Reviewerly/exHarmony huggingface dataset:
Comparison of Yolo, SSD, Faster RCNN for Real Time Tennis Ball Tracking for Action Decision Networks

This paper describes a systemic approach that analyses tennis videos to estimate its trajectory when the ball is tossed by the player. This system will reconstruct the trajectory of the ball by combining various image processing techniques to interpret the video frames using Action Decision networks. The project estimates the ball location using multiple-view geometry and state estimation filtering. Image processing concepts like image segmentation, morphological image processing are employed. We will perform the project using three different algorithms namely YOLO, SSD and Faster RCNN. A comparison is done using the three different algorithms and the performance of the different algorithms will be determined for the detection of a tennis ball. Software has been developed to compare the algorithms and to find the algorithm that is more efficient and has less computational power.
The impact of knowledge management practice on digital financial innovation: the role of bank managers

Purpose This paper aims to examine the impact of knowledge management functions (acquisition, integration and utilization) on digital financial innovation through the moderating role of managers’ demographic characteristics (age, sex, education, experience and position) in commercial banks operating in Lebanon. Design/methodology/approach To accomplish this aim, a conceptual framework based on knowledge-based theory and literature review was developed the data for this research was collected through a self-administered questionnaire with 181 respondents. The target respondents were managers of commercial banks in Lebanon. Findings The empirical findings of the study suggest that the practice of knowledge management functions practice has a positive and significant relationship with digital financial innovation. The findings also provide support for the moderating effect of only two demographic characteristics of bank managers; experience and position on the relationship between knowledge management and digital financial innovation in commercial banks in Lebanon. Practical implications The managers of commercial banks in Lebanon and similar countries could use the study findings to better understand the practices of knowledge management in the banks and also the skills acquired or existing in the individuals working in their organizations and is also helpful to enhance their level of digital financial innovations. Originality/value This study has investigated the unexplored impact of knowledge management on digital financial innovation via moderating role of managers' demographic characteristics among commercial banks operating in Lebanon as a developing country.
Cyber-Risk in Healthcare: Exploring Facilitators and Barriers to Secure Behaviour

There are increasing concerns relating to cybersecurity of healthcare data and medical devices. Cybersecurity in this sector is particularly important given the criticality of healthcare systems, the impacts of a breach or cyberattack (including in the worst instance, potential physical harm to patients) and the value of healthcare data to criminals. Technology design is important for cybersecurity, but it is also necessary to understand the insecure behaviours prevalent within healthcare. It is vital to identify the drivers behind these behaviours, i.e., why staff may engage in insecure behaviour including their goals and motivations and/or perceived barriers preventing secure behaviour. To achieve this, in-depth interviews with 50 staff were conducted at three healthcare sites, across three countries (Ireland, Italy and Greece). A range of seven insecure behaviours were reported: Poor computer and user account security; Unsafe e-mail use; Use of USBs and personal devices; Remote access and home working; Lack of encryption, backups and updates; Use of connected medical devices; and poor physical security. Thematic analysis revealed four key facilitators of insecure behaviour: Lack of awareness and experience, Shadow working processes, Behaviour prioritisation and Environmental appropriateness. The findings suggest three key barriers to security: i) Security perceived as a barrier to productivity and/or patient care; ii) Poor awareness of consequences of behaviour; and iii) a lack of policies and reinforcement of secure behaviour. Implications for future research are presented.
Spatial scale effects on retrieval accuracy of surface solar radiation using satellite data

The presence of nonhomogeneous clouds and their induced radiation interactions result in significant horizontal photon transport and spatially adjacent effects on surface solar radiation (SSR), making spatial estimation scale-dependent. Overlooking scale effects during SSR retrieval from satellite data is responsible for variations in retrieval accuracy and deviations in associated applications. In this paper, the spatial scale effects on SSR retrieval accuracy are investigated using multivariate linear regression and artificial neural network and convolutional neural network models. Scale effects are quantified through changes in retrieval accuracy under varying satellite data input size and compared among different models to reveal the merits and defects of classic linear, ordinary nonlinear, and spatially nonlinear models. The results show that scale effects have considerable impacts on retrieval accuracy in each of the three models for both site-specific and general conditions. The maximum improvement in terms of the root mean square error can reach up to 9% after involving scale information. The performance of site-specific models is continually enhanced with the expansion of spatial scale, while that of general models will drop to some extent beyond a particular threshold. Approximate distances of 20 km and 40 km from the central location are identified as the optimal scale for artificial neural and convolutional neural networks, respectively. This study also concludes that the robustness of general models is relevant to various atmospheric factors, providing perspectives for further improvements including the fusion of time series images, integration with physical modules, and the combination of multi-resolution data.
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity

We address the task of unsupervised Semantic Textual Similarity (STS) by ensembling diverse pre-trained sentence encoders into sentence meta-embeddings.We apply, extend and evaluate different meta-embedding methods from the word embedding literature at the sentence level, including dimensionality reduction (Yin and Schütze, 2016), generalized Canonical Correlation Analysis (Rastogi et al., 2015) and cross-view auto-encoders (Bollegala and Bao, 2018).Our sentence metaembeddings set a new unsupervised State of The Art (SoTA) on the STS Benchmark and on the STS12-STS16 datasets, with gains of between 3.7% and 6.4% Pearson's r over singlesource systems.
One-Shot Neural Architecture Search via Novelty Driven Sampling

One-Shot Neural architecture search (NAS) has received wide attentions due to its computational efficiency. Most state-of-the-art One-Shot NAS methods use the validation accuracy based on inheriting weights from the supernet as the stepping stone to search for the best performing architecture, adopting a bilevel optimization pattern with assuming this validation accuracy approximates to the test accuracy after re-training. However, recent works have found that there is no positive correlation between the above validation accuracy and test accuracy for these One-Shot NAS methods, and this reward based sampling for supernet training also entails the rich-get-richer problem. To handle this deceptive problem, this paper presents a new approach, Efficient Novelty-driven Neural Architecture Search, to sample the most abnormal architecture to train the supernet. Specifically, a single-path supernet is adopted, and only the weights of a single architecture sampled by our novelty search are optimized in each step to reduce the memory demand greatly. Experiments demonstrate the effectiveness and efficiency of our novelty search based architecture sampling method.
High-Performance Analog Front-End (AFE) for EOG Systems

Electrooculography is a technique for measuring the corneo-retinal standing potential of the human eye. The resulting signal is called the electrooculogram (EOG). The primary applications are in ophthalmological diagnosis and in recording eye movements to develop simple human–machine interfaces (HCI). The electronic circuits for EOG signal conditioning are well known in the field of electronic instrumentation; however, the specific characteristics of the EOG signal make a careful electronic design necessary. This work is devoted to presenting the most important issues related to the design of an EOG analog front-end (AFE). In this respect, it is essential to analyze the possible sources of noise, interference, and motion artifacts and how to minimize their effects. Considering these issues, the complete design of an AFE for EOG systems is reported in this work.
Strategizing sustainability in the banking industry using fuzzy cognitive maps and system dynamics

Sustainable banking is an issue that has been growing in importance around the world since the 2008 global crisis. Because the banking system is a major economic and financial intermediary, it has a significant impact on diverse stakeholders. In this context, understanding the determinants of long-term survival of the banking industry appears to be important to ensuring sustainable banking. Based on a constructivist stance, this study sought to identify sustainability strategies in the banking industry through a combination of fuzzy cognitive mapping (FCM) and the system dynamics (SD) approach. The main goal is to develop an easily understood model of sustainable banking to help decision makers analyze the dynamics of the cause-and-effect relationships between determinants of sustainable banking and project long-term scenarios. The insights obtained were derived from two group sessions with a panel of decision makers who deal with banking processes on a daily basis and were validated by the Director of the technical unit for sustainability of Millennium BCP, one of the largest banking corporations in Portugal. Static and dynamic analyses were also carried out, the results of which support the study’s main conclusions and reinforce the relevance of the proposed dual methodology.
Training and Testing of a Single-Layer LSTM Network for Near-Future Solar Forecasting

Increasing integration of renewable energy sources, like solar photovoltaic (PV), necessitates the development of power forecasting tools to predict power fluctuations caused by weather. With trustworthy and accurate solar power forecasting models, grid operators could easily determine when other dispatchable sources of backup power may be needed to account for fluctuations in PV power plants. Additionally, PV customers and designers would feel secure knowing how much energy to expect from their PV systems on an hourly, daily, monthly, or yearly basis. The PROGNOSIS project, based at the Cyprus University of Technology, is developing a tool for intra-hour solar irradiance forecasting. This article presents the design, training, and testing of a single-layer long-short-term-memory (LSTM) artificial neural network for intra-hour power forecasting of a single PV system in Cyprus. Four years of PV data were used for training and testing the model (80% for training and 20% for testing). With a normalized root mean squared error (nRMSE) of 10.7%, the single-layer network performed similarly to a more complex 5-layer LSTM network trained and tested using the same data. Overall, these results suggest that simple LSTM networks can be just as effective as more complicated ones.
Extended Reality Technologies in Nutrition Education and Behavior: Comprehensive Scoping Review and Future Directions

The use of Extended Reality (XR) (i.e. Virtual and Augmented Reality) for nutrition education and behavior change has not been comprehensively reviewed. This paper presents findings from a scoping review of current published research. Articles (n = 92) were extracted from PubMed and Scopus using a structured search strategy and selection approach. Pertinent study information was extracted using a standardized data collection form. Each article was independently reviewed and coded by two members of the research team, who then met to resolve any coding discrepancies. There is an increasing trend in publication in this area, mostly regarding Virtual Reality. Most studies used developmental testing in a lab setting, employed descriptive or observational methods, and focused on momentary behavior change like food selection rather than education. The growth and diversity of XR studies suggest the potential of this approach. There is a need and opportunity for more XR technology focused on children and other foundational theoretical determinants of behavior change to be addressed within nutrition education. Our findings suggest that XR technology is a burgeoning approach in the field of nutrition, but important gaps remain, including inadequate methodological rigor, community application, and assessment of the impact on dietary behaviors.
Automating the Development of Machine Skills and their Semantic Description

In order to approach the vision of quickly integrating new machines and their functionalities into a production plant, machines have to provide a machine-readable description of themselves and their functionalities. With such a description, it becomes possible to find required functions, check compatibility and, finally, execute production processes. In recent years, semantic web technologies have proven to be a promising enabler to realize such descriptions in the form of ontologies. But the creation of such an ontological description is an additional, tedious and error-prone task for a machine developer who might most likely not be an expert in semantic web technologies. In order to support developers in implementing machine functionalities as semantically described skills, we developed a method that highly automates all additional ontology-related tasks. The presented method makes use of information already contained in engineering artifacts, helps in adding additional information, and provides a framework to implement skill behavior. By using the proposed method, machine developers are put in a position to develop formal capability models themselves and with little additional effort.
The Phishing Email Suspicion Test (PEST) a lab-based task for evaluating the cognitive mechanisms of phishing detection

Phishing emails constitute a major problem, linked to fraud and exploitation as well as subsequent negative health outcomes including depression and suicide. Because of their sheer volume, and because phishing emails are designed to deceive, purely technological solutions can only go so far, leaving human judgment as the last line of defense. However, because it is difficult to phish people in the lab, little is known about the cognitive and neural mechanisms underlying phishing susceptibility. There is therefore a critical need to develop an ecologically valid lab-based measure of phishing susceptibility that will allow evaluation of the cognitive mechanisms involved in phishing detection. Here we present such a measure based on a task, the Phishing Email Suspicion Test (PEST), and a cognitive model to quantify behavior. In PEST, participants rate a series of phishing and non-phishing emails according to their level of suspicion. By comparing suspicion scores for each email to its real-world efficacy, we find initial support for the ecological validity of PEST - phishing emails that were more effective in the real world were more effective at deceiving people in the lab. In the proposed computational model, we quantify behavior in terms of participants' overall level of suspicion of emails, their ability to distinguish phishing from non-phishing emails, and the extent to which emails from the recent past bias their current decision. Together, our task and model provide a framework for studying the cognitive neuroscience of phishing detection.
Diagonal-kernel convolutional neural networks for image classification

The recognition performance of convolutional neural networks has surpassed that of humans in many computer vision areas. However, there is a large number of parameter redundancy in deep neural networks, especially the weights of the convolutional kernels. In this work, we propose a simple Diagonal-kernel, in which a standard square kernel is replaced by a diagonal kernel and an anti-diagonal kernel. Diagonal-kernels with fewer parameters can have similar or larger local receptive fields than square kernels. The performance of the Diagonal-kernel is firstly evaluated on two benchmark image classification datasets, CIFAR, and ImageNet. The experimental results indicate that the Diagonal-kernel can effectively reduce parameters and computational cost while maintaining high accuracy. Furthermore, compared with Vector-kernel, Diagonal-kernel has larger local receptive fields and is more efficient. Then, we test the Diagonal-kernel for fine-grained image and imbalanced image dataset. The results show that Diagonal-kernel has larger accuracy loss for fine-grained than the coarse-grain image, but the loss is tolerable. The imbalanced data does not influence the performance of the Diagonal-kernel. The proposed Diagonal-kernel is mainly for traditional convolution but not for depthwise convolution because the number of weights for deep convolution is very small.
Sensor Fusion of Motion-Based Sign Language Interpretation with Deep Learning

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.
HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments

In major Information Technology (IT) companies such as Google, Rackspace and Amazon Web Services (AWS), virtualisation and containerisation technologies are usually used to execute customers' workloads and applications. The computational resources are provided through large-scale datacenters, which consume substantial amount of energy and have, therefore, ecological impacts. Since long, Google runs users' applications in containers, Rackspace offers bare-metal hardware, whereas AWS runs them either in VMs (EC2), containers (ECS) and/or containers inside VMs (Lambda); therefore, making resource management a tedious activity. The role of a resource management system is of the greatest importance, principally, if IT companies practice various kinds of sand-boxing technologies, for instance, bare-metal, VMs, containers, and/or nested containers in their datacenters (hybrid platforms). The absence of centralised, workload-aware resource managers and consolidation policies produces questions on datacenters energy efficiency, workloads performance, and users' costs. In this paper, we demonstrate, through several experiments, using the Google workload data for 12,583 hosts and approximately one million tasks that belong to four different kinds of workload, the likelihood of: (i) using workload-aware resource managers in hybrid clouds; (ii) achieving energy and cost savings, in heterogeneous hybrid datacenters such that the workload performance is not affected, negatively; and (iii) how various allocation policies, combined with different migration approaches, will impact on datacenter's energy and performance efficiencies. Using plausible assumptions for hybrid datacenters set-up, our empirical evaluation suggests that, for no migration, a single scheduler is at most 16.86% more energy efficient than distributed schedulers. Moreover, when migrations are considered, our resource manager can save up to 45.61% energy and can improve up to 17.9% workload performance.
Study of threats associated with cloud infrastructure systems

Abstract Cloud infrastructure is the new euphemism in professional developers view these days round the clock. The rewards of cloud storage and its unique functionalities are absolutely massive and as such it is a concern of phenomenal significance. It offers various outstanding features such as Multi-tenancy, on-demand support, pay per use etc. This paper generates a report on the possible consequences of cloud computing. Processing infrastructure-as-a-Service illustrates the distribution of threats and how cloud doing so can affect their performance. The cloud infrastructure addresses the various situations where the attacks exist also the steps should be followed to mitigate the attacks. The research also tries at classifying steps have to take when using cloud services to reduce the impacts of undesirable consequences and to safeguard data integrity.
Feasibility Study on Virtual Reality Based Basketball Tactic Training

In this article, a VR-based basketball training system comprising a standalone VR device and a tablet is proposed. The system is intended to improve the ability of players to understand offensive tactics and practice these tactics correctly. We compare the training effectiveness of various degrees of immersion, including a conventional basketball tactic board, a 2D monitor, and virtual reality. A multi-camera-based human tracking system was designed and built around a real-world basketball court to record and analyze the running trajectory of each player during tactical execution. The accuracy of the running path and hesitation time at each tactical step were evaluated for each participant. Furthermore, we assessed several subjective measurements, including simulator sickness, presence, and sport imagery ability, to conduct a more comprehensive exploration of the feasibility of the proposed VR framework for basketball tactics training. The results indicate that the proposed system is useful for learning complex tactics. Furthermore, high VR immersion training improves athletes' abilities with regards to strategic imagery.