Reviewerly/exHarmony huggingface dataset:
Development of Deep Learning Based Models for Detecting the Significance of Non-Manual Parameters for Indian Sign Language Interpretation

The interpretation of sign language is a much-needed state of the art for promoting communication between the second-most prominent divyangjan of our society, Deaf people, and the rest of us. The proposed system considers both the manual parameters and the non-manual parameters of the Indian Sign Language signs. Non-manual parameters play a key role in the selected signs for interpretation. With a total of 50 gestures, deep learning-based models-custom CNNs, VGG16, MobileNetV2, and ResNet50 are trained and tested without key points as a feature set and with key points as a feature set. As a conclusion, the interpretation was successful in custom CNN models with a maximum testing accuracy of 73%.
Gesture Controlled Drone Swarm System for Violence Detection Using Machine Learning for Women Safety

The safety of women is an important issue. Major constraints in handling of these cases are that the police are not aware of the location of crime during the time and the crime itself. Victims find it difficult to inform the police. To aid police, this paper proposes the design and development of Gesture Controlled Drone which uses Machine Learning algorithms to track the user and to detect signs of aggression directed towards the user. In such cases an event is detected or suspicious to happen, the complimentary Android App shall alert a selected set of contacts of the user’s woes and where-abouts, while also informing the local police station. The proposed gesture-controlled drone is implemented and performance of the system is analyzed. Experimental results reveal that the proposed system works better and it is effective.
Development of a System for Controlling IoT Devices Using Gaze Tracking

In this work, a prototype based on gaze tracking to control IoT devices was made. This prototype is a web application where the user can interact with interfaces through the gaze. The user can send commands to an IoT device using this application. For the creation of the aforementioned system, the iterative and incremental development methodology was used. The prototype was developed using a webcam and an open source library called WebGazer. These tools were selected based on a state-of-the art review. The prototype also uses an MQTT server and a Broker Communication to establish the connection between the web application and IoT devices. The web application has a user registration function which allows each user to have a channel in Beebotte to control only his/her devices. Finally, the prototype was tested using a light bulb and TV. The commands sent to the light bulb were ON and OFF while the commands sent to the TV were ON, OFF, change the channel (up and down), turn the volume up and turn the volume down. Based on the performed tests, it was possible to measure the effectiveness of the prototype which had 98.7% of precision, 97% of accuracy, and 3.14 s for icon selection.
Finetuned-VGG16 CNN Model for Tissue Classification of Colorectal Cancer

Classifying cancer tissues has been a difficult task ever since Computer Vision and Pattern Recognition were developed. Deep Learning, a modern, state-of-the-art method for texture categorization and localisation of cancer tissues, has replaced traditional machine learning approaches. The third leading cause of mortality from cancer globally is colorectal cancer. In this paper, a fine tuned deep learning model is proposed for image-level texture based classification using CRC dataset. In order to minimise overfitting and considerably increase classification accuracy, the model must be fine-tuned, especially when there are less set of training datasets available. The VGG-16 pretrained model underwent fine-tuning. The nine classes of the histology Kather CRC image collection were used to verify these models (CRC-VAL-HE-7K, NCT-CRC-HE-100K). The outcomes demonstrated that the accuracy of histopathological image recognition was much higher than that of previous approaches.
Development of a Phasogrammetric Measurement System for Error Estimation in Close-Range Photogrammetry

In aeronautics, engineering, medicine, robotics and other industries, optical methods are widely used to measure the geometry and surface deformation of various objects from their images. These methods are based on digital image processing algorithms and can be summarized in a group of methods called close-range photogrammetry. When developing photogrammetric measurement systems, one of the problems to be solved is the estimation of their error. In this work, it is proposed to use physical simulation to estimate the error, which gives more reliable results than theoretical calculations or computer simulation. The phasogrammetric method has been chosen as the reference method for surface shape measurement. This paper presents the results of the development of an optical phasogrammetric measurement system based on the structured illumination method and an evaluation of its accuracy characteristics.
Geospatial Ontology-based Data Access

chapter Share on Geospatial Ontology-based Data Access Author: Konstantina Bereta View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 185–198https://doi.org/10.1145/3581906.3581918Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Geospatial Data Modeling

chapter Share on Geospatial Data Modeling Author: Manolis Koubarakis View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 9–30https://doi.org/10.1145/3581906.3581909Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
On secure communication scheme-based mixed discrete-time hyperchaotic systems

The proposed secure communication system is based on mixed discrete-time chaotic systems in this study. To increase signal transmission security over the public channel, this system is composed of two chaotic systems on the transmitter and receiver sides. Borne and Gentina criterion associated with the Benrejeb arrow form matrix is used for stability study and system description to ensure the synchronization between these chaotic systems. The case of electrocardiogram encryption and decryption is considered an application to illustrate the effectiveness of the proposed chaotic encryption approach. The performance analyses of the proposed secure communication under the Additive White Gaussian Noise (AWGN) channel, brute force attack and some statistical attacks have shown that the proposed encryption scheme exhibited a high level of security, could maintain the confidentiality and integrity of the transmitted information and the original signal can be accurately recovered even in the presence of noise.A comparitive study between different methods is given to show the advantages and the drawbacks of the proposed communication system.
Design and Evaluation of a Personalized Job Recommendation System for Computer Science Students Using Hybrid Approach

This paper presents the design and evaluation of a job recommendation system for computer science students. The system utilizes advanced algorithms and feature selection techniques to provide personalized job recommendations for students based on their skills, interests, and career goals. The performance of the system was evaluated using a dataset of job postings and student profiles, and compared with existing job recommendation systems. Our evaluation results showed that our system outperformed existing systems in terms of all evaluation metrics, including accuracy, precision, recall, and F1-score. Our findings demonstrate the potential of personalized job recommen-dation systems for students and highlight the importance of advanced algorithms and feature selection techniques in improving the accuracy and personalization of job recommendations. The implications of our study for the field of job recommendation systems for students are discussed, including the potential for practical applications and future research directions. Overall, our study contributes to the advancement of knowledge in the field of job recommendation systems and provides a foundation for future research and system design in this area.
Laboratory Setup for Studying the Effect of Atmospheric Discharges on Communication Lines Protected by Quantum Key Distribution Technology

A laboratory setup has been created that makes it possible to conduct comprehensive studies of the influence of atmospheric discharges on the transmission of quantum keys over optical communication lines. The effect of a strong action of an electric field from a charged water aerosol on the polarization of radiation passing along an all-dielectric fiber-optic cable has been found. The dynamics of changes in the optical fiber arising from the action of electrostatic fields of a charged cloud, preceding the electrical breakdown of air, has been registered. An analysis of the need to protect quantum networks from the effects of atmospheric discharges is presented.
MetaStackVis: Visually-Assisted Performance Evaluation of Metamodels

Stacking (or stacked generalization) is an ensemble learning method with one main distinctiveness from the rest: even though several base models are trained on the original data set, their predictions are further used as input data for one or more metamodels arranged in at least one extra layer. Composing a stack of models can produce high-performance outcomes, but it usually involves a trial-and-error process. Therefore, our previously developed visual analytics sys-tem, StackGenVis, was mainly designed to assist users in choosing a set of top-performing and diverse models by measuring their predictive performance. However, it only employs a single logistic regression metamodel. In this paper, we investigate the impact of alternative metamodels on the performance of stacking ensembles using a novel visualization tool, called MetaStackVis. Our interactive tool helps users to visually explore different singular and pairs of metamodels according to their predictive probabilities and multiple validation metrics, as well as their ability to predict specific problematic data instances. MetaStackVis was evaluated with a usage scenario based on a medical data set and via expert interviews.
Unsupervised Intra-Domain Adaptation for Recommendation via Uncertainty Minimization

Given labeled data on a source domain, unsupervised domain adaptation (UDA) for recommendation aims at making desirable prediction for unlabeled data on a target domain which is considerably different from the source domain. Mainstream approaches to UDA for recommendation consist in learning aligned features and minimizing transferred knowledge discrepancy between two domains. However, these methods which focus on overall domain discrepancy tend to ignore intrinsic intra-domain discrimination. Motivated by the uncertainty representation theory, we propose to contrastively minimize the intrinsic intra-domian discrepancy between two domains via uncertainty minimization oriented learning. We constrain the knowledge transfer via uncertainty minimization that hinges on uncertainty representation determined by the pivot similarity between different network branches. More specifically, the Kullback-Leibler (KL) divergence and Euclidean square distance between predicted label distributions of the main classifier and an introduced auxiliary classifier are used to characterize prediction variances. We involve the prediction variance as uncertainty representation into optimization objectives. Experimental results on datasets demonstrate that our proposed method outperforms state-of-the-art methods.
Why Law Firms Must Responsibly Embrace Generative AI

In this article, we explore the compelling reasons why law firms should responsibly embrace generative artificial intelligence (GAI). We believe doing so is imperative for the future of the legal profession and provide several real-world examples of how organizations, including law firms, are already successfully leveraging GAI. We highlight the many advantages GAI can bring to the legal field, but also confront the common arguments against its adoption. Our analysis includes strategies for mitigating these risks, as well as supporting evidence for why a complete ban on GAI technology is not justified. Most importantly, we articulate how law firms can effectively manage these risks by following organizational best practices, adhering to legal and ethical standards, and using GAI responsibly.
Job Recommendation a Hybrid Approach Using Text Processing

This work is an attempt to collate the data and discover the foremost relevant candidate-job association mapping concurring with the skills, interests, and preferences of a user and to provide a possible job opportunity as an efficient solution. Several personalized content-based and case-based approaches are considered in this regard. The investigation involves several feature-based item representation methods along with feature-weighted schemes. A comparative evaluation of the distinctive perspective is performed utilizing Kaggle data respiratory. The investigation of this study has shown that job transitions can be successfully predicted. The delicacy of the model can be evaluated based on various algorithms of machine learning such as Naïve Bayes, Logistic regression, support vector machine, random forest, K-nearest neighbors, and multilayer perceptron. In this work, the hybrid recommender framework is created to analyze the further investigation in advance to make an identification of the region of opportunity for the prediction of a suitable job. To get the most expected outcome optimization techniques has applied and after utilization of various machine learning algorithms on hybrid approaches, the classifier of Random Forest gives better results. By this examination, the system of job recommendation can give proper assistance to job searchers improving accuracy and scalability.
Tackling IoT Security Challenge by Metaheuristics Tuned Extreme Learning Machine

The Internet of Things (IoT) brings together the real and online world to enhance services such as reducing traffic congestion, improving health care, and increasing customer service speed through quicker communication and better analysis. The manufacturing industry's embrace of new technologies that include wireless sensor networks, embedded devices, cloud systems, and big data has enabled the emergence of Industry 4.0. The Fourth Industrial Revolution has resulted in a technologically improved globalized world in which modern technologies may directly manage the manufacturing industry's machinery, facilities, plants, and infrastructure, as well as affect intelligent procedural and strategic decisions. The emphasis of this study is on the implementation of a novel metaheuristic optimization method. It is used for feature selection (FS), hyperparameter tuning, and training of extreme learning machines (ELMs). In terms of hyperparameter optimization (HPO), the count of neural cells in the intermediate coat of the ELM network, accompanied by the initialization of weights and biases, will be taken into account. The proposed methods have been developed for improving IoT security and were therefore tested on a Windows 10 security from the TONIot base.
MAKT: Multichannel Attention Networks based Knowledge Tracing with Representation Learning

As an effective and emerging component of intelligent education, Knowledge Tracing(KT) achieves the combination of artificial intelligence and individualized learning, whose aim is to assess students’ mastery of knowledge concepts and assist in developing learning plans. Several existing KT models either use concepts sequence as input and evaluate students’ knowledge state or treat exercise as input to predict students’ future performance. In this paper, we introduce a constraint factor to extract concepts’ and exercises’ relation matrix, design three methods in representation learning, and propose a Multichannel Attention Networks based KT model(MAKT). Specifically, we restrict the co-occurrence relationship within a time window to extract the relation matrix and then train their representations via graph generative learning, graph contrastive learning, and matrix decomposition, respectively. In MAKT, a sliding window is implemented by multichannel where input sequence is sequentially lagged in turn by one position and attention mechanism is applied. We conduct experiments on several benchmark datasets and demonstrate that MAKT with concepts’ and exercises’ representation trained by matrix decomposition outperforms state-of-the-art models.
Contextualizing Introductory App Development Course for First-Year Engineering Students

This paper presents an experience report of an introductory app development course that uses a context-based approach to teach app development to first-year engineering students in university. This course uses MIT App Inventor as the development environment to teach how to make apps in a variety of contexts (e.g., quiz app, painting app, speech translator app). Pre-course survey showed that many students were motivated to learn app development because they considered it a useful skill in real life. App development attitudes survey shows that a small portion of students had a fallacy of app development and view it as disconnected to the real world. This justifies the importance of contextualizing the learning to help students develop a correct mental model of app development. The quantitative and qualitative results of the course assessment questionnaire demonstrated the effectiveness of the learning design. Overall, this course received positive appraisal from students, and animation, machine learning/AI, and location sensing were perceived as the most attractive and useful contexts.
Introducing Alternative Value Proposition Canvases for Collaborative and Blended Design Thinking Activities in Science and Engineering Education

The value proposition canvas introduced in 2015 by Pigneur et al. is broadly used by entrepreneurs and intrapreneurs to create new services and products. We decided to apply it for collaborative and blended design thinking activities in a specialization in human-Computer interaction for master students in computer and data sciences. While being very useful, the original value proposition canvas relies on a business-oriented vocabulary not familiar to science and engineering students. Moreover, its underlying profit-oriented objective makes it difficult to focus on impact and trust-oriented design activities. In this paper, we propose two alternative value proposition canvases, one dedicated to sustainability for development and one to explainability for artificial intelligence. The motivations why and the ways how these alternative canvases were elicited and used in higher education are also discussed.
Learning Experiences and Analysis in Professional Software Development Subject

Software Engineering or professional software development (PSD) course is an important subject in Computing Science of higher education. It teaches the theory knowledge on software development life cycle (SDLC). The students will also be taught to use appropriate quality assurance techniques, continuous integration, continuous development, software version control, and appropriate configuration management techniques, etc. In the curriculum of the joint degree programme of University of Glasgow and Singapore Institute of Technology, the PSD course which teaches theory of SDLC is offered concurrently with another course named Team Projects that students work in groups for software companies on real software development projects. During the learning journey in the courses of PSD and Team Projects, students have undergone different thoughts and experiences. In this paper, analyses are conducted to provide views and opinions regarding the learning journey for both courses. Limitations of the current learning methods are discussed in this paper, together with the proposed ideas to boost the learning effectiveness of PSD and Team Projects to benefit the next student cohorts.
Analysis of Hardware and Software Tools for Implementation of Cognitive Radio Networks

This paper analyzes of the hardware and software tools for implementing a cognitive radio network. In particular, software-configurable radio modules, which are considered the main element of the cognitive network, were studied according to their main characteristics, among which the widely used ones were comparatively analyzed. As a result of the analysis, the HackRF One device was selected as the most preferred option for cognitive network research, and its working principles, structure, and characteristics were revealed in detail. It also provides information about the GNU Radio software environment and its capabilities, which are used to operate the HackRF One device.
Development of a reliable method for grouping users in user access control based on a Functioning table

Digitization in various fields and the development of digital technologies are giving rise to information security issues such as security management. In addition, a user-friendly method based on the properties of the object should be proposed. Of course, the system will be able to evaluate itself based on improvement criteria. In accordance with the criteria, a formula is presented that evaluates the reliable distribution based on the entered criteria, the base points necessary for the invariant method are selected when choosing the best path of action of the system participants with respect to the defined areas. When selecting the center points of the circles, the observation is determined according to the location density of the wells, to reduce the complexity of the selection of the base points and the reliable grouping problem, the reliable part is divided into sets, a knowledge base is formed, and the values of the knowledge base store the values considered to be highly effective.
An algorithm for parallel processing of traffic signs video on a graphics processor

In the lighting conditions such as snowing, hazing, raining, and weak lighting condition, the accuracy of traffic sign recognition is not very high. It is important to develop an algorithms for real-time fast detection of road signs from video images and use them in driver assistance systems. Firstly, we image preprocessing by using dark channel prior based image dehazing for remove noises from the input image. In this article, we develop a transposed image filtering method combined with other fast parallel filtering algorithms. We propose a high-speed and high-accuracy method by improving image dehazing algorithms and analyzing GPU architecture. In this, we quickly removing fog from HD video images using DCP with image quality and visual effects.