Reviewerly/exHarmony huggingface dataset:
Anti-Counterfeiting and Traceability Consensus Algorithm Based on Weightage to Contributors in a Food Supply Chain of Industry 4.0

Supply chain management can significantly benefit from contemporary technologies. Among these technologies, blockchain is considered suitable for anti-counterfeiting and traceability applications due to its openness, decentralization, anonymity, and other characteristics. This article introduces different types of blockchains and standard algorithms used in blockchain technology and discusses their advantages and disadvantages. To improve the work efficiency of anti-counterfeiting traceability systems in supply chains and reduce their energy consumption, this paper proposes a model based on the practical Byzantine fault tolerance (PBFT) algorithm of alliance chains. This model uses a credit evaluation system to select the primary node and integrates the weightage to contributors (WtC) algorithm based on the consensus mechanism. This model can reduce the decline in the algorithm success rate while increasing the number of malicious transaction nodes, thereby reducing the computing cost. Additionally, the throughput of the algorithmic system increases rapidly, reaching approximately 680 transactions per second (TPS) in about 120 min after the malicious nodes are eliminated. The throughput rapidly increases as the blacklist mechanism reduces the number of malicious nodes, which improves the system’s fault tolerance. To validate the effectiveness of the proposed model, a case study was conducted using data from the anti-counterfeiting traceability system of the real-life supply chain of a food company. The analysis results show that after a period of stable operation of the WtCPBFT algorithm in the proposed model, the overall communication cost of the system was reduced, the throughput and stability were improved, and the fault-tolerant performance of the system was improved. In conclusion, this paper presents a novel model that utilizes the PBFT algorithm of alliance chains and the WtC algorithm to improve the efficiency and security of anti-counterfeiting traceability systems in supply chains. The results of the case study indicate that this model can effectively reduce communication costs, improve throughput and stability, and enhance the fault tolerance of the system.
Single-Image Defogging Algorithm Based on Improved Cycle-Consistent Adversarial Network

With the wave of artificial intelligence and deep learning sweeping the world, there are many algorithms based on deep learning for image defog research. However, there is still serious color distortion, contrast reduction, incomplete fog removal, and other problems. To solve these problems, this paper proposes an improved image defogging network based on the traditional cycle-consistent adversarial network. We add the self-attention module and atrous convolution multi-scale feature fusion module on the basis of the traditional CycleGAN network to enhance the feature extraction capability of the network. The perceptual loss function is introduced into the loss function of the model to enhance the texture sense of the generated image. Finally, by comparing several typical defogging algorithms, the superiority of the defogging model proposed in this paper is proved qualitatively and quantitatively. Among them, on the indoor synthetic data set, the Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) of the network designed by us can reach 23.22 and 0.8809, respectively. On the outdoor synthetic data set, the PSNR and SSIM of our designed network can be as high as 25.72 and 0.8859, respectively. On the real data set, the PSNR and SSIM of our designed network can reach 21.02 and 0.8166, respectively. It is proved that the defogging network in this paper has good practicability and universality.
Augmented Reality for high precision fabrication of Glued Laminated Timber beams

In recent years, the utilization of Augmented-Reality (AR) within the construction industry has increased. However, high-precision applications such as glulam beam fabrication requires tolerances of less than 2 mm, which is smaller than what current AR workflows can offer. This research investigates the use of QR-Code Markers on glulam beams to encode additional positional data in the environment to better interpolate between the physical space, the glulam beam, and the headset. The objective is to understand the effects of Marker placement, size, and frequency on the accuracy of AR projection for glulam fabrication. This paper describes the AR workflow, the effects of Markers, and the framework the Twinbuild software for drift correction in large-scale AR applications. The method can achieve an average tolerance as low as 0.97 mm when Markers are placed in 1.25 ft. increments along the beam edge. The research demonstrates the viability of AR for high-precision fabrication applications.
Uncertainty-guided joint attention and contextual relation network for person re-identification

Due to the influence of factors such as camera angle and pose changes, some salient local features are often suppressed in person re-identification tasks. Moreover, many existing person re-identification methods do not consider the relation between features. To address these issues, this paper proposes two novel approaches: (1) To solve the problem of being confused and misidentified when local features of different individuals have similar attributes, we design a contextual relation network that focuses on establishing the relationship between local features and contextual features, so that all local features of the same person both contain contextual information. (2) To fully and correctly express key local features, we propose an uncertainty-guided joint attention module. The module focuses on the joint representation of individual pixels and local spatial features to enhance the credibility of local features. Finally, our method achieves competitive performance on four widely recognized datasets compared with state-of-the-art methods.
Development of a New Eye Movement Measurement Device Using Eye-Tracking Analysis Technology

Smooth pursuit eye movements and saccadic eye movements are vital for precise vision. Therefore, tests for eye movement are important for assessing nervous or muscular diseases. However, objective measurements are not frequently performed due to the need for a polygraph system, electrodes, amplifier, and personal computer for data analysis. To address this, we developed an all-in-one eye-movement-measuring device that simultaneously presents visual stimuli, records eye positions, and examines its feasibility for evaluating eye movements. This device generates stimulus that induces eye movements and records those movements continuously. The horizontal or vertical eye movements of 16 participants were measured at various visual target speeds of 20–100 deg/s. The maximum cross-correlation coefficient (rho max) between the eye and visual target positions was used as an index of eye movement accuracy. A repeated-measures multi-way analysis of variance was performed, with the main effect being that rho max significantly decreased as the visual target speed increased. The average (±standard deviation) rho max values across all velocities were 0.995 ± 0.008 and 0.967 ± 0.062 in the horizontal and vertical directions, respectively, and were significantly higher for horizontal eye movements than for vertical eye movements. Moreover, rho max and saccadic frequency were significantly correlated for the slowest and fastest visual target motions. These suggest that our device enables accurate measurements of eye movements. We believe our new measurement device can be applied clinically for easily and objectively evaluating eye movements.
Attention-induced semantic and boundary interaction network for camouflaged object detection

The high intrinsic similarity between camouflaged objects and the background makes camouflaged object detection (COD) more challenging than traditional salient object detection (SOD). Existing deep-learning methods often fall into the following shortcomings: (1) When dealing with high-level features, it is difficult to fully and accurately extract semantic information, which is crucial for locating camouflaged targets. (2) The camouflaged targets identified by existing methods in complex scenes have rough boundaries and incomplete spatial information. (3) Existing methods cannot effectively exert the uniqueness and complementarity of multiple features when integrating multiple features. To this end, we propose an attention-induced semantic and boundary interaction network for accurately identifying camouflaged objects. Specifically, we propose a contrastive positioning module (CPM), which adopts a contrastive learning way to separate the camouflaged object from the background, thereby obtaining the exact location of the camouflaged target and retaining more semantic information. Then, we design a boundary exploration module (BEM), which can reduce background noise interference, focus on the structural details of camouflaged targets, and explore rich fine-grained spatial information. Finally, we propose an attention-induced interaction module (AIM) to fuse multivariate information (i.e., semantic location information, boundary information, and the side output information of the backbone), and multivariate information is complementarily fused under attention guidance to generate more powerful camouflaged target features. Extensive experiments show that the proposed method on the different backbones (i.e., ResNet-50, Res2Net-50, PVTv2-B2, EfficientNet-B1) produces state-of-the-art results on several camouflaged benchmarks. The code is available https://github.com/zhangqiao970914/ASBI.
AIONER: all-in-one scheme-based biomedical named entity recognition using deep learning

Biomedical named entity recognition (BioNER) seeks to automatically recognize biomedical entities in natural language text, serving as a necessary foundation for downstream text mining tasks and applications such as information extraction and question answering. Manually labeling training data for the BioNER task is costly, however, due to the significant domain expertise required for accurate annotation. The resulting data scarcity causes current BioNER approaches to be prone to overfitting, to suffer from limited generalizability, and to address a single entity type at a time (e.g., gene or disease). We therefore propose a novel all-in-one (AIO) scheme that uses external data from existing annotated resources to enhance the accuracy and stability of BioNER models. We further present AIONER, a general-purpose BioNER tool based on cutting-edge deep learning and our AIO schema. We evaluate AIONER on 14 BioNER benchmark tasks and show that AIONER is effective, robust, and compares favorably to other state-of-the-art approaches such as multi-task learning. We further demonstrate the practical utility of AIONER in three independent tasks to recognize entity types not previously seen in training data, as well as the advantages of AIONER over existing methods for processing biomedical text at a large scale (e.g., the entire PubMed data).
Parallel Binary Rafflesia Optimization Algorithm and Its Application in Feature Selection Problem

The Rafflesia Optimization Algorithm (ROA) is a new swarm intelligence optimization algorithm inspired by Rafflesia’s biological laws. It has the advantages of high efficiency and fast convergence speed, and it effectively avoids falling into local optimum. It has been used in logistics distribution center location problems, and its superiority has been demonstrated. It is applied to solve the problem of continuity, but there are many binary problems to be solved in the actual situation. Thus, we designed a binary version of ROA. We used transfer functions to change continuous values into binary values, and binary values are used to symmetrically represent the meaning of physical problems. In this paper, four transfer functions are implemented to binarize ROA so as to improve the original transfer function for the overall performance of the algorithm. In addition, on the basis of the algorithm, we further improve the algorithm by adopting a parallel strategy, which improves the convergence speed and global exploration ability of the algorithm. The algorithm is verified on 23 benchmark functions, and the parallel binary ROA has a better performance than some other existing algorithms. In the aspect of the application, this paper adopts the datasets on UCI for feature selection. The improved algorithm has higher accuracy and selects fewer features.
A Multimodal IoT-Based Locomotion Classification System Using Features Engineering and Recursive Neural Network

Locomotion prediction for human welfare has gained tremendous interest in the past few years. Multimodal locomotion prediction is composed of small activities of daily living and an efficient approach to providing support for healthcare, but the complexities of motion signals along with video processing make it challenging for researchers in terms of achieving a good accuracy rate. The multimodal internet of things (IoT)-based locomotion classification has helped in solving these challenges. In this paper, we proposed a novel multimodal IoT-based locomotion classification technique using three benchmarked datasets. These datasets contain at least three types of data, such as data from physical motion, ambient, and vision-based sensors. The raw data has been filtered through different techniques for each sensor type. Then, the ambient and physical motion-based sensor data have been windowed, and a skeleton model has been retrieved from the vision-based data. Further, the features have been extracted and optimized using state-of-the-art methodologies. Lastly, experiments performed verified that the proposed locomotion classification system is superior when compared to other conventional approaches, particularly when considering multimodal data. The novel multimodal IoT-based locomotion classification system has achieved an accuracy rate of 87.67% and 86.71% over the HWU-USP and Opportunity++ datasets, respectively. The mean accuracy rate of 87.0% is higher than the traditional methods proposed in the literature.
Design Proposal for a Virtual Shopping Assistant for People with Vision Problems Applying Artificial Intelligence Techniques

Accessibility is an increasingly important topic for Ecommerce, especially for individuals with vision problems. To improve their online experience, the design of a voice assistant has been proposed to allow these individuals to browse and shop online more quickly and efficiently. This voice assistant forms an intelligent system that can understand and respond to users’ voice commands. The design considers the visual limitations of the users, such as difficulty reading information on the screen or identifying images. The voice assistant provides detailed product descriptions and ideas in a clear, easy-to-understand voice. In addition, the voice assistant has a series of additional features to improve the shopping experience. For example, the assistant can provide product recommendations based on the user’s previous purchases and information about special promotions and discounts. The main goal of this design is to create an accessible and inclusive online shopping experience for the visually impaired. The voice assistant is based on a conversational user interface, allowing users to easily navigate an eCommerce website, search for products, and make purchases.
Feedback Mechanism in Immersive Virtual Reality Influences Physical Hands-on Task Performance and Cognitive Load

AbstractAbstractThe affordance of immersive virtual reality (VR) holds great potential for education, supporting learning through simulation and visualization. However, the literature on STEM education indicates that VR learning is not necessarily more effective than traditional learning, because participants are immersed in learning environments with high visual loads, which increases cognitive load, resulting in poor learning outcomes. In addition, VR studies rarely evaluate learner performance in physical hands-on activities after participating in VR learning programs. Therefore, in this study, we seek to reduce cognitive load by incorporating feedback into the VR learning environment. This study conducts a quasi-experiment and proposes a VR learning environment for embedded electronic circuits and a practical hands-on task, to investigate learners' learning performance on practical hands-on task skills and their cognitive load. Participants were randomly assigned to groups with and without feedback. The results show that feedback in the VR environment is effective in reducing participants' extraneous cognitive load and increasing engagement, with fewer trial-and-error times in the learning units and better performance in the physical hands-on task.Keywords: Immersive virtual realitycognitive loadengagementfeedbackhands-on taskembedded electronic circuits Disclosure statementNo potential conflict of interest was reported by the author(s).Author contributionsAll author(s) read and approved the final manuscript.Additional informationFundingThis research is supported by the National Science and Technology Council (MOST), Taiwan, R.O.C. under grant Nos. MOST 109-2511-H-006-011-MY3 and MOST 110-2511-H-006-008-MY3.Notes on contributorsWei-Sheng WangWei-Sheng Wang is one of the leaders of this research, he is responsible for assisting in the conduct of experiments design, surveying related literature, and proofreading the manuscript.Chia-Ju LinChia-Ju Lin is the leader of this research, she is in charge of the research design, conducting teaching and learning experiments, data analysis, and writing the manuscript.Hsin-Yu LeeHsin-Yu Lee is responsible for assisting in the conduct of experiments and surveying related literature.Ting-Ting WuTing-Ting Wu is responsible for assisting in the conduct of experiments. She is also responsible for revising the manuscript.Yueh-Min HuangYueh-Min Huang is responsible for designing research experiments and providing fundamental education theories and comments on this research. He is also responsible for revising the manuscript. All authors spent more than 4 months discussing and analyzing the data.
Copyright protection on NFT digital works in the Metaverse

Non-fungible token (NFT) emerges with the marvelous explosion of the metaverse. NFT digital works are profoundly impacting the existing copyright system, which needs to be amended and responded to in time. There is a possibility that it will completely change the distribution pattern of rights and interests in the field of copyright law. Currently, the legal nature of the casting and trading of NFT digital works remains controversial. The liability of NFT digital works creators, as well as service platforms, is required to be further identified. This article starts with an analysis of the right attributes of NFT digital works based on intellectual property law. Subsequently, the related legal issues and countermeasures are discussed, including the exhaustion of the right-holders, the duty of platform management, and the assumption of tort liability arising from the transaction. It is suggested that the NFT digital works should be treated with considerable caution and the legal system of NFT digital works should be constructed in the near future.
On-Board Deep-Learning-Based Unmanned Aerial Vehicle Fault Cause Detection and Classification via FPGAs

With the increase in the use of unmanned aerial vehicles (UAVs)/drones, it is important to detect and identify causes of failure in real time for proper recovery from a potential crash-like scenario or postincident forensics analysis. The cause of crash could be either a fault in the sensor/actuator system, a physical damage/attack, or a cyber attack on the drone's software. In this work, we propose novel architectures based on deep convolutional and long short-term memory neural networks to detect (via autoencoder) and classify drone misoperations based on real-time sensor data. The proposed architectures are able to learn high-level features automatically from the raw sensor data. Empirical results show that our solution is able to detect (with over 90% accuracy) and classify various types of drone misoperations [with about 99% accuracy (simulation data) and up to 85% accuracy (experimental data)]. Furthermore, with the help of field programmable gate array-based hardware acceleration, we achieved a speedup of 40x ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim\!\text{2.6 ms}$</tex-math></inline-formula> ) for detection, while consuming half the amount of power compared with onboard GPU devices, such as NVIDIA Jetson TX2.
Multi-UAV Navigation for Optimized Video Surveillance of Ground Vehicles on Uneven Terrains

This paper addresses a trajectory planning problem for a team of UAVs following several ground vehicles on uneven terrain for video surveillance. A model predictive control-based multi-UAV path planning algorithm is designed. A theoretical justification of the path planning algorithm is provided. Extensive simulation studies demonstrate the performance of the proposed method.
The JPEG AI Standard: Providing Efficient Human and Machine Visual Data Consumption

The Joint Photographic Experts Group (JPEG) AI learning-based image coding system is an ongoing joint standardization effort between International Organization for Standardization (ISO), International Electrotechnical Commission (IEC), and International Telecommunication Union - Telecommunication Sector (ITU-T) for the development of the first image coding standard based on machine learning (a subset of artificial intelligence), offering a single stream, compact compressed domain representation, targeting both human visualization and machine consumption. The main motivation for this upcoming standard is the excellent performance of tools based on deep neural networks, in image coding, computer vision, and image processing tasks. The JPEG AI aims to develop an image coding standard addressing the needs of a wide range of applications such as cloud storage, visual surveillance, autonomous vehicles and devices, image collection storage and management, live monitoring of visual data, and media distribution. This article presents and discusses the rationale behind the JPEG AI vision, notably how this new standardization initiative aims to shape the future of image coding, through relevant application-driven use cases. The JPEG AI requirements, the JPEG AI history, and current status are also presented, offering a glimpse of the development of the first learning-based image coding standard.
AI-Powered Vulnerability Detection for Secure Source Code Development

Vulnerable source code in software applications is causing paramount reliability and security issues. Software security principles should be integrated to reduce these issues at the early stages of the development lifecycle. Artificial Intelligence (AI) could be applied to detect vulnerabilities in source code. In this research, a Machine Learning (ML) based method is proposed to detect source code vulnerabilities in C/C++ applications. Furthermore, Explainable AI (XAI) was applied to support developers in identifying vulnerable source code tokens and understanding their causes. The proposed model can detect whether the code is vulnerable or not in binary classification with 0.96 F1-Score. In case of vulnerability type detection, a multi-class classification based on CWE-ID, the model achieved 0.85 F1-Score. Several ML classifiers were tested, and the Random Forest (RF) and Extreme Gradient Boosting (XGB) performed well in binary and multi-class approaches respectively. Since the model is trained on a dataset containing actual source codes, the model is highly generalizable.