Reviewerly/exHarmony huggingface dataset:
Handwriting Letter Recognition on the Steering Wheel Switches

Automotive steering wheel switches technologies are evolving to give easy access to the several interior or exterior functions. This is worth a deep analysis for the current trends in order not to become unintuitive for the driver due to the increasing number of buttons. Through different technologies particularly the capacitive ones, range of innovative solutions have been developed like reconfigurable buttons on the steering wheel to offer commanding several functions twice or triple the number of allocated push buttons. In this paper, we address the problem in a different freely way to offer a wide band-use for the existing functions by developing handwriting letter recognition on the capacitive touch switches acting as touch pads. This approach relies on in-house acquired dataset in its algorithms. This significantly increases the interface capabilities and eases the access to the different functions like ADAS, infotainment, phone, etc. without having to take hands off the wheel. The interest of this solution is a modern access for the steering wheel switches and easy management for configuring the different functions.
A Robust Privacy Preserving Approach for Sanitizing Transaction Databases from Sensitive High Utility Patterns

Utility-Based Mining (UBM) is one of the fundamental problems in data mining technologies that has attracted the research community for many years. UBM algorithms are often applied to transaction databases, where they target the detection of patterns that have the potential to build critical decisions. Unfortunately, these algorithms can be misused, and even lead to major privacy issues for data holders. To cope with these issues, Privacy-Preserving Utility Mining (PPUM) algorithms have been recently employed as a defensive mechanism against the unauthorized use of utility mining techniques. However, the current performance of the existing PPUM algorithms is still limited and can drastically decrease the quality of the database, making it useless and unreliable source of information. In this research, we present a privacy-preserving solution based on the concept of utility. In particular, a heuristic approach; named Selecting the Best two Victim items First (SB2VF), is developed to hide the sensitive patterns that can be extracted from transactional databases after applying a UBM algorithm. Our main goal is to promote data sharing and publishing by providing data owners with an effective way to sanitize their data from the confidential knowledge that might prevent them from releasing their data. The proposed SB2VF adopts an effective technique to identify the appropriate victim items in each sensitive itemset. Also, a novel sorting technique is suggested to identify the appropriate victim transactions for each sensitive itemset. The experimental results show that the proposed SB2VF has very promising and encouraging overall performance and can result in minor side effects than other heuristic approaches.
Semi-automated Ontology Construction Method for Representing Problem-Solving Knowledge: Application on an Online Community of Breastfeeding Difficulties

The growing number of online communities has changed the way users consume information and knowledge in the web. Large amount of users seek information about problem-solving tasks in different domains in web-based communities. In the case of online communities of breastfeeding, mothers are asking and posting questions about breastfeeding difficulties and how to manage them according to the members experiences. In online communities there isn’t intelligent systems capable of extracting and managing this problem-solving knowledge to reuse it. In this paper, we propose a method for extracting and representing problem-solving knowledge shared in an online community about breastfeeding using semi-automated ontology construction method and machine learning. We use for ontology creation Protégé, an ontology development environment. The resulted ontology in evaluated using corpus-based evaluation approach and is compared to the World Health Organization textbook entitled Infant and Young Child Feeding. The results show that our ontology has successfully covered the breastfeeding difficulties domain.
Touchless Secure QR-Based ATM System

We live in an era, where ATMs can be used hundreds of times a day. With rising outbreaks like COVID-19 and many others, it can be one threatening source of spreading contagious diseases more rapidly. This paper proposes a new touchless ATM system, in which authentication can be done by scanning a generated QR code shown on the ATM screen through the bank’s mobile app instead of inserting one’s ATM card into the ATM. The ATM user gets verified by validating the device location and entering an OTP sent to their mobile device on scanning a QR code, after which they can withdraw or deposit cash, performing secure touchless transactions through the bank’s mobile app. This proposed system has been implemented, tested, and proven to fulfill the desired purpose, taking various security countermeasures into consideration.
FNet with Cross-Attention Encoder for Visual Question Answering

Visual question answering (VQA) is a challenging research area where model needs to understand image semantics along with the asked question in order to give a correct answer. Lately transformers showed improvement to the performance of deep learning models than using traditional sequence to sequence models like LSTMs, RNNs. Transformer models use attention mechanism and using it with complex models requires longer time and huge resources for training. In this paper, VQA task is accomplished using three transformer encoders where transformer’s self attention sub-layers are replaced with Fourier transforms which is called FNet that mixes input tokens in question and image encoders. Self attention is used only in the cross modality encoder to enhance accuracy. Experiment is done on two phases: Firstly, Pre-training is done on a subset of LXMERT dataset (5.99% of LXMERT’s instances)due to resources limitations and the Second phase is fine tuning on VQA v.2 dataset. It results in 24% faster Pre-training time but testing accuracy decreased by 5.61% than using encoders with BERT self attention only in all sub-layers. Model is also pre-trained using FNet sub-layers only and it trained faster by 30.6% than using only BERT self-attention sub-layers but got lower testing accuracy result (48.79%).
Satellite Image Dehazing Using Fast Iterative Domain Gaussian Guided Image Filtering

In general, satellite images are foggy due to factors such as noise, snow, thin cloud, dust, and so on, resulting in image contrast reduction. Dehazing is described as the elimination of noise or ambient contaminants from a image in order to improve image quality. However, most state-of-the-art technologies failed to completely eliminate atmospheric influences and noise from satellite images. To address this issue, this study focuses on the creation of a grey world optimization method for the correct estimate of satellite atmospheric light. The study also creates a unique technique for estimating and refining dark channel prior-based transmission maps at the pixel and patch levels. As a consequence, every pixel-based patch contains information about how satellite atmospheric effects were addressed. The fast iterative domain gaussian guided image filtering (FID-GGIF) method was created to provide output that is smooth with dehazing properties. According to the simulation results, the suggested study beats cutting-edge approaches in terms of both quantitative and qualitative results
Predicting the Frequency Bands and the Path Loss in Wireless Communication Systems using Random Forests

Proactive and predictive design in the next wireless generation is critical to avoiding the flaws of prior generations and achieving the 5G goal services pillars. Base stations are needed to perform and make judgments to maintain communication dependability as wireless devices become more commonplace. Machine Learning (ML) is used in this research to help base stations anticipate the frequency bands and the route loss. There is a comparison between the ML algorithms Multilayer Perceptron and Random Forests. In order to keep up with the demands of the new radios, systems that use various bands need an immediate reaction from devices to change bands quickly. For this reason, ML approaches are required to learn and help a radio base station in shifting between multiple bands in response to data-driven decisions. Afterwards, the findings are compared to those of different deep learning approaches. To guarantee that the projected works would succeed, these strategies are used to several case studies. Unsupervised algorithms were added to the random forests in order to improve the accuracy of the learning process.
Survey: An Automatic Parallel Parking using Path Planning Methodologies

Throughout the years, the expectations and capabilities of autonomous vehicles have increased, as has the level of automotive intelligence. In the majority of research studies, longitudinal and lateral control topics have been explored to understand and design intelligent systems. For example: Automatic parallel parking, Adaptive cruise control, co-operative adaptive cruise control, semi and fully autonomous cars. Sensors onboard the vehicle and communications networks transmit scene information to other vehicles and infrastructure. To be able to achieve autonomously driving on complex environments and to utilize the information as part of the motion planning and control schemes, different motion planning and control techniques were implemented. Upon implementing these initiatives, the main task is executed to increase the level of safety, comfort, and energy efficiency in the workplace. As part of the present paper, an in-depth review of various parallel parking methodologies based on automatic parallel parking is presented. In this presentation, the main topics that will be covered will be algorithm types, simulations, and field tests, as well as human factors that influence vehicle behavior. Additionally, various parking information services are also offered for parking guidance, facility management, and even for providing an insight into the parking situation. We have also provided a brief description of the techniques used by research teams, a comparison between these techniques, and additional information about the research teams’ contributions to motion planning. The paper concludes by discussing a future research direction and application.
Wavelet Denoising-Based Deconvolution Algorithm for Radio Telescope Imaging

In this work, wavelets are utilized to perform denoising alongside a deconvolution algorithm in solving the inverse problem in imaging for radio astronomy. In particular, the CLEAN algorithm is utilized to recover an image consisting of user-generated point sources. Wavelet denoising is then applied to both the dirty image before the CLEAN algorithm and to the deconvolved image after the CLEAN algorithm terminates. The results show a clear improvement of the reconstructed image utilizing wavelet denoising in both cases.
3D Image Reconstruction from Multi-View Images using the Encoder-based Feature Map Generation

The geometric feature of the image acquisition-based computer vision application utilizes the 3D image reconstruction for the required scene. The inaccurate reconstruction results and the enormous memory requirement limit the method’s performance. The deep learning-based methods provide promising results with elevated accuracy for computer vision applications. Hence, this research introduces a novel framework based on the deep learning strategy for the 3D image reconstruction of objects from multiple view images. The encoder-decoder-fusion-refiner module is proposed for the image reconstruction, in which the encoder module is proposed to generate the feature map. The significant attributes are taken out from the input multi-view image using the ResNet-50 and AlexNet pre-trained models, and then the channel and spatial information are gathered for obtaining the feature map. The most significant attribute section helps to reduce the computation complexity and elevates the accuracy of the reconstruction process. The newly devised method is evaluated by considering measures like loss and Intersection over Union (IoU) and obtaining the values of 1.04E+00, 1.11E+00, and 2.12E-01 for IoU_3D, IoU_2D and loss, respectively.
Internal Auditors’ Perceptions of Information Technology-Related Risks: A Comparison Between General Auditors and Information Technology Auditors

ABSTRACT With the growing role of information technology (IT), many organizations have incorporated IT governance practices that include keeping executives apprised of IT risks. To perform this function, organizations rely upon their internal audit staff to obtain an independent evaluation of IT risks. Although both general and IT auditors are involved in assessing IT risks, they may not be equally adept at identifying such risks. We draw on the expert versus nonexpert perspective to understand how general and IT auditors perceive IT risks differently. Through a quasi experiment with 70 internal auditors of a financial institution, we found that general auditors perceived IT risks to be lower than their IT audit colleagues. We also found that personal IT risk preferences influenced the level of IT risks that general auditors perceived. Personal IT risk preferences did not affect the risk perceptions of IT auditors. Implications for both research and practice are discussed.
Real-time Vehicle Detection System for Intelligent Transportation using Machine Learning

Vehicle detection plays an important role in analyzing traffic flow data for efficient planning in intelligent transportation. Machine Learning technology has been increasingly used for vehicle detection in traffic flows. However, adverse weather conditions bring challenges for 2D vehicle detection. There is a lack of research on real-time vehicle detection using 3D LiDAR point clouds, which are more resistant to adverse weather conditions. In this paper, we proposed a system for collecting real-time traffic data using both 2D and 3D LiDAR cameras, processing the collected data for vehicle detection, and providing a web-based service with statistical traffic flow data visualization and 2D real-time vehicle detection stream display. We generated 1980 images from the 2D traffic flow videos that were collected in California Highway, and trained a 2D machine learning model on Darknet using YOLO algorithm. Approximately, 7000 frames of LiDAR point cloud data were labeled and pre-processed, and a new deep learning model for 3D vehicle detection was proposed. Compared with YOLO’s original pre-trained mode, our 2D machine learning model improved the vehicle detection that 6 different types of vehicles could be classified with an average precision of 89.25%.
Modified Geometric Mean as an Estimator of Outlier based Artifacts in Natural Images

The Modified geometric mean is proposed as a detector for the removal of high-density salt pepper noise and impulse noise and artifacts like in images. The proposed algorithm initially finds a 3*3 pixel size. In this proposed filter modified geometric mean will act as a measure of central tendency to estimate whether the processed pixel is corrupted or not. If the pixel processed is corrupted then the median of the neighborhood is also checked for noise. If found true then the corrupted pixel is replaced with a modified geometric mean else median of the confined neighborhood pixel is unaltered if found not corrupted. From the results, we found that the proposed filter would eliminate fixed valued outliers, random valued outliers and impulsive artifacts that occur in images due to ageing. The proposed work shows extremely good Quantitative and Qualitative results. In comparison with other algorithms blurring of an image is less and it has high PSNR and SSIM and low MSE. The proposed work shows exquisite noise elimination functionality with more suitable edge preservation functionality.
The Commonalities in Social Engineering Attacks through E-Commerce Shopping Platforms &amp; Online Gaming Programs

After the pandemic, the usage of internet and remote working is on the rise, and the malicious users have also increased. Many users are getting affected by social engineering attacks every day. Social engineering is the practice of gathering personal information from individuals by deceiving them. There are multiple ways in which malicious users can perform social engineering attacks, with approaches like phishing, baiting, pretexting, etc. The gamut of social engineering attacks spreads across anything online i.e., it could be a shopping website, online gaming network, enterprise mailing platform, etc. How to take preventive action against such attacks is very important. In this paper, two user groups are considered - online gamers and shoppers, and their vulnerability to such attacks. The social interactions and psychological factors of online gamers which make them vulnerable to social engineering threats are reviewed. This paper covers extensive secondary research and analysis, on the different types of social engineering attacks and the psychological factors making an individual vulnerable to such social engineering attacks. Few studies have analyzed the behaviors and relationships of gamers and their online interactions as well. This work also examines different preventive action tools that malicious attackers typically use. This study provides a framework for different types of social engineering attacks on online gamers, psychological and behavioral patterns that make them vulnerable, and what preventive actions/tools can be leveraged.
OpenGL-based Beijing Opera Action Intelligent Generation Algorithm Assists Chinese Cultural Confidence Propaganda System

This paper combines the key method of particle system with OpenGL texture mapping technology, and designs a simulation generation algorithm for the intelligent action generation system of Peking Opera. This paper discusses the driving part of the somatosensory game space to the physical performance of Peking Opera. The first is the definition of the somatosensory game space, including its meaning and experience characteristics in the context of the new era. Somatosensory game space In the context of the new era, its intelligence has become an inevitable trend, which is the integration of physical space and information space. In order to build a big pattern and reach a new level in the publicity and ideological work of colleges and universities in the new era, it is necessary to strengthen cultural self-confidence, improve political standing, highlight ideological guidance, innovate public opinion propaganda, and strengthen cultural construction.
Sports Dance Intelligent Training Correction System based on Multimedia Image Action Real-Time Acquisition Algorithm

Sports dance intelligent training correction system based on the multimedia image action real-time acquisition algorithm is the main focus of this paper. The skeleton data is further extended by employing data enhancement technology, the diversity of samples is improved by increasing the observation angle of collected samples. This enhances the generalization ability of the model. Further, the analysis of the multimedia images can be understood from different aspects. According to the traditional method, by extracting the personalized features of multimedia images, using edge pixel information recombination and feature distributed reconstruction methods. Furthermore, the designed model is applied to the dance intelligent training correction system. Robustness and performance are then tested.
Identification of Malignant Attributes in Breast Ultrasound using a Fully Convolutional Deep Learning Network and Semantic Segmentation

Breast ultrasonography is a non-radiation imaging technology that is used to detect and categorize breast malignancies. Individuals have a minimal prevalence of adverse symptoms to it, and it is simple to incorporate therapeutic procedures. The proposed study has used a fully convolutional deep learning network to extract features from breast ultrasound images in order to identify different characteristics of breast imaging and data system terminology. This in turn simplify the procedure of categorizing tumors as benign or malignant. Using the BI-RADS vocabulary, 378 breast ultrasound images are analyzed and from that seven potentially cancerous characteristics are discovered. The mean accuracy and mean IU for the feature extraction are 32.82% and 28.88% respectively. Both the area under the curve and the normalized interconnection over union were observed to be higher than the results obtained by the equivalent feature extraction connections such as SegNet and U-Net by using the same range of data. The area under ROC curve has been 89.47% as well as the graded interconnection placed above a white confederation was 85.35%. By utilizing the ultrasonography to screen for breast cancer, the proposed research study suggests that it would be beneficial to make use of a deep learning network in conjunction with the BI-RADS terminology as a substantial supplementary diagnostic tool.
Stability Analysis of Obstacle Avoidance Ability and Environment Adaptation Modeling of Snake Robot based on Deep Learning and Binocular Vision

Stability analysis of obstacle avoidance ability and environment adaptation modeling of snake robot based on deep learning and binocular vision is studied in the paper. For a robot, another robot is also an agent. If a robot wants to avoid obstacles, it must consider the intention of the other party, that is, the influence of the other party must be considered for the constraint of action. In terms of structure, the sections that make up the snake body are connected horizontally and vertically, with three-dimensional movement ability, hence, the further optimization under different algorithm scenarios will make the detailed implementation of the systematic model. The deep learning and binocular vision are combined to improve the performance of the designed system.
Intelligent Information Retrieval: Handling Variability in Document Structure

Every corporation’s day-to-day activities entail dealing with a vast array of diverse data formats, such as work orders, techno’s, maintenance papers, and so on, many of which are selectable or scanned PDFs. These tasks demand several hours of human labor to extract the necessary data from these papers for further processing, and analysis incurring significant financial toll to these corporations. As a result, there is enormous potential for the creation of a digital solution that enables sophisticated OCR implementation, leading to the automation of the entire information extraction process. This paper provides a thorough examination of information extraction process focusing to deliver a high-quality complete functional solution and suggests a solution that incorporates critical preprocessing required for accurate information extraction and makes use of the capabilities of Faster R-CNN for document layout analysis as well as a range of approaches for efficient data extraction depending on data type. The multistage document analysis and information extraction tool also provides options for template definition enabling their reusability for batch processing large amounts of unstructured data.