Reviewerly/exHarmony huggingface dataset:
Transformer-based personalized attention mechanism for medical images with clinical records

In medical image diagnosis, identifying the attention region, i.e., the region of interest for which the diagnosis is made, is an important task. Various methods have been developed to automatically identify target regions from given medical images. However, in actual medical practice, the diagnosis is made based on both the images and various clinical records. Consequently, pathologists examine medical images with prior knowledge of the patients and the attention regions may change depending on the clinical records. In this study, we propose a method, called the Personalized Attention Mechanism (PersAM) method, by which the attention regions in medical images according to the clinical records. The primary idea underlying the PersAM method is the encoding of the relationships between medical images and clinical records using a variant of the Transformer architecture. To demonstrate the effectiveness of the PersAM method, we applied it to a large-scale digital pathology problem involving identifying the subtypes of 842 malignant lymphoma patients based on their gigapixel whole-slide images and clinical records.
Exploring public cybercrime prevention campaigns and victimization of businesses: A Bayesian model averaging approach

Cybercrime is a pressing concern for governments and businesses around the globe, but little is known about what policy interventions work to prevent and mitigate threats to organizations. Thus, empirical studies on cybercrime prevention policies and tools are needed to understand their effectiveness and to improve implementation and evaluation. This article analyzes whether two UK government schemes aimed at encouraging and helping businesses to adopt cybersecurity controls and policies ('Cyber Essentials’ and ‘10 Steps to Cyber Security’) are associated with safer organizational behavior and whether adopting the recommended measures is related to lower levels of cybercrime victimization and its impacts. Bayesian model averaging is employed on a representative sample of 5,872 businesses from four rounds (2018–2021) of the UK Government's Cyber Security Breaches Survey. The results show that awareness of the Government schemes is associated with more cyber secure practices, but we do not find evidence of lower likelihood of victimization or negative consequences for companies that implement the recommended measures. Findings are discussed in relation to policy, practice and future research.
Heart failure disease prediction and stratification with temporal electronic health records data using patient representation

Accurate early prediction of heart failure and identification of heart failure sub-phenotypes can enable in-time interventions and treatments, assist with policy decisions, and lead to a better understanding of disease pathophysiology in groups of patients. However, decision making more challenging for clinicians since the available data is complex, heterogeneous, temporal, and different in granularity. Even with much data, it is difficult for a cardiologist to pre-judge a patient's heart condition at the next visit by relying on data from only one visit. Moreover, complicated and overloaded information bewilders clinicians, bringing obstacles to the stratification of patients and the mining of disease typical patterns in subgroups. To overcome these issues, this study proposes a novel Patient Representation model based on a temporal Bidirectional neural network with an Attention mechanism deep learning model called tBNA-PR. tBNA-PR effectively models heterogeneous and temporal Electronic Health Records (tEHRs) data from past and future directions to obtain informative patient representation to realize accurate heart failure prediction and reasonable patient stratification. Additionally, this study extracts typical diagnosis and prescriptions for disease patterns exploration and identifies significant features of sub-phenotypes for subgroup explanation in the context of complex clinical settings to provide better quality healthcare services and clinical decision support. This study leverages a real-world dataset MIMIC-III database. We carried out experiments on the prediction of heart failure to investigate tBNA-PR, which obtains prediction accuracy of 0.78, F1-Score of 0.7671, and AUC of 0.7198, showing a certain superiority compared with several state-of-the-art benchmarks. Moreover, we identified three distinct sub-phenotypes in all heart failure patients in the dataset with the clustering method and subgroup analysis. Sub-phenotype I has characteristics of more long-term anticoagulants. This sub-group has more patients who have the thrombotic disease. Sub-phenotype II has features of more patients having kidney disease, pneumonia, urinary tract infection, and coronary heart disease surgery history. Sub-phenotype III has characteristics of more patients having acidosis, depressive disorder, esophageal reflux, obstructive sleep apnea, and acquired hypothyroidism. Statistical tests show that the features, including age, creatinine, hemoglobin, urea nitrogen, and blood potassium, are significantly different among the three sub-phenotypes and have particular high importance. The resultant findings from this work have practical implications for clinical decision support.
Software design pattern selection approaches: A systematic literature review

Abstract Software design patterns have a considerable impact on the software development life cycle. Design pattern (DP) is a reliable and reusable solution provided by software experts to obtain quality software design. However, due to the large number of design patterns, selecting the appropriate one is quite difficult. To overcome this difficulty, several approaches with different methods have been presented to suggest the appropriate DP. Despite conducting a number of studies that have explored some aspects of this field, such as design pattern selection tools and techniques, there is a need for a deeper understanding, analysis, classification, and thorough review of the design pattern selection process. So far, no systematic review of design pattern selection approaches is available. This paper aims to classify existing approaches, provide several criteria for comparing approaches, analyze each one, and identify and analyze the most important elements in this field, including open issues, data sets, and so on. The present investigation paper will help future research to employ the existing approaches taking into account the specification of each one and it also raises awareness about the approaches used in previous research and their potential limitations.
Hardware Acceleration of Deep Neural Networks for Autonomous Driving on FPGA-based SoC

In the last decade, enormous and renewed attention to Artificial Intelligence has emerged thanks to Deep Neural Networks (DNNs), which can achieve high performance in performing specific tasks at the cost of a high computational complexity. GPUs are commonly used to accelerate DNNs, but generally determine a very high power consumption and poor time predictability. For this reason, GPUs are becoming less attractive for resource-constrained, real-time systems, while there is a growing demand for specialized hardware accelerators that can better fit the requirements of embedded systems. Following this trend, this paper focuses on hardware acceleration for the DNNs used by Baidu Apollo, an open-source autonomous driving framework. As an experience report of performing R&D with industrial technologies, we discuss challenges faced in shifting from GPU-based to FPGA-based DNN acceleration when per-formed using the DPU core by Xilinx deployed on an Ultrascale+ SoC FPG A platform. Furthermore, it shows pros and cons of today's hardware accelerating tools. Experimental evaluations were conducted to evaluate the performance of FPGA-accelerated DNNs in terms of accuracy, throughput, and power consumption, in comparison with those achieved on embedded GPUs.
Research on Deep Learning-based Cross-disciplinary Applications

Currently, with the development of artificial intelligence, deep learning is widely applied in several disciplines. This paper presents several cross-disciplinary studies on deep learning, including deep learning for preserving ancient cultural heritage, solving social problems, and industry application. Ancient documents contain a great deal of historical information that needs to be preserved. Therefore, the deep learning-based You Only Look Once is applied to the preservation of ancient cursive script and Oracle Bone inscriptions. Moreover, deep learning effectively solves the problem of lack of labor. Deep learning-based robots are used for tableware recycling. The object detection algorithm provides fast recognition. At the same time, the application of deep learning-based image classification algorithms for egg and apple classification. However, deep learning models are becoming more complex. Finally, research on deep learning in high-performance computing with model compression and acceleration, FPGAs, and neural architecture search is discussed.
Seizing technological advancement; determinants of blockchain supply chain finance adoption in Ghanaian SMEs

ABSTRACTABSTRACTBlockchain has attracted attention within business circles because it promotes secure and cost-saving transactions between anonymous parties without human mediation. This study examines the Technological Organizational and Environmental (TOE) drivers of blockchain supply chain finance adoption in Ghanaian SMEs. Specifically, this study differs from prior studies employing the United Theory of Acceptance and Use of Technology (UTAUT) and the Technology Acceptance Model (TAM) which ignores the TOE elements. Utilising the Partial Least Squares Structural Equation Modelling method (PLS-SEM) and nonlinear non-compensatory PLS-ANN approach, we provide empirical evidence using data from 214 registered SME members of the Association of Ghana Industries (AGI). Our findings suggest that relative advantage, cost, and complexity significantly influence the adoption of blockchain supply chain finance in SMEs. However, competitive pressure, market dynamics, and SME owner/manager support had no statistically significant influence on the intention of SMEs to adopt blockchain supply chain finance. This is because of the relatively low adoption rate of blockchain applications in SMEs in the country. Therefore, extensive education on the applications of blockchain technology is required to stimulate diffusion amongst SMEs.KEYWORDS: Blockchain technologysupply chain financeTOESMEs AcknowledgmentsWe are very grateful to the editors Prof. James Fleck, Lyrez Lopez, and all reviewers. We also thank Mrs. Millicent Adu-Damoah for her support.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research is supported by The Science & Technology Department of Sichuan Province Technology Cooperation Project & Technology Department of Sichuan Province Technology Cooperation Project [grant number 2019YFH0004].Notes on contributorsElijah Asante BoakyeElijah Asante Boakye is a Ph.D. candidate at the School of Management and Economics (University of Electronic Science and Technology of China). His current research is on blockchain technology, fintech, entrepreneurial finance and small business development.Hongjiang ZhaoZhao Hongjiang is an Associate Professor at the School of Management and Economics (University of Electronic Science and Technology of China). His specialty is generalised virtual economies, entrepreneurial financial systems, and fintech.Cephas Paa Kwasi CoffieCephas Paa Kwasi Coffie is a lecturer at the Melbourne Polytechnic - Fuzhou. His research interest focuses on fintech, econometric modelling, and small business finance.Lydia Asare-KyireLydia Asare-Kyire is a Lecturer at the Kumasi Technical University, Ghana. Her current research is focused on entrepreneurship and small business development.
Simultaneous Laplacian embedding and subspace clustering for incomplete multi-view data

In many real multi-view data, some views often lose some information, resulting in incomplete views. It is a challenging task to extract and fuse valuable information from the multi-view data with incomplete views for improving clustering performance. Incomplete multi-view clustering (IMC) is designed to fully mine patterns in data to reduce the negative impact of missing views. Many previous IMC methods are committed to discovers a consensus representation shared by all views. Nevertheless, this representation could severely deviate from the inherent representation of original data due to the loss of information caused by incomplete views. Besides, most existing spectral clustering-based multi-view subspace clustering independently performs similarity graph learning, Laplacian embedding, and discrete indicator matrix learning. This multi-step strategy might result in sharp performance degradation. In this paper, we propose a novel IMC method, referred to as Simultaneous Laplacian Embedding and Subspace Clustering (SLESC), to address the above issues. Specifically, in the paradigm for data self-representation, the proposed SLESC method learns a similarity graph for each view instead of learning a consensus graph for invoking traditional spectral clustering. Similarity graph learning, Laplacian embedding learning, weighting for each view, and discrete indicator matrix learning are seamlessly incorporated into the unified framework. The joint optimal clustering outcomes are therefore possible. Experimental results on real-world datasets in the IMC task demonstrate the effectiveness of the proposed method compared with state-of-the-art baselines.
An Enhanced Phishing Detection Tool Using Deep Learning From URL

Cybersecurity and phishing attacks become critical and important for the global economy because there are many areas and businesses that rely mainly on computer and Internet systems. With the dramatic increase of phishing attacks, user awareness and support remain imperative at individual and organizational levels. In this paper, an enhanced intelligent phishing detection tool is developed using deep learning from URLs. The system scans webpages and uses deep learning techniques to identify potentially harmful phishing content. The software is also supported with a blacklist of websites and some APIs for the reduction of time consumption as possible using a large dataset to train and test the system. The system is found promising and can detect phishing attacks with high and accurate rates.
Inferencing Big Data with Artificial Intelligence &amp; Machine Learning Models in Metaverse

This quantitaive study provides different methods of visualization for processing Big Data sets in augmented and virtual reality. The goal is to provide the detailed implementation of the statistical methods and modeling techniques i.e. using machine learning algorithms and artificial intelligence. The Statistical analysis is performed on the Big Data sets in Metaverse, that points towards real time inferencing infrastructure and techniques. Also, This paper elaborates the importance of pervasive and Ubiquitous Computing Architecture and Applications that enable High-end computing on Big Data sets. In this paper, we evaluate the need for a combination of cognitive mechanism and high-end infrastructure to address the performance, latency, and security issues when working on Big Data sets in environments like virtual or Augmented reality. Despite the modernization and advancement in technology there are increasing number of breaches in cloud and hybrid infrastructures, this justifies the need to meet the requirements of strengthening security, safeguard mechanisms and privacy. This paper discusses methods beyond basic visualizations through a deep dive exploration on improvising the applications of existing analytical methods, and use of advanced exploratory tools and visualization techniques on the next generation platforms.
SAILFISH-I: A Lightweight Block Cipher for Cloud-Enabled Fog Devices

The modern era of computation claims high-speed data transmission, greater performance, and maximizing efficiency using fog devices, faster internet, and decentralized technology. According to the report published by Statista, the number of IoT Devices is expected to reach 29.4 billion in 2030. The devices are liable to a significant number of new vulnerabilities rapidly. One of the best approaches to secure the following devices is to implement cryptographic algorithms. However, it turns out that such algorithms are not ideal for Fog-based IoT devices with their current configuration and to provide high-speed performance. In this research, we propose a new lightweight cryptographic algorithm, SAILFISH-I, based on a conventional Horst Feistel Network, which will gain popularity to meet the requirements of Fog Devices. SAILFISH-I is designed to work on fewer resources with low power and high speed to provide good latency during the transmission and storage of data in a decentralized environment and cloud-based infrastructure. Our experimental results demonstrate that SAILFISH-I has high efficiency and throughput, immune to attacks such as linear, differential, and side-channel attacks to achieve optimum performance compared with today's major cryptographic algorithms.
DA-SLAM: Deep Active SLAM based on Deep Reinforcement Learning

This work presents improvements to the state-of-the-art algorithms for path planning and exploration of unknown and complex environments using Deep Reinforcement Learning. Our novel approach takes into consideration: (i) map information, built online by the robot using a Simultaneous Localization and Mapping algorithm and (ii) uncertainty of the robot's pose, which leads to active loop-closing to encourage exploration and better map generation within two agents. The results show that the map completeness-based reward function outperforms literature's results on shorter trajectories, thus, better performance; while uncertainty-based with loop-closing reward function improves map generation. Both agents showed the ability, to perform Active SLAM over complex environments and generalization to unseen maps capabilities.
PipeEdge: Pipeline Parallelism for Large-Scale Model Inference on Heterogeneous Edge Devices

Deep neural networks with large model sizes achieve state-of-the-art results for tasks in computer vision and natural language processing. However, such models are too compute- or memory-intensive for resource-constrained edge devices. Prior works on parallel and distributed execution primarily focus on training-rather than inference-using homogeneous accelerators in data centers. We propose PipeEdge, a distributed framework for edge systems that uses pipeline parallelism to both speed up inference and enable running larger, more accurate models that otherwise cannot fit on single edge devices. PipeEdge uses an optimal partition strategy that considers heterogeneity in compute, memory, and network bandwidth. Our empirical evaluation demonstrates that PipeEdge achieves 11.88× and 12.78× speedup using 16 edge devices for the ViT-Huge and BERT-Large models, respectively, with no accuracy loss. Similarly, PipeEdge improves throughput for ViT-Huge (which cannot fit in a single device) by 3.93× over a 4-device baseline using 16 edge devices. Finally, we show up to 4.16× throughput improvement over the state-of-the-art PipeDream when using a heterogeneous set of devices.
A Chatbot Intent Classifier for Supporting High School Students

INTRODUCTION: An intent classification is a challenged task in Natural Language Processing (NLP) as we are asking the machine to understand our language by categorizing the users’ requests. As a result, the intent classification plays an essential role in having a chatbot conversation that understand students’ requests.&#x0D; OBJECTIVES: In this study, we developed a novel chatbot called “HSchatbot” for predicting the intent classifications from high school students’ enquiries. Evidently, students in high schools are the most concerned among all students about their future; thus, in this stage they need an instant support in order to prepare them to take the right decision for their career choice. &#x0D; METHODS: The authors in this study used the Multinomial Naive-Bayes and Random Forest classifiers for predicting the students’ enquiries, which in turn improved the performance of the classifiers by using the feature’s extractions.&#x0D; RESULTS: The results show that the random forest classifier performed better than Multinomial Naive-Bayes since the performance of this model is checked by using different metrics like accuracy, precision, recall and F1 score. Moreover, all showed high accuracy scores exceeding 90% in all metrics. However, the accuracy of Multinomial Naive-Bayes classifier performed much better when using CountVectorizers compared to using the TF-IDF.&#x0D; CONCLUSION: In the future work, the results will be analysed and investigated in order to figure out the main factors that affect the performance of Multinomial Naive-Bayes classifier, as well as evaluating the model with using a large corpus of students’ questions and enquiries.
A Bounded Near-Bottom Cruise Trajectory Planning Algorithm for Underwater Vehicles

The trajectory planning algorithm of underwater vehicle near-bottom cruise is important to scientific investigation, industrial inspection, and military affairs. An autonomous underwater vehicle (AUV) often faces the problems of complex underwater environment and large cruise area in a real environment, and some robots must hide themselves during the cruise. However, to the best of our knowledge, few studies have focused on trajectory planning algorithms for AUVs with multiple constraints on large-scale maps. The currently used algorithms are not effective at solving length-constraint problems, and the mainstream trajectory planning algorithms for robots cannot be directly applied to the needs of underwater vehicle sailing near the bottom. Therefore, we present a bounded ridge-based trajectory planning algorithm (RA*) for an AUV to go on a near-bottom cruise. In the algorithm, we design a safety map based on a spherical structure to ensure the safe operation of the robot. In addressing the length-constraint problem and large-scale map planning problem, this paper proposes a two-stage framework for RA*, which designs map compression and parallel computation using a coarse-fine planning framework to solve the large-scale trajectory planning problem and uses a bounded search method to meet the trajectory planning requirements of length constraint. In this study, experiments based on the virtual ocean ridge are conducted, and the results validate the effectiveness and efficiency of the proposed RA* with MCPC algorithm framework.