Reviewerly/exHarmony huggingface dataset:
Vision Transformers for Vein Biometric Recognition

In October 2020, Google researchers present a promising Deep Learning architecture paradigm for Computer Vision that outperforms the already standard Convolutional Neural Networks (CNNs) on multiple image recognition state-of-the-art datasets: Vision Transformers (ViTs).Based on the self-attention concept inherited from Natural Language Processing (NLP), this new structure surpasses the CNN image classification task on ImageNet, CIFAR-100, and VTAB, among others, when it is finetuned (Transfer Leaning) after a previous pre-training on larger datasets.In this work, we confirm this theory and move one step further over the CNN structures applied for Vascular Biometric Recognition (VBR): to the best of our knowledge, we introduce for the first time multiple pure pre-trained and finetuned Vision Transformers in this evolving biometric modality to address the challenge of the limited number of samples in VBR datasets.For this purpose, the ViTs have been trained to extract unique image features on the ImageNet-1k and ImageNet-21k and then fine-tuned for the four main existing VBR variants, i.e., finger, palm, hand dorsal, and wrist vein areas.Fourteen existing vascular datasets have been used to perform the vein identification task in the four previously mentioned modalities, based on the True-Positive Identification Rate (TPIR) and 75-25% train-test sets obtaining the following results: HKPU (99.52%), and FV-USM (99.1%);Vera (99.39%), and CASIA (96.00%);Bosphorus (99.86%);PUT-wrist (99.67%), and UC3M-CV1+CV2 (99.67%).Furthermore, we introduce UC3M-CV3: a hygienic contactless wrist database collected on smartphones and consisting of 5400 images from 100 different subjects.The promising results show the Vision Transformer's versatility in VBR under Transfer Learning and reinforce this new Neural Network architecture paradigm.
An underwater image restoration based on global polarization effects of underwater scene

Currently, most underwater polarization imaging methods assume that the backscattering is polarized and the reflected light of the target is unpolarized. In reality, this assumption is unreliable since both backscattering and reflected light contribute to polarization. Therefore, we propose an underwater image restoration method that combines the polarization effects of reflected light and backscattering. Firstly, Stokes vectors are used to automatically obtain polarization difference images. Based on the enhancement measure evaluation (EME) constraint, the initial estimate of backscattering is obtained. Then, an adaptive local optimization (ALO) method is designed to estimate global backscattering light. A variable factor is introduced for local optimization to obtain global backscattering. Finally, in order to optimize the system, particle swarm optimization (PSO) is introduced, which improves the processing speed of the system. The experimental results show that it is necessary to consider the polarization of the target during underwater polarization descattering, which not only has a good ability to eliminate scattering but also has an excellent effect on restoring target details.
Application of the convolutional neural networks and supervised deep-learning methods for osteosarcoma bone cancer detection

Osteosarcoma is a cancerous tumor that occurs in bones. Although it can occur in any bone, it often occurs in long bones such as arms and legs. The exact cause of this cancerous tumor is still unknown, but according to experts, it occurs due to the deoxyribonucleic acid (DNA) mutations inside the bones. This creates immature, irregular, diseased bone and can destroy healthy body tissue. About 75 out of 100 people who have osteosarcoma can be cured if the cancer is not dispersed to the additional body parts. The bone X-ray is the initial test when a bone tumor is suspected. X-ray and imaging tests are the best way to identify osteosarcoma from the bones. A biopsy is the suggested method that can make a definitive diagnosis. This is a time-consuming and difficult procedure that can be automated. We propose several supervised deep-learning methods and select the most suitable model. The selection is made through the weightage from the users’ data to detect bone cancer. We show the model selected meets the expectations with the highest accuracy 90.36% using the residual neural network(ResNet101) algorithm and 89.51% precision in the prediction tasks.
GWM-view: Gradient-weighted multi-view calibration method for machining robot positioning

Multi-camera vision is widely used for guiding the machining robot to remove flash and burrs on complex automotive castings and forgings with arbitrary initial posture. Aiming at the problems of insufficient field of vision and regional occlusion in actual machining, a gradient-weighted multi-view calibration method (GWM-View) is proposed for the machining robot positioning based on the convergent binocular vision. Specifically, the mapping between each auxiliary camera and the main camera in the multi-view system is calculated by the inverse equation and intrinsic parameter matrix. Then, the gradient-weighted suppression algorithm is introduced to filter out the errors caused by camera angle variation. Next, the spatial coordinates of the feature points after suppression are used to correct the transformation matrix. Finally, the hand-eye calibration algorithm is implemented to transform the corrected data into the robot base coordinate system for the accurate positioning of the robot under multiple views. The experiment on the automotive engine flywheel shell indicates that the average positioning error is controlled within 1 mm under different postures. The stability and robustness of the proposed method are further improved while the positioning accuracy of the machining robot meets the requirements.
An Adaptive Adversarial Patch-Generating Algorithm for Defending against the Intelligent Low, Slow, and Small Target

The “low, slow, and small” target (LSST) poses a significant threat to the military ground unit. It is hard to defend against due to its invisibility to numerous detecting devices. With the onboard deep learning-based object detection methods, the intelligent LSST (ILSST) can find and detect the ground unit autonomously in a denied environment. This paper proposes an adversarial patch-based defending method to blind the ILSST by attacking its onboard object detection network. First, an adversarial influence score was established to indicate the influence of the adversarial noise on the objects. Then, based on this score, we used the least squares algorithm and Bisectional search methods to search the patch’s optimal coordinates and size. Using the optimal coordinates and size, an adaptive patch-generating network was constructed to automatically generate patches on ground units and hide the ground units from the deep learning-based object detection network. To evaluate the efficiency of our algorithm, a new LSST view dataset was collected, and extensive attacking experiments are carried out on this dataset. The results demonstrate that our algorithm can effectively attack the object detection networks, is better than state-of-the-art adversarial patch-generating algorithms in hiding the ground units from the object detection networks, and has high transferability among the object detection networks.
The research of a novel WOG-YOLO algorithm for autonomous driving object detection

Object detection has been one of the critical technologies in autonomous driving. To improve the detection precision, a novel optimization algorithm is presented to enhance the performance of the YOLOv5 model. First, by improving the hunting behavior of the grey wolf algorithm(GWO) and incorporating it into the whale optimization algorithm(WOA), a modified whale optimization algorithm(MWOA) is proposed. The MWOA leverages the population's concentration ratio to calculate [Formula: see text] for selecting the hunting branch of GWO or WOA. Tested by six benchmark functions, MWOA is proven to possess better global search ability and stability. Second, the C3 module in YOLOv5 is substituted by G-C3, and an extra detection head is added, thus a highly optimizable detection G-YOLO network is constructed. Based on the self-built dataset, 12 initial hyperparameters in the G-YOLO model are optimized by MWOA using a score fitness function of compound indicators, thus the final hyperparameters are optimized and the whale optimization G-YOLO (WOG-YOLO) model is obtained. In comparison with the YOLOv5s model, the overall mAP increases by 1.7[Formula: see text], the mAP of pedestrians increases by 2.6[Formula: see text] and the mAP of cyclists increases by 2.3[Formula: see text].
Grid-wise simulation acceleration of the electromagnetic fields of 2D optical devices using super-resolution

The significance of simulation has been increasing in device design due to the cost of real test. The accuracy of the simulation increases as the resolution of the simulation increases. However, the high-resolution simulation is not suited for actual device design because the amount of computing exponentially increases as the resolution increases. In this study, we introduce a model that predicts high-resolution outcomes using low-resolution calculated values which successfully achieves high simulation accuracy with low computational cost. The fast residual learning super-resolution (FRSR) convolutional network model is a model that we introduced that can simulate electromagnetic fields of optical. Our model achieved high accuracy when using the super-resolution technique on a 2D slit array under specific circumstances and achieved an approximately 18 times faster execution time than the simulator. To reduce the model training time and enhance performance, the proposed model shows the best accuracy (R2: 0.9941) by restoring high-resolution images using residual learning and a post-upsampling method to reduce computation. It has the shortest training time among the models that use super-resolution (7000 s). This model addresses the issue of temporal limitations of high-resolution simulations of device module characteristics.
Decentralized Aggregation for Energy-Efficient Federated Learning via D2D Communications

Federated learning (FL) has emerged as a distributed machine learning (ML) technique to train models without sharing users’ private data. In this paper, we introduce a decentralized FL scheme that is called federated learning empowered overlapped clustering for decentralized aggregation (FL-EOCD). The introduced FL-EOCD leverages device-to-device (D2D) communications and overlapped clustering to enable decentralized aggregation, where a cluster is defined as a coverage zone of a typical device. The devices located on the overlapped clusters are called bridge devices (BDs). In the proposed FL-EOCD scheme, a clustering topology is envisioned where clusters are connected through BDs, so as the aggregated models of each cluster is disseminated to the other clusters in a decentralized manner without the need for a global aggregator or an additional hop of transmission. To evaluate our proposed FL-EOCD scheme as opposed to baseline FL schemes, we consider minimizing the overall energy-consumption of devices while maintaining the convergence rate of FL subject to its time constraint. To this end, a joint optimization problem, considering scheduling the local devices/BDs to the CHs and computation frequency allocation, is formulated, where an iterative solution to this joint problem is devised. Extensive simulations are conducted to verify the effectiveness of the proposed FL-EOCD algorithm over FL conventional schemes in terms of energy consumption, latency, and convergence rate.
Feature-Level Deeper Self-Attention Network With Contrastive Learning for Sequential Recommendation

Sequential recommendation, which aims to recommend next item that the user will likely interact in a near future, has become essential in various Internet applications. Existing methods usually consider the transition patterns between items, but ignore the transition patterns between features of items. We argue that only the item-level sequences cannot reveal the full sequential patterns, while explicit and implicit feature-level sequences can help extract the full sequential patterns. Meanwhile, the item-level sequential recommendation also suffers from limited supervised signal issues. In this article, we propose a novel model Feature-level Deeper Self-Attention Network with Contrastive Learning (FDSA-CL) for sequential recommendation. Specifically, FDSA-CL first integrates various heterogeneous features of items into feature-level sequences with different weights through a vanilla attention mechanism. After that, FDSA-CL applies separated self-attention blocks on item-level sequences and feature-level sequences, respectively, to model item transition patterns and feature transition patterns. Moreover, we propose contrastive learning and item feature recommendation tasks to capture the embedding commonality and further utilize the beneficial interaction among the two levels, so as to alleviate the sparsity of the supervised signal and extract the most critical information. Finally, we jointly optimize the above tasks. We evaluate the proposed model using two real-world datasets and experimental results show that our model significantly outperforms the state-of-the-art approaches.
Toward Secured IoT-Based Smart Systems Using Machine Learning

Machine learning (ML) and the internet of things (IoT) are among the most booming research directions. Smart cities, smart campuses (SCs), smart homes, smart cars, early warning systems (EWSs), etc.; or it could be called “Smart x” systems are implemented using ML and IoT. Those systems will alter how various world entities communicate with one another. This paper spots light on the significant roles of the IoT in SS. Also, it focuses on the importance of ML in IoT-based SS. Besides, an overview of smartness and IoT is presented. Then, this paper offers ML benchmarking along with a taxonomy that categorizes the ML models into linear and non-linear ones depending on the problem type (classification or regression). Afterward, the commonly utilized evaluation metrics are provided. In addition, this paper considers the trust techniques used for mitigating different security aspects in IoT networks, which play a crucial part in regulating the new era of communication. Moreover, two case studies devoting ML for IoT-based SS, namely IoT-based SC and IoT-based EWS, are considered for data collection and manipulation with guided research directions. Finally, the paper presents effective recommendations of ML’s significant roles in SC and earthquake EWS for interested scholars.
Classification and Characterization of Memory Reference Behavior in Machine Learning Workloads

With the recent penetration of artificial intelligence (AI) technologies into many areas of computing, machine learning is being incorporated into modern software design. As the in-memory data of AI workloads increasingly grows, it is important to characterize memory reference behaviors in machine learning workloads. In this paper, we perform a characterization study for memory references in machine learning workloads as the learning types (i.e., supervised vs. unsupervised) and the problem domains (i.e., classification, regression, and clustering) are varied. From this study, we uncover the following five characteristics. First, machine learning workloads exhibit significantly different memory reference patterns from traditional workloads, but they are similar regardless of learning types and problem domains. Second, in all workloads, memory reads and writes continue to appear for a wide range of memory addresses, but there is a specific time period where only reads appear. Third, among references to memory areas (i.e., code, data, heap, stack, library), library accounts for about 90% of total memory references. Fourth, there is a low popularity bias between memory pages referenced in machine learning workloads, especially for writes. Fifth, when estimating the likelihood of re-referencing, temporal locality is dominant in top 100 memory pages, but access frequency provides better information after that ranking. It is expected that the characterization of memory references conducted in this paper will be helpful in the design of memory management policies for machine learning workloads.
Exploring the influence of audience familiarity on speaker anxiety and performance in virtual reality and real-life presentation contexts

ABSTRACTABSTRACTVirtual reality (VR) offers immense freedom in the design of virtual instructional environments, but little guidance exists on how to capitalise on this freedom. This article reports on a study exploring how audience familiarity influences public speaking anxiety (PSA) and performance in a presentation speaking task in virtuo and in situ. Questionnaire instruments were used to gauge the PSA, motivation, focus, and self-confidence of 10 undergraduate students who each presented in four different audience conditions across VR and real life. Presentations were transcribed to identify features of performance, including utterance fluency, and speaking breadth and depth. Outcomes indicated that an audience of computer-generated agents resulted in less PSA than an audience of photorealistic people familiar to the speakers. Additionally, presenting to an audience of strangers in real life induced the most anxiety, but the performance features of articulation rate, disfluencies, and frequency of silent pauses were significantly improved in this condition. The main contribution of this study is to show that presentations directed at virtual audiences exhibit less fluent speech in non-native speakers than speeches to a real audience.KEYWORDS: Virtual realitypublic speaking anxietyspeaking performanceaudience familiarity AcknowledgementsThe authors would like to thank Hongji Li, Yu Fu, and Luhan Wang for their expertise in creating the virtual environment used in this study.Disclosure statementNo potential conflict of interest was reported by the author(s).Ethics statementThis research was approved by the university’s ethics committee and followed all required procedures to ensure the safety and consent of the participants.Additional informationFundingThis work was supported by Xi’an Jiaotong-Liverpool University grant number TDF 18/19-R17-115.
An Extensive Preliminary Blockchain Survey from a Maritime Perspective

The maritime industry is moving towards a digital ecosystem to achieve substantial mutual profits. To achieve this, there have been attempts to combine existing, disjointed systems into more efficient, standardized platforms that can be scaled up. However, this transition has faced challenges. To address these issues, it is suggested that innovative technologies such as blockchain be utilized due to their alignment with the sector’s needs. This study uses a triangulation approach by examining a mix of literature, web-based data, applications, and projects to showcase the contribution of blockchain and its potential use cases. We also explore its potential use cases based on other sectors using projection and parallelism. Additionally, the study delves into limitations and possible solutions. This research acts as a preliminary study for the implementation of blockchain in the maritime industry, and advocates for its use as a revolutionary approach. The findings will be beneficial for scholars, policy makers, and practitioners in the maritime industry.
Ubiquitous intelligent federated learning privacy-preserving scheme under edge computing

With the rapid development of artificial intelligence (AI), combining machine learning (ML) and edge computing powered by big data has become a growing trend. However, under edge computing, ML faces problems such as limited energy consumption, insufficient computing power, and data security threats. Therefore, we propose a ubiquitous intelligent federated learning privacy protection scheme(UIFLPP), which provides privacy protection for data under edge computing. First, we train part of the models in embedded devices and add matrix masks to ensure secure transmission between embedded devices and edge servers. In addition, residual model training is performed on the edge server with a differential privacy mechanism. After aggregation in the cloud, noise is added and the model is fed back to the edge server. The experimental results show that the scheme can achieve 86% accuracy on CIFAR10 on the premise of ensuring privacy and reducing the training time by 5% compared with the baseline scheme, which can well meet the needs of ML in edge scenarios.
Detecting Human Falls in Poor Lighting: Object Detection and Tracking Approach for Indoor Safety

Falls are one the leading causes of accidental death for all people, but the elderly are at particularly high risk. Falls are severe issue in the care of those elderly people who live alone and have limited access to health aides and skilled nursing care. Conventional vision-based systems for fall detection are prone to failure in conditions with low illumination. Therefore, an automated system that detects falls in low-light conditions has become an urgent need for protecting vulnerable people. This paper proposes a novel vision-based fall detection system that uses object tracking and image enhancement techniques. The proposed approach is divided into two parts. First, the captured frames are optimized using a dual illumination estimation algorithm. Next, a deep-learning-based tracking framework that includes detection by YOLOv7 and tracking by the Deep SORT algorithm is proposed to perform fall detection. On the Le2i fall and UR fall detection (URFD) datasets, we evaluate the proposed method and demonstrate the effectiveness of fall detection in dark night environments with obstacles.
Social Robotics meets Sociolinguistics

Deploying a social robot in the real world means that it must interact with speakers from diverse backgrounds, who in turn are likely to show substantial accent and dialect variation. Linguistic variation in social context has been well studied in human-human interaction; however, the influence of these factors on human interactions with digital agents, especially embodied agents such as robots, has received less attention. Here we present an ongoing project where the goal is to develop a social robot that is suitable for deployment in ethnically-diverse areas with distinctive regional accents. To help in developing this robot, we carried out an online survey of Scottish adults to understand their expectations for conversational interaction with a robot. The results confirm that social factors constraining accent and dialect are likely to be significant issues for human-robot interaction in this context, and so must be taken into account in the design of the system at all levels.