Reviewerly/exHarmony huggingface dataset:
Pattern Recognition and Deep Learning Technologies, Enablers of Industry 4.0, and Their Role in Engineering Research

The purpose of this study is to summarize the pattern recognition (PR) and deep learning (DL) artificial intelligence methods developed for the management of data in the last six years. The methodology used for the study of documents is a content analysis. For this study, 186 references are considered, from which 120 are selected for the literature review. First, a general introduction to artificial intelligence is presented, in which PR/DL methods are studied and their relevance to data management evaluated. Next, a literature review is provided of the most recent applications of PR/DL, and the capacity of these methods to process large volumes of data is evaluated. The analysis of the literature also reveals the main applications, challenges, approaches, advantages, and disadvantages of using these methods. Moreover, we discuss the main measurement instruments; the methodological contributions by study areas and research domain; and major databases, journals, and countries that contribute to the field of study. Finally, we identify emerging research trends, their limitations, and possible future research paths.
Dynamic Ferroelectric Transistor‐Based Reservoir Computing for Spatiotemporal Information Processing

Reservoir computing (RC) architecture which mimics the human brain is a fundamentally preferred method to process dynamical systems that evolve with time. However, the difficulty in generating rich reservoir states using two‐terminal devices remains challenging, which hinders its hardware implementation. Herein, the 1D array of ferroelectric field‐effect transistor (Fe‐FET) based on α ‐In 2 Se 3 channel, which shows volatile memory effect for realizing various RC systems, is demonstrated. The fading effect in α ‐In 2 Se 3 is sufficiently investigated by polarization dynamic model. The proposed Fe‐FET is capable of experimentally classifying images using MNIST dataset with a high accuracy of 91%. Furthermore, time‐series real‐life chaotic system, for example, Earth's weather, can be accurately forecasted using our Ferro‐RC based on the Jena climate dataset recorded in a 1 year period. Remarkable determination coefficient ( R 2 ) of 0.9983 and normalized root mean square error (NRMSE) of 8.3 × 10 −3 are achieved using a minimized readout network. The demonstration of integrated memory and computation opens a route for realizing a compact RC hardware system.
A binary reptile search algorithm based on transfer functions with a new stochastic repair method for 0–1 knapsack problems

The Reptile Search Algorithm (RSA), inspired by crocodiles' hunting behavior, is a recently introduced nature-inspired algorithm. Although the original version of the RSA shows outstanding performance in optimizing continuous applications, it is not suitable for discrete optimization problems like 0–1 knapsack problems (0–1 KP). To extend RSA to binary optimization issues, binary RSA (BinRSA) is proposed in this study. A wide range of transfer functions (TFs), including the largely used s-shaped and v-shaped, and recently introduced z-shaped, u-shaped, and taper-shaped, are investigated in the proposed algorithm to map the continuous values into binary. In addition, a novel repair method is introduced to cope with infeasible solutions for 0–1 KP and discussed in detail regarding its efficacy in reaching the optimal solution. The proposed method is validated on three benchmark datasets with 63 instances of 0–1 KP. First, the impact of 25 different transfer functions under six categories on the performance of the proposed binary algorithm is thoroughly investigated, and the results indicate that the taper-shaped T1 transfer function is superior to the other variants of the BinRSA. Then, the effectiveness of the proposed BinRSA with T1 transfer function is compared with some well-known and state-of-art algorithms, including Harris hawks optimization (HHO), slime mould algorithm (SMA), and marine predators algorithm (MPA). The experimental results show that compared to other methods, BinRSA considerably increased the solution accuracy and robustness for solving 0–1 KP.
An open automation system for predatory journal detection

The growing number of online open-access journals promotes academic exchanges, but the prevalence of predatory journals is undermining the scholarly reporting process. Data collection, feature extraction, and model prediction are common steps in tools designed to distinguish between legitimate and predatory academic journals and publisher websites. The authors include them in their proposed academic journal predatory checking (AJPC) system based on machine learning methods. The AJPC data collection process extracts 833 blacklists and 1213 whitelists information from websites to be used for identifying words and phrases that might indicate the presence of predatory journals. Feature extraction is used to identify words and terms that help detect predatory websites, and the system's prediction stage uses eight classification algorithms to distinguish between potentially predatory and legitimate journals. We found that enhancing the classification efficiency of the bag of words model and TF-IDF algorithm with diff scores (a measure of differences in specific word frequencies between journals) can assist in identifying predatory journal feature words. Results from performance tests suggest that our system works as well as or better than those currently being used to identify suspect publishers and publications. The open system only provides reference results rather than absolute opinions and accepts user inquiries and feedback to update the system and optimize performance.
Secure Data Transmission of Electronic Health Records Using Blockchain Technology

Electronic Health Records (EHR) serve as a solid documentation of health transactions and as a vital resource of information for healthcare stakeholders. EHR integrity and security issues, however, continue to be intractable. Blockchain-based EHR architectures, however, address the issues of integrity very effectively. In this work, we suggest a decentralized patient-centered healthcare data management (PCHDM) with a blockchain-based EHR framework to address issues of confidentiality, access control, and privacy of record. This patient-centric architecture keeps the patient at the center of control for secured storage of EHR data. It is effective in the storage environment with the interplanetary file system (IPFS) and blockchain technology. In order to control unauthorized users, the proposed secure password authentication-based key exchange (SPAKE) implements smart contract-based access control to EHR transactions and access policies. The experimental setup comprises four hyperledger fabric nodes with level DB database and IPFS off-chain storage. The framework was evaluated using the public hepatitis dataset, with parameters such as block creation time, transactional computational overhead with encryption key size, and uploading/downloading time with EHR size. The framework enables patient-centric access control of the EHR with the SPAKE encryption algorithm.
Development of a Vision-Based Unmanned Ground Vehicle for Mapping and Tennis Ball Collection: A Fuzzy Logic Approach

The application of robotic systems is widespread in all fields of life and sport. Tennis ball collection robots have recently become popular because of their potential for saving time and energy and increasing the efficiency of training sessions. In this study, an unmanned and autonomous tennis ball collection robot was designed and produced that used LiDAR for 2D mapping of the environment and a single camera for detecting tennis balls. A novel method was used for the path planning and navigation of the robot. A fuzzy controller was designed for controlling the robot during the collection operation. The developed robot was tested, and it successfully detected 91% of the tennis balls and collected 83% of them.
Engineering surface topography analysis using an extended discrete modal decomposition

Surface topography is a crucial link to connect precision manufacturing processes and product performance. Discrete modal decomposition (DMD) is recently developed filtering method for surface topography analysis, which projects the engineering surface onto a series of modal basis. Comparing with traditional filtering methods, DMD method is well applied to various engineering surfaces with holes effectively without end effect. However, each decomposed mode of DMD has not been mapped to the surface components, which lacks physical dimensions. Hence, a novel extended discrete modal decomposition (EDMD) is proposed to achieve the multi-scale engineering surface topography analysis in this paper. The proposed EDMD method including data transforming, surface inverse modeling, extended discrete modal decomposition and quantitative evaluation. Gaussian filter, robust Gaussian regression filter, spline filter and extended tetrolet transform are used as comparisons of the proposed method in numerical simulations. The EDMD method perform well both in continuous and discontinuous engineering surfaces, which overcomes the border distortion especially for discontinuous engineering surfaces and complements the physical dimension of each decomposed mode. Three real engineering surface case studies illustrate the proposed EDMD method is effective and applicable.
An method for power lines insulator defect detection with attention feedback and double spatial pyramid

In recent years, with the wide application of UAVs in electric power inspection, inspection efficiency is promoted rapidly. However, how to address massive aerial images is a big challenge. Deep learning applied to automated processing and analysis of aerial images has become a hot topic in current research on insulator defect detection. Most of the existing methods based on deep learning are highly sensitive to complex background which makes detection precision of multi-scale insulators and its defects susceptible to a great influence. Moreover, sheltered insulators cannot be detected in current study. In order to improve the detection precision of insulator defects under complex background and detect sheltered insulators, in this paper, we propose a method that applies the Attention Feedback (AF) and the Double Spatial Pyramid (DSP). AF can enhance attention ability of discriminative features. Furthermore, DSP can combine different pooling response values by using a set of scale factors and two pooling ways. The experiment results show that the proposed method for power lines insulator defect detection with Attention Feedback and Double Spatial Pyramid can achieve Precision of 98.9%, Recall of 99.5%, F1-score of 99.0%, and reaches mAP of 97.1%. In complex background images, the method not only can detect insulator defects, but also identify the sheltered insulators.
Underwater Object Detection Method Based on Improved Faster RCNN

In order to better utilize and protect marine organisms, reliable underwater object detection methods need to be developed. Due to various influencing factors from complex and changeable underwater environments, the underwater object detection is full of challenges. Therefore, this paper improves a two-stage algorithm of Faster RCNN (Regions with Convolutional Neural Network Feature) to detect holothurian, echinus, scallop, starfish and waterweeds. The improved algorithm has better performance in underwater object detection. Firstly, we improved the backbone network of the Faster RCNN, replacing the VGG16 (Visual Geometry Group Network 16) structure in the original feature extraction module with the Res2Net101 network to enhance the expressive ability of the receptive field of each network layer. Secondly, the OHEM (Online Hard Example Mining) algorithm is introduced to solve the imbalance problem of positive and negative samples of the bounding box. Thirdly, GIOU (Generalized Intersection Over Union) and Soft-NMS (Soft Non-Maximum Suppression) are used to optimize the regression mechanism of the bounding box. Finally, the improved Faster RCNN model is trained using a multi-scale training strategy to enhance the robustness of the model. Through ablation experiments based on the improved Faster RCNN model, each improved part is disassembled and then the experiments are carried out one by one, which can be known from the experimental results that, based on the improved Faster RCNN model, mAP@0.5 reaches 71.7%, which is 3.3% higher than the original Faster RCNN model, and the average accuracy reaches 43%, and the F1-score reaches 55.3%, a 2.5% improvement over the original Faster RCNN model, which shows that the proposed method in this paper is effective in underwater object detection.
DisenPOI: Disentangling Sequential and Geographical Influence for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation plays a vital role in various location-aware services. It has been observed that POI recommendation is driven by both sequential and geographical influences. However, since there is no annotated label of the dominant influence during recommendation, existing methods tend to entangle these two influences, which may lead to sub-optimal recommendation performance and poor interpretability. In this paper, we address the above challenge by proposing DisenPOI, a novel Disentangled dual-graph framework for POI recommendation, which jointly utilizes sequential and geographical relationships on two separate graphs and disentangles the two influences with self-supervision. The key novelty of our model compared with existing approaches is to extract disentangled representations of both sequential and geographical influences with contrastive learning. To be specific, we construct a geographical graph and a sequential graph based on the check-in sequence of a user. We tailor their propagation schemes to become sequence-/geo-aware to better capture the corresponding influences. Preference proxies are extracted from check-in sequence as pseudo labels for the two influences, which supervise the disentanglement via a contrastive loss. Extensive experiments on three datasets demonstrate the superiority of the proposed model.
Text Input for Non-Stationary XR Workspaces: Investigating Tap and Word-Gesture Keyboards in Virtual and Augmented Reality

This article compares two state-of-the-art text input techniques between non-stationary virtual reality (VR) and video see-through augmented reality (VST AR) use-cases as XR display condition. The developed contact-based mid-air virtual tap and wordgesture (swipe) keyboard provide established support functions for text correction, word suggestions, capitalization, and punctuation. A user evaluation with 64 participants revealed that XR displays and input techniques strongly affect text entry performance, while subjective measures are only influenced by the input techniques. We found significantly higher usability and user experience ratings for tap keyboards compared to swipe keyboards in both VR and VST AR. Task load was also lower for tap keyboards. In terms of performance, both input techniques were significantly faster in VR than in VST AR. Further, the tap keyboard was significantly faster than the swipe keyboard in VR. Participants showed a significant learning effect with only ten sentences typed per condition. Our results are consistent with previous work in VR and optical see-through (OST) AR, but additionally provide novel insights into usability and performance of the selected text input techniques for VST AR. The significant differences in subjective and objective measures emphasize the importance of specific evaluations for each possible combination of input techniques and XR displays to provide reusable, reliable, and high-quality text input solutions. With our work, we form a foundation for future research and XR workspaces. Our reference implementation is publicly available to encourage replicability and reuse in future XR workspaces.
<i>FaaSLight</i> : General Application-level Cold-start Latency Optimization for Function-as-a-Service in Serverless Computing

Serverless computing is a popular cloud computing paradigm that frees developers from server management. Function-as-a-Service (FaaS) is the most popular implementation of serverless computing, representing applications as event-driven and stateless functions. However, existing studies report that functions of FaaS applications severely suffer from cold-start latency. In this article, we propose an approach, namely, FaaSLight , to accelerating the cold start for FaaS applications through application-level optimization. We first conduct a measurement study to investigate the possible root cause of the cold-start problem of FaaS. The result shows that application code loading latency is a significant overhead. Therefore, loading only indispensable code from FaaS applications can be an adequate solution. Based on this insight, we identify code related to application functionalities by constructing the function-level call graph and separate other code (i.e., optional code) from FaaS applications. The separated optional code can be loaded on demand to avoid the inaccurate identification of indispensable code causing application failure. In particular, a key principle guiding the design of FaaSLight is inherently general, i.e., platform - and language-agnostic . In practice, FaaSLight can be effectively applied to FaaS applications developed in different programming languages (Python and JavaScript), and can be seamlessly deployed on popular serverless platforms such as AWS Lambda and Google Cloud Functions, without having to modify the underlying OSes or hypervisors, nor introducing any additional manual engineering efforts to developers. The evaluation results on real-world FaaS applications show that FaaSLight can significantly reduce the code loading latency (up to 78.95%, 28.78% on average), thereby reducing the cold-start latency. As a result, the total response latency of functions can be decreased by up to 42.05% (19.21% on average). Compared with the state-of-the-art, FaaSLight achieves a 21.25× improvement in reducing the average total response latency.
Performing Wash Trading on NFTs: Is the Game Worth the Candle?

Wash trading is considered a highly inopportune and illegal behavior in regulated markets. Instead, it is practiced in unregulated markets, such as cryptocurrency or NFT (Non-Fungible Tokens) markets. Regarding the latter, in the past many researchers have been interested in this phenomenon from an “ex-ante” perspective, aiming to identify and classify wash trading activities before or at the exact time they happen. In this paper, we want to investigate the phenomenon of wash trading in the NFT market from a completely different perspective, namely “ex-post”. Our ultimate goal is to analyze wash trading activities in the past to understand whether the game is worth the candle, i.e., whether these illicit activities actually lead to a significant profit for their perpetrators. To the best of our knowledge, this is the first paper in the literature that attempts to answer this question in a “structured” way. The efforts to answer this question have enabled us to make some additional contributions to the literature in this research area. They are: (i) a framework to support future “ex-post” analyses of the NFT wash trading phenomenon; (ii) a new dataset on wash trading transactions involving NFTs that can support further future investigations of this phenomenon; (iii) a set of insights of the NFT wash trading phenomenon extracted at the end of an experimental campaign.
Leveraging explanations in interactive machine learning: An overview

Explanations have gained an increasing level of interest in the AI and Machine Learning (ML) communities in order to improve model transparency and allow users to form a mental model of a trained ML model. However, explanations can go beyond this one way communication as a mechanism to elicit user control, because once users understand, they can then provide feedback. The goal of this paper is to present an overview of research where explanations are combined with interactive capabilities as a mean to learn new models from scratch and to edit and debug existing ones. To this end, we draw a conceptual map of the state-of-the-art, grouping relevant approaches based on their intended purpose and on how they structure the interaction, highlighting similarities and differences between them. We also discuss open research issues and outline possible directions forward, with the hope of spurring further research on this blooming research topic.
Blockchain-enabled advances (BEAs): Implications for consumers and brands

Blockchain-enabled advances (BEAs) are state-of-the-art innovations based on blockchain technology. Recent years have witnessed the proliferation of the four BEAs: smart contracts, cryptocurrencies, play-to-earn games, and non-fungible tokens. These BEAs have implications for the marketing field as they affect consumers and brands. We propose a novel theoretical framework that articulates how the principles that underpin BEAs can impact consumers and then explains how brands can use BEAs to innovate their products and services. The core principles of blockchain technology, as well as enhanced digital connectivity, imply that consumers can become more in control of their data and privacy rights, responsible for their choices, and digitally connected. To cater to consumers, brands can use BEAs to roll out technology-focused service innovations, customer-focused service innovations, and product innovations. Based on this perspective, we then advance ten future research questions. This article aims to advance the nascent field of blockchain in marketing.