Reviewerly/exHarmony huggingface dataset:
Stored Energy Forecasting of Small-Scale Photovoltaic-Pumped Hydro Storage System Based on Prediction of Solar Irradiance, Ambient Temperature, and Rainfall Using LSTM Method

This paper presents the implementation of forecasting of photovoltaic (PV) power and stored energy on small-scale pumped hydro storage (PHS) systems. The proposed forecasting approach considers the results of predicting solar irradiance, ambient temperature, and rainfall. Prediction of these three parameters was done using a one-month weather dataset that was split into 80% for training and 20% data for testing and validation. The prediction algorithm used is the bidirectional long short-term memory (LSTM) method. Furthermore, PV power and stored energy were calculated using power and energy models of the photovoltaic-pumped hydro storage system modified by considering the prediction results of solar irradiance, ambient temperature, and rainfall. The proposed forecasting approach was simulation-tested on a small-scale photovoltaic-pumped hydro storage system with a capacity of PV is 2 KW, as well as a capacity of the upper reservoir is 5 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> , The performance of the forecasting model is done by measuring the mean square error (MSE) and the mean absolute error (MAE) values. From the simulation test, the results obtained that the suggested approach produces PV power forecasting performance with an MSE of 8.942 watts and MAE of 0.044 watts, and excess power forecasting performance with an MSE of 81.203 watts and MAE of 0.075 watts. The stored energy forecasting performance for MSE and MAE parameters are <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$2.2\times 10^{-7}$</tex> Wh and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.35\times 10^{-6}$</tex> Wh, respectively.
Diversified Recommendation Using Graph Neural Networks Invariant to User-level Category Preference

Recommendation systems designed to address information overload increasingly need more diversity, significantly impacting user satisfaction. In this paper, we introduce two strategies to enhance diversity within these systems without drastically reducing accuracy. The first strategy involves using an invariant loss to category preference, effectively distinguishing between item-specific and category preferences while minimizing the latter’s influence. The second strategy proposes a graph neural network-based learning sample selection process, mitigating the undue influence of specific categories on certain users. Utilizing a dataset from the web service Taobao, we quantitatively demonstrate the effectiveness of these methods, offering a balanced approach to maintaining both accuracy and diversity in recommendation systems.
A Novel Collaborative Knowledge Sharing and Self-Learning Framework for Robotic Systems in Search and Rescue Operations

Robots may face difficulty detecting all the required objects with their current vision in a search and rescue (SAR) operation in a disaster scenario. This can be due to a partial view of some objects from the whole disaster scenario, or the training model of the robots may not be perfect enough to recognize all the available objects. This research established a method for SAR robots to learn without human interaction by combining their own and other robots' knowledge. It assumes that all robots have a simple machine learning model that can detect objects from their whole perspective. Yolov8, trained with a custom dataset, has been used as the basic machine learning model in this study to detect the object. Two robots and one object are used for the proof of concept, with one robot seeing the object fully and the other partially. Partial view images were auto-labeled, and the fundamental machine learning model of the robot with partial view was retrained using auto-labeled data until a predetermined condition was met. Then the other robot in the environment received the final retrained model with partial view information. The suggested system was tested in numerous scenarios and usually works well.
Improvement of Pose Transfer by Introducing 3D Pose Estimation

Pose Transfer is a technique for generating images in which the posture of a person in an image is converted to a specified pose without changing the person’s appearance. PISE, a conventional Pose Transfer technique, has an advantage to generate high quality images based on clothes parsing analysis and 2D pose input, but the generated images may have unnatural postures. One of the reasons for this is that the front-back relationship of the bone joints is unclear because the input pose is in 2D. In this paper, we propose a PISE based Pose Transfer method to improve the accuracy of generated images by introducing 3D poses as input for PISE. MeTRAbs, a method for estimating 3D poses from real images, is used in our method. The encoder structure of PISE is also modified because the dimension of the input feature is increased. Experimental results show that the results obtained with our method are better than those obtained with conventional methods.
EmoZen: A Robust Word Embedding for Implicit and Explicit Expressions of Emotion

Machine perception of emotions is integral to the development of human-centric Artificial Intelligence (AI) in sustainable industrial applications. Human expressions of emotions are not always direct. Word embeddings are mature techniques that can extract the semantics of such indirect expressions from text data. However, they are not primed to extract emotions. In this paper, we propose a novel approach that generates robust word embeddings for implicit and explicit expressions of emotion. This approach consists of two techniques, mask and rogue, we evaluate both techniques on two benchmark datasets for emotion classification. Our results confirm the effectiveness of the proposed approach in extracting emotions from diverse contexts. We have shared the emotion word embedding for public use.
Path Planning of AUV for Obstacle Avoidance with Improved Artificial Potential Field

An improved artificial potential field (IAPF) algorithm is proposed in this paper to promote the path planning characteristics of autonomous underwater vehicle (AUV). There are two critical problems restricting the application of obstacle avoidance path planning for the artificial potential field (APF) algorithm, one is the local minima and the other is the target unreachability problem. Therefore, the distance between the target and the AUV in attractive field is introduced into the repulsive potential field to modify the repulsive force so as to solve the problem of target unreachability. Moreover, an additional force is added to the resultant force to break the equilibrium when the local minima situation occurs. Ultimately, the simulation experiment validates that the improved artificial potential field method is an efficient way to realize the path planning for AUV in 3D space.
Super-Resolution Using Deep Learning Focusing on Local Reproducibility

Detailed information, such as textures, in images can be challenging to reproduce owing to their relatively small size. Therefore, we propose a super-resolution method that focuses on small regions to reproduce texture. The method consists of three components: spatial similarity loss, a discriminator, and multitask learning of super-resolution and segmentation. Spatial similarity loss reproduces the texture by bringing the correlation, not the individual pixel values, closer to the target. The discriminator assesses whether the generated image effectively reproduces the desired texture. The accuracy of texture reproduction was improved by applying these two methods to each small region, rather than applying them to the entire image.
A Dynamic Obstacle Avoidance Method for Mobile Robots Based on Stochastic Reachable Sets

This paper utilizes the Artificial Potential Field (APF) method as the obstacle avoidance strategy for robots, and integrate it with Stochastic Reachable (SR) sets to calculate the collision probability distribution between the robot and obstacles based on their relative distances. The collision estimation with SR Sets can effectively improve the success rate of avoiding dynamic obstacles while reaching the target point. The APF method generates a virtual repulsive force field around obstacles and an attractive force field around the target point. The robot is then subjected to a combined force of repulsion from obstacles and attraction towards the target point, allowing it to avoid obstacles and reach the target point. The collision probability distribution between the robot and obstacles can be estimated by using the Stochastic Reachable Sets based on their relative distances. The estimated probability helps to modify the repulsive force generated by APF. The newly proposed repulsive force with various integration methods for are tested in the simulation results, which reveal significant improvements in collision rate and time consumption.
A Controllable Recoloring Method for Novel Views Using Segment Anything Model

This paper presents a controllable view recoloring method based on Neural Radiance Fields. Recently, a new palette-based recoloring approach has been proposed to manipulate the entire novel view synthesis in 3D scenes. However, conventional methods performed well only when the color target object is significantly different from the environment. In this paper, we propose a method that can provide a controllable way to recolor a specific object only in the 3D scene by introducing the state-of-the-art Segment Anything Model. The experimental results demonstrate the adaptability of our method to novel view synthesis manipulation.
Comparison of YOLO architectures for face mask detection in images

Traditionally, image recognition has been tackled as a classification problem. You only look once (YOLO) real-time object detection models take a different approach, treating recognition as a regression problem. Models utilize very efficient neural networks, making YOLO models suitable for real-time observation. Recent events have brought to light the importance of medical face masks in preventing the spread of highly contagious pathogens, as well as the extent the individuals are willing to go to avoid wearing them. A robust model for detecting medical masks, capable of running in real-time could help improve the toolset available for preventing the spread of pathogens and reduce the impact contagious diseases could have in the future. This work compares the performance of several lightweight YOLOv8 models suitable for real-time application for mask detection. The advantages and shortcomings of various model architectures are assessed and their potential for use is explored.
Novel Regularization Method for Text-to-Image Generation Using Deep Learning

Generating images from natural language has recently become a promising research field in multimodal learning. The performance in this field has rapidly improved, and powerful tools such as the diffusion model have been released. Learning text-to-image generation models is considered difficult, and various issues arise. Even in the case of StackGAN, a representative model, room for improvement remains in terms of generation accuracy, and mode collapse can be observed. Incorporating the learning stabilization method ICR and endowing the discriminator with semantic understanding capability are believed to be effective solutions in addressing these two issues; however, applying ICR to conditional generation may result in generating content that ignores the input conditions. Therefore, in this study, we propose a regularization method called ICCR that is adapted to conditional regularization and generates various images along with the input text. StackGAN with ICCR incorporated yielded results that were 16% better than those of StackGAN in terms of the inception score using the CUB dataset and 9.7% better than those of StackGAN using the Oxford-102 dataset.
Risk Countermeasures Based on Five Whys Analysis Considering Offensive Security

As digital transformation progresses, security incidents are becoming more sophisticated and more numerous. In general, it is not clear who the attackers are or what their motives may be. In such a situation, the defender is at a keen disadvantage because the only thing to do is simply take countermeasures to protect itself. The concept of "offensive security" has emerged as a way to overcome the attacker's advantage. This approach takes on the attacker's thought processes and other factors and predicts what methods and technologies would be most advantageous to it. Developing countermeasures in advance based on the concept of offensive security thus enables risk countermeasures to be incorporated in advance. At present, specific countermeasures based on the concept of offensive security have yet to be sufficiently studied. We therefore developed a new risk countermeasure utilizing the concept of offensive security based on the attacker's point of view. Specifically, we identified common factors by extracting the "factors behind" 20 types of cyber-attacks—including targeted attacks, which are cyber-attacks that have a major impact on today's society—using a "five whys" analysis. We then utilized the results as a basis for creating risk countermeasures and clarified their effectiveness through qualitative evaluation.
Improving Visual Counterfactual Explanation Models for Image Classification via CLIP

Deep learning models have achieved remarkable success in the field of computer vision. However, improving the quality of visual counterfactual results continues to be a significant challenge. Visual counterfactual explanation, a task that highlights image regions that need alterations to reclassify them into a different category, allows for explanations that are more intuitively understandable to humans. In this paper, we propose a method that introduces Contrastive Language-overview Image Pretraining (CLIP) as an auxiliary model to obtain a better feature pair of the query class and the distractor class, leading to more accurate visual counterfactual explanations. Experimental results on CUB-200-2011 Dataset demonstrate that our method yields a 3% improvement in Near-KP and a 0.1 increase in the "number of edits" metric when generating explanations, outperforming existing state-of-the-art methods in the image classification task.
Study on the Appropriate Triggers for Initiating Micro-learning

Learning foreign languages through mobile applications has been a prominent trend for several decades, with its popularity further intensified by the impact of the Covid-19 pandemic incorporating micro-learning, aiming to enhance learning motivation and cultivate new learning habits. However, as users increasingly engage with various applications, the influx of notifications can become burdensome if users receive an excessive number of them, hindering users from accessing critical information. Inspired by the results of a preliminary survey, we propose a novel approach to push notifications by delivering vocabulary lessons whenever users open or use social media applications. This streamlined notification system ensures robust delivery without causing annoyance. Additionally, the application embraces micro-learning principles by providing users with concise, time-efficient lessons conducive to effective vocabulary practice within a short duration. This research contributes to the fields of language acquisition and technology-mediated learning by introducing an innovative approach that harnesses users’ existing digital behaviors to enhance their vocabulary. However, the research is currently in the initial stages of ideation and implementation. The presentation of results and assessments is yet to be included.
Noise Search Method of Controlling Image Transformation in Diffusion Model

The diffusion model possesses the potential to enhance individuals’ expressive capabilities. One of its functions involves image transformation, wherein an image can be converted to match specific conditions by conditioning it with text. However, it is common for other parts of the image to change alongside the text content. This cause underlying this behavior is that the diffusion model operates by reconstructing lost information through the introduction and subsequent removal of noise. In other words, the noise added during transformation is determined probabilistically, thereby rendering the transformation result noise-dependent. Our objective is to accomplish an image transformation that exclusively alters the text content while leaving other aspects unchanged. To achieve this, capturing the universal tendencies inherent in textual commands is necessary. Our approach employs a two-step process for acquiring the appropriate noise. Initially, the same image was reconstructed using a series of Gaussian noises. Subsequently, the average of these reconstructed images was computed to identify universal trends. The resultant image tends to be blurry, necessitating a gradient descent-based search for noise in order to regenerate the image. The outcomes indicate a discernible influence on the source image’s visual concepts associated with the text.
Experimental Investigations of the Watermarking System for Detecting of Cloning Attacks

In a recent paper, the authors proposed algorithms for embedding and extracting of digital watermarks and theoretically investigated the effectiveness of the attack detection method.It is assumed that some certificate (paper or plastic) is used to guarantee the high quality of the item, provided that the following certificate can be copied, printed and applied to other but similar items (usually of lower quality) in order to falsify prices for real things and, as a result, get some benefit from its sale.In this article, we recall the results of the previous paper and present the results of experiments obtained using a real scanner and printer.Comparison of experiment and theory shows their acceptable agreement under some additional conditions. This fact confirms that the proposed method can be effectively used in practice.
A Fast and Smooth Planning Framework for Autonomous Mobile Robot in Complex Environments

This technique note investigates the problem of path and motion planning for autonomous mobile robots in complex environments. A hybrid algorithm composed of a smoother A-star <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(\mathrm{s}-\mathrm{A}^{*})$</tex> algorithm for global path planning and a faster & more accurate Dynamic Window Approach (fma-DWA) is proposed. Firstly, three technical tricks are designed based on the standard <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{A}^{*}$</tex> algorithm to ensure the smoothness and safety of the generated paths, which are the improvement of evaluation function, the redundant node deletion strategy and the B-splinebased path smoothing respectively. Then regarding the motion planning phase after path generation, we further advance the traditional DWA to choose a faster and more accurate motion command of the mobile robot. To this end, a composite evaluation function of the DWA is proposed by further considering the curvature of the generated path and the distance between the mobile robot and the target. Besides, the weight of the robot's velocity in the evaluation function is adjusted according to the closest distance to obstacles instead of keeping fixed in the traditional DWA method. Finally, extensive simulation results show the efficiency and accuracy of the proposed methods.
Design of a Ternary Fredkin Reversible Gate

Several realizations of a reversible ternary Fredkin gate are developed based on Muthukrishnan-Stroud controlled-controlled Sum modulo 3 gates and controlled scaling gates. It is shown that under positive polarity, the proposed Fredkin gates have the expected controlled swap behaviour and that under mixed polarity, other behaviours are possible. Most introduced realizations have a lower realization complexity than realizations found in the literature.
CyberAttacks and Artificial Intelligence: A Systematic Mapping

With the growing implementation of artificial intelligence in products that are used in everyday life arises the threat of exploitation of vulnerabilities in them, which could lead to, sometimes, lasting damage to the system and the user. This article examines the possible connections, threats and implementations for artificial intelligence in cybersecurity. It represents a literature review of current trends of AI applications and problems in the field of cybersecurity for offensive and defensive use, with a focus on the technological as well as the ethical perspective.
Automatic Code Commenting in Integrated Development Environments Based on Indirect Interaction with Chatbots

Efficient code commenting is critical to improving code readability, maintainability, and collaboration.This paper introduces automated code commenting within an integrated development environment IDE using chatbots.The introduced system, implemented in Python with Selenium, automates the comments generation process, allowing coders to focus on code logic.Results obtained demonstrates successful interactions with chatbots, comment retrieval and handling delay.Challenges identified include instruction selection and extended conversations, offering opportunities for improvement.Future prospects include reusable libraries, user-friendly interfaces, and streamlined code-commenting.
Reinforcement Learning Using for Planning With Compromises of a Rescue Operation

This article explores the creation of a library of plans, including the possibilities offered by the use of available specialized means and equipment in rescue operations. As a hazardous environment, a 3D model of an electrical substation is considered, and intelligent agent behavior is modeled with application in a serious game. The proposed modification of the Reinforcement Q-learning algorithm includes the introduction of matrices for the intensity of the characteristics of the considered disaster; safety thresholds; and new rules for choosing the next possible position in the evacuation path.
Generalization of a Genetic Algorithm for Optimal Coding of the States of a Finite State Machine

In this paper we describe an approach to finding the optimal encoding of a state machine in memory. An overview of methods used for optimal encoding of a finite state machine in logic circuits is discussed. A probabilistic approach toward finding optimal encoding using Hamming codes and genetic algorithms is generalized for state machines with an arbitrary number of states. Shortcomings of the generalized algorithms are observed and possible improvements are proposed.
Economic Typology of Cybercrimes in Bulgaria

The purpose of the present study is to identify the specifics of economic typology of cybercrimes in Bulgaria. This identification will allow some conclusions, concerning the effectiveness of cybercrimes counteractions in EU member-states from Eastern Europe to be made. Some of these countries are Eurozone member-states or they are applying for membership in it. The study summarizes also statistical data about cybercrimes, which is not publicly available in English.
An Algorithm to Predict Hepatitis Diagnosis

We propose an effective approach and the corresponding algorithm for modeling the dataset with multiclass label of observations. We apply our algorithm to solve a classification task using random forest, decision tree, and support vector machines models. We obtain higher values of evaluated measures compared to other authors on the Egyptian Hepatitis dataset and Contraceptive dataset.