Reviewerly/exHarmony huggingface dataset:
Framework of Meta-Heuristic Variable Length Searching for Feature Selection in High-Dimensional Data

Feature Selection in High Dimensional Space is a combinatory optimization problem with an NP-hard nature. Meta-heuristic searching with embedding information theory-based criteria in the fitness function for selecting the relevant features is used widely in current feature selection algorithms. However, the increase in the dimension of the solution space leads to a high computational cost and risk of convergence. In addition, sub-optimality might occur due to the assumption of a certain length of the optimal number of features. Alternatively, variable length searching enables searching within the variable length of the solution space, which leads to more optimality and less computational load. The literature contains various meta-heuristic algorithms with variable length searching. All of them enable searching in high dimensional problems. However, an uncertainty in their performance exists. In order to fill this gap, this article proposes a novel framework for comparing various variants of variable length-searching meta-heuristic algorithms in the application of feature selection. For this purpose, we implemented four types of variable length meta-heuristic searching algorithms, namely VLBHO-Fitness, VLBHO-Position, variable length particle swarm optimization (VLPSO) and genetic variable length (GAVL), and we compared them in terms of classification metrics. The evaluation showed the overall superiority of VLBHO over the other algorithms in terms of accomplishing lower fitness values when optimizing mathematical functions of the variable length type.
Calibration and Validation of Global Horizontal Irradiance Clear Sky Models against McClear Clear Sky Model in Morocco

This study calibrated and compared the capabilities of hourly global horizontal irradiance (GHI) clear sky models for six Moroccan locations, using the McClear clear sky model as a reference. Complex clear sky models, namely Bird, Simplified Solis, Ineichen and Perez, and simple clear sky models, namely Adnot–Bourges–Campana–Gicquel (ABCG), Berger–Duffie, and Haurwitz were tested. The SOLCAST satellite-based dataset estimates were validated against the McClear clear sky model. pvlib python was used to configure the models, and ERA5 hourly fractional cloud cover was used to identify clear-sky days. The study period was from 2014 to 2021, and the study sites were in different climatic regions in Morocco. Bar graphs, tables, and quantitative statistical metrics, namely relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2), were used to quantify the skill of the clear sky model at different sites. The overall rMBE was negative in 5/6 sites, indicating consistent overestimation of GHI, and positive in Tantan (14.4%), indicating frequent underestimation of GHI. The overall rRMSE varied from 6 to 22%, suggesting strong agreement between clear sky models and the McClear clear sky model. The overall correlation was greater than 0.96, indicating a very strong relationship. Overall, the Bird clear sky model proved to be the most feasible. Complex clear sky models outperformed simple clear sky models. The SOLCAST satellite-based dataset and ERA5 cloud fraction information could well be used with quantifiable certainty as an accurate clear sky model in the study region and in other areas where complex clear sky models’ inputs are not available.
Magnitude and Similarity Based Variable Rate Filter Pruning for Efficient Convolution Neural Networks

The superior performance of the recent deep learning models comes at the cost of a significant increase in computational complexity, memory use, and power consumption. Filter pruning is one of the effective neural network compression techniques suitable for model deployment in modern low-power edge devices. In this paper, we propose a loss-aware filter Magnitude and Similarity based Variable rate Filter Pruning (MSVFP) technique. We studied several filter selection criteria based on filter magnitude and similarity among filters within a convolution layer, and based on the assumption that the sensitivity of each layer throughout the network is different, unlike conventional fixed rate pruning methods, our algorithm using loss-aware filter selection criteria automatically finds the suitable pruning rate for each layer throughout the network. In addition, the proposed algorithm adapts two different filter selection criteria to remove weak filters as well as redundant filters based on filter magnitude and filter similarity score respectively. Finally, the iterative filter pruning and retraining approach are used to maintain the accuracy of the network during pruning to its target float point operations (FLOPs) reduction rate. In the proposed algorithm, a small number of retraining steps are sufficient during iterative pruning to prevent an abrupt drop in the accuracy of the network. Experiments with commonly used VGGNet and ResNet models on CIFAR-10 and ImageNet benchmark show the superiority of the proposed method over the existing methods in the literature. Notably, VGG-16, ResNet-56, and ResNet-110 models on the CIFAR-10 dataset even improved the original accuracy with more than 50% reduction in network FLOPs. Additionally, the ResNet-50 model on the ImageNet dataset reduces model FLOPs by more than 42% with a negligible drop in the original accuracy.
Machine Learning and Blockchain for 5G-Enabled IIoT

With one of the most potent technologies ever developed in human history, the world is moving toward a new digitalized era. These technological advancements are enabling people to make things that, in the past, were merely the stuff of fairy tales. The model put out by this research incorporates one of the newest and most potent technologies of the decade. This research proposes to integrate the 5G network with the industrial internet of things, which is based on machine learning to develop an intelligent machine capable of mimicking humans. A system of this power is extremely susceptible to issues like hacking, cyberattacks, and other issues. This problem is solved with the blockchain. Since blockchain offers a decentralized approach to maintaining transparency, the research incorporates it into the model to make it more efficient and secure. IoT with blockchain has been the subject of other studies, but this study is an enhanced version that also incorporates industrial IoT with AI to create an intelligent internet of things.
Fusion-based Multilevel Thresholding For Image Segmentation Using Evolutionary Algorithm

Image segmentation is one of the crucial steps involved in the process of digital image processing. Bi-level segmentation is a simple task, but it can only be used when there are only two distinct regions. However, more challenging real-world situations, such as medical images, have more than two distinct regions, which require multilevel segmentation. The histogram-based multilevel thresholding method is the most widely used as it is simple and fast. Most of the research work used one of the three fitness functions such as Otsu, Kapur's entropy, and Tsallis entropy. However, they don't provide good performance for different types of datasets. To overcome the drawback of the existing multilevel thresholding techniques, a new fusion-based multilevel segmentation technique is proposed. Six popular evolutionary algorithms are investigated to find the optimal threshold. Experiments are performed on two different publicly available MRI datasets to demonstrate the effectiveness of the proposed fusion-based multilevel thresholding for tumor segmentation. The proposed fusion-based segmentation technique, in combination with the BAT algorithm achieves better performance in comparison to existing multilevel thresholding methods in terms of Accuracy, F-measure, Jaccard Index, and Dice Score.
Integer Wavelet Transform and Dual Decomposition Based Image Watermarking scheme for Reliability of DICOM Medical Image

Image watermarking techniques provides security, reliability copyright protection for various multimedia contents. In this paper Integer Wavelet Transform Schur decomposition and Singular value decomposition (SVD) based image watermarking scheme is suggested for the integrity protection of dicom images. In the proposed technique 3-level Integer wavelet transform (IWT) is subjected into the Dicom ultrasound image of liver cover image and in HH sub-band Schur decomposition is applied. The upper triangular matrix obtained from Schur decomposition of HH sub-band is further processed with SVD to attain the singular values. The X-ray watermark image is pre-processed before embedding into cover image by applying 3-level IWT is applied into it and singular matrix of LL sub-band is embedded. The watermarked image is encrypted using Arnold chaotic encryption for its integrity protection. The performance of suggested scheme is tested under various attacks like filtering (median, average, Gaussian) checkmark (histogram equalization, rotation, horizontal and vertical flipping, contrast enhancement, gamma correction) and noise (Gaussian, speckle, Salt & Pepper Noise). The proposed technique provides strong robustness against various attacks and chaotic encryption provides integrity to watermarked image.
Therapeutic Prediction task on Electronic Health Record using DeBERTa

The performance of several deep learning models used in the past to conduct jobs on electronic health records (EHR), such as prediction tasks, is fairly good, but they need a lot of training data. There are many rare disorders, such as CTX, Type II glycogen disease caused by metabolic issue, which may explain why there are not as many patients with the disease as there should be. The amount of data needed is very high, which the healthcare system can not provide in most situations. A large percentage of EHR data, including patient data from a single visit, is lost because the majority of the work done in the past employs patients with multiple visits, which causes a problem with therapy prediction. In order to maintain the performance of the prediction task, This paper includes EHR data from a patient with a single visit in to address the issue of the limited dataset. We use two pre-trained models for this purpose: BERT (Bidirectional Encoder Representations from Transformers) and its extension DeBERTa (Decoding improved BERT disentangled attention), which we then fine-tune using electronic medical records. This paper uses the models BERT and DeBERTa to show the effectiveness of the suggested approach. BERT and DeBERTa outperform other baseline models on a number of evaluation metrics like the Jaccard score, AUC, and F1. We also got to the conclusion that DeBERTa performed better than BERT.
A systematic review of Wikidata in Digital Humanities projects

Abstract Wikidata has been widely used in Digital Humanities (DH) projects. However, a focused discussion regarding the current status, potential, and challenges of its application in the field is still lacking. A systematic review was conducted to identify and evaluate how DH projects perceive and utilize Wikidata, as well as its potential and challenges as demonstrated through use. This research concludes that: (1) Wikidata is understood in the DH projects as a content provider, a platform, and a technology stack; (2) it is commonly implemented for annotation and enrichment, metadata curation, knowledge modelling, and Named Entity Recognition (NER); (3) Most projects tend to consume data from Wikidata, whereas there is more potential to utilize it as a platform and a technology stack to publish data on Wikidata or to create an ecosystem of data exchange; and (4) Projects face two types of challenges: technical issues in the implementations and concerns with Wikidata’s data quality. In the discussion, this article contributes to addressing three issues related to coping with the challenges in the specific context of the DH field based on the research findings: the relevance and authority of other available domain sources; domain communities and their practices; and workflow design that coordinates technical and labour resources from projects and Wikidata.
Generating a sentiment dictionary in R and dictionary-based sentiment analysis in Turkish texts

Abstract Dictionary-based sentiment analysis is a text mining application that allows comments about the sentimental states of the text or documents through the sentimental poles of the words. In recent years, it has become quite popular in many disciplines such as trade, health, education, usage for various purposes. It is applied in many languages depending on the sentiment dictionaries, which are the main component, are created. Unlike inflectional languages such as English and German, the number of dictionary-based sentiment analysis studies using the Turkish language, which is a sequencing language, is very limited. When the literature of sentiment analysis is examined separately in terms of the material used; it is seen that the number of studies on long-format literary texts, which have been functioning as sentiment transmission throughout history, is also quite limited. In the light of these knowledges, a comprehensive method of creating a domain-specific sentiment dictionary and applying dictionary-based sentiment analysis on Turkish texts is proposed in the R software.
Recent trends towards privacy‐preservation in Internet of Things, its challenges and future directions

Abstract The Internet of Things (IoT) is a self‐configuring, intelligent system in which autonomous things connect to the Internet and communicate with each other. As ‘things’ are autonomous, it may raise privacy concerns. In this study, the authors describe the background of IoT systems and privacy and security measures, including (a) approaches to preserving privacy in IoT‐based systems, (b) existing privacy solutions, and (c) recommending privacy models for different layers of IoT applications. Based on the results of our study, it is clear that new methods such as Blockchain, Machine Learning, Data Minimisation, and Data Encryption can greatly impact privacy issues to ensure security and privacy. Moreover, it makes sense that users can protect their personal information easier if there is fewer data to collect, store, and share by smart devices. Thus, this study proposes a machine learning‐based data minimisation method that, in these networks, can be very beneficial for privacy‐preserving.
Design and prototyping of a robotic hand for sign language using locally-sourced materials

People living with disability constitute a significant percentage of the world population. For many people with disabilities, assistance and support are prerequisites for participating in societal activities. This research work developed a hardware prototype of a robotic hand forfor sign language communication with persons living with hard-of-hearing disabilities (deaf and/or dumb). The prototype has three basic modules: the input unit, the control unit, and the robotic hand. The input unit is designed as an Android-based mobile application with speech recognition capabilities while the control unit is ATMEGA 2560 microcontroller board. The robotic hand is constructed using locally available materials (bathroom Slippers, expandable rubber, straw pipe, and tiny rope) together with three servo motors and is designed to look and perform movements similar to a human hand. The prototype was evaluated quantitatively in terms of empirical accuracy and response time. It was also evaluated qualitatively by thirty-five (35) users which included fifteen (15) experience ASL users, eighteen (18) non-experience ASL users, and two (2) ASL experts, who completed questionnaires to rate the prototype on a 5-point Likert scale in terms of five parameters: functionality, reliability, ease of use, efficiency, and portability. An accuracy of 78.43% with an average response time of 2 s was obtained from empirical experiments. Statistical analysis of user responses showed that 97%, 68%, 77%, 80%, and 83% of users rated the system as above average for functionality, reliability, ease of use, efficiency, and portability, respectively. The robotic hand effectively communicates American Sign Language which includes English Alphabets, numbers (1–9), and some selected common words, which can be demonstrated with a single hand for hard of hearing persons. To the best of our knowledge, this work is the first ASL robotic hand that is based on locally sourced cost-effective materials, and we build on flaws from existing literature, most of which are either template-based, not real-time, or expensive. In terms of future work, the prototype can be improved by extending the single robotic hand to a fully robotic body with two hands.
Comparison, evaluation and prioritization of solar photovoltaic tracking systems using multi criteria decision making methods

The purpose of the present study is to evaluate and compare solar tracking systems connected to the grid. The solar systems considered were Fixed Tilt (FT), Vertical Continuous Adjustment (VCA), Horizontal adjustment (Monthly Adjustment (HMA), Continuous Adjustment (HCA)) and Two-Axis (TA) tracking system. Technical, economic and environmental assessment for an industrial consumer in southern Iran was performed by HOMER software. Unlike previous studies, multi-criteria approaches have been used to evaluate solar tracking systems. Due to the economic or technical superiority of each tracking system, solar tracking systems were prioritized using multi-criteria decision-making approaches. SWARA was used to weight criteria and WASPAS, EDAS and ARAS methods were used for prioritization of solar tracking systems. Technical evaluation showed that most of the electricity was generated by the TA and it was 31.8 % more than the FT system. Economic evaluation showed that the minimum LCOE was related to FT and the maximum LCOE was related to horizontal tracking systems (8.8 % more than FT system). Environmental assessment also showed that the reduction of CO2 emissions in the TA system is more than the FT system. This evaluation, taking into account all technical, economic and environmental aspects, showed that the priority of systems in the case study was HMA < HCA < FT < VCA < TA.
2D Image head pose estimation via latent space regression under occlusion settings

Head orientation is a challenging Computer Vision problem that has been extensively researched having a wide variety of applications. However, current state-of-the-art systems still underperform in the presence of occlusions and are unreliable for many task applications in such scenarios. This work proposes a novel deep learning approach for the problem of head pose estimation under occlusions. The strategy is based on latent space regression as a fundamental key to better structure the problem for occluded scenarios. Our model surpasses several state-of-the-art methodologies for occluded HPE, and achieves similar accuracy for non-occluded scenarios. We demonstrate the usefulness of the proposed approach with: (i) two synthetically occluded versions of the BIWI and AFLW2000 datasets, (ii) real-life occlusions of the Pandora dataset, and (iii) a real-life application to human-robot interaction scenarios where face occlusions often occur. Specifically, the autonomous feeding from a robotic arm.
Leveraging feature-level fusion representations and attentional bidirectional RNN-CNN deep models for Arabic affect analysis on Twitter

Arabic affect analysis on Twitter avidly helps to capture the emotional states of individuals being expressed regarding many targets, such as world-level events, products, and services. It is the key to monitoring and advancing human intelligence, which impacts human decision-making processes efficiently. However, state-of-the-art models have not witnessed serious developments yet since they have just achieved an accuracy of around 54%. This inaccuracy is mainly due to the agglutination, dialectal variation, and morphological richness of the Arabic language, as well as the unique features of tweets, such as shortness, noisiness, and informal language. This paper presents an approach that tackles these challenges and then improves the performance of Arabic affect analysis on Twitter. First, we propose a novel feature-based fusion representation for Arabic tweets to capture the polysemy, semantic/syntactic information, and conveyed emotional knowledge; and to deal with Out-Of-Vocabulary (OOV) words. Second, we propose an attentional deep learning model based on Bidirectional Gated Recurrent Unit (BiGRU), Bidirectional Long Short-Term Memory (BiLSTM), and Convolution Neural Network (CNN) to effectively learn local and global features and provide a multilabel emotional classification. The experimental results indicate that our proposal outperforms twelve state-of-the-art and baseline methods with a significant improvement of 6% in accuracy.
Triplet attention fusion module: A concise and efficient channel attention module for medical image segmentation

Concise segmentation of medical images is vital for diagnosing and treating diseases. In recent years, convolutional neural networks (CNNs) have yielded satisfactory results in the field of medical image segmentation, and researchers are striving to improve the segmentation performance of CNNs by using the channel attention mechanism. However, most existing channel attention modules have numerous parameters and use a single compression and activation function in the process of attention realization. This limits the effect of attention promotion. To solve this problem, in this paper, we propose the triplet attention fusion (TAF) module. It combines direct and indirect mapping to achieve effective fusion of global-local information. In addition, this method has low computational complexity. Because of its lightweight and convenient advantages, the TAF module can be integrated into the existing semantic segmentation networks. The experimental results on ISIC-2018 and LiTS datasets show that TAF can greatly improve the performance of the network. Compared with other attention modules, it introduces fewer parameters. In addition, the performance of UNet with TAF module is close to or even surpasses that of the other state-of-the-art network models. It proves the superiority and effectiveness of our proposed module in medical image segmentation.
How Low Can You Go? A Limbo Dance for Low-Latency Network Functions

Abstract Throughput is a commonly used performance indicator for networks. However, throughput may be considered insignificant if data is outdated or networks become unpredictable or unreliable. Critical services may even prioritize latency, predictability, and reliability at the expense of throughput to avoid detrimental effects on service operation. Latency, predictability, and reliability are distinct qualities realized in real-time systems. Real-time systems often require additional effort using non-standard interfaces, requiring customized software, or providing low throughput figures. This work picks up the challenge and investigates a single-server network function—a building block for end-to-end low-latency network applications. Assessing reliability and quantifying low latency is equally challenging, as sub-microsecond latency and $$1/10^{5}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>1</mml:mn> <mml:mo>/</mml:mo> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>5</mml:mn> </mml:msup> </mml:mrow> </mml:math> loss probability leave little room for error. Both, our measurement and the investigated platforms, rely on Linux running on off-the-shelf components. Our paper provides a comprehensive study on the impact of various components on latency and reliability, such as the central processing unit (CPU), the Linux Kernel, the network card, virtualization features, and the networking application itself. We chose Suricata, an intrusion prevention system (IPS), representing a widely deployed, typical network application as our primary subject of investigation.