Reviewerly/exHarmony huggingface dataset:
Compatibility Issues in Deep Learning Systems: Problems and Opportunities

Deep learning (DL) systems are complex component-based systems, which consist of core program (code implementation and data), Python (language and interpreter), third-party libraries, low-level libraries, development tools, OS, and hardware environments. Incompatible interaction between components would cause serious compatibility issues, substantially affecting the development and deployment processes. What types of compatibility issues are frequently exposed in DL systems? What are the root causes of such issues and how do developers fix them? How far are we from automatically detecting and fixing DL compatibility issues? Although there are many existing studies on DL bugs, the characteristics of DL compatibility issues have rarely been systematically studied and the above questions remain largely unexplored. To fill this gap, we conduct the first comprehensive empirical study to characterize compatibility issues in DL systems. Through analyzing 352 DL compatibility issues classified from 3,072 posts in Stack Overflow, we present their types, manifestation stages, and symptoms. We further summarize the root causes and common fixing strategies, and conduct a tool survey on the current research status of automated detection and repair of DL compatibility issues. Our study allows researchers and practitioners to gain a better understanding of DL compatibility issues and can facilitate future tool development.
Cybersecurity as a Service

With the increasing sophistication and sheer number of cyberattacks, more and more companies come to the conclusion that they have to strengthen their cybersecurity posture. At the same time, well-educated information technology (IT) security personnel are scarce. Cybersecurity as a service (CSaaS) is one possible solution to tackle this problem by outsourcing security functions to managed security service providers (MSSP). This chapter gives on overview of common CSaaS functions and their providers. Moreover, it provides guidance especially for small- and medium-sized businesses, for asking the appropriate questions when it comes to the selection of a specific MSSP.
Examining the exposure-reception-retention link in realistic communication environments via VR and eye-tracking: The VR billboard paradigm

Exposure is key to message effects. No effects can ensue if a health, political, or commercial message is not noticed. Yet, existing research in communication, advertising, and related disciplines often measures ‘opportunities for exposure’ at an aggregate level, whereas knowing whether recipients were ‘actually exposed’ to a message requires a micro-level approach. Micro-level research, on the other hand, focuses on message processing and retention, takes place under highly controlled laboratory conditions with forced message exposure, and largely ignores how recipients attend selectively to messages under more natural conditions. Eye-tracking enables us to assess actual exposure, but its previous applications were restricted to screen-based reading paradigms lacking ecological validity or field studies that suffer from limited experimental control. Our solution is to measure eye-tracking within an immersive VR environment that creates the message delivery and reception context. Specifically, we simulate a car ride down a highway alongside which billboards are placed. The VR headset (HP Omnicept Pro) provides an interactive 3D view of the environment and holds a seamlessly integrated binocular eye tracker that records the drivers’ gaze and detects all fixations on the billboards. This allows us to quantify the nexus between exposure and reception rigorously, and to link our measures to subsequent memory, i.e., whether messages were remembered, forgotten, or not even encoded. An empirical study shows that incidental memory for messages differs based on participants’ gaze behavior while passing the billboards. The study further shows how an experimental manipulation of attentional demands directly impacts drivers’ gaze behavior and memory. We discuss the large potential of this paradigm to quantify exposure and message reception in realistic communication environments and the equally promising applications in new media contexts (e.g., the Metaverse).
Get Me Out of This Payment! Bailout: An HTLC Re-routing Protocol

The Lightning Network provides almost-instant payments to its parties. In addition to direct payments requiring a shared payment channel, parties can pay each other in the form of multi-hop payments via existing channels. Such multi-hop payments rely on a 2-phase commit protocol to achieve balance security; that is, no honest intermediary party loses her coins. Unfortunately, failures or attacks in this 2-phase commit protocol can lead to coins being committed (locked) in a payment for extended periods of time (in the order of days in the worst case). During these periods, parties cannot go offline without losing funds due to their existing commitments, even if they use watchtowers. Furthermore, they cannot use the locked funds for initiating or forwarding new payments, reducing their opportunities to use their coins and earn fees. We introduce Bailout, the first protocol that allows intermediary parties in a multi-hop payment to unlock their coins before the payment completes by re-routing the payment over an alternative path. We achieve this by creating a circular payment route starting from the intermediary party in the opposite direction of the original payment. Once the circular payment is locked, both payments are canceled for the intermediary party, which frees the coins of the corresponding channels. This way, we create an alternative route for the ongoing multi-hop payment without involving the sender or receiver. The parties on the alternative path are incentivized to participate through fees. We evaluate the utility of our protocol using a real-world Lightning Network snapshot. Bailouts may fail due to insufficient balance in alternative paths used for re-routing. We find that attempts of a node to bailout typically succeed with a probability of more than 94% if at least one alternative path exists.
Defending Against Free-Riders Attacks in Distributed Generative Adversarial Networks

Generative Adversarial Networks (GANs) are increasingly adopted by the industry to synthesize realistic images using competing generator and discriminator neural networks. Due to data not being centrally available, Multi-Discriminator (MD)-GANs training frameworks employ multiple discriminators that have direct access to the real data. Distributedly training a joint GAN model entails the risk of free-riders, i.e., participants that aim to benefit from the common model while only pretending to participate in the training process. In this paper, we first define a free-rider as a participant without training data and then identify three possible actions: not training, training on synthetic data, or using pre-trained models for similar but not identical tasks that are publicly available. We conduct experiments to explore the impact of these three types of free-riders on the ability of MD-GANs to produce images that are indistinguishable from real data. We consequently design a defense against free-riders, termed DFG, which compares the performance of client discriminators to reference discriminators at the server. The defense allows the server to evict clients whose behavior does not match that of a benign client. The result shows that even when 67% of the clients are free-riders, the proposed DFG can improve synthetic image quality by up to 70.96%, compared to the case of no defense.
ReFit: A Framework for Refinement of Weakly Supervised Semantic Segmentation Using Object Border Fitting for Medical Images

Weakly Supervised Semantic Segmentation (WSSS) relying only on image-level supervision is a promising approach to deal with the need for Segmentation networks, especially for generating a large number of pixel-wise masks in a given dataset. However, most state-of-the-art image-level WSSS techniques lack an understanding of the geometric features embedded in the images since the network cannot derive any object boundary information from just image-level labels. We define a boundary here as the line separating an object and its background, or two different objects. To address this drawback, we are proposing our novel ReFit framework, which deploys state-of-the-art class activation maps combined with various post-processing techniques in order to achieve fine-grained higher-accuracy segmentation masks. To achieve this, we investigate a state-of-the-art unsupervised segmentation network that can be used to construct a boundary map, which enables ReFit to predict object locations with sharper boundaries. By applying our method to WSSS predictions, we achieved up to 10% improvement over the current state-of-the-art WSSS methods for medical imaging. The framework is open-source, to ensure that our results are reproducible, and accessible online at https://github.com/bharathprabakaran/ReFit .
FRC-GIF: Frame Ranking-Based Personalized Artistic Media Generation Method for Resource Constrained Devices

Generating video highlights in the form of animated graphics interchange formats (GIFs) has significantly simplified the process of video browsing. Animated GIFs have paved the way for applications concerning streaming platforms and emerging technologies. Existing studies have led to large computational complexity without considering user personalization. This paper proposes lightweight method to attract users and increase views of videos through personalized artistic media, i.e., static thumbnails and animated GIF generation. The proposed method analyzes lightweight thumbnail containers (LTC) using the computational resources of the client device to recognize personalized events from feature-length sports videos. Next, the thumbnails are then ranked through the frame rank pooling method for their selection. Subsequently, the proposed method processes small video segments rather than considering the whole video for generating artistic media. This makes our approach more computationally efficient compared to existing methods that use the entire video data; thus, the proposed method complies with sustainable development goals. Furthermore, the proposed method retrieves and uses thumbnail containers and video segments, which reduces the required transmission bandwidth as well as the amount of locally stored data. Experiments reveal that the computational complexity of our method is 3.73 times lower than that of the state-of-the-art method.
Robust Multi-agent Communication with Graph Information Bottleneck Optimization

Recent research on multi-agent reinforcement learning (MARL) has shown that action coordination of multi-agents can be significantly enhanced by introducing communication learning mechanisms. Meanwhile, graph neural network (GNN) provides a promising paradigm for communication learning of MARL. Under this paradigm, agents and communication channels can be regarded as nodes and edges in the graph, and agents can aggregate information from neighboring agents through GNN. However, this GNN-based communication paradigm is susceptible to adversarial attacks and noise perturbations, and how to achieve robust communication learning under perturbations has been largely neglected. To this end, this paper explores this problem and introduces a robust communication learning mechanism with graph information bottleneck optimization, which can optimally realize the robustness and effectiveness of communication learning. We introduce two information-theoretic regularizers to learn the minimal sufficient message representation for multi-agent communication. The regularizers aim at maximizing the mutual information (MI) between the message representation and action selection while minimizing the MI between the agent feature and message representation. Besides, we present a MARL framework that can integrate the proposed communication mechanism with existing value decomposition methods. Experimental results demonstrate that the proposed method is more robust and efficient than state-of-the-art GNN-based MARL methods.
Assessing Listening Comprehension Skills in Indonesian Islamic Higher Education EFL Classrooms: Current Practices, Challenges and Solutions

The present study aims to examine the current practices, constraints, and potential enablers when assessing students' listening skills in Indonesian Islamic higher education EFL classrooms. This study used a qualitative approach with a case study research design. The selection of the participants used purposive sampling. The study used interviews as a data gathering technique, involving six EFL lecturers in one Indonesian Islamic higher education institution in South Sulawesi. The findings show participants’ awareness of the various alternative methods to assess students’ listening skills. However, their discussions tend to focus on what is easiest to use as opposed to what matters most for students’ listening skills. Furthermore, participants’ descriptions of EFL lecturers’ constraints in assessing listening skills encompass various educational aspects, which could be classified into resources and practical constraints. The former includes a lack of representative language laboratories and learning resources for conducting quality listening assessment practices. The latter relates to time constraints, auditory surroundings, and designing and developing listening assessment instruments. Therefore, the participants raised several key strategies to enhance quality listening assessment practices, ranging from resources to practical strategies. This includes a high quality and sufficient quantity of educational resources, the use of diverse and authentic assessment tasks, and more collaborative work to share best practices and experience in conducting quality listening assessment practices. These findings imply the need for reformulation of existing practices and policies related to foreign language assessment that could have significant impacts on EFL students, teachers, and classrooms.
Students' English Skills and Their Ways of Learning

at the Faculty of Languages and Literature, Makassar State University. The subjects of this research were fourth-semester students of the English Education Study Program, Department of English, because in that semester there were Learning and Teaching courses. This research wants to see what the student's language learning model is. Students in the fourth semester of the 2021/2022 academic year who took this course in 5 classes consisted of 210 students. However, this research was only carried out in 2 (two) classes, namely classes D and E with a total of 82 students. This research uses a qualitative approach, namely Qualitative Descriptive research. The results of this research show that Students' English language skills are obtained a little differently with only 2 (two) ways of learning English, namely formal and informal, Students' ways of learning English are slightly varied and utilize technology, including: Watching English films/videos; Listening to English songs; YouTube; TikTok; Internet/ Google; Memorizing vocabulary; English podcasts; Practice; Social media; book; and Games.
The Implementation of Cooperative Integrated Reading and Composition (CIRC) Strategy in Teaching Students Writing Skill at Junior High School

This research aimed at knowing the teacher implement Cooperative Integrated Reading and Composition (CIRC) in teaching writing. The researcher applied qualitative research design. The instruments of the research were observation sheet and interview guide. The participants of the research were two teachers in second grade at SMP Telkom Makassar. The result showed that the implementation of cooperative integrated reading and composition in teaching writing is good and accordance with the actual procedure, the teacher prepare teaching materials that will be discussed in class, the material must be in accordance with the needs for implementing this strategy, because of the collaborative implementation of the CIRC strategy. CIRC can contribute to develop students' abilities to be more creative, independent, collaborative, communicative, and self-confident CIRC can also improve vocabulary mastery, familiarize students with conveying ideas by extending the topic, and build students' self-confidence in the area of writing skill. As a result, they can communicate improve writing skill.
WHISPERS OF MOTIVATION: EXPLORING THE GROWING TREND OF ASMR VIDEOS AMONG UNIVERSITY STUDENTS

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 8606-8614ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2194Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
EXPLORING THE NEED FOR COMPETENCE DEVELOPMENT FOR E-PROFESSIONALISM

Appears in: ICERI2023 Proceedings Publication year: 2023Page: 9010 (abstract only)ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.2297Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
The Students’ Perceptions of the Asynchronous Online Learning Model at Institut Parahikma Indonesia Post-Pandemic Covid-19

The objectives of the research were to discover students' perceptions of the asynchronous online learning model at Institut Parahikma Indonesia. The researcher applied phenomenological design in this research. Data obtained from the semi-structured interviews regarding the student’s perceptions of the asynchronous online learning model at Institut Parahikma Indonesia There were seven respondents, three males and four females in the Institut Parahikma Indonesia. They were selected by convenience sampling. The results of this study revealed the students' perceptions of the asynchronous learning model. Trough the interview mentioned several facts that the asynchronous learning model is attractive in learning model because of flexible, and useful, leaving traces and discipline learning models.
SECURING ONLINE LABORATORY MANAGEMENT SYSTEMS USING BLOCKCHAIN TECHNOLOGY: A COMPREHENSIVE REVIEW

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 7318-7326ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.1818Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
IMPACT OF THE COVID-19 PANDEMIC ON COMPUTER SCIENCE EDUCATION. CASE STUDIES: SLOVAKIA AND SPAIN

Appears in: ICERI2023 Proceedings Publication year: 2023Pages: 2402-2406ISBN: 978-84-09-55942-8ISSN: 2340-1095doi: 10.21125/iceri.2023.0659Conference name: 16th annual International Conference of Education, Research and InnovationDates: 13-15 November, 2023Location: Seville, Spain
CONTRIBUTION OF CHALLENGE BASED LEARNING ELEMENTS TO THE DEVELOPMENT OF TRANSVERSAL AND PROFESSIONAL COMPETENCES

CONTRIBUTION OF CHALLENGE BASED LEARNING ELEMENTS TO THE DEVELOPMENT OF TRANSVERSAL AND PROFESSIONAL COMPETENCES
Research on the Application of Machine Learning in Smart Finance

The promotion of machine learning and the depth of financial digital transformation have laid the foundation for the development of intelligent finance. Traditional machine learning methods such as logistic regression can be applied to statistical analysis of data, image recognition, and natural lan
Masked and Phishing URL Detection using Machine Learning

With the escalating threats in the digital landscape of cybersecurity, the rapid and widespread of masked and phishing URLs poses a significant threat to online users. Detecting these malicious URLs is a paramount concern to safeguard sensitive information and prevent unauthorized access. This study delves into the application of machine learning algorithms for the accurate identification of masked and phishing URLs. Specifically, Decision Tree, Random Forest, and XGBoost algorithms are employed to create predictive models capable of distinguishing between legitimate and malicious URLs. The research involves the collection of a comprehensive dataset comprising both legitimate and various forms of malicious URLs. Feature engineering techniques are applied to extract relevant information from the URLs, transforming them into numerical representations suitable for machine learning. The three selected algorithms are individually trained and finetuned using the dataset, exploiting their unique capabilities to distinguish patterns indicative of phishing attempts and masked URLs. The performance of each model is evaluated using metrics such as accuracy, precision, recall, and web traffic. This study examines the application of machine learning algorithms to identify masked and phishing URLs. By comparing the results of these algorithms, a predictive model capable of distinguishing between legitimate and malicious URLs is created. Experimental results showed promising accuracy rates and potential to contribute to online security efforts. The implications of this research extend to advanced cybersecurity systems, offering enhanced protection against evolving threats in the digital domain.
ANALYSIS OF THE EXISTENCE OF THE ACCOUNTING PROFESSION IN THE ERA OF SOCIETY 5.0 WITH THE NAIVE BAYES METHOD

Society 5.0 has opened a new phenomenon that allows humans to use modern-based science (Artificial Intelligence) for human needs with the goal of human life more easily. This phenomenon opens up various new possibilities and risks simultaneously. These changes have had a significant impact on the development of the accounting profession, resulting in fewer human resources needed in business, including accounting staff. This research aims to 1) Apply sentiment analysis to the existence of the accounting profession in the era of society 5.0. 2) Application of the naive Bayes method to classify sentiment analysis for the existence of the accounting profession in the era of society 5.0. 3) Testing the naive Bayes algorithm in sentiment analysis for the existence of the accounting profession in the era of society 5.0. This research design uses quantitative research, analysis of student sentiment on the existence of the accounting profession was carried out using data from 432 student opinions from several campuses in the East Java region. Test data using the Naïve Bayes algorithm with Term-Frequency Inverse Document Frequency feature selection and the Lexicon method. The results of this research are divided into five parts consisting of the first part 424 students' opinions on the level of accuracy (78%), the second part 427 students' opinions on the level of accuracy (62%), the third part 429 students' opinions on the level of accuracy (47%), the fourth part 425 students' opinions level of accuracy (56%), fifth part 429 student opinions level of accuracy (52%). It was found that the number of positive sentiments was more dominant than negative or neutral sentiments