Reviewerly/exHarmony huggingface dataset:
PEMANFAATAN MEDIA PEMBELAJARAN ICT DALAM MENINGKATKAN HASIL BELAJAR BAHASA INGGRIS PADA SISWA KELAS VIII-3 SMP NEGERI 9 TEBING TINGGI

The purpose of this research was to develop learning english subject based on ICT method.This study used a qualitative descriptive analysis technique.This classroom action research conducted by 2 cycles of the four phases: planning, implementation, observation, reflection. The subjects were students from class VIII-3 SMP Negeri 9 Tebing Tinggi which amounted to 38 students. The results showed that the method of the problem posing in English subject can improve student learning result characterized by increased mastery learning students, namely pre-cycle (60,52%), the first cycle (78,94%), cycle II (89,47 %) and complete learn the clasical equal to 89,47 %. Key word : ICT, English, Media
Analisis Sentimen Movie Review menggunakan Word2Vec dan metode LSTM Deep Learning

The increasing number of internet users is directly in line with the increasing number of data on the internet that is available for analysis, especially data in text form. The availability of this text data encourages a lot of sentiment analysis research. However, it turns out that the availability of abundant text data is also one of the challenges in sentiment analysis research. Datasets that consist of long and complex text documents require a different approach. In this study, LSTM was chosen to be used as a sentiment classification method. This research uses a movie review dataset that consists of 25,000 review documents, with an average length per review is 233 words. The research uses CBOW and Skip-Gram methods on word2vec to form a vector representation of each word (word vector) in the corpus data. Several dimensions of the word vector was used in this research, there are 50, 60, 100, 150, 200, and 500, this tuning parameter is used to determine their effect on the resulting accuracy. The best accuracy around 88.17% is obtained at the word vector 100 dimension and the lowest accuracy is 85.86% at the word vector 500 dimension.
Towards a formal multi-agent organizational modeling framework based on category theory

Multi-agent organizational modeling frameworks can be considered as an efficient solution regarding the distributed applications' problems such as task bundle mechanisms, supply chain management, as well as air traffic control. The main objective of this paper is to provide a solution based on a solid mathematical theory for the modeling, the analysis and the verification of artificial organizations properties, and particularly those of Multi-Agent Systems (MASs). Category theory is a mathematical formalism used to categorically study the logics of organizations in different societies. Therefore, it can be projected on artificial organizations in a categorical way. Our work is revolved around the idea of modeling Multi-Agents organization using category theory. In other words, it consists on the transformation of Agent-Group-Role (AGR) organizational model in a categorical way in order to obtain a formal semantics model describing the MAS organization. This formal model allows the analysis, the verification and also the validation of the main concepts of an organization at a high level of abstraction
Analysis of Website Security of SMKN 1 Pangandaran Against SQL Injection Attack Using OWASP Method

Every technological development is usually accompanied by an increase in security on a digital platform that is widely used by a large audience. However, with the rapid development of information technology, some of the security gaps found can be used as loopholes to commit crimes where these actions can harm others. These actions are often carried out by irresponsible people to benefit from the actions taken. Some of the gaps that are often found on digital platforms, especially on websites are SQL Injection where from BSSN data from January to April 2019, 73% of the vulnerability reports received are SQL Injection vulnerabilities. In addition, SQL Injection is also the number one threat to the security of a website application, where this SQL Injection attack is a code injection attack technique that is carried out by exploiting the security gaps that exist in the database layer of a website. In this study, taking the theme of SQL Injection which aims to conduct security analysis from the website of the school agency SMKN 1 Pangandaran, using OWASP which is used to carry out the analysis process. The result of testing the system on the website is where the attacker can perform injection using SQL payload to enter the database. Where the results of system analysis and testing will be recommended to close gaps on existing websites.
Automated Design of Unfolded Metaheuristics and the Effect of Population Size

Metaheuristics are a fairly standard approach for solving optimisation problems due to their success, flexibility, and simplicity. However, there is a plethora of metaheuristics available, with different performance levels for various problems. This work proposes a methodology for designing heuristic-based procedures to solve continuous optimisation problems and study how the population size affects its performance. The technique comprises the well-known Simulated Annealing algorithm as a hyper-heuristic, and a heuristic sequence taken from unfolding the conventional scheme of population-based metaheuristics reported in the literature. Our results show that the proposed approach is a reliable alternative for tackling optimisation problems. We find exciting insights, according to our data, about this primary implementation when varying the population size in different challenging problems.
Bagging ensemble for deep learning based gender recognition using test-time augmentation on large-scale datasets

We present a bagging ensemble of convolutional networks in combination with the test-time augmentation technique to improve performance on the cross-dataset gender recognition problem. The bagging ensemble combines the predictions from multiple homogeneous models into the ensemble prediction. Augmentation techniques are often used in the learning phase of the CNNs to improve the generalization ability. On the other hand, test-time augmentation is not a common method used in the testing phase of the learned model. We conducted experiments on models trained using different hyperparameters. We augmented the test data and combine the predictive outputs from these network models. Experiments performed on diverse gender datasets, including Adience, AFAD, CelebA, Gallagher, Genki-4K, IMDb, LFW, Morph, VGGFace2, and Wiki, showed that the use of bagging ensemble of convolutional networks and test-time augmentation outperforms standalone models. We obtained the highest cross-dataset accuracy in the literature on seven out of eleven datasets. For the remaining four datasets we reported the cross-dataset results for the first time. According to our experiments, VGGFace2, IMDb, and CelebA datasets provided the highest cross-dataset classification results for most of the test datasets in the gender recognition problem.
Evolutionary Gradient Descent for Non-convex Optimization

Non-convex optimization is often involved in artificial intelligence tasks, which may have many saddle points, and is NP-hard to solve. Evolutionary algorithms (EAs) are general-purpose derivative-free optimization algorithms with a good ability to find the global optimum, which can be naturally applied to non-convex optimization. Their performance is, however, limited due to low efficiency. Gradient descent (GD) runs efficiently, but only converges to a first-order stationary point, which may be a saddle point and thus arbitrarily bad. Some recent efforts have been put into combining EAs and GD. However, previous works either utilized only a specific component of EAs, or just combined them heuristically without theoretical guarantee. In this paper, we propose an evolutionary GD (EGD) algorithm by combining typical components, i.e., population and mutation, of EAs with GD. We prove that EGD can converge to a second-order stationary point by escaping the saddle points, and is more efficient than previous algorithms. Empirical results on non-convex synthetic functions as well as reinforcement learning (RL) tasks also show its superiority.
Inferring Time-delayed Causal Relations in POMDPs from the Principle of Independence of Cause and Mechanism

This paper introduces an algorithm for discovering implicit and delayed causal relations between events observed by a robot at regular or arbitrary times, with the objective of improving data-efficiency and interpretability of model-based reinforcement learning (RL) techniques. The proposed algorithm initially predicts observations with the Markov assumption, and incrementally introduces new hidden variables to explain and reduce the stochasticity of the observations. The hidden variables are memory units that keep track of pertinent past events. Such events are systematically identified by their information gains. A test of independence between inputs and mechanisms is performed to identify cases when there is a causal link between events and those when the information gain is due to confounding variables. The learned transition and reward models are then used in a Monte Carlo tree search for planning. Experiments on simulated and real robotic tasks, and the challenging 3D game Doom show that this method significantly improves over current RL techniques.
Reasoning-Based Learning of Interpretable ML Models

Artificial Intelligence (AI) is widely used in decision making procedures in myriads of real-world applications across important practical areas such as finance, healthcare, education, and safety critical systems. Due to its ubiquitous use in safety and privacy critical domains, it is often vital to understand the reasoning behind the AI decisions, which motivates the need for explainable AI (XAI). One of the major approaches to XAI is represented by computing so-called interpretable machine learning (ML) models, such as decision trees (DT), decision lists (DL) and decision sets (DS). These models build on the use of if-then rules and are thus deemed to be easily understandable by humans. A number of approaches have been proposed in the recent past to devising all kinds of interpretable ML models, the most prominent of which involve encoding the problem into a logic formalism, which is then tackled by invoking a reasoning or discrete optimization procedure. This paper overviews the recent advances of the reasoning and constraints based approaches to learning interpretable ML models and discusses their advantages and limitations.
Evaluating Relaxations of Logic for Neural Networks: A Comprehensive Study

Symbolic knowledge can provide crucial inductive bias for training neural models, especially in low data regimes. A successful strategy for incorporating such knowledge involves relaxing logical statements into sub-differentiable losses for optimization. In this paper, we study the question of how best to relax logical expressions that represent labeled examples and knowledge about a problem; we focus on sub-differentiable t-norm relaxations of logic. We present theoretical and empirical criteria for characterizing which relaxation would perform best in various scenarios. In our theoretical study driven by the goal of preserving tautologies, the Lukasiewicz t-norm performs best. However, in our empirical analysis on the text chunking and digit recognition tasks, the product t-norm achieves best predictive performance. We analyze this apparent discrepancy, and conclude with a list of best practices for defining loss functions via logic.
Comparative Analysis on the Impact of Distance Learning Between Russian and Japanese University Students, During the Pandemic of COVID-19

This article discusses the impact of the full transition to distance learning, during spread of coronavirus infection COVID-19.The advantages and disadvantages of distance learning under the conditions of self-isolation have been identified.A comparative analysis of two groups students from Russian (18 students) and Japan (31 students) were analyzed based on survey data, which revealed qualitative differences in the psychological condition of students during the self-isolation, and after the transition to the full-time education system.Special attention was paid to such aspects as: the level of psychological (mental) condition, efficiency, productivity, and overall assessment of the form of education.We have found that after the transition to full distance learning, both groups of students observed a decrease in the positive average level of psychological (mental) state, indicating that the mental states were similar regardless of territorial belonging.However, after a partial return to the full-time education system, the average positive level of psychological (mental) state increased, which indicates the importance of full-time education as the only one that creates conditions for emotional, energetic and positive influence between communicators.
Self-Supervised Convolutional Subspace Clustering Network with the Block Diagonal Regularizer

The practical visual data do not necessarily lie in linear subspaces, so deep convolutional subspace clustering network is proposed to segment the practical visual data into multiple categories accurately. The original convolutional subspace clustering network contains the stacked convolutional encoder module, the stacked convolutional decoder module and the self-expression module. We firstly alter the self-expression module, i.e., add a new k-block diagonal regularizer to the weights of the self-expression module. It means that the $$\ell _1$$ or $$\ell _2$$ regularizer is abandoned. The k-block diagonal regularizer is proposed to directly pursue the block diagonal matrix, so introducing this regularizer to the self-expression module will make the learned representation matrix conform with the block diagonal matrix better. Secondly, we add a new spectral clustering module to this convolutional subspace clustering network, in which the spectral clustering result is used to supervise the learning of the representation matrix. This subspace structured regularizer is introduced to the spectral clustering module, which further refines the learned representation matrix. Experimental results on three challenging datasets have demonstrated that the proposed deep learning based subspace clustering method achieves the better clustering effect over the state-of-the-arts.
A Path-based Fuzzy Approach to Color Image Segmentation

Color is one of the most used features for image segmentation. However, two uncertainty problems arise in this scope: the color feature is imprecise by nature, and the boundaries between regions in real images are usually blurred. In this paper, we propose a fuzzy color-based image segmentation, where not only the obtained regions are fuzzy, but also the color feature used in this process. This way, our approach takes into account the blurriness of boundaries between regions, as well as the uncertainty associated to the color feature. On the one hand, colors are modeled through fuzzy sets related to the linguistic terms used by humans, providing membership degrees associated to them. On the other hand, we propose a path-based fuzzy segmentation technique, where the relationship between two pixels is quantified by means of fuzzy connectivity. This way, given a seed point, the connectivity between this point and the rest of pixels in the image can be used to obtain the corresponding fuzzy region.
Learning Stochastic Equivalence based on Discrete Ricci Curvature

Role-based network embedding methods aim to preserve node-centric connectivity patterns, which are expressions of node roles, into low-dimensional vectors. However, almost all the existing methods are designed for capturing a relaxation of automorphic equivalence or regular equivalence. They may be good at structure identification but could show poorer performance on role identification. Because automorphic equivalence and regular equivalence strictly tie the role of a node to the identities of all its neighbors. To mitigate this problem, we construct a framework called Curvature-based Network Embedding with Stochastic Equivalence (CNESE) to embed stochastic equivalence. More specifically, we estimate the role distribution of nodes based on discrete Ricci curvature for its excellent ability to concisely representing local topology. We use a Variational Auto-Encoder to generate embeddings while a degree-guided regularizer and a contrastive learning regularizer are leveraged to improving both its robustness and discrimination ability. The effectiveness of our proposed CNESE is demonstrated by extensive experiments on real-world networks.
ANALISIS DEBIT PUNCAK PADA DAS AIR MANNA BAGIAN HILIR MENGGUNAKAN PENDEKATAN METODE HIDROGRAF SATUAN SINTETIS (HSS) SNYDER DAN HEC-RAS 5.0.7

Floods is a disaster which is one of the causes of failure of watershed managemen. High rainfall is also a factor in flooding. As a result the river cross section is not able to accommodate the flow of water and cause the river to overflow. This research was conducted to determine the amount of peak discharge Sub-DAS Air Manna Bagian Hilir and identify areas prone to flooding. This study uses a synthetic unit hydrograph (HSS) Snyder and HEC-RAS 5.0.7. Hydrological analysis results obtained from the peak discharge at Sub-DAS Rindu Hati using HSS Snyder for a return period of 2, 5, 10, 25, 50 and 100 yearsthat is 743,627 m3/sec, 839,491 m3/sec, 897,761 m3/sec, 962,599 m3/sec, 1008,717 m3/sec, and 1052,800 m3/sec with a peak time of 5,915 hours. After analysis using software HEC-RAS 5.0.7, DAS Air Manna no longer able to accommodate flow rates occur.
The Power of the Weisfeiler-Leman Algorithm for Machine Learning with Graphs

In recent years, algorithms and neural architectures based on the Weisfeiler-Leman algorithm, a well-known heuristic for the graph isomorphism problem, emerged as a powerful tool for (supervised) machine learning with graphs and relational data. Here, we give a comprehensive overview of the algorithm's use in a machine learning setting. We discuss the theoretical background, show how to use it for supervised graph- and node classification, discuss recent extensions, and its connection to neural architectures. Moreover, we give an overview of current applications and future directions to stimulate research.
A differentiable VMAF proxy as a loss function for video noise reduction

Traditional metrics for evaluating video quality do not completely capture the nuances of the Human Visual System (HVS), however they are simple to use for quantitatively optimizing parameters in enhancement or restoration. Modern Full-Reference Perceptual Visual Quality Metrics (PVQMs) such as the video multi-method assessment fusion (VMAF) function are more robust than traditional metrics in terms of the HVS, but they are generally complex and non-differentiable. This lack of differentiability means that they cannot be readily used in optimization scenarios for enhancement or restoration. In this paper we look at the formulation of a perceptually motivated restoration framework for video. We deploy this process in the context of denoising by training a spatio-temporal denoiser deep convultional neural network (DCNN). We design DCNNs as a differentiable proxy for both a spatial and temporal version of VMAF. These proxies are used as part of the proposed loss function in updating the weights of the spatio-temporal DCNNs. We use these proxies and traditional losses to propose a perceptually motivated loss function for video. Our results show that using the perceptual loss function as a fine tuning step yields a higher VMAF score and lower PSNR, when compared to the spatio-temporal network that is trained using the traditional mean squared error loss. Using the perceptual loss function for the entirety of training yields a lower VMAF and PSNR, but has visibly less noise in its output.
Promoting reading culture, the role of stakeholders and ICT for societal development

Reading is a culture and intellectual action mostly formed as a habit from childhood.This paper carefully examined the promotion of reading culture for better societal development and the role ICT and stakeholders played to bring back the culture of reading in our society most especially in schools.The paper provides a thorough review on stakeholders involvement in reading culture, they include, the government, schools, teachers, students and librarians.It identify roles ICTs played to transform reading culture in the knowledge age.The study concluded that reading culture builds morals and societal values in students and serves as a pathway to success in any economic driving society.
Image Denoising Using Multiwavelet Transform with Different Filters and Rules

&lt;p class="0abstract"&gt;Image denoising is a technique for removing unwanted signals called the noise, which coupling with the original signal when transmitting them; to remove the noise from the original signal, many denoising methods are used. In this paper, the Multiwavelet Transform (MWT) is used to denoise the corrupted image by Choosing the HH coefficient for processing based on two different filters Tri-State Median filter and Switching Median filter. With each filter, various rules are used, such as Normal Shrink, Sure Shrink, Visu Shrink, and Bivariate Shrink. The proposed algorithm is applied Salt&amp;amp; pepper noise with different levels for grayscale test images. The quality of the denoised image is evaluated by using Peak Signal to Noise Ratio (PSNR). Depend on the value of PSNR that explained in the result section; we conclude that the (Tri-State Median filter) is better than (Switching Median filter) in denoising image quality, according to the results of applying rules the result of the Shrinking rule for each filter shows that the best result using first the Bivariate Shrink.&lt;/p&gt;
A Rule Mining-based Advanced Persistent Threats Detection System

Advanced persistent threats (APT) are stealthy cyber-attacks that are aimed at stealing valuable information from target organizations and tend to extend in time. Blocking all APTs is impossible, security experts caution, hence the importance of research on early detection and damage limitation. Whole-system provenance-tracking and provenance trace mining are considered promising as they can help find causal relationships between activities and flag suspicious event sequences as they occur. We introduce an unsupervised method that exploits OS-independent features reflecting process activity to detect realistic APT-like attacks from provenance traces. Anomalous processes are ranked using both frequent and rare event associations learned from traces. Results are then presented as implications which, since interpretable, help leverage causality in explaining the detected anomalies. When evaluated on Transparent Computing program datasets (DARPA), our method outperformed competing approaches.
BKT-POMDP: Fast Action Selection for User Skill Modelling over Tasks with Multiple Skills

Creating an accurate model of a user's skills is necessary for intelligent tutoring systems. Without an accurate model, sample problems or tasks must be selected haphazardly by the tutor. Once an accurate model has been trained, the tutor can selectively focus on training essential or deficient skills. Prior work offers mechanisms for optimizing the training of a single skill or for multiple skills when individual tasks involve testing only a single skill at a time, but not for multiple skills when individual tasks can contain evidence for multiple skills. In this paper, we present a system that estimates user skill models for multiple skills by selecting tasks which maximize the information gain across the entire skill model. We compare our system's policy against several baselines and an optimal policy in both simulated and real tasks. Our system outperforms baselines and performs almost on par with the optimal policy.