Reviewerly/exHarmony huggingface dataset:
Realising the Good University: Social Innovation, Care, Design Justice and Educational Infrastructure

This paper is a contribution to the collective work of imagining better universities. It starts from Raewyn Connell’s account of the good university, and develops four main ideas. Connell’s insistence on thinking about universities as real workplaces, with real workforces doing real work that has real consequences, provides a disciplining foundation. On this basis, we must acknowledge that although changes are often set in motion outside a university, their realisation always depends on the work of university staff and students. Secondly, research on learning and teaching and the spaces in which they unfold can contribute to real change by providing stronger concepts and clearer language in which to imagine, discuss and plan. Thirdly, course and curriculum redesign, oriented to the great challenges that our students will be tackling in the next few decades, would benefit from an infusion of practices and values from the fields of social innovation and participatory design, and that design for social innovation needs a grounding in design justice. Finally, I outline some of the implications that we can infer for better learning spaces — understood from both a postdigital and a postcritical perspective. A connecting theme within this exploration is the realisation of care as thoughtful work.
Learning Dual Transformer Network for Diffeomorphic Registration

Diffeomorphic registration is widely used in medical image processing with the invertible and one-to-one mapping between images. Recent progress has been made to diffeomorphic registration by utilizing a convolutional neural network for efficient and end-to-end inference of registration fields from an image pair. However, existing deep learning-based registration models neglect to employ attention mechanisms to handle the long-range cross-image relevance in embedding learning, limiting such approaches to identify the semantically meaningful correspondence of anatomical structures. In this paper, we propose a novel dual transformer network (DTN) for diffeomorphic registration, consisting of a learnable volumetric embedding module, a dual cross-image relevance learning module for feature enhancement, and a registration field inference module. The self-attention mechanisms of DTN explicitly model both the inter- and intra-image relevances in the embedding from both the separate and concatenated volumetric images, facilitating semantical correspondence of anatomical structures in diffeomorphic registration. Extensive quantitative and qualitative evaluations demonstrate that the DTN performs favorably against state-of-the-art methods.
A Transformer Fault Diagnosis Method Based on Parameters Optimization of Hybrid Kernel Extreme Learning Machine

Dissolved gas analysis (DGA) is a widely used method for diagnosing internal transformer defects. The traditional single intelligent diagnostic method cannot efficiently process large amounts of incomplete defect information with DGA, which affects the accuracy of fault diagnosis. To this end, this paper proposes a transformer fault diagnosis method based on the optimization of kernel parameters and weight parameters of a kernel extreme learning machine (KELM). First, based on Mercer’s theorem, we combine the radial basis kernel function with the polynomial kernel function to construct a new hybrid kernel function. Then, the gray wolf optimization (GWO) algorithm and the differential evolution (DE) algorithm are combined to improve the diversity of the gray wolf population, enhance the searchability of GWO, and prevent GWO from falling into a local optimum during the iterative process. Finally, the kernel parameters and weight parameters of the hybrid kernel function are optimized by using the modified grey wolf optimization (MGWO) algorithm. The International Electrotechnical Commission Technical Committee (IEC TC) 10 transformer fault data and constructed hybrid feature set is used as the input set of the model, the model is simulated and analyzed, and the transformer fault data collected at a site are used for training and verification. The simulation results on the two sets of data show that the method can accurately and effectively diagnose transformer faults, and has a higher fault diagnosis accuracy rate than traditional methods.
Energy Efficient Clustering in Wireless Sensor Networks by Opposition-Based Initialization Bat Algorithm

Bacanin, Nebojsa Arnaut, Uros Zivkovic, Miodrag Bezdan, Timea Rashid, Tarik A.Wireless sensor networks belong to the group of technologies that enabled emerging and fast developing of other novel technologies such as cloud computing, environmental and air pollution monitoring, and health applications. One important challenge that must be solved for any wireless sensor network is energy-efficient clustering, that is categorized as NP-hard problem. This led to a great number of novel clustering algorithms, that emerged with sole purpose to establish the proper balance in energy consumption between the sensors, and to enhance the efficiency and lifetime of the network itself. In this manuscript, a modified version of the bat algorithm, that belongs to a group of nature-inspired swarm intelligence metaheuristics, is proposed. Devised algorithm was utilized to tackle the energy-efficient clustering problems. Performance of proposed improved bat metaheuristics has been validated by conducting a comparative analysis with its original version, and also with other metaheuristics approaches that were tested for the same problem. Obtained results from conducted experiments suggest that the proposed method’s performance is superior, and that it could bring valuable results in the other domains of use as well.
Smart Policing Technique With Crime Type and Risk Score Prediction Based on Machine Learning for Early Awareness of Risk Situation

In order to quickly and effectively respond to a newly received criminal case, information regarding the type and severity of the case is crucial for authorities. This paper designs and develops a crime type and risk level prediction technique based on machine learning technology and verifies its performance. The designed technology can predict crime type and crime risk level using a text-based criminal case summary, which is criminal case receipt data. For the text-based criminal case summary data, the KICS data format is considered, which is actual policing data that contains information about criminal cases. For the crime type, 21 representative types of crimes are considered; therefore, the system can predict one of 21 types of crime for each criminal case. Furthermore, to predict the crime risk level, we developed a crime risk calculation formula. The developed formula calculates the crime risk level and outputs the risk score in numerical terms considering the severity and damage level of the criminal case. To predict the crime type and crime risk score, both DNN and CNN-based prediction models were designed and developed. The performance evaluation section shows that, in the case of crime type prediction, the proposed prediction models can achieve better performance than traditional classification algorithms such as naïve Bayes and SVM. The performance of the CNN-based crime type prediction model is about 7% and 8% better than those of the SVM algorithm and the naïve Bayes algorithm, respectively. The performance of the designed technology was comprehensively analyzed and verified through various performance measurement parameters. It is also developed in the form of a software platform with a GUI, allowing field personnel (e.g. police officers) to intuitively identify the type of criminal case and the level of risk from a text-based criminal case summary upon receipt of a new criminal case.
Comparison Between A* and RRT Algorithms for 3D UAV Path Planning

This paper aims to present a comparative analysis of the two most utilized graph-based and sampling-based algorithms and their variants, in view of 3D UAV path planning in complex indoor environment. The findings of this analysis outline the usability of the methods and can assist future UAV path planning designers to select the best algorithm with the best parameter configuration in relation to the specific application. An extensive literature review of graph-based and sampling-based methods and their variants is first presented. The most utilized algorithms which are the A* for graph-based methods and Rapidly-Exploring Random Tree (RRT) for the sampling-based methods, are defined. A set of variants is also developed to mitigate with inherent shortcomings in the standard algorithms. All algorithms are then tested in the same scenarios and analyzed using the same performance measures. The A* algorithm generates shorter paths with respect to the RRT algorithm. The A* algorithm only explores volumes required for path generation while the RRT algorithms explore the space evenly. The A* algorithm exhibits an oscillatory behavior at different resolutions for the same scenario that is attenuated with the novel A* ripple reduction algorithm. The Multiple RRT generated longer unsmoothed paths in shorter planning times but required more smoothing over RRT. This work is the first attempt to compare graph-based and sampling-based algorithms in 3D path planning of UAVs. Furthermore, this work addresses shortcomings in both A* and RRT standard algorithms by developing a novel A* ripple reduction algorithm, a novel RRT variant and a specifically designed smoothing algorithm.
The Effectiveness of the Finite Differences Method on Physical and Medical Images Based on a Heat Diffusion Equation

Abstract In this paper, we study the influence of applying the well-known finite differences method on medical and physical images. These images will be used as coefficients in the steps of the solution after the images being imported and converted to arrays. The aim of the study is to show and analyze the changes that could happen to images for the sake of an enhancement. Experiments of one dimension and two dimensions will be illustrated by applying the explicit and the implicit methods using MATLAB to explain the way of how these methods affect images. Furthermore, we will study and measure the quality of the proposed images in each phase of the experiments using statistical metrics such as PSNR (Peak Signal to Noise Ratio), MSE (Mean Squared Error), MD (Maximum Difference) and some other statistical metrics.
EdgeNets: Edge Varying Graph Neural Networks

Driven by the outstanding performance of neural networks in the structured euclidean domain, recent years have seen a surge of interest in developing neural networks for graphs and data supported on graphs. The graph is leveraged at each layer of the neural network as a parameterization to capture detail at the node level with a reduced number of parameters and computational complexity. Following this rationale, this paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. By extrapolating this strategy to more iterations between neighboring nodes, the EdgeNet learns edge- and neighbor-dependent weights to capture local detail. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs). In writing different GNN architectures with a common language, EdgeNets highlight specific architecture advantages and limitations, while providing guidelines to improve their capacity without compromising their local implementation. For instance, we show that GCNNs have a parameter sharing structure that induces permutation equivariance. This can be an advantage or a limitation, depending on the application. In cases where it is a limitation, we propose hybrid approaches and provide insights to develop several other solutions that promote parameter sharing without enforcing permutation equivariance. Another interesting conclusion is the unification of GCNNs and GATs -approaches that have been so far perceived as separate. In particular, we show that GATs are GCNNs on a graph that is learned from the features. This particularization opens the doors to develop alternative attention mechanisms for improving discriminatory power.
Multi-Scale Hybrid Fusion Network for Single Image Deraining

Deep learning models have been able to generate rain-free images effectively, but the extension of these methods to complex rain conditions where rain streaks show various blurring degrees, shapes, and densities has remained an open problem. Among the major challenges are the capacity to encode the rain streaks and the sheer difficulty of learning multi-scale context features that preserve both global color coherence and exactness of detail. To address the first problem, we design a non-local fusion module (NFM) and an attention fusion module (AFM), and construct the multi-level pyramids' architecture to explore the local and global correlations of rain information from the rain image pyramid. More specifically, we apply the non-local operation to fully exploit the self-similarity of rain streaks and perform the fusion of multi-scale features along the image pyramid. To address the latter challenge, we additionally design a residual learning branch that is capable of adaptively bridging the gaps (e.g., texture and color information) between the predicted rain-free image and the clean background via a hybrid embedding representation. Extensive results have demonstrated that our proposed method is able to generate much better rain-free images on several benchmark datasets than the state-of-the-art algorithms. Moreover, we conduct the joint evaluation experiments with respect to deraining performance and the detection/segmentation accuracy to further verify the effectiveness of our deraining method for downstream vision tasks/applications. The source code is available at https://github.com/kuihua/MSHFN.
Well-to-well correlation and identifying lithological boundaries by principal component analysis of well-logs

Identifying the location of lithological boundaries is one of the essential steps of reservoir characterizations. The manual well-to-well correlation is usually implemented to identify lithological boundaries. Various automated methods were introduced to accelerate this correlation; however, most of them use single well-log data. As each well-log contains specific information of rock and fluid properties, the simultaneous use of various well-logs can enhance the correlation accuracy. We extend an automatic well-to-well correlation approach from the literature to use the benefits of various well-logs by applying principal component analysis on multiple well-logs of a carbonate reservoir. The extracted features (i.e., mean, coefficient of variation, maximum to minimum ratio, trend angle, and fractal dimension) from a reference well are examined across observation wells. The energy of principal components is evaluated to determine the appropriate number of principal components. We examine three different scenarios of applying principal component analysis and determine the best methodology for well-to-well correlation. In the first scenario, the principal component analysis reduces the dependency of statistical attributes extracted from a single well-log. We then apply principal component analysis on multiple well-logs to extract their features (Scenario II). Finally, we check whether principal component analysis can be applied at multiple steps (Scenario III). The analysis of variance and Tukey are used to compare the accuracy of the scenarios. The results show that identifying lithological boundaries in different wells is significantly improved when the principal component analysis approach combines information from multiple well-logs. Generally, it is concluded that principal component analysis is an effective tool for increasing well-to-well correlation accuracy by reducing the dependency of well-to-well correlation parameters (Scenario I) and the feature extraction from log data (Scenario II & III).
DT-SSIM: A Decentralized Trustworthy Self-Sovereign Identity Management Framework

In a ubiquitous environment enclosing cooperative Internet-of-Things (IoT) devices, individuals, and entities, digital identity management (DIM) becomes critical and challenging. DIM pertains to device identities authentication and verification to enable trustworthy service exchange, data collection, and decision making. DIM is the supporting pillar for all online services and the foundation for security and authentication mechanisms. Due to the extreme heterogeneity, scale, and configuration complexity of such environments, enabling trustworthy DIM is crucial and seriously challenging. In an IoT context, devices use local digital identities stored within a tamper-proof unit and verified by a centralized authority for authentication. The recent attacks on IoT systems showed how vulnerable such a design is. It is also an inherent problem that influences humans. From that, self-sovereign identity (SSI) has emerged as a decentralized DIM approach embracing the concept of portable self-possession identity. SSI was presented to couple the digital identity from the owner to enable large-scale cooperation. However, digital identity storage and verification still occur on the device and in a centralized manner. Utilizing a local single-point-of-failure storage memory for verifiable credentials is one of the considerable drawbacks in contemporary SSI. In this regard, this article introduces decentralized trustworthy-self-sovereign identity management (DT-SSIM), a novel decentralized trustworthy SSI management framework. DT-SSIM integrates the secret share scheme with the blockchain-based smart contracts technologies to provide transparent and trustworthy SSI-based DIM services for IoT. Storing IoT identity credentials outside the devices’ local storage preserves the identity credentials from being tampered with or misused. Evaluations and discussions show the resiliency assessment of the system and the cost and estimated running times for verification processes in DT-SSIM.
Broadcast Encryption Scheme for V2I Communication in VANETs

Information dissemination in vehicular ad hoc networks (VANETs) is inseparable from the interaction between vehicles and infrastructure. The trust authority (TA) often plays a pivotal role in VANETs and requires interaction with multiple vehicles. However, when the TA sends the same message to multiple vehicles, there are many redundancies, as it needs to negotiate with each vehicle and send them different ciphertexts. This greatly reduces the work efficiency of the TA. To the best of our knowledge, there is no research on the problem of redundancy that occurs when the same message is sent to multiple vehicles in VANETs. The proposed scheme adopts identity-based broadcast encryption (IBBE) technology, which is a secure data-sharing scheme suitable for the vehicle-to-infrastructure communication mode, in VANETs for the first time. Thus, with only one encryption, the TA can generate a fixed-length ciphertext for a group of vehicles. When there are new vehicles that subsequently request a service, the TA can assign encryption tasks to the proxy server. In terms of security, our scheme meets the particular requirements of VANETs. The encryption overhead of the sender and the length of the ciphertext were comparatively analyzed. The results demonstrated that the performance of the scheme improved significantly. Thus, our scheme can prevent redundancies and effectively improve the work efficiency of TA.
Reinforcement Learning and Physics

Machine learning techniques provide a remarkable tool for advancing scientific research, and this area has significantly grown in the past few years. In particular, reinforcement learning, an approach that maximizes a (long-term) reward by means of the actions taken by an agent in a given environment, can allow one for optimizing scientific discovery in a variety of fields such as physics, chemistry, and biology. Morover, physical systems, in particular quantum systems, may allow one for more efficient reinforcement learning protocols. In this review, we describe recent results in the field of reinforcement learning and physics. We include standard reinforcement learning techniques in the computer science community for enhancing physics research, as well as the more recent and emerging area of quantum reinforcement learning, inside quantum machine learning, for improving reinforcement learning computations.
Automated Diagnosis of Leukemia: A Comprehensive Review

Leukemia is the rapid production of abnormal white blood cells that consequently affects the blood and damages the bone marrow. The overproduction of abnormal and immature white blood cells leads to the damage of the immune system due to the reduced production of red blood cells and platelets by the bone marrow of the body. This hematological malignancy is generally diagnosed by manual methods such as complete blood count (CBC), bone marrow aspiration, or microscopic examination of the blood smear. Nevertheless, the manual methods of leukemia diagnosis are economical but are found to be less reliable, time-consuming, and hectic. Technological advancement in the medical field has effectively addressed these issues in the past. The problems in the manual diagnosis of leukemia detection have been overcome by the development of automated methods using the computer-aided diagnostic (CAD) systems for efficient and reliable leukemia diagnosis. Since the last decade, multiple approaches have been proposed for the CAD systems regarding pre-processing, segmentation, feature extraction, feature selection, and for the improvement of the classification accuracy of the CAD system for the leukemia detection. This paper presents a comprehensive review of the CAD systems for the detection of the various types of leukemia. The review presented here entails the details of various CAD systems for the automated diagnosis of various types of leukemia and analyses their methodologies in terms of their efficiency in pre-processing, segmentation, feature extraction and selection, and overall classification accuracy of the CAD system.
Uncertainty propagation for dropout-based Bayesian neural networks

Uncertainty evaluation is a core technique when deep neural networks (DNNs) are used in real-world problems. In practical applications, we often encounter unexpected samples that have not seen in the training process. Not only achieving the high-prediction accuracy but also detecting uncertain data is significant for safety-critical systems. In statistics and machine learning, Bayesian inference has been exploited for uncertainty evaluation. The Bayesian neural networks (BNNs) have recently attracted considerable attention in this context, as the DNN trained using dropout is interpreted as a Bayesian method. Based on this interpretation, several methods to calculate the Bayes predictive distribution for DNNs have been developed. Though the Monte-Carlo method called MC dropout is a popular method for uncertainty evaluation, it requires a number of repeated feed-forward calculations of DNNs with randomly sampled weight parameters. To overcome the computational issue, we propose a sampling-free method to evaluate uncertainty. Our method converts a neural network trained using dropout to the corresponding Bayesian neural network with variance propagation. Our method is available not only to feed-forward NNs but also to recurrent NNs such as LSTM. We report the computational efficiency and statistical reliability of our method in numerical experiments of language modeling using RNNs, and the out-of-distribution detection with DNNs.