Reviewerly/exHarmony huggingface dataset:
Literasi Digital Dalam Kehidupan Masyarakat di Sulawesi Utara

The ability of people to identify, comprehend, and accept the usage of digital technology is essential in this day and age of pervasive digitalization. Digital literacy is crucial to society, particularly in the North Sulawesi Province's educational system. The Importance of Digital Literacy to the North Sulawesi society was conducted by North Sulawesi University of Technology in cooperation with the North Sulawesi Provincial Government and the Ministry of Communication and Information Technology's Directorate of Applications and Informatics. With the help of digital literacy, it is envisaged that members of the public, particularly students, will learn about and be able to utilize the four pillars of digital skills in their daily lives.
Literasi Digital Masyarakat Dalam Menghadapi Tantangan Digital Abad 21 Melalui Program Gerakan Literasi Digital di Kabupaten Garut

Digital literacy is a competency that is needed by all elements of society. Digital literacy can help society in facing the digital challenges of the 21st century. Therefore, there is a need for special assistance to the public in understanding digital literacy as a whole both in aspects of digital skills, digital culture, digital ethics and digital security. The purpose of digital literacy assistance is to convey knowledge about the importance of understanding digital literacy in the community, the 4 pillars of digital literacy, stimulate the community in building a paradigm of the importance of digital security in facing the digital development of the 21st century, and be active in finding solutions to the digital literacy problems faced, as well as increasing public understanding of digital literacy after participating in mentoring activities. The partners of this mentoring activity are the Ministry of Communication and Information Technology of the Republic of Indonesia and Cibatu District, Garut Regency. The participants of this digital literacy assistance activity were 200 people in Cibatu-Garut District. This activity is carried out with expository and reflection methods. Community digital literacy activities through digital literacy seminars are public understanding of digital literacy related to digital literacy pillars, digital problems and their handling are increasing, this can be seen from the results of the community understanding questionnaire after participating in mentoring activities, namely 64.2% of people strongly agree and 33.8% agree with positive statements from digital literacy indicators. Then the community has begun to have a paradigm of the importance of digital security in facing digital developments in the 21st century, and is active in finding solutions to the digital literacy problems faced.
Issues and Concerns in Teaching Mother Tongue-Based Multilingual Education during the Pandemic

https://orcid.org/0009-0004-2635-0399, https://orcid.org/0009-0000-9987-1495, https://orcid.org/0009-0009-3823-1772, https://orcid.org/0000-0001-6347-6442, https://orcid.org/0000-0002-3515-3889, https://orcid.org/0000-0001-8981-7399, https://orcid.org/0000-0002-5229-8350, agusan.jealene@clsu2.edu.ph, deocampo.kenneth@clsu2.edu.ph, domingo.erikha@clsu2.edu.ph, jpacol@clsu.edu.ph, parchamentojackson84@clsu.edu.ph, michaelangelo.santos@clsu2.edu.ph, nenitafabrigas@clsu.edu.ph Central Luzon State University, Science City of Muňoz, Nueva Ecija, Philippines
A Web3 Service Architecture with Self-Sovereign Identity

The authors propose a design methodology for a Web 3 Service Architecture that enables Self-Sovereign Services. Sovereign and Decentralized ID requires a suitable policy agent. This paper provides a basic description of blockchain, smart contracts, and Self-Sovereign services. The policy agent is determined by a score based on policies pre-established by the client and user logs. This provides dynamic access control. The authors implemented the service infrastructure of Self-Sovereign in Web3 and confirmed its feasibility. In addition, a delivery service is presented as an example of a Web3 Service Architecture.
Investigation on Verification of the Integration Neural Networks Theory by Weight Analysis Based on Weierstrass' approximation theorem

Simulation-based optimization often requires many simulations, which can be challenging to adapt due to time constraints. To address this issue, constructing approximators for simulations, such as the finite element method using machine learning, has gained attention. However, constructing these approximators requires a significant amount of training data. For this problem, we proposed an integration neural network as a highly accurate approximator with small data. Our method is based on Weierstrass’ approximation theorem, which uses a polynomial approximation. We constructed the integration neural network by integrating a linear approximator, which uses deductive knowledge to constrain the shape of the approximation curve between training points through multiple regression analysis, with a nonlinear approximator, which uses inductive learning to reduce overlearning of the linear approximator and correct errors. In this paper, by applying the approximator theorem one step further, we reduced the computational complexity of the learning process by simplifying and improving the network structure. Our experiments show that we can construct approximators with almost the same accuracy as previous methods while reducing the number of weight updates in the learning process to about 5%. Furthermore, by analyzing the weights of the approximators, we confirmed that the basic concept drove and that the improved integration neural network could learn with appropriate weights.
PROBLEMS OF ATMOSPHERIC AIR CLEANING

This paper considers a mathematical model for the development of the process of atmospheric air pollution, diseases of the population associated with this process, and also analyzes the measures taken to clean the atmosphere from harmful impurities.
MOBILE LEARNING AS THE KEY TO HIGHER EDUCATION INNOVATION

The background of mobile learning and its application in the higher education sector is included with the problem statements that have been identified and explained in order to give the present research a clear direction. The issues such as Hesitation in adopting mobile learning, Low growth rate of mobile learning in higher education, and Unhealthy addiction to smartphones are included. A lot of literature is explored to get an initial understanding. The qualitative research in primary data collection method is included in order to develop understanding on the research topic.
Target perception and behavioural recognition algorithms based on saliency and feature extraction

In response to the problems of target loss and insufficient accuracy in existing target perception and action recognition algorithms, this paper proposes a target perception and action recognition algorithm based on saliency and feature extraction. This algorithm uses saliency detection techniques to obtain salient regions in images or videos to focus attention on the target. At the same time, feature extraction techniques are combined to extract key nodes and inter-frame correlations from the target information. The experimental results of the measurement data show that this algorithm is superior to traditional detection methods in detecting target behaviour. In addition, it has successfully solved the problems of motion misalignment and jumping in pedestrian detection. Although the node localisation of the algorithm needs further improvement, it has shown good application prospects in smart cities and intelligent surveillance. Future work will focus on improving the positioning accuracy of key nodes to enhance its adaptability to different environments and scenarios, providing better support for smart city and other applications.
Knowing is Half the Battle: Enhancing Clean Data Accuracy of Adversarial Robust Deep Neural Networks via Dual-model Bounded Divergence Gating

Significant advances have been made in recent years in improving the robustness of deep neural networks, particularly under adversarial machine learning scenarios where the data has been contaminated to fool networks into making undesirable predictions. However, such improvements in adversarial robustness has often come at a significant cost in model accuracy when dealing with uncontaminated data (i.e., clean data), making such defense mechanisms challenging to adapt for real-world practical scenarios where data is primarily clean and accuracy needs to be high. Motivated to find a better balance between adversarial robustness and clean data accuracy, we propose a new model-agnostic adversarial defense mechanism named Dual-model Bounded Divergence (DBD), driven by a theoretical and empirical analysis of the bias-variance trade-off within an adversarial machine learning context. More specifically, the proposed DBD mechanism is premised on the observation that the variance in deep neural networks tends to increase in the presence of adversarial perturbations in the input data. As such, DBD employs a gating mechanism to decide on the final model prediction output based on a novel dual-model variance measure (coined DBD Variance), which is a bounded version of KL-Divergence between models. Not only is the proposed DBD mechanism itself training-free, but it can be combined with existing adversarial defense mechanisms to boost the balance between clean data accuracy and adversarial robustness. Comprehensive experimental results across over 10 different state-of-the-art adversarial defense mechanisms using both CIFAR-10 and ImageNet benchmark datasets across different adversarial attacks (e.g., APGD, AutoAttack) demonstrates that the integration of DBD can lead to as much as a 6% improvement on clean data accuracy without compromising much on adversarial robustness.
GCMA: An Adaptive Multi-Agent Reinforcement Learning Framework With Group Communication for Complex and Similar Tasks Coordination

Coordinating multiple agents with diverse tasks and changing goals without interference is a challenge. Multi-Agent Reinforcement Learning (MARL) aims to develop effective communication and joint policies using group learning. Some of the previous approaches required each agent to maintain a set of networks independently, resulting in no consideration of interactions. Joint communication work causes agents receiving information unrelated to their own tasks. Currently, agents with different task divisions are often grouped by action tendency, but this can lead to poor dynamic grouping. This paper presents a two-phase solution for multiple agents, addressing these issues. The first phase develops heterogeneous agent communication joint policies using a Group Communication MARL framework (GCMA). The framework employs a periodic grouping strategy, reducing exploration and communication redundancy by dynamically assigning agent group hidden features through hyper-network and graph communication. The scheme efficiently utilizes resources for adapting to multiple similar tasks. In the second phase, each agent's policy network is distilled into a generalized simple network, adapting to similar tasks with varying quantities and sizes. GCMA is tested in complex environments like StarCraft II and UAV take-off, showing its well-performing for large-scale, coordinated tasks. It shows GCMA's effectiveness for solid generalization in multi-task tests with simulated pedestrians.
Review of Methods for Automatic Speaker Verification

A review of techniques to identify speakers from their voices is presented, noting strengths and weaknesses of various methods. Similar acoustic analysis has been often used for both speech and speaker recognition, despite the two tasks being quite different. Speaker biometrics from voice is far more indirect and subtle than the estimation of phoneme sequences for automatic speech recognition from periodic evaluations of the spectral envelope of the vocal tract output. Speech signals are discussed from the point of view of how to recognize their textual content versus estimating other aspects of speakers. Common speech analysis methods such as filter banks, linear prediction, and mel-frequency cepstrum are examined. Approaches such as hidden Markov models, i-vectors, and artificial neural networks are shown to be useful for multiple speech applications. Focus is on how various types of networks can accomplish automatic speaker verification (ASV). Suggestions to improve these methods are made.
Latent Combinational Game Design

We present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">latent combinational game design</i> —an approach for generating playable games that blend a given set of games in a desired combination using deep generative latent variable models. We use Gaussian Mixture Variational Autoencoders (GMVAEs) which model the VAE latent space via a mixture of Gaussian components. Through supervised training, each component encodes levels from one game and lets us define blended games as linear combinations of these components. This enables generating new games that blend the input games as well as controlling the relative proportions of each game in the blend. We also extend prior blending work using conditional VAEs and compare against the GMVAE and additionally introduce a hybrid conditional GMVAE (CGMVAE) architecture which lets us generate whole blended levels and layouts. Results show that these approaches can generate playable games that blend the input games in specified combinations. We use both platformers and dungeon-based games to demonstrate our results.
Anomaly Detectors for Self-Aware Edge and IoT Devices

With the growing processing power of computing systems and the increasing availability of massive datasets, machine learning algorithms have led to major breakthroughs in many different areas. This applies also to resource-constrained IoT and edge devices, which will often benefit from relatively small – but smart – local anomaly detection tasks that aim at protecting the device, or the information they convey from sensors towards a central node. This provides the device with fault detection capabilities that are typically required when engineering dependable devices, services or systems. This paper overviews a pitfall-free process to provide small devices with anomaly detection capabilities, to make them self-aware of their health condition, and possibly take appropriate countermeasures. Our methodology applies to a wide range of Linux-based devices: we show an application to a specific ARANCINO device, which has already been successfully used in many smart cities and sensing applications. We craft anomaly detectors that are very effective in detecting most of the anomalies. Additionally, we comment on the beneficial impact of time-series analysis, which could help improve detection performance even further, allowing to equip any small device with responsive and accurate anomaly detection machinery.
StructureTester: Automatic Machine Translation Testing Based on Variation Feature Vector

In recent years, the performance of machine translation systems has made remarkable progress, primarily due to the rapid advancements in neural network language models. These state-of-the-art models enable the swift translation of vast amounts of text, leading to considerable time and cost savings. In pursuit of enhancing machine translation accuracy, researchers have devoted attention to developing automated translation testing tools. A prominent approach in this context involves comparing the translation results of “similar” source sentences, anticipating the correctness of translation by similarities in sentence structure. However, despite the potential of this approach, the current studies still face certain challenges. Notably, false negatives and false positives persist as issues. Moreover, achieving high detection accuracy for all types of translation errors remains an ongoing challenge. To address these challenges, we propose the StructureTester, a novel approach that not only leverages the differences between the structure trees of two sentences but also employs changes in sentence purpose as crucial judgmental features. Our proposed method yields significant improvements, elevating the overall detection accuracy to an impressive 98.17%. Furthermore, StructureTester effectively identifies various types of translation errors.
History of World's First Commercialization of Image Stabilizers for Handheld Cameras

For more than a decade, Image Stabilization (IS) technology has been considered essential to delivering improved image quality in professional cameras. More recently, as a result of advancing technology, IS has become increasingly popular to handheld device makers who want to propose high-end features for their products. Hence, manufacturers like Panasonic have worked hard on its technologies and methods for IS to significantly improve camera shutter speed and to offer precise suppression of camera vibration. This article overviews the history of world's first commercialization of image stabilizers for handheld cameras.
Voltage over Scaling for Approximate Newton Method in Total Variation-based Image Denoising

Approximate computing is an energy efficient technique that allows acceptable errors to improve circuits' energy and latency performance in image processing. This paper presents a runtime-based approximate integrated method by applying the voltage over scaling (VOS) technique to the approximate Newton method for total variation (TV)-based image denoising. The proposed technique reduces the energy dissipation of the approximate Newton method by decreasing the supply voltage of adder cells. The output delay thus increases beyond the worst-case delay, thereby causing errors in the output; the number of output errors varies without changing the circuit structure. The results illustrate that the output image quality is kept constant when the number of approximate bits (NAB) is smaller than 15 for both exact and approximate cell using VOS; meanwhile, the energy consumption decreases gradually. When the NAB value is high at around NAB=17 with 50% noise, the exact full adder shows better performance than the approximate cell (AMA1) when the approximation is low (less than five errors); the maximum number of iterations and energy consumption for AMA1 (16.2 and 14.33nJ) is larger than for exact full adder (8.6 and 7.26nJ).
The Historic Acoustic-Phonetic Collection of TU Dresden After Finishing the Catalogue

The historic acoustic-phonetic collection (HAPS) of the Technical University Dresden includes more than 1.000 objects from the history of experimental phonetics and speech technology. The rather young collection is a composition of historic devices from mainly three places: the TU Dresden and the former Phonetic Institutes of the universities in Berlin and Hamburg. It was a challenge to explore these inhomogeneous convolutes scientifically. After two decades, this process is now finished for the material exhibits. The paper summarizes the results which are hitherto achieved in cataloguing and describes the remaining tasks with respect to the audiovisual and the archival materials.
Online Data Drift Detection for Anomaly Detection Services based on Deep Learning towards Multivariate Time Series

Deep learning models have been successfully adopted in anomaly detection for multivariate time series data in various fields. These models are good at capturing complex time dependencies and extracting meaningful patterns from time series data. However, the trained models may become outdated due to unforeseen changes in real-world data, which can lead to a decrease in the quality of model service. Therefore, it is crucial to continuously monitor the performance of the model and analyze its behavior to ensure its reliability and availability. We propose an online data drift detection method that uses an unsupervised deep learning network, Variational Autoencoder (VAE), to monitor deep learning models in the field of multivariate time series anomaly detection. This method consists of three main steps namely data collection and statistical analysis, real-time drift detection, and drift interpretation. We collect raw time series data and model prediction data non-invasively from the model server. Then they are separated into windows for drift detection. Furthermore, the method can provide analysis and interpretation when drift is detected. Our evaluation experiments involve three real-world datasets from various industrial domains and four different structured anomaly detection models. We validate the effectiveness of drift detection in multivariate time series, and then test how the anomaly detection models perform during data drift detection. The highest improvement in F1 score is approximately 0.16. In addition, we provide an analysis of the interpretability of the model performance.
Abstract Syntax Tree for Method Name Prediction: How Far Are We?

Method name prediction (MNP) aims to recommend a proper name for a method given by the developer, which can ease the programming task and improve programmer productivity. Due to the excellent expressiveness of code representation, abstract syntax trees (AST) have been widely exploited by MNP techniques. However, it is a complex process to manipulate AST, including AST parsing, AST preprocessing, and AST encoding, of which a change in the scheme may change the AST embeddings and thus affects the performance of MNP. In this paper, we first conduct a comprehensive empirical study to systematically investigate the impact of the sub-processes of AST usage on MNP performance. The empirical findings of this study unmistakably demonstrate that AST has a positive impact on promoting MNP. Moreover, the selection of schemes for AST parsing, preprocessing, and encoding exerts a profound influence on the effectiveness of MNP. Properly combining AST (e.g., using JDT, AST Path <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">full</inf> , and code2seq as AST parsing, preprocessing, and encoding methods, respectively) can improve MNP performance by 164% in terms of F1-score compared to using only code tokens.
Combined Deterministic and Stochastic Streams for Visual Prediction Using Predictive Coding

We present a hybrid predictive coding framework designed for the prediction of future video frames. This model draws its conceptual foundation inspired from the predictive coding theories within the realm of cognitive science. The framework is imbued with a novel amalgamation of bottom-up and top-down information flows, fostering heightened interconnectivity among diverse tiers between prediction and reality. Notably, conventional predictive coding models primarily entail hierarchical event anticipation rather than prospective prediction. To address this limitation, our proposed model adopts a multi-scale paradigm, characterized by a Coarse-to-Fine schema. In relation to the network architecture, we integrate the encoder-decoder network within the Long Short-Term Memory (LSTM) module. This integration facilitates the sharing of ultimate encoded high-level semantic insights across varying strata of the neural network. Consequently, a profound interplay is established between the prevailing input and the historical LSTM states. This stands in stark contrast to the conventional Encoder-LSTM-Decoder configuration. The outcome is an erudite grasp of temporal and spatial dependencies, thereby engendering more verisimilar predictions. Empirical evaluations of our approach on benchmark datasets KTH.