Reviewerly/exHarmony huggingface dataset:
RoBERTa-GRU: A Hybrid Deep Learning Model for Enhanced Sentiment Analysis

This paper proposes a novel hybrid model for sentiment analysis. The model leverages the strengths of both the Transformer model, represented by the Robustly Optimized BERT Pretraining Approach (RoBERTa), and the Recurrent Neural Network, represented by Gated Recurrent Units (GRU). The RoBERTa model provides the capability to project the texts into a discriminative embedding space through its attention mechanism, while the GRU model captures the long-range dependencies of the embedding and addresses the vanishing gradients problem. To overcome the challenge of imbalanced datasets in sentiment analysis, this paper also proposes the use of data augmentation with word embeddings by over-sampling the minority classes. This enhances the representation capacity of the model, making it more robust and accurate in handling the sentiment classification task. The proposed RoBERTa-GRU model was evaluated on three widely used sentiment analysis datasets: IMDb, Sentiment140, and Twitter US Airline Sentiment. The results show that the model achieved an accuracy of 94.63% on IMDb, 89.59% on Sentiment140, and 91.52% on Twitter US Airline Sentiment. These results demonstrate the effectiveness of the proposed RoBERTa-GRU hybrid model in sentiment analysis.
YOLO-LWNet: A Lightweight Road Damage Object Detection Network for Mobile Terminal Devices

To solve the demand for road damage object detection under the resource-constrained conditions of mobile terminal devices, in this paper, we propose the YOLO-LWNet, an efficient lightweight road damage detection algorithm for mobile terminal devices. First, a novel lightweight module, the LWC, is designed and the attention mechanism and activation function are optimized. Then, a lightweight backbone network and an efficient feature fusion network are further proposed with the LWC as the basic building units. Finally, the backbone and feature fusion network in the YOLOv5 is replaced. In this paper, two versions of the YOLO-LWNet, small and tiny, are introduced. The YOLO-LWNet was compared with the YOLOv6 and the YOLOv5 on the RDD-2020 public dataset in various performance aspects. The experimental results show that the YOLO-LWNet outperforms state-of-the-art real-time detectors in terms of balancing detection accuracy, model scale, and computational complexity in the road damage object detection task. It can better achieve the lightweight and accuracy requirements for object detection for mobile terminal devices.
BCTC-KSM: A blockchain-assisted threshold cryptography for key security management in power IoT data sharing

With the rapid advancement of the manufacturing industry from digital stage to network stage, the massive data generated by the production, consumption and other links in power IoT (PIoT) needs to be analyzed and processed urgently. However, due to the limited storage and computing capacity of PIoT devices, the collected industrial data are usually encrypted and stored in the cloud. Data security will be highly dependent on key security, and it is crucial for data privacy protection in PIoT to securely manage keys. Therefore, this paper proposes a Blockchain-Assisted Threshold Cryptography for Key Security Management in Power IoT Data Sharing (BCTC-KSM). We use the Pedersen threshold secret sharing algorithm off-chain to split the symmetric keys used to encrypt cloud-outsourcing data in PIoT into multiple key fragments and publish them on-chain, and use the ledger technology of blockchain to record the rotation history of each key fragment and its access subjects, and then restrict the user access to the key fragments through attribute access control policies on-chain. Security analysis and experimental results show that the BCTC-KSM can significantly improve the security and availability in PIoT data sharing at a slightly higher time cost than the state-of-the-art cryptography scheme.
Learning latent embedding via weighted projection matrix alignment for incomplete multi-view clustering

Multi-view data are generated from multiple perspectives or diverse domains. Although many multi-view clustering methods have achieved a lot of successes based on the assumption that the data is integrity, in fact, these data may exist the case of missing instances in real applications, resulting in incomplete multi-view data. Thus, incomplete multi-view clustering has been proposed to handle this issue, which has gained considerable attention. However, for the most of existing approaches, there still have the following limitations: (i) the latent common representation information within views is not well exploited. (ii) the potential information hidden in missing views are ignored to some extent. Therefore, we proposed a novel Learning Latent Embedding via weighted projection matrix alignment for Incomplete Multi-view Clustering, termed as LLE-IMC. Specifically, a view completion model is introduced in latent embedding learning to infer the missing information. To further explore the consistency information of different views, different projection matrices are enforced to align to cluster centers by ℓ2,1 norm regularization. Furthermore, an efficient optimization algorithm is presented to resolve the proposed model with convergence guarantee. On several incomplete multi-view datasets, experimental results show that our proposed LLE-IMC performs better in comparison to the state-of-the-art methods, in terms of many metrics.
Towards better long-tailed oracle character recognition with adversarial data augmentation

Deciphering oracle bone script is of great significance to the study of ancient Chinese culture as well as archaeology. Although recent studies on oracle character recognition have made substantial progress, they still suffer from the long-tailed data situation that results in a noticeable performance drop on the tail classes. To mitigate this issue, we propose a generative adversarial framework to augment oracle characters in the problematic classes. In this framework, the generator produces synthetic data through convex combinations of all the available samples in the corresponding classes, and is further optimized through adversarial learning with the classifier and simultaneously the discriminator. Meanwhile, we introduce Repatch to generalize samples in the generator. Since tail classes do not have sufficient data for convex combinations, we propose the TailMix mechanism to generate suitable tail class samples from other classes. Experimental results show that our proposed algorithm obtains remarkable performance in oracle character recognition and achieves new state-of-the-art average (total) accuracy with 86.03% (89.46%), 86.54% (93.86%), 95.22% (96.17%) on the three datasets Oracle-AYNU, OBC306 and Oracle-20K, respectively.
The Effect of Deep Learning Methods on Deepfake Audio Detection for Digital Investigation

Voice cloning methods have been used in a range of ways, from customized speech interfaces for marketing to video games. Current voice cloning systems are smart enough to learn speech characteristics from a few samples and produce perceptually unrecognizable speech. These systems pose new protection and privacy risks to voice-driven interfaces. Fake audio has been used for malicious purposes and is difficult to classify what is real and fake during a digital forensic investigation. This paper reviews the issue of deep-fake audio classification and evaluates the current methods of deep-fake audio detection for forensic investigation. Audio file features were extracted and visually presented using MFCC, Mel-spectrum, Chromagram, and spectrogram representations to further study the differences. Harnessing the different deep learning techniques from existing literature were compared using five iterative tests to determine the mean accuracy and the effects thereof. The results showed a Custom Architecture gave better results for the Chromagram, Spectrogram, and Me-Spectrum images and the VGG-16 architecture gave the best results for the MFCC image feature. This paper contributes to further assisting forensic investigators in differentiating between synthetic and real voices.
An image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation

This study proposes a novel color image encryption algorithm based on a 3D chaotic Hopfield neural network and random row–column permutation. First, a 3D chaotic Hopfield neural network is proposed to produce the random sequence for generating the diffusion and permutation keys. Then, the rows and columns of the original image are randomly arranged according to the permutation key in the permutation process. Three subgraphs are formed by separating the R, G, and B components of the color image in the diffusion process. Each of the three subgraphs is split along the columns to form three parts; the left and middle parts are exchanged. Three diffusion keys are used to encrypt each of the three parts. Finally, the individually encrypted subgraphs are stitched together to obtain the final encrypted image. Simulation results using MATLAB and FPGA and security analysis demonstrate that the encryption scheme has good performance.
Material Testing 2.0: A brief review

Abstract With the advent of camera‐based full‐field measurement techniques such as digital image correlation (DIC), researchers have been trying to exploit such rich data sets through the use of more complex test configurations than the standard ones (uniaxial tension/compression, bending etc.). This new paradigm in mechanical testing of materials has recently been christened ‘Material Testing 2.0’. This paper provides a brief overview of this field, which is currently seeing a large increase in research effort.
Entropy regularization for weakly supervised object localization

The goal of weakly-supervised object localization (WSOL) is to train a localization model without the location information of the object(s). Recently, most existing WSOL methods capture the object with an attention map extracted from a classification network. However, it has been observed that we need to sacrifice classification performances to achieve the best WSOL score. We conjecture that this is because the objective of classification training, minimizing entropy between one-hot ground truth and predicted class probability, is not entirely consistent with that of localization. In this paper, we investigate how the entropy of predicted class probability affects localization performances, where we conclude that there is a sweet spot for localization with respect to entropy. Hence, we propose a new training strategy that adopts entropy regularization for finding the optimal point effectively. Specifically, we add an additional term to the loss function, which minimizes the entropy between a uniform distribution and the predicted class probability vector. The proposed method is easy to implement since we do not need to modify the architecture or data pipeline. In addition, our method is efficient in that almost zero additional resources are required. Most importantly, our method improves WSOL scores significantly, which has been shown through extensive experiments.
Agile Technology and Artificial Intelligent Systems in Business Development

This article examines how Agile methodology and concepts are used to the delivery of artifical intelligence as well as how Agile has transformed over time. Artificial Intelligence (AI) are wide-ranging set of technologies that can promise various benefits for the company in terms off added business value and customer satisfaction.In earlier times, organizations/companies are increasingly turning to intelligence technology in order to gain more business value following a deluge of data as well as a strong increase in computational capacity. This is encouraging the incorporation of AI into business operations, but the effects of this adoption need to be investigated more thoroughly. The way that enterprises as well as consumers use information has evolved as a result of the internet’s and smart devices’ exponential growth in data volume. As a result, companies are starting to use AI technologies to embrace agility. Agile technique is the capacity to adapt quickly and effectively to these external situations in order to prosper in an industry that is frequently developing and unpredictable. This research focuses on the entire effects of AI on enterprises, including future changes in business models as well as research, innovation, and market deployment. Additionally, it focuses on several ways to incorporate artificial intelligence methods into scrum methodolgy.It also provides the information regarding iterative development process can be adapted to A.I evolvment well as the comparison of agile technology and artiofical intelligence in terms of business management development.
Breast UltraSound Image classification using fuzzy-rank-based ensemble network

Breast Cancer is one of the most commonly occurring cancers in women. Detecting cancer at an earlier stage increases the chance of survival. Thus the development of an automatic CAD system is important for cancer diagnosis. Researchers have used various image modalities like mammograms and Magnetic Resonance Images to detect cancer. This manuscript uses UltraSound Images for detecting breast cancer. We have proposed a fuzzy-rank-based ensemble network for detecting breast cancer. The proposed model uses four different base learners, namely VGG-Net, DenseNet, Xception, and Inception, and it tries to take advantage of the predictions made by the base learners. The weights of the initial layers of the base learners are pre-trained on the ImageNet dataset. In contrast, the final five layers are fine-tuned using a publicly available Breast Ulta Sound Image dataset. The fuzzy rank of the base learners' predictions is used to make the final classification. Conducting five-fold cross-validation using the base learners an accuracy of 77.69±3.22, 83.23±3.14, 78.31±2.27, and 78.62±4.23 were obtained. Furthermore, using the proposed fuzzy-rank-based model, an accuracy of 85.23±2.52 is obtained. We have proved that the proposed fuzzy-rank-based ensemble network increases the classification performance.
Mining online reviews for ranking products: A novel method based on multiple classifiers and interval-valued intuitionistic fuzzy TOPSIS

Online reviews can assist customers in making purchasing decisions, however, when multiple alternative products need to be compared based on online reviews, a large number of reviews information cannot be processed manually. Therefore, this study develops a novel method for product ranking based on mining online reviews and an interval-valued intuitionistic fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Concretely, it contains three major steps: (1) preprocessing, (2) calculating sentiment scores, and (3) ranking alternative products. First, the results of preprocessing are vectorization representation of online reviews, which are obtained with the Bidirectional Encoder Representations from Transformers (BERT) model. Then, sentiment orientations of online reviews of alternative products regarding different attributes are determined by a multiple classifiers system, which consists of multi-class base classifiers constructed using Support Vector Machine (SVM) with One-Vs-One (OVO) strategy. Lastly, based on the sentiment scores, the ranking result is obtained by the interval-valued intuitionistic fuzzy TOPSIS method. A case study on real-world datasets is provided to illustrate the application of our proposed method, which indicates the validity of our proposal. Thus, the method proposed in this paper can effectively assist consumers in selecting products based on online reviews that meet their preferences.
FlowGNN: A Dataflow Architecture for Real-Time Workload-Agnostic Graph Neural Network Inference

Graph neural networks (GNNs) have recently exploded in popularity thanks to their broad applicability to graph-related problems such as quantum chemistry, drug discovery, and high energy physics. However, meeting demand for novel GNN models and fast inference simultaneously is challenging due to the gap between developing efficient accelerators and the rapid creation of new GNN models. Prior art focuses on accelerating specific classes of GNNs, such as Graph Convolutional Networks (GCN), but lacks generality to support a wide range of existing or new GNN models. Furthermore, most works rely on graph pre-processing to exploit data locality, making them unsuitable for real-time applications. To address these limitations, in this work, we propose a generic dataflow architecture for GNN acceleration, named FlowGNN, which is generalizable to the majority of message-passing GNNs. The contributions are three-fold. First, we propose a novel and scalable dataflow architecture, which generally supports a wide range of GNN models with message-passing mechanism. The architecture features a configurable dataflow optimized for simultaneous computation of node embedding, edge embedding, and message passing, which is generally applicable to all models. We also propose a rich library of model-specific components. Second, we deliver ultra-fast real-time GNN inference without any graph pre-processing, making it agnostic to dynamically changing graph structures. Third, we verify our architecture on the Xilinx Alveo U50 FPGA board and measure the on-board end-to-end performance. We achieve a speed-up of up to 24–254× against CPU (6226R) and 1.3–477× against GPU (A6000) (with batch sizes 1 through 1024); we also outperform the SOTA GNN accelerator I-GCN by 1.26× speedup and 1.55× energy efficiency over four datasets. Our implementation code and on-board measurement are publicly available on GitHub. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>
Trusted Traceability and Certification of Refurbished Medical Devices Using Dynamic Composable NFTs

Medical devices play a crucial role in the global healthcare system, but their high cost has led to the increasing adoption of refurbished medical devices as a sustainable alternative for hospitals and patients around the world. The repositioning of refurbished devices into the market, however, is accompanied by a number of challenges, including concerns about quality and safety, as well as the risk of fraudulent activities such as counterfeiting. To address these challenges, we propose an NFT-based solution for managing refurbished medical devices that creates a secure, transparent, and verifiable record of the refurbishment process to ensure the safety and quality of these devices. The proposed solution utilizes dynamic composable NFTs as digital representations of medical devices, with replacement parts and certificate documents embedded in a parent-child NFT hierarchy, and reprocessing steps captured and reflected through the evolution of the dynamic tokens. This serves to authenticate and track the movement of refurbished devices while also providing a trustworthy means of managing individual devices and their ownership. Furthermore, the integration of non-transferable NFTs as certificates of refurbishment acts as an effective mechanism for detecting suspect medical devices and instances of fraudulent labeling, thereby increasing buyer confidence and promoting user safety. We leverage the Interplanetary File System to store and keep track of the metadata of the tokenized components of the system. We present the system architecture and implementation details with tested algorithms. We develop a front-end decentralized application (DApp) to interact with the designed smart contracts and showcase their functionalities. We also conduct security analysis to demonstrate our system is resistant to common vulnerabilities and exploits. The smart contract code is made available on GitHub.
Non-carcinogenic health risk assessment and predicting of pollution indexing of groundwater around Osisioma, Nigeria, using artificial neural networks and multi-linear modeling principles

Non-carcinogenic health risk assessment and prediction of pollution indexing of groundwater around Osisioma, Nigeria, using artificial neural networks and multi-linear modeling principles has been done. Thirty groundwater samples were collected systematically and analyzed for organic and heavy metal pollutants. The results of the analysis showed that the heavy metals and organic pollutants contributed to the pollution of groundwater resources in the locality. 63.3% of the entire water samples had As above the WHO standard, same as Fe (60%), Cr (100%), Pb (56.7%), E (16.7%), X (13.3%), B (40%). Correlation matrix results indicated a weak correlation. For the Principal Component Analysis, PC1 showed that 60% of the entire variable had loadings, PC2 had 40%, PC3 had 30%, PC4 had 10% loadings of parameters within the study area, and that organic pollutants were major contributors to the loadings. The Contamination factor, Pollution load index, Metal pollution index, Geoaccumulation index, Potential ecological risk index, Elemental Contamination Index, and Overall Metal Contamination Index showed no significant pollution, whereas the Heavy Metal Evaluation Index, Pollution Index of Groundwater results showed the worrisome impact of the anthropogenic activities on the groundwater quality. Health risk assessment showed that children are more at risk than adults as it related to taking polluted with a Hazard Quotient and Hazard Index trend is Cr > As > T > E > m-X > o-X > B > Pb > Cu > Fe. This trend is the same for both children and adults. Seven mathematical models were generated for the prediction of pollution indices. Based on the results, this study recommends regular monitoring of groundwater resources and the integration of ANN and MLR modeling approaches for the prediction of pollution indices.
The HeDiCom framework: Higher Education teachers’ digital competencies for the future

There is little consensus about the nature of teachers' digital competencies in Higher Education. Moreover, existing digital competence frameworks have largely been developed for teachers in secondary education. In response to this, the current study focuses on developing and validating a framework of digital competencies for teachers in Higher Education. First, a review was conducted to determine the state of digital competence research regarding dimensions and definition of digital competence. In a next step, similarities and differences between existing digital competence frameworks were identified. Based on the outcomes of the review and the framework comparison, a framework was developed in an iterative process through expert meetings with policy makers, experts in the field of educational technology, and validated with practitioners. The new framework includes four dimensions of teachers' digital competencies: (1) Teaching practice, (2) Empowering students for a digital society, (3) Teachers' digital literacy, and (4) Teachers' professional development. The resulting Higher Education Digital Competence (HeDiCom) framework will provide guidance and clearer expectations of teachers' digital competency. Ultimately, improving teachers' digital competencies will contribute to improving the quality of digital competencies of the students.