Reviewerly/exHarmony huggingface dataset:
A Proposal of an Android Mobile Application for Senior Citizen Community with Multi-lingual Sentiment Analysis Chatbot

Throughout these years, technology has transformed our world and has become an integral part of our everyday lives. This massive digitalization needs to take into consideration the elderly community too. However, the elderly community is usually digitally excluded, but despite these various and diverse difficulties, they remain driven, engaged, and eager to make an effort, to incorporate developing digital technologies into their daily life. Hence, this research implements the functionality of analyzing and determining the emotions of senior citizens by utilizing various natural languages processing-based machine learning techniques like SVM, Random Forest, and Decision Tree. Different algorithms were used, and various parameters were compared to obtain the results. It is seen that SVM gave the best results with an accuracy of 88%. The android application “Bandhu – your forever friend” would be accessible and usable by the elderly community. In addition, this proposed application offers various other features, including learning a new language and listening to the regional songs which would cater to the multicultural requirements in a diverse country like India, recording their audio stories to preserve the ethnic culture and inspire others, and lastly becoming tech-savvy by following easy video tutorials. These features would not only keep them engaged but also tackle their loneliness and isolation to some extent, as all these features were considered after surveying around 100 senior citizens' lifestyles and needs. This app will also make them more digitally independent. All the above-mentioned functionalities have been implemented using the programming language Java and the android application is built in Android Studio. Also, the entire app is in the Hindi language, considering that this language is the most preferred and spoken language in India.
A blockchain-based computerized network infrastructure for the transparent, immutable calculation and dissemination of quantitative, measurable parameters of academic and medical research publications

Digital transformation of healthcare systems should rely on decentralized computer networks and take advantage of the unique characteristics of blockchain technology. Decentralization ensures process transparency and data transparency for all relevant stakeholders. These values are essential in the realms of populations' healthcare information communications and processing, control and tracking of medical logistics supply chains, clinical research management, and control of certified healthcare services organizations. Mounting decentralized processes onto a blockchain-based computerized network will endow the values of immutability, improved cybersecurity, and potential for incentivizing stakeholders for relevant, pre-determined activities. One of the most relevant processes that would benefit from a decentralized, blockchain-based architecture is the submission, review, and publishing of scientific manuscripts. Current structures and processes in this world are non-transparent, poorly incentivizing significant stakeholders such as manuscripts' reviewers, and many are potentially corrupted. In this review, we suggest a blockchain-based architecture for such systems and advocate further research and development in several domains of modern healthcare systems-offering medicine to become "the new guy on the block (chain)."
An enhanced method for dialect transcription via error‐correcting thesaurus

Abstract Automatic speech recognition (ASR) has been widely used in the field of customer service, but the performance of general ASR in dialect transcription is not satisfactory, especially in Guangdong Province. Targeted training of ASR transcription engine will produce effect, but the training cost is high, and it is not suitable for small‐scale training with multiple dialects and frequencies. The complaint problems in the customer service field have obvious clustering and are suitable for few‐shot and multi‐frequency training. In view of this, in the actual engineering application, the method of ASR transcribed into the dialect error correction thesaurus is tried to be used to replace the wrong words, and have achieved good results. The optimization technology after automatic speech transcription proposed in this study can improve the recognition accuracy of general ASR by 13.75% for dialect words.
Deep Reinforcement Learning for Risk and Disaster Management in Energy-Efficient Marine Ranching

The marine ranching industry in China is transitioning from traditional farming to a digital and intelligent model. The use of new technologies, algorithms, and models in the era of artificial intelligence (AI) is a key focus to enhance the efficiency, sustainability, and resilience of marine ranch operations, particularly in risk and disaster management. This study proposes a methodology for applying deep reinforcement learning to decision making in this domain. The approach involves creating an environmental model based on decision objects and scenarios, determining the number of decision makers, and selecting a single or multi-agent reinforcement learning algorithm to optimize decision making in response to randomly generated disasters. Three core innovations are presented: the development of a disaster simulator for marine ranching scenarios, the application of reinforcement learning algorithms to address risk and disaster management problems in marine ranching. Future research could focus on further refining the methodology by integrating different data sources and sensors and evaluating the social and economic impacts of AI-driven marine ranching. Overall, this study provides a foundation for further research in this area, which is expected to play an increasingly important role in global food production, environmental sustainability, and energy efficiency.
Graph Embedding with Similarity Metric Learning

Graph embedding transforms high-dimensional graphs into a lower-dimensional vector space while preserving their structural information and properties. Context-sensitive graph embedding, in particular, performs well in tasks such as link prediction and ranking recommendations. However, existing context-sensitive graph embeddings have limitations: they require additional information, depend on community algorithms to capture multiple contexts, or fail to capture sufficient structural information. In this paper, we propose a novel Graph Embedding with Similarity Metric Learning (GESML). The core of GESML is to learn the optimal graph structure using an attention-based symmetric similarity metric function and establish association relationships between nodes through top-k pooling. Its primary advantage lies in not requiring additional features or multiple contexts, only using the symmetric similarity metric function and pooling operations to encode sufficient topological information for each node. Experimental results on three datasets involving link prediction and node-clustering tasks demonstrate that GESML significantly improves learning for all challenging tasks relative to a state-of-the-art (SOTA) baseline.
Radar Active Jamming Recognition under Open World Setting

To address the issue that conventional methods cannot recognize unknown patterns of radar jamming, this study adopts the idea of zero-shot learning (ZSL) and proposes an open world recognition method, RCAE-OWR, based on residual convolutional autoencoders, which can implement the classification of known and unknown patterns. In the supervised training phase, a residual convolutional autoencoder network structure is first constructed to extract the semantic information from a training set consisting solely of known jamming patterns. By incorporating center loss and reconstruction loss into the softmax loss function, a joint loss function is constructed to minimize the intra-class distance and maximize the inter-class distance in the jamming features. Moving to the unsupervised classification phase, a test set containing both known and unknown patterns is fed into the trained encoder, and a distance-based recognition method is utilized to classify the jamming signals. The results demonstrate that the proposed model not only achieves sufficient learning and representation of known jamming patterns but also effectively identifies and classifies unknown jamming signals. When the jamming-to-noise ratio (JNR) exceeds 10 dB, the recognition rate for seven known jamming patterns and two unknown jamming patterns is more than 92%.
Lipschitz Variational Approximation of Total Variation Distance

In this work, we build on the family of Integral Probability Metrics and design a new distance metric between probability distributions that belong to this family.The new metric is termed Lipschitz Variational Total Variation Distance and is a relaxation of the integral probability metric representation of the well-known total variation distance.We propose simple procedures to estimate this distance metric and demonstrate its convergence.Based on the Lipschitz smoothness of the proposed metric family, the proposed metrics, hence its empirical estimate, can provide meaningful and tight lower bounds for the total variation distance between two probability distributions.Finally, we extend our results to general measures and provide an application of the proposed estimators to bounding the Neyman-Pearson region.
Artificial Intelligence text generators for overcoming language barriers in ecological research communication

Abstract Language barriers can impede the dissemination of research findings, restrict collaboration and exclude non‐English‐speaking researchers from the global scientific community. To overcome this challenge, we explore the potential of Generative Artificial Intelligence (GenAI) text generators to assist non‐anglophone researchers in producing high‐quality academic texts for publication in scientific journals, with a focus on the field of ecological research. These tools can produce grammatically correct, coherent and contextually appropriate text, improving scientific communication quality. Improving scientific communication is vital in Ecology, where research findings can have important implications for the environment and public policy. GenAI text generators can generate summaries of research findings, abstracts and social media posts promoting research findings. Nonetheless, researchers must exercise caution and use these tools together with human review and editing to ensure accuracy and clarity. As natural language processing and machine learning continue to evolve, the use of GenAI text generators in scientific communication is poised to become increasingly important.
Passive Imaging Detection on Identity Attributes of Targets

Visible marks describing various meanings are present on many equipment and carrying platforms, such as alphanumeric numbers and patterns.
RepNet: A Lightweight Human Pose Regression Network Based on Re-Parameterization

Human pose estimation, as the basis of advanced computer vision, has a wide application perspective. In existing studies, the high-capacity model based on the heatmap method can achieve accurate recognition results, but it encounters many difficulties when used in real-world scenarios. To solve this problem, we propose a lightweight pose regression algorithm (RepNet) that introduces a multi-parameter network structure, fuses multi-level features, and combines the idea of residual likelihood estimation. A well-designed convolutional architecture is used for training. By reconstructing the parameters of each level, the network model is simplified, and the computation time and efficiency of the detection task are optimized. The prediction performance is also improved by the output of the maximum likelihood model and the reversible transformation of the underlying distribution learned by the flow generation model. RepNet achieves a recognition accuracy of 66.1 AP on the COCO dataset, at a computational speed of 15 ms on GPU and 40 ms on CPU. This resolves the contradiction between prediction accuracy and computational complexity and contributes to research in lightweight pose estimation.
A method of automatic field of view generation and path planning for automated x‐ray inspection

Abstract With the increasing accuracy and stability requirements of SMT equipment, automatic x‐ray inspection (AXI) is used as a new popular type of testing technology. First, an imaging constraint model is proposed to ensure that the detected object is not truncated. Second, an improved iterative self‐organizing clustering algorithm was proposed to realize an automatic optimal generation of the field of view and form a Hamiltonian path. Finally, the solution of the shortest Hamiltonian path problem is given by the proposed VTSP heuristic algorithm and VTSP + transformer deep learning algorithm. Experimental results show that, compared with the binary state compression DP method and the local shortest path planning method, the proposed method can support large‐scale nodes, and the performance is improved by 16% on 75 nodes and 31% on 200 nodes. The proposed method achieves a balance between x‐ray imaging efficiency and performance.
EvIcon: Designing High‐Usability Icon with Human‐in‐the‐loop Exploration and IconCLIP

Abstract Interface icons are prevalent in various digital applications. Due to limited time and budgets, many designers rely on informal evaluation, which often results in poor usability icons. In this paper, we propose a unique human‐in‐the‐loop framework that allows our target users, that is novice and professional user interface (UI) designers, to improve the usability of interface icons efficiently. We formulate several usability criteria into a perceptual usability function and enable users to iteratively revise an icon set with an interactive design tool, EvIcon. We take a large‐scale pre‐trained joint image‐text embedding (CLIP) and fine‐tune it to embed icon visuals with icon tags in the same embedding space (IconCLIP). During the revision process, our design tool provides two types of instant perceptual usability feedback. First, we provide perceptual usability feedback modelled by deep learning models trained on IconCLIP embeddings and crowdsourced perceptual ratings. Second, we use the embedding space of IconCLIP to assist users in improving icons' visual distinguishability among icons within the user‐prepared icon set. To provide the perceptual prediction, we compiled IconCEPT10K , the first large‐scale dataset of perceptual usability ratings over 10,000 interface icons, by conducting a crowdsourcing study. We demonstrated that our framework could benefit UI designers' interface icon revision process with a wide range of professional experience. Moreover, the interface icons designed using our framework achieved better semantic distance and familiarity, verified by an additional online user study.
Advanced Bayesian Network for Task Effort Estimation in Agile Software Development

Effort estimation is always quite a challenge, especially for agile software development projects. This paper describes the process of building a Bayesian network model for effort prediction in agile development. Very few studies have addressed the application of Bayesian networks to assess agile development efforts. Some research has not been validated in practice, and some has been validated on one or two projects. This paper aims to bring the implementation and use of Bayesian networks for effort prediction closer to the practitioners. This process consists of two phases. The Bayesian network model for task effort estimation is constructed and validated in the first phase on real agile projects. A relatively small model showed satisfactory estimation accuracy, but only five output intervals were used. The model was proven to be useful in daily work, but the project manager wanted to obtain more output intervals, although increasing the number of output intervals reduces the prediction accuracy. In the second phase, the focus is on increasing the number of output intervals while maintaining satisfactory accuracy. The advanced model for task effort estimation is developed and tested on real projects of two software firms.
Universal Price Tag Reader for Retail Supermarket

Retail supermarkets are an essential part of today's economy, and managing them is a tedious task. One of the major problems faced by supermarkets today is to keep track of the items available on the racks. Currently, the track of the product on the shelf is kept by price tag readers, which work on a barcode detection methodology that has to be customized for each store. On the other hand, if barcodes are not present on the price tags, the data is manually fed by the staff of the store, which is really time-consuming. This paper presents a universal pipeline that is based on Optical Character recognition and can be used across all kinds of price tags, and is not dependent on barcodes or any particular type of price tag. This project uses various image-possessing techniques to determine and crop the Area of Interest. It detects the price of the product and the name of the product by filtering the OCR outputs based on the area and dimensions of the bounding boxes of the text detected. Additionally, the presented pipeline is also capable of capturing discounted prices, if any, for the products. It has been tested over price tags of five different types, and the accuracy ranges from 78% to 94.5%.
Classification Tool to Predict the Presence of Colon Cancer Using Histopathology Images

The proposed model compares the efficiency of CNN and ResNet50 in the field of digital pathology images. Deep learning methods are widely used in all fields of disease detection, diagnosis, segmentation, and classification. CNN is the widely used image classification algorithm. But it may show less accuracy in case of complex structures like pathology images. Residual Networks are a good choice for pathology image classification because the morphology of digital pathology images is very difficult to distinguish. Colon cancer is one of the common cancers, and it is one of the fatal diseases. If early-stage detection has been done using biopsy results, it will decrease the mortality rate. ResNet50 is selected among the variants as its computational complexity is moderate and provides high accuracy in classification as compared to others. The accuracy metric used here is the training and validation accuracy and loss. The training and validation accuracy of ResNet50 is 89.1% and 90.62%, respectively, whereas the training loss and validation loss are 26.7% and 24.33%, respectively. At the same time, for CNN, the accuracy is 84.82% and 78.12% and the loss is 36.51% and 47.33% .
A Zero-Shot Low Light Image Enhancement Method Integrating Gating Mechanism

Photographs taken under harsh ambient lighting can suffer from a number of image quality degradation phenomena due to insufficient exposure. These include reduced brightness, loss of transfer information, noise, and color distortion. In order to solve the above problems, researchers have proposed many deep learning-based methods to improve the illumination of images. However, most existing methods face the problem of difficulty in obtaining paired training data. In this context, a zero-reference image enhancement network for low light conditions is proposed in this paper. First, the improved Encoder-Decoder structure is used to extract image features to generate feature maps and generate the parameter matrix of the enhancement factor from the feature maps. Then, the enhancement curve is constructed using the parameter matrix. The image is iteratively enhanced using the enhancement curve and the enhancement parameters. Second, the unsupervised algorithm needs to design an image non-reference loss function in training. Four non-reference loss functions are introduced to train the parameter estimation network. Experiments on several datasets with only low-light images show that the proposed network has improved performance compared with other methods in NIQE, PIQE, and BRISQUE non-reference evaluation index, and ablation experiments are carried out for key parts, which proves the effectiveness of this method. At the same time, the performance data of the method on PC devices and mobile devices are investigated, and the experimental analysis is given. This proves the feasibility of the method in this paper in practical application.
Textual emotion analysis using improved metaheuristics with deep learning model for intelligent systems

Abstract Complex, intelligent systems, namely social network services (SNS), internet‐of‐things (IoT), augmented reality (AR), virtual reality (VR), and so on have become a crucial part of our day‐to‐day life. The dynamics of this system alter human behaviors in various forms. Therefore, it is important to understand the fundamental dynamics of the system and what it involves for user sentiment. Emotion analysis is a commonly used and very powerful analysis methodology in data mining. It gives an outstanding option to evaluate, determine, understand, and monitor the sentiments of consumers with respect to a service or a product. This article focuses on the design of textual emotion analysis using the improved metaheuristics with deep learning (TEA‐IMWDL) model for intellectual systems. The goal of the TEA‐IMWDL technique lies in the identification and classification of emotions that exist in social media content. To attain this, the presented TEA‐IMWDL technique undergoes preprocessing to make the input data compatible with the latter stages of processing. For the word embedding process, the Fast text approach is utilized in this study. Next, the long short term memory‐recurrent neural network (LSTM‐RNN) method can be enforced for the emotion classification procedure. To boost the classification efficiency of the LSTM‐RNN model, an improved gravitational search optimization algorithm (IGSA) based hyperparameter tuning process is involved in this study. A brief set of simulation analyses of the TEA‐IMWDL technique is carried out and the results are tested on a benchmark dataset. The experimental outcomes demonstrate the enhanced performance of the TEA‐IMWDL technique over other recent approaches.
An Empirical Study of SETA Program Sustaining Educational Sector’s Information Security vs. Information Systems Misuse

Information systems misuse and data breaches are among the most common information security threats at the organisational and individual levels. Security, Education, Training and Awareness (SETA) program can be effective tools in addressing and preventing such risks for sustaining the educational sector’s information security, although it is costly to implement and achieves limited results. Several studies have shown that SETA implementation can improve corporate employees’ information security protection behaviours. This study adopts the method of quantitative research, deterrence theory with selected perceived cost and information security awareness (ISA) as intermediate variables and explores how SETA programs affect information system abuse on campuses. The results show that implementing the SETA program positively impacts perceived cost and ISA; perceived cost and information security positively impact reducing misuse behaviour of information systems. At last, we provide rationalisation suggestions for individual students and schools to help SETA programs to be better implemented.