state-of-the-art methods in terms of speed and accuracy.&lt;/p&gt;&lt;/abstract&gt;
Blockchain Technology-Based Holiday Exchange Network

This study discusses the application of blockchain for club membership sharing. The integration of blockchain technology not only increases the scalability of the system but also reduces the cost and saves a lot of time for the members of holiday resort companies. This technology is still in its nascent stage but has proven its application in many areas. Many government and corporate groups are investigating and/or investing in this technology and thought leaders expect significant changes in the society.
Deep Learning-Based Sign Language Recognition and Translation

In recent years, deep learning algorithms have recently been shown to be extremely successful for a variety of computer vision applications, including sign recognition and translation. Although verbal communication is necessary for exchanging thoughts, ideas, and much more, for people who have difficulty in hearing or speaking, sign language is the primary way of communication. According to the World Health Organization, there are currently 466 million people worldwide who have hearing problems, with that number expected to climb to 900 million by 2050. Deep neural networks, namely convolutional neural networks, have been shown to attain state-of-the-art outcomes in sign recognition by effectively learning and extracting significant characteristics from input photos. The paper introduces a deep learning-based system for sign translation and identification in multi-class picture classification. The system is able to understand and translate a wide range of sign languages because it was trained on a vast dataset of sign images.
An Analysis of Deep Learning-Based Image Segmentation Techniques

Among the numerous applications that stand to gain tremendously from further investigation into picture segmentation are medical image analysis, robotic perception, video surveillance, augmented reality (AR), and image compression. There have been several advances in image segmentation techniques throughout the years. Research into using deep learning models for image segmentation has increased as a result of the models' remarkable effectiveness in vision applications. We survey the state of the art in image segmentation using deep learning, including a broad range of innovative work at the semantic and instance levels. We go through a broad variety of deep learning models, including those that use completely convolutional pixel labelling, encoder–decoder designs, multi-scale and pyramid-based approaches, recurrent networks, visual attention models, and generative models in adversarial settings. We discuss the pros and cons of various models, analyse the most often used datasets, report on their findings, and suggest promising new research directions.
Robust Image Hashing Based on Histogram Reconstruction

In this paper, we propose a perceptual image hashing method based on histogram reconstruction. Our method consists of three steps, pre-processing, histogram reconstruction and hash vector generation based on the reconstructed histogram. In image pre-processing, the color image is converted to a gray one, then the gray image is standardized and the standardized image is processed with a low-pass filter. Then, we reconstruct the histogram of a gray image by using the relationship between the pixels in different groups. Finally, the hash vector is extracted from the reconstructed histogram. As the experimental results show, the proposed image hashing method is robust to most of the content-preserving operations, such as JPEG compression, noise addition and rotation. Moreover, the method proposed can effectively solve the problems in traditional histogram-based methods.
Chinese Named Entity Recognition Based BERT-Decoder-CRF in Turbine Generator Set Fault Diagnosis

It is promising to apply the Knowledge Graph (KG) in turbine generator set fault diagnosis. Named Entity Recognition (NER) is a key technology in constructing KG. However, currently, the quality of Chinese Named Entity Recognition (CNER) in the domain of turbine generator set fault diagnosis cannot meet the actual requirements. To solve the problem, the BERT-Decoder-CRF model is proposed for CNER. Specially, the BERT pre-trained language model is used to obtain character vectors based on the context, which can enhance the semantic representation of the characters. The Decoder can better fuse characters to labels. The CRF can obtain better prediction sequences through introducing and learning some constraints. To validate the validity and generalization of the proposed model, the turbine generator set fault diagnosis dataset and the public People's Daily news corpus labeled dataset are selected, respectively. By comparing with three well-known models, the results show that the proposed model is capable of high-quality identification of named entities.
Analysis of the Architecture of Perceiving a Dynamic Environment for an Unmanned Aerial Vehicle

Unmanned aerial vehicles (UAVs) every year demonstrate more and more advanced capabilities in detecting and avoiding obstacles, as well as in identifying and pursuing targets. The use of computer vision systems, artificial intelligence methods is an urgent task for ensuring the future of mobility and autonomous transport. The article discusses the analysis of existing image databases to prepare data for decision-making during the autonomous flight of an unmanned aerial vehicle, even in the absence of navigation systems. The choice of the coefficient for evaluating the quality of the semantic segmentation of video stream objects is carried out, a review of the methods of training the neural network based on the available data is carried out. The applied goal is the development of architecture and implementation of semantic segmentation methods for small UAVs used for research and training of automatic flight capabilities. The paper proposes to use the data obtained during the flight with the operator of the Parrot Mambo mini UAV, then manual segmentation of individual images of the received video is performed. After segmentation, the resulting data set is transferred to the neural network for training. The completed subsystem is used to simulate flight using the Cannon block in the Simulink support package for Parrot Minidrones. As a result of several flights, IoU = 0.63 was obtained, which is an acceptable result for the study of SS methods and further application in UAV automatic flight algorithms.
Use of Information Systems in Disclosure of Criminal Offenses

With the introduction in modern life of such technologies as the telephone, computer, laptop, tablet and the use of the Internet, social networks and chat rooms, the number of crimes has increased. But these technologies are also used to solve crimes. New technological innovations have been developed to prevent crime and to improve the performance of the police, but we know remarkably little about how and why certain innovations are adopted, and the consequences–both intended and unintended of technology-driven solutions to the problem of crime. Prevention of criminal acts is possible on the basis of IT, information systems of justice, digital transformations in society, law and economy. Investigating IT crimes requires speed, completeness, accuracy, evidence and reduced uncertainty. A prompt, efficient and complete investigation will require the involvement of IT, which will reduce the complexity of the investigation. This article provides an examination of a wide range of new technological innovations that have applications in the areas of crime prevention generally, and crime control (by police) in particular. We provide a description of recent technological innovations, summarize the available research on the extent of adoption in the United States, Great Britain, India, and Ukraine. Let’s take a closer look at electronic surveillance, automatic ticketing technology, and video surveillance systems (CCTV).
Instant Chats in Collaborative Learning: Its Merits and Demerits in Bangladeshi Tertiary Education

Various technological platforms have become prominent in recent times for communication. It is hard to survive for educated people in this modern world without effective communication. People use various technological tools for mutual interaction. These technological aspects are Facebook, Twitter, YouTube, Messenger, WhatsApp, and others. Among these instruments, instant messaging applications are the popular means through which students can share their ideas on how they can improve their English language skills. Group messenger or WhatsApp chatting are significant ways through which more than two students can discuss their study materials collaboratively. In this context, the main objective of this research is to find out the contributions of online group chatting to interactive English language learning. Many Bangladeshi tertiary-level students do not have proper knowledge about the use of technological tools. This study will help them to understand how messenger\WhatsApp chats can create an interactive and friendly environment among English language learners. The researcher followed qualitative research methods for this study. The researcher took semi-structured interviews with ten participants, all drawn from a renowned private university of Bangladesh. However, findings inform us that online messaging platforms help students to improve their English language in a rapid way. On the contrary, the researcher also found that students sometimes face difficulties while sharing their ideas in English through messenger\WhatsApp or other online applications. Finally, some recommendations have been proposed in light of the research findings.
Lossy Compression of Three-Channel Remote Sensing Images Based on Controlled Visual Quality

Remote sensing image compression plays an important role in Earth observation applications due to limitations of on-board data storage and downlink communication bandwidth. Lossy compression techniques are efficient means to reduce the size of images to reach high compression ratio. However, the distortions are unavoidable, which can affect value of remote sensing images. Consequently, the visual quality should be controlled carefully to ensure that distortions do not have too large negative impact on the efficiency of final image processing. This paper aims to discuss this topic. We review the visual quality metrics which are used in remote sensing to evaluate the quality of compressed images. We also compare approaches to lossy compression of remote sensing images. Afterwards, we propose an approach to providing a desired quality, and adopt the AGU coder to compress three-channel remote sensing data. The results show that it is possible to conduct the lossy compression based on controlled visual quality.
3D立体視を活用した効果的な学習教材の開発

The progress of VR/AR/MR technology has been remarkable, and it is widely used for evaluation and examination in various fields. Effective educational programs can be expected by introducing these methodologies and technologies into engineering field, which requires practice, and by developing and utilizing appropriate teaching materials and contents. In this study, from the viewpoint of utilization in engineering education, 3D models of machine tools and mechanics, 3D images as real experiences, and a completely virtual space for experiencing microstructures are examined. Details of content development and its implementation are presented for three types of trials: creation and arrangement of a virtual space serves as an environment for experiencing 3D viewing, VR experience content for practical class, and proposals for effective utilization of 3D view.
Breast Cancer Classification and Survival Prediction Using Proteomic Analysis

Breast cancer refers to a malignant tumor that is formed from the cells in the breast. Initially, it starts in the milk-producing glands or ducts, the path that takes milk to the nipples. Breast cancer is most intrusive in women and is one of the main causes of their deaths. It can be successfully treated if it is found in the initial stage but most of the time it can’t be diagnosed for many years. So, there is a requirement for a cheap diagnostic method in the early stages for its successful treatment. The work in this paper is all about producing the cheapest method for the diagnosis of breast cancer. I aim at exploring different machine learning algorithms like Support Vector Machine (SVM), Classification and Regression Tree (CART), K-Nearest Neighbors (KNN), and Random Forest on proteomic data of affected patients and predict the class of breast cancer. Besides we have ensembled two of the least correlated algorithms by stacking them to improve accuracy.
NiftyPlace: An NFT Marketplace Using Blockchain

Blockchain is a notably advanced and innovative technology which has caused a dramatic shift from the previously established centralized framework to the underrated decentralized framework. Non-fungible tokens (NFTs) are digital assets that can be anything like pictures, videos, games, songs, etc., and they can also be property deeds, medical records, and a lot more. The popularity and commendation of NFTs have only increased in the current years. Since NFTs are based on the intrinsic concepts of Blockchain, they form the backbone of any NFT marketplace. This paper explores the creation of NiftyPlace, an NFT marketplace using Blockchain which is nothing but a platform that supports features like minting, listing, buying, selling, relisting, and canceling of NFTs. These functions are possible by the virtue of an ERC-721 token standard-based on a smart contract that works on the Ethereum Blockchain. Research has been done on the existing prototypes of the NFT marketplaces, and this has helped in gaining a good insight in the limitations associated with the existing systems and ways in which the current system can be improved upon. This paper also delves into the use of Interplanetary File System (IPFS) for the storage of the metadata associated with the NFTs and the need or prevalence of developing such a decentralized application.
Chaos Rafflesia Optimization Algorithm

Intelligent optimization algorithms are one of the crucial methods to solve optimization problems at present. When faced with practical issues in multi-dimensional space, intelligent optimization algorithms can be used to find optimal parameters in the global area to solve the problem. This paper improves the problem that the Rafflesia Optimization Algorithm (ROA) recently proposed is slow and easy to fall into a local optimum. This paper studies the performance of chaotic mapping to optimize the ROA algorithm and uses the random walk strategy to improve the ROA algorithm’s performance and the algorithm’s ability to jump out of the local optimum. This paper tests the improved algorithm (CROA) through the CEC2013 data test set and compares it with other optimization algorithms. The experimental results show that the newly proposed ROA algorithm is more effective.
The Use of Fuzzy Information and Linguistic Model in the Virtual Reconstruction of Defensive Borders in the 17th and 18th Centuries

The paper describes: the use of fuzzy data and linguistic models for virtual reconstruction of historically significant defensive structures intended to protect the borders of Russia in the seventeenth and eighteenth centuries from the raids of Mongolian nomads; the procedural model for determining the values of the characteristics of virtual space objects; the procedure for creating a realistic virtual model of a historically significant territory, which helps to track the history of its development; information analysis of inaccurate data from a variety of primary sources using fuzzy set theory. The technology of creating a virtual historical space using the developed information and logical model, 3DVista Virtual Tour Pro and Twinmotion software environments is described. As a result, we have developed a virtual tour, which can be used for various purposes: remote study of issues related to objects of historical reconstruction; teaching schoolchildren and students; taking educational quests.
Ensuring Fairness Using Streaming Tool in Face-To-Face Classes of the Same Subject Across Multiple Classes

In the National Institute of Technology, Kitakyushu College, the students in the third grade choose one specific engineering course from five specialized ones. The scores in the first and second grade are used to determine where students are assigned, so the fairness of scores in the lower grades is required. On the other hand, many teaching methods using online tools are now in practice after the COVID-19 craze. Therefore, we used the streaming tool for multiple face-to-face classes of the same subject at the same time. This attempt of teaching was found to be highly beneficial in that it enabled effective use of limited human resources, and student questionnaires showed favorable opinions about the fairness of the teaching method.
Recent Challenges in a New Distributed Learning Paradigm

The majority of online transactions between the parties can be reported in real-time publicly by relying on smart contracts (SCs) and federated learning (FL). They are both decentralized and reinforced. By aggregating the gradient values from client devices, FL enables a large number of clients to create deep learning models anonymously. Yet, it lacks an incentive system for the contributing clients. Conversely, since self-executing contracts with immutable data records are resilient to failure, the virtues of SCs can be a tenable solution as an incentive mechanism in the FL system. The clients can claim the rewards by providing a proof transaction function and stating their contribution arbitrarily through SCs. However, because the transactions are made public, directly implementing SCs in the CL system could jeopardize the users’ privacy. The observer can deduce the characteristics of the client’s resources. Therefore, in this research, we elaborate on the critical points to be taken into account in adopting SCs as an incentive mechanism for the FL environment. We also state an empirical investigation and the open challenges that can address the aforementioned issues and concerns. Eventually, we recapitulate the essential points to be considered in developing a new distributed learning paradigm.
デジタルツイン基盤型の創成教育

The aim of this paper is to put forward an educational model for engineering design based on digital twin technology. The development of 3D technology in diverse forms and the proliferation of multifunctional sensors have facilitated the creation of digital twins. The COVID-19 pandemic has exposed the necessity for dynamic, research-driven learning that incorporates ICT. As a result, I have proposed a PBL that uses an engineering digital twin to go to the site, makes a direct observation, and determines the facts. Furthermore, I have presented a case study that demonstrates the effectiveness of this approach in community nursing education through the use of a digital diorama. This digital diorama offers a platform for cooperative learning in educational institutions and communities alike.
Explainable AI for Event and Anomaly Detection and Classification in Healthcare Monitoring Systems

Artificial intelligence (AI) has the potential to revolutionize healthcare by automating the detection and classification of events and anomalies. In the scope of this work, events and anomalies are abnormalities in the patient’s data, where the former are due to a medical condition, such as a seizure or a fall, and the latter are erroneous data due to faults or malicious attacks. AI-based event and anomaly detection (EAD) and their classification can improve patient outcomes by identifying problems earlier, enabling more timely interventions while minimizing false alarms caused by anomalies. Moreover, the advancement of Medical Internet of Things (MIoTs), or wearable devices, and their high processing capabilities facilitated the gathering, AI-based processing, and transmission of data, which enabled remote patient monitoring, and personalized and predictive healthcare. However, it is fundamental in healthcare to ensure the explainability of AI systems, meaning that they can provide understandable and transparent reasoning for their decisions. This paper proposes an online EAD approach using a lightweight autoencoder (AE) on the MIoT. The detected abnormality is explained using KernelSHAP, an explainable AI (XAI) technique, where the explanation of the abnormality is used, by an artificial neural network (ANN), to classify it into an event or anomaly. Intensive simulations are conducted using Medical Information Mart for Intensive Care (MIMIC) dataset for various physiological data. Results showed the robustness of the proposed approach in the detection and classification of events, regardless of the percentage of the present anomalies.
Creating a Good Experience

A good “user experience” (from using digital products) is highly desirable. A good user experience arises from the easy coping with the product which we find aesthetically attractive and effectively pleasing.
Encouragement of Learning Analytics to Improve Engineering Education

With the increase in online classes and our access to large amounts of learning and educational data, Learning Analytics (LA) has received much attention. LA is essential for student-centered education and can make a significant contribution to transforming the educational model. This paper describes the procedures and significance of LA and introduces classes on mechanical engineering using LA tools. In our class, we used the LEAF system consisting of BookRoll and LAView as the LA tool. By using the BookRoll to deliver explanations of pre-class assignments for a flipped classroom, we could analyze the learning activities in the LAView and monitor student understanding. Furthermore, we found a statistically significant difference in the time spent reading the material between the highest- and lowest-performing students.
The Fading Treatment Effects of a Multi-Faceted Asset-Transfer Program in Ethiopia

We study the long-run effects of a big-push “graduation” program in Ethiopia in which very poor households received a one-time transfer of productive assets (mainly livestock), technical training, and access to savings accounts. After seven years, treatment effects on wealth and consumption remain economically meaningful but dissipated relative to the two- and three-year results. Treatment effects on other outcomes attenuated further. Based on absolute wellbeing (e.g., food security) not dropping, we argue that the treatment effect dissipation is driven primarily by improved living standards for control households, rather than losses of the previously accrued benefits for the treatment households.