Reviewerly/exHarmony huggingface dataset:
Deep Knowledge Tracking Based on Double Attention Mechanism and Cognitive Difficulty

Knowledge tracking is a key technique of artificial intelligence-assisted education to predict learners' future answers based on their historical learning interaction data. Current studies on knowledge-tracking models are well-established, but often with limited accuracy in tracking learners' knowledge acquisition. This paper proposes a knowledge tracking model based on double attention mechanism and cognitive difficulty, to achieve a better understanding of the relationships among knowledge concepts, their influence on learners' knowledge acquisition and the impact of absolute difficulty and the relative difficulty of the exercises based on learners' cognitive levels. We designed a mechanism to explore the correlation between exercises-knowledge concepts and knowledge concepts-knowledge concepts, and dynamically analyze the cognitive difficulty of exercises. The model also introduces a multi-headed self-attentive mechanism to capture the long-term dependence of learners' knowledge acquisition. The results suggest that the model can effectively track learners' knowledge acquisition.
Automatic Surgical Caption Generation in Nephrectomy Surgery Videos

Captioning surgical images is used in computer- aided diagnosis, intervention, and surgical training, however, it is a challenging task that requires expertise. Using automatic surgical captioning, the time-consuming and error-prone process of reporting can be carried out automatically and quickly. In addition to assisting doctors in making more precise and timely diagnoses, this procedure can shorten intra and post-operative reporting procedures, allowing doctors to provide patients with better care. Recently, several deep learning approaches have been proposed for the recognition of activities performed in surgical videos, however, there are not yet many studies on surgical image captioning with natural language. In this study, we generate surgical captions for nephrectomy surgery images automatically, using the Inception-v3 encoder to extract the visual features and the Gated Recurrent Unit (GRU) decoder with an attention mechanism. In our model, we used the Bahdanau attention mechanism, which learns attention weights directly from data using a neural network and takes into account the previous attention state and the current decoder state for the calculation of these weights. We tested our model on the Robotic Scene Segmentation Challenge dataset using the Bleu-N, Rouge-N, and Rouge-L metrics and compared them to a similar model using the Luong attention mechanism. Our model using Bahdanau attention outperformed an identical model using the Luong attention mechanism with an average of 0.654 Bleu-N, 0.737 Rouge-N, and 0.802 Rouge-L scores.
Sorting Strategies for Interactive Conflict Resolution in ASP

Answer set programs in practice are often subject to change.This can lead to inconsistencies in the modified program due to conflicts between rules which are the results of the derivation of strongly complementary literals.To facilitate the maintenance of consistency in answer set programs, in this paper we continue work on a recently presented framework that implements interactive conflict resolution by extending the bodies of conflicting rules by suitable literals, so-called λ -extensions.More precisely, we present strategies to choose λ -extensions that allow for resolving several conflicts at a time in an order that aims at minimizing (cognitive) efforts.In particular, we present a graphical representation of connections between conflicts and their possible solutions.Such a representation can be utilized to efficiently guide the user through the conflict resolution process by displaying conflicts and suggesting solutions in a suitable order.
LEARNING OF POEM WRITING BY IMPLEMENTING PROJECT-BASED LEARNING FOR 8TH GRADE STUDENTS

The purpose of this study is to describe the experiences of eighth-grade junior high school students who use Project Based Learning as a paradigm for learning to write poems. This study was also organized to see the implementation of the Project Based Learning model in learning to write poetry for eighth-grade junior high school students. This research was conducted at SMP Kristen Taraitak and was organized using an analytic descriptive approach. The participants in this study were 22 eighth-grade students from SMP Kristen Taraitak. Data were collected using methods including interviews, surveys, and testing. The percentage method was used to analyze the data. Findings from the Project Based Learning method for teaching poetry writing Students in grade VIII of SMP Kristen Taraitak were considered "competent" because their average scores were in the 80-89 percentile. Thus, the use of the Project Based Learning model is one of the right and suitable steps for teachers to improve the learning process of writing poetry, which certainly brings many benefits to students' development in learning to write, especially in terms of learning to write poetry for class VIII students of SMP Kristen Taraitak.
Machine Learning Prediction of Time Series Covid-19 Data in West Java, Indonesia

In 2019, the COVID-19 pandemic appeared. There have been several efforts to curb the spread of this virus. West Java, Indonesia, employs social restrictions to prevent the spread of this disease. However, this method destroyed the economy of the people. If no instances were detected in the region, the World Health Organization (WHO) authorized the social restrictions to be relaxed. If the government lifts the social limitation, the decision must also consider the potential of future confirmed instances. By utilizing machine learning, it is possible to forecast future data. This work utilized the following algorithms: linear regression (LR), locally weighted learning (LWL), multi-layer perceptron (MLP), radial basis function regression (RBF), and support vector machine (SVM). The study investigated daily new instances of COVID-19 in West Java, Indonesia, from March 2, 2020, to October 15, 2020. The RBF algorithm was the best in this investigation. Mean absolute error (MAE), root mean squared error (RMSE), mean absolute percentage error (MAPE), and relative absolute error (RAE) were 48.85, 89.73, 88.67, 62.99, and 60.88, respectively. The RBF prediction model may be proposed to the government of West Java for assessing data on COVID-19 instances, particularly in social restriction management. It is anticipated that West Java would have a minimum of 275 new cases every day for the following 30 days beginning on October 16, 2020. Consequently, the easing of societal limitations requires careful consideration.
Fast Calibration of Radar and Camera Images

In this study, we developed a simple and efficient method using radar-measured longitudinal distance to transform the coordinates on the 2D image into the 3D camera coordinate system through the neural network, where the inputs are the millimeter-wave radar measurement and the outputs are the target's horizontal and vertical axis coordinates in the camera coordinate system. To validate the proposed method, we used corner reflectors to generate the target's coordinates with a total of 700 data sets. The experimental results show an excellent calibration performance with a mean absolute error (MAE) of target 3D coordinate location between the prediction and actual camera coordinate of 6.8 cm and a variance of 5.5 cm.
Coexistence of 1 Tbps classical optical communication and quantum key distribution over a 100.96 km few-mode fiber

The integration of quantum key distribution (QKD) and classical optical communication has attracted widespread attention. In this Letter, we experimentally demonstrate a real-time co-propagation of 1 Tbps for 10 classical channels with one discrete-variable QKD channel in the weakly coupled few-mode fiber (FMF). Based on the selection of optimal device parameters and wavelength assignment of classical channels, as well as the optimization of equipment performance, a secure key rate of as high as 2.7 kbps of coexistence transmission of QKD and classical optical communication can be achieved using a 100.96 km weakly coupled FMF. Therefore, this study is a step toward realizing long-distance quantum–classical coexistence transmission.
DT-k-group structures on digital objects and an answer to an open problem

The paper examines various properties of [Formula: see text]-[Formula: see text]-subgroup structures and addresses an open problem on the existence of topological group structures on the [Formula: see text]-dimensional Khalimsky ([Formula: see text]-, for brevity) topological space and Marcus–Wyse ([Formula: see text]-, for short) topological plane. In particular, we obtain many types of totally [Formula: see text]-disconnected or [Formula: see text]-connected subgroups of a [Formula: see text]-connected [Formula: see text]-[Formula: see text]-group. Besides, we prove that each of the [Formula: see text]-dimensional [Formula: see text]-topological space and the [Formula: see text]-topological plane cannot be a typical topological group. Unlike an existence of a [Formula: see text]-[Formula: see text]-group structure of [Formula: see text] (see Proposition 4.7), we prove that neither of [Formula: see text] and [Formula: see text] is a topological group, where [Formula: see text] (respectively, [Formula: see text]) is a simple closed [Formula: see text]- (respectively, [Formula: see text]-) topological curve with [Formula: see text] elements in [Formula: see text] (respectively, [Formula: see text]) and the operation “∗” is a special kind of binary operation for establishing a group structure of each of [Formula: see text] and [Formula: see text]. Finally, given a [Formula: see text]-[Formula: see text]-group structure of [Formula: see text], we find several types of [Formula: see text]-[Formula: see text]-subgroup structures of it (see Theorem 5.7).
DEFINING THE STRATEGIES USED BY TEACHERS WORKING WITH STUDENTS WITH INTELLECTUAL DISABILITIES IN READING COMPREHENSION STUDIES

The general purpose of this study was to define the strategies used by teachers working with&#x0D; students with intellectual disabilities in teaching them reading comprehension. In line with&#x0D; this general purpose, the study sought to find out which cognitive and metacognitive&#x0D; strategies these teachers working used before, during and after reading in reading&#x0D; comprehension activities in the classroom. The study used semi-structured interviews, one&#x0D; of the data collections techniques employed in qualitative studies. Interviews were&#x0D; conducted with 13 special education teachers working with students with mild intellectual&#x0D; disabilities. These students are educated at a special education and practice school high&#x0D; school. The data obtained from the teachers were analyzed using descriptive analysis. The&#x0D; findings obtained as a result of this analysis showed that special education teachers used a&#x0D; limited number of strategies in teaching reading comprehension.
Applicability of Augmented and Virtual Reality for Education in Robotics

The rapid development of automatic control and robotics requires an innovative approach to teaching. This is especially important in the case of studies at the academic level and in vocational schools, where training with expensive robotic stations is necessary. A solution for the problem may be substituting these with augmented reality (AR) and virtual reality (VR) due to their significantly lower costs. Therefore, AR/VR technologies have an advantage in terms of low-cos and effective practice in robotics. The described content is an outcome of the MILAN project, wherein those technologies are used for the online course purpose. This includes providing access to virtual laboratories via a mobile application and the real-life training stations connected to the network. The paper provides an overview of the available AR/VR applications implemented into robotics education and a detailed description of related best practices. The main sections contain a detailed methodology of designing an AR mobile tool for learning robotics in the AR environment. Moreover, the main challenges encountered during the development phase were listed and analysed. Additionally, the paper presents the possible future use of the application with the associated benefits. The literature overview and conclusions may be used to design similar online courses with an interactive form of teaching practical industrial robots programming.
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 : 146 • Number of submissions sent for review : 84 • Number of submissions accepted : 61 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 41.8 • Average number of reviews per paper : 2 • Total number of reviewers involved : 4 • Contact person for queries: Name : Novizar Nazir Email : nazir_novizar@ae.unand.ac.id Affiliation : Andalas University, Indonesia
DEVELOPMENT OF INFORMATION TECHNOLOGY-BASED LEARNING MEDIA FOR EDUCATORS IN ELEMENTARY SCHOOLS

This research aims to determine whether instructional media can be used to share learning messages and data. Students will better understand and understand the material in a well-designed learning environment. The learning environment is also increasingly advanced in the information and globalization era. Learning to use information technology (IT) is a must. Media functions in the learning process as a teaching tool and as a source of information and news. Each learning environment has advantages and disadvantages. Therefore, the use of the learning environment must be adequately planned. The learning environment is growing in the current information and globalization era. There is an argument that using information technology (IT) as a learning method is necessary.
On the Security of a Cloud Auditing Storage Protocol

In 2022, Han et al. proposed a public cloud storage auditing scheme for shared encrypted data within cloud storage group. They introduced a certificateless signature scheme in their proposal. However in this paper, we show their protocol for cloud storage auditing is not as secure as they claimed. There exist some attacks which can forge the tags for authentication of data blocks. Finally we give some remark on how to strengthen it to be more secure.
A Short Introduction to SHACL for Logicians

The Shapes Constraint Language (SHACL) was recommended by the W3C in 2017 for describing constraints on web data (specifically, on the so-called RDF graphs) and validating them. At first glance, it may not seem to be a topic for logicians, but as it turns out, SHACL can be approached as a formal logic, and actually quite an interesting one. In this paper, we give a brief introduction to SHACL tailored towards logicians and frame key uses of SHACL as familiar logic reasoning tasks. We discuss how SHACL relates to description logics, which are the basis of the OWL Web Ontology Languages, a related yet orthogonal standard for web data. Finally, we summarize some of our recent work in the SHACL world, hoping that this may shed light on how ideas, results, and techniques from well-established areas of logic can advance the state of the art in this emerging field.
Assessing the Impact of Noise on Quantum Neural Networks: An Experimental Analysis

In the race towards quantum computing, the potential benefits of quantum neural networks (QNNs) have become increasingly apparent. However, Noisy Intermediate-Scale Quantum (NISQ) processors are prone to errors, which poses a significant challenge for the execution of complex algorithms or quantum machine learning. To ensure the quality and security of QNNs, it is crucial to explore the impact of noise on their performance. This paper provides a comprehensive analysis of the impact of noise on QNNs, examining the Mottonen state preparation algorithm under various noise models and studying the degradation of quantum states as they pass through multiple layers of QNNs. Additionally, the paper evaluates the effect of noise on the performance of pre-trained QNNs and highlights the challenges posed by noise models in quantum computing. The findings of this study have significant implications for the development of quantum software, emphasizing the importance of prioritizing stability and noise-correction measures when developing QNNs to ensure reliable and trustworthy results. This paper contributes to the growing body of literature on quantum computing and quantum machine learning, providing new insights into the impact of noise on QNNs and paving the way towards the development of more robust and efficient quantum algorithms.
Defending Against Membership Inference Attack by Shielding Membership Signals

Member Inference Attack (MIA) is a key measure for evaluating privacy leakage in Machine Learning (ML) models, aiming to distinguish private members from non-members by training the attack model. In addition to the traditional MIA, the recently proposed Generative Adversarial Network (GAN)-based MIA can help the adversary know the distribution of the victim's private dataset, thereby significantly improving attack accuracy. For traditional attacks and this new type of attack, previous defense schemes cannot handle the trade-off between privacy and utility well. To this end, we propose a defense solution using multi-model ensemble framework. Specifically, we train multiple submodels to hide membership signals and resist MIA, achieving reduced privacy leakage while guaranteeing the effectiveness of the target model. Our security analysis shows that our scheme can provide privacy protection while preserving model utility. Experimental results on widely used datasets show that our scheme can effectively resist MIAs with negligible utility loss.
A Countermeasure to Power Analysis Attack by Arbitrarily Injecting Multiple Types of Noise

As the Internet of Things (IoT) society develops, cryptographic security becomes even more important. However, side channel security remains a vulnerability without a definitive solution. We focus on power analysis attacks that estimate internal information from processor power consumption which is one of side channels and propose lightweight countermeasures to reduce the area and delay overhead compared with conventional countermeasures. This paper proposes a novel very large-scale integrated circuit (VLSI) design method, arbitrary noise injector (ANI), that randomly selected noise into vulnerable circuit to achieve improvement in tamper resistance. In our evaluation, it has been confirmed that ANI achieves comparable tamper resistance to the conventional design method, WDDL, with a small area overhead of approximately 7%.
Leveraging Deep Learning Models for Cross-function Null Pointer Risks Detection

The progress made in deep learning for natural language understanding has inspired researchers to explore similar techniques for programming language understanding. Various methods have been proposed for identifying vulnerabilities in code, including those that work on raw code or use abstract syntax tree (AST) and data-flow analysis. However, these methods only perform single-function analysis and cannot precisely pinpoint bugs. This study introduces a pipeline for detecting and locating null pointer vulnerabilities in C++ source code through cross-function analysis. The pipeline includes a data-flow analyzer capable of analyzing function call relationships and a deep learning model. We evaluate our approach on an industrial dataset and compare it with cppcheck using a user study. Our findings indicate that our method is an effective complement to cppcheck.
Few-Shot Personality-Specific Image Captioning via Meta-Learning

In standard captioning, the characteristics of the end-user whom we generate the caption for are ignored. This is mainly because more than often we do not have access to the entire spectrum of personality characteristics for our user. In other words, each user in test time can exhibit different traits to which we need to adapt our model. Therefore, we focus on generating personalized image captioning and formulate the problem as a few-shot learning setting. To the best of our knowledge, we are the first to study this problem and shed light on the challenges involved with this setting. Furthermore, we propose a MAML-based few-shot learner enabling the model to learn a new personality style from only a handful of annotated samples. Finally, we set up baselines for the problem and show that our proposed method is superior in performance when compared with baselines on the benchmark dataset. Ablation studies are conducted to investigate different design choices' effects on the model performance.
SVDFed: Enabling Communication-Efficient Federated Learning via Singular-Value-Decomposition

Federated learning (FL) is an emerging paradigm of distributed machine learning. However, when applied to wireless network scenarios, FL usually suffers from high communication cost because clients need to transmit their updated gradients to a server in every training round. Although many gradient compression techniques like sparsification and quantization are proposed, they compress clients’ gradients independently, without considering the correlations among gradients. In this paper, we propose SVDFed, a collaborative gradient compression framework for FL. SVDFed utilizes Singular Value Decomposition (SVD) to find a few basis vectors, whose linear combination can well represent clients’ gradients at a certain round. Due to the correlations among gradients, these basis vectors can still well approximate new gradients in many subsequent rounds. With the help of basis vectors, clients only need to upload the coefficients of the linear combination to the server, which greatly reduces communication cost. In addition, SVDFed leverages the classical PID (Proportional, Integral, Derivative) control to determine the proper time to update basis vectors to maintain their representation ability. Through experiments, we demonstrate that SVDFed outperforms existing gradient compression methods in FL. For example, compared to a popular gradient quantization method QSGD, SVDFed can reduce the communication overhead by 66 % and pending time by 99 %.