Reviewerly/exHarmony huggingface dataset:
Price Prediction of Bitcoin Based on Adaptive Feature Selection and Model Optimization

Bitcoin is one of the most successful cryptocurrencies, and research on price predictions is receiving more attention. To predict Bitcoin price fluctuations better and more effectively, it is necessary to establish a more abundant index system and prediction model with a better prediction effect. In this study, a combined prediction model with twin support vector regression was used as the main model. Twenty-seven factors related to Bitcoin prices were collected. Some of the factors that have the greatest impact on Bitcoin prices were selected by using the XGBoost algorithm and random forest algorithm. The combined prediction model with support vector regression (SVR), least-squares support vector regression (LSSVR), and twin support vector regression (TWSVR) was used to predict the Bitcoin price. Since the model’s hyperparameters have a great impact on prediction accuracy and algorithm performance, we used the whale optimization algorithm (WOA) and particle swarm optimization algorithm (PSO) to optimize the hyperparameters of the model. The experimental results show that the combined model, XGBoost-WOA-TWSVR, has the best prediction effect, and the EVS score of this model is significantly better than that of the traditional statistical model. In addition, our study verifies that twin support vector regression has advantages in both prediction effect and computation speed.
On Filtering and Smoothing Algorithms for Linear State-Space Models Having Quantized Output Data

The problem of state estimation of a linear, dynamical state-space system where the output is subject to quantization is challenging and important in different areas of research, such as control systems, communications, and power systems. There are a number of methods and algorithms to deal with this state estimation problem. However, there is no consensus in the control and estimation community on (1) which methods are more suitable for a particular application and why, and (2) how these methods compare in terms of accuracy, computational cost, and user friendliness. In this paper, we provide a comprehensive overview of the state-of-the-art algorithms to deal with state estimation subject to quantized measurements, and an exhaustive comparison among them. The comparison analysis is performed in terms of the accuracy of the state estimation, dimensionality issues, hyperparameter selection, user friendliness, and computational cost. We consider classical approaches and a new development in the literature to obtain the filtering and smoothing distributions of the state conditioned to quantized data. The classical approaches include the extended Kalman filter/smoother, the quantized Kalman filter/smoother, the unscented Kalman filter/smoother, and the sequential Monte Carlo sampling method, also called particle filter/smoother, with its most relevant variants. We also consider a new approach based on the Gaussian sum filter/smoother. Extensive numerical simulations—including a practical application—are presented in order to analyze the accuracy of the state estimation and the computational cost.
Tri-Training Algorithm for Adaptive Nearest Neighbor Density Editing and Cross Entropy Evaluation

Tri-training expands the training set by adding pseudo-labels to unlabeled data, which effectively improves the generalization ability of the classifier, but it is easy to mislabel unlabeled data into training noise, which damages the learning efficiency of the classifier, and the explicit decision mechanism tends to make the training noise degrade the accuracy of the classification model in the prediction stage. This study proposes the Tri-training algorithm for adaptive nearest neighbor density editing and cross-entropy evaluation (TTADEC), which is used to reduce the training noise formed during the classifier iteration and to solve the problem of inaccurate prediction by explicit decision mechanism. First, the TTADEC algorithm uses the nearest neighbor editing to label high-confidence samples. Then, combined with the relative nearest neighbor to define the local density of samples to screen the pre-training samples, and then dynamically expand the training set by adaptive technique. Finally, the decision process uses cross-entropy to evaluate the completed base classifier of training and assign appropriate weights to it to construct a decision function. The effectiveness of the TTADEC algorithm is verified on the UCI dataset, and the experimental results show that compared with the standard Tri-training algorithm and its improvement algorithm, the TTADEC algorithm has better classification performance and can effectively deal with the semi-supervised classification problem where the training set is insufficient.
Domain-Aware Adaptive Logarithmic Transformation

Tone mapping (TM) aims to display high dynamic range scenes on media with limited visual information reproduction. Logarithmic transformation is a widely used preprocessing method in TM algorithms. However, the conventional logarithmic transformation does not take the difference in image properties into account, nor does it consider tone mapping algorithms, which are designed based on the luminance or gradient-domain features. There will be problems such as oversaturation and loss of details. Based on the analysis of existing preprocessing methods, this paper proposes a domain-aware adaptive logarithmic transformation AdaLogT as a preprocessing method for TM algorithms. We introduce the parameter p and construct different objective functions for different domains TM algorithms to determine the optimal parameter values adaptively. Specifically, for luminance-domain algorithms, we use image exposure and histogram features to construct objective function; while for gradient-domain algorithms, we introduce texture-aware exponential mean local variance (EMLV) to build objective function. Finally, we propose a joint domain-aware logarithmic preprocessing method for deep-neural-network-based TM algorithms. The experimental results show that the novel preprocessing method AdaLogT endows each domain algorithm with wider scene adaptability and improves the performance in terms of visual effects and objective evaluations, the subjective and objective index scores of the tone mapping quality index improved by 6.04% and 5.90% on average for the algorithms.
SuperDet: An Efficient Single-Shot Network for Vehicle Detection in Remote Sensing Images

Vehicle detection in remote sensing images plays an important role for its wide range of applications. However, it is still a challenging task due to their small sizes. In this paper, we propose an efficient single-shot-based detector, called SuperDet, which achieves a combination of a super resolution algorithm with a deep convolutional neural network (DCNN)-based object detector. In SuperDet, there are two interconnected modules, namely, the super resolution module and the vehicle detection module. The super resolution module aims to recover a high resolution sensing image from its low resolution counterpart. With this module, the small vehicles will have a higher resolution, which is helpful for their detection. Taking the higher resolution image as input, the vehicle detection module extracts the features and predicts the location and category of the vehicles. We use a multi-task loss function to train the network in an end-to-end way. To assess the detection performance of SuperDet, we conducted experiments between SuperDet and the classical object detectors on both VEDAI and DOTA datasets. Experimental results indicate that SuperDet outperforms other detectors for vehicle detection in remote sensing images.
Controller Design for Parallel Mechanism Solar Tracker

Solar energy is considered a sustainable solution that has proven its technological competence for electricity generation among renewable energy sources. While green resources provide high energy security, they also reduce environmental pollution and support the use of local resources. In this article, a dual axis solar tracker that can operate with high accuracy in harsh operating conditions is proposed using the Stewart platform. The Stewart platform is designed using linear actuators using direct current (DC) motors. An embedded controller is designed to control the motors and to realize the sun tracking algorithm of the system. An STM32 board is adopted as a real-time controller to implement the decoupled control algorithm. Therefore, the proposed solar tracker panel control system monitors the daily trajectory of the sun by the photovoltaic panel, ensuring that the system’s energy production remains at its maximum throughout the day. First, the Simulink model of the system was created and the proportional derivative integral (PID) control algorithms were simulated. Experimental studies were carried out by producing the system; the experimental results exhibited a better performance, with an increase in the collected energy of about 32% compared with the fixed one.
Fusion of CCTV Video and Spatial Information for Automated Crowd Congestion Monitoring in Public Urban Spaces

Crowd congestion is one of the main causes of modern public safety issues such as stampedes. Conventional crowd congestion monitoring using closed-circuit television (CCTV) video surveillance relies on manual observation, which is tedious and often error-prone in public urban spaces where crowds are dense, and occlusions are prominent. With the aim of managing crowded spaces safely, this study proposes a framework that combines spatial and temporal information to automatically map the trajectories of individual occupants, as well as to assist in real-time congestion monitoring and prediction. Through exploiting both features from CCTV footage and spatial information of the public space, the framework fuses raw CCTV video and floor plan information to create visual aids for crowd monitoring, as well as a sequence of crowd mobility graphs (CMGraphs) to store spatiotemporal features. This framework uses deep learning-based computer vision models, geometric transformations, and Kalman filter-based tracking algorithms to automate the retrieval of crowd congestion data, specifically the spatiotemporal distribution of individuals and the overall crowd flow. The resulting collective crowd movement data is then stored in the CMGraphs, which are designed to facilitate congestion forecasting at key exit/entry regions. We demonstrate our framework on two video data, one public from a train station dataset and the other recorded at a stadium following a crowded football game. Using both qualitative and quantitative insights from the experiments, we demonstrate that the suggested framework can be useful to help assist urban planners and infrastructure operators with the management of congestion hazards.
Efficient secure data storage based on novel blockchain model over IoT-based smart computing systems

In recent years, edge-related smart computing is the way to process and store data via outsourcing environments. Security is the main progressive concept in smart-related Internet of Things (IoT) software denied networks to share and increase the scalability and efficiency in data storage. Because of the rapid growth of different smart services, different security-related problems may appear in resource processing and sharing data in real-time computing systems. To increase the reliability in secure data storage and satisfy the basic requirements related to IoT-related smart computing networks. So propose and implement a Novel Artificial Intelligence based Blockchain Secure Model (NAIBSM) to provide efficient secure data storage in IoT-related smart computing systems. This model flexibly captures each user authentication for the detection of different user-related attacks (i.e. distributed denial-of-service (DDOS)) in the storage of data via applying Artificial Intelligence (AI) calculation method i.e. Leverage Bat algorithm to explore complex features. Build a blockchain at the server side to provide secure communication and reliability of storing data on the terminal of IoT. This approach provides random hash values to each data to ensure blockchain for the integrity of data and uses a weight-based data storage procedure to arrange/store and classify data to each user with secure and unsecured storage in smart computing networks. The experimental results of the proposed model are to explore complex security features and ensure the performance of secure authentication to each user and better security, accuracy, and low communication overhead in IoT-related smart computing data storage and sharing systems.
Combining object‐oriented metrics and centrality measures to predict faults in object‐oriented software: An empirical validation

Abstract Many object‐oriented metrics have been proposed in the literature to measure various structural properties of object‐oriented software. Furthermore, many centrality measures have been introduced to identify central nodes in large networks. However, few studies have used them to measure dependencies in software systems. In fact, centrality measures, as opposed to most traditional object‐oriented metrics that mainly focus on intrinsic properties of classes, can be used to better model the control flow and to identify the most important classes in a software system. This paper aims (1) to investigate the relationships between object‐oriented metrics and centrality measures and (2) to explore the ability of their combination to support fault‐proneness prediction from different perspectives (fault‐prone classes, fault severity, and number of faults). Many studies in the literature have addressed the prediction of fault‐prone classes, from different perspectives, using object‐oriented metrics. The main motivation here is in fact to investigate if the information captured by centrality measures is related to fault proneness and complementary to the information captured by object‐oriented metrics and to investigate if the combination of object‐oriented metrics and centrality measures improves the performance of fault‐proneness prediction significantly. We used size, complexity, and coupling object‐oriented metrics in addition to various centrality measures. We collected data from 20 different versions of five open‐source Java software systems. We first studied the relationships between selected metrics and their relationships to fault proneness. Then, we built different models to predict fault‐prone classes using several machine learning algorithms. In addition, we built models to predict if a class contains a high severity fault, and the number of faults in a class. Results indicate that using centrality measures in combination with object‐oriented metrics improves the prediction of fault‐prone classes as well as the prediction of the number of faults in a class. However, the combination has no significant impact, according to the data we collected, on the quality of the prediction of fault severity. Moreover, using centrality measures in combination with object‐oriented metrics also improves the prediction performance of fault proneness and the number of faults in both cross‐version and cross‐system validation.
Ontology Learning Applications of Knowledge Base Construction for Microelectronic Systems Information

Knowledge base construction (KBC) using AI has been one of the key goals of this highly popular technology since its emergence, as it helps to comprehend everything, including relations, around us. The construction of knowledge bases can summarize a piece of text in a machine-processable and understandable way. This can prove to be valuable and assistive to knowledge engineers. In this paper, we present the application of natural language processing in the construction of knowledge bases. We demonstrate how a trained bidirectional long short-term memory or bi-LSTM neural network model can be used to construct knowledge bases in accordance with the exact ISO26262 definitions as defined in the GENIAL! Basic Ontology. We provide the system with an electronic text document from the microelectronics domain and the system attempts to create a knowledge base from the available information in textual format. This information is then expressed in the form of graphs when queried by the user. This method of information retrieval presents the user with a much more technical and comprehensive understanding of an expert piece of text. This is achieved by applying the process of named entity recognition (NER) for knowledge extraction. This paper provides a result report of the current status of our knowledge construction process and knowledge base content, as well as describes our challenges and experiences.
G-Loss: A loss function with gradient information for super-resolution

For image super-resolution task, loss function plays an important role to guide the model optimization. Different loss functions could cause different performance of models in terms of peak signal-to-noise ratio (PSNR) and perceptual quality. Generally, pixel loss tends to obtain a higher PSNR, while perceptual loss tends to improve the perceptual quality. It is difficult to improve these two evaluation indicators at the same time. We speculate that adding image features to the loss function is effective, while it is necessary to avoid redundant features. Among image features, gradient feature is often used to suppress noise and improve line quality. Therefore, we propose to extract gradient feature and incorporate it into loss function. Experiments confirm that this method can improve both PSNR and perceptual quality on various models. This method has great generality and could be used as an optional component of loss function to improve image quality.
Modelling and Analysis of Neuro Fuzzy Employee Ranking System in the Public Sector

Human resource management has a significant influence on the performance of any public body. Employee classification and ranking are definitely time-consuming processes, which in many cases lead to controversial results. In addition, assessing employee efficiency through a variety of skills could lead to never-ending calculations and error-prone statistics. On the other hand, hard skill selection is proven to formulate a base for further investigation since subjectivity is not included in the performance equation. This research proposes a ranking model of employee selection based on certain criteria and attributes. The proposed prototype shows a series of results with a low error rate using ANFIS as the base methodology approach. This research was explanatory, and the population of this study consisted of employees with the majority of the sample in the wider region of Western Greece. The results showed a harmonic co-existence of the factors that proportionally affect the productivity of the employees in public service. Therefore, it provides the HR department with valuable information regarding the overall productivity of the public body, as well as significant material based on each profile separately. Therefore, efficiency was achieved through an automated time-saving procedure. The final output will enhance any personnel selection system with data extracted directly from the system, ensuring that the current method outperformed traditional approaches and secured a non-subjective procedure on employee management applied to the public sector.
Natural Language Processing and Sentiment Analysis on Bangla Social Media Comments on Russia–Ukraine War Using Transformers

The Bangla Language ranks seventh in the list of most spoken languages with 265 native and non-native speakers around the world and the second Indo-Aryan language after Hindi. However, the growth of research for tasks such as sentiment analysis (SA) in Bangla is relatively low compared to SA in the English language. It is because there are not enough high-quality publically available datasets for training language models for text classification tasks in Bangla. In this paper, we propose a Bangla annotated dataset for sentiment analysis on the ongoing Ukraine–Russia war. The dataset was developed by collecting Bangla comments from various videos of three prominent YouTube TV news channels of Bangladesh covering their report on the ongoing conflict. A total of 10,861 Bangla comments were collected and labeled with three polarity sentiments, namely Neutral, Pro-Ukraine (Positive), and Pro-Russia (Negative). A benchmark classifier was developed by experimenting with several transformer-based language models all pre-trained on unlabeled Bangla corpus. The models were fine-tuned using our procured dataset. Hyperparameter optimization was performed on all 5 transformer language models which include: BanglaBERT, XLM-RoBERTa-base, XLM-RoBERTa-large, Distil-mBERT and mBERT. Each model was evaluated and analyzed using several evaluation metrics which include: F1 score, accuracy, and AIC (Akaike Information Criterion). The best-performing model achieved the highest accuracy of 86% with 0.82 F1 score. Based on accuracy, F1 score and AIC, BanglaBERT outperforms baseline and all the other transformer-based classifiers.
Blockchain-enabled cross-chain collaboration model for elderly health information from a whole process perspective

Due to people having less children and the aging population, the demand for elderly health services is increasing, which leads to an increase in demand for elderly health information. However, there is a gap between elderly medical health information and elderly care information due to different storage institutions and storage methods, which makes it difficult for the medical service industry and the elderly service industry to fully grasp and utilize the health information of the elderly. Therefore, it is difficult to provide whole process services that combine elderly medical health and elderly care. To solve the problem of the poor collaborative utilization of elderly healthcare information, this paper, based on blockchain cross-chain technology and the literature and field research, studies the specific contexts that are needed to realize elderly health information collaboration. Based on the system theory viewpoint, the component-based modular design concept is used to identify the attributes and types of current health information of the elderly from health information related to the five modules of prevention, detection, diagnosis, treatment, and rehabilitation in the process of elderly healthcare. This paper explores the structure, elements, and interactions between the medical health information chains and the elderly care information chains. We build a blockchain-enabled cross-chain collaboration model of elderly health information from the perspective of the whole process with the help of the underlying logic of virtual chain, and to realize the applicability and flexibility of cross-chain collaboration for health information for the elderly in the whole process. The research results show that the proposed cross-chain collaboration model can realize the cross-chain collaboration of health information for the elderly with easy implementation, high throughput, and strong privacy protection.