Reviewerly/exHarmony huggingface dataset:
Android Based Object Detection System for Visually Impaired

Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gauge the emotions of those they interact with. The app integrates pre-trained deep learning models for object detection and facial emotion analysis, computer vision techniques for distance estimation, and optical character recognition for currency recognition. User feedback and engagement are central to the ongoing improvement of the application, ensuring that it remains a valuable resource for the community it serves. Ethical considerations and privacy concerns are also addressed, with a focus on data security and user privacy. By presenting the development and functionalities of this app, this review paper not only contributes to the field of assistive technology but also underscores the importanceof harnessing cutting-edge technology to improve the quality of life for visually impaired individuals.
Survey on Fraud App Detection Using Sentimental Analysis and Machine Learning

Abstract: Ranking fraud is the practice of acting dishonestly or deceitfully to artificially improve an App's position on a popularity list in the mobile app market. In actuality, app makers are increasingly using ranking fraud. These behaviors include posting phoney app reviews or exaggerating the sales of their apps. Although the importance of combating ranking fraud has long been recognized, there is little literature and research available in this area. To achieve this, we give a thorough examination of fraud app detection in this work utilizing sentiment analysis and spam filtering and propose a technique for identifying it in mobile apps. We specifically suggest mining the active times, or leading sessions, of mobile Apps to precisely locate the ranking scam in the first place.
The metaverse in the world of health: The present future. Challenges and opportunities

The World Health Organization has defined "digital health" as the use of information and communication technologies to improve health.In recent years, there has been a strong acceleration in the adoption of these digital tools, which has had a major impact on traditional healthcare models.We are currently witnessing the emergence of a large immersive virtual environment called the "metaverse."Its emergence creates new and challenging opportunities in health care.This article explores some metaverse-related concepts, provides specific examples of its use in pediatrics, describes experiences in the hospital setting, and finally delves into the resulting challenges and opportunities.
MFI-Net: Multi-Feature Fusion Identification Networks for Artificial Intelligence Manipulation

Tampered images can easily be used for illegal activities, such as spreading rumors, economic fraud, fabricating false news, and illegally obtaining experience benefits, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . With the improvement and development of artificial intelligence (AI), image manipulation technology has also been further improved, more and more retouching software in daily life adopts AI technology. So far, there is no AI-based tampered dataset. To address this challenge, we propose a dataset-IPM15K. It utilizes the most advanced image processing technology and contains a total of 150,00 doctored vital images. This dataset also could serve as a catalyst for progressing many vision tasks, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g</i> ., localization, segmentation, and alpha-matting, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">etc</i> . Additionally, we propose an effective multi-feature fusion identification network (MFI-Net) to identify these challenging images. Our model consists of four modules: the detail extraction module (DEM), which utilizes different sizes of convolutions and perceptual fields to extract more valuable information of tampered locations; the multi-branch attention fusion module (MAFM), which fully exploits contextual information of different levels to capture subtle traces of tampering; the feature decoder component (FDC), which combines fused features to identify tampered regions; and the detail enhancement block (DEB), which continues to supplement the detailed information of the detected regions. Extensive experiments on three public datasets and the proposed dataset show that MFI-Net outperforms various state-of-the-art (SOTA) manipulation detection baselines.
Neurodynamics for Equality-Constrained Time-Variant Nonlinear Optimization Using Discretization

Time-variant problems are widespread in science and engineering, and discrete-time recurrent neurodynamics (DTRN) method has been proved to be an effective way to deal with a variety of discrete time-variant problems. However, this DTRN method is usually based on the study of continuous time-variant problems and lacks a direct study of discrete time-variant problems. To solve the above problem, based on a pioneering direct discretization technique, we study and develop a new DTRN method to solve equality-constrained discrete time-variant nonlinear optimization (EC-DTVNO) problem. Specifically, firstly, to solve the EC-DTVNO problem, the recent method widely used by researchers is Lagrange multiplier method. By introducing Lagrange multiplier to construct Lagrange function, the objective function and equality constraint are integrated into a discrete time-variant nonlinear system. Then, the corresponding error function is defined, and the corresponding DTRN method for solving the EC-DTVNO problem can be obtained by direct discretization technique. Thereafter, this DTRN method is analyzed theoretically and its convergence is proved. In addition, numerical experiments and application experiments further confirm the effectiveness and superiority of DTRN method.
An advanced data fabric architecture leveraging homomorphic encryption and federated learning

Data fabric is an automated and AI-driven data fusion approach to accomplish data management unification without moving data to a centralized location for solving complex data problems. In a Federated learning architecture, the global model is trained based on the learned parameters of several local models that eliminate the necessity of moving data to a centralized repository for machine learning. This paper introduces a secure approach for medical image analysis using federated learning and partially homomorphic encryption within a distributed data fabric architecture. With this method, multiple users or clients (hospitals/medical data centers) can collaborate in training a machine-learning model without exchanging raw data. The approach complies with laws and regulations such as HIPAA and GDPR, ensuring the privacy and security of the data. The study demonstrates the method’s effectiveness through a case study on pituitary tumor classification, achieving a significant accuracy of 83.31%. However, the primary focus of the study is using the data fabric architecture to securely store and analyze medical images while complying with HIPAA and GDPR regulations. The results highlight the potential of these techniques to be applied to other privacy-sensitive domains and contribute to the growing body of research on secure and privacy-preserving machine learning.
The role of ethical climates in employee information security policy violations

In the context of information security, the organizational social environment plays an important role in deterring employee violations of the Information Security Policy (ISP). As extant research has not yet determined the role ethical climates may play, this study seeks to understand their influence on employee ISP violations. To this end, we classified ISPs into two major categories: those related to communal information technology (IT) resources in the organization, such as databases or software applications, and those related to connective IT resources, such as email or Internet access. Then, we explored how rules-, performance-, and friendship-oriented organizational ethical climates affect ISP violations. Based on a survey of 177 professionals employed in the United States, we found that rules-oriented and friendship-oriented ethical climates deter ISP violations, whereas a performance-oriented one does not. Moreover, rules- and friendship-oriented ethical climates lead to fewer communal ISP violations, while performance-oriented ethical climates lead to more connectivity ISP violations. These findings underscore the importance of considering the type of IT resources that an ISP aims to protect as well as the ethical climate of an organization in order to better understand the causes of ISP violations.
Keyword-enhanced recommender system based on inductive graph matrix completion

Going beyond the user–item rating information, recent studies have utilized additional information to improve the performance of recommender systems. Graph neural network (GNN) based approaches are among the most common. However, existing models that utilize text data require a lot of computing resources and have a complex structure that makes them difficult to utilize in real-world applications. In this research, we propose a new method, keyword-enhanced graph matrix completion (KGMC), which utilizes keyword sharing relationships in user–item graphs. Our model has a simpler structure and requires less computing resources than existing models that utilize text data, but it has the advantage of cross-domain transferability while providing an intuitive understanding of the inference results. KGMC consists of three steps: (1) keyword extraction from the review text, (2) subgraph extraction and keyword-enhanced subgraph construction, and (3) GNN-based rating prediction. We have conducted extensive experiments over eight benchmark datasets to examine the relative superiority of the proposed KGMC method, compared to state-of-the-art baselines. Additional experiments and case studies have been also conducted to demonstrate the transferability as well as keyword-based explainability of KGMC. Our findings highlight the practical advantages of our model for recommender systems and support its effectiveness in inductive graph-based link prediction.
Embracing RAISA in restaurants: Exploring customer attitudes toward robot adoption

Humanity has experienced four main technological revolutions; the last two being the digital revolution (Internet) and the emerging artificial intelligence (AI) revolution. With robots, artificial intelligence, and service automation (RAISA) increasingly applied in all industries, knowledge on people's attitudes toward RAISA is limited. Factors affecting RAISA adoption in restaurants, an industry known for its quick adoption of new technology, are largely unknown. This study explores customer attitudes toward robot adoption in restaurants through a quantitative survey of 293 valid respondents. The results, analyzed using partial least squares structural equation modeling (PLS-SEM), show that positive attitudes toward technology, interest in RAISA, perceived relevance, and enjoyment positively impact RAISA adoption. Furthermore, customers' overall attitude toward technology positively affects their intention to use RAISA. Therefore, restaurant owners can start implementing new technologies since consumers are ready to embrace RAISA in restaurants.
Multi-level graph contrastive learning

Graph representation learning has attracted a surge of interest recently, which targets learning discriminant representation for each node in the graph. Most of these representation methods focus on supervised learning and heavily depend on label information. However, annotating graphs are expensive in the real-world, especially in specialized domains (i.e. biology), as it requires the annotators with the domain knowledge to label the graph. To approach this problem, self-supervised learning provides a feasible solution for graph representation learning. In this paper, we propose a Multi-Level Graph Contrastive Learning (MLGCL) framework for learning robust representation of graph data by contrasting space views of graphs. Specifically, we introduce a novel contrastive view — space view. The original graph is a first-order approximation structure in the topological space where nodes are linked by feature similarity, relationship, etc. While the k-nearest neighbor (kNN) graph with community structure generated by encoding features preserves high-order proximity in feature space, it not only provides a complementary graph to the original graph from the feature space view but also is suitable for GNNs encoder. Furthermore, we develop a multi-level contrastive mode to preserve the local similarity and semantic similarity of graph-structured data simultaneously. Extensive experiments indicate MLGCL achieves promising results compared with the existing state-of-the-art graph representation learning methods on seven node classification datasets and three graph classification datasets.
Triple competitive differential evolution for global numerical optimization

As optimization problems become more and more complex in real-world scenarios, the effectiveness of many existing differential evolution (DE) methods is critically challenged. To circumvent this predicament, this paper proposes a triple competitive DE (TCDE) to tackle increasingly complicated optimization problems. Specifically, a triple competition mechanism is devised to first randomly arrange individuals into exclusive triples and then let individuals in each triple compete with each other. Subsequently, heterogeneous mutation schemes are designed to update the three individuals in each triple based on the competition results. With the cooperation between these two techniques, on the one hand, better individuals in the triples tend to exploit the areas where they lie to find high-accuracy solutions, while worse individuals in the triples prefer to explore the intricate problem space to locate promising regions. On the other hand, different individuals are mutated by following the guidance of different leading exemplars, which ensures that individuals traverse the sophisticated solution space in diverse directions along with slight intensification to optimal regions. Furthermore, a modified adaptive parameter adjustment scheme is designed for individuals in each triple by considering their differences so as to meet the expectation that better individuals focus on exploitation while worse individuals concentrate on exploration. Finally, experiments are performed to first compare TCDE with totally 10 advanced DE variants and then further compare it with totally 10 CEC winners and their latest variants on the CEC'2014 and the CEC'2017 problem suites with totally 4 different dimensionality settings. Experimental results prove that TCDE achieves highly competitive or even much better performance than the totally 20 compared methods. Particularly, TCDE is demonstrated to be especially capable of solving complicated optimization problems. Furthermore, the efficacy of TCDE in tackling real-world optimization problems is substantiated through experiments conducted on the CEC'2011 real-world optimization problem set.
Deep feature extraction with tri-channel textual feature map for text classification

The complexity and diversity of texts make it difficult for shallow text classification models to capture deeper text features. Therefore, this paper takes advantage of the BiLSTM-CNN hybrid network based on the self-attention mechanism to extract text features, and constructs a new text feature representation similar to RGB-channel images, that is, tri-channel text feature maps. Drawing on the effectiveness of ResNet and SA-Net in deep networks, we designed a deep feature extraction network to capture deeper features in the text. We use the tri-channel text feature map as the input to the deep feature extraction network, and propose the Deeper Feature Text Classification (DFTC) end-to-end text classification model. Experiments prove that the DFTC model is competitive compared to the most advanced methods on five challenging text classification datasets.
Shape-margin knowledge augmented network for thyroid nodule segmentation and diagnosis

Thyroid nodule segmentation is a crucial step in the diagnostic procedure of physicians and computer-aided diagnosis systems. However, prevailing studies often treat segmentation and diagnosis as independent tasks, overlooking the intrinsic relationship between these processes. The sequencial steps of these independent tasks in computer-aided diagnosis systems may lead to the accumulation of errors. Therefore, it is worth combining them as a whole by exploring the relationship between thyroid nodule segmentation and diagnosis. According to the diagnostic procedure of thyroid imaging reporting and data system (TI-RADS), the assessment of shape and margin characteristics is the prerequisite for radiologists to discriminate benign and malignant thyroid nodules. Inspired by TI-RADS, this study aims to integrate these tasks into a cohesive process, leveraging the insights from TI-RADS, thereby enhancing the accuracy and interpretability of thyroid nodule analysis. Specifically, this paper proposes a shape-margin knowledge augmented network (SkaNet) for simultaneous thyroid nodule segmentation and diagnosis. Due to the visual feature similarities between segmentation and diagnosis, SkaNet shares visual features in the feature extraction stage and then utilizes a dual-branch architecture to perform thyroid nodule segmentation and diagnosis tasks respectively. In the shared feature extraction, the combination of convolutional feature maps and self-attention maps allows to exploitation of both local information and global patterns in thyroid nodule images. To enhance effective discriminative features, an exponential mixture module is introduced, combining convolutional feature maps and self-attention maps through exponential weighting. Then, SkaNet is jointly optimized by a knowledge augmented multi-task loss function with a constraint penalty term. The constraint penalty term embeds shape and margin characteristics through numerical computations, establishing a vital relationship between thyroid nodule diagnosis results and segmentation masks. We evaluate the proposed approach on a public thyroid ultrasound dataset (DDTI) and a locally collected thyroid ultrasound dataset. The experimental results reveal the value of our contributions and demonstrate that our approach can yield significant improvements compared with state-of-the-art counterparts. SkaNet highlights the potential of combining thyroid nodule segmentation and diagnosis with knowledge augmented learning into a unified framework, which captures the key shape and margin characteristics for discriminating benign and malignant thyroid nodules. Our findings suggest promising insights for advancing computer-aided diagnosis joint with segmentation.
Virtual quantum resource distillation: General framework and applications

We develop the general framework of virtual resource distillation, an alternative distillation strategy proposed in Phys. Rev. Lett. 132, 050203 (2024), which extends conventional quantum resource distillation by integrating the power of classical postprocessing. The framework presented here is applicable not only to quantum states, but also to dynamical quantum objects such as quantum channels and higher-order processes. We provide a general characterization and benchmarks for the performance of virtual resource distillation in the form of computable semidefinite programs as well as several operationally motivated quantities. We apply our general framework to various concrete settings of interest, including standard resource theories such as entanglement, coherence, and magic, as well as settings involving dynamical resources such as quantum memory, quantum communication, and non-Markovian dynamics. The framework of probabilistic distillation is also discussed.
Application of a reconfigurable all-optical activation unit based on optical injection into a bistable Fabry–Perot laser in multilayer perceptron neural networks

In this Letter, we theoretically investigate the application of a bistable Fabry-Perot semiconductor laser under optical injection as an all-optical activation unit for multilayer perceptron optical neural networks. The proposed device is programmed to provide reconfigurable sigmoid-like activation functions with adjustable thresholds and saturation points and benchmarked on machine learning image recognition problems. Due to the reconfigurability of the activation unit, the accuracy can be increased by up to 2% simply by adjusting the control parameter of the activation unit to suit the specific problem. For a simple two-layer perceptron neural network, we achieve inference accuracies of up to 95% and 85%, for the MNIST and Fashion-MNIST datasets, respectively.
Comprehensive Sensitivity Analysis Framework for Transfer Learning Performance Assessment for Time Series Forecasting: Basic Concepts and Selected Case Studies

Recently, transfer learning has gained popularity in the machine learning community. Transfer Learning (TL) has emerged as a promising paradigm that leverages knowledge learned from one or more related domains to improve prediction accuracy in a target domain with limited data. However, for time series forecasting (TSF) applications, transfer learning is relatively new. This paper addresses the need for empirical studies as identified in recent reviews advocating the need for practical guidelines for Transfer Learning approaches and method designs for time series forecasting. The main contribution of this paper is the suggestion of a comprehensive framework for Transfer Learning Sensitivity Analysis (SA) for time series forecasting. We achieve this by identifying various parameters seen from various angles of transfer learning applied to time series, aiming to uncover factors and insights that influence the performance of transfer learning in time series forecasting. Undoubtedly, symmetry appears to be a core aspect in the consideration of these factors and insights. A further contribution is the introduction of four TL performance metrics encompassed in our framework. These TL performance metrics provide insight into the extent of the transferability between the source and the target domains. Analyzing whether the benefits of transferred knowledge are equally or unequally accessible and applicable across different domains or tasks speaks to the requirement of symmetry or asymmetry in transfer learning. Moreover, these TL performance metrics inform on the possibility of the occurrence of negative transfers and also provide insight into the possible vulnerability of the network to catastrophic forgetting. Finally, we discuss a sensitivity analysis of an Ensemble TL technique use case (with Multilayer Perceptron models) as a proof of concept to validate the suggested framework. While the results from the experiments offer empirical insights into various parameters that impact the transfer learning gain, they also raise the question of network dimensioning requirements when designing, specifically, a neural network for transfer learning.