Reviewerly/exHarmony huggingface dataset:
ML-driven classification scheme for dynamic interference-aware resource scheduling in cloud infrastructures

Computing systems continue to evolve, resulting in increased performance when processing workloads in large data centers due to the virtualization benefits. This technology is the key factor that allows multiple applications to share resources, thereby enhancing the overall hardware utilization of cloud computing environments. However, multiple cloud-services contending for shared resources are susceptible to cross-application interference, which can lead to significant performance degradation and, consequently, an increase in Service Level Agreements violations. Nevertheless, state-of-the-art resource scheduling still relies mainly on resource capacity, adopting heuristics such as bin-packing and overlooking this source of overhead. But in recent years, interference-aware scheduling has gained traction, with the investigation of ways to classify applications regarding their interference levels and the proposal of static interference models and policies for scheduling co-hosted cloud applications. The preliminary results already show a considerable improvement in resource utilization and can be considered as the first steps toward a dynamic scheduling strategy. In this scenario, this paper proposes a machine learning-driven classification scheme for dynamic interference-aware resource scheduling in cloud computing environments. The main goal is to present how a classification approach, that better represents the workload variations, affects resource scheduling. In the first place, we analyze how hardware resources react to different applications with dynamic workloads. Then, we explore distinct interference classification formats and evaluate their efficiency, taking the dynamic nature of cloud workloads into account. Lastly, we present an interference-aware application classifier based on machine learning techniques and compare it with related work, adopting a variety of workload patterns. Preliminary results revealed an improvement in resource utilization efficiency by 27%, on average, when applying our classification approach in cloud infrastructures.
Visualizing Computer-Based Activity on Ambient Displays to Reduce Sedentary Behavior at Work

Workplace health interventions have predominantly been designed around visualizations of physical activity data in the work routine. Yet, contextual factors, such as computer-based activity, appears to be crucial to support healthier behaviors at work. In this research, we explore the effect of visualizing computer-based activity to prompt physical activity at work, through desktop-based ambient displays. Based on our prototypes Yamin and Apphia, we conducted an exploratory qualitative user study in a lab setting with office workers (N=16). Results showed that visualizing one's computer-based activity could potentially increase the awareness, self-reflection, and social interactions for individuals to become physically active. With our findings, we discuss design implications for using computer activity data in a physical form as a motivational factor to encourage physically active workstyles. We present directions for future field studies to gain further insights on this topic.
Accelerating exact constrained shortest paths on GPUs

The recently emerging applications such as software-defined networks and autonomous vehicles require efficient and exact solutions for constrained shortest paths (CSP), which finds the shortest path in a graph while satisfying some user-defined constraints. Compared with the common shortest path problems without constraints, CSP queries have a significantly larger number of subproblems. The most widely used labeling algorithm becomes prohibitively slow and impractical. Other existing approaches tend to find approximate solutions and build costly indices on graphs for fast query processing, which are not suitable for emerging applications with the requirement of exact solutions. A natural question is whether and how we can efficiently find the exact solution for CSP. In this paper, we propose Vine , a framework that parallelizes the labeling algorithm to efficiently find the exact CSP solution using GPUs. The major challenge addressed in Vine is how to deal with a large number of subproblems that are mostly unpromising but require a significant amount of memory and computational resources. Our solution is twofold. First, we develop a two-level pruning approach to eliminate the subproblems by making good use of the GPU's hierarchical memory. Second, we propose an adaptive parallelism control model based on the observations that the degree of parallelism (DOP) is the key to performance optimization with the given amount of computational resources. Extensive experiments show that Vine achieves 18× speedup on average over the widely adopted CPU-based solution running on 40 CPU threads. Vine also has over 5× speedup compared with a GPU approach that statically controls the DOP. Compared to the state-of-the-art approximate solution with preprocessed indices, Vine provides exact results with competitive or even better performance.
Blockchain based Access Control and Data Sharing Systems for Smart Devices

Abstract Internet-of-Things and Blockchain technology are evolving all around today’s modern world to solve many problems such as security, communications, data collection and analysis, etc. However, we also have some issues, such as efficient data sharing, restriction of access, reliable authentication, etc. The proposed framework is designed to solve the problems related to security and authorization in IoT network access control. In addition, the system’s aim is to accomplish security, authorizing, and encryption for information exchange through IoT networks. In this article, a novel system is introducing to provide the data sharing system that integrates blockchain based access control system for IoT devices. Here, we are creating three different smart contracts to offers an efficient access control management like contract to provide access control, contract to provide authentication, and contract to provide a judgment. Finally, the effectiveness of the proposed approach is measured against the cost consumption of smart contracts and cryptographic functions. The cost utilization comparison is carried out against certain current approaches as well as the findings clearly demonstrate that the proposed approach is cost-effective.
Systematic Literature Review: American Sign Language Translator

Sign Language Recognition (SLR) is a relatively popular research area yet contrary to its popularity, the implementation of SLR in daily basis is rare; this is due to the complexity and various resources required. In this literature review, the authors have analyzed various techniques that can be used to implement an automated sign-language translator through the analysis of the methodologies and models used to make a working model of any sign-language translator from various sources. The purpose of this study is to explore various possible ways to implement Artificial Intelligence technology to improve the automated American Sign Language translator that is applicable. The authors have identified 22 different research papers within the period of the years 2015 - 2020. The analysis showed that every research studies picked have achieved respectable results, however, they are not perfect, since each research demonstrates its own unique strengths and weaknesses. There are some methods that might be suitable for our need to create an applicable Sign Language Translator, that is by using standard video camera for obtaining data, and either Convolutional Neural Network or Support Vector Machine can be used for the classification.
Salp swarm optimizer for modeling the software fault prediction problem

This paper proposes the salp swarm algorithm (SSA) combined with a backpropagation neural network (BPNN) to solve the software fault prediction (SFP) problem. The SFP problem is one of the well-known software engineering problems that are concerned with anticipating the software defects that are likely to appear during a software project or thereafter. In order to find the optimal BPNN parameters, a combination of SSA optimizer and BPNN named (SSA-BPNN) is proposed, so as to enhance prediction accuracy. The proposed method is evaluated against several datasets for the SFP problem. These datasets vary in both size and complexity. The results obtained are evaluated using a variety of performance measures (i.e., the AUC, Confusion Matrix, Sensitivity, Specificity, Accuracy, and Error Rate). The results obtained by SSA-BPNN are better than those obtained by the conventional BPNN over all of the datasets. The proposed method also has the ability to outperform several state-of-the-art methods over the same datasets in respect of most of the aforementioned performance measures. Therefore, the hybridization of SSA with BPNN is a valuable addition to the software engineering issues and can be utilized to achieve higher prediction accuracy for a variety of prediction problems.
Model-Based Quality-Diversity Search for Efficient Robot Learning

Despite recent progress in robot learning, it still remains a challenge to program a robot to deal with open-ended object manipulation tasks. One approach that was recently used to autonomously generate a repertoire of diverse skills is a novelty based Quality-Diversity (QD) algorithm. However, as most evolutionary algorithms, QD suffers from sample- inefficiency and, thus, it is challenging to apply it in real-world scenarios. This paper tackles this problem by integrating a neural network that predicts the behavior of the perturbed parameters into a novelty based QD algorithm. In the proposed Model-based Quality-Diversity search (M-QD), the network is trained concurrently to the repertoire and is used to avoid executing unpromising actions in the novelty search process. Furthermore, it is used to adapt the skills of the final repertoire in order to generalize the skills to different scenarios. Our experiments show that enhancing a QD algorithm with such a forward model improves the sample-efficiency and performance of the evolutionary process and the skill adaptation.
Reinforced Grounded Action Transformation for Sim-to-Real Transfer

Robots can learn to do complex tasks in simulation, but often, learned behaviors fail to transfer well to the real world due to simulator imperfections (the "reality gap"). Some existing solutions to this sim-to-real problem, such as Grounded Action Transformation (gat), use a small amount of real-world experience to minimize the reality gap by "grounding" the simulator. While very effective in certain scenarios, gat is not robust on problems that use complex function approximation techniques to model a policy. In this paper, we introduce Reinforced Grounded Action Transformation (rgat), a new sim-to-real technique that uses Reinforcement Learning (RL) not only to update the target policy in simulation, but also to perform the grounding step itself. This novel formulation allows for end-to-end training during the grounding step, which, compared to gat, produces a better grounded simulator. Moreover, we show experimentally in several MuJoCo domains that our approach leads to successful transfer for policies modeled using neural networks.
Patient triage system for supporting the operation of dispatch centres and rescue teams

Abstract Background The Thai medical application for patient triage, namely Triagist, is an mHealth application designed to support the pre-hospital process. However, since the functions of the application that are necessary for the pre-hospital process have been found not to be fully developed, the addition of a back-end system has been considered to increase its performance and usability. Objective To determine the ability of the previous version to effectively manage the pre-hospital process and analyse the current problems with the pre-hospital operation. Therefore, the new system was developed to support the connection of dispatch centres or operational centres to the Triagist mobile application and system evaluation. Method Design thinking methodology was used to analyse, design and develop a patient triage system to support the pre-hospital process in Thailand based on users’ requirements. 68 active members of the rescue teams and emergency medical staff in Chiang Mai and Lampang provinces were recruited to test the reliability of the system based on a prototype application. Results The new medical mobile application for patient triage in Thailand was validated for use due to containing the two essential functions of Initial Dispatch Code (IDC) geolocation and IDC management. When the system was tested by emergency staff who were responsible for using it, those with the least experience were found to use it better than their highly experienced colleagues. Moreover, in cases where the system had been implemented, it was found to determine the frequency of symptoms, the time period during which cases occurred, and the density of cases in each area. Conclusion This system, which has been developed based on the use of smart technology, will play an important role in supporting emergency services in Thailand by enhancing the efficiency of the pre-hospital process. Emergency centres will receive IDC information from the geolocation system so that they can determine patients’ location without undue delay. Emergency services will be able to rapidly prepare the necessary resources and administrative tasks will be supported by linking the dispatch centre to central rescue teams.
Outlier Detection in Multivariate Time Series Data Using a Fusion of K-Medoid, Standardized Euclidean Distance and Z-Score

Data mining technique has been used to extract potentially useful knowledge from big data. However, data mining sometimes faces the issue of incorrect results which could be due to the presence of an outlier in the analyzed data. In the literature, it has been identified that the detection of this outlier could enhance the quality of the dataset. An important type of data that requires outlier detection for accurate prediction and enhanced decision making is time series data. Time series data are valuable as it helps to understand the past behavior which is helpful for future predictions hence, it is important to detect the presence of outliers in time series dataset. This paper proposes an algorithm for outlier detection in Multivariate Time Series (MTS) data based on a fusion of K-medoid, Standard Euclidean Distance (SED), and Z-score. Apart from SED, experiments were also performed on two other distance metrics which are City Block and Euclidean Distance. Z-score performance was compared to that of inter-quartile. However, the result obtained showed that the Z-score technique produced a better outlier detection result of 0.9978 F-measure as compared to inter-quartile of 0.8571 F-measure. Furthermore, SED performed better when combined with both Z-score and inter-quartile than City Block and Euclidean Distance.
SCPM-CR: A Novel Method for Spatial Co-Location Pattern Mining With Coupling Relation Consideration

Spatial co-location pattern mining (SCPM) aims to discover subsets of spatial features frequently located together in proximate areas. Previous studies of SCPM solely concern the inter-features association of a pattern, but neglect the interesting intra-feature behavior. In this paper, we propose the task of spatial co-location pattern mining with coupling relation consideration (SCPM-CR) to capture complex relations embedded in a co-location. Specifically, InterPCI measure is designed to capture the inter-features coupling by considering the comprehensive interaction of objects for the features in a pattern, and luckily it possesses the anti-monotone property. Another measure, IntraCAI, is proposed to capture the congregating behavior of intra-feature objects under the restriction of a co-location. A general framework is designed for SCPM-CR task and experiments show that a large fraction of computation time is devoted to identifying the participating objects of a candidate pattern. To tackle this calculation bottleneck, a novel candidate-and-search algorithm is suggested, CS-HBS, equipped with heuristic backtracking search. Extensive experiments are conducted on real and synthetic datasets to demonstrate the superiority of SCPM-CR compared with traditional SCPM methods, and also to validate the efficiency and scalability of CS-HBS. Experimental results show that CS-HBS outperforms the baselines by several times or even orders of magnitude.
Use of a Simulated Social Network as an Educational Tool to Enhance Teacher Digital Competence

There is a worrying gap between the digital competence that teachers must have to effectively develop their students' digital competence and the one they actually have, especially in the area related to the safe and responsible use of technology. Further investigation is needed on the use of training activities, methods and tools aimed at enhancing this competence. This article examines, in the context of an online course in MOOC format, the usefulness of Social Lab, a simulated social network, as an educational tool to improve the digital competence of teachers in the area of safe and responsible use of technology.
A CNN-GCN Framework for Multi-Label Aerial Image Scene Classification

As one of the fundamental tasks in aerial image understanding, multi-label aerial image scene classification attracts increasing research interest. In general, the semantic category of a scene is reflected by the object information and the topological relations among objects. Most of existing deep learning-based aerial image scene classification methods (e.g., convolutional neural network (CNN)) classify the image scene by perceiving object information, while how to learn spatial relationships from image scene is still a challenging problem. In literature, graph convolutional network (GCN) has been successfully used for learning spatial characteristics of topological data, but it is rarely adopted in aerial image scene classification. To simultaneously mine both the object visual information and spatial relationships among multiple objects, this paper proposes a novel framework combining CNN and GCN to address multi-label aerial image scene classification. Extensive experimental results on two public datasets show that our proposed method can achieve better performance than the state-of-the-art methods.
Rethinking Weight Decay for Efficient Neural Network Pruning

Introduced in the late 1980s for generalization purposes, pruning has now become a staple for compressing deep neural networks. Despite many innovations in recent decades, pruning approaches still face core issues that hinder their performance or scalability. Drawing inspiration from early work in the field, and especially the use of weight decay to achieve sparsity, we introduce Selective Weight Decay (SWD), which carries out efficient, continuous pruning throughout training. Our approach, theoretically grounded on Lagrangian smoothing, is versatile and can be applied to multiple tasks, networks, and pruning structures. We show that SWD compares favorably to state-of-the-art approaches, in terms of performance-to-parameters ratio, on the CIFAR-10, Cora, and ImageNet ILSVRC2012 datasets.
Improving HW/SW Adaptability for Accelerating CNNs on FPGAs through A Dynamic/Static Co-Reconfiguration Approach

With the continuous evolution of Convolutional Neural Networks (CNNs) and the improvement of the computing capability of FPGAs, the deployment of CNN accelerator based on FPGA has become more and more popular in various computing scenarios. The key element of implementing these accelerators is to take full advantage of underlying hardware characteristics to adapt to the computational features of the software-level CNN model. To achieve this goal, however, previous designs mainly focus on the static hardware reconfiguration pattern, which is not flexible enough and can hardly make the accelerator architecture and the CNN features fully fit, resulting in inefficient computations and data communications. By leveraging the dynamic partial reconfiguration technology equipped in the modern FPGA devices, in this article, we propose a new accelerator architecture for implementing CNNs on FPGAs in which static and dynamic reconfigurabilities of the hardware are cooperatively utilized to maximize the acceleration efficiency. Based on this architecture, we further present a systematic design and optimization methodology for implementing the specific CNN model in the particular computing scenario, in which a static design space exploration method and a reinforcement learning-based decision method are proposed to obtain the optimal static hardware configuration and run-time reconfiguration strategy respectively. We evaluate our proposal by implementing three widely used CNN models, AlexNet, VGG16C, and ResNet34, on the Xilinx ZCU102 FPGA platform. Experimental results show that our implementations on average can achieve 683 GOPS under 16-bit fixed data type and 1.37 TOPS under 8-bit fixed data type for three targeted CNN models, and improve the computational density from 1.1× to 1.91× compared with previous implementations on the same type of FPGA platform.
Improving hash-q exact string matching algorithm with perfect hashing for DNA sequences

Exact string matching algorithms involve finding all occurrences of a pattern P in a text T. These algorithms have been extensively studied in computer science, primarily because of their applications in various fields such as text search and computational biology. The main goal of exact string matching algorithms is to find all pattern matches correctly within the shortest possible time frame. Although hash-based string matching algorithms run fast, there are shortcomings, such as hash collisions. In this study, a novel hash function has been proposed that eliminates hash collisions for DNA sequences. It provides us perfect hashing and produces hash values in a time-efficient manner. We have proposed two exact string matching algorithms based on the proposed hash function. In the first approach, we replace the traditional Hash-q algorithm's hash function with the proposed one. In the second approach, we improved the first approach by utilizing the shift size indicated at the (m−1)th entry in the good suffix shift table when an exact matching is found. In these approaches, we eliminate the need to compare the last q characters of the pattern and text. We have included six algorithms from the literature in our evaluations. E. Coli and Human Chromosome1 datasets from the literature and a synthetic dataset produced randomly are utilized for comparisons. The results show that the proposed approaches achieve better performance metrics in terms of the average runtime, the average number of character comparisons, and the average number of hash comparisons.