Reviewerly/exHarmony huggingface dataset:
Interactive Vehicle Trajectory Prediction Considering Abnormal Emotion Based on SOR Cognitive Framework

The precise anticipation of vehicle trajectory is a fundamental technological requirement for vehicle active safety systems to work, especially in scenarios with strong interaction. The majority of existing algorithms for trajectory prediction rely on the driving behavior exhibited by drivers in a normal emotional state. The impact of abnormal emotions is not taken into account. It leads to low prediction accuracy of trajectory, resulting in false alarms. To address this issue, this paper built a CARLA-SUMO co-simulation platform and recruited 26 volunteers, who were induced to experience abnormal emotions of anger and fright for driving simulation experiments in scenarios with strong interaction. Based on Dynamic Bayesian Network (DBN), the key causal chains from anger, and fright to severe collision risk are deduced. We constructed the trajectory prediction scheme of Double-GCN-BiLSTM-Attention with reference to the stimulus-organism-response (SOR) cognitive framework. This model integrates emotional factors and enhances the precision of trajectory prediction. This study proves that the introduction of the SOR cognitive framework can significantly improve the accuracy of trajectory prediction. The research results can be promoted to car companies, and the feasibility of landing is strong, which is of great significance to improving driving safety and creating social and economic value.
Evaluation on Computer Region Location Algorithm Based on Fully Convolutional Network

The research of computer region location algorithm can obtain a more accurate location. In the field of computer science, target location has always been a very important issue. The traditional target location method usually uses the image as the basis to extract its features, and then carries out the research of regional recognition and location. Through this method, the image can be subdivided into many grouped images with features, and then use the prior knowledge of these images to match. However, there are several problems in doing so. First of all, they would generate a large amount of data in the training process, which is not a good representation of the target. Secondly, these feature extraction algorithms do not consider the structure of the target region itself in the training process, so they cannot effectively extract the target information. Finally, they often use many image features to train the double network structure. In view of the above problems, this article proposed a more excellent and convenient method to extract and recombine a large amount of feature information contained in the image. The algorithm used Fully Convolutional Network (FCN) to extract the feature map of the image. In the training phase, people train the network by representing the original image as a multi-layer FCN. Then the network was connected to the computer screen, and the architecture was optimized by adjusting the Convolutional Neural Network (CNN) to calculate the better results, and feedback to the model of the full CNN. During the experiment, a large number of experiments were carried out on two public data sets (Cityscapes and OpenImage Segmentation, respectively). The results showed that the method proposed in this article has achieved good results: the accuracy of the computer region location algorithm based on FCN studied in this article can reach more than 98%.
Detection of Acute Lymphoblastic Leukemia Using CollateNet

Acute Lymphoblastic Leukemia (ALL), a malignant blood cancer is very common in children. This cancer shows good response to treatment provided it is diagnosed in time. As this is a fast-growing cancer, timely diagnosis of this cancer is crucial. The diagnosis of this cancer requires identification of unhealthy lymph blast cells. As the unhealthy lymphoid blast cells are morphologically similar to the healthy lymphoblast cells, distinguishing between them is a very difficult task and requires advanced methods to be applied. Deep Learning algorithms like Convolution Neural Network (CNN) have shown great results in image classification tasks but they are prone to overfitting. Bearing this in mind, this paper proposes CollateNet, a fully convolutional network which uses collation blocks. This architecture has been applied on CNMC 2019 dataset to classify single cell blood smear images into ALL and normal cells. The proposed architecture achieved 89.88 % and 87.96% training and validation accuracies respectively and a training and validation Precision of 92.58% and 92.24% respectively. The model is able to overcome the challenge of overfitting and vanishing gradient due to the collation blocks used in the neural network. The post training and post testing analysis shows the effectiveness of the proposed architecture for timely detection of ALL from single cell blood smear images and hence can be used in future for the diagnosis of ALL at an earlier phase.
Authentication of Voter Using Convolutional Neural Network and Blockchain

For elected officials and government policies to be credible and legitimate in today's democracies, voting systems must be fair and honest. Voter impersonation and ballot stuffing are just two examples of the many fraud and manipulation schemes that can be used against traditional voting systems. We suggest a novel method for voter authentication that combines blockchain technology and convolutional neural networks (CNNs) to address these issues. Our suggested method entails gathering voter biometric information, such as facial recognition data, and putting it in a safe and immutable blockchain ledger. This ledger is virtually impossible to hack or manipulate because it is decentralized and replicated across numerous network nodes. After that, we compare the voter's identity to the information kept in the blockchain using a face recognition model based on CNN. This method enables us to protect the confidentiality and privacy of the personal data of voters while ensuring that only authorized voters can participate in the electoral process. On a real-world dataset, we put our suggested methodology to the test and show that it can accurately and securely authenticate voters. We also talk about the ethical issues that arise when implementing such a system, such as privacy, consent, and technology access. The advancement of democratic processes around the world can be aided by our suggested methodology, which has applications for creating more reliable and secure voting systems.
USING MUNSELL SOIL-COLOUR CHARTS ON MARS AND EARTH

We computed Munsell notations for a set of fifty-five clay samples, analogous to those existing in Mars, under three different illuminants: The CIE standard illuminant D65 and two illuminants proposed for Mars, named M54 and M46, with correlated colour temperatures of 5400 K and 4600 K, respectively. The magnitudes of CIELAB colour differences (average ± standard deviation) between clay samples and their nearest Munsell chips were very similar for D65, M46 and M54: 3,8 ± 1,7, 3,8 ± 1,6 and 3,8 ± 1,7, respectively. Lightness differences were the main components (above 50%) in these average CIELAB colour differences. The change of illuminant generated changes in Munsell notations for few clay samples: 18,2% and 7,3% for changes from D65 to M46 and from D65 to M54, respectively. Assuming the CAT16 model, the average colour inconstancy of the clay samples when the illuminant changes from D65 to M46 was 1,8 CIELAB units.
Whence Principal Components?

The historical roots of principal component analysis are traced from its beginnings under Harold Hotelling in the 1930s to modifications and developments that were made shortly thereafter by several prominent psychometricians, such as Truman Lee Kelley and L. L. Thurstone. The emphasis in the present historical survey is on the origins of principal components per se. Other papers in this Festschrift for Shizuhiko Nishisato are expected to concentrate on the computational engine behind principal components as used for a number of areas in categorical data analysis.
The Relationship Between Interest and Citizen Participation in Learning Computer Skills at PKBM Biba Course West Pasaman

ABSTRACTThis research aims to find out the relationship between interest and the participan of learning residents in taking computer skills at the PKBM Biba Course, west Pasanan. This research uses analysis techniques based on the percentage formula and Product Moment correlation formula. The result of this research are that people’s interest in learning computer skills at PKBM Biba Course West Pasaman was categorized as low, this was because respondents predominantly chose alternative answers with a score of two. There is a significant relationship between interest and student participation in taking computer skills at the PKBM Biba Course West Pasaman. Keywords: Interest, participation, Computer Skills, PKBM Biba Course
Machine learning explanations by design: a case study explaining the predicted degradation of a roto-dynamic pump

The field of explainable Artificial Intelligence (AI) has gained growing attention over the last few years due to the potential for making accurate data-based predictions of asset health. One of the current research aims in AI is to address challenges associated with adopting machine learning (ML) (i.e., data-driven) AI ‐ that is, understanding how and why ML predictions are made. Despite ML models successfully providing accurate predictions in many applications, such as condition monitoring, there are still concerns about the transparency of the prediction-making process. Therefore, ensuring that the models used are explainable to human users is essential to build trust in the approaches proposed. Consequently, AI and ML practitioners need to be able to evaluate any available eXplainable AI (XAI) tools’ suitability for their intended domain and end users, while simultaneously being aware of the tools’ limitations. This paper provides insight into various existing XAI approaches and their limitations to be considered by practitioners in condition monitoring applications during the design process for an MLbased prediction. The aim is to assist practitioners in engineering applications in building interpretable and explainable models intended for end users who wish to improve a system’s reliability and help users make better-informed decisions based upon a predictive ML algorithm output. It also emphasizes the importance of explainability in AI. The paper applies some of these tools to an explainability use case in which real condition monitoring data is used to predict the degradation of a roto-dynamic pump. Additionally, potential avenues are explored to enhance the credibility of explanations generated by XAI tools in condition monitoring applications, aiming to offer more reliable explanations to domain experts.
Detection of Vulnerability in Websites Predominantly Against CSRF Using Machine Learning

Nowadays, web applications serve as the primary edge for accessing security-sensitive data and functionality. These applications are frequently used for tasks such as filing taxes, accessing medical screening results, conducting financial communications, and sharing thoughts with groups. Unfortunately, this also means that web requests are attractive goals for malicious operators (invaders) who aim to cause financial harm, gain unauthorized admittance to private data, or embarrass their sufferers. In this article, a methodology is proposed for utilizing Machine Learning (ML) in detecting vulnerabilities in web applications. Analysing web applications can be difficult due to their varied nature and custom programming practices. ML proves to be a valuable tool in web application security, leveraging labelled data to incorporate human considerate of web request semantics into computerized analysis apparatuses. The practice is applied to develop Mitch, the first ML resolution for sensing Cross-Site Request Forgery (CSRF) vulnerabilities in a black-box setting. Mitch successfully identified 35 novel CSRFs across 20 foremost websites besides 3 new CSRFs in manufacture software.
Securing the Connected Future: A Comprehensive Analysis of Challenges, Threats, and Prospects in IoT Security

With new technology comes new challenges and opportunities. These opportunities try to leverage Internet of Things (I oT) and embark on creating a connected business. IoT promises technical advances, improved efficiencies, greater revenues and enhanced customer experiences. The rapid proliferation of IoT devices has revolutionized the way we interact with the world, offering unprecedented levels of connectivity and convenience. However, this proliferation has also exposed the IoT ecosystem to a myriad of security challenges and barriers. This research paper presents a comprehensive review of the current state of IoT security, focusing on the challenges and barriers, critical security issues that pose significant threats, and the potential future of IoT.
Solar Panel Control Using A Single Axis Automatic Tracking System Based on the Pilot Method to Maximise Solar Energy Extraction

At present, two different kinds of rooftop sections panels have been put into use. resolved sections are those that have a repaired tilt and are positioned at an acceptable angle based on their geographic spot. But the daily radiation exposure is only longer than six a while. every day. The additional type consists of revolving panels that either follow known as sun's path continually or they rotate through a predetermined angle at a predetermined time. sadly, neither the ongoing tracking nor the built schemes work well. On the initial instance, a motor operating that low speeds needs a lot of acceleration, which calls for a lot of electricity, which results in greater operating power. The whole thing spins at specified narrow angles with the following scheme regardless of whether known as new establish generates additional vitality or just not. The outcome could even be contrary, with every single the powering system might utilize the collected energy. These two shortcomings were successfully solved by a novel approach that is presented in this cardboard. The layout optimizes solar power removal through the use of a microprocessor-based control system. The following is accomplished through the creation of some observatory called PILOT as well as a turning that spins for cells called PANEL. This constellation is initially pointed eastward while awaiting sunrise. While this occurs, an PILOT continues to follow the sun. An LED to time Converters (LTF) attached upon a tiny electric motors is used to accomplish this matter. The heavenly body remains lined up against an PILOT within this converters. similar resistors that vary in light in two Both the power source PILOT along with the PANEL have (LDR) attached on them. Following every one PILOT location, the comparing handle is taken over. The previously PANEL coincides against their PILOT when the electrical current caused through known as PILOT LDR exceeds than which one caused by known as PANEL LDR in addition to some predetermined offset; alternatively, it remains in place and patiently awaits to allow the PILOT to make a shift to another location before repeating the procedure once more. Thus, only after the second one generates more energy does known as PANEL shift to its new position. The solar system comes back with initial location that an final moment of the working day once sundown and awaits to feed the next day.
Unveiling Gender from Speech: An Investigation into Acoustic Features for Accurate Gender Detection

This paper focuses on the development of a classification model for gender recognition based on voice speech using various acoustic parameters. The aim is to predict whether a voice sample belongs to a male or female speaker. The initial step in the paper involves dividing the dataset into two parts: training data and test data, through a test-train data split. Following this, the study explores the performance of various machine learning algorithms for gender classification based on voice features. The algorithms under consideration include Support Vector Machine (SVM), Decision Tree Classifier, k-Nearest Neighbors (KNN), Random Forest, and Logistic Regression. Each of these algorithms is individually trained on the training data and subsequently evaluated on the test data to gauge their effectiveness in gender classification. The paper then analyzes and compares the results obtained from each algorithm, assessing their accuracy, precision, recall, and F1-score to identify the model that performs best in predicting gender based on voice features. The performance of every model is assessed using a confusion matrix and classification report, offering valuable information about accuracy, precision, recall, and F1-score for each gender class. The model with the highest accuracy is determined as the best performer among them. By employing these classification algorithms and analysing the acoustic parameters of voice speech, this paper aims to provide a reliable method for gender recognition. The results obtained from this study can have potential applications in various fields, including speech processing, voice-based user interfaces, and automatic gender identification systems.
ESOA:Classification of Autism Spectrum Disorder based on Gaze Tracking Imaging using Egret Swarm Optimization Algorithm

Autism spectrum disorder (ASD) may be appropriately identified using a set of cognitive tests, which is a time-consuming procedure, but early identification is usually difficult since people without ASD display comparable behaviors. To improve the ASD detection process, this paper presents a novel Egret Swarm Optimization Algorithm (ESOA) based on rough sets to detect ASD in patients by tracing the key features in their eye gaze. The noisy and irrelevant scan paths in the input high-dimensional eye gaze image are eliminated via the ESOA-based rough sets. The three steps in the proposed model are feature extraction, high dimensionality reduction, and classification. The accuracy of the Bayesian Network (BN) classifier is improved via the ESOA algorithm whose fitness is mainly based on the number of features selected and the rough set dependency degree. In this manner, the detection performance of the BN classifier is improved. The experimental results conducted on the eye tracking dataset show that the proposed model offers an improved accuracy nearly equal to 98%.
Solar Panel Control Using a Single Axis Automatic Tracking System Based on The Pilot Method to Maximise Solar Energy Extraction

At present, two different kinds of rooftop sections panels have been put into use. resolved sections are those that have a repaired tilt and are positioned at an acceptable angle based on their geographic spot. But the daily radiation exposure is only longer than six a while. every day. The additional type consists of revolving panels that either follow known as sun's path continually or they rotate through a predetermined angle at a predetermined time. sadly, neither the ongoing tracking nor the built schemes work well. On the initial instance, a motor operating that low speeds needs a lot of acceleration, which calls for a lot of electricity, which results in greater operating power. The whole thing spins at specified narrow angles with the following scheme regardless of whether known as new establish generates additional vitality or just not. The outcome could even be contrary, with every single the powering system might utilize the collected energy. These two shortcomings were successfully solved by a novel approach that is presented in this cardboard. The layout optimizes solar power removal through the use of a microprocessor-based control system. The following is accomplished through the creation of some observatory called PILOT as well as a turning that spins for cells called PANEL. This constellation is initially pointed eastward while awaiting sunrise. While this occurs, an PILOT continues to follow the sun. An LED to time Converters (LTF) attached upon a tiny electric motors is used to accomplish this matter. The heavenly body remains lined up against an PILOT within this converters. similar resistors that vary in light in two Both the power source PILOT along with the PANEL have (LDR) attached on them. Following every one PILOT location, the comparing handle is taken over. The previously PANEL coincides against their PILOT when the electrical current caused through known as PILOT LDR exceeds than which one caused by known as PANEL LDR in addition to some predetermined offset; alternatively, it remains in place and patiently awaits to allow the PILOT to make a shift to another location before repeating the procedure once more. Thus, only after the second one generates more energy does known as PANEL shift to its new position. The solar system comes back with initial location that an final moment of the working day once sundown and awaits to feed the next day.
Performance Evaluation of Anti-Predatory Nature-Inspired Algorithm on the CEC'14 Test Suites

Anti-Predatory Nature-Inspired Algorithm (APNIA) is a recent proposal in evolutionary computations. It emulates the self-preservation strategies of frogs. It has several advantages, like a reasonable convergence rate, a balance between exploration and exploitation, and preserving diversity in solutions during the evolution process. Apart from different types of evaluations, CEC benchmarking problems are one of the finest test suites that measure the performance of an evolutionary algorithm. With this motive, this work evaluates the performance of the APNIA in solving CEC'14 benchmarking problems. We evaluate the performance of APNIA based on the CEC'14 guidelines. Our experimental evaluations demonstrate the moderated performance of APNIA for solving the CEC'14 test suite problems in different dimensions.
Analysis of Artificial intelligence Financial Innovation System based on Big Data Technology

This study suggests a novel employing big data technologies, innovative finance framework for commercial banking entities order to address the challenges associated with the transformation of Concerning large quantities of data, banks are commercial. The proposed system fully utilizes big data technology and suggests several financial innovation paths and financial innovation strategies in accordance with the various traits of both established and newly founded commercial banks. The study’s findings demonstrate the innovation system’s ability to comprehensively and methodically suggest how commercial banks should use big data technology and the Internet as a platform for institutional innovation, management innovation, and product innovation. AI is able to be used in a variety of ways in the context of credit finance and businesses, including credit risk assessment, fraud detection, and customer service automation. Credit finance Capability: A company’s ability to obtain finance from banks, financial institutions, or investors is referred to as having credit financing capacity. It depends on elements including the business’s financial stability, creditworthiness, and capacity to pay its debts.
Enabling Speech Recognition for Lesser-Known Language

An overview of Automatic Speech Recognition (ASR) technology, including its history and challenges in implementing it for regional languages. The challenges include lack of training data, dialectal and pronunciation variations, limited vocabulary, code mixing, and lack of standardization. Addressing these challenges requires significant research and development efforts to create accurate and reliable ASR systems for regional languages. In this research paper we have discussed various Automatic Speech Recognition model along with the application used in various field finally we have evaluated the accuracy of ASR for major Indian languages.