Reviewerly/exHarmony huggingface dataset:
Development of a hand classification system for smart hand wearables

Research purpose: This study aimed to lay a research foundation for smart hand wearable design by classifying the right-hand data of 4545 adults aged 20 to 69. Further, to increase the practical applicability of the hand classification system, a hand type discrimination method and regression equations for the hand dimensions of each type were presented. Methods: This study statistically analyzed eighth Size Korea data with IBM SPSS Ver.26.0. Cluster analysis was performed to classify both finger length and circumference type. Discriminant analysis was conducted, yielding discriminant functions to aid potential smart hand wearable wearers in self-diagnosing their hand types. Linear regression analysis yielded regression equations for the detailed finger dimensions for the pattern-making of smart hand wearables. Results: The finger length type was categorized into four types: the Uphill type, Downhill type, Mountain type, and Horizon type for both men’s and women’s hands. The finger circumference type was categorized into two types, the Cone and Cylinder types, for both men’s and women’s hands. The discriminant function showed a mean accuracy rate of 89.9% and the regression equations a mean explanatory power of 72.9%. Conclusion: The hand classification system proposed in this study aimed to improve the fingertip fit of smart hand wearable products by analyzing the configuration of motion tracking gloves or haptic gloves. In addition, considering the practical applicability for both wearers and designers of smart hand wearables, a discrimination method of finger types for wearers’ self-diagnosis and regression functions of finger dimensions for designers’ pattern making were provided.
Requirements Engineering in Machine Learning Projects

Over the last decade, machine learning methods have revolutionized a large number of domains and provided solutions to many problems that people could hardly solve in the past. The availability of large amounts of data, powerful processing architectures, and easy-to-use software frameworks have made machine learning a popular, readily available, and affordable option in many different domains and contexts. However, the development and maintenance of production-level machine learning systems have proven to be quite challenging, as these activities require an engineering approach and solid best practices. Software engineering offers a mature development process and best practices for conventional software systems, but some of them are not directly applicable to the new programming paradigm imposed by machine learning. The same applies to the requirements engineering best practices. Therefore, this article provides an overview of the requirements engineering challenges in the development of machine learning systems that have been reported in the research literature, along with their proposed solutions. Furthermore, it presents our approach to overcoming those challenges in the form of a case study. Through this mixed-method study, the article tries to identify the necessary adjustments to (1) the best practices for conventional requirements engineering and (2) the conventional understanding of certain types of requirements to better fit the specifics of machine learning. Moreover, the article tries to emphasize the relevance of properly conducted requirements engineering activities in addressing the complexity of machine learning systems, as well as to motivate further discussion on the requirements engineering best practices in developing such systems.
Secure Multi-Party Computation-Based Privacy-Preserving Authentication for Smart Cities

The increasing concern for identity confidentiality in the Smart City scenario has fostered research on privacy-preserving authentication based on pseudonymization.Pseudonym systems enable citizens to generate pseudo-identities and establish unlinkable anonymous accounts in cloud service providers.The citizen's identity is concealed, and his/her different anonymous accounts cannot be linked to each other.Unfortunately, current pseudonym systems require a trusted certification authority (CA) to issue the cryptographic components (e.g.credentials, secret keys, or pseudonyms) to citizens.This CA, generally a Smart City governmental entity, has the capability to grant or revoke privacy rights at will, hence posing a serious threat in case of corruption.Additionally, if the pseudonym system enables de-anonymization of misusers, a corrupted CA can jeopardize the citizens' privacy.This paper presents a novel approach to construct a pseudonym system without a trusted issuer.The CA is emulated by a set of Smart City service providers by means of secure multi-party computation (MPC), which circumvents the requirement of assuming an honest CA.The paper provides a full description of the system, which integrates an MPC protocol and a pseudonym-based signature scheme.The system has been implemented and tested.
Reinforcing Local Structure Perception for Monocular Depth Estimation

Monocular depth estimation is a basic and critical task in computer vision that finds wide applications in various domains, including robot navigation and autonomous driving. A prevailing method nowadays is leveraging hybrid depth datasets obtained from various depth sensors to predict affine-invariant depth under supervised learning. However, the varying depth ranges in hybrid datasets can result in an unstable network. While some affine-invariant loss functions have been introduced, existing methods may lead to suboptimal geometric structures, such as blurred boundaries and details. To tackle this issue, our approach is centered on reinforcing the local structural perception of images. Specifically, we propose a novel pixel-level supervised loss, called the windowed correlation regression (WCR) loss. It computes the windowed Pearson correlation coefficient (PCC) to constrain the similarity of data distribution within a local region. In addition, we introduce a new coarse-to-fine multiscale normal (CFMN) loss in conjunction with the former loss to further improve geometric accuracy. Our experimental results on six zero-shot datasets demonstrate that our method outperforms state-of-the-art (SOTA) methods. In terms of local geometric structural precision, our method achieves sharper edges and more consistent local grayscale.
Wireless Federated Learning With Asynchronous and Quantized Updates

Federated learning (FL) is a framework of large-scale distributed learning with user privacy protection through local training and global aggregation. However, FL may suffer from asynchronous updates with quantized gradients in practice, which has not been well addressed in existing works. In this letter, we consider improving the convergence rate under the asynchronous FL system with performance guarantee and quantized. Specifically, our goal is to find the tradeoff between the quantization error (QE) and the staleness by taking the constraints on the quantization level and staleness into account. In order to make the optimization problem more efficient to solve, we derive an upper bound of QE and reformulate the optimization problem to obtain the optimal solutions. Simulation results quantify the impact of different quantization levels on the convergence rate, and demonstrate the performance improvement by achieving a tradeoff between the convergence rate and the QE.
Hate Speech Detection via Dual Contrastive Learning

The fast spread of hate speech on social media impacts the Internet environment and our society by increasing prejudice and hurting people. Detecting hate speech has aroused broad attention in the field of natural language processing. Although hate speech detection has been addressed in recent work, this task still faces two inherent unsolved challenges. The first challenge lies in the complex semantic information conveyed in hate speech, particularly the interference of insulting words in hate speech detection. The second challenge is the imbalanced distribution of hate speech and non-hate speech, which may significantly deteriorate the performance of models. To tackle these challenges, we propose a novel dual contrastive learning (DCL) framework for hate speech detection. Our framework jointly optimizes the self-supervised and the supervised contrastive learning loss for capturing span-level information beyond the token-level emotional semantics used in existing models, particularly detecting speech containing abusive and insulting words. Moreover, we integrate the focal loss into the dual contrastive learning framework to alleviate the problem of data imbalance. We conduct experiments on two publicly available English datasets, and experimental results show that the proposed model outperforms the state-of-the-art models and precisely detects hate speeches.
Toward Hierarchical Adaptive Alignment for Aerial Object Detection in Remote Sensing Images

The aerial objects tend to distribute with a major variation on the scale and arbitrary orientations in remote sensing images. To meet such characteristics of the aerial object, most of the existing anchor-based detectors rely on preset anchors with variable scales, angles, and aspect ratios, which leads to the misalignment of the selection of candidate regions, the extraction of object features, and the label assignment of preset boxes, interfering the performance of the detector. To address this issue, we propose a Hierarchical Adaptive Alignment Network (HAA-Net). Specifically, we first design the Region Refinement Module (RRM), Feature Alignment module (FAM), and Potential Label Assignment Module (PLAM) to alleviate the misalignment of the region, feature, and label levels respectively; furthermore, we use the gradient equalization strategy to jointly optimize these modules at different levels, so that the whole network can be fully trained to significantly improve detection performance. Extensive experiments demonstrate that our approach can achieve superior performance in three common aerial object datasets (e.g., DOTA, HRSC2016, and UCAS-AOD) when compared with state-of-the-art detectors.
An Effective Instance-Level Contrastive Training Strategy for Ship Detection in SAR Images

Existing ship detection approaches in SAR images often suffer from inadequate learning of the detector and sub-optimal detection performance. To this end, Based on self-supervised contrastive learning, this letter consider using the relationship between samples to develop a more effective training strategy. First, the Instance-based RoI encode head is proposed, named InsRen head, a simple yet effective network structure. Its purpose is to encode the samples into a contrastive feature space, facilitating the measurement of contrastive learning. Furthermore, to adapt contrastive learning to ship detection, we have redefined some basic terms, such as query, positive key, and negative key, which can help the model build the training pipeline. Finally, we design the Instance-based Contrastive loss that does not require label supervision, named InsCon loss. With the penalty of the InsCon loss, the queries and positive key can learn more similar representations in the contrastive feature space. Simultaneously, the query and negative key are as far away as possible to increase the difference. With the help of InsRen head and InsCon loss, the training of the detection model is more effective. Experimental results demonstrate the superiority of our method.
Probabilistic neural data fusion for learning from an arbitrary number of multi-fidelity data sets

In many applications in engineering and sciences analysts have simultaneous access to multiple data sources. In such cases, the overall cost of acquiring information can be reduced via data fusion or multi-fidelity (MF) modeling where one leverages inexpensive low-fidelity (LF) sources to reduce the reliance on expensive high-fidelity (HF) data. In this paper, we employ neural networks (NNs) for data fusion in scenarios where data is very scarce and obtained from an arbitrary number of sources with varying levels of fidelity and cost. We introduce a unique NN architecture that converts MF modeling into a nonlinear manifold learning problem. Our NN architecture inversely learns non-trivial (e.g., non-additive and non-hierarchical) biases of the LF sources in an interpretable and visualizable manifold where each data source is encoded via a low-dimensional distribution. This probabilistic manifold quantifies model form uncertainties such that LF sources with small bias are encoded close to the HF source. Additionally, we endow the output of our NN with a parametric distribution not only to quantify aleatoric uncertainties, but also to reformulate the network's loss function based on strictly proper scoring rules which improve robustness and accuracy on unseen HF data. Through a set of analytic and engineering examples, we demonstrate that our approach provides a high predictive power while quantifying various sources uncertainties.
Energy Optimization of Distributed Video Processing System using Genetic Algorithm with Bayesian Attractor Model

For the future cyber-physical system (CPS) society, it is necessary to construct digital twins (DTs) of a real world in real time using a lot of cameras and sensors. Hence, the energy efficiency of both networks and computers for largescale distributed video analysis is a major challenge for the full-scale spread of CPSs and DTs. Toward this goal, we first propose a model to arbitrarily split and distribute the video analysis task to terminals, edge servers, and cloud servers and dynamically assign appropriate CNN models to them. System-wide optimization of such distributed processing can reduce overall system power consumption by reducing network bandwidth and efficiently utilizing distributed CPU/GPU resources. To realize this optimization in a real system, we also propose a model to estimate the GPU load, processing time, and power consumption of these devices based on massive experimental measurements. Since such a large-scale optimization is difficult because of the dynamic and multi-objective nature of the problem, we propose a new optimization algorithm composed of Genetic Algorithm and Bayesian Attractor Model. Finally, simulation evaluations are performed to demonstrate that the proposed method can minimize system power consumption and satisfy latency and recognition accuracy requirements of each video analysis, even under changing environmental conditions.
TinyGraphHAR: Enhancing Human Activity Recognition With Graph Neural Networks

Graph Neural Networks (GNNs) have improved prediction accuracy and reduced model size in many applications. In this paper, we explore a new way of human activity recognition that leverages GNNs. Our technique focuses on inertial sensor data that has been acquired from accelerometers and gyroscopes in wearable devices. We introduce TinyGraphHAR, a novel GNN with attention and recurrence that constructs graphs that reflect temporal relationships of human events in the signals. TinyGraphHAR has superior discriminative power for complex events that are interconnected in time. For the benchmarks KU-HAR, PAMAP2, UCI-HAR, and Daphnet we achieve Macro F1 scores of up to 95.46 %, 75.59 %, 90.16 %, and 59.75 %, respectively. In addition, model sizes of TinyGraphHAR are up to 3 orders of magnitude smaller than comparative state-of-the-art neural architectures for the same data sets, thanks to the abstraction of the graph representation. TinyGraphHAR thus opens up new opportunities for a multitude of applications at the edge, including wearables and mobile devices with limited memory and energy budgets.
Ant Colony Optimization Algorithm Enhancement for Better Performance

The scientific field of swarm intelligence is related to swarms existing in natural systems. Numerous systems and Algorithms built on swarm intelligence have appeared addressing optimization problems, including: Firefly Algorithm (FA), Ant System (AS), Particle Swarm Optimization (PSO), Intelligent Water Drops (IWD), Ant Colony Optimization (ACO), Artificial Bee Colony (ABC), and many others. The foraging behavior of the ant’s system inspired the researchers to generate the ACO and use it to solve optimization problems. In this research, we introduce the ACOStar algorithm to improve the performance of ACO by including the evaluation function of A* algorithm on the transition-probability function of ACO. To demonstrate the success of the proposed algorithm, we applied the suggested algorithm to the shortest path problem. All experiments demonstrate the success and the accuracy of the proposed algorithm.
Adaptive Governance for Blockchain Networks

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KT-BT: A Framework for Knowledge Transfer Through Behavior Trees in Multirobot Systems

Multirobot and multiagent systems demonstrate collective (swarm) intelligence through systematic and distributed integration of local behaviors in a group. Agents sharing knowledge about the mission and environment can enhance performance at individual and mission levels. However, this is difficult to achieve, partly due to the lack of a generic framework for transferring part of the known knowledge (behaviors) between agents. This article presents a new knowledge representation framework and a transfer strategy called KT-BT: knowledge transfer through behavior trees. The KT-BT framework follows a query-response-update mechanism through an online behavior tree framework, where agents broadcast queries for unknown conditions and respond with appropriate knowledge using a condition-action-control subflow. We embed a novel grammar structure called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stringBT</i> that encodes knowledge, enabling behavior sharing. We theoretically investigate the properties of the KT-BT framework in achieving homogeneity of high knowledge across the entire group compared to a heterogeneous system without the capability of sharing their knowledge. We extensively verify our framework in a simulated multirobot search and rescue problem. The results show successful knowledge transfers and improved group performance in various scenarios. We further study the effects of opportunities and communication range on group performance, knowledge spread, and functional heterogeneity in a group of agents, presenting interesting insights.
Application of blockchain technology in the agri-food system: a systematic bibliometric visualization analysis and policy imperatives

Purpose Blockchain technology (BCT), since its emergence touted to be disruptive, is gaining momentum, especially in the agri-food system owing to its multiple benefits. Design/methodology/approach The authors attempted to conduct a systematic bibliometric visualization analysis of the BCT in the agri-food system. The analysis investigated the list of countries and institutions that conducted research on BCT in agriculture, growth trend analysis in research publications, bibliographic coupling of journals using the VOSviewer tool, and the countries and institutions researching BCT. Findings The authors discovered that China, the USA and India were the highly active countries in BCT research and publication. However, India has only limited research collaboration with other countries as compared to China and the USA. The keyword analysis indicates the role of BCT in order to maintain the transparency of the supply chain by means of protecting the privacy of the personal data of the stakeholders. Research limitations/implications More research related to the implementation of BCT in livestock, fishery and agro-forestry sector is recommended. Social implications The case examined is of particular interest as it is concerned with efficient supply chain management. Originality/value This study adds value and evidence to the scope and benefits of BCT by providing a comprehensive literature review, with a special focus on the opportunities and challenges concerned with implementation of BCT in the Indian agri-food system. Highlights Blockchain technology (BCT) – a promising tool to resolve issues in agriculture supply chain. BCT ensures transparency and protection of information along the supply chain transactions. China, the USA and India are the highly active countries in BCT research and publication. Multiple potential benefits to stakeholders are attributed to the BCT in the agri-food system. The key challenge is to bridge the digital gap between developed and developing nations. Future research on BCT should aim at easing and undistorted competition among stakeholders.
A simple method for obtaining artificial 3D forms of 2D mammograms in diagnosis of breast cancer

Breast cancer is one of the most common types of cancer among women worldwide and mammography is the primary method which plays a major role in early diagnosis of breast cancer. Mammograms can be obtained in two-dimensional (2D) or three-dimensional (3D) forms. 3D images contain more information than their 2D forms, however, they involve more computational and time complexities and are more challenging to produce. This study proposes a simple method to convert mammograms into artificial 3D forms. In this method, first, the gray level values of a 2D image are defined as an artificial third dimension for the same image and then the obtained image is saved in a different viewpoint again in the 2D form to make it include more information than its original form. Both, the original mammograms and their converted forms are classified as mass or normal mammograms using the ResNet. The method is tested on masses and normal mammograms taken from MIAS, INBreast and CBIS-DDSM databases, which are frequently used in the literature. The results show that the classification performed on the images obtained with the original 2D forms provided 82.4% accuracy for MIAS, 86.7% for INbreast and 96.7% for CBIS-DDSM databases. Whereas the transformed forms provided 97.8% accuracy for MIAS, 100% for INbreast and 100% for CBIS-DDSM databases, in terms of mass and normal classification. According to these results, it is seen that the transformed forms of the images provided an average of 10.67% higher accuracy for all three databases compared to their original forms.
Adaptive-sized residual fusion network-based segmentation of biomedical images

Medical imaging is crucial in examinations and for providing appropriate patient care. Medical images are used to diagnose and identify specific conditions. However, the diagnostic procedures are confounded by the complexity of these images, which have several overlapping characteristics. Various techniques have enhanced biomedical image segmentation, and strategies to improve the contrast and accuracy of medical images have been presented in the literature. This article presents a recurrent grasshopper herd-based optimized histogram-based contrast enhancement technique, in which sharp-edge morphological characteristics can be enhanced using inclusive Laplacian fractional order Savitzky–Golay differentiation. An adaptive-sized residual fusion network approach is used to isolate the target tissue in the biomedical image. The effectiveness of the proposed system is verified using different medical data sets and a high level of accuracy is achieved. The proposed method outperforms other state-of-the-art methodologies in terms of the scale, shape and morphological attributes of the medical image.