Reviewerly/exHarmony huggingface dataset:
QUARK: A Framework for Quantum Computing Application Benchmarking

Quantum computing (QC) is anticipated to provide a speedup over classical HPC approaches for specific problems in optimization, simulation, and machine learning. With the advances in quantum computing toward practical applications, the need to analyze and compare different quantum solutions increases. While different low-level benchmarks for QC exist, these benchmarks do not provide sufficient insights into real-world application-level performance. We propose an application-centric benchmark method and the QUantum computing Application benchmaRK (QUARK) framework to foster the investigation and creation of application benchmarks for QC. This paper establishes three significant contributions: (1) it makes a case for application-level benchmarks and provides an in-depth "pen and paper" benchmark formulation of two reference problems: robot path and vehicle option optimization from the industrial domain; (2) it proposes the open-source QUARK framework for designing, implementing, executing, and analyzing benchmarks; (3) it provides multiple reference implementations for these two reference problems based on different known, and where needed, extended, classical and quantum algorithmic approaches and analyzes their performance on different types of infrastructures.
Learning unseen coexisting attractors

Reservoir computing is a machine learning approach that can generate a surrogate model of a dynamical system. It can learn the underlying dynamical system using fewer trainable parameters and, hence, smaller training data sets than competing approaches. Recently, a simpler formulation, known as next-generation reservoir computing, removed many algorithm metaparameters and identified a well-performing traditional reservoir computer, thus simplifying training even further. Here, we study a particularly challenging problem of learning a dynamical system that has both disparate time scales and multiple co-existing dynamical states (attractors). We compare the next-generation and traditional reservoir computer using metrics quantifying the geometry of the ground-truth and forecasted attractors. For the studied four-dimensional system, the next-generation reservoir computing approach uses ∼ 1.7 × less training data, requires 10 × shorter "warmup" time, has fewer metaparameters, and has an ∼ 100 × higher accuracy in predicting the co-existing attractor characteristics in comparison to a traditional reservoir computer. Furthermore, we demonstrate that it predicts the basin of attraction with high accuracy. This work lends further support to the superior learning ability of this new machine learning algorithm for dynamical systems.
Predicting medical specialty from text based on a domain-specific pre-trained BERT

Owing to the prevalence of the coronavirus disease (COVID-19), coping with clinical issues at the individual level has become important to the healthcare system. Accordingly, precise initiation of treatment after a hospital visit is required for expedited processes and effective diagnoses of outpatients. To achieve this, artificial intelligence in medical natural language processing (NLP), such as a healthcare chatbot or a clinical decision support system, can be suitable tools for an advanced clinical system. Furthermore, support for decisions on the medical specialty from the initial visit can be helpful.In this study, we propose a medical specialty prediction model from patient-side medical question text based on pre-trained bidirectional encoder representations from transformers (BERT). The dataset comprised pairs of medical question texts and labeled specialties scraped from a website for the medical question-and-answer service. The model was fine-tuned for predicting the required medical specialty labels among 27 labels from medical question texts. To demonstrate the feasibility, we conducted experiments on a real-world dataset and elaborately evaluated the predictive performance compared with four deep learning NLP models through cross-validation and test set evaluation.The proposed model showed improved performance compared with competitive models in terms of overall specialties. In addition, we demonstrate the usefulness of the proposed model by performing case studies for visualization applications.The proposed model can benefit hospital patient management and reasonable recommendations for specialties for patients.
Proposal-Free Temporal Action Detection via Global Segmentation Mask Learning

Existing temporal action detection (TAD) methods rely on generating an overwhelmingly large number of proposals per video. This leads to complex model designs due to proposal generation and/or per-proposal action instance evaluation and the resultant high computational cost. In this work, for the first time, we propose a proposal-free Temporal Action detection model via Global Segmentation mask (TAGS). Our core idea is to learn a global segmentation mask of each action instance jointly at the full video length. The TAGS model differs significantly from the conventional proposal-based methods by focusing on global temporal representation learning to directly detect local start and end points of action instances without proposals. Further, by modeling TAD holistically rather than locally at the individual proposal level, TAGS needs a much simpler model architecture with lower computational cost. Extensive experiments show that despite its simpler design, TAGS outperforms existing TAD methods, achieving new state-of-the-art performance on two benchmarks. Importantly, it is $$-{20\times }$$ faster to train and $$-{1.6\times }$$ more efficient for inference. Our PyTorch implementation of TAGS is available at https://github.com/sauradip/TAGS .
Data-Driven Deepfake Forensics Model Based on Large-Scale Frequency and Noise Features

With the rapid development of deep learning and communication technology, the application of streaming media services and social software have gone deep into life. However, in the face of many uncertain factors in data dissemination, protecting privacy and security is particularly important. In order to solve the above problems, this study proposes a deep face forgery forensics method with frequency domain and noise features. In this method, DCT (i.e. Discrete Cosine Transform) transform is proposed to perceive the forgery trace features of different frequency bands in the frequency domain. At the same time, the spatial rich model (SRM) is used for guidance to enhance the traces of forged noise. Then, large-scale network and single center loss function are introduced to improve the forensics ability of the model. Experimental results on several databases such as faceforensics++, celeb DF and DFDC show that this method can effectively improve the accuracy of forensics.
NeuMan: Neural Human Radiance Field from a Single Video

Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences. We propose a novel framework to reconstruct the human and the scene that can be rendered with novel human poses and views from just a single in-the-wild video. Given a video captured by a moving camera, we train two NeRF models: a human NeRF model and a scene NeRF model. To train these models, we rely on existing methods to estimate the rough geometry of the human and the scene. Those rough geometry estimates allow us to create a warping field from the observation space to the canonical pose-independent space, where we train the human model in. Our method is able to learn subject specific details, including cloth wrinkles and accessories, from just a 10 s video clip, and to provide high quality renderings of the human under novel poses, from novel views, together with the background. Code will be available at https://github.com/apple/ml-neuman .
Strengthening Supply Chain Visibility With Blockchain: A PRISMA-Based Review

Over the last 5 years, research into the use of blockchain technology in supply chains (SCs) has increased significantly. This is because many firms are faced with the necessity of enhancing SC visibility not just to fulfill statutory obligations, but also to streamline operations, assure the quality of deliverables, and ensure the sustainability of activities. This review article provides a summary of all scientific work published since 2016, with a special emphasis on the business management & accounting and decision science domains, in order to better comprehend the constantly rising knowledge clusters linked with this topic. The prominent contributors and knowledge clusters were identified using suitable bibliometric algorithms on a review corpus of 308 articles extracted from the SCOPUS database. There are five identified knowledge clusters: 1) blockchain for food SC transparency, 2) distributed ledger for sustainable SCs, 3) traceability systems using smart contracts, 4) internet of things for logistics, and 5) the emergence of Ethereum and hyperledger in SCs. This scholarly assessment, in conjunction with PageRank analysis and content mining, assisted in identifying research articles that are prominent in each cluster, which in turn helped in formulating future research avenues for aspiring researchers.
In-depth analysis of blockchain, cryptocurrency and sharia compliance

The high return on cryptocurrency has not only attracted the Muslim investors but it has also provided an opportunity for the Muslim entrepreneurs to raise funds by issuing the online coin. Most of the scholars believe that blockchain technology is consistent with sharia requirements. Still, cryptocurrency is considered controversial and declared haram by most of the sharia scholars. Against such a backdrop, the present study tries to achieve two objectives. The first objective of this study is to investigate the technical architecture of blockchain technology and its application in cryptocurrencies. The second objective of the study is to find out the sharia compliance of the cryptocurrency and online tokens like Bitcoin.
Explaining Deepfake Detection by Analysing Image Matching

This paper aims to interpret how deepfake detection models learn artifact features of images when just supervised by binary labels. To this end, three hypotheses from the perspective of image matching are proposed as follows. 1. Deepfake detection models indicate real/fake images based on visual concepts that are neither source-relevant nor target-relevant, that is, considering such visual concepts as artifact-relevant. 2. Besides the supervision of binary labels, deepfake detection models implicitly learn artifact-relevant visual concepts through the FST-Matching (i.e. the matching fake, source, target images) in the training set. 3. Implicitly learned artifact visual concepts through the FST-Matching in the raw training set are vulnerable to video compression. In experiments, the above hypotheses are verified among various DNNs. Furthermore, based on this understanding, we propose the FST-Matching Deepfake Detection Model to boost the performance of forgery detection on compressed videos. Experiment results show that our method achieves great performance, especially on highly-compressed (e.g. c40) videos.
Artificial Intelligence Based Object Detection and Tracking for a Small Underwater Robot

Object recognition and tracking is a challenge for underwater vehicles. Traditional algorithm requires a clear feature definition, which suffers from uncertainty as the variation of occlusion, illumination, season and viewpoints. A deep learning approach requires a large amount of training data, which suffers from the computation. The proposed method is to avoid the above drawbacks. The Siamese Region Proposal Network tracking algorithm using two weights sharing is applied to track the target in motion. The key point to overcome is the one-shot detection task when the object is unidentified. Various complex and uncertain environment scenarios are applied to evaluate the proposed system via the deep learning model’s predictions metrics (accuracy, precision, recall, P-R curve, F1 score). The tracking rate based on Siamese Region Proposal Network Algorithm is up to 180 FPS.
Image super-resolution reconstruction based on multi-scale dual-attention

Image super-resolution reconstruction is one of the methods to improve resolution by learning the inherent features and attributes of images. However, the existing super-resolution models have some problems, such as missing details, distorted natural texture, blurred details and too smooth after image reconstruction. To solve the above problems, this paper proposes a Multi-scale Dual-Attention based Residual Dense Generative Adversarial Network (MARDGAN), which uses multi-branch paths to extract image features and obtain multi-scale feature information. This paper also designs the channel and spatial attention block (CSAB), which is combined with the enhanced residual dense block (ERDB) to extract multi-level depth feature information and enhance feature reuse. In addition, the multi-scale feature information extracted under the three-branch path is fused with global features, and sub-pixel convolution is used to restore the high-resolution image. The experimental results show that the objective evaluation index of MARDGAN on multiple benchmark datasets is higher than other methods, and the subjective visual effect is better. This model can effectively use the original image information to restore the super-resolution image with clearer details and stronger authenticity.
HFedMS: Heterogeneous Federated Learning with Memorable Data Semantics in Industrial Metaverse

Federated Learning (FL), as a rapidly evolving privacy-preserving collaborative machine learning paradigm, is a promising approach to enable edge intelligence in the emerging Industrial Metaverse. Even though many successful use cases have proved the feasibility of FL in theory, in the industrial practice of Metaverse, the problems of non-independent and identically distributed (non-i.i.d.) data, learning forgetting caused by streaming industrial data, and scarce communication bandwidth remain key barriers to realize practical FL. Facing the above three challenges simultaneously, this paper presents a high-performance and efficient system named <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFedMS</small> for incorporating practical FL into Industrial Metaverse. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFedMS</small> reduces data heterogeneity through dynamic grouping and training mode conversion ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic Sequential-to-Parallel Training, STP</i> ). Then, it compensates for the forgotten knowledge by fusing compressed historical data semantics and calibrates classifier parameters ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Semantic Compression and Compensation, SCC</i> ). Finally, the network parameters of the feature extractor and classifier are synchronized in different frequencies ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Layer-wise Alternative Synchronization Protocol, LASP</i> ) to reduce communication costs. These techniques make FL more adaptable to the heterogeneous streaming data continuously generated by industrial equipment, and are also more efficient in communication than traditional methods (e.g., Federated Averaging). Extensive experiments have been conducted on the streamed non-i.i.d. FEMNIST dataset using 368 simulated devices. Numerical results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">HFedMS</small> improves the classification accuracy by at least 6.4% compared with 8 benchmarks and saves both the overall runtime and transfer bytes by up to 98%, proving its superiority in precision and efficiency.
Proposed artificial intelligence algorithm and deep learning techniques for development of higher education

Artificial intelligence (AI) has been increasingly impacting various aspects of our daily lives, including education. With the rise of digital technologies, higher education has also been experiencing a transformation, and AI has been playing a crucial role in this transformation. The application of AI in higher education has been rapidly increasing, with a focus on improving student engagement, increasing efficiency, and enhancing the learning experience. The use of AI in higher education is not without its challenges and ethical considerations. One of the biggest challenges is ensuring the accuracy and fairness of AI algorithms, as well as avoiding potential biases. In addition, there are concerns about the privacy of student data, as well as the potential for AI to replace human instructors and support staff. Another challenge is ensuring that AI is used in a way that supports the overall goals of higher education, such as promoting critical thinking and creativity, rather than just being used as a tool for automating tasks and increasing efficiency. In this article, we will discuss the various ways in which AI is being applied in higher education where a proposed model for improving the cognitive capability of students is proposed and compared to other existing algorithms. It will be shown that the proposed model shows better performance compared to other models.
Perceived anthropomorphism and purchase intention using artificial intelligence technology: examining the moderated effect of trust

Purpose This study explores features that impact consumers' purchase intention through artificial intelligence (AI), because it is believed that through artificial intelligence, consumers' intention to purchase grows significantly, especially in the retail sector, whereby retailers provide lucrative offers to motivate consumers. The study develops a theoretical framework based on media-richness theory to investigate the role of perceived anthropomorphism toward an intention to purchase products using AI. Design/methodology/approach The study is based on cross-sectional data through an online survey. The data have been analyzed using PLS-SEM and SPSS PROCESS macro. Findings The results show that consumers tend to demand anthropomorphized products to gain a better shopping experience and, therefore, demand features that attract and motivate them to purchase through artificial intelligence via mediating variables, such as perceived animacy and perceived intelligence. Moreover, trust in artificial intelligence moderates the relationship between perceived anthropomorphism and perceived animacy. Originality/value The study investigates and concludes with managerial and academic insights into consumer purchase intention through artificial intelligence in the retail and marketing sector.
ARTIFICIAL INTELLIGENCE AND EDUCATION 4.0

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 6309-6317ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1670Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
On the privacy and security for e-health services in the metaverse: An overview

Metaverse-enabled healthcare systems are expected to efficiently utilize an unprecedented amount of health-related data without disclosing sensitive or private information of individuals during data collection, communications, and learning-based processing. In this paper, we study the privacy and security of the metaverse healthcare from different aspects. Specifically, we first investigate the security of data collection and communications in the access layer of the metaverse. We then go through the privacy and security issues inside the metaverse, by addressing the privacy and security threats of utilizing clinical machine learning for intelligent e-health. From a human-centric perspective, privacy of social interactions among patients in a metaverse-enabled healthcare platform is also studied. We try to provide a holistic and less-investigated approach to help metaverse-as-a-health service providers facilitate the realization of secure and private e-health services from different aspects, ranging from the access layer to the social interactions among clients. Future vision and directions are also discussed to bring further insights for the network designers in the metaverse era.
A modified PKM environment for the security enhancement of IEEE 802.16e

IEEE 802.16 standard developed Privacy Key Management (PKM) security protocol for WiMAX. However, it still suffers from various attacks such as forgery attack, replay attack, Denial of Service (DoS) attack and eavesdropping attack. To mitigate these attacks, a modified PKM environment has been developed for the security enhancement of IEEE 802.16e. This system integrates multiple techniques like Random Number Generator (RNG), Rivest, Shamir and Adelman (RSA), Diffie–Hellman (DH) algorithm, Data Connection Core (DCC) and Salsa20 stream cipher to improve the end to end security of the WiMAX environment. The random numbers have been used as the inputs to the Diffie–Hellman Public Key Distribution System (DH–PKDS) to provide the public keys and common secret keys to it. The DCC contains RSA triple keys, which prevents the system from different attacks. Employment of RSA and DH algorithm in the proposed system leads to powerful data connection for authentication and enhanced key exchange flow respectively. The presence of TEK 3 way handshake in the TEK exchanges process prevents the system from different attacks. Stream cipher Salsa20 makes the system more secure and faster during data transmission process. The proposed system has been compared with standard WiMAX and existing work with respect to different security attributes. Moreover, the performance of the proposed system has been compared with Mutual Authentication with Dynamic Keys (MAWDK) in terms of key generation time, encryption and decryption time. Finally, the computational cost, total computational time and time complexity of the proposed system has been compared with existing systems.
A Water Surface Cleaning Robot

Abstract This paper describes the design of a robot for cleaning rubbish floating on the water surface. Three important issues for designing the aquatic robots are a cost-effective solution along with robustness and durability. Due to the nature of the cleaning work, we designed the vehicle structure that can provide high stability, good ability in maneuver and can easily collect all the waste flowing in between. A pontoon shaped hull works best for this case and fulfils all the hydrostatic, structural stability criteria. For removal and collection of surface waste, a motor-driven collecting-arm system has been designed for collecting the wastes and redeploying it into a rectangular basket on the hull. This design provides simple and effective waste removal and accommodates large amounts of waste within a small space. For the prototype, the hulls were made up of styrofoam which is wrapped by fibre and then coated with waterproof and resin. It supported together by aluminum. This light and tough structure support the total weight of the system. The propulsion system based on a differential drive mechanism has been designed, which allows the robots to take a 360 turn on the spot and provides high thrust. Electronic circuit and motors have been placing inside the hull, in order to protect them from water. The robot is manually controlled by remote control based on Xbee Pro wireless modules. The testing of the robot prototype proved to be effective in waste collecting and removal. The maximum trash loads that robot can bear is up 16 kg.