Reviewerly/exHarmony huggingface dataset:
Privacy preserving Generative Adversarial Networks to model Electronic Health Records

Hospitals and General Practitioner (GP) surgeries within National Health Services (NHS), collect patient information on a routine basis to create personal health records such as family medical history, chronic diseases, medications and dosing. The collected information could be used to build and model various machine learning algorithms, to simplify the task of those working within the NHS. However, such Electronic Health Records are not made publicly available due to privacy concerns. In our paper, we propose a privacy-preserving Generative Adversarial Network (pGAN), which can generate synthetic data of high quality, while preserving the privacy and statistical properties of the source data. pGAN is evaluated on two distinct datasets, one posing as a Classification task, and the other as a Regression task. Privacy score of generated data is calculated using the Nearest Neighbour Adversarial Accuracy. Cosine similarity scores of synthetic data from our proposed model indicate that the data generated is similar in nature, but not identical. Additionally, our proposed model was able to preserve privacy while maintaining high utility. Machine learning models trained on both synthetic data and original data have achieved accuracies of 74.3% and 74.5% respectively on the classification dataset; while they have attained an R2-Score of 0.84 and 0.85 on synthetic and original data of the regression task respectively. Our results, therefore, indicate that synthetic data from the proposed model could replace the use of original data for machine learning while preserving privacy.
Investment strategy for blockchain technology in a shipping supply chain

In the post-COVID-19 pandemic era, how to promote blockchain technology to improve the efficiency of port customs clearance and logistics transparency has become a hot research question in the shipping industry. In this paper, we investigate the value of blockchain-based vertical cooperation led by a port or a shipping company in a one-to-two shipping service competition model. A status quo scenario and two different investment scenarios led by different stakeholders are constructed, and equilibrium solutions of the Stackelberg game in three scenarios are proposed. Meanwhile, consumer surplus and social welfare under different cooperation frameworks are discussed. We find that i) investment in blockchain technology can significantly increase the profits of shipping supply chain participants. ii) From the point of view of profit, when the investment efficiency of the port and the shipping company satisfies a certain relationship, there is a balanced strategy for both parties to invest in blockchain technology. iii) The more intense the competition for the services of shipping companies, the lower the level of blockchain technology to improve the logistics capabilities of the shipping supply chain participants. iv) The port's investment in blockchain technology brings more consumer surplus and social welfare. The abovementioned findings can provide managerial insights for ports and shipping companies and present decision support for the government to formulate blockchain technology promotion policies.
Human Evaluation of English–Irish Transformer-Based NMT

In this study, a human evaluation is carried out on how hyperparameter settings impact the quality of Transformer-based Neural Machine Translation (NMT) for the low-resourced English–Irish pair. SentencePiece models using both Byte Pair Encoding (BPE) and unigram approaches were appraised. Variations in model architectures included modifying the number of layers, evaluating the optimal number of heads for attention and testing various regularisation techniques. The greatest performance improvement was recorded for a Transformer-optimized model with a 16k BPE subword model. Compared with a baseline Recurrent Neural Network (RNN) model, a Transformer-optimized model demonstrated a BLEU score improvement of 7.8 points. When benchmarked against Google Translate, our translation engines demonstrated significant improvements. Furthermore, a quantitative fine-grained manual evaluation was conducted which compared the performance of machine translation systems. Using the Multidimensional Quality Metrics (MQM) error taxonomy, a human evaluation of the error types generated by an RNN-based system and a Transformer-based system was explored. Our findings show the best-performing Transformer system significantly reduces both accuracy and fluency errors when compared with an RNN-based model.
Cyber Security at the Heart of Open Banking

During the last year, the pandemic prompted a heavier reliance on technology, as well as the adoption of interconnected devices and hybrid work settings. As a result, we are more vulnerable to cyber-attacks than ever before. The chapter introduces open banking and cyber security concepts briefly, presenting the reasons for rising of open banking. An insight to open banking in India is also presented. In India, the non-banking financial companies are intermediaries responsible for open banking and customer consent management. The trend of open banking systems is depicted from recent years. A short description of various threats and key security measures are discussed. Further, the need and implementation of cyber security in open banking are elaborated. Finally, future perspectives and research challenges are described to extend work on cyber security mechanisms in open banking.
Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes

Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.
A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

The rapid development of Cloud Computing (CC) has led to the release of many services in the cloud environment. Service composition awareness of Quality of Service (QoS) is a significant challenge in CC. A single service in the cloud environment cannot respond to the complex requests and diverse requirements of the real world. In some cases, one service cannot fulfill the user's needs, so it is necessary to combine different services to meet these requirements. Many available services provide an enormous QoS and selecting or composing those combined services is called an Np-hard optimization problem. One of the significant challenges in CC is integrating existing services to meet the intricate necessities of different types of users. Due to NP-hard complexity of service composition, many metaheuristic algorithms have been used so far. This article presents the Artificial Bee Colony and Genetic Algorithm (ABCGA) as a metaheuristic algorithm to achieve the desired goals. If the fitness function of the services selected by the Genetic Algorithm (GA) is suitable, a set of services is further introduced for the Artificial Bee Colony (ABC) algorithm to choose the appropriate service from, according to each user's needs. The proposed solution is evaluated through experiments using Cloud SIM simulation, and the numerical results prove the efficiency of the proposed method with respect to reliability, availability, and cost.
A fusion of a deep neural network and a hidden Markov model to recognize the multiclass abnormal behavior of elderly people

With a rapidly aging population, the health problems of older individuals have attracted increasing attention. Elderly people are exposed to more health risks, and their behavior can often indicate signs of crises and diseases. This paper proposes a method for multiclass abnormal behavior recognition based on the integration of sensors (accelerometer, gyroscope, and orientation sensor) in smartphones with positioning monitoring systems. An attention-convolutional neural network (CNN)-long short-term memory (LSTM) algorithm is introduced for human action recognition, which can handle time-dependent data with multiple features of varying importance. Furthermore, based on the long-term activity data (action and position) of the human body, a hidden Markov model (HMM) of the individual’s daily behavior activity state is constructed. Experimental results show that compared with the existing approaches, the proposed attention-CNN-LSTM algorithm performs better in recognizing different human behaviors, with 94.2% precision, 95.1% recall, and F1-score of 94.6%. The developed daily behavior HMM for an individual has been proven to be able to detect changes in human behavioral patterns and indicate the specific behaviors that have changed. The method proposed in this paper can provide a powerful technical guarantee for the health and care of elderly individuals at home.
On-chain analysis-based detection of abnormal transaction amount on cryptocurrency exchanges

Cryptocurrency exchanges play an indispensable role in the cryptocurrency market. However, some exchanges are suspected to be involved in various abnormal or malicious behaviors while providing services to users, such as money laundering, wash trading and even running away. Besides, these behaviors are reported to be often accompanied by an anomalous increase in the transaction amount. Therefore, it is a topic worthy of study to detect whether the abnormal transaction amount occurs in the exchange and when it occurs. This paper uses web crawler tools to collect a relatively complete dataset of exchanges and then conducts a correlation analysis to obtain the most important factors that influence the transaction amount of different exchanges. Then, the prediction model of the influence of various factors on the transaction amount is obtained based on deep learning. The deviation between the predicting transaction amount and the actual transaction amount is calculated to provide a basis for abnormal transaction amount detection. Finally, through a case study on the detection results, some abnormal transaction amounts are related to policy changes and industry events, while the others are suspected to be related to illegal behaviors.
A Multitask Framework for Sentiment, Emotion and Sarcasm aware Cyberbullying Detection from Multi-modal Code-Mixed Memes

Detecting cyberbullying from memes is highly challenging, because of the presence of the implicit affective content which is also often sarcastic, and multi-modality (image + text). The current work is the first attempt, to the best of our knowledge, in investigating the role of sentiment, emotion and sarcasm in identifying cyberbullying from multi-modal memes in a code-mixed language setting. As a contribution, we have created a benchmark multi-modal meme dataset called MultiBully annotated with bully, sentiment, emotion and sarcasm labels collected from open-source Twitter and Reddit platforms. Moreover, the severity of the cyberbullying posts is also investigated by adding a harmfulness score to each of the memes. The created dataset consists of two modalities, text and image. Most of the texts in our dataset are in code-mixed form, which captures the seamless transitions between languages for multilingual users. Two different multimodal multitask frameworks (BERT+ResNET-Feedback and CLIP-CentralNet) have been proposed for cyberbullying detection (CD), the three auxiliary tasks being sentiment analysis (SA), emotion recognition (ER) and sarcasm detection (SAR). Experimental results indicate that compared to uni-modal and single-task variants, the proposed frameworks improve the performance of the main task, i.e., CD, by 3.18% and 3.10% in terms of accuracy and F1 score, respectively.
An Attribute-Driven Mirror Graph Network for Session-based Recommendation

Session-based recommendation (SBR) aims to predict a user's next clicked item based on an anonymous yet short interaction sequence. Previous SBR models, which rely only on the limited short-term transition information without utilizing extra valuable knowledge, have suffered a lot from the problem of data sparsity. This paper proposes a novel mirror graph enhanced neural model for session-based recommendation (MGS), to exploit item attribute information over item embeddings for more accurate preference estimation.
Online obstacle avoidance path planning and application for arc welding robot

The offline path planning problem for arc welding robots becomes increasingly difficult to achieve with the increasing product complexity and the open work environment, such as the welding tasks in ship formation. Most research considers the path planning problem without time limitations or other constraints that conform to the actual production process. During the welding process, the placement of workpieces seriously affects the efficiency of finding feasible paths for the welding robots for weld tasks that cannot be repeated accurately. Thus, in the flow welding workshop, the improved lazy Probabilistic Roadmap (PRM) algorithm based online collision-free path planning method is proposed to finish the welding tasks of general stiffened plates at an arbitrary position on the workbench within the robot's reach. The work environment is built with the grid method to reduce the computational dimension of collision detection. The local path planner is implemented by the lazy-PRM algorithm with the repulsive force field to rapidly search the collision-free path for the welding robot with specific safe distances. The robot posture adjustment strategy is also applied to the collision detection process to adjust some poses of the robot at candidate paths to be feasible. The improved path planning method is tested on both the simulated environment and actual application scenario and proved to be suitable for the path planning problem of the arc welding robot. The results of this study have been integrated into a computer approach, which can accelerate the motion planning process and reduce human efforts.
Explainable digital forensics AI: Towards mitigating distrust in AI-based digital forensics analysis using interpretable models

The present level of skepticism expressed by courts, legal practitioners, and the general public over Artificial Intelligence (AI) based digital evidence extraction techniques has been observed, and understandably so. Concerns have been raised about closed-box AI models' transparency and their suitability for use in digital evidence mining. While AI models are firmly rooted in mathematical, statistical, and computational theories, the argument has centered on their explainability and understandability, particularly in terms of how they arrive at certain conclusions. This paper examines the issues with closed-box models; the goals; and methods of explainability/interpretability. Most importantly, recommendations for interpretable AI-based digital forensics (DF) investigation are proposed.
LKASR: Large kernel attention for lightweight image super-resolution

Image super-resolution, aims to recover a corresponding high-resolution image from a given low-resolution image. While most state-of-the-art methods only consider using fixed small-size convolution kernels (e.g., 1 × 1, 3 × 3) to extract image features, few works have been made to large-size convolution kernels for image super-resolution (SR). In this paper, we propose a novel lightweight baseline model LKASR based on large kernel attention (LKA). LKASR consists of three parts, shallow feature extraction, deep feature extraction and high-quality image reconstruction. In particular, the deep feature extraction module consists of multiple cascaded visual attention modules (VAM), each of which consists of a 1 × 1 convolution, a large kernel attention (acts as Transformer) and a feature refinement module (FRM, acts as CNN). Specifically, VAM applies lightweight architecture like swin transformer to realize iterative extraction of global and local features of images, which greatly improves the effectiveness of SR method (0.049s in Urban100 dataset). For different scales ( × 2, × 3, × 4), extensive experimental results on benchmark demonstrate that LKASR outperforms most lightweight SR methods by up to 0.17∼0.34 dB, while the total of parameters and FLOPs remains lightweight.
A Comparative Analysis on Blockchain versus Centralized Authentication Architectures for IoT-Enabled Smart Devices in Smart Cities: A Comprehensive Review, Recent Advances, and Future Research Directions

Smart devices have become an essential part of the architectures such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), and Internet of Everything (IoE). In contrast, these architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. The adoption of these smart devices expands to different cyber-physical systems in smart city architecture, i.e., smart houses, smart healthcare, smart transportation, smart grid, smart agriculture, etc. The edge of the network connects these smart devices (sensors, aggregators, and actuators) that can operate in the physical environment and collects the data, which is further used to make an informed decision through actuation. Here, the security of these devices is immensely important, specifically from an authentication standpoint, as in the case of unauthenticated/malicious assets, the whole infrastructure would be at stake. We provide an updated review of authentication mechanisms by categorizing centralized and distributed architectures. We discuss the security issues regarding the authentication of these IoT-enabled smart devices. We evaluate and analyze the study of the proposed literature schemes that pose authentication challenges in terms of computational costs, communication overheads, and models applied to attain robustness. Hence, lightweight solutions in managing, maintaining, processing, and storing authentication data of IoT-enabled assets are an urgent need. From an integration perspective, cloud computing has provided strong support. In contrast, decentralized ledger technology, i.e., blockchain, light-weight cryptosystems, and Artificial Intelligence (AI)-based solutions, are the areas with much more to explore. Finally, we discuss the future research challenges, which will eventually help address the ambiguities for improvement.
DDU-Net: A dual dense U-structure network for medical image segmentation

Medical image segmentation is one of the important steps in medical image analysis and has a wide range of applications and research values in medical research and practice. However, it is still a challenging task due to the characteristics of medical images with diverse lesion scales, blurred structural boundaries and numerous modalities. To address these challenges, we design a simple yet powerful dual dense u-structured network (called DDU-Net) for medical image segmentation. Specifically, we first construct dual encoders with the densely connected, where the first encoder uses DenseNet, whose backbone network is pre-trained on ImageNet, as a fixed feature extractor, and the second encoder uses a network structure similar to the first encoder. Both encoders try to encode information on the input image, and each layer is directly connected to the next layer in a feed-forward manner. This approach not only greatly reduces the number of parameters in the network, but also makes it easier to train with smaller samples. We then design up-sampling paths applicable to the dual encoder structure, and construct deeper decoders through densely connected convolutional layers, fusing the low to high level multi-scale semantic information learned by the two encoders. Finally, we employ a multiple loss function that combines boundary and content information to make DDU-Net more focus on the accuracy of boundary delineation to improve the overall segmentation performance. We have conducted comprehensive experiments on five different medical image segmentation datasets, including skin lesion segmentation, nuclei segmentation, lung segmentation, gland segmentation and vessel segmentation (IoU metrics for DDU-Net are 0.790, 0.860, 0.925, 0.812 and 0.696 respectively). Compared to baseline U-Net and other state-of-the-art methods, DDU-Net achieves competitive segmentation performance in both qualitative and quantitative evaluation.
A novel multiple grayscale image encryption method based on 3D bit-scrambling and diffusion

Since a large number of digital images are shared widely in our daily life, the demand and need for multiple-image encryption algorithms are increasing to ensure the security of the shared images. To present a new perspective to this request, a secure multiple-image encryption method based on 3D bit-scrambling and diffusion operations is proposed for grayscale images in this paper. A three-dimensional bit array is created by extending the eight bit planes of each grayscale image to a third dimension. This 3D bit array is pseudo-randomly scrambled by changing the order of the rows, columns, and layers. The scrambled bit planes are then used to reconstruct the shuffled images. A controlled diffusion operation is also applied to the shuffled and merged images. Both scrambling and diffusion parameters are obtained separately by iterating the chaotic Henon map twice. The initial values of the Henon map are plain images dependent. Several performance analyses including key space and sensitivity, histogram, correlation, entropy, differential, data loss, noise, and running time are carried out to test the proposed method’s resistance against various cryptanalytic attacks. Simulation results show that the proposed method can encrypt multiple images effectively and securely.