Reviewerly/exHarmony huggingface dataset:
Urban Cool Island Effect through Innovative Architecture and Sustainable Urban Design

This paper investigates the phenomenon of Urban Cool Islands (UCIs) in desert regions, focusing on highly Sustainable Design Feature (SDF) areas in Dubai and Sharjah. The research explores the role of sustainable architectural and urban design practices in mitigating the Urban Heat Island (UHI) effect and fostering UCIs. The study combines remote sensing and survey data to analyze temperature variations, vegetation cover, and the influence of innovative architecture on UCIs. The results demonstrate that high SDF areas in Dubai, equipped with sustainable design elements and innovative architecture, exhibit cooler temperatures and negative UHI indices, supporting the hypothesis that sustainable practices enhance UCIs. In contrast, Sharjah's high SDF areas, despite abundant vegetation, experience higher temperatures and positive UHI indices. The study also reveals the positive impact of public awareness and education initiatives on adopting sustainable design practices. This research contributes valuable insights into the formation of UCIs in desert regions and highlights the importance of sustainable design in creating cooler, environmentally friendly urban environments. Future research directions are proposed, including expanded geographic scope, longitudinal data collection, and interdisciplinary collaboration to address the challenges of urban heat islands. The environmental and societal impact of UCIs, including improved air quality, enhanced biodiversity, energy savings, and sustainable development, underscores the significance of this research in promoting a healthier and more sustainable urban future.
An Introduction to Blockchain

An Introduction to Blockchain is a primer for those not familiar with Blockchain. This paper introduces the reader to how Blockchain works and how it can potentially provide resilience and security to the United States 16 critical infrastructure sectors, as listed by FEMA (DHS).
An Economic Model for AIGC and the Digital Economy

In the realm of the digital economy, data, computing power, and storage form a powerful trio. Our model unveils how they interact: data accumulation is bound by storage limits, efficient data use depends on rival computing power, and data drives both production and innovation. This dynamic scenario reveals challenges like excessive computing investment, innovation limits, insufficient data-sharing, and data overuse. Our study also delves into the role of algorithm development, adding an extra layer of complexity and importance.
Intellectual Property, Personality Rights, and Artificial Intelligence: Chatbots, Robots, and Vocaloids, oh my....

The fact is that many manufacturers and nearly all internet service providers as well as online service providers utilize some form of artificial intelligence systems, including algorithms and proprietary software programs to mine, collect, analyze, and organize the content created by users, or the users of their clients, that is hosted on their own web platforms, hosted on private third party network servers or hosted by their advertising partners platforms. What legally cognizable rights do humans have to control and or benefit from the commercial use of their user-generated content, which may include their intellectual property rights protected works, as well as their own name, image, voice, and other personally distinguishing identity characteristics, by proprietary machine learning systems (aka artificial intelligence software) deployed by private companies?
GMMSampling: a new model-based, data difficulty-driven resampling method for multi-class imbalanced data

Abstract Learning from multi-class imbalanced data has still received limited research attention. Most of the proposed methods focus on the global class imbalance ratio only. In contrast, experimental studies demonstrated that the imbalance ratio itself is not the main difficulty in the imbalanced learning. It is the combination of the imbalance ratio with other data difficulty factors, such as class overlapping or minority class decomposition into various subconcepts, that significantly affects the classification performance. This paper presents GMMSampling—a new resampling method that exploits information about data difficulty factors to clear class overlapping regions from majority class instances and to simultaneously oversample each subconcept of the minority class. The experimental evaluation demonstrated that the proposed method achieves better results in terms of G-mean, balanced accuracy, macro-AP, MCC and F-score than other related methods.
Hypergraph-based locality-enhancing methods for graph operations in Big Data applications

The need for speeding up data analytics increases inevitably due to the need for extracting valuable information from social media, data generated by smart devices with sensors, patterns of people’s communications over the web, items viewed and bought by global-scale customers, cloud applications, etc., all of which take part in the “Big Data.” Such kind of interaction data is very well represented as sparse graphs to enable the graph analytics, which requires efficient underlying kernels. The breadth-first search (BFS)-based traversal is a commonly used kernel in graph algorithms such as the betweenness centrality algorithm for centrality analysis. In this work, we focus on parallel BFS operations and propose hypergraph-based combinatorial models that aim at reducing cache misses and hence exploiting data locality during the parallel BFS operations. Our models are based on finding new vertex visit orders so that locality in accessing the data associated with vertices is exploited. Experiments on graphs arising in a wide range of applications show that our proposed models achieve on average 9% performance improvement in the CPU-based Ligra data analytics framework.
A chaotic-based watermarking scheme for ensuring integrity of a face recognition system in public large gathering scenario

Secure data transmission is critical in public video surveillance applications such as tracking missing persons in large crowd gatherings. It may help the security personnel to monitor & record the live feeds from multiple cameras. However, this live streaming could be falsified. Various cryptographic techniques have been proposed to ensure the integrity of transmitted images, but continuous attacks still pose serious challenges to ensuring the integrity and authenticity of transmitted images. This work proposes a new chaotic-based watermarking technique to ensure the integrity of the transmitted video frames, and since the frames come from multiple cameras, the scheme should be as simple as possible and as fast as possible, the simplicity manifests in designing a fragile watermarking scheme therefore, the watermark is to be embedded in the spatial domain by direct manipulation of the values of the cover's pixels. To ensure the security of the proposed scheme, chaotic maps have been deployed to reach an acceptable level of security with minimum computational overhead. The proposed scheme is novel in design in terms of the chaotic maps used and it's tested in challenging large gathering scenario. Experimental results demonstrate that the proposed method successfully locates any falsification in the transmitted images, and the performance of the scheme evaluates different types of attacks. Our testing results in good rate tamper of detection with TPR=99.2005, TNR=99.0777 and with a good quality of imperceptibility for the watermarked image with PSNR=43.82dB, SSIM=00.9955.
A corpus of English learners with Arabic and Hebrew backgrounds

Learner corpora—datasets that reflect the language of non-native speakers—are instrumental for research of language learning and development, as well as for practical applications, mainly for teaching and education. Such corpora now exist for a plethora of native–foreign language pairs; but until recently, none of them reflected native Hebrew speakers, and very few reflected native Arabic speakers. We introduce a recently-released corpus of English essays authored by learners in Israel. The corpus consists of two sub-corpora, one of them of Arabic native speakers and the other consisting mainly of Hebrew native speakers. We report on the composition and curation of the datasets; specifically, we processed the data so that both sub-corpora are now uniformly represented, facilitating seamless research and computational processing of the data. We provide statistical information on the corpora and outline a few research projects that had already used them. This is the first and only learner corpus in Israel including two major native languages of people in the same educational system regarding the English syllabus. All the resources related to the corpus are freely available.
Alignment-Enhanced Interactive Fusion Model for Complete and Incomplete Multimodal Hand Gesture Recognition

Hand gesture recognition (HGR) based on surface electromyogram (sEMG) and Accelerometer (ACC) signals is increasingly attractive where fusion strategies are crucial for performance and remain challenging. Currently, neural network-based fusion methods have gained superior performance. Nevertheless, these methods typically fuse sEMG and ACC either in the early or late stages, overlooking the integration of entire cross-modal hierarchical information within each individual hidden layer, thus inducing inefficient inter-modal fusion. To this end, we propose a novel Alignment-Enhanced Interactive Fusion (AiFusion) model, which achieves effective fusion via a progressive hierarchical fusion strategy. Notably, AiFusion can flexibly perform both complete and incomplete multimodal HGR. Specifically, AiFusion contains two unimodal branches and a cascaded transformer-based multimodal fusion branch. The fusion branch is first designed to adequately characterize modality-interactive knowledge by adaptively capturing inter-modal similarity and fusing hierarchical features from all branches layer by layer. Then, the modality-interactive knowledge is aligned with that of unimodality using cross-modal supervised contrastive learning and online distillation from embedding and probability spaces respectively. These alignments further promote fusion quality and refine modality-specific representations. Finally, the recognition outcomes are set to be determined by available modalities, thus contributing to handling the incomplete multimodal HGR problem, which is frequently encountered in real-world scenarios. Experimental results on five public datasets demonstrate that AiFusion outperforms most state-of-the-art benchmarks in complete multimodal HGR. Impressively, it also surpasses the unimodal baselines in the challenging incomplete multimodal HGR. The proposed AiFusion provides a promising solution to realize effective and robust multimodal HGR-based interfaces.
Scale-Aware Graph Convolutional Network with Part-Level Refinement for Skeleton-Based Human Action Recognition

Graph Convolutional Networks (GCNs) have been widely used in skeleton-based human action recognition and have achieved promising results. However, current GCN-based methods are limited by their inability to refine semantic-guided joint relations and perform adaptive multi-scale analysis. These limitations impair their performance, particularly for analogical actions involving the interaction of the same body parts (e.g., drinking water and eating) as well as deficient actions with limited spatial-temporal information (e.g., subtle action writing and transient action sneezing). To solve these problems, we propose Part-level Refined Spatial Graph Convolution (PR-SGC) and Scale-aware Temporal Graph Convolution (Sa-TGC) for optimal action representation. The PR-SGC divides the skeleton into body parts and embeds this high-level semantics to refine the physical adjacency matrix. The Sa-TGC leverages the dynamic scale-aware mechanism to extract context-dependent multi-scale features. On this basis, we develop a novel Scale-aware Graph Convolutional Network with Part-level Refinement (SaPR-GCN), which is on par with state-of-the-art benchmarks on NTU RGB+D 60, NTU RGB+D 120, and NW-UCLA datasets.
A Critical Analysis of Benchmarks, Techniques, and Models in Medical Visual Question Answering

This paper comprehensively reviews medical VQA models, structures, and datasets, focusing on combining vision and language. Over 75 models and their statistical and SWOT (Strengths, Weaknesses, Opportunities, Threats) analyses were compared and analyzed. The study highlights whether the researchers in the general field influence those in the medical field. According to an analysis of text encoding techniques, LSTM is the approach that is utilized the most (42%), followed by non-text methods (14%) and BiLSTM (12%), whereas VGGNet (40%) and ResNet (22%) are the most often used vision methods, followed by Ensemble approaches (16%). Regarding fusion techniques, 14% of the models employed non-specific methods, while SAN (13%) and concatenation (10%) were frequently used. The study identifies LSTM-VGGNet and LSTM-ResNet combinations as the primary approaches in medical VQA, with 18% and 15% usage rates, respectively. The statistical analysis of medical VQA from 2018 to 2023 and individual yearly analyses reveals consistent preferences for LSTM and VGGNet, except in 2018 when ResNet was more commonly used. The SWOT analysis provides insights into the strengths and weaknesses of medical VQA research, highlighting areas for future exploration. These areas include addressing limited dataset sizes, enhancing question diversity, mitigating unimodal bias, exploring multi-modal datasets, leveraging external knowledge, incorporating multiple images, ensuring practical medical application integrity, improving model interpretation, and refining evaluation methods. This paper’s findings contribute to understanding medical VQA and offer valuable guidance for future researchers aiming to make advancements in this field.
Sentiment Analysis Based on Hybrid Neural Network Techniques Using Binary Coordinate Ascent Algorithm

Sentiment analysis is a technique for determining whether data is positive, negative, or neutral using Natural Language Processing (NLP). The particular challenge in classifying huge amounts of data is that it takes a long time and requires the employment of specialist human resources. Various deep learning techniques have been employed by different researchers to train and classify different datasets with varying outcomes. However, the results are not satisfactory. To address this challenge, this paper proposes a novel Sentiment Analysis approach based on Hybrid Neural Network Techniques. The preprocessing step is first applied to the Amazon Fine Food Reviews dataset in our architecture, which includes a number of data cleaning and text normalization techniques. The word embedding technique is then used to capture the semantics of the input by clustering semantically related inputs in the embedding space on the cleaned dataset. Finally, generated features were classified using three different deep learning techniques, including Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Hybrid CNN-RNN models, in two different ways, with each technique as follows: classification on the original feature set and classification on the reduced feature set based on Binary Coordinate Ascent (BCA) and Optimal Coordinate Ascent (OCA). The experimental results show that a hybrid CNN-RNN with the BCA and OCA algorithms outperforms state-of-the-art methods with 97.91% accuracy.
Advancements of Artificial Intelligence Techniques in the Realm About Library and Information Subject—A Case Survey of Latent Dirichlet Allocation Method

To investigate the advancements of artificial intelligence techniques in the realm of library and information subject, we have chosen the Latent Dirichlet Allocation method as a case study to explore its current study status and implementations. Traditional theme mining analyses utilize methods such as word frequency statistics, co-occurrence analysis, community detection, and citation analysis to capture external quantitative features of words or documents. In contrast, the Latent Dirichlet Allocation theme modelling method employs a three-layer Bayesian structure of document-topic-word to describe the themes of documents and the semantic relationships among words, enabling a better exploration of latent semantic information in text. This method plays a pivotal role in fine-grained knowledge extraction and analysis. We systematically review more than a decade of relevant literature in the realm about library and information subject. Through content analysis, we construct an analytical architecture for the implementation of the Latent Dirichlet Allocation method. This architecture, viewed from the perspective of the implementation process of Latent Dirichlet Allocation, comprehensively summarizes the core stages and technical challenges, including text pre-processing, model construction (i.e., theme model selection and optimal theme number determination), and model solving. Additionally, we provide a comprehensive overview of the current study status of the Latent Dirichlet Allocation method across various implementation domains, such as theme exploration, knowledge organization, academic evaluation, sentiment analysis, and recommendation study. Our findings indicate that the Latent Dirichlet Allocation method has formed a mature analytical process in the realm of library and information subject, with ongoing growth in study interest.
New Security Proofs and Complexity Records for Advanced Encryption Standard

Common block ciphers like AES specified by the NIST or KASUMI (A5/3) of GSM are extensively utilized by billions of individuals globally to protect their privacy and maintain confidentiality in daily communications. However, these ciphers lack comprehensive security proofs against the vast majority of known attacks. Currently, security proofs are limited to differential and linear attacks for both AES and KASUMI. For instance, the consensus on the security of AES is not based on formal mathematical proofs but on intensive cryptanalysis over its reduced rounds spanning several decades. In this work, we introduce new security proofs for AES against another attack method: impossible differential (ID) attacks. We classify ID attacks as reciprocal and nonreciprocal ID attacks. We show that sharp and generic lower bounds can be imposed on the data complexities of reciprocal ID attacks on substitution permutation networks. We prove that the minimum data required for a reciprocal ID attack on AES using a conventional ID characteristic is 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">66</sup> chosen plaintexts whereas a nonreciprocal ID attack involves at least 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">88</sup> computational steps. We mount a nonreciprocal ID attack on 6-round AES for 192-bit and 256-bit keys, which requires only 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">18</sup> chosen plaintexts and outperforms the data complexity of any attack. Given its marginal time complexity, this attack does not pose a substantial threat to the security of AES. However, we have made enhancements to the integral attack on 6-round AES, thereby surpassing the longstanding record for the most efficient attack after a period of 23 years.
Parallel Multi-Head Graph Attention Network (PMGAT) Model for Human-Object Interaction Detection

Human-object interaction (HOI) detection is an advanced task in the field of computer vision and is crucial for deep scene understanding. However, current HOI detection models face serious challenges in the following aspects: first, they overly rely on appearance features and neglect the local details of human-object interactions; second, the training cost of the existing detection model is quite high. To overcome these challenges, this study proposes a Parallel Multi-Head Graph Attention Network (PMGAT) model for detecting human-object interaction correlations. First, the close relationship between facial landmarks and body keypoints with objects is recognized, thereby introducing a local feature module to construct a relational graph model between facial keypoints, body keypoints, and objects. A multi-head graph attention network was utilized to accurately capture the interaction correlations between keypoints, addressing the issue of neglecting local details. Furthermore, the global feature module is designed to extract absolute spatial pose features and relative spatial pose features based on the positions of human keypoints relative to objects, enabling a more in-depth extraction of interactions between humans and objects. To reduce the training cost of the model, it adopts a multi-branch parallel structure and employs a multi-threaded multi-GPU scheme for parallel training acceleration. The empirical results demonstrate that the PMGAT model outperforms the current state-of-the-art ViPLO method in terms of mAP on the V-COCO and HICO-DET datasets. On V-COCO, it exhibits a notable improvement of up to 0.8% mAP over ViPLO, while on the more demanding HICO-DET, the improvement reaches up to 1.47% mAP. Furthermore, PMGAT stands out for its minimal training time compared to existing approaches. Overall, these results corroborate the dual augmentation of PMGAT in accuracy and training efficiency.
Robust and Secure Medical Image Watermarking for Edge-Enabled e-Healthcare

Advancements in networking technologies have enabled doctors to remotely diagnose and monitor patients using the Internet of Medical Things (IoMT), telemedicine, and edge-enabled healthcare. In e-healthcare, medical reports and patient records are typically outsourced to a server, which can make them vulnerable to unauthorized access and tampering. Therefore, it is crucial to ensure the authorization, security, confidentiality, and integrity of medical data. To address these challenges, this paper proposes a novel reversible watermarking approach with a high payload and low computational cost. Firstly, the input medical image is divided into a Border region (BR) and a Non-Border region (NBR). The NBR region is upscaled using Neighbour Mean Interpolation (NMI) to ensure reversibility. The Electronic Patient Record (EPR) is encrypted using a pseudorandom key that is generated adaptively from the host medical image and Enigma machine. Then, the encrypted EPR is embedded in the medical image using NMI. Two-level tamper detection (global and local level) is performed at the receiver’s end for higher accuracy. A Global Integrity Code is generated and embedded in BR using LSB embedding technique for global tamper detection. The experimental results show that the visual quality and robustness are both high (Avg. PSNR = 41.03 dB and Avg. SSIM = 0.99, NC = 0.99, and BER = 0.0019 calculated for 100 images). The subjective and objective experimental analysis indicates that the proposed scheme is highly secure and the computational cost is also low. The average embedding and extraction time (including embedding, encryption and decryption, extraction process respectively) is 0.88 s and 0.83 s. It is resistant to various image processing attacks. A comparison with some of the most recent popular schemes confirms the scheme’s effectiveness.