Reviewerly/exHarmony huggingface dataset:
A Comprehensive Survey on Blockchain-Based Decentralized Storage Networks

Blockchains are a new approach to creating distributed networks that were first introduced in 2008. It allows the formation of peer-to-peer networks based on consensus, forming chains from accepted blocks without requiring a central authority or centralized controller. A prominent application of this technology is its use in decentralized storage systems. Individuals in decentralized storage networks rent unused hardware storage space to other individuals. A decentralized network utilizing end-to-end encryption eliminates the risk of data loss associated with centralized data control by enabling clients to transmit their files securely. The storage providers must prove that they have kept unaltered files in this network for this time. Many studies have been conducted in this specific domain, most targeting storage capacity and efficiency, but a security, integrity and privacy loophole need to be addressed. This paper presents an overview of blockchain-based storage systems and how they work, followed by a comparison with cloud-based storage networks and a survey of various decentralized storage networks like SIA, File coin, and Storj available on the market. Next, we discuss the advantages and disadvantages of blockchain-based storage. In our final discussion, we examine the security problems of decentralized storage networks and explore potential solutions and research directions for the future.
Synchrosqueezed Fractional Wavelet Transform: A New High-Resolution Time-Frequency Representation

The synchrosqueezed wavelet transform (SSWT) has been proven to be a powerful time-frequency analysis tool. However, this transform is unable to deal with signals with fast varying instantaneous frequencies. The objective of this paper is to overcome this deficiency using the fractional wavelet transform (FRWT), which is a generalization of the conventional wavelet transform. We first propose a synchrosqueezed FRWT (SSFRWT), which shares many properties of its SSWT counterpart while offering attractive new features. Then, we present a theoretical analysis of the SSFRWT, including the derivation of its basic properties. Moreover, we show that the discrete form of the SSFRWT admits efficient numerical implementation akin to that of the SSWT. Finally, the theoretical derivations are validated via simulations.
Explainable concept drift in process mining

The execution of processes leaves trails of event data in information systems. These event data are analyzed to generate insights and improvements for the underlying process. However, companies do not execute these processes in a vacuum. The fast pace of technological development, constantly changing market environments, and fast consumer responses expose companies to high levels of uncertainty. This uncertainty often manifests itself in significant changes in the executed processes. Such significant changes are called concept drifts. Transparency about concept drifts is crucial to respond quickly and adequately, limiting the potentially negative impact of such drifts. Three types of knowledge are of interest to a process owner: When did a drift occur, what happened, and why did it happen. This paper introduces a framework to extract concept drifts and their potential root causes from event data. We extract time series describing process measures, detect concept drifts, and test these drifts for correlation. This framework generalizes existing work such that object-centric event data with multiple case notions, non-linear relationships, and an arbitrary number of process measures are supported. We provide an extendable implementation and evaluate our framework concerning the sensitivity of the time series construction and scalability of cause–effect testing. Furthermore, we provide a case study uncovering an explainable concept drift.
Intentions to adopt the blockchain: investigation of the retail supply chain

Purpose Blockchain can track the material from the manufacturer to the end customers. Therefore, it can ensure the product's authenticity, transparency and trust in the retail supply chain (SC). There is a need to trace and track the retail products before it reaches the customers to check the quality of the products so that expired products can be recycled and reused, which in turn will help gain customers' trust. This research aims to investigate retail employees' behavioural intention to adopt blockchain in the retail SC. Design/methodology/approach To examine the behavioural intention of employees in the retail SC, the research uses three theories – the technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour. The technology acceptance model measures the employee's acceptance of blockchain in the retail SC. The unified theory of acceptance is used in this research to measure how blockchain adoption will improve the performance of the employees. The theory of planned behaviour is used in this research to measure whether the employees intend to adopt blockchain. A survey was carried out in the retail stores of India. Exploratory factor analysis and structural equation modelling were used for data analysis. Findings This study found that the employees of the retail stores have a positive intention and attitude to adopt blockchain technology. Further, it was found that perceived behavioural control and effort expectancy was not promoting blockchain adoption in the retail sector. Practical implications This study will help the retail stores' employees understand the blockchain in their operations and will motivate the top management of the retail companies to adopt this technology. The study is limited to the retail SC in India only. Originality/value This study uses three theories technology acceptance model; the unified theory of acceptance and use of technology; and the theory of planned behaviour, which were not used in earlier studies of blockchain adoption in the retail SC.
An Improved SSD-Like Deep Network-Based Object Detection Method for Indoor Scenes

The indoor scene object detection technology is of important research significance, which is one of the popular research topics in the field of scene understanding for indoor robots. In recent years, the solutions based on deep learning have achieved good results in object detection. However, there are still some problems to be further studied in indoor object detection methods, such as lighting problem and occlusion problem caused by the complexity of the indoor environment. Aiming at these problems, an improved object detection method based on deep neural networks is proposed in this article, which uses a framework similar to the single-shot multibox detector (SSD). In the proposed method, an improved ResNet50 network is used to enhance the transmission of information, and the feature expression capability of the feature extraction network is improved. At the same time, a multiscale contextual information extraction (MCIE) module is used to extract the contextual information of the indoor scene, so as to improve the indoor object detection effect. In addition, an improved dual-threshold non-maximum suppression (DT-NMS) algorithm is used to alleviate the occlusion problem in indoor scenes. Finally, the public dataset SUN2012 is further screened for the special application of indoor scene object detection, and the proposed method is tested on this dataset. The experimental results show that the mean average precision (mAP) of the proposed method can reach 54.10%, which is higher than those of the state-of-the-art methods.
Robot path planning based on artificial potential field with deterministic annealing

In the context of motion planning in robotics, the problem of path planning based on artificial potential fields has been examined using different algorithms to avoid trapping in local minima. With this objective, this paper proposes a novel method based on a deterministic annealing strategy to improve the potential field function by introducing a temperature parameter to increase the robot's obstacle avoidance efficiency. The annealing and tempering strategies prevent the robot from being trapped at the local minima and allow it to continue towards its destination. The initial path is optimised using an annealing algorithm to enhance the overall performance. The time, length and success rate of the planned path measures the quality of the solution. Simulation results and comparative experiments demonstrate that the proposed algorithm can solve path planning in different environments. The proposed algorithm is suitable for complex environments with convex or non-convex polygon obstacles.
SSMTL++: Revisiting self-supervised multi-task learning for video anomaly detection

A self-supervised multi-task learning (SSMTL) framework for video anomaly detection was recently introduced in literature. Due to its highly accurate results, the method attracted the attention of many researchers. In this work, we revisit the self-supervised multi-task learning framework, proposing several updates to the original method. First, we study various detection methods, e.g. based on detecting high-motion regions using optical flow or background subtraction, since we believe the currently used pre-trained YOLOv3 is suboptimal, e.g. objects in motion or objects from unknown classes are never detected. Second, we modernize the 3D convolutional backbone by introducing multi-head self-attention modules, inspired by the recent success of vision transformers. As such, we alternatively introduce both 2D and 3D convolutional vision transformer (CvT) blocks. Third, in our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps through knowledge distillation, solving jigsaw puzzles, estimating body pose through knowledge distillation, predicting masked regions (inpainting), and adversarial learning with pseudo-anomalies. We conduct experiments to assess the performance impact of the introduced changes. Upon finding more promising configurations of the framework, dubbed SSMTL++v1 and SSMTL++v2, we extend our preliminary experiments to more data sets, demonstrating that our performance gains are consistent across all data sets. In most cases, our results on Avenue, ShanghaiTech and UBnormal raise the state-of-the-art performance bar to a new level.
Combining a deep learning model with multivariate empirical mode decomposition for hourly global horizontal irradiance forecasting

Accurate and reliable global horizontal irradiance forecasting is one of the solutions for the associated problems with grid-integrated PV plants. This study proposes a novel hybrid MEMD-PCA-GRU model for an hour ahead of GHI forecasting. The multivariate empirical mode decomposition (MEMD) breaks the multidimensional data into multivariate subseries termed intrinsic mode functions (IMFs). MEMD helps to remove the naturally produced non-stationary and nonlinear deficiencies within the target series and meteorological predictors. A large number of obtained IMFs necessitates the application of a dimensionality reduction technique. Principal component analysis (PCA) is used here to identify the most informative features from a large set of IMFs. Finally, the gated recurrent unit (GRU) is utilized to predict GHI at four places in India. The performance of the proposed model is tested against some hybrid and standalone models. Double decomposition techniques enhanced the GRU performance by a minimum % RMSE (% MAE) improvement of 48.38 (24.97). The proposed model reported an average nRMSE (RMSE) of 7.82% (36.85 W/m2) across four locations. The lowest error metrics of the proposed model reflect the relatively stable and good performance compared to studied single-stage and hybrid benchmark models under different climatic conditions.
Deep learning-based visual detection of marine organisms: A survey

Most recently, deep learning-based visual detection has attracted rapidly increasing attention paid to marine organisms, thereby expecting to significantly benefit ocean ecology. Suffering from underwater visual degradation including low contrast, color distortion and blur, etc., both advances and challenges on visual detection of marine organisms (VDMO) co-exist in the literature. In this survey, deep learning-based VDMO techniques are comprehensively revisited from a systematic viewpoint covering advances in underwater image preprocessing, deep learning-based detection approaches, benchmark dataset and intensively quantitative comparisons. Furthermore, in terms of inherent features of marine organisms and complexity of underwater visual environments, underlying challenges are unfolded in depth. Such a self-contained survey is expected to exploit potential breakthroughs and explore probable trends in deep learning-based VDMO techniques.
Mapping and Tracing the Postdigital: Approaches and Parameters of Postdigital Research

Abstract The launch of Postdigital Science and Education helped generate a burst of new scholarship about this emerging turn in educational research and theory. Yet, what it means to do postdigital research remains obscure to many. Ongoing debates around definitions, combined with the complexity of analysing digital activity within rich contexts that are also social, material, political, economic, and so on, make it challenging to understand what constitutes postdigital research. Meanings of the postdigital emerge from within the processes of postdigital research. Furthermore, while some individual contributions to postdigital research may be grounded in particular disciplines, we argue that postdigital research, in general, benefits from transdisciplinary knowledge. All of this points to a need for flexibility, and principled, rather than prescriptive, research and scholarship practices. It situates postdigital research in the tradition of compositional and inventive research approaches, and this paper traces that relationship.
Machine learning-based diagnosis of breast cancer utilizing feature optimization technique

Breast cancer disease is recognized as one of the leading causes of death in women worldwide after lung cancer. Breast cancer refers to a malignant neoplasm that develops from breast cells. Developed and less developed countries both are suffering from this extensive cancer. This cancer can be recuperated if it is detected at an early stage. Many researchers have proposed several machine learning techniques to predict breast cancer with the highest accuracy in the past years. In this research work, the Wisconsin Breast Cancer Dataset (WBCD) has been used as a training set from the UCI machine learning repository to compare the performance of the various machine learning techniques. Different kinds of machine learning classifiers such as support vector machine (SVM), Random Forest (RF), K-nearest neighbors(K-NN), Decision tree (DT), Naïve Bayes (NB), Logistic Regression (LR), AdaBoost (AB), Gradient Boosting (GB), Multi-layer perceptron (MLP), Nearest Cluster Classifier (NCC), and voting classifier (VC) have been used for comparing and analyzing breast cancer into benign and malignant tumors. Various matrices such as error rate, Accuracy, Precision, F1-score, and recall have been implemented to measure the model's performance. Each Algorithm's accuracy has been ascertained for finding the best suitable one. Based on the analysis, the result shows that the Voting classifier has the highest accuracy, which is 98.77%, with the lowest error rate. Finally, a web page is developed using a flask micro-framework integrating the best model using react.
Embedding patient-reported outcomes at the heart of artificial intelligence health-care technologies

Integration of patient-reported outcome measures (PROMs) in artificial intelligence (AI) studies is a critical part of the humanisation of AI for health. It allows AI technologies to incorporate patients' own views of their symptoms and predict outcomes, reflecting a more holistic picture of health and wellbeing and ultimately helping patients and clinicians to make the best health-care decisions together. By positioning patient-reported outcomes (PROs) as a model input or output we propose a framework to embed PROMs within the function and evaluation of AI health care. However, the integration of PROs in AI systems presents several challenges. These challenges include (1) fragmentation of PRO data collection; (2) validation of AI systems trained and validated against clinician performance, rather than outcome data; (3) scarcity of large-scale PRO datasets; (4) inadequate selection of PROMs for the target population and inadequate infrastructure for collecting PROs; and (5) clinicians might not recognise the value of PROs and therefore not prioritise their adoption; and (6) studies involving PRO or AI frequently present suboptimal design. Notwithstanding these challenges, we propose considerations for the inclusion of PROs in AI health-care technologies to avoid promoting survival at the expense of wellbeing.
Toward Robust Visual Object Tracking With Independent Target-Agnostic Detection and Effective Siamese Cross-Task Interaction

Advanced Siamese visual object tracking architectures are jointly trained using pair-wise input images to perform target classification and bounding box regression. They have achieved promising results in recent benchmarks and competitions. However, the existing methods suffer from two limitations: First, though the Siamese structure can estimate the target state in an instance frame, provided the target appearance does not deviate too much from the template, the detection of the target in an image cannot be guaranteed in the presence of severe appearance variations. Second, despite the classification and regression tasks sharing the same output from the backbone network, their specific modules and loss functions are invariably designed independently, without promoting any interaction. Yet, in a general tracking task, the centre classification and bounding box regression tasks are collaboratively working to estimate the final target location. To address the above issues, it is essential to perform target-agnostic detection so as to promote cross-task interactions in a Siamese-based tracking framework. In this work, we endow a novel network with a target-agnostic object detection module to complement the direct target inference, and to avoid or minimise the misalignment of the key cues of potential template-instance matches. To unify the multi-task learning formulation, we develop a cross-task interaction module to ensure consistent supervision of the classification and regression branches, improving the synergy of different branches. To eliminate potential inconsistencies that may arise within a multi-task architecture, we assign adaptive labels, rather than fixed hard labels, to supervise the network training more effectively. The experimental results obtained on several benchmarks, i.e., OTB100, UAV123, VOT2018, VOT2019, and LaSOT, demonstrate the effectiveness of the advanced target detection module, as well as the cross-task interaction, exhibiting superior tracking performance as compared with the state-of-the-art tracking methods.
A systematic review on recent advances in autonomous mobile robot navigation

Recent years have seen a dramatic rise in the popularity of autonomous mobile robots (AMRs) due to their practicality and potential uses in the modern world. Path planning is among the most important tasks in AMR navigation since it demands the robot to identify the best route based on desired performance criteria such as safety margin, shortest time, and energy consumption. The complexity of the problem can however become intractable when challenging scenarios are considered, which include navigation in a dynamic environment and solving multi-objective optimizations. Various classical and heuristic techniques have been proposed by researchers to mitigate such issues. The purpose of this paper is to provide a comprehensive and up-to-date literature review of the path planning strategies that have received a considerable attention over the past decade. A systematic analysis of the strengths, shortcomings, and scope of each method is presented. The trends as well as challenges in practical implementation of the strategies are also discussed at the end of this paper. The outcome of this survey provides useful guidance for future research into creating new strategies that can enhance the autonomy level of AMRs while preserving their robustness against unforeseen circumstances in practice.
A whale optimization algorithm with combined mutation and removing similarity for global optimization and multilevel thresholding image segmentation

Because of the problem of the whale optimization algorithm (WOA), such as weak local search ability, easy fall into local optimality, unable to better balance the exploration and exploitation abilities of the algorithm, a whale optimization algorithm with combined mutation and removing similarity (CRWOA) is proposed. Firstly, CRWOA proposed an adaptive adjustment method of the parameters A→ and C→ in the encircling prey stage and an improved bubble net attack update position formula, so that the updated whale is located around the prey, which improves the local search ability of the algorithm. Secondly, a roulette selection operator is proposed to randomly select individuals participating in position updates from the population, which improves the possibility of producing excellent individuals in the random search stage. Thirdly, a combined mutation operator based on individual fitness value is proposed to better balance the exploration and exploitation abilities of the algorithm. Finally, to better maintain the diversity of the population, an improved removing similarity operation is added. In addition, in the first set of experiments, the CEC 2017 benchmark test functions are selected to compare the performance of CRWOA with other group intelligent optimization algorithms. In the second group of experiments, Kapur and Otsu are used as objective functions to perform multilevel threshold segmentation on 10 grayscale images. The experimental results show that CRWOA is better than the five comparison algorithms in terms of convergence and segmentation quality.
Interpretable and explainable machine learning: A methods‐centric overview with concrete examples

Abstract Interpretability and explainability are crucial for machine learning (ML) and statistical applications in medicine, economics, law, and natural sciences and form an essential principle for ML model design and development. Although interpretability and explainability have escaped a precise and universal definition, many models and techniques motivated by these properties have been developed over the last 30 years, with the focus currently shifting toward deep learning. We will consider concrete examples of state‐of‐the‐art, including specially tailored rule‐based, sparse, and additive classification models, interpretable representation learning, and methods for explaining black‐box models post hoc. The discussion will emphasize the need for and relevance of interpretability and explainability, the divide between them, and the inductive biases behind the presented “zoo” of interpretable models and explanation methods. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Explainable AI Technologies &gt; Machine Learning Commercial, Legal, and Ethical Issues &gt; Social Considerations