Reviewerly/exHarmony huggingface dataset:
Creating Games for Treating Anxiety and Phobia Through Virtual Reality

Virtual reality exposure therapy is used as a therapeutic approach to effectively treat phobias. Research suggests that in phobias, the amount of fear experienced does not necessarily correspond to the potential amount of danger posed by the stimulus. In traditional therapeutic methods, patients may be unable to experience the stimulus as the treatment lacks the projection of the stimulus. However, with exposure-based treatment, a virtual environment is created to enable patients to undergo treatment. Patients are examined and categorized based on the degree of fear experienced, and a virtual environment is modelled accordingly for each category. An examination is conducted through mediated pathways, which suggests a reduction in the severity of phobia symptoms during treatment. Significant changes in behaviour are recorded for iterative refinements. The project focuses on the development of a Virtual Reality-based Serious Game platform for overcoming phobias.
Improved Email Spam Detection Using Integrated Approach of BiGRU-A-LSTM Approach

In today's world, email is used by nearly everyone. It has been called the most efficient, widely used, and low-cost method of communication currently available. However, the widespread use of email has been hampered by the proliferation of unwanted and potentially fraudulent messages that must be quickly identified and isolated using a spam detection system. Spam identification is crucial for the safety of email users and for avoiding the many harmful applications to which email has been put recently. There is a pressing need for improved spam detection techniques due to the adaptive nature of unsolicited emails that are sent through mailing programmes, which has severely reduced and sometimes rendered the efficiency of existing spam detection methods. Despite the fact that several algorithms for detecting spam have been presented and tested, the reported accuracy indicates that further research is needed in this area. Preprocessing, feature selection, and model training are all used in the suggested method. Preprocessing makes use of tools like HTML tag removal, stop word removal, Tokenization, and word frequency. Feature selection is performed using TF-IDF. CNN-A-BiLSTM is utilized for model training. The proposed method has superior results to those of LSTM and BiLSTM, two well-established alternatives.
Object Detection

Computer Vision is the branch of the science of computers and software systems which can recognize as well as understand images and scenes. Computer Vision is consists of various aspects such as image recognition, object detection, image generation, image super-resolution and many more. Object detection is widely used for face detection, vehicle detection, pedestrian counting, web images, security systems and self-driving cars. In this project, we are using highly accurate object detection-algorithms and methods such as R-CNN, Fast-RCNN, Faster-RCNN, RetinaNet and fast yet highly accurate ones like SSD and YOLO. Using these methods and algorithms, based on deep learning which is also based on machine learning require lots of mathematical and deep learning frameworks understanding by using dependencies such as TensorFlow, OpenCV, imageai etc, we can detect each and every object in image by the area object in an highlighted rectangular boxes and identify each and every object and assign its tag to the object. This also includes the accuracy of each method for identifying objects
One Time Password Authentication for Machine Activation Monitoring System Based on Wireless Network

Digital data in manufacturing production is helpful for companies in planning, evaluating, and developing innovations. The Industrial Revolution 4.0 made integration between conventional production machines effortless and possible. The application of the Internet of Things makes it possible to monitor and control devices via the Internet network. Conversion of machine activity recording from paper-based to application based is a necessity in the manufacturing world. Machine activity recording validation must be able to be carried out. In this paper, an authentication mechanism for machine activation is proposed. One-Time Password (OTP) is used to authenticate. This method is integrated into a web-based DW Tracking application with Arduino and Raspberry Pi for physical hardware. For the result, the web-based DW Tracking application can send the OTP number to the gateway and forward the activation command to the machine. With this process, DW Tracking can monitor and record when the machine is active. In this study, authentication mechanism through wireless communication can be operated successfully until 60 meters with a processing time of 765 milliseconds.
Research on Error Handling Techniques for Speech Interaction

With the rapid advancement of speech recognition and semantic understanding, speech interaction systems have gained widespread usage. This paper investigates the use of intelligent error handling approaches in speech interaction systems when faced with recognition or comprehension challenges. A case study focusing on the common task of “electronic spreadsheet editing” is conducted, wherein an experimental environment is developed by combining an open-source neural network code repository with a third-party speech recognition interface. Drawing from historical experience, a “triplet” paradigm is constructed to parse user command texts, and an improved approach for intelligent selection of error recovery strategies is proposed. This approach automatically determines the appropriate strategy for different speech interaction errors based on user logs. Experiments on 20 users demonstrate that our proposed intelligent error handling techniques for speech interaction, exhibit performance similar to empirically designed systems in practical tasks. Moreover, they significantly enhance user satisfaction and task completion efficiency when utilizing the system.
A random number generator with hardware switching devices

Utilizing a hardware-based random number generator is a viable strategy for enhancing the security of the fundamental procedures in handling sensitive data within a computer system. Given the substantial volume of data generated by intelligent devices, there is an increasing need for a random number generator that is quick, cost-effective, simplified, and trustworthy. Such a generator is considered the most suitable fundamental for performing encryption algorithms. This work presents a novel approach to developing a real random number generator for general uses, utilizing MEMS switch technology. In order to enhance the level of unpredictability, the implementation involves the utilization of a voltage-controlled switch array and the allocation of dedicated reference comparison devices. The examination of circuit simulation performance demonstrates that the suggested method exhibits verifiable randomness in the test model and efficiency in energy consumption.
Blockchain Technology for Secure and Efficient Land Registration Management: A Promising Paradigm Shift in India

Land registration in India is a critical process that helps to ensure secure and efficient land ownership. However, traditional land registration systems are often faced with challenges such as corruption, fraud, and inefficiencies. The emergence of blockchain technology provides a hopeful resolution to these challenges. It provides a secure as well as transparent decentralized platform for the management of land registration. This research work explores the potential of “blockchain technology” in enhancing land registration management. The study examines the current state of land registration systems and identifies the challenges faced by traditional systems. Furthermore, the research discusses the fundamental concepts of blockchain technology and how it can be applied to land registration management. The study presents a proposed blockchain-based land registration system that provides transparency, immutability, and security of land ownership records. The proposed system is built on a decentralized platform that eliminates the need for intermediaries, reduces the cost of transactions, and increases the efficiency of land registration management. The prototype demonstrated the potential of blockchain technology in enhancing land registration management by providing a secure, transparent, and efficient system. In conclusion, the study highlights the potential of blockchain technology in revolutionizing land registration management. The proposed system offers a more secure, transparent, and efficient way of managing land registration. This research contributes to the growing body of literature on blockchain technology applications and provides insights for policymakers and practitioners in the land registration sector.
An Evaluation of Machine Learning Techniques for Detecting Banking Frauds

Credit cards are an essential part of every person's daily life in the modern world. Customers use their credit cards and online transactions to make purchases for their requirements. To reduce the risk of defaulters, banks and other financial institutions consider customer credit applications. Credit risk is the increase in debt owed by a customer who repeatedly misses payments on their bills. The precision and recall measures received greater attention than the metrics for accuracy. Based on the False Negative value of the confusion metrics, logistic regression is the best model after comparison with the precision-recall curve. Additionally, a GUI (Graphical User Interface) was built after altering the logistic regression's threshold value and it successfully forecasted whether a customer was a defaulter or not. It is a problem to categorize clients' credit payment defaulters and non-defaulters. The basic objective of risk prediction is to evaluate each customer's credit risk and reduce it using information from financial statements, customer transactions, and repayment history. The damage and unpredictable nature. Different methods, including logistic regression and decision trees, are used to create risk prediction models.
Underwater Object Detection Algorithm Based on Improved YOLOv7-tiny

Due to the complex and ever-changing underwater environment, underwater object detection is prone to issues such as missed detection, false detection, and low precision in precision measurement. This article proposes an improved YOLOv7-tiny underwater object detection model. Firstly, the convolutional block attention module (CBAM) module is introduced in the Neck section to improve the feature extraction ability for feature objects by adjusting the weight parameters between each channel during feature extraction; Secondly, soft Non-maximum suppression(Soft -NMS) algorithm is used to replace the Non-maximum suppression algorithm in the original network, and the detection accuracy of overlapping targets is improved by appropriately retaining overlapping detection boxes. Finally, the content-aware reassembly of features (CARAFE) is used to replace the nearest neighbor interpolation in the original model, fusing feature information from different scales to improve the model's expression ability. The experimental results show that the improved object detection model has improved the average accuracy by 3.72 %, 5.33%, and 1.7% compared to the original model, YOLOv5, and YOLOv8, respectively, achieving good detection results.
Global Path Planning of Unmanned Underwater Vehicle with Asexual Reproduction Optimization

A global path planning algorithm with asexual reproduction optimization (ARO) is proposed to solve the local optimum of path planning problem for unmanned underwater vehicles (UUVs). Firstly, the working spaces of UUV are modeled and encoded, and each path is represented by a chromosome. Secondly, in order to increase the diversity of paths, a reproduction operator suitable for path planning is put forward and a new bud path is generated by the reproduction operator from the parent path. Finally, a cost function is designed to evaluate the parent and bud paths, retaining the better path among the parent and bud paths. The proposed method is a global path planning algorithm with high convergence speed and high stability in complex working environments. The performance of global planning, convergence ability and stability in different working spaces are simulated and verified respectively. Simulation experiments verify the feasibility and reliability of the algorithm.
Improved Sonar Target Detection Method Based on YOLOv5

Side-scan sonar target detection will soon be used in the field of detect sunken ships and various underwater targets, and has received an increasing attention of scientific researchers in recent years. However, many existing deep-CNNs side-scan sonar detectors still suffer from weaker small target detection performance. It may be attributed to the fact that the layers of a deep convolutional neural network are too deep, which leads to the loss of high frequency information. In this study, we propose a new framework to achieve the robust detection of small targets in side-scan sonar images. Firstly, considering that small targets require more high-frequency features, we propose a Spatial Pyramid Polling Fast++ (SPPF++) module and apply it to the backbone layer network to avoid the loss of feature information for sonar small targets. Secondly, we apply an attention module to the backbone network to improve the recognition ability of small targets. Finally, the Efficient Intersection over Union (EIoU) loss is applied to the regression branch, reducing the network's dependence on the target aspect ratio and improving the accuracy of the regression. Comparing with the state-of-arts detectors, our model has got the better performance. Experiments show that the accuracy of our model has greatly improved compared with YOLOv5-s, and the model parameters have only increased very little, which has practical significance for target detection in side-scan sonar images.
Performance Comparison of Conventional and Deep Learning Classifiers for Punjabi Dialect Identification

Dialect identification is the process of identifying the dialect from spoken form and this paper focuses on identifying the four dialects of Punjabi language. For any speech processing activity, the need of database arises at first stage and for Punjabi language, a database primarily for four dialectal variations is constructed. To extract the information from the dataset, Mel Frequency Cepstral Coefficients (MFCC) feature extraction technique is employed along with various pre-processing activities like pre-emphasis, framing and windowing. Then for classification of dialects, conventional classifiers like Support Vector Machine (SVM) and Logistic Regression (LR) are used. Also, Recurrent Neural Network (RNN) and Convolutional Neural Network (CNN) are implemented under the category of deep learning classifiers. Performance comparison is done to conclude that deep learning classifiers are giving better outcomes in terms of both accuracy and F1 scores as compared to conventional ones.
Machine Learning-Based Intrusion Detection for Mitigating Denial of Service Attacks in Wireless Sensor Networks

Due to the characteristics of certain networks, particularly their constrained hardware capabilities, and infrastructure-less design, the widespread adoption of Wireless Sensor Networks (WSNs) has resulted in a number of security vulnerabilities. One of the most frequent threats to these networks is the denial-of-service attack. It is difficult to build an intrusion detection and prevention system to reduce the consequences of a denial-of-service attack. The use of a few machine learning techniques is suggested in this research as a way to identify vulnerabilities on a specified dataset. Regular profiles and various Denial of Service attack scenarios in WSNs are both included in the dataset that was used. According to the experimental findings, the hyperparameter tuning technique outperformed other techniques with greater true positive rates (higher) and better false positive rates (lower).
Towards investigation of transfer learning framework for Globotruncanita genus and Globotruncana genus microfossils in Genus-Level and Species-Level prediction

The applicability of digital imaging techniques and machine learning models to paleontological datasets is exploring the possibility of predicting microfossils extracted from the rock samples instead of the traditional identifying methodologies under the microscope in a one-by-one way via a domain expert. However, these processes, including labeling, are carried out manually and take a high time-consuming, especially for many quantities and diversity of complex morphological microfossil specimens. In this work, we propose a transfer learning framework based on a custom model CNN (Convolutional Neural Network) and diverse pre-trained deep models (ResNet50, Xception, InceptionV3, VGG6, MobileNet) trained with the millions of images for Globotruncanita genus and Globotruncana genus in genus-level and species-level prediction. The second primary advantage of our framework is able to provide better and more robust decisions for a limited number of microfossil images captured by the low-cost light microscope imaging technology. The comparison of the diverse methods was evaluated with different performance metrics, and the observation of the framework was made to perform high prediction scores reaching up to the outcomes (>99 % accuracy and > 0.99 AUC score for genus-level/>81 % accuracy and > 0.89 AUC score for species-level). As far as we know, this research study is the first attempt to investigate a transfer learning framework to predict the Globotruncanita genus and Globotruncana genus families at the genus-level and species-level microfossils. Overall, it may extend the existing literature on paleontological science and automated/quick classification manner.
Implementation of Video-Based Human Anomalous Activity Detection Using LSTM-RNN Network

Increased interest in video analysis technology, and in particular automatic human activity recognition, has arisen in response to the rising requirements of a wide range of applications, including surveillance, entertainment, and healthcare systems. Using automated reporting, authorities might be alerted to the presence of a possible criminal or dangerous actor, such as a person hanging about an airport or railway station with a bag. Similarly, activity identification can improve human-computer interaction in the gaming industry by, say, automatically identifying the motions of different players in a tennis match so that an avatar can assume control of the game on the player's behalf. Steps one through four of the proposed procedure are preprocessing the data, segmenting the data, selecting features, and training the model. Preprocessing videos is used to observe people's actions. Background subtraction and GMM both play a role in the segmentation process. Parameters such as speed, measurement, and movement play a significant role in feature extraction. The models are then trained with LSTM-RNN after feature extraction. The proposed method outperforms the two most used alternatives, LSTM and RNN.
Safe Implementation of PID Controllers for Unmanned Aerial Vehicles Using a Test Platform

This work presents the design and implementation of a PID controller applied to an unmanned aerial vehicle. The main objective is to implement a control algorithm, which allows the vehicle in question to remain flying at a desired angular position, controlling the angular velocity of the vehicle. This thanks to the design of a gyroscope-type structure that limits spatial but not angular mobility. The PID controllers are of standard type, and were designed under simulation schemes using a dynamic model of the vehicle. Finally, using the test platform, the implementation of the designed controllers was performed and their performance was analyzed. The results show that the PID controller is able to stabilize the vehicle and keep it in the desired angular position.
Automatic Detection of Plasmodium Parasite Using Convolution Neural Network

The health industry is quite active and malaria is the deadliest disease on earth. A laboratory or skilled technician will often do a schematic analysis of a patient's blood film under a microscope to look for parasite-infected red blood cells. The procedure is ineffective, and the diagnosis is based on the expertise and experience of the individuals who will be performing the examination. Malaria diagnosis using a blood smear has already been carried out using deep learning algorithms. The suggested method uses image-based diagnosis with CNNs and VGG models to accomplish this goal, yielding a high accuracy of 94.7% and precision in differentiating infected and uninfected cells.
Pengembangan Multimedia Interaktif Menggunakan Aplikasi Canva Pada Mata Pelajaran Dasar-Dasar Elektronika Kelas X Di SMK Negeri 1 Batipuh

This research is motivated by the low learning outcomes of students due to the teaching process still predominantly relying on printed books, the absence of interactive multimedia learning tools, and continued use of traditional teaching methods. The primary aim of this study is to determine the validity and practicality of interactive multimedia using the Canva application in the subject of Basic Electronics for 10th-grade students at SMK Negeri 1 Batipuh. To carry out this development, the 4-D research model was employed: Define, Design, Development, and Disseminate. The results of expert validation for the subject matter obtained an average score of 0.883, categorizing it as "valid." The average score from media experts' verification was 0.914, also falling into the "valid" category. The practicality test of the Basic Electronics learning multimedia indicated its practicality for use in the learning process, as the obtained respondent percentage averaged 91%, which falls under the category of "Very practical."
What makes a visual scene more memorable? A rapid serial visual presentation (RSVP) study with dynamic visual scenes

The visual system has been characterized as having limited processing capacity. Research suggests that not all visual information is equal and that certain visual scenes are registered better than others. The present study investigated how people process biological stimuli under time constraint using a Rapid Serial Visual Presentation (RSVP) paradigm with dynamic movie scenes. The results of Experiments 1 and 2 indicated that recognition memory as well as identification performance got better with longer duration (400 ms vs. 200 ms). Most importantly, biological stimuli led to better memory and lower reaction times. Lastly, Experiment 3 was conducted to replicate previously observed dynamic advantage and to disentangle the role of motion from content. The results indicated that dynamic scenes were remembered better than static scenes for both the biological and non-biological stimuli. The ecological validity and retrieval benefit of dynamic scenes were discussed in relation to image memorability.