Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Computing Resource Trading in Autonomous Multi-Access Edge Network Slicing: A Dueling Double Deep Q-Learning Approach

We investigate the computing resource allocation in multi-access edge network slicing (NS) in the context of revenue and multi-access edge computing (MEC) resource management. The significant variety of slice resource utilization levels across slice tenants (i.e., Mobile Virtual Network Operators (MVNOs)) challenges MEC resource management in NS with MEC, leading to virtual machine resource (VMR) (i.e., computing resource) wastage or scarcity. As a result, for efficient MEC resource management, the infrastructure provider (InP) encourages dynamic resource sharing and trading (DRST) of unutilized slice VMR quotas. Nevertheless, cellular network security and privacy issues deter MVNOs from collaborating on effective DRST. The security characteristics inherent in blockchain have recently gained much interest for secure resource trading. Thus, this paper proposes a unique hierarchical blockchain-based inter-slice computing resource trading (ISCRT) scheme for peer-to-peer (P2P) MVNOs in an autonomous multi-sliced MEC-based 5G network. For secure ISCRT transactions, a consortium blockchain network with hyperledger smart contracts (SC) is designed. We model the demand and pricing problems of buyer and seller MVNOs for the unutilized VMRs using a two-stage Stackelberg game. Then, to obtain the Stackelberg equilibrium (SE), an enhanced dueling double deep Q-network (D3QN) algorithm is proposed, which intelligently determines the optimal demand and pricing policies of MVNOs for the unutilized VMRs during ISCRT transactions at negotiation intervals. Simulation analysis shows that the proposed enhanced D3QN algorithm outperforms benchmark schemes in terms of the MVNO slice-level satisfaction and VMR utilization while reducing double-spending attacks in ISCRT settings by 16% and increasing both players’ utility.
Learning Good Features to Transfer Across Tasks and Domains

Availability of labelled data is the major obstacle to the deployment of deep learning algorithms for computer vision tasks in new domains. The fact that many frameworks adopted to solve different tasks share the same architecture suggests that there should be a way of reusing the knowledge learned in a specific setting to solve novel tasks with limited or no additional supervision. In this work, we first show that such knowledge can be shared across tasks by learning a mapping between task-specific deep features in a given domain. Then, we show that this mapping function, implemented by a neural network, is able to generalize to novel unseen domains. Besides, we propose a set of strategies to constrain the learned feature spaces, to ease learning and increase the generalization capability of the mapping network, thereby considerably improving the final performance of our framework. Our proposal obtains compelling results in challenging synthetic-to-real adaptation scenarios by transferring knowledge between monocular depth estimation and semantic segmentation tasks.
What Really Drives Financial Sector Development in Ghana?

The chapter addresses two voids in the financial development scholarship on Ghana. First is the preferential selection of covariates deemed influential for financial development, and second, the use of traditional methods that lack regularisation powers for predicting financial development. To fill these gaps, we employ machine learning techniques to achieve two objectives—first, for identifying variables key for driving financial development in Ghana, and second, for presenting a model best for predicting financial development. To this end, we run three machine learning regularisation models namely, the Standard lasso, the Adaptive lasso and the minimum Schwarz Bayesian Information Criterion lasso, based on a dataset containing 81 potential predictors of financial development. We find that, for both regularisation and prediction objectives, the Adaptive lasso is the best. The evidence suggests that covariates such as economic globalisation, internet access and institutional quality matter for Ghana’s financial sector development. Policy recommendations are provided in the end.
Hardware-Efficient DWT Architecture for Image Processing in Visual Sensors Networks

This article proposes a fractional wavelet filter (FrWF)-based 2-D discrete wavelet transform (DWT) architecture for the 9/7 Cohen–Daubechies–Feauveau (CDF) filter employed at the wireless visual sensor nodes (VSNs) for preprocessing of images. Two very large-scale integration (VLSI) architectures of an FrWF-based 2-D DWT for a 9/7 CDF filter are proposed here. The first design employs raster scanning order as the data access scheme and is termed horizontal-FrWF (H-FrWF), whereas the second design employs vertical scanning order as the data access scheme and is termed vertical-FrWF (V-FrWF). The proposed architectures and best of the existing 2-D DWT architectures are modeled in Verilog Hardware Description Language (HDL), simulated using Vivado 2019.1, and synthesized using the gpdk090 library by Cadence Genus Synthesis Solution. The implementation results reveal that both the proposed architectures are memory-efficient and significantly improve area and power compared to state-of-the-art designs. The proposed V-FrWF architecture has constant latency, and the dependency of the memory requirement for FrWF computation on the resolution of the image is eliminated. Moreover, the proposed architectures are capable of computing the 2-D DWT of high-resolution (HR) images with high image quality, making them expedient for wireless sensor networks (WSNs).
Triplet Multi-task Learning Strategy for Person Re-identification Using Deep Learning

The majority of existing person re-identification methods are based on human part partitioning, semantic segmentation of the human body, or metric learning. However, they failed to recognize their joint benefits and mutual complementary effect. In this paper, we employ a Triplet Multi-task Learning (TML) strategy that combines three tasks simultaneously, including person re-identification, semantic segmentation, and triplet prediction. The usefulness of local and global features created by the Region Aligned Pooling (RAP) module is highlighted, as it makes the framework robust to posture variation, backdrop clutter, and occlusion. The part segmentation module is considered with the goal of handling spatial misalignment. In addition, the triplet prediction module is added to decrease the intraclass separability and increase the inter-class distance. Extensive tests show that our method outperforms prior-art techniques and consistently achieves excellent results on popular benchmark datasets such as CUHK03, Market-1501, and DukeMTMC-reID.
Hybrid Genetic-Bees Algorithm in Multi-layer Perceptron Optimization

The Multi-layer Perceptron (MLP) is extensively used in solving real-world problems. Backpropagation (BP) is often employed to deal with training data in MLP. However, this method is quickly faced with premature convergence problems and local traps. In this study, the Hybrid Genetic-Bees Algorithms (HGBA) in training MLP is reported for the first time. The genetic algorithm (GA) is used to improve the global search phase of the Bees Algorithm (BA), which is then to search for better solutions. The proposed HGBA is tested on four standard UCI classification datasets with different levels of difficulty. Experimental results show that HGBA provides significantly better performance than Particle Swarm Optimization (PSO) in training MLP with higher accuracy.
Laplacian Matrix Sampling for Communication- Efficient Decentralized Learning

We consider the problem of training a given machine learning model by decentralized parallel stochastic gradient descent over training data distributed across multiple nodes, which arises in many application scenarios. Although extensive studies have been conducted on improving the communication efficiency by optimizing what to communicate between nodes (e.g., model compression) and how often to communicate, recent studies have shown that it is also important to customize the communication patterns between each pair of nodes, which is the focus of this work. To this end, we propose a framework and efficient algorithms to design the communication patterns through Laplacian matrix sampling (LMS), which governs not only which nodes should communicate with each other but also what weights the communicated parameters should carry during parameter aggregation. Our framework is designed to minimize the total cost incurred until convergence based on any given cost model that is additive over iterations, with focus on minimizing the communication cost. Besides achieving a theoretically guaranteed performance in the special case of additive homogeneous communication costs, our solution also achieves superior performance under a variety of network settings and cost models in experiments based on real datasets and topologies, saving 24–50% of the cost compared to the state-of-the-art design without compromising the quality of the trained model.
Efficiency of a Visual Search Explained by the Small-World Features of a Gaze Position Network

A visual search is implemented when the eye moves to find a target symbol amongst many other symbols (distractors). The efficiency of a visual search is described by Hick's law, which shows that the search time increases logarithmically as the number of symbols increases. In this paper, the efficiency of visual search was analyzed from the perspective of the network features of a conceptual ‘unobservable’ gaze position network superimposed on a monitor screen filled with many symbols (search array board). We assume that the gaze position does not move freely around the search array board, but rather moves in a way restricted to the unobservable gaze position network. First, we statistically verified that the artificial gaze position network designed from the data of visual search experiments have small-world features, and depends on the ratio of the saccades. Second, by implementing gaze step simulations on such small-world networks, we statistically verified that the simulation search times were close to those obtained from the experiments and also to the minimum search times. Thus, this study suggests that an efficient visual search can be explained by a small-world architecture hidden in the unobservable gaze position network and thus has to be artificially designed.
Surface and Sublevel Hate

On the face of it, contemporary “alt-tech” platforms appear more moderate than legacy hate havens. Yet it's also clear that virulent hate in the form of misogyny, white supremacy, and xenophobia has not disappeared. Probing this tension, this article conceptualizes two forms of hate: Surface “Hate” (moderate content that is highly visible and easily accessible) and Sublevel Hate (explicit content that is more marginal and less discernible). These terms are illustrated by examining several viral videos on Rumble. This twinned mechanism explains how alt-tech platforms can be both accessible and extreme at the same time. Stratified hate is strategic, heightening the appeal and durability of online communities. Recognizing this dangerous dynamic is key for interventions seeking to counter it.
Fuzzy attack tree analysis of security threat assessment in an internet security system using algebraic t-norm and t-conorm

In this fast-growing world, the internet has become one of the important tools of modern society in various ways, such as communication, transactions, security, and real-time monitoring. At the same time, internet security threats play a crucial role in accomplishing all the intended tasks safely. So internet security threat assessment becomes a greater concern due to the advancement of information technology. To analyze internet security threat assessment, a novel fuzzy attack tree analysis (FATA) method under uncertainty has been proposed in this chapter. The method integrates attack tree analysis (ATA) for modeling the problem, triangle intuitionistic fuzzy set (TIFS) for quantifying data uncertainty and hesitation factor, while algebraic t-norm and t-conorm-based arithmetic operations defined on TIFS are applied to evaluate membership and nonmembership degrees of top goal failure probability in terms of TIFS. The benefit of using algebraic t-norm and t-conorm-based arithmetic is that they involve membership and nonmembership degrees of each bottom event in evaluating top goal failure probability in terms of TIFS. The proposed FATA method has been applied to analyze the fuzzy failure probability of a security threat in an internet security system. The computed results are compared with results obtained from three other existing methods to show the effectiveness of the proposed FATA method. The results conclude that the proposed FATA method optimizes the prediction range of failure probability at any cut level α.
Deep Learning-Based Signal Detection for Rate-Splitting Multiple Access Under Generalized Gaussian Noise

In this paper, we propose a long short-term memory-based deep learning (DL) architecture for signal detection in uplink and downlink rate-splitting multiple access systems with multi-carrier modulation, over Nakagami-m fading and generalized Gaussian noise (GGN).The proposed DL detector completely eliminates the need for the use of successive interference cancellation (SIC), which suffers from disadvantages such as error propagation.In an orthogonal frequency division multiplexing setting, we show that the proposed DL detector outperforms the standard SIC receivers such as the least squares detector and the minimum mean-squared error receiver, and attains the performance of the optimal maximum likelihood detector, in terms of the symbol error rate (SER).Furthermore, we study the effects of the shaping parameter of GGN, hyperparameters of the DL network such as batch size and learning rate on the SER performance.INDEX TERMS Deep learning (DL), generalized Gaussian noise (GGN), long short-term memory (LSTM), non-orthogonal multiple access (NOMA), rate-splitting multiple access (RSMA). I. INTRODUCTION A. BACKGROUND
Blockchain and Smart Contracts for Secure and Sustainable Development

Smart contracts play an important role of simplifying business and trade between the identified and anonymous parties without need of any intermediary to handle the transaction, as well as the intermediary's associated fees and time delays. A business can easily bring the transparency and build the trust of the customers by storing the encrypted records of transaction across participants. It also prevents hackers from breaking the integrity of the transaction because each record is connected on a distributed ledger, and if hackers want to alter any particular record, he/she would have to modify the entire chain for a single record. Therefore, a smart contract has huge potential to streamline and automate the business work across industries around the world. In this chapter, the authors will explore the potential of blockchain and smart contracts in the different aspects of business world, like supply chain, insurance, mortgage loans, financial settlements, and many more applications in the financial industry.
Developing MFCC-CNN Based Voice Recognition System with Data Augmentation and Overfitting Solving Techniques

The ever-increasing need for intelligent systems technology capable of enhancing human-machine interaction in different areas of life has become a very important topic. However, since speech is the main used method of communication, deep Learning-based speech recognition systems still have poor performance when trained on small datasets. To solve this problem, an automated speech recognition system based on a convolutional neural network and MFCC algorithm, is proposed in this paper. Firstly, the data augmentation technique is applied to the dataset in order to double it. Then, MFCCs are extracted from all the audio files. After that, CNN model is trained on the MFCCs with the help of early stopping and dropout techniques to prevent the overfitting problem. Finally, the best model with the highest performance is selected. Simulation results on Kaggle TensorFlow Speech Recognition Challenge dataset, which contains 20 basic command words, showed that the selected model achieved 88.35% accuracy.
Improved Weakly Supervised Image Semantic Segmentation Method Based on SEC

The study of weakly supervised image semantic segmentation based on weak supervision is received increasing attention. The segmentation algorithm based on seed, expansion, and constraint (SEC) principles is relatively effective. However, this method also suffers from sparsity and discontinuity of initialized seeds. It causes over-growth or under-growth of seeds. This paper proposes an improved SEC weakly supervised image semantic segmentation method. This method enhances the initial seed leads generated by SEC and guides the seed growth through the saliency map generated by the self-attentive network. We also introduce a new loss function to enhance the model’s ability to acquire edge information and optimize the final segmentation network. The effectiveness of the proposed method is verified by training the segmentation network on the PASCAL VOC 2012.
A Weighted Naive Bayes for Image Classification Based on Adaptive Genetic Algorithm

Naive Bayes (NB) is a simple and widely used classification model, but due to the conditional independence assumption, the accuracy of NB is not very competitive in the field of image recognition. Therefore, this paper proposes a Weighted Naive Bayes classification algorithm with an Adaptive Genetic Algorithm (AGA_WNB), which is used to reduce the impact of this assumption. First, reduce the dimensionality of the image and binarize the image. Then, the initial weights of the features are used as the initial population, and the classification accuracy of the Weighted Naive Bayes (WNB) model is used as the fitness function. Adjust the crossover probability and mutation probability according to the fitness function, and select the better chromosome to enter the next generation. Finally, the optimal weights are selected by iteration. The experimental results on the public dataset MNIST show that under the same environment, the average accuracy of AGA_WNB is 3.25% higher than Weighted Naive Bayes based on Genetic Algorithm (GA_WNB) and 9.7% higher than NB. The single digit accuracy of AGA_WNB is 19% higher than NB. Compared with the comparison methods, the accuracy of AGA_WNB is also improved, and it has a good application prospect.
Diagnosis of breast cancer from histopathological images with deep learning architectures

Breast cancer is one of the most common cancer types among women worldwide. If not treated in earlier stages, it may be fatal. Therefore early diagnosis of breast cancer can minimize the human life risk. Mammograms and ultrasound imaging technologies play a crucial role to detect intraductal papillomas. However, the determination process of intraductal papillomas requires histopathological image analysis which may be mostly time-consuming, subjective, and tedious if carried out manually by the experts. To cover issue, computer-aided diagnosis (CAD) systems came into consideration. However, earlier CAD systems could not achieve significant improvement in the diagnosis process and their usage of them did not become widespread for more than a decade. Since deep learning has made so many significant advances in a wide variety of image applications, CAD systems that use its principles perform as well as the experts in stand-alone mode, and even perform better when used in support mode. In this chapter, we utilized various deep learning architectures for the detection process of breast cancer on the invasive ductal carcinoma (IDC) dataset which is one of the most popular and remarkable datasets in this field. According to the results, the pretrained VGG16 and MobileNet architectures obtain the best detection performance, reaching nearly 92% classification accuracy.
Technological Shock in Digital Transformation of Higher Education in Bangladesh

The outbreak of the novel coronavirus has acutely disrupted the higher education system in Bangladesh. Its erratic nature forced the government of Bangladesh to close all the educational institutions on March 18, 2020. A digital transformation in the education industry facilitated education during this prolonged lockdown. Undoubtedly, this dramatic change introduced both teachers and students to unexpected challenges. This study aimed to unveil the challenges the students and teachers faced and focus on their overall experience adapting to the digital transformation in higher education during COVID-19. This study employed the semistructured interview with open-ended questions using qualitative content analysis and thematic analysis. The interview was conducted with two focused groups, including 109 students and 49 teachers from seven private universities in Bangladesh. The result shows that most of the respondents mainly struggled with lack of technical preparation, unavailability of resources, poor infrastructure of the Internet, and additional costs associated with e-learning.
Automated Threshold Learning for Feature Selection Optimization

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Optimization of TiO2/Ag photoanode thickness for improvement dye-sensitized solar cell (DSSC) performance with simulation method

Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Edy Supriyanto, Lutfiatun Ni’mah, Sujito Sujito, Nova Alviati, Antin Dikayanti, Agus Geter Edy Sutjipto; Optimization of TiO2/Ag photoanode thickness for improvement dye-sensitized solar cell (DSSC) performance with simulation method. AIP Conference Proceedings 7 February 2023; 2682 (1): 030002. https://doi.org/10.1063/5.0118273 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAIP Publishing PortfolioAIP Conference Proceedings Search Advanced Search |Citation Search
EMOD: Efficient Moving Object Detection via Image Eccentricity Analysis and Sparse Neural Networks

This paper proposes an efficient moving objects detection pipeline focusing on dynamic object detection on video streams captured by traffic monitoring cameras. While developing autonomous vehicle systems, we found that views from self-driving vehicles can be occluded by dynamic or static objects on the street. Whereas infrastructure nodes such as traffic monitoring cameras having broader field-of-views and better perspectives can be used as auxiliary sensors to share traffic information with nearby self-driving cars in real-time. However, these infrastructure cameras usually have constrained computation resources, and detecting hundreds of static background objects in consecutive video frames is wasteful. In our detection pipeline, we leverage the image eccentricity analysis as a pre-processing step to fast generate moving objects segmentation maps. These maps are used to mask the original images to get images that only contain the moving objects in the scene. These sparse images are then passed to an object detection model built with a sparse convolution backbone network, resulting in significant reduction in computational costs. Our quantitative experiments illustrate that the proposed detection pipeline can achieve up to 50% inference speedup with negligible detection accuracy drop in images obtained from traffic monitoring cameras.