Reviewerly/exHarmony huggingface dataset:
A hybrid formation path planning based on A* and multi-target improved artificial potential field algorithm in the 2D random environments

Traditional artificial potential field algorithm for multi-robot formation is easy to fall into local minima and the path planning efficiency is low. To this end, we propose a new method of a hybrid formation path planning based on A* and multi-target improved artificial potential field algorithm (A*-MTIAPF) that provides the optimal collision free path and improves the efficiency for multi-robot formation path planning. The A*-MTIAPF algorithm integrates global path planning and local path planning. The novelties include combining A* with the improved artificial potential field algorithm and dividing multiple virtual sub-target points on the global optimal path of A* planning. Firstly, A* algorithm is used to complete the global path planning. Secondly, the improved artificial potential field algorithm which takes multiple sub-target points divided by the global optimal path as virtual target points is used to complete local path planning by switching target points. In addition, we propose a double priority judgment control algorithm (DPJC) to solve the collision problem among multiple robots by setting double priority to determine the movement order of each robot. Then, a new experimental method is designed by using the randomly generated 2D maps to verify the effectiveness of the proposed method. The results show that our method has advantages that it solves the local minimum problem, improves the efficiency of formation path planning and avoids collision among multiple robots over existing methods.
Copy-move Forgery Detection using SIFT and DWT detection Techniques

Copy-Move forgery practices are defamatory tampering processes, affecting digital images. Such practices have become much more common nowadays because of the advent of significantly effective image processing tools being readily available. In copy-move forgery technique, an area from the original image is copied and pasted over another image area. This technique provides a way to conceal the important details and information in a digital image without leaving much traces behind to observe the tampering or manipulation of the image. Such practices question the authenticity of an image to forensic. In this paper, the techniques are proposed for the detection of cloning or copy-move forgery that involve both block based extraction using Discrete Wavelet Transform (DWT) and feature point extraction based detection technique i.e., Scale Invariant Feature Transform (SIFT) to extract the tampered regions more precisely. The proposed algorithm mainly involves tentacle matching of features of same features, which can be extracted from each of the blocks by calculating the dot products between their respective unit vectors. The proposed algorithm is able to achieve an efficiency of 98.12%, precision factor of 97% of and recall factor of 100%.
PLENARY: Explaining black-box models in natural language through fuzzy linguistic summaries

We introduce an approach called PLENARY (exPlaining bLack-box modEls in Natural lAnguage thRough fuzzY linguistic summaries), which is an explainable classifier based on a data-driven predictive model. Neural learning is exploited to derive a predictive model based on two levels of labels associated with the data. Then, model explanations are derived through the popular SHapley Additive exPlanations (SHAP) tool and conveyed in a linguistic form via fuzzy linguistic summaries. The linguistic summarization allows translating the explanations of the model outputs provided by SHAP into statements expressed in natural language. PLENARY accounts for the imprecision related to model outputs by summarizing them into simple linguistic statements and for the imprecision related to the data labeling process by including additional domain knowledge in the form of middle-layer labels. PLENARY is validated on preprocessed speech signals collected from smartphones from patients with bipolar disorder and on publicly available mental health survey data. The experiments confirm that fuzzy linguistic summarization is an effective technique to support meta-analyses of the outputs of AI models. Also, PLENARY improves explainability by aggregating low-level attributes into high-level information granules, and by incorporating vague domain knowledge into a multi-task sequential and compositional multilayer perceptron. SHAP explanations translated into fuzzy linguistic summaries significantly improve understanding of the predictive modelling process and its outputs.
Deep Evidential Learning with Noisy Correspondence for Cross-modal Retrieval

Cross-modal retrieval has been a compelling topic in the multimodal community. Recently, to mitigate the high cost of data collection, the co-occurred pairs (e.g., image and text) could be collected from the Internet as a large-scaled cross-modal dataset, e.g., Conceptual Captions. However, it will unavoidably introduce noise (i.e., mismatched pairs) into training data, dubbed noisy correspondence. Unquestionably, such noise will make supervision information unreliable/uncertain and remarkably degrade the performance. Besides, most existing methods focus training on hard negatives, which will amplify the unreliability of noise. To address the issues, we propose a generalized Deep Evidential Cross-modal Learning framework (DECL), which integrates a novel Cross-modal Evidential Learning paradigm (CEL) and a Robust Dynamic Hinge loss (RDH) with positive and negative learning. CEL could capture and learn the uncertainty brought by noise to improve the robustness and reliability of cross-modal retrieval. Specifically, the bidirectional evidence based on cross-modal similarity is first modeled and parameterized into the Dirichlet distribution, which not only provides accurate uncertainty estimation but also imparts resilience to perturbations against noisy correspondence. To address the amplification problem, RDH smoothly increases the hardness of negatives focused on, thus embracing higher robustness against high noise. Extensive experiments are conducted on three image-text benchmark datasets, i.e., Flickr30K, MS-COCO, and Conceptual Captions, to verify the effectiveness and efficiency of the proposed method. The code is available at \urlhttps://github.com/QinYang79/DECL.
Cross-Modal Retrieval with Heterogeneous Graph Embedding

Conventional methods address the cross-modal retrieval problem by projecting the multi-modal data into a shared representation space. Such a strategy will inevitably lose the modality-specific information, leading to decreased retrieval accuracy. In this paper, we propose heterogeneous graph embeddings to preserve more abundant cross-modal information. The embedding from one modality will be compensated with the aggregated embeddings from the other modality. In particular, a self-denoising tree search is designed to reduce the "label noise" problem, making the heterogeneous neighborhood more semantically relevant. The dual-path aggregation tackles the "modality imbalance" problem, giving each sample comprehensive dual-modality information. The final heterogeneous graph embedding is obtained by feeding the aggregated dual-modality features to the cross-modal self-attention module. Experiments conducted on cross-modality person re-identification and image-text retrieval task validate the superiority and generality of the proposed method.
Dynamic Prototype Mask for Occluded Person Re-Identification

Although person re-identification has achieved an impressive improvement in recent years, the common occlusion case caused by different obstacles is still an unsettled issue in real application scenarios. Existing methods mainly address this issue by employing body clues provided by an extra network to distinguish the visible part. Nevertheless, the inevitable domain gap between the assistant model and the ReID datasets has highly increased the difficulty to obtain an effective and efficient model. To escape from the extra pre-trained networks and achieve an automatic alignment in an end-to-end trainable network, we propose a novel Dynamic Prototype Mask (DPM) based on two self-evident prior knowledge. Specifically, we first devise a Hierarchical Mask Generator which utilizes the hierarchical semantic to select the visible pattern space between the high-quality holistic prototype and the feature representation of the occluded input image. Under this condition, the occluded representation could be well aligned in a selected subspace spontaneously. Then, to enrich the feature representation of the high-quality holistic prototype and provide a more complete feature space, we introduce a Head Enrich Module to encourage different heads to aggregate different patterns representation in the whole image. Extensive experimental evaluations conducted on occluded and holistic person re-identification benchmarks demonstrate the superior performance of the DPM over the state-of-the-art methods.
Blockchain digital technology empowers E-commerce supply chain sustainable value co-creation decision and coordination considering online consumer reviews

From the perspective of value co-creation, this study adopted Stackelberg game theory to construct traditional e-commerce supply chain and BOCR technology e-commerce supply chain, their optimal decisions and profits are solved under decentralized and centralized decision-making. We anatomized the influence of key factors and compared different models. Then we proposed the investment decision condition, cost-sharing and profit-sharing contract to achieve coordination, then validated conclusions via numerical simulation. Findings: When the producer’s attention to consumer value increases, under decentralized decision-making, optimal sales price and service level decrease, the producer’s optimal profit increases, while the e-commerce platform’s optimal profit will first increase, then decrease; under centralized decision-making, optimal sales price decreases, and optimal service level and overall optimal profit increase. Under decentralized decision-making, in the traditional e-commerce supply chain, optimal decisions and profits first increase, then decrease with increased misrepresentation in favorable product ratings; in the BOCR technology e-commerce supply chain, members can optimize costs and increase profits by extracting more valuable information. The optimal sales price and service level show different size relationships with production cost changes. When investment costs meet specific conditions, using BOCR technology can make members more profitable. The cost-sharing and profit-sharing contract enables e-commerce coordination.
Imbalance fault diagnosis under long-tailed distribution: Challenges, solutions and prospects

Intelligent fault diagnosis based on deep learning has yielded remarkable progress for its strong feature representation capability in recent years. Nevertheless, in engineering scenarios, machines often operate under normal condition and the probability of various faults is also different, which leads to the extreme class imbalance and long-tailed distribution between different health conditions. Long-tailed distribution usually degrades the performance of the model, since the model tends to focus on dominant classes and exhibits a poor performance on tail classes. To address this problem, numerous researches have been carried out and fruitful achievements have been achieved in recent years. However, a comprehensive summary of the latest achievements is still lacking. In addition, existing studies solely concentrate on the issue of class imbalance, and lack of analysis and future research directions of long-tailed problem. Hence, this paper specifically defines the long-tailed distribution and discusses the relations between long-tailed problem and class imbalance problem. Then, research achievements on class imbalance problems are reviewed. Specifically, we divide the existing research achievements into three categories: class rebalance, information augmentation and module design. Finally, this paper summarizes the challenges of the long-tailed problem in fault diagnosis and provides some future research directions.
Towards Accurate and Robust Domain Adaptation Under Multiple Noisy Environments

In many non-stationary environments, machine learning algorithms usually confront the distribution shift scenarios. Previous domain adaptation methods have achieved great success. However, they would lose algorithm robustness in multiple noisy environments where the examples of source domain become corrupted by label noise, feature noise, or open-set noise. In this paper, we report our attempt toward achieving noise-robust domain adaptation. We first give a theoretical analysis and find that different noises have disparate impacts on the expected target risk. To eliminate the effect of source noises, we propose offline curriculum learning minimizing a newly-defined empirical source risk. We suggest a proxy distribution-based margin discrepancy to gradually decrease the noisy distribution distance to reduce the impact of source noises. We propose an energy estimator for assessing the outlier degree of open-set-noise examples to defeat the harmful influence. We also suggest robust parameter learning to mitigate the negative effect further and learn domain-invariant feature representations. Finally, we seamlessly transform these components into an adversarial network that performs efficient joint optimization for them. A series of empirical studies on the benchmark datasets and the COVID-19 screening task show that our algorithm remarkably outperforms the state-of-the-art, with over 10% accuracy improvements in some transfer tasks.
Energy trading scheme based on consortium blockchain and game theory

With the gradual opening of the electricity sales market, distributed energy trading is becoming an important research topic. However, it is not easy to design practical energy trading schemes in distributed scenario. In particular, known distributed energy trading schemes do not address the security of transaction data and the maximization of benefits among all the participants. In this paper, we propose a distributed energy trading scheme based on consortium blockchain and game theory. In our scheme, a peer-to-peer trading platform is constructed to realize direct transactions among all the participants by the property of decentralization in consortium blockchain. The direct transactions greatly reduce operating costs of energy trading, and at the same time, the security of transaction data can be obtained by the cryptographic techniques such as digital signatures and hash functions associated with the underlying blockchain. Moreover, we design an energy transaction matching mechanism by game theory in our scheme. In the matching mechanism, we construct a game model among all the participants and design an equilibrium solving algorithm, which are the key techniques to realize the maximization of benefits among all the participants in energy trading. The security analysis and experimental results show that our scheme can realize the best transaction price and quantity in the transaction matching and has high security in distributed energy transaction scenarios.
Cyber security threats: A never-ending challenge for e-commerce

This study explores the challenge of cyber security threats that e-commerce technology and business are facing. Technology applications for e-commerce are attracting attention from both academia and industry. It has made what was not possible before for the business community and consumers. But it did not come all alone but has brought some challenges, and cyber security challenge is one of them. Cyber security concerns have many forms, but this study focuses on social engineering, denial of services, malware, and attacks on personal data. Firms worldwide spend a lot on addressing cybersecurity issues, which grow each year. However, it seems complicated to overcome the challenge because the attackers continuously search for new vulnerabilities in humans, organizations, and technology. This paper is based on the conceptual analysis of social engineering, denial of services, malware, and attacks on personal data. We argue that implementing modern technology for e-commerce and cybersecurity issues is a never-ending game of cat and mouse. To reduce risks, reliable technology is needed, training of employees and consumer is necessary for using the technology, and a strong policy and regulation is needed at the firm and governmental level.
The Impact of Agile Methodology on Project Success, with a Moderating Role of Person’s Job Fit in the IT Industry of Pakistan

Computing software plays an essential role in almost every sector of the digital age, but the process of efficient software development still faces several challenges. Effective software development methodology can be the difference between the success and failure of a software project. This research aims to evaluate the overall impact of Agile Software Development (ASD) on the individual, organizational, software development, and project management dimensions. For this purpose, we surveyed several software development professionals from a variety of backgrounds (experience, location, and job ranks) to explore the impact of ASD on the IT industry of Pakistan. Our analysis of the collected information is two folds. First, we summarized the findings from our surveys graphically clearly show the opinions of our survey respondents regarding the effectiveness of the Agile methodology for software development. Secondly, we utilized quantitative measures to analyze the same data statistically. A comparison is drawn between the graphical and statistical analysis to verify the reliability of our findings. Our findings suggest the existence of a strong relationship between effective software development and the use of Agile processes. Our analysis shows that the job fit of software development professionals and ASD are critical factors for software development project success in terms of cost, quality, stakeholders satisfaction, and time. Although the study focuses on the IT industry of Pakistan, the findings can be generalized easily to other developing IT industries worldwide.
Detecting group concept drift from multiple data streams

Concept drift may lead to a sharp downturn in the performance of streaming in data-based algorithms, caused by unforeseeable changes in the underlying distribution of data. In this paper, we are mainly concerned with concept drift across multiple data streams, and in situations where the drift of each data stream cannot be detected in time, due to slight underlying distribution drifts. We call this group concept drift. When compared to the detection of concept drift for a single data stream, the challenges of detecting group concept drift arise from three aspects: first, the training data become more complex; second, the underlying distribution becomes more complex; and third, the correlations between data streams become more complex. To address these challenges, the key idea of our method is to construct a distribution free test statistic, free from any underlying distribution in multiple data streams. Then, for streaming data, we design an online learning algorithm to obtain this test statistic, thereby determining the concept drift caused by the hypothesis test. The experiment evaluations with both synthetic and real-world datasets prove that our method can accurately detect concept drift from multiple data streams.
Zernike polynomials and their applications

Abstract The Zernike polynomials are a complete set of continuous functions orthogonal over a unit circle. Since first developed by Zernike in 1934, they have been in widespread use in many fields ranging from optics, vision sciences, to image processing. However, due to the lack of a unified definition, many confusing indices have been used in the past decades and mathematical properties are scattered in the literature. This review provides a comprehensive account of Zernike circle polynomials and their noncircular derivatives, including history, definitions, mathematical properties, roles in wavefront fitting, relationships with optical aberrations, and connections with other polynomials. We also survey state-of-the-art applications of Zernike polynomials in a range of fields, including the diffraction theory of aberrations, optical design, optical testing, ophthalmic optics, adaptive optics, and image analysis. Owing to their elegant and rigorous mathematical properties, the range of scientific and industrial applications of Zernike polynomials is likely to expand. This review is expected to clear up the confusion of different indices, provide a self-contained reference guide for beginners as well as specialists, and facilitate further developments and applications of the Zernike polynomials.
Fuzzy-based hunger games search algorithm for global optimization and feature selection using medical data

Abstract Feature selection (FS) is one of the basic data preprocessing steps in data mining and machine learning. It is used to reduce feature size and increase model generalization. In addition to minimizing feature dimensionality, it also enhances classification accuracy and reduces model complexity, which are essential in several applications. Traditional methods for feature selection often fail in the optimal global solution due to the large search space. Many hybrid techniques have been proposed depending on merging several search strategies which have been used individually as a solution to the FS problem. This study proposes a modified hunger games search algorithm (mHGS), for solving optimization and FS problems. The main advantages of the proposed mHGS are to resolve the following drawbacks that have been raised in the original HGS; (1) avoiding the local search, (2) solving the problem of premature convergence, and (3) balancing between the exploitation and exploration phases. The mHGS has been evaluated by using the IEEE Congress on Evolutionary Computation 2020 (CEC’20) for optimization test and ten medical and chemical datasets. The data have dimensions up to 20000 features or more. The results of the proposed algorithm have been compared to a variety of well-known optimization methods, including improved multi-operator differential evolution algorithm (IMODE), gravitational search algorithm, grey wolf optimization, Harris Hawks optimization, whale optimization algorithm, slime mould algorithm and hunger search games search. The experimental results suggest that the proposed mHGS can generate effective search results without increasing the computational cost and improving the convergence speed. It has also improved the SVM classification performance.
An Enhanced Evaporation Rate Water-Cycle Algorithm for Global Optimization

Water-cycle algorithm based on evaporation rate (ErWCA) is a powerful enhanced version of the water-cycle algorithm (WCA) metaheuristics algorithm. ErWCA, like other algorithms, may still fall in the sub-optimal region and have a slow convergence, especially in high-dimensional tasks problems. This paper suggests an enhanced ErWCA (EErWCA) version, which embeds local escaping operator (LEO) as an internal operator in the updating process. ErWCA also uses a control-randomization operator. To verify this version, a comparison between EErWCA and other algorithms, namely, classical ErWCA, water cycle algorithm (WCA), butterfly optimization algorithm (BOA), bird swarm algorithm (BSA), crow search algorithm (CSA), grasshopper optimization algorithm (GOA), Harris Hawks Optimization (HHO), whale optimization algorithm (WOA), dandelion optimizer (DO) and fire hawks optimization (FHO) using IEEE CEC 2017, was performed. The experimental and analytical results show the adequate performance of the proposed algorithm.