Reviewerly/exHarmony huggingface dataset:
The Use of Wordwall Learning Media on Student Interest and Motivation in IPAS Learning at Elementary School

Learning media is a teacher's need to facilitate students' understanding of learning concepts. Learning with wordwall media is very helpful for students when mastering material in learning. The purpose of this study was to determine the effect of Wordwall application on student interest and motivati
Academic Librarian Competence to Support Scientific Writing and Publication

This study aims to determine what competencies are needed by librarians at the Sebelas Maret University library to support scientific writing and publication services. This study uses a qualitative method with a case study approach. The researcher chose a case study in this study because Sebelas Mar
The Role of Parents in Providing Exemplary Characteristics of Reading Children as Elementary School Students in the Widyatama Group Korwilcam Dindik Kemranjen Banyumas Regency

This research is motivated by several problems that exist in the Widyatama Group, Kemranjen Banyumas Regency including relatively low attendance of students to the library, relatively low general knowledge of students, relatively low awareness of students in reading activities, lack of parental atte
Analysis of English Grammatical Terms Learning Experience of SMP Muhammadiyah 1 Cilacap 3rd Grade Students

In English education, it is really difficult to teach English grammar effectively in a limited time. Many students have difficulties in learning English grammar because they do not understand grammatical terms properly. This study was conducted to find ways to help middle school students who have di
Exploration of Teaching Reform Innovation under the Background of Education Informatization—Taking the Teaching of Leadership Science in Universities as an Example

With the continuous development of teaching informatization and the development and improvement of various online teaching apps, many traditional classroom teaching methods and teaching methods in universities have also undergone significant changes. Online and offline blended teaching has become the main direction for many universities to explore teaching reform in traditional courses. This article explores various problems that arise in the teaching process of leadership science, one of the traditional courses in full-time undergraduate administrative management majors in higher education institutions, and explores how to strengthen teaching quality, improve classroom teaching efficiency, and better help students master relevant knowledge points through online and offline blended teaching reform.
(3+1)D printing towards the scalable and efficient integration of high-performance hybrid platforms

We employ one-and two-photon polymerizatin, i.e. flash -TPP printing, which is compatible with metal-oxid-semiconductor (CMOS) technology, to fabricate polymer-cladded and single-mode 3D pho-tonic waveguides and adiabatic splitters. Our 3D technology is a major step forward towards the highly-interconnection required in optical neural networks, which removes the high energy dissipation of electronics and where 3D integration enables scalability that is challenging to realize in 2D.
Investigation of a Photonic Reservoir Computing based Equalizer for Short Reach Applications

We propose an optical pre-equalizer which relaxes the need for power-hungry DSP in IM/DD applications. A reservoir computing-based approach is pursued demonstrating under FEC performance up to 6km at 50Gbaud NRZ outperforming digital FFE.
Optimization Strategies for College English Education Model under the Guidance of New Liberal Arts Concepts

During the period of reform in college English education, it is necessary to firmly adhere to the new liberal arts concept as the guide, in order to further optimize the model of college English education, so as to make college English education more effective and in line with the requirements of the times. In this context, this paper provides a detailed analysis of the significance of the new liberal arts concept in optimizing the college English education model and the difficulties in optimizing the college English education model under the new liberal arts concept. Based on this, it proposes the opinions and suggestions on how to better promote the optimization of the college English education model under the new liberal arts concept, in order to make college English education more in line with the requirements of the times, and to further demonstrate the effectiveness of college English education and provide some reference.
An Approach of Improved Traversal Merging of Transaction Data for Faster Apriori Algorithm

In order to improve the operational efficiency of the Apriori algorithm in the data preprocessing stage of large-scale data and achieve overall optimization of the Apriori project, a fast traversal merge pre-processing method is proposed by integrating an adaptive association mining threshold determination method. Firstly, the proposed fast traversal merging method is analyzed and compared with two benchmark algorithms, and the experimental results show that the running time of the fast traversal merging method is much lower than that of the two benchmark methods; secondly, according to the central limit theorem, a data adaptive support threshold setting method is proposed, which can avoid the subjectivity of the minimum support threshold setting in association mining; finally, the two proposed algorithms are applied to Apriori and the results show that the application of the proposed improved method for association mining gives significantly better results than association mining under the better processing of the benchmark algorithm, and thus can significantly improve the efficiency of solving the shopping basket problem.
PokerKit: A Comprehensive Python Library for Fine-Grained Multi-Variant Poker Game Simulations

PokerKit is an open-source Python library designed to overcome the restrictions of existing poker game simulation and hand evaluation tools, which typically support only a handful of poker variants and lack flexibility in game state control. In contrast, PokerKit significantly expands this scope by supporting an extensive array of poker variants and it provides a flexible architecture for users to define their custom games. This paper details the design and implementation of PokerKit, including its intuitive programmatic API, multi-variant game support, and a unified hand evaluation suite across different hand types. The flexibility of PokerKit allows for applications in diverse areas, such as poker AI development, tool creation, and online poker casino implementation. PokerKit's reliability has been established through static type checking, extensive doctests, and unit tests, achieving 99% code coverage. The introduction of PokerKit represents a significant contribution to the field of computer poker, fostering future research and advanced AI development for a wide variety of poker games. The source code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/uoftcprg/pokerkit</uri>
Traceable Transformer-Based Anomaly Detection for a Water Treatment System

As industrial control system malfunctions caused by attacks become more complex and frequent, anomaly detection and subsequent forensic analyses are more important than ever. When an anomaly is detected, security professionals need to accurately identify the components that are under attack. However, traditional methods do not provide enough traces, which makes it difficult to identify the targeted components. This chapter describes a traceable anomaly detection method that leverages unsupervised learning using industrial control system component time series data. The method generates customized transformer-encoder classifiers for industrial control system components. The final detection result is ensembled from all the classifier outputs. Experiments with water treatment testbed data indicate that the method achieves good performance with low false positive rates and delays, and strong traceability.
Knowledge Graph-Based Behavior Denoising and Preference Learning for Sequential Recommendation

Sequential recommendation seeks to predict users' next behaviors and recommend related items over time. Existing research has mainly focused on modeling users' dynamic preferences from their sequential behaviors. However, most of these studies have ignored the negative effects of noise behaviors in the given sequences, which may mislead the recommender. In addition, users' behavior data is always sparse, which makes it difficult to effectively learn users' preferences purely from their historical behaviors. Most recently, knowledge graphs (KGs) have been exploited by few researchers for sequential recommendation. However, they always assume all information in KGs or KG paths with limited length are useful for recommendation, which may bring irrelevant information from KGs into the recommender and further mislead the recommender. To address these issues, we propose a novel KG-based behavior denoising and preference learning model named KGDPL for sequential recommendation. We argue that the paths in KGs that reflect semantic relations between entities can not only help to remove noise behaviors and recommend successive items for users, but also provide relevant explanations. Therefore, we first devise a supervised knowledge path selection module to select effective paths between items from KGs for behavior prediction, which aims to filter out irrelevant information from KGs for the given recommendation task. Then, we design a knowledge-enhanced behavior denoising module to mitigate the negative effects of the noise behaviors contained in historical sequences by using the knowledge path information. After that, we propose a knowledge-enhanced preference learning module to better learn users' personalized and dynamic preferences from their historical behavior sequences and related knowledge information, which can also help tag users and provide explanations for recommendation results. Experimental results on four real-world datasets demonstrate the effectiveness and interpretability of the proposed model KGDPL.
Occlusion-Aware Plane-Constraints for Monocular 3D Object Detection

The task of 3D object detection poses a significant challenge for 3D scene understanding and is primarily employed in the fields of robot control and autonomous driving. Monocular-based 3D detection methods are more cost-effective and practical than stereo-based or LiDAR-based methods. Monocular image 3D detection methods have garnered considerable attention from researchers. However, the impact of occlusion scenarios of the objects on the keypoints prediction is often overlooked. To address this issue, the present paper proposes a novel 3D monocular object detection method named MonOAPC, which is equipped with occlusion-aware plane-constraints. This method can adaptively utilize partial keypoints to infer the plane location of the object based on the level of occlusion and highlights that the introduction of plane constraints is advantageous for the 3D detection task. First, the plane information of the object in 3D space is beneficial to optimize the keypoints regression, and considering that each plane holds different significance, an adaptive plane location inference module is proposed to enhance the keypoints location regression. Second, a novel co-depth estimation module is proposed to jointly estimate the object’s spatial location through various depth inference methods, thereby improving the generalization of the object depth estimation. Furthermore, the paper demonstrates that the accuracy of 3D object detection can be indirectly improved by introducing plane information to promote keypoints regression, and that plane information is effective for monocular 3D detection. The experimental outcomes show that the MonOAPC method can attain competitive results.
FENet: A Feature Explanation Network with a Hierarchical Interpretable Architecture for Intelligent Decision-Making

As an increasing number of intelligent decision-making problems of vehicles are addressed using implementations of deep learning (DL) methods, the interpretability of intelligent decision-making strongly determines the degree of human trust in the technology and implementation process; therefore, it is urgently necessary to improve the interpretability of DL. To enhance the interpretability of intelligent decision-making processes based on DL, we propose a feature explanation network (FENet), which consists of a correlation clustering module, a feature extraction module, and a decision module. First, to determine the source of each feature before the feature extraction module, input variables are clustered according to correlation. We design dynamic strategy and Dset strategy, the former strategy comprises four specific implementations corresponding to different parameter selections and noise processing methods. Then, in the feature extraction module, any DL model can be employed to extract features from each correlation cluster separately. Finally, a probabilistic graphical model-Bayesian network is constructed based on the features, and the intelligent decision is realized by Bayesian inference. In the experiments, we first design five indicators to quantify the interpretability and verify the effectiveness of each module in FENet by an ablation study. Compared with other DL models, FENet demonstrates significant improvements in interpretability while maintaining comparable decision accuracy. Moreover, when applied to the practical problem of UAV intrusion detection, FENet can provide an explainable process and interpretation of final results. These findings indicate that the proposed network architecture can be effectively utilized with various DL-based algorithms for decision-making problems to enhance interpretability.
Background: Propositional Axiomatics

In this chapter we construct an axiomatic proof system for classical propositional logic. In the next chapter we revisit the logic, but with semantic tableaus as the main proof method. There are many logics in use today but all have a certain commonality. Syntactically there is a specification of a formal language. There is some notion of a semantics, providing a mathematically defined meaning for formulas of the language. There is some specification of a proof system. And finally, there are connections established between the semantics and the proof system. All this is at its clearest and simplest for classical propositional logic. It is the most well-behaved of all the logics. Think of our presentation in this and the next chapter as providing a foundation on which many more elaborate logics can be built. Our interest, of course, will be on those that are modal.
Federated Learning in Industrial IoT: A Privacy-Preserving Solution That Enables Sharing of Data in Hydrocarbon Explorations

Applying artificial intelligence (AI) to data from Industrial Internet of Things (IIoT) devices is a novel direction in geological studies. However, privacy and security concerns hinder the sharing of data, thus affecting the performance of current AI-based approaches. In this article, we propose a novel data management style to address the privacy and security issues in joint hydrocarbon explorations. Federated learning can facilitate the analysis of multiple datasets without the need to share them, protecting private information of different companies in a virtual joint venture. We use the inference of petroleum reservoirs in karst stratigraphy as a case study. A federated learning-based enterprise data management framework is proposed to virtually integrate the information from different organizations. Our key contributions are summarized as follows. 1) A method for karst identification and inference is proposed, which uses neural networks to recognize the size of petroleum reservoirs in different karst areas. 2) A federated learning algorithm is applied to virtually aggregate data samples from different companies. 3) The performance of the new privacy-preserving integration model is compared with those of the individual/local deep learning models. Our results show that the proposed approach can substantially improve the accuracy of petroleum reservoir explorations.
Tableau Soundness and Completeness

We have seen several soundness and completeness proofs so far. And we have just introduced four more tableau systems in need of such proofs. For each of the modal logics from the Lesser Modal Cube, Fig. 7.1 , we have a quantified version that is constant domain and a quantified version that is varying domain. For each of these we have versions with and without equality. And we have predicate abstract extensions for which terms always designate and extensions for which terms might not designate. Rather than give a multiplicity of soundness and completeness arguments in full detail we just summarize what needs to be added to earlier proofs and we do this for a single representative example.
KAJIAN IMPORTANCE PERFORMANCE ANALYSIS SEBAGAI MATRIKS EVALUASI KINERJA: STUDI KASUS WORKSHOP PENGEMBANGAN E-LEARNING SMAN 39 JAKARTA

Along with the industrial revolution 4.0, the rapid development of information and communication technology (ICT) has changed organizations and individuals in carrying out daily activities. In the world of education, the increasingly sophisticated capabilities of mobile phones and portable computers are one of the factors that influence the development of online learning (e-learning). However, digital literacy is still needed to improve understanding and skills in the development of online learning (e-learning). This study propose a digital literacy workshop design to improve the quality of human resources; and evaluate the performance of the digital literacy workshop using the Importance Performance Analysis (IPA) matrix. The data collection process was carried out in two stages. The first stage is to design workshop activities and the second stage is to evaluate the quality of the workshop program. Based on data analysis, it was found that socialization and workshops were the right solution to improve the competence of teachers and students of SMAN 39 in carrying out learning innovations by utilizing information technology. In general, the level of satisfaction obtained by the workshop participants has met the level of expectations and nothing is excessive. However, there is one item no 5 (Active involvement in the program) which is a top priority for improvement. Because it has a high level of hope, but the level of perceived satisfaction is still relatively low.
Multisensory Immersive Experiences: From Monitoring of Human Influential Factors to New Applications in Healthcare

While virtual and extended reality applications are on the rise, existing experiences are not fully immersive, as only two senses (audio-visual) are typically stimulated. In this keynote talk, I will describe our ongoing work on developing multisensory immersive experiences, which combine auditory, visual, olfactory, and haptic/somatosensory stimuli. I will show the impact that stimulating more senses can have on user quality of experience, sense of presence and immersion, and engagement levels.
Sparse Sequential Recommendation with Interactions and Intentions Contrastive Learning

Sequential recommendation models user behavior dynamically based on historical interactions. However, data sparsity affects the performance of sequential recommendation consistently. Existing research mainly uses contrastive learning and auxiliary information to alleviate data sparsity. But existing contrastive learning methods only perform contrastive learning on interaction sequences. Auxiliary information enriches item representations, especially auxiliary intentions are widely adopted in the sequential recommendation. But the intent information is also sparse. And it is necessary to perform contrastive learning of the intention sequences corresponding to the interaction sequences. Previous research only uses contrastive learning and auxiliary intentions independently, which can result in general recommendation efficiency. How to combine contrastive learning and auxiliary intentions organically becomes more and more challenging. Therefore, we propose sparse sequential recommendation with interactions and intentions contrastive Learning, namely I <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> CL, which solves the data sparsity problem by employing both contrastive learning and auxiliary intentions in the sequential recommendation. First, the intent representation is learned by selecting the intent information from a large amount of auxiliary information to compensate for the deficiency that the current sequential recommendation only focuses on recent items due to data sparsity. In this paper, the auxiliary intentions use the seller and classification information of items for user interaction, which can effectively capture users’ long-term preferences. Second, for reasons of learning high-quality representations, we utilize a contrastive learning framework to extract self-supervised signals from raw user behavior sequences and intent sequences and optimize user representation models so as to improve sequential recommender systems. Finally, a multitask training strategy is adopted to optimize recommendations jointly through parameter and structure sharing. Experiments are conducted on two sparse recommendation datasets to demonstrate the effectiveness of the proposed model in this paper.
Performance Modeling of Blockchains with Fixed Block Intervals

With the emergence of various application scenarios, various chains have been developed to meet their requirements. Among them, chains with fixed block intervals (fixed chains for short) occupy an increasingly significant position. Performance has always been a key bottleneck of blockchains and modeling for them is the most common method for performance analysis. But til now, few models for fixed chains exist and they are not precise and applicable enough. This paper proposes a model for fixed chains via the bulk-service queuing theory, which can reflect the real scenario more precisely and apply to the high load. We consider the continuous time and the transaction (TX for short) pool with limited capacity and reflect the chains’ features of fixed intervals and empty blocks to improve accuracy and applicability. We give an expression for three significant measurements: the average confirmation delay of TXs, the blockchain throughput, and the TX rejection rate. We use Ethereum to validate our model. And moreover, we use the model for analysis to assist designers in operating chains.
AEWF: Autoencoder-based Website Fingerprinting in Data-limited Scenarios

With the assistance of deep learning (DL), website fingerprinting (WF) attacks, which enable a local passive attacker to ascertain the website visited by a user, even when utilizing an anonymous network such as Tor, have become increasingly effective. Nevertheless, the majority of these attacks necessitate a substantial amount of labeled data, posing a practical burden. Recent attempts at mitigating this issue involve the utilization of auxiliary data. However, these methods either require the auxiliary data to be labeled or fail to achieve satisfactory results. In this study, we propose a novel approach that leverages autoencoders to better exploit the unlabeled auxiliary data. Through the reconstruction task, the network learns to extract representative features, thereby enhancing the classifier’s performance. We validate the efficacy of our method, which outperforms competing algorithms in the majority of our experimental setups, encompassing both closed-world (CW) and open-world (OW) scenarios.