Reviewerly/exHarmony huggingface dataset:
A Literature Review of Internet of Vehicle Based Blockchain

The Internet of Vehicles (IoV) is an emerging concept that helps to realize intelligent transportation systems (ITSs). IoV is an area of great interest given the rapid evolution of vehicle technologies, which enables communication between intelligent vehicles and the various system components related to their environment. However, the IoV suffers from security issues regarding the storage and management of exchanged information, in addition to the inability to manage traffic due to the increasing number of connected vehicles. To overcome these constraints, blockchain represents a promising technology in the IoV that provides a distributed, transparent and highly confidential database by recording immutable transactions. This study proposes a systematic review of the literature on the application of blockchain technology in the IoV. The objective is to identify the current challenges in the IoV and highlight the value of blockchain in solving these challenges based on papers published from 2017 to 2022 in Web of Science (WoS)-indexed scientific journals. In addition, the limitations removed from the theoretical foundations of several research works were identified, as well as the perspectives and directions to be addressed for future research.
Brain Tumor Segmentation Using Active Contour on Model Mumford-Shah Algorithm, Simple Standard Deviation, and Mathematical Morphology in Medical Images MRI

The segmentation of brain MRI is a critical step in many clinical applications. Artifacts inherent in this type of image, poor contrast, and substantial individual variances make it impossible to introduce a priori information. In this study, we offer a novel approach to brain MRI segmentation in which we integrate tissue and structural segmentation. We tested our proposed approach’s performance on both simulated and actual images, particularly its robustness to artifacts at low computation time. Statistical models appear to be a useful approach for medical image segmentation.
Smooth Collision-Free Trajectory Planner for AMR Based on B-spline Interpolation and I-GWO

The problem of trajectory planning is one of the important tasks of autonomous mobile robots (AMR). For that, in this paper, a trajectory planning algorithm is developed to generate a smooth trajectory for AMR in a static environment. The generated trajectory links a starting point with an end point while avoiding static obstacles. The trajectory curve is parameterized with the B-spline interpolation due to its smoothness properties and to reduce the number of points needed to define. Therefore, to find the optimal B-spline parameters, the trajectory planning problem has turned into an optimization problem. Improved Grey Wolf Optimizer (I-GWO) was selected as optimization algorithm because of its excellent optimization performance and global search capability. Different I-GWO settings were tested and the best selected setting was compared with other optimization algorithms. The results show that the selected optimization algorithm is more efficient and accurate.
A Comparison Between LSTM and Transformers for Image Captioning

Image captioning is the process of generating a textual description of images, which integrates both computer vision and natural language processing. Approaches based on encoder-decoder architectures have been recently proposed to solve image captioning problems. The main objective of this paper is to conduct a comparative study between the two most widely used approaches for natural language processing tasks, namely, LSTMs and Transformers. We used the Flickr8k dataset as input images. Regarding image feature extraction, we used the VGG16 model. To evaluate the obtained descriptions generated by the models, the BLEU score metric is used to measure the performance of both models. The latter were able to generate grammatically correct and expressive captions.
Blockchain Technology for Audit Firms: Perspectives and Challenges

Blockchain technology has spread to different business areas beyond cryptocurrency since 2008. Its operating mode has generated innovative and secure practices to insure reliable, immutable and ‘real-time’ information within an organization. This paper is a research on Blockchain technology, discussing its implication in audit procedures such as planning, gathering audit evidence and finally, exercising the professional judgement on the financial statements. The conducted study investigates the operation of external auditing by revealing the fundamental shift operated by the implementation of Blockchain within audit procedures. Empirical data were collected through semi-directive interviews with 25 subject audit experts. Lastly, the study exhibits the requirements for the application of Blockchain in audit firms.
Data Augmentation and Deep Learning Applied for Traffic Signs Image Classification

Traffic sign recognition is an essential phase for intelligent autonomous driving systems. In this work, we have presented a recognition solution which is based on the classification performed by supervised learning method through deep learning an artificial using Convolutional Neural Network (CNN). Three pre-trained neural networks such as, GoogLeNet, VGG-16, AlexNet, were adapted for transfer learning algorithm. In order to improve the classification accuracy and to solve the problem of the insufficient amount of training dataset or the unequal number of images for each class, we have used the data augmentation technique applied on German Traffic Sign Benchmark dataset (GTSRB). The obtained experimental results show that the size of the dataset as the number of training images has a significant impact on the classification accuracy of the CNN model. The number of images was increased from 51,840 to 96,750 images by using data augmentation technique. Therefore the classification accuracy was increased from 86.72% to 97.33%.
Machine Learning with Reinforcement for Optimal and Adaptive Learning

In this article, we focus on Artificial Intelligence (AI) as a teaching topic. At the moment, it is important that every citizen is not a simple consumer of technology. We have challenges to overcome around digital sciences which must have the same place as life and earth sciences in the training of an individual in school curricula. Artificial intelligence is particularly concerned. How to overcome the initial representations of students who may think that these little machines work like magic. It is crucial that students can understand how these voices can inform us about time. The school has a big role to play. As for the teaching and learning of programming which has experienced an exceptional boom in recent years, the idea is not to make young people trained at the school of computer engineers. Rather, it is a question of fully preparing them for the world of tomorrow and understanding what AI is by asking these crucial questions. Beyond the learning mode (individual and / or collaborative), the modeling of user profiles and educational resources of a training domain represent essential parameters for the realization of adaptation to a user in order to recommend relevant educational resources and more finely customize a learning path for a single user in individual mode, but also for a group of users during the synchronization of events in collaborative mode. In this article, after an overview of the existing AI in education, we propose an architecture for the management of this type of adaptive learning.
Android-Based Attendance Management System

Over the years, the manual attendance monitoring process has been the predominant practice. Given the fact that manual processing of attendance can be time-consuming and may produce errors, this has prompted the majority of organizations to use automated attendance monitoring systems to reduce the need for manual attendance tracking. In view of the above, we solve the problem of attendance monitoring via an innovative Android mobile app on smartphone using offline wireless technology, thus reducing the need for Internet connection. The BSSID of the access point can be detected by each student’s smartphone in order to use it as a tracking identifier for attendance. Our application aims to reduce the time taken during the manual process, minimize human errors, and free teachers and administrations from the loads of work often associated with classical attendance checking methods so that efforts can be invested in other more important activities.
Path Planning of Underwater Biomimetic Robot

Path planning is one of its primary robot technologies.
Cost and Performance Analysis of MPI-Based SaaS on the Private Cloud Infrastructure

The paper presents the cost and performance analysis of parallel MPI-based software as a service (SaaS) deployed on the OpenStack cloud infrastructure. The parallel SaaS was developed by using C++ programming language and MPI library for the scientific discrete element method (DEM) computations of granular flows. The performance measured on KVM-based virtual machines was slightly higher than that on Docker containers of the OpenStack cloud. Round up and proportional pricing schemes were examined and compared from the user’s perspective. The difference in cost computed by using alternative pricing schemes varied from 0.6% to 15.4%. However, this difference can be reduced to 1.0%, increasing execution time of considered tasks. The investigation of a trade-off between the execution time and cost was performed by using Pareto front analysis and a linear scalarization method. Bi-objective decision making revealed the preferable configurations of virtual machines specific to memory bound DEM computations, exploiting higher bandwidth.
Using AI-based Edge Processing in Monitoring the Pedestrian Crossing

In edge processing, data collection devices are used to pre-filter data. As a result, only data of interest will be written to memory. This approach significantly reduces the amount of transferred data. Various algorithms, such as background segmentation or artificial intelligence (AI) techniques based on various neural networks, can be used to detect data of interest. This paper uses an AI-based technique in the edge processing environment to perform the learning process with a chosen neural network. The environment containing Nvidia Jetson Xavier NX is employed to train the MobileNetV3 network dedicated to detecting objects of interest like people or vehicles while monitoring the pedestrian crossing. The network consists of the initial fully connected convolution layer with 32 filters, followed by 19 residual bottleneck layers. This paper also proposes a learning process that uses the collected data after manual validation and significantly improves accuracy over the original network.
Pedestrian Detection Model Algorithm Optimization Based on Deep Convolutional Neural Network

Pedestrian detection is a classical problem in computer vision and has been a difficult problem to solve for a long time. Compared with face detection, it is very difficult to accurately detect pedestrians in various scenarios because of the complex posture of human body, larger deformation, and more serious problems such as attachment and occlusion. This paper focuses on the typical pedestrian detection model - YOLO model. Through experiments, the principle of pedestrian detection model algorithm and its model effect are studied to solve the difficulties in pedestrian detection. In YOLO, logistic regression is used to predict the object score of each boundary box, and multi-scale fusion is used to make prediction. By observing the mAP index, it is concluded that the YOLO algorithm has a good effect on single-label pedestrian detection, and the calculation efficiency is high.
Compact In-Memory Representation of Decision Trees in GPU-Accelerated Evolutionary Induction

Decision trees (DTs) are popular techniques in the field of explainable machine learning. Traditionally, DTs are induced using a top-down greedy search that is usually fast; however, it may lead to sub-optimal solutions. Here, we deal with an alternative approach which is an evolutionary induction. It provides global exploration that results in less complex DTs but it is much more time-demanding. Various parallel computing approaches were considered, where GPU-based one seems to be the most efficient. To speed up the induction further, different GPU memory organization/layouts could be dealt with. In this paper, we introduce a compact in-memory representation of DTs. It is a one-dimensional array representation where links between parent and children tree nodes are explicitly stored next to the node data (testes in internal nodes, classes in leaves, etc.). On the other side, when the complete representation is applied, children positions are calculated based on the parent place. However, it needs a spacious one-dimensional array as if all DT levels would be completely filled, no matter if all nodes actually exist. Experimental validation is performed on real-life and artificial datasets with various sizes and dimensions. Results show that by using the compact representation not only the memory requirements are reduced but also the time of induction is decreased.
Dataset Related Experimental Investigation of Chess Position Evaluation Using a Deep Neural Network

The idea of training Artificial Neural Networks to evaluate chess positions has been widely explored in the last ten years. In this paper we investigated dataset impact on chess position evaluation. We created two datasets with over 1.6 million unique chess positions each. In one of those we also included randomly generated positions resulting from consideration of potentially unpredictable chess moves. Each position was evaluated by the Stockfish engine. Afterwards, we created a multi class evaluation model using Multilayer Perceptron. Solution to the evaluation problem was tested with three different data labeling methods and three different board representations. We show that the accuracy for the model trained for the dataset without randomly generated positions is higher than for the model with such positions, for all data representations and 3, 5 and 11 evaluation classes.
The Color Sensor

The Robot Inventor includes a Color Sensor, pictured in Figure 9-1, that works with light. It can measure the color of objects placed in front of the sensor, as well as the brightness of light that hits the sensor. The Color Sensor includes a circular light on its front surface to provide a source of light for objects to reflect. An exercise in this chapter will work with this brightness reflection function to build a beeping tone generator. Then to use the namesake function of the Color Sensor, the chapter project builds a machine that sorts liftarms by their color.
Using GitHub and Grafana Tools: Data Visualization (DATA VIZ) in Big Data

We’ll talk about data visualization, why it’s important, and how to use data visualization tools called GitHub and Grafana in this article. There are several approaches to perform automatic text summarization. It can be done with supervised or unsupervised learning. And inside these categories, there are a wide variety of methods. In terms of types of summaries, there are two extractive and abstractive. The program presented here uses unsupervised learning and generates an extractive summarization. Extractive summarization is when the summary is a subset of the original text because all words in the summary are included in the original text. Data visualizations are surprisingly widespread in our daily lives, yet they frequently take the form of familiar charts and graphs. It can be utilized to find out about new things. When communication, data science, and design all come together, good data visualizations result. When done correctly, data visualizations provide significant and intuitive insights into complex datasets. This article provides a basic data visualization overview. The components regarding data visualization that should be considered throughout operational steps such as an initial argument for breakdown, communication, procedure, encoder, framework, station, and translator—which equate to six fundamentals in the framework of data visualization—are used.
Hands-Free Eye Gesture Authentication Using Deep Learning and Computer Vision Principles

Authentication systems are security measures that are put in place to safeguard data and systems for users to access a system. Personal identification numbers are used to authenticate and secure users. Password verification using PINs necessitates users entering a physical PIN, which might be subject to password cracking or hacking via shoulder surfing or thermal tracking. To develop an application that will authenticate users more safely and protect the user from shoulder surfing and thermal tracking. The proposed system uses a blend of Deep Learning and Computer Vision techniques to protect the user by combining eye blink count-based PIN entry, and face recognition to avoid shoulder surfing and thermal tracking attacks. Face detection is accomplished by the use of the Haar Cascade classifier, which is a cascade function learned from a large number of positive and negative pictures and is used to recognize objects (faces) in other images, and Dlib’s frontal face detector, which employs the HOG and Linear SVM models. The face is recognized by calculating the features of the face and matching it with the available feature set calculated from the dataset. Eyeblink is counted using the Eye aspect ratio which uses the Euclidean distance formula between the eye coordinates. By taking the count of blinks for every digit, the PIN is generated. PIN authentication with hands-off eye blinks, on the other hand, leaves no physical footprints and thus provides a more secure password entry method when combined with face recognition.
Assembling Fragments of Ancient Papyrus via Artificial Intelligence

The knowledge of humanity passes through the ancient texts whose acquisition, reconstruction and interpretation become tasks of fundamental importance. The simultaneous spread of equipment capable of exploiting new technologies for data acquisition together with the opportunities offered by Artificial Intelligence open new unimaginable horizons in different applications including the conservation of cultural heritage. In this work, we refer to the opportunity inherent the acquisition of texts from papyri via machine learning and deep learning applications. The theme of assembling fragments, will be investigated by referring to some recent interesting contributions of the scientific community.
Real-Time Vehicle Detection and Tracking System Using Cascade Classifier and Background Subtractor

The utilization of video surveillance in the domain of Inspection and Supervision for security concern is extensive. The areas such as navigation in Military, Smart Transportation, etc. is aided by video Surveillance System. Vehicle counting systems, intelligent parking systems along with Autonomous Driving Assistant Systems (ADAS), there is a high demand for Real-time Vehicle Detection System for detecting any mobile architecture. The importance of processing a real-time stream of frames of a video stands out to be the major outlook for creating a lightweight and efficient model for giving potent results. For this purpose, we need an algorithm which can be easily configured according to the hardware and perform well, even in low processing power thereby reducing inventory costs. In this paper, we have scrutinized the Cascade Classifier and the Background Subtractor MOG2 modus operandi’s for assuaging the competency of each model on different devices based on Frames Per Second (FPS) value and the amount of processing required to run in real-time. The comparison of these models has also been carried out with MobileNet and Xailient architectures for validation. The results gave us an idea of the need of including a complexity perspective into an algorithm for its actuation for proving its need in real-time.
Tweet Classification on the Base of Sentiments Using Deep Learning

Twitter data has been used to improve political campaigns, product quality, and sentiment analysis over the last several years. An essential and collaborative effort for many businesses is to classify tweets based on user sentiment. A machine learning classifier is proposed in this research in order to aid in sentiment analysis for these types of organizations. The classifier employs a soft voting process based on Logistic Regression (LR) to determine the final prediction. Based on the content and tone of the tweets, we divided them into three categories: “good,” “negative,” and “neutral.” Additionally, the accuracy and F1-scores were used to assess the performance of several machine learning classifiers. Classification accuracy was also examined in terms of feature extraction strategies, such as term frequencies, Inverse Document Frequencies (TF-IDF), and Words-To-Vectors (W2V). Furthermore, the performance of the Deep Long-Term Memory (DLTM) network was evaluated on the dataset. Compared to other classifiers, the presented classifier performs better. With TF-IDF feature extraction, the LR can attain an accuracy of 0.9616 and an F1-score of 0.7633. According to these findings, ensemble classifiers outperform non-ensemble classifiers. According to experiments, using TF-IDF as a feature extraction approach improves the performance of machine learning classifiers. The extraction of W2V features is less efficient than the extraction of TF-IDF features.
Group Fairness in Outlier Detection Ensembles

Detection of anomalies in regular visual patterns is a severely complex and challenging problem of machine learning and computer vision. For outlier detection, ensembles are considered to outperform individual outlier detection methods. However, due to several open challenges, ensembles may make the results biased toward some particular groups. In this paper, we address the task of inducing group fairness in the outputs of outlier detection ensembles. We propose a rank-based post-processing method for multiple multi-valued sensitive attributes using Kruskal–Wallis H test. The effectiveness of the proposed method is demonstrated through extensive empirical analysis on five benchmark datasets and using a variety of ensemble methods. We analyze the trade-off between performance and fairness and illustrate that the proposed method achieves significant improvement in group fairness; the performance results are comparable.
Adaptive filter method in Bendlet domain for biological slice images

The biological cross-sectional images majorly consist of closed-loop structures, which are suitable to be represented by the second-order shearlet system with curvature (Bendlet). In this study, an adaptive filter method for preserving textures in the bendlet domain is proposed. The Bendlet system represents the original image as an image feature database based on image size and Bendlet parameters. This database can be divided into image high-frequency and low-frequency sub-bands separately. The low-frequency sub-bands adequately represent the closed-loop structure of the cross-sectional images and the high-frequency sub-bands accurately represent the detailed textural features of the images, which reflect the characteristics of Bendlet and can be effectively distinguished from the Shearlet system. The proposed method takes full advantage of this feature, then selects the appropriate thresholds based on the images' texture distribution characteristics in the database to eliminate noise. The locust slice images are taken as an example to test the proposed method. The experimental results show that the proposed method can significantly eliminate the low-level Gaussian noise and protect the image information compared with other popular denoising algorithms. The PSNR and SSIM obtained are better than other methods. The proposed algorithm can be effectively applied to other biological cross-sectional images.