Reviewerly/exHarmony huggingface dataset:
Pemanfaatan Learning Management System (LMS) Berbasis Moodle dalam Pembelajaran Sejarah Pada Kelas X SMK Negeri 3 Magelang

The purpose of this study is to know the use of LMS Moodle in history study and to know of the obstacles encountered by teachers and trainees in making use of the LMS Moodle. The study uses a qualitative approach with case study strategies. Data collection techniques of observation, interview, and document review. The data collected will then be processed to inference. Research shows that during learning done from home 1) 1) history study with an LMS Moodle is done well. The planning, implementation and evaluations of the history teacher have been systematic. The casting of learning with the LMS Moodle was presensi, matter waiver, and evaluation by taking an formative question. During the online study the history teacher has made good use of, but in doing so has not been fully effective. 2) the challenges teachers experience are the making of attractive materials, the media selection to explain the material, the teachers cannot control the learning activities, and the activation of the learners. The performance of learning is inadequate because of age factors, and learners' accessibility is still lacking, and the class's organization is irregular. The challenge for a learner is the often defective LMS Moodle, an unstable Internet network, a lack of understanding of matter, and a usually full cell phone memory.
TEACHERS’ PERCEPTION DURING PANDEMIC TIMES: CHALLENGES, OPPORTUNITIES AND CHANGES

This integrative literature review aimed to analyze research on teaching action and the challenges, opportunities, and changes in teaching from the basic education teachers' perception during the pandemic era considering the methodologies adopted in research carried out.Regarding the challenges, most teachers identified three areas of difficulties: interaction and communication, student motivation and coordination and learning assessment.Although it was a period of tensions, it was also perceived as a window of opportunity for changes in teaching practices.Develop new skills and knowledge, especially about educational technology, and rethink new ways of teaching and learning were the main opportunities perceived by teachers.As for the changes, they realized technological, pedagogical and social changes on the teaching action.
Ideograph: A Language for Expressing and Manipulating Structured Data

We introduce Ideograph, a language for expressing and manipulating structured data.Its types describe kinds of structures, such as natural numbers, lists, multisets, binary trees, syntax trees with variable binding, directed multigraphs, and relational databases.Fully normalized terms of a type correspond exactly to members of the structure, analogous to a Church-encoding.Moreover, definable operations over these structures are guaranteed to respect the structures' equivalences.In this paper, we give the syntax and semantics of the non-polymorphic subset of Ideograph, and we demonstrate how it can represent and manipulate several interesting structures.
Structure Analysis of General Type-2 Fuzzy Controller and Its Application

In this paper, a general type-2 fuzzy controller is proposed. The proposed general type-2 fuzzy controller is based on α-plane representation, which converts the centroid of general type-2 fuzzy sets to centroids of several interval type-2 fuzzy sets. Also, the mathematical expression of general type-2 fuzzy controller is derived by fuzzy product operate and NT type reduction. By the mathematical expression, the relation between general type-2 fuzzy controller output with parameter of triangular secondary membership function is discussed. Finally, the control performances of proposed general type-2 fuzzy controller are compared with conventional PID controller, tradition type-1 fuzzy controller, and interval type-2 fuzzy controller. Furthermore, to show the effective and practical of the proposed controller, a nonlinear inverted pendulum system is tested. The simulation results show that general type-2 fuzzy controller achieves better control efforts than other compared controllers.
MEDIA MANAGEMENT IN THE CONTEXT OF PEDAGOGICAL MANAGEMENT PRACTICES FOR SCHOOL INCLUSION

This study aims to address the role of school management in media management with a focus on the organization of pedagogical work aimed at the teaching-learning process, for school inclusion and its influence on student training and preparation for citizenship.It analyzes the dimensions within the scope of technological management and the process of organizing the school space for the proper use of media in pedagogical practice.
The Influence of Temporal Logic on Finite Automata

The theory of automata combines ideas from engineering, linguistics, mathematics, philosophy, etc.The Entscheidungsproblem asks if it is possible to design a series of steps that replaces a mathematician.An automaton is an abstract machine that processes data.C. Shannon's theory is today's most popular despite having no relationship with the other.The Kt system is called "minimal" because it makes no assumptions about the structure of time.In LKt, we have four monary temporal operators, F, P, G and H, which are mutually interdefinable.Interdefinability means that we will pass logic in the future is the same as saying I will never fail logic, interpreting not passing logic as failing logic.The minimal system syntax of temporal logic introduces operators that have the property of being defined in terms of others.Modal logic studies the reasoning that involves the use of expressions "necessarily" and "possibly".In this article, we will represent through a finite automaton the temporal logic formula Fp.It allows us to see an acceptance pattern for Fp by considering two variables: p and q.Kt's axiomatic system of time expresses the idea that both the present and the past are fixed, if it has always been in the past that it will be some time in the future that p is now.No philosophical argument supports deterministic time flow; the logic of time must be open.Temporal logic has revived many old problems, from the Megaric-Stoics to the minimal system of temporal logic.Our work suggests that the future operators of system Kt follow an evaluation pattern, but we must be cautious because this pattern can only apply to models whose time flow is based on instants and precedence relations.
Observation of Localization Using a Noisy Quantum Computer

Quantum dynamics in a strongly disordered quantum many-body system show localization properties. The initial state memory is maintained owing to slow relaxation when the system is in the localized regime. This work demonstrates how localization can be observed using a noisy quantum computer by evaluating the magnetization and twist overlap in a quantum spin chain after short-time evolution. The quantities obtained from quantum-circuit simulation and real-device computation show their apparent dependence on disorder strength, although real-device computation suffers from noise-induced errors significantly. Using the exact diagonalization of the Hamiltonian, we analyze how noise-induced errors affect those quantities. The analysis also suggests how the twist overlap can reflect the information on the eigenstates of the Hamiltonian.
Interactive Reconstruction of Damaged Historic Landmarks – Al-Qattanin Mosque in Mosul Old City as a Case Study

The use of technology in the field of urban heritage has increased in recent times due to the possibilities that contribute to the management of conservation operations.The use of virtual reality systems is considered as one of the most important methods of digital preservation through the production of 3D models of historical buildings and then inserting them into interactive environments, such as the use of augmented reality systems, which allow the user to identify historical buildings through tablets and mobile devices to make them quickly and accessible.The research problem lies in the absence of studies on re-creating interactive models of historical buildings in Mosul old city.The current applied research aims to provide a working methodology to form an interactive model for a lost or damaged historical landmark through a methodology that works on collecting information sources on the damaged building and trying to rebuild it or recover the damaged parts of it by linking the information to produce a stereoscopic building , and then linking them to books and historical sources .So that the user can identify it with a three-dimensional model that appears on the screen of the mobile phone or tablet after making a digital scan of the relevant book page and using the Unite AR application.
Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network

In recent years, End-to-End speech recognition technology based on deep learning has developed rapidly. Due to the lack of Turkish speech data, the performance of Turkish speech recognition system is poor. Firstly, this paper studies a series of speech recognition tuning technologies. The results show that the performance of the model is the best when the data enhancement technology combining speed perturbation with noise addition is adopted and the beam search width is set to 16. Secondly, to maximize the use of effective feature information and improve the accuracy of feature extraction, this paper proposes a new feature extractor LSPC. LSPC and LiGRU network are combined to form a shared encoder structure, and model compression is realized. The results show that the performance of LSPC is better than MSPC and VGGnet when only using Fbank features, and the WER is improved by 1.01% and 2.53% respectively. Finally, based on the above two points, a new multi-feature fusion network is proposed as the main structure of the encoder. The results show that the WER of the proposed feature fusion network based on LSPC is improved by 0.82% and 1.94% again compared with the single feature (Fbank feature and Spectrogram feature) extraction using LSPC. Our model achieves performance comparable to that of advanced End-to-End models.
Self-supervised Meta-Prompt Learning with Meta-Gradient Regularization for Few-shot Generalization

Prompt tuning is a parameter-efficient method, which learns soft prompts and conditions frozen language models to perform specific downstream tasks. Though effective, prompt tuning under few-shot settings on the one hand heavily relies on a good initialization of soft prompts. On the other hand, it can easily overfit to few-shot training samples, thereby undermining generalizability. Existing works leverage pre-training or supervised meta-learning to initialize soft prompts but they fail to data-efficiently generalize to unseen downstream tasks. To address the above problems, this paper proposes a novel Self-sUpervised meta-Prompt learning framework with MEta-gradient Regularization for few-shot generalization (SUPMER). SUPMER leverages self-supervised meta-learning with a diverse set of well-designed meta-training tasks to learn a universal prompt initialization for efficient adaptation using only unlabeled data. Additionally, it jointly meta-learns a gradient regularization function to transform raw gradients into a domain-generalizable direction, thus alleviating the problem of overfitting. Extensive experiments show that SUPMER achieves better performance for different few-shot downstream tasks, and also exhibits a stronger domain generalization ability. The code for SUPMER will be available at https://github.com/beepkh/SUPMER.
Plutus: Bandwidth-Efficient Memory Security for GPUs

Graphic-Processing Units (GPUs) are increasingly used in systems where security is a critical design requirement. Such systems include cloud computing, safety-critical systems, and edge devices, where sensitive data is processed or/and generated. Thus, the ability to reduce the attack surface while achieving high performance is of utmost importance. However, adding security features to GPUs comes at the expense of high-performance overheads due to the extra memory bandwidth required to handle security metadata. In particular, memory authentication metadata (e.g., authentication tags) along with encryption counters can lead to significant performance overheads due to the memory bandwidth used to fetch the metadata. Such metadata can lead to more than 200% extra bandwidth usage for irregular access patterns.In this work, we propose a novel design, Plutus, which enables low-overhead secure GPU memory. Plutus has three key ideas. The first is to leverage value locality to reduce authentication metadata. Our observation is that a large percentage of memory accesses could be verified without the need to bring the authentication tags. Specifically, through comparing decrypted blocks against known/verified values, we can with high confidence guarantee that no tampering occurred. Our analysis shows that the probability of the decryption of a tampered (and/or replayed) block leading to a known value is extremely low, in fact, lower than the collision probability in the most secure hash functions. Second, based on the observation that many GPU workloads have limited numbers of dirty block evictions, Plutus proposes a second layer of compact counters to reduce the memory traffic due to both the encryption counters and integrity tree. Third, by exploring the interesting tradeoff between the integrity tree organization vs. metadata fetch granularity, Plutus uses smaller block sizes for security metadata caches to optimize the number of security metadata memory requests. Based on our evaluation, Plutus can improve the GPU throughput by 16.86% (up to 58.38%) and reduce the memory bandwidth usage of secure memory by 48.14% (up to 80.30%).
Training Multilayer Perceptrons by Sampling with Quantum Annealers

A successful application of quantum annealing to machine learning is training restricted Boltzmann machines (RBM). However, many neural networks for vision applications are feedforward structures, such as multilayer perceptrons (MLP). Backpropagation is currently the most effective technique to train MLPs for supervised learning. This paper aims to be forward-looking by exploring the training of MLPs using quantum annealers. We exploit an equivalence between MLPs and energy-based models (EBM), which are a variation of RBMs with a maximum conditional likelihood objective. This leads to a strategy to train MLPs with quantum annealers as a sampling engine. We prove our setup for MLPs with sigmoid activation functions and one hidden layer, and demonstrated training of binary image classifiers on small subsets of the MNIST and Fashion-MNIST datasets using the D-Wave quantum annealer. Although problem sizes that are feasible on current annealers are limited, we obtained comprehensive results on feasible instances that validate our ideas. Our work establishes the potential of quantum computing for training MLPs.
Exploiting d-DNNFs for Repetitive Counting Queries on Feature Models

Feature models are commonly used to specify the valid configurations of a product line. In industry, feature models are often complex due to a large number of features and constraints. Thus, a multitude of automated analyses have been proposed. Many of those rely on computing the number of valid configurations which typically depends on solving a #SAT problem, a computationally expensive operation. Further, most counting-based analyses require numerous #SAT computations on the same feature model. In particular, many analyses depend on multiple computations for evaluating the number of valid configurations that include certain features or conform to partial configurations. Instead of using expensive repetitive computations on highly similar formulas, we aim to improve the performance by reusing knowledge between these computations. In this work, we are the first to propose reusing d-DNNFs for performing efficient repetitive queries on features and partial configurations. Our empirical evaluation shows that our approach is up-to 8,300 times faster (99.99\% CPU-time saved) than the state of the art of repetitively invoking #SAT solvers. Applying our tool ddnnife reduces runtimes from days to minutes compared to using #SAT solvers.
BiCro: Noisy Correspondence Rectification for Multi-modality Data via Bi-directional Cross-modal Similarity Consistency

As one of the most fundamental techniques in multimodal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, massive and correctly aligned data pairs are required for model training. However, unlike unimodal datasets, multimodal datasets are extremely harder to collect and annotate precisely. As an alternative, the co-occurred data pairs (e.g., image-text pairs) collected from the Internet have been widely exploited in the area. Unfortunately, the cheaply collected dataset unavoidably contains many mismatched data pairs, which have been proven to be harmful to the model's performance. To address this, we propose a general framework called BiCro (Bidirectional Cross-modal similarity consistency), which can be easily integrated into existing cross-modal matching models and improve their robustness against noisy data. Specifically, BiCro aims to estimate soft labels for noisy data pairs to reflect their true correspondence degree. The basic idea of BiCro is motivated by that -- taking image-text matching as an example -- similar images should have similar textual descriptions and vice versa. Then the consistency of these two similarities can be recast as the estimated soft labels to train the matching model. The experiments on three popular cross-modal matching datasets demonstrate that our method significantly improves the noise-robustness of various matching models, and surpass the state-of-the-art by a clear margin.
A Representation Transformation of Parametric Feynman Integrals

A transformation is applied to the parametric representation of Feynman integrals. The obtained representation and the original parametric representation are dual to each other. For integrals with momentum-space correspondences, the ``new'' representation is just the Baikov's representation.
Is it possible to simulate the cosmological stage of the nucleon mass generation?

The mechanism of elementary particle mass generation is a fundamental issue of physics. While cardinal progress has recently been achieved in the case of leptons and current quarks through the Brout-Engler-Higgs mechanism, this issue is at the stage of searching for the constituent quarks forming the nucleon mass. A deep similarity is noted in the paper between the constituent quark states arising in central inelastic $NN$ collisions within a certain energy interval and the QCD matter states at the cosmological stage of nucleon mass generation. This gives rise to special attention to the investigation of both issues.
BlockChain and Decentralized Apps

Blockchain, the backbone of Bitcoin, has recently gained a lot of attention. Blockchain functions as an immutable record that enables decentralized transactions. Blockchain-based applications are sprouting up in a variety of industries, including financial services, reputation systems, and the Internet of Things (IoT), among others. However, many hurdles of blockchain technology, including scalability and security issues, have to be overcome. Many industries, including finance, medicine, manufacturing, and education, use blockchain applications to capitalize on this technology's unique set of properties. Blockchain technology (BT) has the potential to improve trustworthiness, collaboration, organization, identity, credibility, and transparency. We provide an overview of blockchain architecture, various different kinds of blockchain as well as information about the Decentralized apps which are also known as Dapps. This paper provides an in-depth look at blockchain technology
Wasserstein Auto-encoded MDPs: Formal Verification of Efficiently Distilled RL Policies with Many-sided Guarantees

Although deep reinforcement learning (DRL) has many success stories, the large-scale deployment of policies learned through these advanced techniques in safety-critical scenarios is hindered by their lack of formal guarantees. Variational Markov Decision Processes (VAE-MDPs) are discrete latent space models that provide a reliable framework for distilling formally verifiable controllers from any RL policy. While the related guarantees address relevant practical aspects such as the satisfaction of performance and safety properties, the VAE approach suffers from several learning flaws (posterior collapse, slow learning speed, poor dynamics estimates), primarily due to the absence of abstraction and representation guarantees to support latent optimization. We introduce the Wasserstein auto-encoded MDP (WAE-MDP), a latent space model that fixes those issues by minimizing a penalized form of the optimal transport between the behaviors of the agent executing the original policy and the distilled policy, for which the formal guarantees apply. Our approach yields bisimulation guarantees while learning the distilled policy, allowing concrete optimization of the abstraction and representation model quality. Our experiments show that, besides distilling policies up to 10 times faster, the latent model quality is indeed better in general. Moreover, we present experiments from a simple time-to-failure verification algorithm on the latent space. The fact that our approach enables such simple verification techniques highlights its applicability.
A Perceptual Quality Assessment Exploration for AIGC Images

\underline{AI} \underline{G}enerated \underline{C}ontent (\textbf{AIGC}) has gained widespread attention with the increasing efficiency of deep learning in content creation. AIGC, created with the assistance of artificial intelligence technology, includes various forms of content, among which the AI-generated images (AGIs) have brought significant impact to society and have been applied to various fields such as entertainment, education, social media, etc. However, due to hardware limitations and technical proficiency, the quality of AIGC images (AGIs) varies, necessitating refinement and filtering before practical use. Consequently, there is an urgent need for developing objective models to assess the quality of AGIs. Unfortunately, no research has been carried out to investigate the perceptual quality assessment for AGIs specifically. Therefore, in this paper, we first discuss the major evaluation aspects such as technical issues, AI artifacts, unnaturalness, discrepancy, and aesthetics for AGI quality assessment. Then we present the first perceptual AGI quality assessment database, AGIQA-1K, which consists of 1,080 AGIs generated from diffusion models. A well-organized subjective experiment is followed to collect the quality labels of the AGIs. Finally, we conduct a benchmark experiment to evaluate the performance of current image quality assessment (IQA) models.
OcTr: Octree-based Transformer for 3D Object Detection

A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering from inadequate receptive fields or coarse-grained holistic correlations. In this paper, we propose an Octree-based Transformer, named OcTr, to address this issue. It first constructs a dynamic octree on the hierarchical feature pyramid through conducting self-attention on the top level and then recursively propagates to the level below restricted by the octants, which captures rich global context in a coarse-to-fine manner while maintaining the computational complexity under control. Furthermore, for enhanced foreground perception, we propose a hybrid positional embedding, composed of the semantic-aware positional embedding and attention mask, to fully exploit semantic and geometry clues. Extensive experiments are conducted on the Waymo Open Dataset and KITTI Dataset, and OcTr reaches newly state-of-the-art results.