Reviewerly/exHarmony huggingface dataset:
ANIMAL HEALTHCARE PLATFORM BETTER TREATMENT FOR PETS WITH GOOGLE MAP INTEGRATION: SURVEY AND RESEARCH POTENTIALS

This work summarizes the design and development of an Android-based animal healthcare application that allows users to quickly identify nearby veterinary clinics and consult with veterinarians about their pets through a user-friendly interface.In this essay, we suggest an online conference for creatures and a medical services application.This application for animal healthcare includes all pertinent information on pets, veterinarians, and major diseases that affect animals, along with advice on how to prevent them.Certain emergency situations prevent users or people from treating ill animals right away or from reaching the appropriate doctors and specialists right once.The primary cause of death in said critters is this problem.As a result, programmers have the idea to create an Android-based application system that keeps track of authenticated physicians, appropriate details, and location, among other things.The idea is to create an android-based application system that maintains a record of authenticated physicians, appropriate information, location, etc.Based on their therapy and the best moment for consumers, the android application concentrates on high-quality specialists.Users will be able to locate and select which doctor will be based on the review system.The administrator will keep track of all the registered physicians, and if any of them receive repeated unfavorable comments from users, the administrator will stop their access as well.Customers will only find reputable doctors here, and quality assurance will aid in quick and simple treatment.
Assessment of multi-plenoptic 2.0 camera depth maps for DIBR

We aim to evaluate the depth map quality by the plenoptic 2.0 cameras for DIBR applications, by simulating a multi-plenoptic camera array, and synthesizing virtual images based on the obtained color and depth maps. The real-time generated depth maps by plenoptic cameras are compared with the offline and high-quality depth maps estimated by the MPEG-I Depth Estimation Reference Software (DERS). Results show that synthesized virtual image quality obtained by RGBD plenoptic cameras has acceptable quality for real-time DIBR applications, but lower than using the depth estimated by DERS, due to a loss of inter-view depth consistency.
Multi-party Privacy Set Intersection protocol with an Untrusted Cloud Server

The development of cloud computing has brought many conveniences to life, its advantages of low cost and high computing power bring convenience to our life, but the main problem is how to protect user data on untrustworthy cloud servers. To solve this problem, we have proposed a new private set intersection protocol, denoted as. The protocol uses a representation of sets with polynomial point-value pairs. All elements should be transmitted and computed in the protocol as ciphertext by introducing random polynomials to re-randomize the encryption elements. To reduce the computational burden of each party. The parties send the processed polynomials to the cloud server, which calculates the intersection. In addition, our protocol achieves fairness in the case of collusion between parties. Finally, the security of the protocol is demonstrated under the UC model. Comparing the performance of this protocol with other related protocols show that the protocol is better than the other associated protocols in terms of low computational overhead.
Human like programming using SPADE BDI agents and the GPT-3-based Transformer

Programming an application requires multiple people with skills and experience in that field. It will also take a lot of time with multiple steps before achieving the final result of an application. Today, developers are assisted by various tools, software, or applications based on Artificial Intelligence (AI) such as OpenAI's ChatGPT. These AI that automatically generates source code helps developers to develop applications much faster. However, although code generators are numerous and very helpful, we are not yet at the stage where we can generate a fully functional application, but just generate pieces of source code. And we don’t know yet how to understand textual descriptions of Software Requirements to generate an application directly. Or where to find data to train an AI capable of generating a functional application from textual descriptions. Therefore, we created a new architecture composed of virtual intelligent agents called SPADE BDI to create virtual developers. The virtual intelligent agents were responsible for keyword extraction, Software Requirements synthesis, and source file creation. Then we used a transformer based on pre-trained GPT-3 for source code generation. This transformer is orchestrated by a virtual intelligent agent. To solve the problem of training data, we collected and created a new dataset called WSBL. The data came from several projects developed with the Laravel Framework over 4 years. The result allowed us to have a functional application directly from a textual description. Each intelligent virtual agent played a role like a developer by analyzing textual of Software Requirements and then generating source code. With a 15% reduction in time to develop an application compared to brute development. Our new architecture allows for processing textual descriptions (Software Requirements) step by step using intelligent virtual agents named SPADE BDI and source code generation is done by a transformer based on pre-trained GPT-3 to have a directly functional application
Service-Oriented Integration of SuperTuxKart

Recent developments in the automotive industry show a rising demand for in-car gaming and entertainment. Series-produced vehicles offer high-performance hardware, displays, sensors, and actors, which can be used for gaming. This trend was recently confirmed in particular by the Mercedes-Benz Group AG, which integrated the racing game SuperTuxKart in its series-produced Mercedes-Benz CLA Coupé. However, integrating and interacting with C++ games, such as SuperTuxKart, is cumbersome as developers need deep technical knowledge about the internal structure of the game and its APIs. This makes development processes time-consuming, knowledge-intensive, and error-prone. To overcome this issue, we developed (i) a domain model for SuperTuxKart and (ii) a REST API supporting this model. The developed API serves as an abstraction layer that enables developers as well as researchers to integrate SuperTuxKart in a service-oriented manner into other applications.
Placing (Historical) Facts on a Timeline: A Classification Cum Coref Resolution Approach

A timeline provides one of the most effective ways to visualize the important historical facts that occurred over a period of time, presenting the insights that may not be so apparent from reading the equivalent information in textual form . By leveraging generative adversarial learning for important sentence classification and by assimilating knowledge based tags for improving the performance of event coreference resolution we introduce a two staged system for event timeline generation from multiple (historical) text documents. We demonstrate our results on two manually annotated historical text documents. Our results can be extremely helpful for historians, in advancing research in history and in understanding the socio-political landscape of a country as reflected in the writings of famous personas. The dataset and the code are available at https://github.com/sayantan11995/Event-Timeline-Generation-from-Documents .
Learning to Control Local Search for Combinatorial Optimization

Combinatorial optimization problems are encountered in many practical contexts such as logistics and production, but exact solutions are particularly difficult to find and usually NP-hard for considerable problem sizes. To compute approximate solutions, a zoo of generic as well as problem-specific variants of local search is commonly used. However, which variant to apply to which particular problem is difficult to decide even for experts. In this paper we identify three independent algorithmic aspects of such local search algorithms and formalize their sequential selection over an optimization process as Markov Decision Process (MDP). We design a deep graph neural network as policy model for this MDP, yielding a learned controller for local search called NeuroLS. Ample experimental evidence shows that NeuroLS is able to outperform both, well-known general purpose local search controllers from the field of Operations Research as well as latest machine learning-based approaches.
USE OF VIRTUAL INTERACTIVE ENVIRONMENTS FOR THE EDUCATIONAL PROCESS

The article discusses the possibility of using virtual reality technology to solve the problems of the educational process. Using the VRChat platform, a virtual room with additional features has been created, which is proposed to be used for conducting various types of classes (lectures, seminars, etc.)
Technical University as an Innovative Factor of Culture

This study aims to consider the technical university as a factor for innovation in modern culture. The author’s task is to identify the phenomenological (sociocultural) and ontological (pedagogical, personal, organizational) foundations of a technical university as an innovative factor. The methodological basis of the study is made up of identifying value concepts, a pragmatic method, and a criterion of effectiveness. The technical university as an innovative factor is revealed through research: concentration of innovations in engineering and technical activities; the cultural meaning of innovation; the peculiarities of the formation and development of the humanitarian environment by the technical university and its spiritual and moral component. The criterion of effectiveness refers to the use of pedagogical models by a technical university and a spiritual and moral component. The study deepens the understanding of the innovative factors of culture that have developed in science and makes it possible in practice to improve the technical university in line with the innovative trends of our time.
Modified Iterative Shrinkage Thresholding Algorithm for Image De-blurring in Medical Imaging

Computer-assisted medical diagnosis has grown its popularity among researchers and practitioners due to its high applicability and cost-effective applications. The use of learning methods in facilitating the processing and analyses of Medical Images has become necessary. Image de-blurring problem in Medical Imaging applications is a challenging task to improve the quality of the images. The class of Iterative Shrinkage Thresholding Algorithms (ISTA) is considered for handling the image de-blurring problems. These algorithms use first-order information and are tempting due to their simplicity. However, they converge pretty slowly. In this work, first, a faster version of ISTA is proposed by utilizing Nestrove’s updating rule. Later, the proposed approach is used to solve the image de-blurring problems arising in Medical Imaging. A theoretical study is performed to verify the improvements in the convergence rate of the modified fast ISTA. Numerical experiments suggest that the presented method for the image de-blurring problem outperforms the baseline ISTA method.
Development of an Interaction Concept to Illustrate the Energy Transformation on an AR-Surface

Augmented Reality (AR) enables new forms of interaction and knowledge transfer for users. This paper describes a novel approach to visualize information of energy transformation on an AR-Surface. Aiming to provide a playful tangible interaction method with real data for citizens, especially for those locally effected by the changes in future energy production facilities; we analyzed which bifacial aspects can be drawn from the two novel interaction and visualization methods. Therefore, we projected databased information on the AR-Surface so that users were able to interact with the displayed information by placing or moving our own designed tangibles on the surface. In our user study, the participants interacted with a data set on renewable energy production. Through the tangible interaction, the user receives important information, e.g. about renewable energies and their performance. As an outcome of the study, we showed that the use of tangibles offers many advantages, such as fast and easy interaction on an AR-Surface as well as an improved user understanding. The gained insights will help for further implementations and visualizations in the field of AR combined with simple communication of energy topics.
Sentiment Analysis Using an Improved LSTM Deep Learning Model

Sentiment analysis is one of the subfields of data mining that helps to detect the sentiment of a person. Analyzing and examining textual data quantifies the global society’s feelings or attitudes toward their belief and perception. Along with product review and healthcare domain, sentiment analysis is also widely applied in the hotel industry review domain where online feedback data obtained from stakeholders via social media are extracted, processed, analyzed, and evaluated. Sentiment analysis provides many benefits like utilizing sentiment classification insight information to achieve improved quality and services. Sentiment analysis is performed in our work using machine learning and improved deep learning methods. Deep learning approaches are found to produce an effective solution to various text mining problems such as document clustering, classification, web mining, summarization, and sentiment analysis compared to machine learning approaches. Experiments are performed on the 515 K hotel review dataset. Classification results were compared between deep learning approaches and machine learning-based approaches. Finally, we discuss our proposed improved deep learning approach in this paper. Deep learning is a machine learning and artificial intelligence concept aimed at making robots as proficient at making judgments as humans are. We were able to get superior results using the LSTM model compared to traditional machine learning models and the sequential RNN model, and the empirical results were further strengthened using bidirectional LSTM (BiLSTM) and improved LSTM (ILSTM) and gained a performance improvement 2.73% and 5.67%, respectively.
R-Calculi for Tableau/Gentzen Deduction Systems

A logic consists of a logical language, syntax and semantics, where the logical language specifies what symbols can be used in the logic, and the symbols are decomposed into two classes: logical and nonlogical, where the logical symbols are the ones used in each language of the logic, and the nonlogical symbols are those which are different for different logical languages of the logic; the syntax specifies what strings of symbols are meaningful (formulas) in logic, and the semantics specifies the truth-values of formulas under an assignment (or a model).
R-Calculi for $$\rightsquigarrow $$-Propositional Logic

By taking $$\lnot $$ as a logical connective, in traditional Getzen deduction system, we have the following deduction rules
Introduction

Propositional logic is basic, based on which we developed first-order logic and modal logic, which compose of classical logics.
R-Calculi $$\textbf{R}^{Q_1Q_2}/\textbf{R}_{Q_1Q_2}$$

Let $$Q_1,Q_2\in \{\textbf{A},\textbf{E}\}$$ . We consider $$\textbf{G}^{Q_1Q_2}$$ -valid sequents and $$\textbf{G}_{Q_1Q_2}$$ -valid co-sequents, Gentzen deduction systems $$\textbf{G}^{Q_1Q_2}, \textbf{G}_{Q_1Q_2}$$ , and corresponding R-calculi $$\textbf{R}^{Q_1Q_2},\textbf{R}_{Q_1Q_2}$$ .
A Detailed Review on Reversible Data Hiding and Its Applications

Reversible data concealment is a strategy that quietly alters digital material to keep secret data while allowing the original digital media to be totally restored without any error after extracting the concealed information. Reversible data hiding is often used for the content identification of visual data such as photos, films, digital records, and so forth because of its expanding demand in fields like enforcement agencies, diagnostic imaging, scientific research, and so forth. Data hiding is a mechanism of securely conveying data within a multimedia container. Reversible data concealment is a way of hiding data by lightly changing digital media while permitting the original digital information to be completely restored without error if the hidden data is found. Data hiding is a secure method of sending data within a multimedia container. In uses such as medical image transmission, military, and forensics, it is unlawful to destroy the original cover. Data-concealing solutions are popular in these domains. After recovering the concealed data, reversible data hiding (RDH) may precisely rebuild the cover image. Without knowing the encryption key or the original content of the picture, further information can be inserted in the encrypted image. Reversible data-hiding techniques completely restore the original carrier when the secret encrypted data is removed. The classification of reversible data concealing schemes is determined by the manner of implementation. We will cover all of the ways based on Histogram Shifting, Difference Expansion, Prediction error-based approach, and Compression embedding in this paper’s review.
Development of a Low-Cost Autonomous Healthcare Digital Assistant Lead RoBot (DALBOT)

Autonomous robots' applications have been increasing in various fields, including healthcare. However, the use of autonomous robots has posed various challenges in terms of cost, navigation and operational complexity. Many researchers have explored the applications of autonomous robots in healthcare services. Some developed virtual assistants, like Chatbots, Hospital Care Watch (HCW), Intelligent Diabetes Assistant (IDA), etc. Others focused on developing robot navigation systems. However, those research works have not focused on developing healthcare assistants that would accompany patients while visiting medical facilities. This paper presented one of the vital applications of autonomous robots in healthcare services. The primary aim of the work is to develop a low-cost autonomous healthcare Digital Assistant Lead RoBot (DALBOT) to serve patients and guide them to the desired location while visiting medical facilities. The locations were identified using codes with corresponding Cartesian coordinates. The results demonstrated the feasibility of integrating DALBOT with other healthcare systems, such as stretchers, wheelchairs, medical supply deliveries, etc.
Multi-area Target Individual Detection with Free Drawing on Video

This paper has provided a novel design idea and some implementation methods to make a real time detection of multi-areas with multiple detecting areas that are generated by the real time drawing on the screen display of the video. The drawing on the video will remain the output as polylines, and the colors of the outlines will change when the stage of drawing or detecting is changed. The shape of the drawn area is free to be customized and real-time effective. The configuration of the drawn areas can be renewed and the detecting areas are working individually. The detection result should be shown with a GUI designed by Tkinter. The object recognition model was developed on YOLOv5 but can be also changed to others, which means the core design and implementation idea of this paper is model-independent. With PIL and OpenCV and Tkinter, the drawing effect is real time and efficient. The design and code of this research is basic and can be extended to be implemented in numerous monitoring and detecting situations.
Enhanced Mobile IoT Security Protection Method Based on CPK and Zero Trust

The security protection of mobile Internet of Things (IoT) is becoming increasingly important. In the traditional Single Package Authorization (SPA) protocol, the authenticity of the terminal cannot be proved. This paper propose a Combined Public Key (CPK) and zero-trust based architecture to protect mobile IoT from identity forgery attacks. Specifically, we adopt an enhanced SPA protocol based on CPK, where the CPK matrix merges of multi-dimensional information about users, devices, events and time. In this way, every event is encrypted. Besides, through adopting CPK, the deficiency of subject authenticity proof in SPA can be compensated. Experiments verify the efficiency and validity of the proposed CPK-based SPA authentication method in comparison with the state-of-the-art SPA protocol.
Keynote Talk 1 Cognition and Future

The main topic of this lecture will be related to the Information security and the role of the Cognitive Science in Enhancing this security. The main topics will be: 1.Role of Future Parameters in Advance Technologies 2.Main Features towards New Technologies 3.Multidisciplinary and its main role in progress of the new science and technologies applications 4.Modeling Cognitive Security 5.Cognitive Cryptography 6.Some Important Parameters 7.International Scientific support
A Federated Deep Reinforcement Learning-based Approach to Secure Resource Allocation in Power Communication Networks

In order to alleviate the shortcomings of the current power communication network resource security allocation method, which has a large service blockage rate, a federal deep reinforcement learning-based power communication network resource security allocation method is designed. Using the maximum throughput rate as a reference, the network resource target is obtained using beam assignment, a priority classification model for power communication services is constructed, spectrum utilisation is improved, a shared protocol is established to upload training data and a secure resource allocation method is designed based on a federal deep reinforcement learning algorithm. Experimental results: The average value of service blockage rate of the power communication network resource security allocation method in the paper is 27.374%, which indicates that this designed power communication network resource security allocation method is more applicable after combining with the federal deep reinforcement learning algorithm.
Transfering RGB-D Models to RGB by Multi-View Embedding Space Learning for Object Detection

In this paper, we propose a new framework for detecting objects in RGB images captured by conventional cameras by leveraging a set of labeled RGB-D data. We formulate this problem into a new multi-view learning problem, where we have the depth information in the training set that is not available in the testing set. To solve this problem, we use a three-view CCA learning method which combines visual features, depth features, and label information together to get the projection matrices. Sufficient experiments are done to demonstrate the effectiveness of our method, and improvement measures in the future are also proposed.