Reviewerly/exHarmony huggingface dataset:
How IT Course Improves Digital Competencies: An Experimental Study in Science Education

The increasing importance of technology in society has led to a growing demand for individuals to possess digital competencies. In today's world, digital skills are becoming increasingly essential for both personal and professional development. Governments have recognized the importance of digital competencies and have included them as part of basic competencies in many countries. Pre-service teachers play a crucial role in shaping future generations, and it is vital that they possess strong digital competencies. This study focuses on the impact of an "Information Technologies in Science Education" course on the digital competency development of pre-service teachers. The study employed a one-group, pre-test post-test experimental design to evaluate the effectiveness of the course on improving the digital literacy skills of pre-service teachers. The results of the study indicated a significant positive impact of the IT course structured for the science field on the digital competency development of pre-service teachers. The post-test mean scores indicated a high level of digital competency among the study group. The study also highlights the lack of experimental studies in this area, emphasizing the importance of further research in this field. The findings of this study have implications for both teacher education and the incorporation of digital technologies in science education. Based on the study results, it is recommended that teacher education programs provide opportunities for pre-service teachers to improve their digital competencies through courses and training programs. Additionally, it is essential to integrate digital technologies into science education to equip students with the necessary skills to thrive in a digital world.
EQNAS: Evolutionary Quantum Neural Architecture Search for Image Classification

Quantum neural network (QNN) is a neural network model based on the principles of quantum mechanics. The advantages of faster computing speed, higher memory capacity, smaller network size and elimination of catastrophic amnesia make it a new idea to solve the problem of training massive data that is difficult for classical neural networks. However, the quantum circuit of QNN are artificially designed with high circuit complexity and low precision in classification tasks. In this paper, a neural architecture search method EQNAS is proposed to improve QNN. First, initializing the quantum population after image quantum encoding. The next step is observing the quantum population and evaluating the fitness. The last is updating the quantum population. Quantum rotation gate update, quantum circuit construction and entirety interference crossover are specific operations. The last two steps need to be carried out iteratively until a satisfactory fitness is achieved. After a lot of experiments on the searched quantum neural networks, the feasibility and effectiveness of the algorithm proposed in this paper are proved, and the searched QNN is obviously better than the original algorithm. The classification accuracy on the mnist dataset and the warship dataset not only increased by 5.31% and 4.52%, respectively, but also reduced the parameters by 21.88% and 31.25% respectively. Code will be available at https://gitee.com/Pcyslist/models/tree/master/research/cv/EQNAS, and https://github.com/Pcyslist/EQNAS.
Constructing Industrial Knowledge Graph Through Ontology and Link Prediction

Given that the numerous data embedded in manufacturing processes and products are separated, it is challenging to tackle and integrate heterogeneous data in industrial scenarios. In this context, an industrial knowledge graph (iKG) has been developed as a promising semantic organisation to leverage the rich information from multiple resources. However, relations are usually missing and hidden in original iKGs, which results in the necessity for iKG completion. Given these two perspectives, a framework of iKG construction is proposed based on ontology and link prediction in this study. Firstly, an ontology design framework is deployed to generate domain-centric ontologies after extracting numerous data (e.g., entities and relations). Secondly, the missing relations between each couple of entities are discovered over existing knowledge to increase the number of edges that complete and refine iKGs. Thirdly, iKG visualisation is conducted by importing data into the generated ontology. The feasibility and effectiveness of the proposed framework are substantiated and demonstrated in a case study using real-world data.
A New Approach to Video Coding Leveraging Hybrid Coding and Video Frame Interpolation

In this work, we propose the use of a video coding method, dubbed Decoupled Interpolated Video Coding (DIVC), which blends traditional hybrid video coding with novel approaches based on Neural Networks (NNs). The DIVC approach provides a base-level representation with a reduced bit rate by dropping frames in a regular manner which can be decoded by standard hybrid video coding. Meanwhile, we also regenerate the dropped frames from the reconstructed video from the base-level representation using NN-based Video Frame Interpolation (VFI) to recover the original number of frames per second (fps). We show that the DIVC approach can improve video coding efficiency considering the perceptual quality measured with the Structural Similarity (SSIM) metric. More specifically, such approach achieved a reduction of 38.96% (AI configuration) and 5% (RA configuration) in terms of SSIM <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">dB</inf> -based Bøntegaard Delta Bit Rate (BD-Rate) when compared to traditional video coding. Meanwhile, considering the Peak Signal-to-Noise Ratio (PSNR), which may not correlate as well to human perception, the discussed approach resulted in PSNR BD-Rate reduction of 16.19% in AI configuration and increase of 19.33% in RA configuration. Employing VFI to improve video coding efficiency tends to be more suited to processing video sequences with high fps or slower motion.
Toward a Better Understanding of the Emotional Dynamics of Negotiation with Large Language Models

Current approaches to building negotiation agents rely either on model-based techniques that explicitly implement key principles of negotiation or model-free techniques leveraging algorithms developed via training on large amounts of human-generated text. We bridge these two approaches by combining a model-based approach with large language models for natural language understanding and generation. We find large language models perform well at recognizing dialogue acts and an opponent's emotions; perform reasonably well at recognizing opponents' preferences in the negotiation; and perform worse at understanding opponent offers. We also perform a qualitative comparison of the capabilities of our hybrid approach with a model-free method and find our hybrid agent provides safeguards against hallucinations and guarantees more control over aspects of negotiation such as emotional expressions, information sharing, and concession strategies.
Brussels mobility in 2050: Participatory mixed-method scenario building to explore alternative futures

Mobility is deeply embedded in people’s everyday lives and fundamental in shaping society. Therefore, transport policy needs to be embedded in a well-grounded view of the future, while also being resilient to external shocks. One of the difficulties in ensuring the resilience of transportation systems is the large variety of factors of uncertainty that characterizes the field. Scenario building is one method that can address this issue by exploring alternative futures besides a most likely or desired one. But to increase the likelihood of translating exploratory scenarios into policy making, these should be developed in a participatory manner. One of the difficulties encountered when developing scenarios using quantitative methods is that these methods are often a black box for participants, while qualitative methods on their own can lack legitimacy. Mixed-method approaches can therefore be an interesting avenue for research to facilitate participation in transport scenario building, by providing a mathematical structuring of the scenarios, enriched with qualitative stakeholder input. In this paper, we explore how participation can be facilitated in mixed-method scenario building. We further elaborate on the combination of a systems dynamic-approach (cross-impact balance analysis) with creative workshops by testing three approaches to facilitate participation: a Delphi survey, personas, and the use of visual harvesting. We develop four scenarios for the future of urban mobility in Brussels, Belgium, by 2050, and report on the lessons learnt. We find that the use of Delphi as a stakeholder engagement method too rapidly closes down the scenario funnel when used for selecting factors of uncertainty. The use of personas allows for the development of engaging narratives, and could be helpful for the involvement of people less familiar with participatory exercises. Lastly, the use of visuals throughout the workshops was found to stimulate active discussion and resulted in a more transparent scenario development process, by increasing traceability.
Development of Android-Based Science Learning Media "Wismaya" with an Inquiry Approach in Improving Critical Thinking Ability

This study aims to develop an android-based game learning media by applying the steps of the ADDIE model RnD research method. The data collection technique was in the form of a validated questionnaire for media experts, linguists, materials, teachers, and student responses. The results of media, material, and language validation were 0.917; 0.898, and 0.791 with very feasible criteria for media and material validation, while for language validation it shows feasible criteria. The classical mastery of student learning outcomes reached 94% and the N percent gain of 59.34 indicated that the criteria were quite effective. Student responses were seen from the usefulness, convenience, and satisfaction indicators each of 0.82; 0.9; and 0.92 indicating very good response criteria. The teacher's response questionnaire, seen from the indicators of presentation and use of language, was 0.9 and 0.875 respectively, indicating very good response criteria, while in the indicators of instructions for use, the suitability of the material and suitability for student needs were each 0.75; 0.8; and 0.79 indicates good response criteria. The results of the application of android-based science learning media with an inquiry approach can improve the critical thinking skills of class IV students on style material.
Reusability Analysis of K-Nearest Neighbors Variants for Classification Models

The K-nearest neighbors (KNN) algorithm is a powerful example-based method that is easy to implement in classification tasks. As a result, in the literature, there are proposals for KNN variants, such as KNN with weights, which are meant to improve KNN without straying too far from the original algorithm. To select a KNN variant using the design of experiments, a complete implementation of each KNN variant must be stored. Combining these variants with a range of K values and a set of distance metrics can produce a set of configurations to choose from. Even with these options, there is a chance that none of the possible configurations will suit the research scenario. Finding patterns in KNN variants would allow their features to be separated so that they can be reused and recombined, expanding the range of possible configurations. It is proposed to analyze the variants of KNN to determine the essence of their building patterns, which can be exploited for reusability purposes. This analysis will make it clear that there are patterns among the strategies that these variants use. The main contribution of this work is to distinguish the compositional differences between KNN variants and to identify strategies that can be adapted as part of a reusable approach.
A Collaborative Filtering Algorithm with Improved User Similarity Calculation for Enhanced Recommendation Accuracy and Personalization

The conventional method for calculating user similarity in the collaborative filtering recommendation algorithm based on users fails to address the challenge of variations in the number, value, and popularity of items that are collaboratively rated by multiple users. In order to solve the above problems, a new user similarity calculation method is proposed. By fusing cosine similarity and Jackard similarity, the problem of difference in the number of common scoring items is alleviated, so as to reduce the error of scoring prediction. Finally, experiments were performed on the MovieLens dataset, and the findings demonstrated that the enhanced algorithm outperforms the established benchmark similarity algorithm regarding mean absolute error (MAE). In addition, the improved algorithm has good robustness in predicting the accuracy of user ratings and recommendation quality.
A Novel Attribute based Encryption with implicit user authentication for Cloud

Cloud computing is gaining popularity steadily. It has evolved into a method for data storage and retrieval from any location at any time. It is one of the most popular settings for the maintenance and administration of data, information, and services internationally in this era of globalisation. Before stating that cloud computing is the way of the future, however, there are a few security concerns that must be resolved. The challenges include unauthorised access, confidentiality, and data privacy. Out of numerous suggested security systems, attribute-based encryption (ABE) might be considered one of the most viable security and privacy-preserving solutions in cloud settings. The majority of ABE approaches now in practice rely on expensive bilinear maps and pairing operations. In this paper, a novel ABE for cloud environments is suggested that makes use of elliptic curve cryptography. The main operation of the suggested method is simple scalar point multiplication over an elliptic curve, and uses bottom up approach across the access tree to provide implicit authentication of the user. The intractability of the Elliptic Curve Discrete Logarithm Problem (ECDLP) serves as the foundation for the suggested technique’s security assumption.
Current Ethical Issues in Healthcare Settings

The healthcare industry is one of the industries that has undergone a substantial digital transformation. The paperwork initially used by hospitals has been changed to cloud services, smart devices, and the introduction of the internet of medical things (IoMT). Electronic health records have improved healthcare quality, among other factors. The increased cyber security in the healthcare system and workers' limited knowledge of cyber security has been attributed to workers' negligence. The inability to secure healthcare information and data has intense implications for the healthcare organization. These threats may be limited through ethical decision-making models and organizational culture models. The leadership theory, decision-making model, strategy model theory, change managerial theory, and desired state of the organizational culture are some of the methodologies that can be used in securing cyber threats. The topic concerning the various methods of protecting healthcare information from cyberbullies is important because it protects all data categories from damage and theft.
The Recursive Arrival Problem

We study an extension of the Arrival problem, called Recursive Arrival, inspired by Recursive State Machines, which allows for a family of switching graphs that can call each other in a recursive way. We study the computational complexity of deciding whether a Recursive Arrival instance terminates at a given target vertex. We show this problem is contained in NP \cap coNP, and we show that a search version of the problem lies in UEOPL, and hence in EOPL = PLS \cap PPAD. Furthermore, we show P-hardness of the Recursive Arrival decision problem. By contrast, the current best-known hardness result for Arrival is PL-hardness.
BiERL: A Meta Evolutionary Reinforcement Learning Framework via Bilevel Optimization

Evolutionary reinforcement learning (ERL) algorithms recently raise attention in tackling complex reinforcement learning (RL) problems due to high parallelism, while they are prone to insufficient exploration or model collapse without carefully tuning hyperparameters (aka meta-parameters). In the paper, we propose a general meta ERL framework via bilevel optimization (BiERL) to jointly update hyperparameters in parallel to training the ERL model within a single agent, which relieves the need for prior domain knowledge or costly optimization procedure before model deployment. We design an elegant meta-level architecture that embeds the inner-level’s evolving experience into an informative population representation and introduce a simple and feasible evaluation of the meta-level fitness function to facilitate learning efficiency. We perform extensive experiments in MuJoCo and Box2D tasks to verify that as a general framework, BiERL outperforms various baselines and consistently improves the learning performance for a diversity of ERL algorithms.
LoSS: Local Structural Separation Hypergraph Convolutional Neural Network

Graph classification is a classic problem with practical applications in many real-life scenes. Existing graph neural networks, including GCN, GAT, and GIN, are proposed to extract useful features from complex graph structures. However, most existing methods’ feature extraction and aggregation inevitably mix the useful and redundant features, which will disturb the final classification performance. In this paper, to handle the above drawback, we put forward the Local Structural Separation Hypergraph Convolutional Neural Network (LoSS) based on two discoveries: most graph classification tasks only focus on a few groups of adjacent nodes, and different categories have their specific high response bits in graph embeddings. In LoSS, we first decouple the original graph into different hypergraphs and aggregate the features in each substructure, which aims to find useful features for the final classification. Next, the low-correlation feature suppression strategy is devised to suppress the irrelevant node-level and bit-level features in the forward inference process, effectively reducing the disturbance of redundant features. Experiments on five datasets show that the proposed LoSS can effectively locate and aggregate useful hypergraph features and achieve SOTA performance compared with existing methods.
Domain Knowledge Injection in Bayesian Search for New Materials

In this paper we propose DKIBO, a Bayesian optimization (BO) algorithm that accommodates domain knowledge to tune exploration in the search space. Bayesian optimization has recently emerged as a sample-efficient optimizer for many intractable scientific problems. While various existing BO frameworks allow the input of prior beliefs to accelerate the search by narrowing down the space, incorporating such knowledge is not always straightforward and can often introduce bias and lead to poor performance. Here we propose a simple approach to incorporate structural knowledge in the acquisition function by utilizing an additional deterministic surrogate model to enrich the approximation power of the Gaussian process. This is suitably chosen according to structural information of the problem at hand and acts a corrective term towards a better-informed sampling. We empirically demonstrate the practical utility of the proposed method by successfully injecting domain knowledge in a materials design task. We further validate our method's performance on different experimental settings and ablation analyses.
BEDCOE: Borderline Enhanced Disjunct Cluster Based Oversampling Ensemble for Online Multi-Class Imbalance Learning

Multi-class imbalance learning usually confronts more challenges especially when learning from streaming data. Most existing methods focus on manipulating class imbalance ratios, disregarding other data properties such as the borderline and the disjunct. Recent studies have shown non-negligible impact of disregarding these properties on deteriorating predictive performance. Online multi-class imbalance would further exacerbate such negative impact. To abridge the research gap of online multi-class imbalance learning, we propose to enhance the number of training times of borderline samples based on the disjunct class-wise clusters that are adaptively constructed over time for each class individually. Specifically, we propose a borderline enhanced strategy for ensemble aiming to increase the number of training times of samples neighboring to borderline areas of different classes. We also propose to generate synthetic samples for training based on the adaptively learned disjunct clusters that are maintained for each class individually online, catering for online multi-class imbalance problem directly. These two components construct the Borderline Enhanced Disjunct Cluster Based Oversampling Ensemble (BEDCOE). Experimental studies are conducted and demonstrate the effectiveness of BEDCOE and each of its components in dealing with online multi-class imbalance.
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning

Model-based reinforcement learning (RL) has demonstrated remarkable successes on a range of continuous control tasks due to its high sample efficiency. To save the computation cost of conducting planning online, recent practices tend to distill optimized action sequences into an RL policy during the training phase. Although the distillation can incorporate both the foresight of planning and the exploration ability of RL policies, the theoretical understanding of these methods is yet unclear. In this paper, we extend the policy improvement of Soft Actor-Critic (SAC) by developing an approach to distill from model-based planning to the policy. We then demonstrate that such an approach of policy improvement has a theoretical guarantee of monotonic improvement and convergence to the maximum value defined in SAC. We discuss effective design choices and implement our theory as a practical algorithm—Model-based Planning Distilled to Policy (MPDP)—that updates the policy jointly over multiple future time steps. Extensive experiments show that MPDP achieves better sample efficiency and asymptotic performance than both model-free and model-based planning algorithms on six continuous control benchmark tasks in MuJoCo.
Heteroscedastic Causal Structure Learning

Heretofore, learning the directed acyclic graphs (DAGs) that encode the cause-effect relationships embedded in observational data is a computationally challenging problem. A recent trend of studies has shown that it is possible to recover the DAGs with polynomial time complexity under the equal variances assumption. However, this prohibits the heteroscedasticity of the noise, which allows for more flexible modeling capabilities, but at the same time is substantially more challenging to handle. In this study, we tackle the heteroscedastic causal structure learning problem under Gaussian noises. By exploiting the normality of the causal mechanisms, we can recover a valid causal ordering, which can uniquely identify the causal DAG using a series of conditional independence tests. The result is HOST (Heteroscedastic causal STructure learning), a simple yet effective causal structure learning algorithm that scales polynomially in both sample size and dimensionality. In addition, via extensive empirical evaluations on a wide range of both controlled and real datasets, we show that the proposed HOST method is competitive with state-of-the-art approaches in both the causal order learning and structure learning problems.