Reviewerly/exHarmony huggingface dataset:
RRFMDS: Rapid Real-Time Face Mask Detection System for Effective COVID-19 Monitoring

The primary mode of COVID-19 transmission is through respiratory droplets that are produced when an infected person talks, coughs, or sneezes. To avoid the fast spread of the virus, the WHO has instructed people to use face masks in crowded and public areas. This paper proposes the rapid real-time face mask detection system or RRFMDS, an automated computer-aided system to detect a violation of a face mask in real-time video. In the proposed system, single-shot multi-box detector is utilized for face detection, while fine-tuned MobileNetV2 is used for face mask classification. The system is lightweight (low resource requirement) and can be merged with pre-installed CCTV cameras to detect face mask violation. The system is trained on a custom dataset which consists of 14,535 images, of which 5000 belong to incorrect masks, 4789 to with masks, and 4746 to without masks. The primary purpose of creating such a dataset was to develop a face mask detection system that can detect almost all types of face masks with different orientations. The system can detect all three classes (incorrect masks, with mask and without mask faces) with an average accuracy of 99.15% and 97.81%, respectively, on training and testing data. The system, on average, takes 0.14201142 s to process a single frame, including detecting the faces from the video, processing a frame and classification.
BTAD: A binary transformer deep neural network model for anomaly detection in multivariate time series data

In the context of big data, if the task of multivariate time series data anomaly detection cannot be performed efficiently and accurately, it will bring great security risks to industrial systems. However, fast model inference requirements, unlabeled datasets and excessively long time series make it a challenging problem to build an accurate and fast anomaly detection model. In this paper, we propose an unsupervised Bi-Transformer anomaly detection method (BTAD) for multivariate time series data, which uses Bi-Transformer structure to extract dataset association features, and uses an improved adaptive multi-head attention mechanism to infer trends in each meta-dimension of multivariate time series data in parallel. The modified Decoder structure prevents the reconstructed output of BTAD from being disturbed by the input information. Self-conditioning mechanism could enhance the robustness to noisy data, and improve model’s generalization ability. Experiments show that BTAD could outperform other models in detection performance and training efficiency. Taking NAB dataset as an example, the AUC and F1 of BTAD are increased by more than 4.78% and 1.40% separately. Finally, we look forward to the future development trend of BTAD, and put forward the corresponding improvement ideas.
Garbage detection and classification using a new deep learning-based machine vision system as a tool for sustainable waste recycling

Waste recycling is a critical issue for environment pollution management while garbage classification determines the recycling efficiency. In order to reduce labor costs and increase garbage classification capacity, a machine vision system is established based on the deep learning and transfer learning. In this new method, an improved MobileNetV2 deep learning model is proposed for garbage detection and classification, where the attention mechanism is introduced into the first and last convolution layers of the MobileNetV2 model to improve the recognition accuracy and the transfer learning uses a set of pre-trained weight parameters to extend the model generalization ability. In addition, the principal component analysis (PCA) is employed to reduce the dimension of the last fully connected layer to enable real-time operation of the developed model on an edge device. The experimental results demonstrate that the proposed method generates 90.7 % of the garbage classification accuracy on the "Huawei Cloud" datasets, the average inference time is 600 ms on the raspberry Pi 4B microprocessor, and the model volume compression is 30.1 % of the basic MobileNetV2 model. Furthermore, a garbage sorting porotype is designed and manufactured to evaluate the performance of the proposed MobileNetV2 model on the real-world garbage identification, which turns out that the average garbage classification accuracy is 89.26 %. Hence, the developed garbage sorting porotype can be used a effective tool for sustainable waste recycling.
Federated Machine Learning, Privacy-Enhancing Technologies, and Data Protection Laws in Medical Research: Scoping Review

The collection, storage, and analysis of large data sets are relevant in many sectors. Especially in the medical field, the processing of patient data promises great progress in personalized health care. However, it is strictly regulated, such as by the General Data Protection Regulation (GDPR). These regulations mandate strict data security and data protection and, thus, create major challenges for collecting and using large data sets. Technologies such as federated learning (FL), especially paired with differential privacy (DP) and secure multiparty computation (SMPC), aim to solve these challenges.This scoping review aimed to summarize the current discussion on the legal questions and concerns related to FL systems in medical research. We were particularly interested in whether and to what extent FL applications and training processes are compliant with the GDPR data protection law and whether the use of the aforementioned privacy-enhancing technologies (DP and SMPC) affects this legal compliance. We placed special emphasis on the consequences for medical research and development.We performed a scoping review according to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). We reviewed articles on Beck-Online, SSRN, ScienceDirect, arXiv, and Google Scholar published in German or English between 2016 and 2022. We examined 4 questions: whether local and global models are "personal data" as per the GDPR; what the "roles" as defined by the GDPR of various parties in FL are; who controls the data at various stages of the training process; and how, if at all, the use of privacy-enhancing technologies affects these findings.We identified and summarized the findings of 56 relevant publications on FL. Local and likely also global models constitute personal data according to the GDPR. FL strengthens data protection but is still vulnerable to a number of attacks and the possibility of data leakage. These concerns can be successfully addressed through the privacy-enhancing technologies SMPC and DP.Combining FL with SMPC and DP is necessary to fulfill the legal data protection requirements (GDPR) in medical research dealing with personal data. Even though some technical and legal challenges remain, for example, the possibility of successful attacks on the system, combining FL with SMPC and DP creates enough security to satisfy the legal requirements of the GDPR. This combination thereby provides an attractive technical solution for health institutions willing to collaborate without exposing their data to risk. From a legal perspective, the combination provides enough built-in security measures to satisfy data protection requirements, and from a technical perspective, the combination provides secure systems with comparable performance with centralized machine learning applications.
Blockchain for healthcare systems: Architecture, security challenges, trends and future directions

Blockchain has become popular in recent times through its data integrity and wide scope of applications. It has laid the foundation for cryptocurrencies such as Ripple, Bitcoin, Ethereum, and so on. Blockchain provides a platform for decentralization and trust in various applications such as finance, commerce, IoT, reputation systems, and healthcare. However, prevailing challenges like scalability, resilience, security and privacy are yet to be overcome. Due to rigorous regulatory constraints such as HIPAA, blockchain applications in the healthcare industry usually require more stringent authentication, interoperability, and record sharing requirements. This article presents an extensive study to showcase the significance of blockchain technology from both application and technical perspectives for healthcare domain. The article discusses the features and use-cases of blockchain in different applications along with the healthcare domain interoperability. The detailed working operation of the blockchain and the consensus algorithms are presented in the context of healthcare. An outline of the blockchain architecture, platforms, and classifications are discussed to choose the right platform for healthcare applications. The current state-of-the-art research in healthcare blockchain and available blockchain based healthcare applications are summarized. Furthermore, the challenges and future research opportunities along with the performance evaluation metrics in realizing the blockchain technology for healthcare are presented to provide insight for future research. We also layout the various security attacks on the blockchain protocol with the classifications of threat models and presented a comparative analysis of the detection and protection techniques. Techniques to enhance the security and privacy of the blockchain network is also discussed.
Exploring Consumer-Robot interaction in the hospitality sector: Unpacking the reasons for adoption (or resistance) to artificial intelligence

Over the years, service robots and artificial intelligence (AI) have been gaining traction in the hospitality sector for frontline service automation. Although a significant body of literature exists pertinent to AI services in the hospitality sector, the research on the AI service encounters and factors affecting the acceptance (or rejection) of AI services among consumers requires further attention. Against this background, the purpose of this study is to explore the underlying factors that influence the acceptance (or rejection) of AI and robotic services in the hospitality industry, specifically in the context of restaurants in Pakistan. This study adopted a case study approach and conducted semi-structured interviews with the users and non-users of AI/robotic services in restaurants. These interviews aimed to uncover the values, motives, and reasons that affect consumers' decisions to adopt or resist AI services. The findings revealed that, in addition to customer values and global motives, there also exist context-specific reasons that influence the adoption (or resistance) of robots and AI services in restaurants. This research is one of the first studies to explore the reasons for facilitating or inhibiting consumers' adoption of AI and robotic services in the Pakistani hospitality sector. The study's contribution lies in providing a nuanced understanding of the factors that affect consumer behavior toward AI and robotic services, which can inform the development and implementation of AI and robotic services in the hospitality industry.
A Current Review of Machine Learning and Deep Learning Models in Oral Cancer Diagnosis: Recent Technologies, Open Challenges, and Future Research Directions

Cancer is a problematic global health issue with an extremely high fatality rate throughout the world. The application of various machine learning techniques that have appeared in the field of cancer diagnosis in recent years has provided meaningful insights into efficient and precise treatment decision-making. Due to rapid advancements in sequencing technologies, the detection of cancer based on gene expression data has improved over the years. Different types of cancer affect different parts of the body in different ways. Cancer that affects the mouth, lip, and upper throat is known as oral cancer, which is the sixth most prevalent form of cancer worldwide. India, Bangladesh, China, the United States, and Pakistan are the top five countries with the highest rates of oral cavity disease and lip cancer. The major causes of oral cancer are excessive use of tobacco and cigarette smoking. Many people's lives can be saved if oral cancer (OC) can be detected early. Early identification and diagnosis could assist doctors in providing better patient care and effective treatment. OC screening may advance with the implementation of artificial intelligence (AI) techniques. AI can provide assistance to the oncology sector by accurately analyzing a large dataset from several imaging modalities. This review deals with the implementation of AI during the early stages of cancer for the proper detection and treatment of OC. Furthermore, performance evaluations of several DL and ML models have been carried out to show that the DL model can overcome the difficult challenges associated with early cancerous lesions in the mouth. For this review, we have followed the rules recommended for the extension of scoping reviews and meta-analyses (PRISMA-ScR). Examining the reference lists for the chosen articles helped us gather more details on the subject. Additionally, we discussed AI's drawbacks and its potential use in research on oral cancer. There are methods for reducing risk factors, such as reducing the use of tobacco and alcohol, as well as immunization against HPV infection to avoid oral cancer, or to lessen the burden of the disease. Additionally, officious methods for preventing oral diseases include training programs for doctors and patients as well as facilitating early diagnosis via screening high-risk populations for the disease.
Hierarchical structure-based joint operations algorithm for global optimization

Joint operations algorithm (JOA) is a metaheuristic algorithm based on joint operations strategy in military theory, which incorporates three core operations – offensive, defensive and regroup – and has excellent performance in global optimization problems. To enhance the optimization performance of the original JOA, we re-examine the positioning of the three core operations in balancing global exploration and local exploitation, and propose a hierarchical structure-based JOA variant (abbreviated as HSJOA) by adjusting their execution mechanism. In addition, we redesign the three core operations to give full play to their synergy effect. In the new offensive operations, we simplify its specific execution strategy, but introduce an adjustment parameter to retain valuable position information. In the modified defensive operations, we integrate the influence of officer and elite comrades on soldiers to design a Gaussian distribution-based conservative defensive strategy and one Cauchy distribution-based aggressive defensive strategy. In the novel regroup operations, we replace the original random division strategy with a sorting-based division scheme. To evaluate the optimization performance of HSJOA, we conduct comparison experiments using nine excellent algorithms to deal with four real-life optimization problems and thirty test functions from IEEE CEC 2014 testbed. The comparison results show that the overall optimization performance of HSJOA is significantly better than the original JOA, the outstanding variants (TAPSO and GODE) of two well-known algorithms and four recently proposed algorithms (EO, SOA, MPA and WOA), while inferior to two winners of IEEE CEC competition (L-SHADE and EBOwithCMAR), but HSJOA clearly outperforms L-SHADE and EBOwithCMAR in terms of the runtime consumption.
Use of ChatGPT in ESP Teaching Process

The emergence of ChatGPT, a chatbot launched by OpenAI in November 2022, opened a large amount of opportunities for using the artificial intelligence (AI) for dealing with creation and processing of textual materials, including the use in teaching and learning of foreign languages at all educational and age levels. In terms of the teaching process regarding English for Specific Purposes (ESP), Chat GPT can be used as an effective and time-saving tool for various aspects of preparation and implementation of teaching units and evaluation of students’ written assignments, and that is the topic that will be presented end elaborated in the central part of the paper. Before that, an introduction regarding AI, its use in education and language teaching and learning, ESP and ChatGPT will be made. The final part of the paper will contain the conclusions relating the overall use of this AI tool in teaching ESP.
A Fitts’ Law Study of Gaze-Hand Alignment for Selection in 3D User Interfaces

Gaze-Hand Alignment has recently been proposed for multimodal selection in 3D. The technique takes advantage of gaze for target pre-selection, as it naturally precedes manual input. Selection is then completed when manual input aligns with gaze on the target, without need for an additional click method. In this work we evaluate two alignment techniques, Gaze&Finger and Gaze&Handray, combining gaze with image plane pointing versus raycasting, in comparison with hands-only baselines and Gaze&Pinch as established multimodal technique. We used Fitts’ Law study design with targets presented at different depths in the visual scene, to assess effect of parallax on performance. The alignment techniques outperformed their respective hands-only baselines. Gaze&Finger is efficient when targets are close to the image plane but less performant with increasing target depth due to parallax.
Disentangling Hype from Practicality: On Realistically Achieving Quantum Advantage

What are the promising applications to realize quantum advantage?
Adversarial Approaches to Tackle Imbalanced Data in Machine Learning

Real-world applications often involve imbalanced datasets, which have different distributions of examples across various classes. When building a system that requires a high accuracy, the performance of the classifiers is crucial. However, imbalanced datasets can lead to a poor classification performance and conventional techniques, such as synthetic minority oversampling technique. As a result, this study proposed a balance between the datasets using adversarial learning methods such as generative adversarial networks. The model evaluated the effect of data augmentation on both the balanced and imbalanced datasets. The study evaluated the classification performance on three different datasets and applied data augmentation techniques to generate the synthetic data for the minority class. Before the augmentation, a decision tree was applied to identify the classification accuracy of all three datasets. The obtained classification accuracies were 79.9%, 94.1%, and 72.6%. A decision tree was used to evaluate the performance of the data augmentation, and the results showed that the proposed model achieved an accuracy of 82.7%, 95.7%, and 76% on a highly imbalanced dataset. This study demonstrates the potential of using data augmentation to improve the classification performance in imbalanced datasets.
A Comprehensive Survey on Multi-View Clustering

The development of information gathering and extraction technology has led to the popularity of multi-view data, which enables samples to be seen from numerous perspectives. Multi-view clustering (MVC), which groups data samples by leveraging complementary and consensual information from several views, is gaining popularity. Despite the rapid evolution of MVC approaches, there has yet to be a study that provides a full MVC roadmap for both stimulating technical improvements and orienting research newbies to MVC. In this article, we review recent MVC techniques with the purpose of exhibiting the concepts of popular methodologies and their advancements. This survey not only serves as a unique MVC comprehensive knowledge for researchers but also has the potential to spark new ideas in MVC research. We summarise a large variety of current MVC approaches based on two technical mechanisms: heuristic-based multi-view clustering (HMVC) and neural network-based multi-view clustering (NNMVC). We end with four technological approaches within the category of HMVC: nonnegative matrix factorisation, graph learning, latent representation learning, and tensor learning. Deep representation learning and deep graph learning are two technical methods that we demonstrate in NNMVC. We also show 15 publicly available multi-view datasets and examine how representative MVC approaches perform on them. In addition, this study identifies the potential research directions that may require further investigation in order to enhance the further development of MVC.
Strategic blockchain adoption to deter deceptive counterfeiters

Counterfeiting is an ever growing problem worldwide which is exacerbated by the ease of access through e-commerce and online shopping. This calls for innovative technologies, such as blockchain, to identify, track, and prevent fake products from reaching consumers, especially for vital sectors such as the drug industry, which is the main motivation for this work. We investigate the strategic implications of using blockchain technology to deter counterfeiters. We particularly focus on the case of deceptive counterfeits that infiltrate legitimate distribution channels. Deceptive counterfeits lack the quality of genuine products and may pose immense health and safety risks to consumers who are unable to distinguish them from genuine products at the time of purchase. In contrast to prior literature that assumes that blockchain eliminates deceptive counterfeiting, we present a model that realistically considers blockchain as a technology that increases the capability of detecting counterfeits. This capability nonetheless comes at an increasing cost that may financially discourage genuine manufacturers from adopting the technology. The presented model shows that blockchain is not always financially beneficial and demonstrates that manufacturers can strategically balance between product quality and investment in blockchain to combat counterfeiting. Furthermore, our results demonstrate that, with the availability of blockchain, genuine manufacturers may be less interested to differentiate products based on quality, but rather rely on blockchain to block counterfeits.
Value of Artificial Intelligence in Evaluating Lymph Node Metastases

One of the most relevant prognostic factors in cancer staging is the presence of lymph node (LN) metastasis. Evaluating lymph nodes for the presence of metastatic cancerous cells can be a lengthy, monotonous, and error-prone process. Owing to digital pathology, artificial intelligence (AI) applied to whole slide images (WSIs) of lymph nodes can be exploited for the automatic detection of metastatic tissue. The aim of this study was to review the literature regarding the implementation of AI as a tool for the detection of metastases in LNs in WSIs. A systematic literature search was conducted in PubMed and Embase databases. Studies involving the application of AI techniques to automatically analyze LN status were included. Of 4584 retrieved articles, 23 were included. Relevant articles were labeled into three categories based upon the accuracy of AI in evaluating LNs. Published data overall indicate that the application of AI in detecting LN metastases is promising and can be proficiently employed in daily pathology practice.