Reviewerly/exHarmony huggingface dataset:
Benchmark Problems on GPU: Accelerating Experiments on Metaheuristics

Effectiveness analysis on population-based meta-heuristics can be a longstanding task, specifically when dealing with large populations. This makes this class of algorithms computationally expensive. However, its intrinsic parallel characteristic accepts significant reductions in experimentation time using parallel computing, making it possible to obtain results faster and at a lower cost. This work presents a library for speeding up experiments on population-based metaheuristics by computing objective functions on GPUs. Moreover, it provides reliable benchmark functions to test the effectiveness of GPU-based implementations of metaheuristics. The proposed library parallelizes the CEC'22 benchmark problems, and the results demonstrate a significant improvement in experimentation time compared to the original library. With this work, we aim to provide the metaheuristics researcher community with a way to reduce the time required to evaluate these new methods and approaches, allowing the community to focus on more challenging and exciting aspects of the research. We hope this library will be a valuable resource for researchers working on metaheuristics and stimulate the rapid advancement of this important area of study.
Evolutionary Multitask Reinforcement Learning Using Symbiosis in Biocoenosis Optimization

Reinforcement learning has gathered lots of attention as many real-world applications such as complex control and optimization problems can be formulated as reinforcement learning problem. Reinforcement learning aims to find a policy capable of pursuing high return through policy optimization. Evolutionary multitasking is an emerging technique in evolutionary computation field, and it has been proved to be effective in handling multiple complicated optimization problems. This study proposes a novel policy search method: the symbiosis in biocoenosis optimization for reinforcement learning (SBO-RL). The SBO-RL is composed of three main features. First, the SBO-RL copes with multiple RL problems concurrently. Second, the SBO-RL integrates evolutionary-based policy optimization with policy gradient into symbiosis in biocoenosis optimization. Third, the SBO-RL transfers actors across domains by a simple domain adaptation strategy. The present study conducts experiments to examine the effectiveness of the proposed method. Experimental results show the SBO-RL can improve the quality and convergence of advanced policy-gradient and policy-search based deep reinforcement learning methods. Results also discover that the SBO-RL is capable of saving 50.06% and 31.15% computational cost for advanced deep reinforcement learning methods.
KENDALA-KENDALA YANG DIHADAPI DIGITAL NATIVE DALAM PENCARIAN INFORMASI

Digital natives can be referred to as the google generation because most of their information needs are met through the internet and tend to do things by multitasking, working in a network, like interactive games, accessing information randomly, getting information quickly and accurately, and prefer online sources. Compared to sources in the library, this is also the case for digital natives at the Faculty of Ushuluddin Adab and Da'wah IAIN Kerinci. The purpose of this study is to describe the obstacles faced by the digital native in searching for information. The research was conducted using a qualitative descriptive approach to analyze information related to information retrieval conducted by digital natives and find the obstacles they face. The results of the study show that the obstacles faced by digital natives when searching for information are (1). Internet (personal factors) as follows: lack of mastery of information literacy such as the use of keywords, and how to access information; (2) external (external factors) the lack of understanding of students in formulating or analyzing the information needs they need so that they spend a lot of time searching for inappropriate information and this has an impact on the high cost of purchasing internet quota when searching for information.
On the Effect of Quantum Noise in Quantum Genetic Algorithms

In recent years, quantum computing is finding strong application in the development of new evolutionary algorithms. This is mainly due to the intrinsic parallelism induced by this new computational paradigm, which fits well with the structure of population-based optimization algorithms. However, the pioneering nature of quantum hardware makes quantum computations still suffer from noise that affects their accuracy and precision. This study aims to analyze the effect of this noise on quantum genetic algorithms and to quantify how much this can destroy or enhance the guided search of these algorithms in the search space. In detail, the proposed paper introduces a systematic study that assess the effects of quantum noise in a genetic algorithm equipped with a quantum recombination operator, formally known as the Quantum Mating Operator, when applied to solving a well-known optimization problem such as the 01 knapsack.
Blaze: A High-Performance, Scalable, and Efficient Data Transfer Framework with Configurable and Extensible Features : Principles, Implementation, and Evaluation of a Transatlantic Inter-Cloud Data Transfer Case Study

Blaze is a high-speed data transfer framework that enables efficient and scalable data movement between distributed storage systems. In this paper, we describe the design, implementation, and evaluation of Blaze in the context of a case study involving the transfer of 5.6 petabytes of data from OVH cloud storage to an Amazon S3 bucket. We discuss the technical challenges and design choices that led to creating a single-agent architecture, providing flexibility in agent placement while minimizing operational costs. We also demonstrate the orchestration capabilities of Blaze using Apache Airflow to manage hierarchical workflows for data transfer between storage systems. Our evaluation shows that Blaze achieved the expected 20 Gbps throughput during data transfer and provided significant cost savings compared to other architectures. The results demonstrate that Blaze is a practical solution for high-speed data transfer in large-scale distributed storage environments.
Use of Information Communication Technology in Science, Technology, Engineering, and Mathematics

The purpose of the chapter was to evaluate use of ICT in STEM teaching and learning in teacher education during the COVID-19 pandemic in Zimbabwe. A quasi-experimental research design with five teachers' colleges and five universities was used. A posttest and a pretest were given to 220 learners before and after the intervention of ICT tools such as Google classroom and smartphones. Findings show that lecturers prefer the use of simple social media gadgets to deliver lectures in colleges and universities. There is a strong relationship between use of ICT and performance by learners tested at 5% significance level (P&amp;lt;0.005). The chapter concludes that lack of knowledge, skills, application of integrated functions contained in the ICT gadget has resulted in its poor teaching and learning in colleges and universities. Teacher education lecturers are trained on the use of ICT tools and how it can be used for teaching and learning in colleges and universities.
A Personalized Recommendation Model based on Collaborative Filtering and Federated Learning for Cultural Tourism Attractions in Fujian-Taiwan

In the era of big data, the explosive growth of cultural tourism information should provide personalized and privacy requirements for people’s travel. However, the existing travel recommendation algorithms have some obvious challenges, such as weak personalization, significant cold-start problem, and poor user privacy protection, etc. We propose a personalized intelligent recommendation model and method, which constructs a new collaborative filtering recommendation algorithm based on user characteristics portrait and historical behaviors, and designs an user-attraction data correction model based on federal learning to implement user privacy protection. Furthermore, We fully combine user age, gender, hobbies, and explicit and implicit data feedback to improve the accuracy of the proposed recommendation model. A sufficient experiments have been done on the real Fujian-Taiwan cultural and tourist attractions dataset, and the results show that, the proposed model protects the user’s private information and ensures a higher recommendation accuracy. Compared with the traditional collaborative filtering recommendation, it is concluded that the recommended attractions can accurately hit 50%-90% of users’ clear travel destinations, and the satisfaction rate of users can reach 77% among the attractions recommended to users outside the expectation, which shows good performance and can be effectively extended to other recommendation systems for cultural tourism attractions from the experimental results.
Research on the Design and Application Principle of Virtual Reality Technology

With its unique technical advantages, virtual reality (VR) is being used in almost all human practical activities, such as industry, agriculture, commerce, education, military and so on. VR technology will play a key role in our further life. It is necessary to analyze all aspects of VR technology systematically to help the development of virtual reality technology. In this article, the basic concept of VR technology, the basic technologies it contains, the characteristics of VR technology and its development process are introduced, further, the main difficulties faced by the current VR technology development are analyzed, and the possible solutions are discussed. The main difficulties faced by VR technology at present are essentially due to its high-speed development. The rapid development of VR has led to a variety of choices of technical routes, difficulty in establishing industrial standards and confusion of users. On the one hand, these difficulties depend on the technology development itself; on the other hand, the active involvement of the government or relevant institutions can also effectively promote the development of VR.
An Evaluation of the Performance of Convolution Neural Network and Transfer Learning on Face Gender Recognition

Gender detection has become an essential component of the biometric system and protection. It is used to recommend age-appropriate content and to broaden the reach of multilayer advertising and promotions, among others. Many challenges emanate from using contemporary machine learning algorithms for face gender recognition, ranging from low detection accuracy and overfitting. Most recognition tasks, including object identification, segmentation, and classification, are based on convolutional neural networks. The human Face is a nonlinear manifold in which slight input changes can result in the invariance of the output. This prompts the need for continuous research to achieve more accurate recognition, especially with experimenting on ground truth data. This research compares the performance of Convolution Neural Network (CNN) and Transfer Learning (TL) on faces acquired in real-time scenarios to detect gender. The empirical result based on the study demonstrates that TL outperforms CNN in terms of prediction accuracy, with TL having 95.83% and CNN having 83.11%, and also in terms of recognition time, with TL having 0.23 s and CNN having 0.98 s, amongst other evaluation metrics. The novelty of the research is in the recognition time, which is not a metric commonly reported in the literature. The work demonstrates the effectiveness of using pre-trained models based on the dataset used and evaluation metrics for the experiment.
Critical Analysis of Policy Perspectives in Teacher Education

The notion of a teacher and how s/he should be has dominated the policy construction and more so its implementation.
Stronger Lower Bounds for Leakage-Resilient Secret Sharing

Threshold secret sharing allows a dealer to split a secret s into n shares, such that any t shares allow for reconstructing s, but no $$t-1$$ shares reveal any information about s. Leakage-resilient secret sharing requires that the secret remains hidden, even when an adversary additionally obtains a limited amount of leakage from every share. Benhamouda et al. (CRYPTO'18) proved that Shamir's secret sharing scheme is one bit leakage-resilient for reconstruction threshold $$t\ge 0.85n$$ and conjectured that the same holds for $$t=c\cdot n$$ for any constant $$0\le c\le 1$$ . Nielsen and Simkin (EUROCRYPT'20) showed that this is the best one can hope for by proving that Shamir's scheme is not secure against one-bit leakage when $$t=c\cdot n/\log (n)$$ . In this work, we strengthen the lower bound of Nielsen and Simkin. We consider noisy leakage-resilience, where a random subset of leakages is replaced by uniformly random noise. We prove a lower bound for Shamir's secret sharing, similar to that of Nielsen and Simkin, which holds even when a constant fraction of leakages is replaced by random noise. To this end, we first prove a lower bound on the share size of any noisy-leakage-resilient sharing scheme. We then use this lower bound to show that there exist universal constants $$c_1,c_2$$ , such that for sufficiently large n it holds that Shamir's secret sharing scheme is not noisy-leakage-resilient for $$t\le c_1\cdot n/\log (n)$$ , even when a $$c_2$$ fraction of leakages are replaced by random noise.
On the Discrete Logarithm Problem in the Ideal Class Group of Multiquadratic Fields

In this work we show that the discrete logarithm problem in the ideal class group of the multiquadratic field $$K = \mathbb {Q}(\sqrt{d_1}, \ldots , \sqrt{d_n})$$ of degree $$m = 2^n$$ can be solved in classical time $$e^{\widetilde{\mathcal {O}}(\max (\log {m},\sqrt{\log {D}}))}$$ using an adaptation of Pohlig-Hellman approach, where $$D = d_1 \cdot \ldots \cdot d_n$$ . This complexity is for the case when the factorization of the target ideal norm is not given. Thanks to our implementation, we provide numerical examples of discrete logarithm computation in real and imaginary number fields.
Investigating Two-Stage Detection Methods Using Traffic Light Detection Dataset

Object detection is the process of using a camera to track an object or a group of objects over time. It is sometimes referred to as object tracking. It can be used for a variety of things, including human-computer interactions (HCI), security and surveillance, video communication, subfield of works, lane detection (LD), pedestrian detection (PD), traffic light detection (TLD), traffic sign detection (TSD), vehicle detection (VD), and object detection from compressed video public places such as airports. In recent times, object tracking has become a popular topic in computer science particularly in the data science community, thanks to the usage of deep learning (DL) in artificial intelligence (AI). It has become essential as numerous government and private organizations gather enormous amounts of domain-specific data, which can offer insightful data on topics like marketing, national intelligence, cybersecurity, and fraud detection. In the last decades, these applications including core functions of image categorization, localization, and detection have attracted a lot of study interest. Because of significant developments in neural networks, particularly DL, these visual identification algorithms have attained amazing performance. DL which convolutional neural network as one of its techniques usually used two-stage detection methods in TLD. Despite all successes recorded in TLD through the use of two-stage detection methods, there is no study that has analyzed these methods in experimental research, studying the strength and witnesses for informed research by the researchers. Based on the needs, this chapter analyzes the applications and challenges of DL techniques in TLD. In addition, object detection for TLD using five distinct, two-stage detection methods with LARA traffic light dataset using a Jupyter Notebook and the sklearn libraries is implemented. The achievements of two-stage detection methods in TLD are enlightened using standard performance metrics, and it was observed that FASTER-CNN was the best in detection accuracy, F1-score, precision, recall, and running time with 0.89, 0.93, 0.83, 0.90, and 32 s, respectively.
Hardware Design Aspects of Visual Tracking System

Hardware design for object detection is one of the upcoming technical fields in VLSI design. With the increase in popularity of artificial intelligence and machine learning, object detection and visual tracking are becoming crucial as well. These techniques are very complex and frequently require more parallelized approach to the algorithm. General-purpose CPU core thus is not suitable for these applications. GPU and ASICs designed for such parallelized loads are thus usually needed. But with increasing popularity and complexity, developing discrete hardware capable for processing such tasks has become feasible. The visual tracking system is complex and is comprised of many parts. This chapter focuses on proposing a system with all the complexity defined and each part discussed in detail. FPGA and its architecture as needed by the system are also discussed. This entails discussing different algorithms available for hardware tracking. Each technique is thoroughly discussed, and hardware/software co-system for implementing those algorithms is proposed. The chapter underlines the benefits and shortcomings of each algorithm and then enables the reader to create complex systems that are both efficient and fast and also discusses the new implementations for systems using multiple camera and views.
Deep Learning-Based Multi-object Tracking

Multiple object tracking (MOT) is a technique of localizing numerous moving objects over time in a video clip. There are several uses for MOT, including augmented reality, traffic management, medical imaging, surveillance and security, video editing, and video transmission and compression. Generally, MOT is a two-step process that includes object detection and association. Initially, a distinct identifier is allocated to each identified object in the first frame, and then motion trajectories of the detected objects were extracted. All the objects are detected, and their track is preserved in every frame that follows in an image stream. Afterward, the trajectories of each detected object are determined in the existing frame based on its position in the previous frame. MOT aims to determine improved object connections to increase the affinity between objects in the subsequent frames. But exact multiple object tracing is extremely difficult. The challenges are either due to object deformation, namely, pose variation, occlusion, and background clutter, or due to the dynamic environmental variations, namely, fog, snow, rain, and dust particles. In order to cope with these challenges, plenty of work is suggested exploiting deeplearning (DL). In this chapter, we have reviewed the various DL-based MOT algorithms utilized for object detection and tracking. Salient features of these algorithms are reviewed along with performance analysis. In addition, recent performance metrics for MOT algorithms performance evaluation are exhaustively analysed for their application to real-world.
Deploying RShiny-based Applications in Financial Institutions: A Case Study

This paper focuses on a step-by-step approach to deploy RShiny-based applications in risk management of financial institutions on in-house servers, also providing examples and a case study of those applications such as internal rating models, GRC tools, incident management tools as well as risk assessment tools. While there are numerous providers of risk management/ GRC tools in the market (e.g. OneTrust, JIRA, ServiceNow) as well as different ways to deploy proprietary RShiny applications (see e.g. Fay et all 2021, Velasquez 2022), this paper focuses on the in-house server alternative as the one with the highest balance between cost efficiency, API flexibility, data privacy and resources needed. To my knowledge, this is the first paper focusing on a step-by-step approach to deploy RShiny applications in risk management on in-house servers.
IoT-Enabled Secure and Scalable Cloud Architecture for Multi-User Systems: A Hybrid Post-Quantum Cryptographic and Blockchain-Based Approach Toward a Trustworthy Cloud Computing

Cloud computing has revolutionized organizational operations by providing convenient, on-demand access to resources. The emergence of the Internet of Things (IoT) has introduced a new paradigm for collaborative computing, leveraging sensors and devices that generate and process vast amounts of data, thereby resulting in challenges related to scalability and security, making the significance of conventional security methods even more pronounced. Consequently, in this paper, we propose a novel Scalable and Secure Cloud Architecture (SSCA) that integrates IoT and cryptographic techniques, aiming to develop scalable and trustworthy cloud systems, thus enabling multi-user systems and facilitating simultaneous access to cloud resources by multiple users. The design adopts a decentralized approach, utilizing multiple cloud nodes to handle user requests efficiently and incorporates Multicast and Broadcast Rekeying Algorithm (MBRA) to ensure the privacy and confidentiality of user information, utilizing a hybrid cryptosystem that combines MBRA, Post Quantum Cryptography (PQC) and blockchain technology. Leveraging IoT devices, the architecture gathers data from distributed sensing resources and ensures the security of collected information through robust MBRA-PQC encryption algorithms, while the blockchain ensures that the confidential data is stored in distributed and immutable records. The proposed approach is applied to several datasets and the effectiveness is validated through various performance metrics, including response time, throughput, scalability, security, and reliability. The results highlight the effectiveness of the proposed SSCA, showcasing a notable reduction in response time by 1.67 seconds and 0.97 seconds for 250 and 1000 devices, respectively, in comparison to the MHE-IS-CPMT. Likewise, SSCA demonstrated significant improvements in the AUC values, exhibiting enhancements of 6.30%, 6.90%, 7.60%, and 7.30% at the 25-user level, and impressive gains of 5.20%, 9.30%, 11.50%, and 15.40% at the 50-user level when compared to the MHE-IS-CPMT, EAM, SCSS, and SHCEF models, respectively.
Real-time Feedback from a Bird's-eye View: Improving Spatial Perception in Cutoff Man Training Using a Drone

Most sports-assisting technologies have been developed to improve performances only in individual sports such as skiing, batting, and swimming. In team sports, it is important to perceive one's position relative to others. Few studies have focused on team sports, which not only require the motor ability of individual players, but also their perceptual abilities. This pilot study aimed to evaluate the effectiveness of a visual feedback system to improve players' spatial perception relative to others. The visual feedback system was composed of a flying drone that transmitted an image to the participant's smart glasses. The participant was able to see his/her own relative position in real time using the glasses with and without the system. Nine participants tried to position themselves on an imaginary line between two experimenters 30 m away from each other, which simulated the situation of a baseball cutoff man. The results showed that the error in distance between the participants' positions and the line decreased significantly when using the system compared to when not using it. Furthermore, there was also a reduction in positioning error in subsequent trials after participants performed the task using the system, compared to before they used the system. In conclusion, the real-time feedback system from a bird's-eye view has the potential to improve accuracy in spatial perception.