Reviewerly/exHarmony huggingface dataset:
Non-Fungible Token (NFT) in Malaysian Creative Arts: The Status Quo of Tokenisation

The world has been discussing non-fungible tokens (NFT) and this abbreviation is the most searched topic via Google which received widespread attention in 2021. This NFT phenomenon has made the conversion of traditional art into crypto art which also led to the emergence of marketplaces based on the NFT. Rising numbers of users and local NFT marketplaces is somehow an indication of the reception of Malaysia towards the NFT. This paper is to first identify the status-quo of NFT and tokenization in the creative arts in Malaysia since its emergence and, secondly, the potential of this emerging technology carries. The study finds that there are number of local creative talents and brands has started to tokenised their artworks and gained success with their NFT projects. Despite Malaysia having a low percentage of NFT owners, demand in NFT is increasing as the potentials of this advancement has would contribute to the encouragement towards the Metaverse. This research concludes that as more users, brands, and organisations are beginning to create their own NFT collections to not only advertise their brands but also to begin utilising this new technology as they keep pace with other international NFT users, Malaysia’s NFT market is still in its infancy and must be explore.
Data-Free Quantization with Accurate Activation Clipping and Adaptive Batch Normalization

Data-free quantization compresses the neural network to low bit-width without access to original training data. Most existing data-free quantization methods cause severe performance degradation due to inaccurate activation clipping range and quantization error. In this paper, we present a simple yet effective data-free quantization method with accurate activation clipping and adaptive batch normalization. Accurate activation clipping (AAC) improves the model accuracy by exploiting accurate activation information from the full-precision model. Adaptive batch normalization (ABN) firstly proposes to address the quantization error from distribution changes by updating the batch normalization layer adaptively. Extensive experiments demonstrate that the proposed data-free quantization method can yield surprising performance, achieving 64.33% top-1 accuracy of 4-bit ResNet18 on ImageNet dataset, with 3.7% absolute improvement outperforming the existing state-of-the-art methods.
A Local-First Approach for Green Smart Contracts

Shared code in blockchains, known as smart contracts, stands to replace important parts of our digital governance and financial infrastructure. The permissionless execution of smart contracts is tightly coupled to cryptocurrencies and Proof-of-Work blockchains. As a result, smart contracts inherit the environmental impact of Proof-of-Work blockchains, like its energy consumption, carbon footprint, and electronic waste. The four concepts of relaxed consistency, strong identities, probabilistic consensus, and the use of liabilities instead of assets may change the status quo. This work explores the integration of these concepts to decouple smart contracts from Proof-of-Work blockchains. By means of a local-first approach, that may expose users to inconsistent ephemeral contract states, the architecture of smart contracts can be transformed to become green. Because such contract states may be dropped, we base the interactions between users on liabilities. We propose a novel paradigm for smart contract architectures, named Green Smart Contracts, that is based on a local-first approach. Furthermore, we present and implement a prototype solution for this paradigm. We validate the need for a mechanism to resolve consistency violations by replaying the contract calls of a real smart contract. Our simulation shows that violations occur more often (13% of contract invocations) when using liabilities than when using a traditional blockchain (3% of contract invocations). However, we additionally validate that they can be avoided using a consensus mechanism, and our experiments show that a publish-subscribe messaging pattern uses the fewest messages to do so, though it may not be applicable for use cases that disallow the inherent imbalance in the messaging between peers. Our carbon emission estimation shows that a Green Smart Contract approach lowers carbon emissions by 52.31% when compared to the messaging behavior of a typical peer-to-peer blockchain with 1000 nodes.
Multi-scale feature fusion for histopathological image categorisation in breast cancer

ABSTRACTABSTRACTHistopathological images of the breast play a crucial role in diagnosing breast cancer and developing effective treatment strategies. However, the complexity of these images poses challenges for traditional convolutional neural networks, which struggle to extract all relevant features. In this paper, we present a novel classification method that addresses this limitation and enhances the accuracy of diagnosing benign and malignant breast tumors. Our approach involves data augmentation techniques, such as staining normalization, image patch generation, and spatial geometry transformation, to expand the training set. We utilize BCMNet (Breast Classification Fusion Multi-Scale Feature Network), which combines VGG16 and CBAM, to fuse spatial features, channel features, and multi-scale features of the input images. We evaluate our method on the BreakHis dataset and achieve average accuracies of 91.91%, 91.14%, 92.65%, and 87.56% for breast histopathology image classification at four different magnifications. Compared to the benchmark network VGG16 without data augmentation, our approach improves the highest accuracy by 3.69% and the average accuracy by 18.47%. Moreover, our proposed method achieves an F1 score of 95.02% and an average recognition accuracy of 92.20% at the patient level. Our approach surpasses previous breast pathology image classification methods, both single and hybrid networks, and establishes a strong theoretical foundation for computer-assisted breast cancer diagnosis.KEYWORDS: Breast cancerhistopathological imagesconvolutional neural networkfeature fusiondata augmentation AcknowledgementsThe authors wish to express their appreciation to the editors and reviewers for their insightful suggestions which greatly improved the presentation of this paper. This research is supported by National Natural Science Foundation of China (No. 61902158).Disclosure statementNo potential conflict of interest was reported by the author(s).
Multi-Branch Parallel Networks for Object Detection in High-Resolution UAV Remote Sensing Images

Uncrewed Aerial Vehicles (UAVs) are instrumental in advancing the field of remote sensing. Nevertheless, the complexity of the background and the dense distribution of objects both present considerable challenges for object detection in UAV remote sensing images. This paper proposes a Multi-Branch Parallel Network (MBPN) based on the ViTDet (Visual Transformer for Object Detection) model, which aims to improve object detection accuracy in UAV remote sensing images. Initially, the discriminative ability of the input feature map of the Feature Pyramid Network (FPN) is improved by incorporating the Receptive Field Enhancement (RFE) and Convolutional Self-Attention (CSA) modules. Subsequently, to mitigate the loss of semantic information, the sampling process of the FPN is replaced by Multi-Branch Upsampling (MBUS) and Multi-Branch Downsampling (MBDS) modules. Lastly, a Feature-Concatenating Fusion (FCF) module is employed to merge feature maps of varying levels, thereby addressing the issue of semantic misalignment. This paper evaluates the performance of the proposed model on both a custom UAV-captured WCH dataset and the publicly available NWPU VHR10 dataset. The experimental results demonstrate that the proposed model achieves an increase in APL of 2.4% and 0.7% on the WCH and NWPU VHR10 datasets, respectively, compared to the baseline model ViTDet-B.
A Large-Scale Reviews-Driven Multi-Criteria Product Ranking Approach Based on User Credibility and Division Mechanism

Massive online reviews provide consumers with the convenience of obtaining product information, but it is still worth exploring how to provide consumers with useful and reliable product rankings. The existing ranking methods do not fully mine user information, rating, and text comment information to obtain scientific and reasonable information aggregation methods. Therefore, this study constructs a user credibility model and proposes a large-scale user information aggregation method to obtain a new product ranking method. First, in order to obtain the aggregate weight of large-scale users, this paper proposes a consistency modeling method of text comments and star ratings by mining the associated information of user comments, including user interaction information and user personalized characteristics information, combined with sentiment analysis technology, and then constructs a user credibility model. Second, a double-layer group division mechanism considering user regions and comment time is designed to develop the large-scale group ratings aggregation approach. Third, based on the user credibility model and the large-scale ratings aggregation approach, a product ranking method is developed. Finally, the feasibility and effectiveness of the proposed method are verified through a case study for automobile ranking and a comparative analysis is furnished. The analysis results of the application case of automobile ranking show that there is a significant difference between the ranking results obtained by the ratings aggregation method based on the arithmetic mean and the ranking results obtained by this method. The method in this study comprehensively considers user credibility and group division, which can be reflected in user aggregation weights and the group aggregation process, and can also obtain more scientific and reasonable decision results.
Semantic automatic annotation method based on artificial intelligence for electric power internet of things

Abstract The development of the power Internet of Things is currently underway, and a proposal for a semantic Internet of Things based on artificial intelligence algorithms is made to address the challenges in obtaining prior knowledge for heterogeneous data fusion, improving the real‐time performance of the ontology library, and enhancing the efficiency of manual labeling of instance object data in the power field. This proposal introduces an Automatic Semantic Annotation Method to provide an effective knowledge organization model for sensor systems. Data mining knowledge is utilized to drive ontology update and improvement, resulting in more accurate semantic annotation and enhanced machine understanding. Experimental results show that artificial intelligence algorithms can automatically extract concepts from sensory data and achieve automatic semantic annotation during ontology instantiation.
Moderating online child sexual abuse material (CSAM): Does self-regulation work, or is greater state regulation needed?

Social media platforms are crucial public forums connecting users around the world through a decentralised cyberspace. These platforms host high volumes of content and, as such, employ content moderators (CMs) to safeguard users against harmful content like child sexual abuse material (CSAM). These roles are critical in the social media landscape however, CMs’ work as “digital first responders” is complicated by legal and systemic debates over whether the policing of cyberspace should be left to the self-regulation of technology companies, or if greater state-regulation is required. In this empirical policy and literature review, major debates in the area of content moderation and, in particular, the online policing of CSAM are identified and evaluated. This includes the issue of territorial jurisdiction, and how it obstructs traditional policing; concerns over free speech and privacy if CMs are given greater powers, and debates over whether technology companies should be legally liable for user-generated content (UGC). In outlining these issues, a more comprehensive foundation for evaluating current practices for monitoring and combatting online CSAM is established which illustrates both the practical and philosophical challenges of the existing status quo, wherein the state and private companies share these important responsibilities.
Pashto Handwritten Invariant Character Trajectory Prediction Using a Customized Deep Learning Technique

Before the 19th century, all communication and official records relied on handwritten documents, cherished as valuable artefacts by different ethnic groups. While significant efforts have been made to automate the transcription of major languages like English, French, Arabic, and Chinese, there has been less research on regional and minor languages, despite their importance from geographical and historical perspectives. This research focuses on detecting and recognizing Pashto handwritten characters and ligatures, which is essential for preserving this regional cursive language in Pakistan and its status as the national language of Afghanistan. Deep learning techniques were employed to detect and recognize Pashto characters and ligatures, utilizing a newly developed dataset specific to Pashto. A further enhancement was done on the dataset by implementing data augmentation, i.e., scaling and rotation on Pashto handwritten characters and ligatures, which gave us many variations of a single trajectory. Different morphological operations for minimizing gaps in the trajectories were also performed. The median filter was used for the removal of different noises. This dataset will be combined with the existing PHWD-V2 dataset. Various deep-learning techniques were evaluated, including VGG19, MobileNetV2, MobileNetV3, and a customized CNN. The customized CNN demonstrated the highest accuracy and minimal loss, achieving a training accuracy of 93.98%, validation accuracy of 92.08% and testing accuracy of 92.99%.
Robust Low-Rank Graph Multi-View Clustering via Cauchy Norm Minimization

Graph-based multi-view clustering methods aim to explore the partition patterns by utilizing a similarity graph. However, many existing methods construct a consensus similarity graph based on the original multi-view space, which may result in the lack of information on the underlying low-dimensional space. Additionally, these methods often fail to effectively handle the noise present in the graph. To address these issues, a novel graph-based multi-view clustering method which combines spectral embedding, non-convex low-rank approximation and noise processing into a unit framework is proposed. In detail, the proposed method constructs a tensor by stacking the inner product of normalized spectral embedding matrices obtained from each similarity matrix. Then, the obtained tensor is decomposed into a low-rank tensor and a noise tensor. The low-rank tensor is constrained via nonconvex low-rank tensor approximation and a novel Cauchy norm with an upper bound is proposed to handle the noise. Finally, we derive the consensus similarity graph from the denoised low-rank tensor. The experiments on five datasets demonstrate that the proposed method outperforms other state-of-the-art methods on five datasets.
Apriori Algorithm-Based Three-Dimensional Mineral Prospectivity Mapping—An Example from Meiling South Area, Xinjiang, China

Mineral Prospectivity Mapping (MPM) is shifting toward intelligent deep mineralization searches in the era of big data and the increasing difficulties of surface deposit detection. Comparative analysis of two forms of mineralization prediction based on the Apriori algorithm was performed in the Meiling South mining area in the eastern Hami region of Xinjiang, China. In comparison 1, we use the Apriori algorithm to mine ore-forming information and determine the ore-forming voxel positions based on spatial distance and angle analysis. Then, we compare the ore-forming voxel positions determined by Apriori with the ore-forming voxel positions predicted by the mathematical model based on the conceptual model of mineralization, and these mathematical models include Gaussian Naive Bayesian (GNB) and Support Vector Machine (SVM). In comparison 2, the optimal prediction model is SVM, which is trained using the elements of mineralization prediction determined by the conceptual model of mineralization. Then, two sets of new elements of mineralization prediction are extracted from the original elements of mineralization prediction using the Apriori and Chi-square methods and then input into the SVM model for training. After we obtain the mineralization prediction results, we compare them with the original mineralization prediction results. The preceding comparison produced the following results. (1) Using the Apriori algorithm, the distribution characteristics of the high and low-grade ore bodies and the association rules between ore-bearing information were determined. (2) The prediction results of the GNB and SVM models displayed corresponding trends on the high and low-grade ore-bearing voxels identified by Apriori, which matched the rules mined by Apriori. (3) In comparison to the mineralization prediction elements screened by Chi-square and the original mineralization prediction elements based on the conceptual model of mineralization, the elements of mineralization prediction chosen based on Apriori have the best prediction effect in SVM when tested in new drill holes. Based on the mineralization prediction elements screened by Apriori, the number of accurate ore-bearing voxels (prediction probability greater than 0.5) predicted by the SVM model is 6, 5, and 1 in drill holes V1, V2, and V3, respectively. The collective results demonstrated that Apriori is explicit, intuitive, and interpretable for mineralization prediction and has a certain reference value for refining the determination of mineralization prediction elements and discovering mineralization mechanisms and laws.
Lattices-Inspired CP-ABE from LWE Scheme for Data Access and Sharing Based on Blockchain

To address the quantum attacks on number theory-based ciphertext policy attribute-based encryption (CP-ABE), and to avoid private key leakage problems by relying on a trustworthy central authority, we propose a lattice-inspired CP-ABE scheme for data access and sharing based on blockchain in this paper. Firstly, a CP-ABE-based algorithm using learning with errors (LWE) assumption is constructed, which is selective security under linear independence restriction in the random oracle model. Secondly, the blockchain nodes can act as a distributed key management server to offer control over master keys used to generate private keys for different data users that reflect their attributes through launching transactions on the blockchain system. Finally, we develop smart contracts for proving the correctness of proxy re-encryption (PRE) and provide auditability for the whole data-sharing process. Compared with the traditional CP-ABE algorithm, the post-quantum CP-ABE algorithm can significantly improve the computation speed according to the result of the functional and experimental analysis. Moreover, the proposed blockchain-based CP-ABE scheme provides not only multi-cryptography collaboration to enhance the security of data access and sharing but also reduces average transaction response time and throughput.
Homography Matrix-Based Local Motion Consistent Matching for Remote Sensing Images

Feature matching is a fundamental task in the field of image processing, aimed at ensuring correct correspondence between two sets of features. Putative matches constructed based on the similarity of descriptors always contain a large number of false matches. To eliminate these false matches, we propose a remote sensing image feature matching method called LMC (local motion consistency), where local motion consistency refers to the property that adjacent correct matches have the same motion. The core idea of LMC is to find neighborhoods with correct motion trends and retain matches with the same motion. To achieve this, we design a local geometric constraint using a homography matrix to represent local motion consistency. This constraint has projective invariance and is applicable to various types of transformations. To avoid outliers affecting the search for neighborhoods with correct motion, we introduce a resampling method to construct neighborhoods. Moreover, we design a jump-out mechanism to exit the loop without searching all possible cases, thereby reducing runtime. LMC can process over 1000 putative matches within 100 ms. Experimental evaluations on diverse image datasets, including SUIRD, RS, and DTU, demonstrate that LMC achieves a higher F-score and superior overall matching performance compared to state-of-the-art methods.
Video Object Segmentation Using Multi-Scale Attention-Based Siamese Network

Video target segmentation is a fundamental problem in computer vision that aims to segment targets from a background by learning their appearance information and movement information. In this study, a video target segmentation network based on the Siamese structure was proposed. This network has two inputs: the current video frame, used as the main input, and the adjacent frame, used as the auxiliary input. The processing modules for the inputs use the same structure, optimization strategy, and encoder weights. The input is encoded to obtain features with different resolutions, from which good target appearance features can be obtained. After processing using the encoding layer, the motion features of the target are learned using a multi-scale feature fusion decoder based on an attention mechanism. The final predicted segmentation results were calculated from a layer of decoded features. The video object segmentation framework proposed in this study achieved optimal results on CDNet2014 and FBMS-3D, with scores of 78.36 and 86.71, respectively. It outperformed the second-ranked method by 4.3 on the CDNet2014 dataset and by 0.77 on the FBMS-3D dataset. Suboptimal results were achieved on the video primary target segmentation datasets SegTrackV2 and DAVIS2016, with scores of 60.57 and 81.08, respectively.
Mechanical Characterization of Soft Membranes with One-Shot Projection Moiré and Metaheuristic Optimization

Mechanical characterization of soft materials is a complicated inverse problem that includes nonlinear constitutive behavior and large deformations. A further complication is introduced by the structural inhomogeneity of tested specimens (for example, caused by thickness variations). Optical methods are very useful in mechanical characterization of soft matter, as they provide accurate full-field information on displacements, strains and stresses regardless of the magnitude and/or gradients of those quantities. In view of this, the present study describes a novel hybrid framework for mechanical characterization of soft membranes, combining (i) inflation tests and preliminary in-plane equi-biaxial tests, (ii) a one-shot projection moiré optical setup with two symmetric projectors that project cross-gratings onto the inflated membrane, (iii) a mathematical model to extract 3D displacement information from moiré measurements, and (iv) metaheuristic optimization hybridizing harmony search and JAYA algorithms. The use of cross-gratings allows us to determine the surface curvature and precisely reconstruct the shape of the deformed object. Enriching metaheuristic optimization with gradient information and elitist strategies significantly reduces the computational cost of the identification process. The feasibility of the proposed approach wassuccessfully tested on a 100 mm diameter natural rubber membrane that had some degree of anisotropy in mechanical response because of its inhomogeneous thickness distribution. Remarkably, up to 324 hyperelastic constants and thickness parameters can be precisely identified by the proposed framework, reducing computational effort from 15% to 70% with respect to other inverse methods.