Reviewerly/exHarmony huggingface dataset:
Postdigital Practical Axiology

This chapter details an approach to postdigital research focused on systemic modelling and scalable small-scale interventions. In the face of major educational challenges which have arisen in the wake of technology, postdigital research is timely. I argue that the current educational system relies on the codification of knowledge, whereas the modern fluid knowledge environment of technology produces a ‘codification bottleneck’ necessitating more emphasis on tacit knowledge, dialogue, and apprenticeship. Within this context, values are of increasing importance as an underpinning foundation for learning partnerships in fast-moving streams of discourse. I present postdigital research as an active process of inspecting and intervening with the dynamics of value within the existing system, with the aim of constructing better models of its dynamics, and to use this to guide interventions which catalyse processes of educational improvement. This modelling requires an understanding of the ‘liminal dynamics’ of perception, as well as the dynamics of institutional organization. This requires the use of systems tools, and the majority of the chapter is an exposition of tools and approaches to understanding value as a dynamic system. Drawing on work from cybernetics and psychotherapy with these modelling techniques, I discuss how recent examples of practice can be seen to be consistent with a postdigital approach – particularly with regard to understanding self-organisation in learning. This places emphasis on the role of uncertainty and noise created by technology in the production of new understanding and organizational forms.
Variable Discovery with Large Language Models for Metamorphic Testing of Scientific Software

When testing scientific software, it is often challenging or even impossible to craft a test oracle for checking whether the program under test produces the expected output when being executed on a given input – also known as the oracle problem in software engineering. Metamorphic testing mitigates the oracle problem by reasoning on necessary properties that a program under test should exhibit regarding multiple input and output variables. A general approach consists of extracting metamorphic relations from auxiliary artifacts such as user manuals or documentation, a strategy particularly fitting to testing scientific software. However, such software typically has large input-output spaces, and the fundamental prerequisite – extracting variables of interest – is an arduous and non-scalable process when performed manually. To this end, we devise a workflow around an autoregressive transformer-based Large Language Model (LLM) towards the extraction of variables from user manuals of scientific software. Our end-to-end approach, besides a prompt specification consisting of few examples by a human user, is fully automated, in contrast to current practice requiring human intervention. We showcase our LLM workflow over three case studies of scientific software documentation, and compare variables extracted to ground truth manually labelled by experts.
FAIR-FATE: Fair Federated Learning with Momentum

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data heterogeneity.
Pseudo-Reference Point Cloud Quality Measurement Based on Joint 2-D and 3-D Distortion Description

Point cloud (PC) compression inevitably introduces distortion during communication, which can affect users’ visual experience. Thus, efficient point cloud quality measurement (PCQM) tools are highly desired to measure the PC’s visual quality. In this paper, a pseudo-reference PCQM metric based on joint two-dimensional (2D) and three-dimensional (3D) distortion description is proposed. In 2D description, aiming at the visual quality degradation reflected cooperatively by PC’s texture distortion and geometry distortion, a joint texture-geometry distribution with texture projection map and geometry projection map of the video-based point cloud compression (V-PCC) standard is constructed to measure the joint texture-geometry distortion of PC. Since the geometry distortion of PC results in the similar distortion phenomena in the geometry projection map and texture projection map, a self-reference geometry-texture structural similarity (SGT-SSIM) is proposed. The separate statistical features of texture projection map and geometry projection map are also considered. In 3D description, considering the limitations of using full-reference metric and the difficulty of directly reflecting PC cracks and outliers only by the V-PCC projection, a pseudo-reference PC is constructed by performing Poisson surface reconstruction on the distorted PC. Then, the point-to-distribution is used to directly characterize pseudo-referenced geometry distortion, while gray level-gradient co-occurrence matrix based on key points of PC is constructed to measure the texture distortion. Finally, the features with joint 2D and 3D distortion description are combined to measure the PC visual quality more comprehensively. Experimental results on five PC datasets demonstrate that the proposed metric has comparable performance to the existing full-reference metrics.
TCPP: Achieving Privacy-Preserving Trajectory Correlation With Differential Privacy

The prevalence of mobile Internet, smart terminal devices, and GPS positioning technology has generated a vast number of trajectory data that location-based applications can utilize. However, delivering LBSs based on trajectories without extra protection may expose the personal information of users and even their social ties. Despite the fact that many works have been offered to achieve differential privacy for trajectory correlation, the vast majority of them only consider the trajectory correlation of a single user, and privacy protection for trajectory correlation amongst multiple users is not considered. Directly applying these works to protect correlation amongst multiple users may lead to the low availability of published trajectory data. To address the above challenges, we propose a trajectory correlation privacy-preserving mechanism (TCPP) that fulfills differential privacy. Specifically, we first apply the Euclidean distance to filter out a set of trajectories whose correlation needs to be protected. Then, we employ the Kalman filter to generate a dataset with high availability from the set of trajectories. Finally, we present a mechanism for publishing trajectories that preserves the trajectory correlation based on a customized privacy budget allocation strategy. Rigid security analysis shows that our proposed mechanism can well preserve the correlation privacy of trajectories. Experimental results on real-world datasets further demonstrate the privacy, availability and time efficiency advantages of our mechanism.
Ensemble Partition Sampling (EPS) for Improved Multi-Class Classification

Classification is a commonly used technique in data mining and is applied in various fields such as sentiment analysis, fraud detection, and fault diagnosis. Multiclass classification, which involves more than two classes, is more complex than binary classification. There are mainly two ways to approach multiclass classification, one is to expand the binary classifier into a multiclass classifier through various strategies and the other is to divide the multiclass classification problem into multiple binary problems (binarization). Two popular approaches for binarization are One vs One (OvO) and One vs All (OvA). It is simpler to aggregate the outputs of all binary classifiers as the number of classifiers decreases. However, it causes an imbalance of positive and negative sample numbers, which affects the classification effect of each binary classifier. In this article, we contribute to the field of ensemble learning and multi-class classification by proposing a new method called Ensemble Partition Sampling (EPS). This article presents a new approach to multiclass classification using an “Ensemble Partition Sampling” method within the “one-vs-all” (OvA) framework. The primary goal of this method is to tackle the problem of data imbalance by incorporating ensemble learning and preprocessing techniques into each binary dataset. The study found that Ensemble Partition Sampling (EPS) is the most effective method for imbalanced and multiclass imbalanced classification, outperforming other methods including OvA, SMOTE, k-means-SMOTE, Bagging-RB, DES-MI, OvO-EASY, and OvO-SMB. The study used CART, Random Forest, and SVM as classifiers, and the results consistently showed that EPS outperformed all other algorithms. Based on the findings, it can be concluded that the EPS approach is a highly effective method for improving classification performance in imbalanced and multiclass imbalanced datasets.
A Hybrid Deep Learning Method to Extract Multi-features from Reviews and User–Item Relations for Rating Prediction

Abstract Currently, the Internet is widely used for shopping. Online reviews have become a crucial factor in helping people to make purchasing decisions. However, the large amount of data overwhelms most users, leading to the problem of information overload. To address this issue, researchers have proposed recommender systems as a solution. The most commonly used method is the collaborative filtering method, which analyzes users’ purchase history or behavior to make recommendations. In addition to user ratings, by analyzing users’ comments and the relationships between users and items more precise preferences can be obtained. In this study, the aspect-based rating prediction with a hybrid deep learning method (ARPH) is proposed. It consists of five parts: aspect detection, sentiment and semantic analysis, user preference analysis, graph attention network analysis, and rating prediction. It initially extracts the implicit aspect features and aspects’ sentiment–semantic features from user and item reviews. The convolutional neural network and matrix factorization methods are then used to generate the predicted ratings of items. Additionally, a graph attention network was built to calculate the predicted ratings based on the relationships between users and items. Finally, a multilayer perceptron was used to automatically adjust the weights for integrating these two predicted ratings. Our method utilizes user–item relationships to predict ratings when there are fewer user reviews. Conversely, the features derived from textual reviews were employed for rating prediction. The experimental results showed that extracting different features is useful in representing user and product preferences. The proposed method effectively improved the accuracy of the rating predictions.
FF-RRT*: a sampling-improved path planning algorithm for mobile robots against concave cavity obstacle

Abstract The slow convergence rate and large cost of the initial solution limit the performance of rapidly exploring random tree star (RRT*). To address this issue, this paper proposes a modified RRT* algorithm (defined as FF-RRT*) that creates an optimal initial solution with a fast convergence rate. An improved hybrid sampling method is proposed to speed up the convergence rate by decreasing the iterations and overcoming the application limitation of the original hybrid sampling method towards concave cavity obstacle. The improved hybrid sampling method combines the goal bias sampling strategy and random sampling strategy, which requires a few searching time, resulting in a faster convergence rate than the existing method. Then, a parent node is created for the sampling node to optimize the path. Finally, the performance of FF-RRT* is validated in four simulation environments and compared with the other algorithms. The FF-RRT* shortens 32% of the convergence time in complex maze environment and 25% of the convergence time in simple maze environment compared to F-RRT*. And in a complex maze with a concave cavity obstacle, the average convergence time of Fast-RRT* in this environment is 134% more than the complex maze environment compared to 12% with F-RRT* and 34% with FF-RRT*. The simulation results show that FF-RRT* possesses superior performance compared to the other algorithms, and also fits with a much more complex environment.
Optimization of virtual machines performance using fuzzy hashing and genetic algorithm-based memory deduplication of static pages

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
Professional skills training in developing digital materials through augmented and virtual reality applications

Abstract A good deal of research has stressed that teaching staff are expected to continuously improve their profession. However, we observed that there is a need for more practical‐based research investigating the practices being adopted by teaching staff. Therefore, the present research aimed to explore the effect of a 9‐day practical‐based training on faculty members' and teachers' levels of professional competence in developing digital materials through augmented and virtual reality applications. The study group consisted of 15 faculty members and 15 teachers. “Digital Material Development Competence Scale” was applied as pre‐ and posttests, and semi‐structured interview questions were employed to collect data. As a result, both the quantitative and qualitative data evidenced that the training increased both faculty members' and teachers' levels of supposed professional development significantly. Subsequently, the participants' views revealed that the following contributed to the development of their skills in developing digital materials, technology use, integrating their pedagogical knowledge into technological knowledge, and lifelong learning. They are (a) receiving training on the use of augmented and virtual reality applications; (b) practicing sample applications with these tools; (c) experiencing mutually collaborative, hands‐on, and interactive learning; (d) with the help of highly motivated and expert trainers; and (e) with efficient collaboration between faculty members and teachers. In closing, not only did we report and discuss the results in line with relevant professional development and lifelong learning theories, but we also presented research activities and results as concrete examples in the context of efficient learning for the professional development of teaching staff as adults.
Arithmetic Study about Efficiency in Network Topologies for Data Centers

Data centers are getting more and more attention due the rapid increase of IoT deployments, which may result in the implementation of smaller facilities being closer to the end users as well as larger facilities up in the cloud. In this paper, an arithmetic study has been carried out in order to measure a coefficient related to both the average number of hops among nodes and the average number of links among devices for a range of typical network topologies fit for data centers. Such topologies are either tree-like or graph-like designs, where this coefficient provides a balance between performance and simplicity, resulting in lower values in the coefficient accounting for a better compromise between both factors in redundant architectures. The motivation of this contribution is to craft a coefficient that is easy to calculate by applying simple arithmetic operations. This coefficient can be seen as another tool to compare network topologies in data centers that could act as a tie-breaker so as to select a given design when other parameters offer contradictory results.
Atlantic—Automated data preprocessing framework for supervised machine learning

Atlantic is an open-source Python package designed to simplify and automate data preprocessing for supervised ML (Machine Learning) tasks. The Atlantic package integrates multiple customizable preprocessing mechanisms, including datetime feature engineering, automated feature selection, categorical encoding techniques and null imputation methods. In order to provide a comprehensive approach to data processing automation, Atlantic automated preprocessing pipeline follows an optimization method based on tree-based models ensembles. The main goal of Atlantic is to automatically identify the best combination of preprocessing mechanisms for a specific dataset, aiming to improve the performance of future ML models applied to that data.
Cervical cell nuclei segmentation based on GC-UNet

Cervical cancer diagnosis hinges significantly on precise nuclei segmentation at early stages, which however, remains largely elusive due to challenges such as overlapping cells and blurred nuclei boundaries. This paper presents a novel deep neural network (DNN), the Global Context UNet (GC-UNet), designed to adeptly handle intricate environments and deliver accurate cell segmentation. At the core of GC-UNet is DenseNet, which serves as the backbone, encoding cell images and capitalizing on pre-existing knowledge. A unique context-aware pooling module, equipped with a gating model, is integrated for effective encoding of ImageNet pre-trained features, ensuring essential features at different levels are retained. Further, a decoder grounded in a global context attention block is employed to foster global feature interaction and refine the predicted masks.
Secure and Privacy-Preserving Trust Management System for Trustworthy Communications in Intelligent Transportation Systems

As Intelligent Cyber-Physical Transportation Systems (ICPTS) become more complex and interconnected, ensuring secure and reliable communication between components and entities becomes a critical challenge. In this paper, we propose a Context-Aware Cognitive Memory Trust Management System (CACMTM) for ICPTS that leverages game theory to model trust interactions and combines several trust components, such as evaluation, decision, update, and knowledge modules, to provide an effective and reliable trust management solution. This approach takes into account the unique requirements of Customer Centric Communication and Networked Control for Intelligent Cyber-Physical Transportation Systems (CNC-ICTS). We present three algorithms that implement these modules and evaluate the CACMTM’s performance using various metrics and parameters, such as execution time, scalability, and detection of different types of attacks. Our experimental results show that the proposed approach outperforms existing trust management solutions and can effectively detect and mitigate various types of attacks in the context of CNC-ICTS. We also provide recommendations for future research in this area.
Attack-Resilient Supervisory Control of Discrete-Event Systems: A Finite-State Transducer Approach

Resilience to sensor and actuator attacks is a major concern in the supervisory control of discrete events in cyber-physical systems (CPS). In this work, we propose a new framework to design supervisors for CPS under attacks using finite-state transducers (FSTs) to model the effects of the discrete events. FSTs can capture a general class of regular-rewriting attacks in which an attacker can nondeterministically rewrite sensing/actuation events according to a given regular relation. These include common insertion, deletion, event-wise replacement, and finite-memory replay attacks. We propose new theorems and algorithms with polynomial complexity to design resilient supervisors against these attacks. We also develop an open-source tool in Python based on the results and illustrate its applicability through a case study.
Federated Learning: Crop classification in a smart farm decentralised network

In this paper, the application of federated learning to smart farming has been investigated. The Federated averaging model has been used to carry out crop classification using climatic parameters as independent variables and crop types as labels. The decentralised machine learning models have been used to predict chickpea crops. Through experimentation, it has been observed the model converges when learning rates of 0.001 and 0.01 are considered using the Stochastic gradient descent (SGD) and the Adam optimizers. The model using the Adam optimizer converged faster than the SGD optimizer, this was achieved after 100 epochs. Analysis from the farm dataset has shown that the decentralised models achieve faster convergence and higher accuracy than the centralised network models.
A Lightweight Nonlinear White-Box SM4 Implementation Applied to Edge IoT Agents

The white-box implementation of cryptography algorithm can hide key information even in the white-box attack context owing to the means of obfuscation. However, under the deliberately designed attack, there is still a risk of the information being recovered within a certain time complexity. In this paper, a lightweight nonlinear white-box SM4 implementation is proposed to prevent several typical attacks from extracting the secret key, which hides the encryption and decryption process in obfuscated lookup tables. Aiming to improve the diversity and ambiguity of the lookup tables as well as resist the different types of white-box attacks, the random bijective nonlinear mappings are applied as scrambling encodings of the lookup tables. Moreover, the memory occupation of the implementation doesn’t increase significantly by simplifying the structure and using concatenation code. Through several quantitative indicators, including memory size, diversity, ambiguity, the time complexity required to extract the key, and the value space of the key and external encodings, it is proved that the security of the proposed implementation could been enhanced significantly, while no sacrificing the practicality, compared with the existing schemes.