Reviewerly/exHarmony huggingface dataset:
Multi-class semantic segmentation of breast tissues from MRI images using U-Net based on Haar wavelet pooling

MRI images used in breast cancer diagnosis are taken in a lying position and therefore are inappropriate for reconstructing the natural breast shape in a standing position. Some studies have proposed methods to present the breast shape in a standing position using an ordinary differential equation of the finite element method. However, it is difficult to obtain meaningful results because breast tissues have different elastic moduli. This study proposed a multi-class semantic segmentation method for breast tissues to reconstruct breast shapes using U-Net based on Haar wavelet pooling. First, a dataset was constructed by labeling the skin, fat, and fibro-glandular tissues and the background from MRI images taken in a lying position. Next, multi-class semantic segmentation was performed using U-Net based on Haar wavelet pooling to improve the segmentation accuracy for breast tissues. The U-Net effectively extracted breast tissue features while reducing image information loss in a subsampling stage using multiple sub-bands. In addition, the proposed network is robust to overfitting. The proposed network showed a mIOU of 87.48 for segmenting breast tissues. The proposed networks demonstrated high-accuracy segmentation for breast tissue with different elastic moduli to reconstruct the natural breast shape.
AutoPOI: automated points of interest selection for side-channel analysis

Abstract Template attacks (TAs) are one of the most powerful side-channel analysis (SCA) attacks. The success of such attacks relies on the effectiveness of the profiling model in modeling the leakage information. A crucial step for TA is to select relevant features from the measured traces, often called points of interest (POIs), to extract the leakage information. Previous research indicates that properly selecting the input leaking features could significantly increase the attack performance. However, due to the presence of SCA countermeasures and advancements in technology nodes, such features become increasingly difficult to extract with conventional approaches such as principle component analysis (PCA) and the Sum Of Squared pairwise T-difference-based method (SOST). This work proposes a framework, AutoPOI, based on proximal policy optimization to automatically find, select and scale down features. The input raw features are first grouped into small regions. The best candidates selected by the framework are further scaled down with an online-optimized dimensionality reduction neural network. Finally, the framework rewards the performance of these features with the results of TA. Based on the experimental results, the proposed framework can extract features automatically that lead to comparable state-of-the-art performance on several commonly used datasets.
A resilience‐based framework for assessing the evolution of open source software projects

Abstract Open source software (OSS) has been developing for more than two decades. It originated as a movement with the introduction of the first free/libre OSS operating system, became a popular trend among the developer community, led to enterprise solutions widely embraced by the global market, and began garnering attention from significant players in the software industry (such as IBM's acquisition of RedHat). Throughout the years, numerous software assessment models have been suggested, some of which were created specifically for OSS projects. Most of these assessment models focus on software quality and maintainability. Some models are taking under consideration health aspects of OSS projects. Despite the multitude of these models, there is yet to be a universally accepted model for assessing OSS projects. In this work, we aim to adapt the City Resilience Framework (CRF) for use in OSS projects to establish a strong theoretical foundation for OSS evaluation focusing on the project's resilience as it evolves over time. We would like to highlight that our goal with the proposed assessment model is not to compare two OSS solutions with each other, in terms of resilience, or even do a resilience ranking between the available OSS tools. We are aiming to investigate resilience of an OSS project as it evolves and identify possible opportunities of improvements in the four dimensions we are defining. These dimensions are as follows: source code, business and legal, integration and reuse, and social (community). The CRF is a framework that was introduced to measure urban resilience and most specifically how cities' resilience is changing as they evolve. We believe that a software evaluation model that focuses on resilience can complement the pre‐existing models based on software quality and software health. Although concepts that are related to resilience, like sustainability or viability, already appear in literature, to our best knowledge, there is no OSS assessment model that evaluates the resilience of an OSS project. We argue that cities and OSS projects are both dynamically evolving systems with similar characteristics. The proposed framework utilizes both quantitative and qualitative indicators, which is viewed as an advantage. Lastly, we would like to emphasize that the framework has been tested on the enterprise software domain as part of this study, evaluating five major versions of six OSS projects, Laravel, Composer, PHPMyAdmin, OKApi, PatternalPHP, and PHPExcel, the first three of which are intuitively considered resilient and the three latter nonresilient, to provide a preliminary validation of the models' ability to distinguish between resilient and not resilient projects.
Enhancing Microsoft 365 Security: Integrating Digital Forensics Analysis to Detect and Mitigate Adversarial Behavior Patterns

This research article investigates the effectiveness of digital forensics analysis (DFA) techniques in identifying patterns and trends in malicious failed login attempts linked to public data breaches or compromised email addresses in Microsoft 365 (M365) environments. Pattern recognition techniques are employed to analyze security logs, revealing insights into negative behavior patterns. The findings contribute to the literature on digital forensics, opposing behavior patterns, and cloud-based cybersecurity. Practical implications include the development of targeted defense strategies and the prioritization of prevalent threats. Future research should expand the scope to other cloud services and platforms, capture evolving trends through more prolonged and extended analysis periods, and assess the effectiveness of specific mitigation strategies for identified tactics, techniques, and procedures (TTPs).
Information sharing offices and economic growth in sub‐Saharan Africa

Abstract Although credit availability is essential for economic growth, the information asymmetry problem inhibits the credit creation function of financial institutions. Among other means, information‐sharing offices (ISOs) have been established, including private credit bureaus and public credit registries to provide information on the financial characteristics of citizens to reduce information asymmetry. From this background, this study used an explanatory research design with a longitudinal (panel) data approach to assess whether the coverage of information by ISOs affects sectorial and overall economic growth among sub‐Saharan African (SSA) countries using the system generalized method of moments technique on data from 2005 to 2019. The results highlight that information coverage by ISOs enhances sectorial and overall economic growth by solving the information asymmetry problem. From the findings, governments in SSA countries are encouraged to support public credit registries to effectively perform their mandate while providing incentives to attract investments in private credit bureaus since they contribute to economic growth.
Self-Supervised Representation Learning for Quasi-Simultaneous Arrival Signal Identification Based on Reconnaissance Drones

Reconnaissance unmanned aerial vehicles are specifically designed to estimate parameters and process intercepted signals for the purpose of identifying and locating radars. However, distinguishing quasi-simultaneous arrival signals (QSAS) has become increasingly challenging in complex electromagnetic environments. In order to address the problem, a framework for self-supervised deep representation learning is proposed. The framework consists of two phases: (1) pre-train an autoencoder. For learning the unlabeled QSAS representation, the ConvNeXt V2 is trained to extract features from masked time–frequency images and reconstruct the corresponding signal in both time and frequency domains; (2) transfer the learned knowledge. For downstream tasks, encoder layers are frozen, the linear layer is fine-tuned to classify QSAS under few-shot conditions. Experimental results demonstrate that the proposed algorithm can achieve an average recognition accuracy of over 81% with the signal-to-noise ratio in the range of −16∼16 dB. Compared to existing CNN-based and Transformer-based neural networks, the proposed algorithm shortens the time of testing by about 11× and improves accuracy by up to 21.95%.
Distributed Ledger as a Service: A Web 3.0-Oriented Architecture

This paper proposes a general and interoperable Web of Things (WoT)-oriented architecture to support a distributed storage application. In particular, the focus is on a distributed ledger service dedicated to machine-to-machine (M2M) transactions occurring in an intelligent ecosystem. For this purpose, the basic functional modules have been characterized and integrated into a comprehensive framework relying on an IOTA approach. Furthermore, a general protocol that is built upon an underlying publish-and-subscribe framework is proposed to support all the application phases. The proposed approach has been validated by a simulation campaign targeting the achievable latency and throughput and, further, by a qualitative analysis of high-level metrics, both pointing out several advantages in terms of interoperability, scalability, and mobility support, together with addressing some constraints affecting service availability and security.
Exploring issues of online hate speech against minority religious groups in Bangladesh

Purpose Online hate speech (OHS) is becoming a serious problem worldwide including in Bangladesh. This study aims to understand the nature of OHS against religious groups and explore its impact on their social life and mental health. Design/methodology/approach A qualitative approach was followed and 11 in-depth interviews (IDIs) were conducted with the selected OHS victims. This study conducted a semi-structural interview using Google Form following the design questionnaire for selecting IDIs participants. Findings This study found that religious minorities experience online hatred through online media by the major religious group in Bangladesh. Natures of OHS are commenting on social media posts, sharing hateful memes and sending private messages using slang language targeting religious identity, religious food habits and ethnic identities. Victims were offended, abused and bullied by unknown persons, their university friends and colleagues. Victims of OHS did not take any action against it due to fear of insecurity. Victims of OHS felt low-minded, helpless and anxious after the experience of OHS; they felt more insecure and vulnerable socially and mentally. Originality/value The findings of this study suggest that policymakers identify the nature of OHS and take proper steps for reducing the frequency of OHS in Bangladesh. To combat the OHS, authorities have to make legal enforcement equal for everyone.
Language-Led Visual Grounding and Future Possibilities

In recent years, with the rapid development of computer vision technology and the popularity of intelligent hardware, as well as the increasing demand for human–machine interaction in intelligent products, visual localization technology can help machines and humans to recognize and locate objects, thereby promoting human–machine interaction and intelligent manufacturing. At the same time, human–machine interaction is constantly evolving and improving, becoming increasingly intelligent, humanized, and efficient. In this article, a new visual localization model is proposed, and a language validation module is designed to use language information as the main information to increase the model’s interactivity. In addition, we also list the future possibilities of visual localization and provide two examples to explore the application and optimization direction of visual localization and human–machine interaction technology in practical scenarios, providing reference and guidance for relevant researchers and promoting the development and application of visual localization and human–machine interaction technology.
Control Method of Cold and Hot Shock Test of Sensors in Medium

In order to meet the latest requirements for sensor quality test in the industry, the sample sensor needs to be placed in the medium for the cold and hot shock test. However, the existing environmental test chamber cannot effectively control the temperature of the sample in the medium. This paper designs a control method based on the support vector machine (SVM) classification algorithm and K-means clustering combined with neural network correction. When testing sensors in a medium, the clustering SVM classification algorithm is used to distribute the control voltage corresponding to temperature conditions. At the same time, the neural network is used to constantly correct the temperature to reduce overshoot during the temperature-holding phase. Eventually, overheating or overcooling of the basket space indirectly controls the rapid rise or decrease in the temperature of the sensor in the medium. The test results show that this method can effectively control the temperature of the sensor in the medium to reach the target temperature within 15 min and stabilize when the target temperature is between 145 °C and -40 °C. The steady-state error is less than 0.31 °C in the high-temperature area and less than 0.39 °C in the low-temperature area, which well solves the dilemma of the current cold and hot shock test.
Interactive Attention Learning on Detection of Lane and Lane Marking on the Road by Monocular Camera Image

Vision-based identification of lane area and lane marking on the road is an indispensable function for intelligent driving vehicles, especially for localization, mapping and planning tasks. However, due to the increasing complexity of traffic scenes, such as occlusion and discontinuity, detecting lanes and lane markings from an image captured by a monocular camera becomes persistently challenging. The lanes and lane markings have a strong position correlation and are constrained by a spatial geometry prior to the driving scene. Most existing studies only explore a single task, i.e., either lane marking or lane detection, and do not consider the inherent connection or exploit the modeling of this kind of relationship between both elements to improve the detection performance of both tasks. In this paper, we establish a novel multi-task encoder-decoder framework for the simultaneous detection of lanes and lane markings. This approach deploys a dual-branch architecture to extract image information from different scales. By revealing the spatial constraints between lanes and lane markings, we propose an interactive attention learning for their feature information, which involves a Deformable Feature Fusion module for feature encoding, a Cross-Context module as information decoder, a Cross-IoU loss and a Focal-style loss weighting for robust training. Without bells and whistles, our method achieves state-of-the-art results on tasks of lane marking detection (with 32.53% on IoU, 81.61% on accuracy) and lane segmentation (with 91.72% on mIoU) of the BDD100K dataset, which showcases an improvement of 6.33% on IoU, 11.11% on accuracy in lane marking detection and 0.22% on mIoU in lane detection compared to the previous methods.
Road Traffic Sign Detection Method Based on RTS R-CNN Instance Segmentation Network

With the rapid development of the autonomous driving industry, there is increasing research on related perception tasks. However, research on road surface traffic sign detection tasks is still limited. There are two main challenges to this task. First, when the target object's pixel ratio is small, the detection accuracy often decreases. Second, the existing publicly available road surface traffic sign datasets have limited image data. To address these issues, this paper proposes a new instance segmentation network, RTS R-CNN, for road surface traffic sign detection tasks based on Mask R-CNN. The network can accurately perceive road surface traffic signs and provide important information for the autonomous driving decision-making system. Specifically, CSPDarkNet53_ECA is proposed in the feature extraction stage to enhance the performance of deep convolutional networks by increasing inter-channel interactions. Second, to improve the network's detection accuracy for small target objects, GR-PAFPN is proposed in the feature fusion part, which uses a residual feature enhancement module (RFA) and atrous spatial pyramid pooling (ASPP) to optimize PAFPN and introduces a balanced feature pyramid module (BFP) to handle the imbalanced feature information at different resolutions. Finally, data augmentation is used to generate more data and prevent overfitting in specific scenarios. The proposed method has been tested on the open-source dataset Ceymo, achieving a Macro F1-score of 87.56%, which is 2.3% higher than the baseline method, while the inference speed reaches 23.5 FPS.
Metaphor-Enabled Marketplace Sentiment Analysis

Textual data require an analytical trade-off between breadth and depth. Automated approaches locate patterns across large swaths of data points but sacrifice qualitative insight because they are not well equipped to deal with context-determined ways to express meaning, like figurative language. To strengthen the power of automated text analysis, researchers seek hybrid methodologies that combine computer-augmented analysis with sociocultural researcher insights based on qualitative textual interpretation. This article demonstrates a new method, which the authors term metaphor-enabled marketplace sentiment analysis (MEMSA). Building on existing automated text analysis methodologies linking word lists to sentiments, MEMSA adds metaphors that associate topics with sentiments across domains. Using MEMSA, researchers can leverage the sentiment potential of these located metaphors and scale insights to the level of big textual data by employing a dictionary approach enhanced by a specific and useful linguistic property of metaphors: their predictable structure in text (something is something else). This article shows that metaphors add associative detail to sentiments, revealing the targets and sources of sentiments that underlie the associations. Understanding nuanced market sentiments enables marketers to identify sentiment-based trends embedded in market discourse, so they can better formulate, target, position, and communicate value propositions for products and services.
Big Data-Driven Banking Operations: Opportunities, Challenges, and Data Security Perspectives

At present, with the rise of information technology revolution, such as mobile internet, cloud computing, big data, machine learning, artificial intelligence, and the Internet of Things, the banking industry is ushering in new opportunities and encountering severe challenges. This inspired us to develop the following research concepts to study how data innovation impacts banking. We used qualitative research methods (systematic and bibliometric reviews) to examine research articles obtained from the Web of Science and SCOPUS databases to achieve our research goals. The findings show that data innovation creates opportunities for a well-developed banking supply chain, effective risk management and financial fraud detection, banking customer analytics, and bank decision-making. Also, data-driven banking faces some challenges, such as the availability of more data increasing the complexity of service management and creating fierce competition, the lack of professional data analysts, and data costs. This study also finds that banking security is one of the most important issues; thus, banks need to respond to external and internal cyberattacks and manage vulnerabilities.
Performance Optimization and Comprehensive Analysis of Binary Nutcracker Optimization Algorithm: A Case Study of Feature Selection and Merkle–Hellman Knapsack Cryptosystem

In this paper, a binary variant of a novel nature-inspired metaheuristic algorithm called the nutcracker optimization algorithm (NOA) is presented for binary optimization problems. Because of the continuous nature of the classical NOA and the discrete nature of the binary problems, two different families of transfer functions, namely S-shaped and V-shaped, are extensively investigated for converting the classical NOA into a binary variant, namely BNOA, applicable for various binary problems. Additionally, BNOA is improved using a local search strategy based on effectively integrating some genetic operators into the BNOA’s exploitation and exploration; this additional variant is called BINOA. Both BNOA and BINOA are evaluated using three common binary optimization problems, including feature selection, 0-1 knapsack, and the Merkle–Hellman knapsack cryptosystem (MHKC), and are compared to several robust binary metaheuristic optimizers in terms of statistical information, statistical tests, and convergence speed. The experiential findings show that BINOA is better than the classical BNOA and the other rival optimizers for both the 0-1 knapsack problem and attacking MHKC and is on par with some algorithms, like the genetic algorithm for feature selection.
Training Artificial Neural Networks Using a Global Optimization Method That Utilizes Neural Networks

Perhaps one of the best-known machine learning models is the artificial neural network, where a number of parameters must be adjusted to learn a wide range of practical problems from areas such as physics, chemistry, medicine, etc. Such problems can be reduced to pattern recognition problems and then modeled from artificial neural networks, whether these problems are classification problems or regression problems. To achieve the goal of neural networks, they must be trained by appropriately adjusting their parameters using some global optimization methods. In this work, the application of a recent global minimization technique is suggested for the adjustment of neural network parameters. In this technique, an approximation of the objective function to be minimized is created using artificial neural networks and then sampling is performed from the approximation function and not the original one. Therefore, in the present work, learning of the parameters of artificial neural networks is performed using other neural networks. The new training method was tested on a series of well-known problems, a comparative study was conducted against other neural network parameter tuning techniques, and the results were more than promising. From what was seen after performing the experiments and comparing the proposed technique with others that have been used for classification datasets as well as regression datasets, there was a significant difference in the performance of the proposed technique, starting with 30% for classification datasets and reaching 50% for regression problems. However, the proposed technique, because it presupposes the use of global optimization techniques involving artificial neural networks, may require significantly higher execution time than other techniques.