Reviewerly/exHarmony huggingface dataset:
Prototype Structure Design Process Using Information Flow Diagram (IFD) for Mobile Application Learning System

In the current digital technology age in education, using tools as learning aids to develop software for teaching is essential. There is a variety of software design tools, such as Data Flow Diagram (DFD) and Unified Modeling Language (UML), used to depict interactions between systems. However, these tools can be complex and burdensome for system developers, users, and investors who may not fully understand the system’s intricacies. Researchers have proposed a prototype software development framework using an Information Flow Diagram (IFD). It is approach simplifies the software design process, reduces the diversity of diagram designs, and yields genuine efficiency. Some educational institutions have already incorporated this new design tool for using Information Flow Diagram (IFD). There are curriculums, especially in software and engineering courses to teach students how to design mobile-based online plant tissue culture distribution learning system media easily, conveniently, and with fewer steps. This helps students grasp software design concepts more readily and enhances productivity for system developers, users, and investors, meeting their requirements clearly. Researchers aim to further develop software in the education sector, particularly in web development for mobile platforms, within the curriculum of the digital technology for learning innovation discipline in the future.
Optimal AI Model for Industrial AR Application based on Object Detection

In the modern industries and manufacturing techniques, quick and precise maintenance is a vital concept. Several approaches fulfill these requirements, including the Augmented Reality (AR) technology. The AR technology, combined with real-time object detection techniques, offers a possible solution to improve and disrupt the maintenance techniques in the near future. For finding the most suitable AI model for this purpose, this study compared the pre-trained YOLOv5, Resnet50, and SSD MobileNet models in detecting PLCnext components. Using 960 images for training and testing purpose, the models were trained and tested, highlighting their real-time capabilities. The results show that the pre-trained YOLOv5 model is the best to enhance interactive monitoring, minimize downtime, and boost efficiency in industrial settings and suitable for AR application development in automation 4.0 after being trained with required images.
Smoke Testing of UML Activity Diagrams: An Approach for Ensuring System Reliability

In this paper, we discussed the concept of smoke testing on Unified Modelling Language (UML) activity diagrams. We presented a step-by-step procedure to conduct smoke testing on an activity diagram, which involves identifying critical paths, generating test cases, executing the tests, recording results, and documenting any issues or defects found during the testing. We also highlighted the importance of smoke testing in the healthcare domain, where systems must be reliable and accurate for patient safety. We applied the procedure to a hospital management system activity diagram and generated nine test cases, covering the critical paths of the diagram. We then executed the tests and documented any issues or defects found, emphasizing the need to verify that test outcomes meet the expected results. Finally, we discussed the potential for generalizing the approach to other UML diagrams and domains, highlighting the benefits of smoke testing as an efficient and effective way to identify issues early in the software development process. Overall, our discussions demonstrate the importance of thorough testing in the healthcare domain and provide a practical guide for conducting smoke testing on UML activity diagrams.
A Deep CNN-LSTM Model Tuned by PPSO for Spatiotemporal Solar GHI Forecasting

Accurately forecasting solar global horizontal irradiance (GHI) is a challenging and crucial task for both photovoltaic power plants and grid operators. This paper proposes a novel deep learning model based on a convolutional neural network (CNN) and long short-term memory (LSTM) for 24-hour-ahead solar GHI forecasting, utilizing spatiotemporal information. The proposed model can capture both the spatial and temporal dependencies of solar GHI data from multiple locations and time windows for input data. To optimize the parameters/hyperparameters of the model, such as the number of layers, filters, and neurons, a phasor particle swarm optimization (PPSO) algorithm is employed, which is a variant of the particle swarm optimization (PSO) algorithm and utilizes complex numbers to represent the particles and their velocities. The PPSO algorithm is efficient in exploring the search space and avoiding local optima. The proposed model is evaluated using data from different countries and compared with several baseline methods. The results show that the proposed model outperforms the baseline methods in terms of accuracy and robustness.
An Accumulative Method to Time Series Prediction for Vehicle Communication

Accurately modeling and predicting the wireless channel is crucial for improving safety in various applications, particularly in vehicles, and Machine Learning (ML) plays a significant role in achieving this. In certain situations, it is necessary to establish long-distance communication for V2V communication without increasing congestion. Although beam forming is a technique for extending the range, it is critical to choose the optimal beam, and ML can assist in predicting which beam to select and estimating the power level. In this research, an ensemble learning method like Random Forest and an ML method like LSTM are utilized to construct an accumulative time series model. Each ML concept offers distinct advantages when used alone, but our accumulative approach yields superior prediction accuracy. Our accumulative approach is designed for multi-step prediction, which is more predictive than following in the case of one-step prediction. We evaluate the performance of our accumulative approach against that of the standalone algorithms.
A Lightweight Blockchain Scheme for the Internet of Things

Internet of Things (IoT) systems are one of fields, where features and advantages of blockchain technologies are demanded. The possibilities of using blockchain in an IoT are complicated by the fact that an IoT system usually consists of low-resourced devices such as sensors, whereas blockchains are typically based on algorithms with high requirements to the resources and a high level of the resource consumption. The solution of this problem is to use lightweight blockchains. In this paper, we propose a lightweight blockchain scheme to use in IoT systems that is built upon the use of hash-based message authentication codes to provide the integrity and authenticity of circulated data and on a simplistic consensus protocol. The scheme can be used in industrial IoT systems working as a part of critical industry, transport or social objects. Another result of this paper is a method to provide a secure transfer of an object under tracking by IoT sensors from one edge computer to another one; this method can eliminate the main limitation of the proposed scheme.
Forgery Localization in Images Using Deep Learning

Photography has become incredibly popular as a result of camera systems being widely accessible. Photos are essential to our everyday lives because they are so full of information. Consequently, there is a frequent need to enhance photos to extract more meaningful data. However, the availability of various technologies for image enhancement has also led to the proliferation of photo manipulation, contributing to the dis-semination of misinformation. The emergence of image forgeries has become a pressing concern. While conventional frameworks have been established throughout time to detect picture forgeries, the localization of image forgeries has been greatly impacted by the recent development of convolutional neural networks (CNNs), Among the challenging types of image forgeries is the splicing of images, where a segment of one image is inserted into other images. Existing literature on image forgery localization techniques reveals certain limitations, emphasizing the necessity to devise effective methods for accurately pinpointing forgeries in manipulated images. In this context, we propose a robust deep learning-based approach that employs image patches to detect forgery in an image. To determine if a pixel is part of a tampered zone, a deep neural network is trained with an extracted patch around each pixel in the picture. The approach that is being provided shows effectiveness in both identifying the altered region's border pixels and separating them from the remainder of the image. Rigorous evaluations of the technique have been conducted, and the experimental results, particularly on the CASIA 2.0 database, are highly encouraging.
Investigating the Generative-AI Evaluation Methods and Correlation with Fashion Designers

AI drawing tools set off a revolutionary trend in the field of image creation. However, there is still no clear and appropriate evaluation standard to rank AI graphics in fashion. In addition, most fashion industry insiders never used AI tools before. This research aims to evaluate whether AI-generated images could satisfy fashion designers’ needs by comparing automatic and human evaluations. Therefore, AI-generated fashion datasets with 25 images using Leonardo AI were created, and a survey was conducted to check how the experts ranked the AI images. Automatic evaluation methods, such as FID and Clip scores of each picture were measured to observe the correlation with human evaluation. The result showed the correlation coefficient between expert scores to FID scores is only 0.30, while the correlation coefficient between expert scores to Clip scores is 0.05. In other words, human evaluation and automatic evaluation are not so related and both have insufficiencies. Automatic evaluation is unable to provide judgments on fashion and aesthetics. The evaluations of different experts vary greatly due to the subjective consciousness and cannot provide fair and objective standards. Thus, it is necessary to create a new evaluation method that can evaluate the generated image in both fashion and AI aspects.
Massive Face Recognition Via CNN Based on Big Data Framework for Surveillance

Nowadays Tracking and searching criminals via massive surveillance cameras has become a popular method for security departments. But before 2000 Searching criminals wasn’t an economical thing for police or other security departments, because searching specific criminals in massive surveillance videos needs tremendous computing which generally requires a high-performance single computer, however, a high-performance server is extremely expensive. But nowadays, distributed computing makes it economical. We just need multi normal computers, then connect them parallelly. We generally call it cloud computing.
Aerial Robotics in Urban Environments: Optimized Path Planning and SITL Assessments

This paper focuses on guiding aerial robots through city environments. The goal is to create an optimal and safe flight path, avoiding obstacles in urban landscapes with tall buildings. We combine the A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm and the Medial Axis algorithm to develop a well-balanced trajectory that ensures both safety and efficiency. To better understand narrow passages in cities, we analyze and preprocess two-dimensional maps. The A <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">*</sup> algorithm forms the basis, while the Medial Axis algorithm identifies secure routes through tight spaces. Additionally, we explore new methods to seamlessly integrate multiple path planning algorithms, offering a comprehensive solution for navigating urban airspace. Our proposed method is rigorously evaluated using simulation tools, mimicking real-world scenarios. The results consistently demonstrate superior safety and efficiency compared to alternative algorithms. Even when subjected to atmospheric disturbance tests, our approach maintains adherence to critical operational constraints, ensuring enhanced safety and shorter flight paths. This study significantly contributes to advancing aerial robotics in urban areas, providing a promising way to enhance safety and efficiency in complex environments.
Dynamics of Multi-agent Holonomic Mobile Robots System Transporting a Rigid Object

This paper presents the cooperative transport of objects utilizing multi-robot systems. A novel approach to address the intricate issue of object manipulation has been introduced. In this approach, wheeled holonomic robots are employed, and in order to make them move around the object, prismatic and revolute joints are employed. This can model the impact of the forces of other robots. Moreover, connecting the points of the robots to the objects is of great importance. Therefore, several heuristic optimization algorithms have been applied over the Jacobian matrix to obtain the optimal points. Finally, the effectiveness of the proposed solution is evaluated via some simulation results.
Feature-Based Linguistic Text Sentiment Analysis Using Stacked Meta-Ensemble Learning

Sentiment analysis, or opinion mining, is a crucial aspect of Natural Language Processing (NLP) applications. Transformer-based models are commonly used, but their accuracy is limited in languages with limited linguistic resources. Recent studies have combined ensemble learning approaches with deep learning techniques to improve sentiment analysis. This research article focuses on sentiment analysis in the Indian linguistic context. This paperproposes an ensemble architecture for linguistic feature- based sentiment analysis, including a meta-learner, which improves accuracy and recall rates by combining Bert modelsand integrating features into meta-data-based models. The proposed model achieves good accuracy-recall ratings andoutperforms baseline deep learning models, highlighting its effectiveness.
An Automatic Nuclei Segmentation Technique using Unsharp Masking

Nuclei are very useful in understanding the micro-environment. The pathologist's effort may be reduced and precise micro-environment observations can be obtained by using an automatic nuclei segmentation approach in clinical trials. Even though the latest deep learning (DL) based segmentation algorithms are generally outperforming more conventional approaches, however, this segmentation task is difficult in situations where nuclei are coherent and overlap. In the current study, we propose a novel nuclei segmentation method that combines the U-Net model based on a convolution neural network (CNN) with an unsharp masking technique for the pre-processing of the source images. The unsharp masking technique enhances the high-frequency information of the source images. An objective function is proposed for the optimization algorithm to further improve the segmentation performance. On the same dataset, the efficacy of the suggested method to segment nuclei is compared with existing methods. The suggested technique achieved IOU(JI), Accuracy, Precision, and F1Score values as 0.8172, 96.05 %, 0.8672, and 0.8994 respectively. The empirical results reveal that the discussed technique is performing better and is superior to the existing techniques.
Comparative Analysis of Word vs. Character Embedding for Machine Learning Based Detection of Malicious URLs and DGA-Generated Domains

This study presents a comprehensive comparative analysis of the effectiveness of word-level and character-level embeddings in the context of machine learning-based detection of malicious URLs and DGA-generated domains. Utilizing distinct datasets comprising DGA-generated domains and Spam URLs, we systematically evaluate various machine learning models coupled with word-level and character-level tokenization techniques. Our findings indicate that character-level tokenization yields superior results in identifying DGA-generated domains, particularly due to the random character composition of these URLs. Conversely, both word-level and character-level embeddings exhibit comparable success rates in classifying Spam URLs, owing to the non-random nature of their URL structures. The study sheds light on the importance of tailoring tokenization strategies based on the unique characteristics of the data. We recommend character-level embeddings for detecting DGA-generated domains characterized by random characters. In contrast, the choice between word-level and character-level embeddings is less critical when dealing with Spam URLs, as both approaches yield effective results.
Quadratic Interpolation enhanced hybrid Grey Wolf Optimization and Moth Flame Optimization for global optimization

In order to construct a powerful meta-heuristic algorithm, this work suggests a novel hybrid technique namely h-QGMFO (Hybrid Quadratic Interpolation enhanced Grey Wolf - Moth Flame Optimization), which combines the Grey Wolf Optimizer (GWO) and Moth Flame Optimization (MFO) algorithms with Simple Quadratic Interpolation (SQI). Both algorithms suffer with identifying effective solutions, slow rate of convergence, and a propensity to become trapped in local optimal solutions. The h-QGMFO is proposed to combine the effectiveness of GWO and MFO algorithms in order to address the shortcomings concerned with these robust algorithms. The SQI technique, a part of this hybrid strategy, permits diversity in search while inhibiting early convergence of both algorithms. The recommended hybrid methodology is used to test the effectiveness of the algorithm on 23 (twenty-three) familiar benchmark functions and tested on real world problems with satisfactory results. The proposed strategy provides an effectual and capable solution in terms of the eminence of the output, and the convergence rate, according to the findings by comparing the proposed approach with other state-of-the-art algorithms.
GradTOD - A Unified Dialogue State Tracking Model for Task-Oriented and Open Domain Dialogues

The task-oriented dialogue domain system requires classifying intent and replying to a specific goal domain. In the sub-module of Task-oriented, the Dialogue State Tracker (DST) is well-known as a variety processing tracker. However, existing DST models often specialize in only task-oriented domains (ToD), leading to limited performance when applied to scenarios. In this paper, we propose GradTOD, a unified DST model that predicts both two task types, task-oriented dialogue (TOD) and open-domain dialogue (ODD). Our model leverages the recent advances in prompt engineering and conditional generation to perform zero-shot learning. After experiments, GradTOD has achieved an 88.6% and 82.5% score on Joint Goal Accuracy metrics when evaluating the Scheme-Guided Dialogue (SGD) and FusedChat test sets correspondingly, demonstrating the adaption ability for multi-domains.
Fault-tolerant Verifiable Encrypted Search on Blockchain

Verifiable Dynamic Searchable Symmetric Encryption (VDSSE) enables search and update functionalities over encrypted data, allowing users to utilize encrypted data outsourced to an untrusted server. The verifiable property ensures that users do not accept incorrect search results returned by the server. Recent works on VDSSE have focused on the forward-privacy property, which requires that an update operation cannot be linked to previous search queries. However, the majority of existing schemes focus on preventing curious or malicious behavior from the server but ignore faulty update operations by the client. Moreover, most current schemes store data on a single server or in centralized storage, which, in an untrusted environment, raises concerns over data storage reliability and integrity.In this paper, we explore how to solve the above problems with blockchain. First, we introduce a pragmatic approach to storing and accessing data on the blockchain while ensuring forward-privacy. Then, we use a smart contract to build a forward-private keyword mapping for verifying keyword search. Building upon these foundations, we formulate a decentralized, verifiable dynamic SSE scheme featuring a lightweight client and robust fault-tolerant properties. Following this, we use smart contract to establish fairness by devising a model that incentivizes each party to adopt honest behavior. We provide the prototype implementation on Ethereum. Experiments and evaluations demonstrate the practicability of our blockchain VDSSE scheme.
A Steganographic Scheme to Protect Medical Data Using Radiological Images

The progress in data transmission technology has underscored the significance of safeguarding information, particularly with regards to confidentiality. Researchers have therefore dedicated their attention to tackling this concern by introducing various techniques for protecting data confidentiality, including cryptography and steganography. Steganography, a data hiding method that has showcased better results in data security in images, gained significant attention from information security practitioners. Nevertheless, the existing methods show a significant tradeoff between the payload capacity and the stego image's quality, making them not applicable to sensitive domains such as military and medical. In this work, we propose a new steganography method working in medical images based on difference expansions to optimize the alleviation of the imbalance between the payload size and the imperceptibility of the stego image. Our experimental results achieve the best performance of a psnr of 75.1312 dB and the best psnr capacity of 0.6831. Our results showed an improvement in payload capacity from 0.4835 bpp by comparing the existing method to 0.6705 bpp with the same cover image labeled “Leg.”
Vulnerability Assessment of Security Breach and Deadly Threat in Cloud Computing Environment

Cloud computing allows us to access available systems and pay for what we require whenever needed. When there is access to the internet, it uses some techniques like Service-Oriented Architecture (SOA), virtualization, distributed computing, etc. Cloud computing has transformed the way people utilize and handle computer services. It enables sharing, pooling, and accessing resources on the Internet. It offers tremendous advantages that enhance the cost-effectiveness and efficiency of organizations, which is marked by security challenges or threats that can compromise data, service safety and privacy. This paper gives an overview of cloud computing and explores the threats and vulnerabilities related to cloud computing with its countermeasures. It also explores the recent advancement in cloud computing threats and countermeasures. Further, this paper highlights the case studies on recent attacks and vulnerabilities which are compromised. Finally, this paper concludes that cloud computing is efficiently used to mitigate the threats and vulnerabilities with its countermeasures.