Reviewerly/exHarmony huggingface dataset:
Orrs Orchestration of a Resource Reservation System Using Fuzzy Theory in High-Performance Computing

In the computing world, every company is focusing on the early reservation system. In the early reservation the price, quality of service, time, and everything is maintained and reserved for the user on the due interval. ORRS is a step toward this global vision and specifically designed to act as a flexible reservation supporting multiple services according to user's need and pricing (such as traditional immediate reservation (TIR) request and ORRS reservation. Thus, it is both important and challenging to reserve computing services and the cost of the services in an efficient manner. In the ORRS reservation system, the fuzzy rough set feature selection decreases the dimensional structure and problems of a large database in the computing world. The researcher is now going at a rapid speed in the field of maintaining cost based on decision making like on-demand, reserved, the spot to increase resources utilization and profit. The implementation had done in our research work by the use of cloud simulator.
One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy

Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to infer a data record’s membership in a dataset or even reconstruct this data record using a confidence score vector predicted by the target model. However, most existing defense methods only protect against membership inference attacks. Methods that can combat both types of attacks require a new model to be trained, which may not be time-efficient. In this paper, we propose a differentially private defense method that handles both types of attacks in a time-efficient manner by tuning only one parameter, the privacy budget. The central idea is to modify and normalize the confidence score vectors with a differential privacy mechanism which preserves privacy and obscures membership and reconstructed data. Moreover, this method can guarantee the order of scores in the vector to avoid any loss in classification accuracy. The experimental results show the method to be an effective and timely defense against both membership inference and model inversion attacks with no reduction in accuracy.
Attention-Based Multimodal Image Feature Fusion Module for Transmission Line Detection

Transmission line (TL) inspection is important for ensuring a stable supply of electricity to rural areas. Currently, there are several TL detection approaches based on computer vision; however, they have limitations owing to background clutter in visible light images. This article presents a novel multimodal image feature fusion module that utilizes both visible light and infrared images to enhance the TL-detection performance. The proposed module consists of a multibranch feature extraction (MFE) block followed by a channelwise attention (CA) block. The first block extracts the representative features of each modal input using multiple branches. The outputs of the MFE block are jointly aggregated into an attention vector in the CA block. Finally, the attention vector recalibrates each input feature of the proposed module. To reduce the number of additional parameters due to the insertion of the module, we introduced a channel-shrink factor in the MFE block and utilized a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1\times {1}$</tex-math></inline-formula> convolution in the CA block. Comparison experiments with various augmented conditions of day, night, fog, and snow were conducted on a real-world dataset, which we constructed by visible light and infrared images. The results showed that the proposed module outperformed not only the case of single modal input but also the state-of-the-art fusion methods, regardless of the baseline networks. Additionally, the proposed module showed effectiveness in terms of capacity when the baseline network has a large number of weight parameters.
Augmenting Sports Videos with VisCommentator

Visualizing data in sports videos is gaining traction in sports analytics, given its ability to communicate insights and explicate player strategies engagingly. However, augmenting sports videos with such data visualizations is challenging, especially for sports analysts, as it requires considerable expertise in video editing. To ease the creation process, we present a design space that characterizes augmented sports videos at an element-level (what the constituents are) and clip-level (how those constituents are organized). We do so by systematically reviewing 233 examples of augmented sports videos collected from TV channels, teams, and leagues. The design space guides selection of data insights and visualizations for various purposes. Informed by the design space and close collaboration with domain experts, we design VisCommentator, a fast prototyping tool, to eases the creation of augmented table tennis videos by leveraging machine learning-based data extractors and design space-based visualization recommendations. With VisCommentator, sports analysts can create an augmented video by selecting the data to visualize instead of manually drawing the graphical marks. Our system can be generalized to other racket sports (e.g., tennis, badminton) once the underlying datasets and models are available. A user study with seven domain experts shows high satisfaction with our system, confirms that the participants can reproduce augmented sports videos in a short period, and provides insightful implications into future improvements and opportunities.
Identity-Unrelated Information Decoupling Model for Vehicle Re-Identification

As an indispensable part of intelligent transportation system (ITS), vehicle re-identification (Re-ID) aims to retrieve all target vehicle images captured from non-overlapping cameras. However, this task remains very challenging due to the variation of camera perspective and the similar appearance among the vehicles with the same type and color, i.e., large intra-class variances and small inter-class variances. Previous methods have made a great progress on vehicle Re-ID by leveraging local details and aligning local features, this issue is still far from being solved. In this work, we propose to decouple identity-unrelated information from vehicle representation, tackling the problems of camera perspective variation and vehicle appearance similarity. The keypoint of this method is to learn a distinguishable feature embedding that is independent of identity-unrelated information. Specially, the novel Identity-Unrelated Information Decoupling (IUID) paradigm is designed to learn invariant features of the vehicle with the same ID in different scenes. In our approach, identity-unrelated information can be divided into two kinds of information, i.e., camera perspective information and background information. For the former, through a feature-level camera generative adversarial module, we can decouple camera perspective information from the feature embedding after extracting invariant features across different cameras perspective. For the latter, we propose a vehicle-mask transformer to enhance the attention of the model on local details while reducing the impact of background information. Extensive experiments on two public datasets demonstrate the superiority of IUID over the current state-of-the-arts methods.
Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

Although the multi-layer perceptron (MLP) neural networks provide a lot of flexibility and have proven useful and reliable in a wide range of classification and regression problems, they still have limitations.One of the most common is associated with the optimization algorithm used to train them.The most commonly used training method is stochastic gradient descent with backpropagation (or backpropagation for short) because it is mathematically tractable (given that the activation functions are differentiable).However, backpropagation is not guaranteed to find the globally optimal set of weights and biases.As a result, the MLP is often incapable of obtaining a desirable solution to the problem.Clonal selection algorithms (CSA) are optimization procedures that effectively explore a complex and large space to find values near the global optimum.Consequently, CSA can be used to solve the problem of training MLP networks.This paper presents a novel implementation of CSA for training MLP architectures to solve real-world problems such as breast cancer diagnosis, active sonar target classification, wheat classification, and flower classification.The CSA is used to find the optimal weights and biases that will significantly increase the classification accuracy of the MLP.The performance of our proposed approach is compared with other popular training methods: genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), Harris hawks optimization (HHO), moth-flame optimization (MFO), flower pollination algorithm (FPA), and backpropagation (BP).The comparison is benchmarked using five classification datasets: Iris Flower, Sonar, Wheat Seeds, Breast Cancer Wisconsin, and Haberman's Survival.Comparative study results illustrate the improvements in MLP performance gained by using CSA over other training methods, and hence it can be considered a competitive approach to training MLP networks when solving real-world applications in various disciplines.
Orthogonal Transfer for Multitask Optimization

Knowledge transfer (KT) plays a key role in multitask optimization. However, most of the existing KT methods still face two challenges. First, the tasks may commonly have different dimensionalities (DDs), making the KT between heterogeneous search spaces very difficult. Second, the tasks may have different degrees of similarity in different dimensions, making that treating all dimensions with equal importance may be harmful to the KT process. To address these two challenges, this article proposes a novel orthogonal transfer (OT) method that is enabled by a cross-task mapping (CTM) strategy, which can achieve high-quality KT among heterogeneous tasks. For the first challenge, the CTM strategy maps the global best individual of one task from its original search space to the search space of the target task via an optimization process, which can handle the difference in task dimensionality. For the second challenge, the OT method is performed on the CTM-obtained individual and a random individual of the target task to find the best combination of different dimensions in these two individuals rather than treating all the dimensions equally, so as to achieve high-quality KT. To verify the effectiveness of the proposed OT method and the resulted OT-based multitask optimization (OTMTO) algorithm, this article not only uses the existing multitask optimization benchmark but also proposes a new benchmark test suite named multitask optimization problems (MTOPs) with DDs. Comprehensive experimental results on the existing and the proposed benchmarks show that the proposed OT method and the OTMTO algorithm are very advantageous in providing high-quality KT and in handling the heterogeneity of search space in MTOPs compared to the existing competitive evolutionary multitask optimization (EMTO) algorithms.
Bangla Natural Language Processing: A Comprehensive Analysis of Classical, Machine Learning, and Deep Learning-Based Methods

The Bangla language is the seventh most spoken language, with 265 million native and non-native speakers worldwide. However, English is the predominant language for online resources and technical knowledge, journals, and documentation. Consequently, many Bangla-speaking people, who have limited command of English, face hurdles to utilize English resources. To bridge the gap between limited support and increasing demand, researchers conducted many experiments and developed valuable tools and techniques to create and process Bangla language materials. Many efforts are also ongoing to make it easy to use the Bangla language in the online and technical domains. There are some review papers to understand the past, previous, and future Bangla Natural Language Processing (BNLP) trends. The studies are mainly concentrated on the specific domains of BNLP, such as sentiment analysis, speech recognition, optical character recognition, and text summarization. There is an apparent scarcity of resources that contain a comprehensive review of the recent BNLP tools and methods. Therefore, in this paper, we present a thorough analysis of 75 BNLP research papers and categorize them into 11 categories, namely Information Extraction, Machine Translation, Named Entity Recognition, Parsing, Parts of Speech Tagging, Question Answering System, Sentiment Analysis, Spam and Fake Detection, Text Summarization, Word Sense Disambiguation, and Speech Processing and Recognition. We study articles published between 1999 to 2021, and 50% of the papers were published after 2015. Furthermore, we discuss Classical, Machine Learning and Deep Learning approaches with different datasets while addressing the limitations and current and future trends of the BNLP.
Automating Vehicle Insurance Process Using Smart Contract and Ethereum

The existing vehicle insurance system faces number of challenges like frauds, absence of P2P insurance, no timely settlement of claims and absence of on-demand insurance. However, the existing vehicle insurance system is centralized, and not settlement of claims efficiently. Hence, to dissolve this issue, this paper presents a distributed vehicle insurance system. Blockchain is an open, public, cryptographically hashed system that gives immutability and authenticity through digital signature incorporated with hash value. Blockchain exploits hash functions and encryption in the direction of accumulation of incontrovertible records which are more strongly secure over various present very similar cybersecurity resolutions technologies. On the other hand, the existing security deals with a single trusted authority to authenticate information or accumulate encrypted data. In this paper, we proposed a decentralized application (DApp), which deliberates the introduction of blockchain technology followed by Ethereum and smart contracts. Hence, for implementing the smart contract, public blockchain platform used. Finally, the React is used for frontend development.
MISF:Multi-level Interactive Siamese Filtering for High-Fidelity Image Inpainting

Although achieving significant progress, existing deep generative inpainting methods still show low generalization across different scenes. As a result, the generated images usually contain artifacts or the filled pixels differ greatly from the ground truth, making them far from real-world applications. Image-level predictive filtering is a widely used restoration technique by predicting suitable kernels adaptively according to different input scenes. Inspired by this inherent advantage, we explore the possibility of addressing image inpainting as a filtering task. To this end, we first study the advantages and challenges of the image-level predictive filtering for inpainting: the method can preserve local structures and avoid artifacts but fails to fill large missing areas. Then, we propose the semantic filtering by conducting filtering on deep feature level, which fills the missing semantic information but fails to recover the details. To address the issues while adopting the respective advantages, we propose a novel filtering technique, i.e., Multi-level Interactive Siamese Filtering (MISF) containing two branches: kernel prediction branch (KPB) and semantic & image filtering branch (SIFB). These two branches are interactively linked: SIFB provides multi-level features for KPB while KPB predicts dynamic kernels for SIFB. As a result, the final method takes the advantage of effective semantic & image-level filling for high-fidelity inpainting. Moreover, we discuss the relationship between MISF and the naive encoder-decoder-based inpainting, inferring that MISF provides novel dynamic convolutional operations to enhance the high generalization capability across scenes. We validate our method on three challenging datasets, i.e., Dunhuang, Places2, and CelebA. Our method outperforms state-of-the-art baselines on four metrics, i.e., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$L_{1}$</tex> , PSNR, SSIM, and LPIPS.
Sports Data Management, Mining, and Visualization

Data are everywhere. Examples include sports data. Embedded in these data is implicit, previously unknown and potentially useful information or knowledge to be discovered. In this paper, we present a solution for sports data management, mining and visualization. In particular, we focus on basketball data. Basketball is a culture and is respected by fans around the world. Ever since its birth, basketball has changed drastically. Under such effects, basketball discussion and analysis evolved as well. Our solution adapts three different approaches for predicting the win. Evaluation on real-life basketball data show the effectiveness of our solution.
Hierarchical Feature Aggregation Based on Transformer for Image-Text Matching

In order to carry out more accurate retrieval across image-text modalities, some scholars use fine-grained feature to align image and text. Most of them directly use attention mechanism to align image regions and words in the sentence, and ignore the fact that semantics related to an object is abstract and cannot be accurately expressed by object information alone. To overcome this weakness, we propose a hierarchical feature aggregation algorithm based on graph convolutional networks (GCN) to facilitate object semantic integrity by integrating attributes of an object and relations between objects hierarchically in both image and text modalities. In order to eliminate the semantic gap between modalities, we propose a cross-modal feature fusion method based on transformer to generate modal-specific feature representations by integrating both the object feature and global feature from the other modality. Then we map the fusion feature into a common space. Experiment results on the most frequently-used datasets MSCOCO and Flickr30K show the effectiveness of the proposed model compared with the latest methods.
Aeolus: Distributed Execution of Permissioned Blockchain Transactions via State Sharding

Blockchain has attracted lots of attention in recent years. However, the performance of blockchain cannot meet the requirement of massive Internet of Things (IoT) devices. One of the important bottlenecks of blockchain is the limited computing resources on a single server while executing transactions. To address this issue, we propose Aeolus blockchain to achieve the distributed execution of blockchain transactions. There are two key challenges to achieving this for IoT blockchain: transaction structure and state consistency. Facing these challenges, we first propose a distributed blockchain transaction structure, which imports extra parameters to divide the transaction execution into different stages to enable distributed execution. Second, we propose distributed state update sharding, which equips each blockchain peer with its own master and shard servers. In this way, each blockchain peer can be considered as a cluster that distributes the transaction to shorten the processing time and reach the consensus finally. We implement Aeolus on Go-Ethereum to evaluate its feasibility, on a testbed including 132 cloud servers. Our system runs stably for more than 8 h under the workload of 190 000 000 real-world user transactions. Experimental results show the efficiency that Aeolus can achieve more than 100 000 transactions/s of blockchain transactions, which is 15.6 times the throughput of the original blockchain.
3D Multi-Object Tracking With Adaptive Cubature Kalman Filter for Autonomous Driving

A crucial and challenging issue in autonomous driving is dynamic road environment detection and 3D multi object tracking. In this article, we propose a novel framework for online 3D multi-object tracking to eliminate the influence of inherent uncertainty and unknown biases in point cloud. A constant turn rate and velocity (CTRV) motion model is employed to estimate the future motion state, which are smoothed by a cubature Kalman filter (CKF) algorithm. A new affinity model is introduced to evaluate the similarity between trajectories and candidate detections for accurate and reliable data association which can be formulated as a bipartite matching problem. An adaptive cubature Kalman filter (ACKF) is given to remove the influence of unknown bias and to robustly update the tracked state. Accuracy and speed of the proposed tracking method are evaluated on the KITTI 3D multi-object tracking dataset, showing superior performance than the baselines.
A modified adaptive guided differential evolution algorithm applied to engineering applications

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC’2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising.
Multivariate time series forecasting method based on nonlinear spiking neural P systems and non-subsampled shearlet transform

Multivariate time series forecasting remains a challenging task because of its nonlinear, non-stationary, high-dimensional, and spatial-temporal characteristics, along with the dependence between variables. To address this limitation, we propose a novel method for multivariate time series forecasting based on nonlinear spiking neural P (NSNP) systems and non-subsampled shearlet transform (NSST). A multivariate time series is first converted into the NSST domain, and then NSNP systems are automatically constructed, trained, and predicted in the NSST domain. Because NSNP systems are used as nonlinear prediction models and work in the NSST domain, the proposed prediction method is essentially a multiscale transform (MST)-based prediction method. Therefore, the proposed prediction method can process nonlinear and non-stationary time series, and the dependence between variables can be characterized by the multiresolution features of the NSST transform. Five real-life multivariate time series were used to compare the proposed prediction method with five state-of-the-art and 28 baseline prediction methods. The comparison results demonstrate the effectiveness of the proposed method for multivariate time-series forecasting.