Reviewerly/exHarmony huggingface dataset:
A Combination of Term Frequency and Topic Modeling with Public Attention to Detect Hot Topics on Texts of Indonesian Online News

In the age of digitalization, accurately identifying trending topics within online news articles is crucial for understanding public interests and concerns. This study introduces an approach that combines Term Frequency-Inverse Document Frequency (TF-IDF) and Topic Modeling techniques to pinpoint viral keywords in news content. While TF -IDF conventionally assigns word weights based on document frequency, it often struggles to capture emerging trends effectively. To overcome this limitation, our research presents a modified TF -IDF approach that incorporates temporal sensitivity, enabling us to detect trends in a more timely manner. Our methodology takes into account not only word frequency but also factors such as user attention and time in a specific period, on how the keyword appears in every month, thereby improving the accuracy of identifying the burst effect of the keyword. Our results demonstrate that our modified TF-IDF approach surpasses by 0.25 from TF -IDF in identifying viral content through KMeans clustering and exhibits topic modeling accuracy. This is especially evident when analyzing discussions related to events in 2022, as validated using Google Trend viral keywords. Despite our approach's improvements, it remains constrained by the need for periodic data updates and processing, preventing real-time trend detection. In conclusion, our research seeks to enhance the reliability of digital data for informed decision-making, aligning with sustainability goals. By providing a novel approach to identifying viral keywords in online news, we aim to contribute to a better understanding of public interests and concerns in the digital age.
AI in Education-A Transformative Force

Artificial Intelligence (AI) holds the potential to profoundly transform education, offering personalized learning experiences and improving student engagement and outcomes. AI's applications encompass adaptive learning, intelligent tutoring, data-driven insights, administrative automation, content generation, language support, accessibility, and predictive analytics. Nonetheless, ethical concerns, such as data privacy and algorithmic fairness, must be carefully addressed in the responsible implementation of AI. As AI continues to advance, it promises to shape a more dynamic and equitable educational landscape.
Bottlenecks in Secure Adoption of Deep Neural Networks in Safety-Critical Applications

The widespread proliferation of deep learning models has resulted in their extensive adoption in various domains, ranging from image recognition, Natural Language Processing (NLP), medical diagnosis, autonomous driving etc. Features such as high accuracy, quick inference, high scalability and adaptability have been instrumental in this revolution. Nonetheless, there are several elements that pose a threat to secure and reliable implementation of these models in safety-critical applications. Three primary reasons can be attributed to engender this bottleneck. First, Deep Neural Networks (DNN) are vulnerable to attacks that manipulate inputs and/or network parameters, which can compromise their performance. Additionally, faults in the DNN hardware can have adverse effects on the network accuracy. Next, error and uncertainty quantification in DNN-based inference are complex, which can undermine the dependability of their decisions. Finally, the lack of transparency and explainability in black-box models impedes their adoption in highly critical environments. This paper offers a comprehensive exploration of these issues, which must be addressed for improving the safety and reliability in adoption of machine learning models in mission-critical applications.
Experiments to Review Literature on Topic Trends in Technology Development in Educational Information Systems

The smart campus is a phenomenon that occurs in the industrial era 4.0. Many people understand that a smart campus is a campus where all resources can work and produce learning with high effectiveness and maximum efficiency. Everyone talks about elements of information technology that play a role in the sustainability of smart campuses. However, what actually makes the definition of a smart campus itself is not defined with certainty. This is because the world of education itself is experiencing major disruption. This causes innovation in the world of education, especially learning, to pay less attention to the actual aspects of education. This means that education has relied too much on technology which will actually cause a shift in the essence of education itself. In this paper, we will review how effective and efficient learning concepts actually are using information technology. The method used is an analysis of the literature review with an experimental survey and bibliometric analysis. As a result, it will be known that information technology that is in accordance with the essence of education is a matter that does not only focus on technical aspects but also needs to pay attention to aspects of methods, materials, and media. So that the results that are known will provide insight into the development of learning models on an ongoing basis according to the true essence of education.
Recommendation System with Faster R-CNN for Detecting Content Violation in Broadcasting Videos

Television plays a significant role in achieving sustainable development as it can influence behavior and foster violence, hence why setting rules and standards is vital to ensure the well-being of vulnerable populations. Organizations such as Indonesian Broadcasting Commission use the Broadcasting Code of Conduct and Broadcast Program Standards and its five categories (5S) to regulate and monitorbroadcast content in Indonesia. With the growth of content and the urgency to monitor effectively by collaborating Artificial Intelligence with human reviewers, our research aims to develop a recommendation system to detect broadcast violations using Faster R-CNN. Data was collected using scraping based on the categories in the code of conduct and annotated manually. We have conducted four scenarios to train for three categories: SARU, SADIS, and SIHIR, using the Faster-RCNN ResNet-152 pre-trained model from the Tensorflow Object Detection API framework. The models are evaluated using mean Average Precision (mAP). The optimum results obtained are models with reduced learning rates with mAP scores of 0,325 for SARU, 0,2877 for SADIS, and 0,3563 for SIHIR. The models are then integrated into thesystem in which the back end is developed using the Flask framework and front-end using NextJS. Functionality and usability testing are carried out in which the system has met the requirement specifications of 100% and is easy to use with an average score of 6.75.
Self-Supervised Visual Representation Learning Framework for Road Sign Classification

In the landscape of machine learning, the dominance of supervised learning in crafting specialized models for specific tasks is evident, yet its confinement in terms of broader generalization has underscored the quest for more versatile models capable of acquiring multiple skills without an insurmountable reliance on labelled data. A pivotal avenue toward this goal lies in self-supervised learning (SSL), which promises to instill foundational knowledge and a semblance of common sense into AI systems. This investigation undertakes a comprehensive exploration of diverse SSL strategies, elucidating their inherent constraints on the road sign detection and classification usecase. Through an intricate comparative analysis spotlighting Context Autoencoders (CAE) and MoCoV3 - two prominent state-of-the-art methodologies leveraging masked image modelling and contrastive learning, respectively - we unravel their distinct attributes and limitations. Furthermore, we introduce an innovative end-to-end framework meticulously tailored for self-supervised road sign classification. Our roadmap encompasses the infusion of domain-specific expertise in road signs into this framework, followed by rigorous benchmarking against state-of-the-art SSL models across diverse road sign datasets, employing a spectrum of evaluation metrics. This study emerges as a stepping stone, propelling AI closer to comprehesive intelligence by harnessing the potency of self-supervised paradigms.
Distraction Detection in Driving Using Pose Extraction and Machine Learning Techniques

There are a lot of technologies have been implemented in the modern cars these days, ranging from preventive braking system to driving assistance. It is believe that equipping car with modern technology would enhance safety. However, there are other aspects need to be considered in safety driving, that is human factor. Human factors are believed have high contribution in road accidents. Human factors such as emotional control and distraction may leads to dangerous driving behaviours. This study investigates the driver pose distraction. The distracted driver pose detected through pose detection using landmark feature extraction. The dataset used in this study is State Farm Distracted Driving Detection which is a secondary dataset. The extracted driver pose are in numeric form and used as the input for the classification model to detect ten kinds of driving distraction ranging from safety driving to talking to passenger. Four classifiers used to detect distracted driver are CatBoost, Decision Tress, Random Forest and SVM. The model performance evaluated using Precision, Recall, Accuracy, and F1-score. Furthermore, prediction time recorded to determine the best model to detect driver distraction. The results shows that CatBoost model outperformed all other model with F1-score and prediction time of 0.899 and 0.014 second. Random Forest model performance is the second highest compared to four other with F1-score and prediction time of 0.881 and 0.028 seconds. CatBoost model is the best model even though Cat Boost model execution time is not the highest and the model performance is not significantly different compared to Random Forest.
UOD-FPN: An Effective Feature Pyramid Network for Underwater Object Detection

Optical images captured in underwater environments often suffer from degradation problems such as color deviation, low contrast, and haze effects. Faced with this situation, traditional feature pyramid networks (FPNs) have difficulty effectively integrating the multi-scale features of underwater objects, which limits the performance of object detectors in underwater environments. Therefore, this paper proposes a novel underwater object detection feature pyramid network (UOD-FPN), which can effectively enhance the ability to represent underwater object features. This network consists of three different modules. First, the inter-layer feature fusion module (IFM) is capable of generating multiple aggregated feature layers with different receptive fields. Second, the feature adaptive weighted re-layering module (FAWRM) introduces attention mechanisms to guide finer re-layering of the aggregated feature layer. Finally, the feature-selective fusion module (FFM) achieves more effective feature fusion by using cross-correlation responses between different feature layers as guidance, enhancing the ability to express multi-level integrated features. Additionally, the UOD-FPN proposed in this paper can be applied to most FPN-based object detection models. The experiments demonstrate that UOD-FPN can significantly enhance the performance of one-stage and two-stage object detectors on the underwater datasets DUO and RUOD.
A Syllabus on Bio-Inspired Computing with Student Outcomes Evaluation

Bio-inspired algorithms is an important approach in computer science which are based on the principles of the biological evolution and animal or insect behavior. We have considered twelve of the most know bio-inspired algorithms for proposed a syllabus for undergraduate course in a computer science program. The main objective of this paper is to detail the student outcomes evaluation based on accreditation criteria for computer programs proposed by the Institute for Quality and Accreditation of Computer, Engineering and Technology Programs (ICACIT) in 2019. Furthermore, we shows the analysis and results of the student outcomes evaluation in a course of the Computer Science Program of the National University of San Agustin of Arequipa (UNSA) where we obtain over 80% between excellent and satisfactory from five of the eight performance indicators evaluated.
Evaluation of the Digital Competences of University Professors

In this research, we measured the digital competence levels of Peruvian university professors using the DigCompEdu framework. We use a self-assessment questionnaire proposed by the framework to collect the data directly. As a result, 47.06% professors are at the leadership level and 35.29% professors are at the expertise level. In addition, it is important to highlight that within the six areas in which the twenty-two competences are divided, the one that obtained the lowest results is area 04 called Assessment in which we recommended to improve their digital competences.
Joint Maximum a Posteriori - Maximum Likelihood Estimator for Linear Discrete- Time Systems

This paper is concerned with the joint estimation of a hybrid vector with both random and deterministic parameters when a large set of dependent observations is available. This yields the joint Maximum A Posteriori-Maximum Likelihood estimator (MAP/MLE) for which we derive a recursive estimation scheme by leveraging previous results in the deterministic case. The main novelties are the recursive joint MAP-MLE formulation and the derivation of the recursive hybrid Cramér- Rao bound for a mixture of complex and real parameters vector. The relevance of the general bound expressions and the recursive MAP-MLE estimation performance are illustrated with a practical radar estimation problem.
K-Subspaces for Sequential Data

We study the problem of clustering high-dimensional temporal data such as video sequences of human motion, where points that arrive sequentially in time are likely to belong to the same cluster. State-of-the-art approaches to this problem rely on the union-of-subspaces model, where points lie near one of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> unknown low-dimensional subspaces. We propose the first approach to sequential subspace clustering based on the popular <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -Subspaces (KSS) formulation, which we refer to as Temporal <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex> -Subspaces (TKSS). We show how sequential information can be incorporated into the KSS problem and provide an efficient algorithm for approximate minimization of the resulting cost function, proving convergence to a local minimum. Results on benchmark datasets show that TKSS achieves state-of-the-art performance, obtaining an accuracy increase of over 10% compared to existing methods.
SAM-GAN: A Single-Image GAN Model Guided by Synergistic Attention

In real life, there is often an insufficient number of images available for the model to learn from in image generation tasks. Previous work shows that learning the distribution of patches within a single image can possibly gain powerful prior. But training a single-image generative model is still a tough mission since the features of an image patch are considerably less detailed and abstract compared to a distribution of images. That's why the existing methods are relatively rare, such as SinGAN, ConSinGAN, and CCASinGAN. This paper proposes a single-image GAN model (SAM-GAN) guided by a synergistic attention mechanism which consists of low-dimensional and high-dimensional blocks focusing on both global structural and detailed texture features in the meantime. We conduct numerous experiments to evaluate the model's performance qualitatively and quantitatively. The outcomes demonstrate SAM-GAN's superiority over other state-of-the-art models, showcasing its potential to generate more intricate and realistic images.
Graph Learning for Balanced Clustering of Heavy-Tailed Data

Graph learning is an emerging research area with many applications in machine learning, specifically data clustering. There are numerous algorithms for learning a graph, among which, Gaussian models have gained significant attraction. These models, however, are shown not to have promising performance in modelling the data with heavy-tailed distributions. Existing approaches for graph-based clustering also lack a measure of balancedness in the size of the clusters, which often leads to poor classification accuracy. This paper proposes a method for learning a graph structure for heavy-tailed data clustering that firstly enjoys a balanced structure and secondly provides sufficient details of intra-cluster connections. The numerical results involving heavy-tailed financial data, validate the performance of the proposed clustering method.
Research on the Application of Improved Collaborative Filtering Algorithm in Course Recommendation

In the era of big data, the amount of resources on educational platforms is constantly increasing, making it difficult for many learners to choose the courses they really need from a massive number of online courses. Collaborative filtering algorithms are widely used in recommendation systems. Traditional collaborative filtering algorithms usually use the cosine similarity formula for interest similarity calculation, but in many cases, popular courses can affect the recommendation results and cannot reflect student needs well. This article proposes an improved solution to the cosine similarity calculation formula of traditional collaborative filtering algorithms, which can effectively suppress the influence of popular courses on the actual similarity of students. Experimental results show that this method can efficiently recommend online courses on educational platforms.
Multi-Model Federated Learning Optimization Based on Multi-Agent Reinforcement Learning

This paper addresses the problem of Multi-Model Federated Learning (MMFL) in a typical wireless network, where a cellular Base Station (BS) cooperates with multiple clients to simultaneously train several Machine Learning (ML) models. Accordingly, the objective of this paper is to make an efficient joint decision for client association and communication-computation resource allocation to optimize the performance of the MMFL algorithm. In this regard, an optimization problem is formulated to minimize the average global loss of ML models under clients' energy and delay constraints. It is shown that the problem is a mixed-integer optimization whose objective is implicit in terms of the decision variables. To solve the optimization problem, we propose a Multi-Agent Multi-Model Federated Learning (MAMMFL) scheme based on a cooperative multi-agent configuration to intelligently assign models and resources to clients. Specifically, the problem is first converted to a Markov Decision Process (MDP) problem, then it is divided into four sub-MDP problems, where each problem relates to a phase in MMFL. The reinforcement learning algorithm solves each sub-problem, and a team-Q algorithm is adopted to coordinate agents in a cooperative multi-agent setting. Simulation results show that the proposed method can outperform other baselines in terms of average global loss and resource consumption.
Sparse Graph Signal Recovery by the Graph-Based Multiple Generalized Information Criterion (GM-GIC)

This paper investigates the recovery of sparse signals over graphs, which is a common problem in graph signal processing (GSP) applications such as anomaly detection in sensor networks. We represent the sparse graph signals as a graph fil-ter output and pose the problem as hypothesis testing. Based on this representation, we propose the Graph-Based Multiple Generalized Information Criterion (GM-GIC), which leverages the double sparsity of the graph signal and the graph filter. In the first stage of the GM-GIC method, we test each dictionary element (graph filter matrix column) to identify if it captures information on the sparse signal. Next, we partition the subset of informative dictionary elements into smaller subsets that span orthogonal subspaces. Finally, we compute the local GICs over each subset and combine them into a global decision. Simulations show that the GM-GIC method improves the support recovery performance compared with existing methods without significant computational overhead.
A ModelOps-Based Framework for Intelligent Medical Knowledge Extraction

Extracting medical knowledge from healthcare texts enhances downstream tasks like medical knowledge graph construction and clinical decision-making. However, the construction and application of knowledge extraction models lack automation, reusability and unified management, leading to inefficiencies for researchers and usage barriers for non-AI experts like doctors. To address these issues, we propose a ModelOps-based intelligent medical knowledge extraction framework that offers a low-code system for model construction and application. Specifically, the framework includes a dataset abstraction mechanism based on multi-layer callback functions, a reusable model training, monitoring and management mechanism, alongside a model recommendation method based on dataset similarity for quickly finding potentially suitable models for a given dataset. Our frame-work provides convenience for researchers to develop models and simplifies model access for non-AI experts like doctors.
Matrix Low-Rank Trust Region Policy Optimization

Most methods in reinforcement learning use a Policy Gradient (PG) approach to learn a parametric stochastic policy that maps states to actions. The standard approach is to implement such a mapping via a neural network (NN) whose parameters are optimized using stochas-tic gradient descent. However, PG methods are prone to large policy updates that can render learning inefficient. Trust region algorithms, like Trust Region Policy Optimization (TRPO), constrain the policy update step, ensuring monotonic improvements. This paper introduces low-rank matrix-based models as an efficient alternative for estimating the parameters of TRPO algorithms. By gathering the stochastic policy's parameters into a matrix and applying matrix-completion techniques, we promote and enforce low rank. Our nu-merical studies demonstrate that low-rank matrix-based policy models effectively reduce both computational and sample complexities compared to NN models, while maintaining comparable aggregated rewards.
Robust Deep Image Recovery from Sparsely Corrupted and Sub-Sampled Measurements

Lately, two frameworks, namely deep image prior (DIP) and compressed sensing generative models (CSGM), have demonstrated remarkable performance in inverse problem solving. They accomplish this by fitting over-parameterized deep neural networks to undersampled measurements, where the network can be either trained or untrained. However, their effectiveness diminishes considerably in the presence of noise, as the over-parameterized deep neural network can easily overfit the measurements given sufficient training iterations. This study proposes a double-overparameterization approach to achieve robust image recovery when the measurements are corrupted by sparse noise. More specifically, we separate the sparse measurement noise from the clean measurement by introducing an extra sparse over-parameterization term into the training objective of DIP or CSGM. Our proposed double over-parameterization algorithm successfully mitigates the adverse effects of sparse corruptions, as demonstrated through experiments on various inverse problems with sparse corruptions.