Reviewerly/exHarmony huggingface dataset:
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Single Anonymous • Conference submission management system : Morressier • Number of submissions received : 42 • Number of submissions sent for review : 39 • Number of submissions accepted : 37 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 88.1 • Average number of reviews per paper : 1.162162162162162 • Total number of reviewers involved : 9 • Contact person for queries : Name : Rizky Chandra Ariesta Email : chandra@its.ac.id Affiliation : Institut Teknologi Sepuluh Nopember
Secure Aggregation in Heterogeneous Federated Learning for Digital Ecosystems

Privacy-preserving federated learning (PPFL) is vital for Industry 5.0 digital ecosystems due to the increasing number of interconnected devices and the volume of shared sensitive data. Secure aggregation (SA) protocols are essential components to fulfill the privacy properties of PPFL. However, there are still fundamental challenges to be tackled. For example, statistical and model heterogeneous characteristics across terminal devices, communication bottlenecks, and the requirement of inputting integers rather than real values in cryptographic operations. To overcome these problems, we propose RF-HFL, a novel secure aggregation scheme for PPFL in digital ecosystems with the capability to act on non-independent and identically distributed (non-IID) data. The scheme distills Representative Factors from each edge device through a self-attention-based neural network and transfers the average of these factors to the server for aggregation. The central model is then sent back to the devices for regularizing decentralized training models. The data transmitted between the devices and server are greatly reduced, henceforth significantly decreasing the communication overhead in PPFL. Theoretical analyses are provided for correctness, security and convergence. We set a benchmark for comparing our proposed scheme with several state-of-the-art SA protocols or algorithms in heterogeneous PPFL. Results demonstrate the effectiveness and efficiency of RF-HFL on multiple datasets.
FaceCLIP: Facial Image-to-Video Translation via A Brief Text Description

The existing image-to-video translation methods generally follow a frame-by-frame generative paradigm, while extracting the temporal information from a reference video or an audio stream. Inspired by the recent success in text-guided image generation, we explore a more challenging but promising task, Text-guided Image-to-Video (TI2V) translation. Given an image and a brief text description as input, TI2V aims to generate a facial expression video following the image and text. To this end, we first propose an automatic video captioning pipeline to generate dense textual descriptions for facial video datasets, using both expression labels and action units. These dense textual descriptions provide precise semantic guidance for TI2V learning. Then we design and train an efficient framework, FaceCLIP, on these datasets to deal with the TI2V translation task. FaceCLIP adopts a video autoencoder to model the temporal information of training videos, and a pretrained CLIP model to embed the video frames and the text description. We design a reconstruction loss and an embedding alignment loss to train the autoencoder to obtain the text-guided video generative ability. Recognizing that expressions are closely tied to facial landmark motions, the reconstruction loss is applied to facial landmarks rather than each video frame, significantly enhancing training efficiency. We compare FaceCLIP with several potential baseline methods, and extensively evaluate the performance using multiple metrics. Both qualitative and quantitative results validate the superiority of FaceCLIP in terms of both visual quality and expression-text consistency. Moreover, the unique ability of FaceCLIP to generate videos based on abstract texts demonstrates its stronger generalization capability.
Short-Term Fine-Grained Regional MUF Prediction for HF Communication Based on Time-Series Decomposition

High-frequency (HF) communication plays a vital role in military and commercial fields because of its characteristics of transhorizon propagation. Accurate prediction of the maximum usable frequency (MUF) is critical to improve the efficiency of HF ionospheric propagation. Deep learning has demonstrated remarkable performance in time-series prediction in recent years. However, the MUF is a nonstationary time series with complex properties of multiple time scales. Therefore, we must fully explore the features of the MUF sequence to provide reliable support for selecting deep-learning prediction models. In this article, we first decompose the real-world MUF-measured data from vertical sounding stations to obtain different components. After analyzing the phenomena of each component at different positions, we propose the capability requirements of the MUF prediction model and verify the capability requirements by comparing the existing most advanced deep-learning prediction models. Then, we calculate the complexity of the MUF sequence based on multiscale entropy. Through the entropy series at different time scales, the key to improving the model’s prediction performance is the fitting ability of the nonlinear change of the long-term trend component in the short-period scale. Furthermore, we propose a quantitative prediction scheme of MUF based on multiscale entropy similarity tolerance. Based on the Fano inequality, we calculate and compare the upper and lower bounds of the predictability on the quantization sequence and the original data and verify the effectiveness of the proposed scheme. Through experimental results, as the latitude decreases, the models based on time-series decomposition have better prediction performance over the state-of-the-art schemes. Compared to nontime-series decomposition deep-learning models, the root mean square error (RMSE) of the predicted MUF values for four cities, ranging from low to high latitude, decreased by an average of 0.11, 0.20, 0.02, and 0.03 MHz, respectively.
CHAPTER SEVEN: CHAOS GAME REPRESENTATION

Finding hidden patterns in long sequences can be both difficult and valuable. Representing these sequences visually can often help. The human brain is very adept at detecting patterns visually. Indeed, sometimes too adept: when humans detect spurious patterns that aren't really there, it's called pareidolia. That human tendency is what originally provoked the “heavy warning” against pictures that Littlewood complained about above. Nonetheless, pictures can help a lot to generate conjectures and sometimes even indicate a path toward a proof, even if sometimes “only” an experimental proof using comparison with data.
REPORTS ON ACTIVITIES R6: REPORTS ON MANDELBROT ACTIVITIES

R6.1 Mandelbrot Activity 1Read the Wikipedia entry on the Mandelbrot set and the entry on Mandelbrot. Mandelbrot's educational books and papers are amazing, and amazingly clearly written. If you don't come back from this activity, well, at least you left going in a good direction.
Hundreds Guide Millions: Adaptive Offline Reinforcement Learning With Expert Guidance

Offline reinforcement learning (RL) optimizes the policy on a previously collected dataset without any interactions with the environment, yet usually suffers from the distributional shift problem. To mitigate this issue, a typical solution is to impose a policy constraint on a policy improvement objective. However, existing methods generally adopt a "one-size-fits-all" practice, i.e., keeping only a single improvement-constraint balance for all the samples in a mini-batch or even the entire offline dataset. In this work, we argue that different samples should be treated with different policy constraint intensities. Based on this idea, a novel plug-in approach named guided offline RL (GORL) is proposed. GORL employs a guiding network, along with only a few expert demonstrations, to adaptively determine the relative importance of the policy improvement and policy constraint for every sample. We theoretically prove that the guidance provided by our method is rational and near-optimal. Extensive experiments on various environments suggest that GORL can be easily installed on most offline RL algorithms with statistically significant performance improvements.
REPORTS ON ACTIVITIES R4: REPORTS ON JULIA SET ACTIVITIES

R4.1 Julia Set Activity 1Write a Python program to draw the Sierpinski gasket (perhaps by using binomial coefficients mod 2).This turns out to be a very popular question, and there are several web tutorials and examples on the subject. For instance, Jayaprakash Joshi has a good YouTube video.304 Another (maybe more detailed) one has a program that uses the Turtle package.305 Some links show recursive programs.304https://youtu.be/G2A_Yg-JXxY305https://youtu.be/ssK2Z9Opokc
Security Analysis of WAGE Against Division Property Based Cube Attack

In recent years, as more Internet of Things (IoT) devices are connected to the internet, lightweight cryptography has become more and more important. WAGE is a LFSR-based authenticated encryption algorithm and one of the candidates in the NIST standard Lightweight Cryptography competition. It offers 128-bit security. In the literature, the best cryptanalytic estimates available for WAGE pertain to a correlation power attack that recovers the secret key up to 12 out of 111 rounds. In this paper, we evaluate the security of this cipher following the (bit-based) division property based cube attack using mixed-integer-linear-programming (MILP) models. Specifically, we investigate the security of the nonlinear feedback based initialization phase. To the best of our knowledge, our attack is the first one that investigates the security of the nonlinear feedback-based initialization phase of WAGE cipher. Theoretically, the results of our attack enable us to recover the secret key up to the reduced 18-round of the initialization phase utilizing $$2^{123}$$ time complexity and $$2^{6.32}$$ keystream bits.
Research on Practical Pathways of Strengthening Labor Education for College and University Students in the New Era

Labor education is an essential component of college and university education that can help students to develop a strong work ethic, acquire practical skills, and better understand the value of work. Strengthening labor education for college and university students is an urgent need of the high-quality development of the society and the internal requirement of promoting the all-round development of individuals. This study analyzes the importance of strengthening labor education for college and university students in the new era and proposes four practical pathways which draw on labor courses and campus activities, social practices, scientific research projects, and internships. After implementing these pathways, a survey of 967 students showed that students’ understanding and awareness of labor was deepened, their hands-on skills and interests in science and labor practices were improved, and they became more cordially respectful to the working class. Taken together, the exploration and practice of these pathways helps college and university students to recognize their abilities, strengths, and interests, and guides them to form good labor habits that permeate all aspects of their studies and lives.
Research and Practice of Teaching Algorithmic Dynamic Visualization for Secondary School CSP

The problem of abstract and difficult-to-understand algorithms in secondary school CSP has led to the proposal of a teaching mode called RAVADIPO for junior high school students. This mode utilizes visualization technology to make the process of abstract and complex algorithms more understandable. The proposed teaching mode has been implemented in school-based courses and has shown positive results. It has increased students' learning enthusiasm and efficiency, facilitated their understanding and application of algorithms, reduced the difficulty of comprehending algorithms, and improved the rate and level of awards. Overall, this teaching mode has significantly enhanced the effectiveness of teaching.
Research on Effective Ways of English Teaching in Primary and Secondary Schools under the Background of Core Competencies

Core literacy refers to the correct values, essential qualities, and key abilities that meet the lifelong and social development needs of students. It is a comprehensive expression of students' knowledge, skills, emotions, attitudes, values, and other requirements. English teaching has gone through a transformation from emphasizing only instrumental aspects, to emphasizing the comprehensive language application ability that combines instrumental and humanistic aspects, and finally to emphasizing the cultivation of core competencies in the English subject. The core competencies of English subject in primary and secondary schools mainly include four aspects: language ability, thinking quality, cultural character, and learning ability. Among them, language ability is the core competency of English subject, thinking quality reflects the mental characteristics of English subject core competencies, cultural awareness reflects the value orientation of English subject core competencies, and learning ability constitutes the development conditions of English subject core competencies. This article proposes effective paths around these four aspects.
STUDENTS’ EXPERIENCE ON LEARNING ENGLISH ESSAY WRITING WITH TEACHER’S APPLICATION OF ONLINE TOOLS

Recently, the outbreak of the Covid-19 pandemic which has broadly initiated the rise of digital literacy and teaching has inevitably been taking place on online platform. Converted from the conventional teaching mode to the online mode, both teachers and students certainly faced difficulties, especially in the area of EFL. Teaching and learning English essay writing has always been a challenging task for EFL teachers and learners. This paper aims to examine the non-English major third-year students‟ experience on learning an English Essay Writing course with teacher‟s application of online tools at University of Foreign Language Studies-The University of Da Nang (UFLS-UD). 22 Oriental Studies major thirdyear students attending the course Viet luan tieng Anh-01 DPH (English Essay Writing course-01 DPH) in academic year 2021-2022 were surveyed with questionnaires and in-depth interviews. Data analysis shows that students experienced many difficulties in learning English Essay Writing virtually, among which the most difficult one is “Lacking face-to-face communication in solving group tasks….” Their feedback on the efficiency of the online tools applied in the course was also detected. Some suggestions for improving the quality of teaching and learning English Essay Writing with the help of online tools were also proposed.
SCPatcher: Mining Crowd Security Discussions to Enrich Secure Coding Practices

Secure coding practices (SCPs) have been proposed to guide software developers to write code securely to prevent potential security vulnerabilities. Yet, they are typically one-sentence principles without detailed specifications, e.g., “Properly free allocated memory upon the completion of functions and at all exit points.”, which makes them difficult to follow in practice, especially for software developers who are not yet experienced in secure programming. To address this problem, this paper proposes SCPatcher, an automated approach to enrich secure coding practices by mining crowd security discussions on online knowledge-sharing platforms, such as Stack Overflow. In particular, for each security post, SCPatcher first extracts the area of coding examples and coding explanations with a fix-prompt tuned Large Language Model (LLM) via Prompt Learning. Then, it hierarchically slices the lengthy code into coding examples and summarizes the coding explanations with the areas. Finally, SCPatcher matches the CWE and Public SCP, integrating them with extracted coding examples and explanations to form the SCP specifications, which are the wild SCPs with details, proposed by the developers. To evaluate the performance of SCPatcher, we conduct experiments on 3,907 security posts from Stack Overflow. The experimental results show that SCPatcher outperforms all baselines in extracting the coding examples with 2.73 % MLine on average, as well as coding explanations with 3.97 % F1 on average. Moreover, we apply SCPatcher on 447 new security posts to further evaluate its practicality, and the extracted SCP specifications enrich the public SCPs with 3,074 lines of code and 1,967 sentences.
Aircraft Inspection Using Drones – Benefits and Related Safety Concerns

This chapter investigates benefits and associated safety concerns imposed by new technology and innovative process of aircraft inspection using drones. The assessment of related risks is conducted by using HAZOP (hazard and operability) method in correlation with safety risk assessment matrix. The results of research show that the aircraft inspection using drones have a lot of benefits both for airlines and MRO (maintenance, repair, and overhaul) organizations including improving efficiency of process and cost savings, but also improving the safety of inspection process by means of better working conditions for engineers. It is recommended to adopt some of preventive measures in order to mitigate associated safety risks.
Research on multilingual sentiment analysis based on N-Gram and gating mechanism

The development of the Internet makes online reviews tend to be multilingual. In the study of aspect sentiment analysis tasks, traditional monolingual methods is unable to fully extract the reviews sentiment information in multilingual situations. To solve the limitations of traditional monolingual sentiment analysis in multilingual situations, a ngram-based multilingual gated convolutional neural network model was proposed. The model divided the review text into the left context and the right context, and uses convolutional neural network to obtain ngram features of different size between the left and right contexts. After max pooling, the gating mechanism is used to realize the interaction between ngram features. Finally, the features extracted from the left context and the right context is used to detect aspect categories. In this paper, several experiments have been conducted for the proposed model. The experimental results show that the performance of the proposed model is significantly better than other comparison models, which can more effectively extract the emotional information of user reviews more effectively and improve the accuracy of aspect category detection.
A novel machine learning based hybrid approach for breast cancer relapse prediction

&lt;span&gt;The second leading cause of death for women is breast cancer, which is growing. Some cancer cells may remain in the body, so relapse is possible even if treatment begins soon after diagnosis. Since there are now many machine learning (ML) approaches to recurrence prediction in breast cancer, it is important to compare and contrast them to find the most effective one. Datasets with many features often lead to incorrect predictions because of this. In this study, correlation-based feature selection (CFS) and the flower pollination algorithm (FPA) are used to improve the quality of the wisconsin prognostic breast cancer (WPBC) and University Medical Centre, Institute of Oncology (UMCIO) breast cancer relapse datasets respectively. Data imputation, scaling, pre-process raw data. The second stage uses CFS to select discriminative features based on important feature correlations. The FPA chose the optimum attribute combination for the most precise answer. We tested the approach using 10-fold cross-validation stratification. Various trials show 84.85% and 83.92% accuracy on the WPBC and UMCIO breast cancer relapse datasets, respectively. The hybrid method performed well in feature selection, increasing the accuracy of the relapse classification for breast cancer.&lt;/span&gt;
Analysis of linear congruent methods and multiplicative random number generator in computer-based test

This research focuses on the implementation of computer - based test exams in high schools which face the problem of not having differences in exam questions which results in weak security and validity of exam results. Therefore, a randomization method is needed to overcome this problem. The method used in random ization is linear congruent methods and multiplicative random number generator. There are 101 random questions, but only 40 questions are displayed for each student with a reference value of Xn and C, 2 will be added for each package of exam questions and to avoid question code=0, the calculation results will be added 1. The linear congruent methods (LCM) results achieve 100% accuracy, while the method The multiplicative random number generator (MRNG) only achieved 62.5% accuracy in randomizing the exam que stions. This accuracy comparison highlights the difference in the ability of the two methods to generate random permutations of test item packages. LCM randomization accuracy ensures that each student will receive a different set of test questions in a con sistent manner. However, the low accuracy of randomization using MRNG indicates a weakness in generating permutations of exam question packages. The results of this study show that the LCM method is better than the MRNG in conducting exams.
Cybersecurity awareness among university students in Mogadishu: a comparative study

&lt;span&gt;This study aimed to assess the level of cyber security awareness among graduate and undergraduate students in five universities in Mogadishu. The study used a one-way analysis of variance (ANOVA) to examine the difference in cyber security awareness levels between graduate and undergraduate students across five reputable universities. The questionnaire method was used to collect data from 250 graduate and undergraduate students from SIMAD, SIU, UNISO, Jamhuriya, and Mogadishu universities. The cross-tabulation result showed that there was a significant difference in cyber security awareness levels between the universities. Specifically, the results showed that students from SIMAD and Jamhuriya universities suffered from virus attacks, while SIU students struggled with password strength and social network misuse. Mogadishu students faced phishing and virus attacks, and UNISO students dealt with both virus attacks and password strength issues. The study recommended that universities educate their students and parents on safe internet usage and cybersecurity and monitor and secure their internet and computer services. Additionally, the authors recommended the development of cybersecurity software to help students use their data confidently and securely.&lt;/span&gt;
FedLANE: a federated U-Net architecture for lane detection

Lane detection is a crucial module for today’s autonomous driving cars. Detecting road lanes is a challenging task as it varies in color, texture, boundaries and markings. Traditional lane detection techniques detect the lane by applying a model trained with centralized data. As roads vary in urban and rural areas, a more localized and decentralized training technique is desired for accurate and personalized lane detection. Federated learning has recently proved to be a promising technology that trains and prunes the model using local data. Applying federated learning-based lane detection improves the accuracy of detection and also ensures the security and privacy of autonomous cars. This paper proposes FedLANE, a federated learning-based lane detection technique. U-Net, U-Net long short-term memory (LSTM) and &lt;br /&gt; AU-Net architectures were explored using a federated learning approach. Experimental analysis using TuSimple and CuLane dataset shows that the FedLANE based lane detection performs similar to that of the traditional deep learning lane detection models.