Reviewerly/exHarmony huggingface dataset:
Hybrid Nearest-Neighbor Ant Colony Optimization Algorithm for Enhancing Load Balancing Task Management

Many computer problems that arise from real-world circumstances are NP-hard, while, in the worst case, these problems are generally assumed to be intractable. Existing distributed computing systems are commonly used for a range of large-scale complex problems, adding advantages to many areas of research. Dynamic load balancing is feasible in distributed computing systems since it is a significant key to maintaining stability of heterogeneous distributed computing systems (HDCS). The challenge of load balancing is an objective function of optimization with exponential complexity of solutions. The problem of dynamic load balancing raises with the scale of the HDCS and it is hard to tackle effectively. The solution to this unsolvable issue is being explored under a particular algorithm paradigm. A new codification strategy, namely hybrid nearest-neighbor ant colony optimization (ACO-NN), which, based on the metaheuristic ant colony optimization (ACO) and an approximate nearest-neighbor (NN) approaches, has been developed to establish a dynamic load balancing algorithm for distributed systems. Several experiments have been conducted to explore the efficiency of this stochastic iterative load balancing algorithm; it is tested with task and nodes accessibility and proved to be effective with diverse performance metrics.
Real-Time Optimizing Weighted Gaussian Curvature for 4K Videos

Weighted Gaussian curvature is an important measurement for surfaces, images and videos. However, since it is a second order quantity, its optimization usually leads to high order geometric flows that cause difficulties for practical applications. In this paper, we propose a novel optimization method for weighted Gaussian curvature. Our method does not require the image (video) to be second-order differentiable, thus, avoiding the high order geometric flows. In addition, we propose a new 4-D look-up table method to accelerate the optimization of weighted Gaussian curvature. Therefore, our algorithm is very efficient and can achieve real-time processing for high resolution videos. For example, our method can process 4K videos with 50 frames per second on a single graphic card (NVIDIA 3090). Several numerical experiments are carried out to confirm the efficiency and effectiveness of the proposed method. Thanks to the high performance, our method can be applied in a large range of applications that involve weighted Gaussian curvature, such as image restoration, registration, enhancement, etc.
Evaluating the Performances of Biomarkers over a Restricted Domain of High Sensitivity

The burgeoning advances in high-throughput technologies have posed a great challenge to the identification of novel biomarkers for diagnosing, by contemporary models and methods, through bioinformatics-driven analysis. Diagnostic performance metrics such as the partial area under the ROC (pAUC) indexes exhibit limitations to analysing genomic data. Among other issues, the inability to differentiate between biomarkers whose ROC curves cross each other with the same pAUC value, the inappropriate expression of non-concave ROC curves, and the lack of a convenient interpretation, restrict their use in practice. Here, we have proposed the fitted partial area index (FpAUC), which is computable through an algorithm valid for any ROC curve shape, as an alternative performance summary for the evaluation of highly sensitive biomarkers. The proposed approach is based on fitter upper and lower bounds of the pAUC in a high-sensitivity region. Through variance estimates, simulations, and case studies for diagnosing leukaemia, and ovarian and colon cancers, we have proven the usefulness of the proposed metric in terms of restoring the interpretation and improving diagnostic accuracy. It is robust and feasible even when the ROC curve shows hooks, and solves performance ties between competitive biomarkers.
k-primal spaces

A function f:X→X determines a topology P(f)={U⊆X:f−1(U)⊆U}. A topological space (X,τ) is primal (or functional Alexandroff) if τ=P(f) for some function f, and is k-primal if τ is the supremum of a set of k primal topologies on X. Using the associated specialization quasiorder, we give necessary and sufficient conditions for a finite topological space to be k-primal. We show that the k-primal topologies on a finite set X form a lattice and discuss lattice complements.
Classification of Breast Cancer Based on Improved PSPNet

the effect of traditional PSPNet neural network on the semantic segmentation of mammographic images is not obvious enough, so the network is improved, and the practical application effect of two kinds of network on mammography detection is compared. the multi-layer image features extracted from the pyramid image features of PSPNet network were fused, which not only retained the original network to improve the effect of the receptive field, but also retained the detail features of the image through the fusion of multi-layer information. Results the detection performance of the experimental network was verified by using the mammography images of DDSM Database. Through the project verification, the improved network not only improves the semantic segmentation accuracy of the images, but also has obvious effect on the classification and diagnosis of breast cancer images. the improved PSPNet neural network has obvious semantic segmentation effect because it integrates the multi-level details of the images.
Static Indoor Pathfinding with Explicit Group Two-Parameter Over Relaxation Iterative Technique

A solution to Laplace’s equation referred to as harmonic potential fields is commonly employed in robot pathfinding as an indication for robot navigation in an identifiable environment. The simulations computation of these harmonic functions frequently requires a high-performance computer. In the quest to address the pathfinding problem, the article presents a technique called Block Two-Parameter Over-Relaxation, otherwise known as Explicit Group Two-Parameter Over-Relaxation (EGTOR). The simulations of robot pathfinding were executed in a static known enclosed environment to validate the competency of EGTOR. Multiple tests are provided to assess the effectiveness of the suggested technique. Different departure and goal positions, in particular, are used to assess the paths generated by the simulations. The outcomes demonstrate the advantages of the proposed technique. In the context of iteration number, EGTOR improves by around 4.4% when compared to EGAOR and 17.1% in comparison with EGSOR. While as to computational timing, EGTOR outperforms EGAOR and EGSOR by 6.3% and 14.5%, respectively. The study concludes that the suggested method in computing harmonic functions is appealing and attainable for solving path planning problems.
A Modeling and Engineering Methodology for Developing Internet of Services from Scratch

In the Internet of Services(IoS), user requirements are becoming more and more complex, which leads to higher requirements put forward for the design and implementation of services. On the other hand, service systems are moving to the cloud, and the new requirements for the service are high availability, high scalability, easy deployment and maintenance, etc. To address these challenges, this paper proposes a modeling and engineering approach for developing services from scratch. This modeling approach uses the classical MDA concept to abstractly model the user’s value quality expectation and correlate the functional model with the user’s value quality in a concrete business scenario. At the same time, it incorporates the concept of DevOps and uses microservice architecture to realize service systems, and then obtains a service solution through the design of value-quality-constrained microservices. We implement a modeling tool to assist designers in the design and implementation of services according to the modeling approach, and validate the feasibility and practicality of the modeling approach on the health care case.
Code-switched inspired losses for spoken dialog representations

Spoken dialog systems need to be able to handle both multiple languages and multilinguality inside a conversation (e.g in case of codeswitching).In this work, we introduce new pretraining losses tailored to learn multilingual spoken dialog representations.The goal of these losses is to expose the model to codeswitched language.To scale up training, we automatically build a pretraining corpus composed of multilingual conversations in five different languages (French, Italian, English, German and Spanish) from OpenSubtitles, a huge multilingual corpus composed of 24.3G tokens.We test the generic representations on MIAM, a new benchmark composed of five dialog act corpora on the same aforementioned languages as well as on two novel multilingual downstream tasks (i.e multilingual mask utterance retrieval and multilingual inconsistency identification).Our experiments show that our new code switched-inspired losses achieve a better performance in both monolingual and multilingual settings.
Health Assistant Bot

This paper presents the design and development of an efficient and intelligent chatbot. The proposed idea is to create a health companion which can be used for a daily well-being. An agile approach was used for its development and deployed on a platform such that all types of users can communicate with it. This virtual assistant makes our life easier and saves time via automated user query replies. Responding to user queries regarding home remedies as well as the recent pandemic COVID-19, the bot is well-equipped and reliable.
SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments

Recent advancements in deep learning, computer vision, and embodied AI have given rise to synthetic causal reasoning video datasets. These datasets facilitate the development of AI algorithms that can reason about physical interactions between objects. However, datasets thus far have primarily focused on elementary physical events such as rolling or falling. There is currently a scarcity of datasets that focus on the physical interactions that humans perform daily with objects in the real world. To address this scarcity, we introduce SPACE: A Simulator for Physical Interactions and Causal Learning in 3D Environments. The SPACE simulator allows us to generate the SPACE dataset, a synthetic video dataset in a 3D environment, to systematically evaluate physics-based models on a range of physical causal reasoning tasks. Inspired by daily object interactions, the SPACE dataset comprises videos depicting three types of physical events: containment, stability and contact. These events make up the vast majority of the basic physical interactions between objects. We then further evaluate it with a state-of-the-art physics-based deep model and show that the SPACE dataset improves the learning of intuitive physics with an approach inspired by curriculum learning. Repository: https://github.com/jiafei1224/SPACE
One‐class tensor machine with randomized projection for large‐scale anomaly detection in high‐dimensional and noisy data

The modern industrial sector generates enormous amounts of high-dimensional heterogeneous data daily. However, mostly the vectored data (rank-one tensor) have been considered for anomaly detection, whereas the data in real-life is high dimensional. The expressive power of methods based on vector data is restrictive as they may destroy the structural information embedded in data and lead to the curse-of-dimensionality and overfitting. In this paper, we present a novel anomaly detection approach for large-scale tensor data. We first present novel one-class support tensor machines (OCSTM) with bounded loss function. We further extend it by leveraging the randomness to design a scalable approach that can also be used for large-scale anomaly detection. To solve the corresponding optimization of the objective function, we utilize half-quadratic optimization followed by solving it like a traditional OCSTM optimization at each iteration. We demonstrate the proposed randomized OCSTM with bounded hinge loss through experiments on 14 benchmark data sets. Experimental results demonstrate the effectiveness of the proposed approach against anomalies and a significant reduction in the computational complexity.
Image Colorization: A Survey of Methodolgies and Techniques

Image Colorization is the problem of defining colors for grayscale images. Deep neural networks proved a great success in different fields recently. Therefore, it is used to solve the image colorization problem; moreover, it proved to be a very good choice. In literature, few review papers addressed the colorization problem. Most of them classified the research papers according to one or two criteria as input image type, a number of colored output images, colorization methodology, techniques or networks used in colorization, and network paths. This review classifies the papers according to these criteria intagrally and with a relatively large number of papers. Besides, the review displays the commonly used datasets and measures of comparison. It is found that deep learning is a widely used solution methodology to the problem. Unifying the comparison measures and data sets might help show the advances of the new models.
Finding a range of perceived natural visual walking speed for stationary travelling techniques in VR

Travelling is one of the significant interactions in virtual reality (VR). Until now, researchers have come up with many Virtual Locomotion Techniques (VLT) to supply natural, efficient and usable ways of navigating in VR while not causing VR sickness. Stationary VLTs are those which does not demand any physical movement from the user to travel in the virtual environment. In order to improve the experience of walking in this VLT, it is essential to show the view transition speed that mimics the natural walking speed of the user. In this paper, we describe a within-subject study that was performed to establish a range of perceptually natural walking speeds while being stationary. In the study, we provided vibrotactile feedbacks behind the ears of the subjects to avoid motion sickness. The subjects were exposed to visuals with gains ranging from 1.0 to 3.0. The slowest speed was the estimated natural speed of the user, and the highest speed was three times faster than this. The perceived naturalness of the speed was evaluated using the self-report. We found the range of the visual gain to be 1.40 to 1.78.
Development of Swarm Intelligence Leader-Vicsek-Model for Multi-AGV Path Planning

Automatic guided vehicle (AGV) is the mobile robot widely used in the industry, and multiple AGVs are usually involved for industrial intelligence. Path planning for multi-AGV is essential for automatic transportation, and we propose a swarm-based approach that is inspired by biological subjects. The dynamic virtual leader is assigned for updating the locations and angles for the Leader-Vicsek-Model, and multiple agents are assigned through the centralized approach while the path planning is achieved through the decentralized method.
A Scalable Cloud Deployment Architecture for High-Performance Real-Time Online Applications

We study high-performance Real-Time Online Interactive Applications (ROIAs), with use cases like product configurators in the Configure-Price-Quote market, e-learning, multiplayer online gaming, and digital twins of production facilities for the Industry 4.0 market. While core components of ROIAs, e.g., interactive real-time 3D rendering, still widely run on local devices, it is very desirable to run them on cloud resources to benefit from the advantages of cloud computing, e.g., better quality provided by high-performance compute resources and accessibility. In this paper, we design and implement a novel cloud service deployment architecture for ROIAs, which addresses three major challenges: meeting the high Quality of Service (QoS) requirements, auto-scalability, and resource usage optimization. Compared to previous work, our deployment approach is based on the concept of session slots that combines a high level of QoS with the economic use of resources like CPU, GPU, and memory. We describe a prototype implementation of a ROIA use case - a car configurator running on a Kubernetes cluster. Experimental evaluation demonstrates that our architecture avoids the traffic and latency bottleneck of a classical cloud load balancer, provides significantly more efficient resource usage, and can autoscale well.
Poster: The Need for a Collaborative Approach to Cyber Security Education

Traditional forms of cyber security education mainly focus on knowledge transmission, which means that knowledge is perceived as a tangible object being transferred from an expert (i.e., the teacher) to a beginner. When practiced well, the learner may acquire such knowledge, but not the resilience to apply it in various contexts [1], [2]. This is especially troubling for the cyber security domain, given the dynamic and constantly changing nature of the field and the environments in which it is required. We therefore need forms of education that aim at understanding the interdisciplinary nature of the field of cyber security as well as at the development of joint action in context: being able to quickly analyse and understand evolving and possibly previously unseen situations and take collaborative action to prevent, detect and recover from incidents.
Movie Recommender System with Visualized Embeddings

Recommender Systems are a fundamental building block of E-commerce and Online Streaming services. With the advent and subsequent globalization of the internet and data, integration of Machine Learning and Deep Learning Algorithms into these online services has happened. In this approach combination, There are be methods implemented, like providing input-based embeddings for users and items (which are generated via models obtained from a Standard Deep Neural Network), to get results. By utilizing the t-Distributed stochastic Neighbor Embedding Tool, visualizing this complex data in lower dimensions has been done. KNN has been implemented to search for similar embeddings. Thus a combination of methods has been utilized to both recommend movies and visualize the generated embeddings, along with being applied on the movielens 100K dataset. Approach mayor may not vary depending on the dataset.
Extended Reality (XR) Applications in Architectural Practice: Towards a Development Framework

Extended Reality (XR) applications allow designers to experiment with design concepts and examine design solutions in mixed-reality environments. XR technologies, from virtual reality to augmented reality, have proven potentials to enhance the design process and improve design outcomes. However, XR applications in architecture, engineering, and construction are limited mainly due to primitive XR technology—both software and hardware. Further research on how to develop these applications thus deems necessary. This study focused on XR use cases in architectural practice, identified six key XR affordances through a case study and then discussed their relationships with three components of the creative design process in architecture (concept, knowledge, and environment). The results are presented as a framework that can serve a reference for developing the next generation of XR applications for architectural practice.
Bir Hibrid Balina Optimizasyon Algoritminin Gezgin Satıcı Problemi için Analizi

Whale Optimization Algorithm (WOA) is a fairly new algorithm developed in 2016. WOA was applied to continuous optimization problems and engineering problems in the literature. However, WOA demonstrates lower performance than others in traveling salesman problems. Therefore, in this study, an application of the hybrid algorithm (WOA+NN) has been done in the traveling salesman problem. A set of classical datasets which have cities scale ranged from 51 to 150 was used in the application. The results show that the hybrid algorithm (WOA+NN) outperforms AS (Ant system), WOA, GA, and SA for 50% of all datasets. Ant system (AS) is the second algorithm that is better than other metaheuristics for 40% of all datasets. In addition, it was given that a detailed analysis presents the number of best, worst, average solutions, standard deviation, and the average CPU time concerning meta-heuristics. The metrics stress that the hybrid algorithm (WOA+NN) demonstrates a performance rate over 50% in finding optimal solutions. AS (Ant system) is better at 40% of all optimal solutions. Finally, the hybrid algorithm solves the discrete problem in reasonable times in comparison to other algorithms for medium-scale datasets.
A comparative study of neural machine translation models for Turkish language

Machine translation (MT) is an important challenge in the fields of Computational Linguistics. In this study, we conducted neural machine translation (NMT) experiments on two different architectures. First, Sequence to Sequence (Seq2Seq) architecture along with a variation that utilizes attention mechanism is performed on translation task. Second, an architecture that is fully based on the self-attention mechanism, namely Transformer, is employed to perform a comprehensive comparison. Besides, the contribution of employing Byte Pair Encoding (BPE) and Gumbel Softmax distributions are examined for both architectures. The experiments are conducted on two different datasets: TED Talks that is one of the popular benchmark datasets for NMT especially among morphologically rich languages like Turkish and WMT18 News dataset that is provided by The Third Conference on Machine Translation (WMT) for shared tasks on various aspects of machine translation. The evaluation of Turkish-to-English translations’ results demonstrate that the Transformer model with combination of BPE and Gumbel Softmax achieved 22.4 BLEU score on TED Talks and 38.7 BLUE score on WMT18 News dataset. The empirical results support that using Gumbel Softmax distribution improves the quality of translations for both architectures.
RETRACTED: Umpire Gesture Detection and Recognition using HOG and Non-Linear Support Vector Machine (NL-SVM) Classification of Deep Features in Cricket Videos

Abstract Gesture Recognition pertains to recognizing meaningful expressions of motion by a human, involving the hands, arms, face, head, and/or body. It is of utmost importance in designing an intelligent and efficient human–computer interface. The applications of gesture recognition are manifold, ranging from sign language through medical rehabilitation, monitoring patients or elder people, surveillance systems, sports gesture analysis, human behaviour analysis etc., to virtual reality. In recent years, there has been increased interest in video summarization and automatic sports highlights generation in the game of Cricket. In Cricket, the Umpire has the authority to make important decisions about events on the field. The Umpire signals important events using unique hand on signals and gestures. The primary intention of our work is to design and develop a new robust method for Umpire Action and Non-Action Gesture Identification and Recognition based on the Umpire Segmentation and the proposed Histogram Oriented Gradient (HOG) feature Extraction oriented Non-Linear Support Vector Machine (NL-SVM) classification of Deep Features. Primarily the 80% of Umpire action and non-action images in a cricket match, about 1, 93, 000 frames, the Histogram of Oriented Gradient Deep Features are calculated and trained the system having six gestures of Umpire pose. The proposed HOG Feature Extraction oriented Non-Linear Support Vector Machine classification method achieves the maximal accuracy of 97.95%, the maximal sensitivity of 98.87%, the maximal specificity of 98.89% and maximal Precision of 97.02% which indicates its superiority.