Reviewerly/exHarmony huggingface dataset:
Mining Frequent Patterns from Temporal Dataset Using Backtracking Search Tree of GenMax Algorithm

In recent days, many real-life applications incorporate databases that involve time factors to be considered for any decision-making. The knowledge discovery deals with the mining of frequent and valuable patterns from massive databases as they are the critical step in association rule mining. The temporal data is not considered a significant attribute in many traditional data mining techniques. This feature contains timestamping data that can affect the result of the mining process. Thus, there is a decrease in mining efficiency without considering the temporal data. This paper proposes a modified GenMax algorithm to discover the temporal patterns from a huge database. The proposed algorithm employs the basic concept of the GenMax algorithm, where all maximal patterns are efficiently computed with the help of a backtracking search. In addition, it uses a temporal cube to consider the time factor of the data for efficient temporal pattern mining. It also has some advantages as GenMax works efficiently with fast frequency computation and eliminates non-maximal itemsets.
An Innovative User Authentication Method: Replacements of Text Based Passwords

Authentication is an important aspect for information security. Text based passwords have been the choice for authentication over the years, but many security threats and vulnerabilities are reported now. Researchers and academicians have suggested many alternatives with some advantages and limitations. It has been noted that few alternative methods are very promising but very complicated to implement hence they have not been adopted in real life applications. Keeping this in mind, a simple, innovative and easily implementable method for user authentication as an alternative of text-based passwords is proposed in this paper. User needs to upload an image of his choice as a sign-up process initially. This image is analysed by server and decomposed into many components. When the user wants to get access to the system next time, the server gives him a challenge to arrange all the image components in a sequence. These image components are randomly jumbled and server plays an intelligent role here. Based on the pattern and log in attempt by user, server can increase or decrease number of image components based on an algorithm and it enhances the randomness and security level of the authentication process. The server learns about variations gradually and in case of any suspicious activity, server can instruct the user to change the image which acts as a password. The test results and security analysis along with proposed method is discussed here in order to prove the feasibility and validity of the proposed method. It is believed that the proposed method has the strength to replace text-based passwords and the method can be incorporated with various machine learning based cryptographic procedures which will enhance the overall utility of the proposed work.
Simulation of Internet of Things Network for Big Data Analytics

In recent years there has been a tremendous amount of data generated from various wireless sensor networks. These sensor networks generate a large volume of data. Traditional data processing systems do not process these data. An effective analytical model is required for efficient design and implementation of WSN’s and the data collection, processing, and analytics to process this type of data. The complex data collected from the sensors can be called big data. Drawing the insights from the data collected is a challenging task in real-time analysis. This paper discusses big data characteristics, types of data analytics and their applications in the IoT environment. All the WSN’s consist of sensing devices associated with few resources, and they are connected wirelessly. As a part of IoT analysis, in this paper Contiki operating system is used along with the Cooja simulator to simulate an IoT network. The number of motes can be used to communicate with each other. This paper focuses on IoT generation and its analysis using Hadoop framework tools.
Obstacle Finding and Path Planning of Unmanned Vehicle by Hybrid Techniques

With the wide application of unmanned vehicles (UV) in a complex environment, the research on path optimization and obstacle detection system has gradually become an important research part in the field of unmanned vehicle (UV) systems. The basic concept of path planning methods has always been a basic and difficult problem, especially in complex environments, in the effect of the dynamic environment, the safety, smoothness, and real-time requirement. To perform the task in a different environment and follow the exact movement and find the accurate path as well as remove the minimum human hurdle. In this research paper to find the shortest path with the avoidance of human obstacles with optimization techniques with meta-heuristic algorithms such as A*, fuzzy logic, GA, etc. we compare A* and fuzzy logic algorithms with the terms of processing time and path length with obstacle detection for high traffic areas. This research paper was practically implemented by the MATLAB software to simulate UV traversing from starting to endpoints.
2D Transparent Soil Imaging and Digital Image Cross-Correlation

Transparent soil is used to model natural soils in geotechnical research. The laser speckle effect generated when a laser light goes through the transparent soil were used to provide the displacement information of soil particles. Digital image cross-correlation is usually adopted to process the laser speckle images of the transparent soil.This chapter presented a typical experimental set up for 2D transparent soil modelling, 2D and 3D digital image cross correlation methods, and influences of particles size on digital image deformation measurement.
Intra- and inter-semantic with multi-scale evolving patterns for dynamic graph learning

Dynamics and heterogeneity are two principal challenges in recent graph learning research and are promising to solve many real-world applications better. Existing work usually decomposes heterogeneous graphs as different semantics via meta-path and learns each space representation separately. For dynamic graphs, they mostly learn the evolving patterns based on a general assumption that dynamic graphs change smoothly along with timestamps. Although much progress has been achieved, they still suffer from at least two open problems. First, different semantics are not always independent of each other because more information is reflected in their mutual interactions. Ignoring this latent influence might cause insufficiency in downstream tasks. Second, the smoothness assumption for graph evolving relies on temporal granularity. Specifically, two adjacent snapshots may change dramatically when the time interval is very coarse. To this end, this paper wishes to push forward this research area by learning interactions between different semantics and revisiting short-term changes within local areas and long-term changes at the global level. In particular, to address the first problem, we propose a novel cross-view mechanism to capture the mutual influence of different views. To solve the second problem, we construct a temporal graph with both short-term and long-term information and then design a graph attention network to learn multi-scaled features at both local and global levels. Extensive experiments on various graph tasks demonstrate the superiority of our model over state-of-the-art methods.
Safe and Topographic Path Planning with Deep Neural Networks

Path planning with the topographical characteristics of real-world terrains has been investigated in the solution of problems in simulation systems and computer games. Path planning in real-world terrains involves finding a path with a low cost in terms of distance traveled according to the terrain relief. This work investigates how the computations of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{\ast}$</tex> algorithm have to be performed for an agent to obtain a hidden path from the view of an observer in such terrains with topography. This indicates that terrain features are important for obtaining a path with reduced topographical cost that permits visually hiding an agent from an observer in a given terrain location. Moreover, this work evaluates the use of deep neural networks in the approximation of the heuristic used by the pathfinding algorithm, optimizing the execution time and number of expanded nodes required to compute safe routes with lower topographic costs that are generated with the proposed Deep Safe and Topographic <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$A^{\ast}$</tex> (DSTA*) algorithm.
Impact of Different Noise Distributions in the Application of Kalman Filter in Sensor Fusion

This paper explains the concept of the Kalman filter as an estimator of the system state based on measurements in the presence of noise. Sensor fusion allows for the measurement of factors that are not immediately measurable as well as the creation of more accurate data. The theoretical underpinning for the definition of a filter and Kalman-based sensor fusion, and filter and sensor fusion algorithms are explained and implemented in simulation. The application of the filter for sensor fusion is presented, and in the end, simulation results are obtained for different types of noise distribution - normal as the usual one for physical quantities and uniform as dominant in digitized values.
Evolutionary computation plus Mathematical Programming for the Traveling Car Renter Salesman Problem

The Traveling Car Renter Salesman Problem (CaRS) is a generalization of the Traveling Salesman Problem. A new variant of the Adaptive Local Search Procedure (ALSP) algorithm called Iterated Adaptive Local Search Procedure (IALSP) is presented in this work. Two mathematical formulations are presented to model the CaRS problem. These formulations are compared using a MIP solver. The formulation with the best result is used in the IALSP algorithm. To deal with the CaRS problem, we propose a hybrid algorithm composed of an evolutionary algorithm called Scientific Algorithm (ScA) and the IALSP algorithm. We call the proposed hybrid algorithm ScA + IALSP. We have carried out computational experiments on a set of 15 instances extracted from the literature. We have compared the proposed algorithm with the best-known algorithm in the literature. The results show that the IALSP algorithm is competitive. Six new best results are reported.
Effect of Peer Influence and Self-Reflection on Scaffolded Out-of-Class Activity Administered Using a Mobile Application

Student engagement with out-of-class activities is becoming more difficult as students spend fewer hours outside the classroom studying the content. This research developed a mobile educational platform, Dysgu, to provide students with an optimal learning experience outside of the classroom. Dysgu includes social networking and gamification features to increase student engagement. The platform offers interactive auto-graded assessments to help students practice concepts and take tests. Students can see their scores and a summary of the performance of the rest of the class. We used Dysgu for multiple out-of-class activities at two universities with different student demographics for two semesters. The data shows that students obtain better grades when using Dysgu. We also saw more on-time or ahead-of-time submissions with Dysgu. Survey responses indicated several Dysgu features which students found helpful. We conclude that digital educational platforms should consider features to support scaffolding to master the concept, peer influence to keep students engaged, self-reflection to foster critical thinking, and easy adaption of the platform to reduce faculty workload and improve students’ acceptance of the system.
Trust Management: A Cooperative Approach Using Game Theory

Trust, defined as the willingness to accept risk and vulnerability based upon positive expectations of the intentions or behaviours of another. The qualities or behaviours of one person that create good expectations in another are referred to as trustworthiness. Because of its perceived link to cooperative behaviour, many social scientists regard trust as the backbone of effective social structures. With the advancement in technology, through these online social media people can explore various products, services and facilities. Through these networks the end users want to communicate are usually physically unknown with each other, the evaluation of their trustworthiness is mandatory. Mathematical methods and computational procedures do not easily define trust. Psychological and sociological factors can influence trust. End users are vulnerable to a variety of risks. The need to define trust is expanding as businesses try to establish effective marketing strategies through their social media activities, and as a result, they must obtain consumer trust. Game theory is a theoretical framework for analysing strategic interactions between two or more individuals, in the terminology of game theory, called players. Thus, a conceptual framework for trust evaluation can be designed using a game theory approach that can indicate the conditions under which trustworthy behaviour can be determined.
Named Entity Recognition for Russian Judicial Rulings Text

The article presents the solution of named entity recognition problem for legal Russian-language texts. We studied CRF, LSTM, BERT and BiLSTM and their combinations. The models were tested with various parameters of text preprocessing and words vector representations. The best result was shown by fastext vectorization with BiLSTM and CRF model, the value <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F-score$</tex> is 0.86.
Driving Safe Speed Estimation Based on Outside Environment Vision Analysis

Using predefined speed limits on roads had huge positive safety effects on decreasing the vehicle accident rate. On the other hand using these static limits was the 20th-century solution. With the recent evolution of machine learning, driver assistant systems, and autonomous driving the necessity for dynamic speed limits are raised. In this paper we propose a novel method to analyze the scene and adjust the speed limits according to the environment's dynamic changes taking into account static constraints. Our proposed system is a recommendation system to estimate safe speed limits. It will consider the static predefined speed limits as well as other values like (the number of vehicles on the scene, the relative distance to the closest vehicle, the road width and curvature, the weather state, and the day/night (illumination) states.
Thesis Review and Analysis Automated System

In this article, we propose the design, construction, and validation of a technological solution with the ability to automate the process of reviewing and analyzing thesis paper by using natural language processing and a deep learning training algorithm. This project seeks to be a proof of concept because current solutions in the academic field focus on abstracting and analyzing scientific articles but not in theses. Another point to consider is that these solutions are in English language. The resulting language model was compared with other language models based on the Transformers architecture. The result of this comparison gives us an objective for future research on the A.S.T.R.A. project.
Channel Traffic Minimizing Key Sharing Protocol Intended for the Use over the Internet and Secure without any Cryptographic Assumptions

A further development of the key sharing protocol presented at previous FRUCT -31 session is proposed in the current paper. In contrast to the previous protocol executing by an exchange of real integers over Internet channel, a new protocol version operates with binary bits and all operations are modulo two additions. Such version significantly decreases channel traffic required for key sharing and a complexity of signal processing. The full description of protocol is given. Optimality of signal processing proposed in the paper for eavesdropper is proved.
An Event-Driven Approach to the Recognition Problem in Video Surveillance System Development

Many video surveillance systems (VSS) have been already developed for various application domains. Such systems are based on well-elaborated recognition algorithms of Artifi-cial Intelligence (AI) and implemented as Ambient Intelligence (AmI) services in Internet of Things (IoT) environments. In particular, algorithms support such smart VSS functions of video data processing as human detection, human identification, object location within an image, human activity recognition. Many software tools have been developed to implement various recognition algorithms for VSS development. In this paper, we consider the following VSS development problems: a) a generic model of events in video data for a given problem domain, b) a hardware-software architecture for data processing with existing recognition algorithms, and c) a model to construct a required smart VSS function using existing software tools. We introduce our event-oriented approach to solve the above VSS development problems. The approach is experienced in several use cases. Our experimental study shows the applicability of the proposed approach in terms of the accuracy and performance.
Opinion Mining for Modeling User Experience of Online Education: Sentiment Analysis and Keywords Extraction of Student Reviews

The paper discusses the possibilities of applying modern natural language processing technologies of opinion mining to investigate and improve the user experience of online-courses students. We analyzed 27 000 student reviews of projects within the Python programming language course. First, we applied keyword extraction algorithms as a way of semantic compression to receive a generalized picture of what users' main impressions are. Then we performed sentiment analysis to understand the feelings of students towards the learning process. The used methodology proved to be effective for analyzing user experience and allowed to find out some discrepancies between information in project descriptions and what users' reflection on the project. Two instruments of SA were applied to receive data on users' feelings in general.
GAN based Visible to Thermal Image Translation

The increasing demands of deep learning based solutions for security and surveillance applications has opened various new challenges in computer vision. Multimodal face recognition is one of the important tasks where face recognition may be required to be performed on infrared or thermal camera images in locations with low visibility and challenging lighting conditions. However, the data available for such modalities is not enough as compared to data available for visible light images. This paper presents a generative adversarial network (GAN) based solution to solve the problem of data scarcity. The closed loop GAN system CycleGAN is used to generate novel thermal images from visible light images. The results are very impressive and close to reality.
Prediction of Corrosion of Oil Pipelines in Ecuador based on Machine Learning

The corrosion in oil pipelines not only causes economic losses to Ecuador, but also has harmful effects on both people’s health and the environment. This study applied Machine Learning algorithms to predict the level of corrosion based on data collected regarding production and the chemical processes in the oil pipelines. Support Vector Machine (SVM), Random Forest (RF) and XGBoost classification models were implemented and evaluated according to a set of metrics, such as accuracy, precision, and f1-measure, and based on these results a model was selected to tune the hyperparameters to improve the performance of it. In addition, a feature engineering stage was considered, which includes the normalization of the data, the elimination of variables with high correlation and the balance of the data set. 10-fold-cross-validation was also performed. In this case, the results of the analysis showed that the RF model has better performance results than the other models, for instance, RF accuracy was about 20% better than SVM and 5% better than XGBoost, so RF model was selected for the hyperparameters tuning. Besides, a dashboard web application was developed so the end user can make the prediction in a friendly way.
Learning Neural Processes on the Fly

Deep neural networks (DNNs) have performed impressively on a wide range of tasks, but they usually require a significant number of training samples to achieve good performance. Thus, DNNs do not work well in low-data regimes because they tend to overfit a small dataset and make poor predictions. In contrast, shallow neural networks (SNNs) generally are robust against overfitting in low-data regimes and converge more quickly than DNNs, but they struggle to represent very complex systems. Hence, DNNs and SNNs have a complementary relationship, and combining their benefits can provide fast-learning capability with high asymptotic performance, as meta-learning does. However, aggregating heterogeneous methods with opposite properties is not trivial, as it can make the combined method inferior to each base method. In this paper, we propose a new algorithm called anytime neural processes that combines DNNs and SNNs and can work in both low-data and high-data regimes. To combine heterogeneous models effectively, we propose a novel aggregation method based on a generalized product-of-exports and a winner-take-all gate network. Moreover, we discuss the theoretical basis of the proposed method. Experiments on a public dataset show that the proposed method achieves comparable performance with other state-of-the-art methods.
Regressing Image Sub-Population Distributions with Deep Learning

Regressing the distribution of different sub-populations from a batch of images with learning algorithms is not a trivial task, as models tend to make errors that are unequally distributed across the different sub-populations. Obviously, the baseline is forming a histogram from the batch after having characterized each image independently. However, we show that this approach can be strongly improved by making the model aware of the ultimate task thanks to a density loss for both sub-populations related to classes (on three public datasets of image classification) and sub-populations related to size (on two public datasets of object detection in image). For example, class distribution was improved two-fold on the EUROSAT dataset and size distribution was improved by 10% on the PASCAL VOC dataset with both RESNET and VGG backbones. The code is released in the GitHub archive at achanhon/AdversarialModel/tree/master/proportion.
Scalable Algorithms for Simultaneous Mapping and Localization of Mobile Robot Swarms

The chapter is devoted to the development of scalable algorithms for multi-agent solution of the SLAM problem. These algorithms are applicable to robots with limited computational resources, having limited computational power and memory, small spatial size, and power from a portable battery. To simplify the description, only robots equipped with LIDAR are considered. The main focus is as follows: a scalable multi-agent SLAM algorithm based on Dempster-Shafer theory; an algorithm for filtering two-dimensional laser scans to free up computational resources; evaluation of the accuracy of the map and trajectory constructed by the multi-agent algorithm; performance evaluation on resource-limited computing devices.