Reviewerly/exHarmony huggingface dataset:
Piksellerden Paragraflara: Inception v3 ve Dikkat Mekanizmalarını Kullanarak Gelişmiş Görüntüden Metin Üretimi Keşfetme

Processing visual data and converting it into text plays a crucial role in fields like information retrieval and data analysis in the digital world. At this juncture, the "image-to-text" transformation, which bridges the gap between visual and textual data, has garnered significant interest from researchers and industry experts. This article presents a study on generating text from images. The study aims to measure the contribution of adding an attention mechanism to the encoder-decoder-based Inception v3 deep learning architecture for image-to-text generation. In the model, the Inception v3 model is trained on the Flickr8k dataset to extract image features. The encoder-decoder structure with an attention mechanism is employed for next-word prediction, and the model is trained on the train images of the Flickr8k dataset for performance evaluation. Experimental results demonstrate the model's satisfactory ability to accurately perceive objects in images.
A Zeroing Neurodynamic Approach for Solving Time-Varying Optimization Problem with Interval-Valued Cost Function

Most of the research on uncertainty and imprecision that is widespread in the real world can be abstracted into interval-valued optimization problems whose cost function or constraint function is a closed interval. In this paper, a zeroing neurodynamic (ZND) approach is designed for solving a class of constrained time-varying optimization problems with closed interval-valued cost functions. By virtue of the projection operator and the optimality conditions of the interval-valued optimization problem, the time-varying interval-valued optimization problem is transformed into a nonlinear equation system to be solved. For the reformulated nonlinear equation system, the proposed ZND solver has the ability to track its theoretical solution within pre-defined time. This significantly improves the convergence rate of the ZND solver, making the ZND solver more promising for complex engineering tasks. To the best of our knowledge, it is the first time applying the ZND solver to solve the time-varying interval value optimization problem. Furthermore, the introduction of the projection operator greatly reduces the computational complexity of this ZND solver, saving costs for its hardware implementation. Finally, a numerical example is given to demonstrate the effectiveness of the proposed approach.
An Improved Path-planning Method for Multi-agent by Velocity Obstacle Dynamic Window Approach

Dynamic window approach (DWA) is a frequently-used local path planning algorithm for avoiding dynamic obstacles in multi-agent path planning. Velocity obstacle (VO) is a local path planning algorithm that uses the relative velocity of robots to determine whether there will be collisions between robots in the future for collision avoidance. This paper proposes an improved method combining the velocity obstacle and dynamic window approach. The proposed algorithm can solve the problem of path redundancy caused by the influence of robots on each other in the local path planning of multiple robots, solve the problem that the robot can not reach the target point due to the influence of other robots and limitations of robot's kinematics model, reduce the phenomenon of dead cycle around the target point. Finally, the simulation experiment verifies the feasibility and effectiveness of the algorithm proposed in this paper. The simulation result analysis demonstrates the improved algorithm's path planning effect and the reduced running time.
Segmentation-Based Adversarial Denoising Auto-Encoder for Anomaly Detection in Multivariate Time Series Data

Abnormal detection is crucial for maintaining critical processes' stability, security, and efficiency in various domains. Traditional methods for abnormal detection in time series data have limitations in capturing temporal dependencies and providing interpretable anomaly scores. The authors propose a Segmentation-based Adversarial Denoising Auto-encoder (SADA) model to address these challenges. The SADA model uses two individual auto-encoders to establish an adversarial training process and incorporates segmentation techniques and inter-correlation encoding units to capture temporal dependencies and inter-correlations in multivariate time series data. Experimental results on multiple datasets demonstrate the effectiveness of the SADA model in detecting anomalies and achieving high precision and recall rates. The proposed model shows promise in improving the adaptability of anomaly detection algorithms to different types of data.
Multi-Domain Dataset for Moroccan Arabic Dialect Sentiment Analysis in Social Networks

Social media generates a massive amount of textual data every day, users post comments, tweets, messages, reviews, and ratings, creating very large and diverse data. The comments are often very authentic, as users express their opinions freely and without influence. This can help capture people's true feelings about a topic in real-time. Currently, the majority of research and projects primarily focus on Indo-European languages, particularly English, leaving a significant portion of dialect-speaking communities underserved and overlooked. In this work, we focus our study on Moroccan dialect sentiment analysis in different social networks; we have collected and manually annotated a rich dataset from different social media (Facebook, Twitter, YouTube, Instagram, and websites. To the best of our knowledge, there is currently no publicly accessible sentiment analysis dataset that encompasses all social networks and specifically caters to the sentiment analysis task. Furthermore, the dataset we have collected stands out as the largest dataset focused on sentiment analysis specifically for Morocco. It is characterized by its size (70 332), quality, variety (covering a lot of domains), novelty (it contains all topics and trends from 2020 to now) Keywords─ Moroccan Arabic dialect, sentiment analysis, dataset, social media.
Comparing Different Simulated Annealing Cooling Variants for Fuzzy Logic Systems Modeling

Simulated Annealing (SA) has been used successfully with a broad spectrum of optimization problems, including the optimization of Fuzzy Logic Systems (FLS). A suitable formation of SA, however, is dependent on the selection of an appropriate cooling process and initial temperature. Previous attempts to optimize FLS using SA appear to have been based only on static cooling schedules, with little insight into the cooling schedule rule. The problem of determining the best cooling schedule should aid in the development of a suitable solution for balancing speed and performance when training fuzzy logic systems. In this study, we examined the application of three static cooling schedule variants and two variants of adaptive cooling schedules to determine how far the choice of cooling schedules can affect the final modeling accuracy of FLSs. Two widely-used benchmark problems in the field of fuzzy logic served as the basis for the study's application of these schedules. The results show that the choice of adaptive cooling schedules provides extra improvement to modeling accuracy.
A Lightweight Network for Human Pose Estimation Based on ECA Attention Mechanism

This paper presents a novel approach to address the problem of increasing the number of parameters in existing human posture estimation network models while trying to improve prediction accuracy. We propose a human posture estimation optimization network model, called BDENet, which is based on the high-resolution detection network (HRNet). BDENet incorporates a bottleneck structure and depth-separable convolution to reduce the number of parameters. Additionally, it introduces an efficient channel attention (ECA) lightweight attention mechanism to enhance accuracy. We evaluate the proposed model using the MSCOCO dataset and compare it with HRNet. The experimental results demonstrate that BDENet achieves a 41.4% reduction in the number of parameters compared to HRNet, while achieving a 0.6% increase in accuracy. These findings confirm that the network model proposed in this paper can effectively improve accuracy while reducing the number of parameters.
A novel PSO-ViT approach for facial emotion recognition

Facial Emotion Recognition is gaining interest from researchers in various fields due to its numerous applications. The Vision Transformer (ViT) outperforms convolutional neural network (CNN)-based systems significantly in terms of performance when compared to contemporary image categorisation algorithms. In this context, the primary objective of this paper is to present an innovative and efficient deep learning model for facial emotion recognition. This hybrid intelligent model combines the Vision Transformer with Particle Swarm Optimization (PSO). The proposed model accurately identified the majority of emotions in images, achieving an accuracy of 95.93% for the Fer2013plus dataset and 100% for the CK+ dataset, as demonstrated through experiments conducted on both datasets. According to the results, the proposed facial expression recognition approach has either outperformed or closely matched the performance of state-of-the-art methods.
Development of health-environmental competences in teaching chemistry through a system of learning tasks

The choice of the teaching tasks as the main didactic tool for developing of health-ecological competences in chemistry education is reasoned. Criteria for the classification of chemistry tasks with health-ecological orientation, which can be used in the composition and selection of such tasks, are derived. On the basis of the proposed criteria, a classification of chemistry teaching tasks with health-ecological content is created. It is the basis of the developed system of chemistry tasks in the learning of specific educational content in view of forming health-ecological competencies in students. The appropriateness of the composed teaching tasks with health-ecological content is proved by pedagogical experiment. Its results show that the designed didactic tools succeed in achieving the goals of forming health-ecological knowledge, skills and value attitudes in adolescents.
PENINGKATAN SKILL EDITING FOTO &amp; VIDEO MELALUI PELATIHAN PENGENALAN APLIKASI EDITING PADA SISWA KELAS X

The increasing demand from companies and the need for job vacancies in the field of photo and video editing in the media today has made it urgent for students majoring in broadcasting to hone their editing skills before jumping in and getting involved in the media industry. Based on observations, the limited human resources available in schools often prevent students from realizing the importance of these skills, so in order to accelerate knowledge, cooperation, and participation of students is needed to improve photo and video editing skills beyond those provided by the school. This community service aims to improve photo and video editing skills through training in the introduction of Adobe Photoshop and Adobe Premiere editing applications, for class X students majoring in Broadcasting at SMKN 1 Demak. The method used in this activity is Participatory Action Research (PAR), with stages, namely, observation, planning, action, and evaluation. The training includes an introduction to several photo and video editing applications, Adobe Photoshop and Adobe Premiere, as well as effective editing techniques. The results of this activity obtained a percentage increase in knowledge about photo and video editing skills of 82.7%. High student enthusiasm also shows an increase in theoretical understanding of how to edit photos and videos. This training has helped students apply photo and video editing skills in terms of promotions, digital marketing, company profiles, and personal branding of a product. Furthermore, this skill also opens up opportunities for students to be able to create businesses independently in the field of creating strategies and creative content.
Creators fight back against AI

Tech giants are gearing up for a series of potentially bitter legal battles over claims they used copyrighted material in training data, says Alex Wilkins
The Interpretation of Husserl’s Time-Consciousness in the Reconstruction of the Concept of Anthropic Time. Part Two

The purpose of the article is to comprehend the Husserlian model of constituting temporal modes through the ability of intentional "retentional-protentional" consciousness, as well as to clarify the possibility of interpreting its positions in the reconstruction of the concept of anthropic time. Theoretical basis. The theoretical framework of the research includes: 1) the interpretation of the phenomenological reflection of "time-consciousness" by E. Husserl in the context of solving the problem of phased-differentiation of this form of temporality; 2) the concept of anthropic time (V. Khanzhy). Originality. For the first time in the research literature, the possibilities of applying the ideas of Husserl to the reconstruction of the concept of anthropic time are considered through the interpretation of the phenomenological solution to the problem of temporality, proposed and specified in Husserl’s "time-consciousness" concept. Conclusions. According to Husserl, the structure of human time-consciousness is instantiated in three spheres of passivity: prerefleсtive cogito, embodiment, and intersubjectivity. Within the framework of the problem of phase differentiation of phenomenological time, an analysis of the potencies of consciousness in constituting the phases of time-consciousness, namely protentional and retentional potencies, has been proposed. In the context of the reconstruction of the concept of anthropic time, several aspects of Husserl’s model of time-consciousness have been interpreted, including the paradoxical reconciliation of two quasi-incompatible ideas: the idea of the vagueness of the boundaries between temporal modes and the thesis of the formal capacity of preserving temporal units within their respective temporal phases. The property of multilevel complexity in the system of human temporality accounts for the diversity in the relationship of unique temporal units based on formal and content-related criteria ('temporal matryoshka').
Proposal for a gamified educational mobile application to improve the learning process

Currently, the education system continues to adopt a traditional teaching approach, which has proven to be ineffective in providing a quality learning process to students, given the persistent poor academic performance observed year after year. Likewise, the disconnect between current educational methodology and changing student expectations has led to a decline in interest in learning subjects. The objective of this research is to present the results, a review of the literature and the development of a proposal for an educational mobile application. The methodology used was the review of the literature and the construction of the mobile application design using the Octalysis framework. As results, key elements were identified such as frameworks, Octalysis being the most used, Android was identified as operating systems, and C# was identified as programming languages. In the gamification elements, scores, challenges, rankings, rewards and levels are proposed as essential components to improve student motivation. In addition, for the mobile application design proposal the Octalysis framework will be used and a Three-Tier model is considered in the architecture.
Digital Competence of Mathematics teachers based on DigCompEdu in Regular Basic Education in the Lambayeque region

The current study aimed to assess the digital competence level among 41 mathematics teachers from the regular basic education sector in the public domain of the Lambayeque region. Utilizing the European Framework for the Digital Competence of Educators (DigCompEdu), key areas such as Professional Engagement, Teaching and Learning, and Open Education were scrutinized. Findings indicated a competent level in Professional Engagement, yet they revealed that teachers are still at initial stages regarding the integration of digital tools in teaching and the use of open educational resources. Although variables like gender, age, and academic background showed no significant differences, it was noted that both the technological infrastructure of institutions and support for professional development positively impact digital competences. This underlines the importance of reinforcing educational policies related to training and digital resources to promote a substantial improvement in mathematics teaching, as a key factor for the development of students’ critical and computational thinking.
Fuzzy Sandwich Control of Some Nonlinear Systems with a Number of Stochastically Occurring Impulses

In this paper, rather than have a fixed constant matrix in the sandwich control of the nonlinear system, a fuzzy control matrix was used. In addition, simple inequalities that do not require additional computational time and skills in computer programming were used rather than the usual LMI. The result of this method is that the period T required in the control process is shorter and the control time is also reached much faster or reduced compared to the existing sandwich methods that used constant matrices. In other words, the control time of this method is shorter than the control time achieved when using constant matrices. Besides, this method also can be used to control more nonlinear chaotic systems such as Lorenz's, Rossler's and Chen's systems. Numerical simulations were used to demonstrate the effectiveness of this method.
Imperceptible Image Watermark Embedding using Multi-resolution Wavelet Decomposition and Deep Convolutional Neural Networks

Due to the ease of access to and unauthorized copying of multimedia data, especially images, digital watermarking techniques have become a necessity. In this research, we propose a method that combines the power of deep learning, and the multiresolution property of wavelet transforms for the purpose of practical and imperceptible digital image watermarking. The proposed method utilizes a pre-trained CNN model to fuse the watermark image into the approximation wavelet sub-band of the host image. The effectiveness of the proposed method is demonstrated through several experiments at different levels of wavelet decomposition. In addition, the method was tested under common third-party attacks such as compression and filtering. When compared with some existing techniques, experimental results showed the superior performance of the proposed method in terms of imperceptibility and hiding capacity.
Kalman Filter Based Improved Beluga Whale Optimization Algorithm

Beluga Whale Optimization (BWO) algorithm is a novel heuristic approach that has shown promising performance in optimization tasks. However, it still exhibits certain limitations in terms of convergence. To address this, we propose an improved version called Kalman Filter-based Beluga Whale Optimization algorithm (KBWO). KBWO algorithm utilizes a logistic growth model to update the balance factor. It facilitates a more balanced distribution of whales engaged in both global and local searches. Moreover, by incorporating the Kalman Filter during the process of state updating, KBWO achieves a more comprehensive exploration of the searching space during the local exploitation stage. Comparative test results demonstrate that the KBWO algorithm outperforms the original BWO algorithm in terms of stability and convergence, enabling it to approach the global optima more effectively. This provides a more reliable solution for applying the Beluga Whale Optimization algorithm to complex optimization problems.
Semi-Supervised Classifier Ensemble with High-Quality Subspace for High-Dimensional Data

Due to the noisy and high-dimensional character of the data combined with a small sample size, semi-supervised classification of high-dimensional data with few labeled samples is a significant problem in machine learning and data mining. To address these challenges, we first propose a novel feature evaluation function that combines the advantages of the Laplacian score and intra-class and inter-class smoothness score to select high-quality features. Subsequently, we combine the selected high-quality features with some randomly selected features to generate a set of high-quality subspaces (HQS). Building upon the concept of HQS, we further propose an ensemble framework (E-HQS) specifically designed for semi-supervised classification tasks on high-dimensional data. We thoroughly validate the effectiveness of our proposed method on 8 real high-dimensional datasets.
Electronic Tourism Using Decision Support Systems to Optimize the Trips

Pagar Alam is a tourist destination city in the province of South Sumatra, which has many very diverse tourist destinations. The problem is that there is still a lack of information about tourism that tourists can access. This research aimed to build electronic tourism to make it easier for tourists to get the best information and recommendations about tourism in the city of Pagar Alam, which can be accessed anytime and anywhere, as well as improve tourist experience in planning their tourist trips because this electronic tourism platform includes decision making support system, which helps tourists manage their tours according to their needs and abilities. The research method used was analysis by collecting data by observing tourist attractions, calculating predictions using the simple additive weighting method, and from the results of testing with several alternatives, it can be concluded that electronic tourism meets the criteria chosen by tourists after being carried out. The calculation produced the highest preference value for tourist attractions, namely Tugu Rimau, with a value of 13.25. The highest preference value for hotels is Villa Gunung Gare Pagar Alam, with a score of 8.91, and the highest preference score for eating places is Warung Ridwan, with a score of 13.25. The next stage was system design using data flow diagrams, and the final stage was implementation by building electronic tourism using the CodeIgniter framework.
Detection of Crucial Power Side Channel Data Leakage in Neural Networks

Neural network (NN) accelerators are now extensively utilized in a range of applications that need a high degree of security, such as driverless cars, NLP, and image recognition. Due to privacy issues and the high cost, hardware implementations contained within NN Propagators were often not accessible for general populace. Additionally with power and time data, accelerators also disclose critical data by electro-magnetic (EM) sided channels. Within this study, we demonstrate a side-channel information-based attack that can successfully steal models from large-scale NN accelerators deployed on real-world hardware. The use of these accelerators is widespread. The proposed method of attack consists of two distinct phases: 1) Using EM side-channel data to estimate networking's underlying architecture; 2) Using margin-dependent, attackers learning actively in estimating parameters, notably weights. Deducing the underlying network structure from EM sidechannel data. Inferring the underlying network structure from EM sidechannel data. Experimental findings demonstrate that the disclosed attack technique can be used to precisely retrieve the large-scale NN via the use of EM side-channel information leaking. Overall, our attack shows how critical it is to conceal electromagnetic (EM) traces for massive NN accelerators in practical settings.