Reviewerly/exHarmony huggingface dataset:
A Secured Biometric Authentication with Hybrid Face Detection and Recognition Model

Now-a-days biometrics is used to recognize an individual, and is thought of a more secure way to authorize an individual.Face is the most important factor to recognize an individual.In order to enforce security features, many verification systems use face recognition to identify a person, such as -automobile security, security of home, smartphones owner recognition, attendance system, identification of criminals, etc. Face biometric works in two phases -face detection and face recognition.In this paper we are proposing a hybrid model that works in two phases, face detection is the first phase which includes two stages, in first stage it detects the face from the acquired image and then in the second stage acquired image is subjected to intensity equalization.The second phase is of Face recognition, where finally the individual is recognized as authenticated user or not.We are using Viola-Jones algorithm to detect the face, histogram stretching for intensity equalization and principal component analysis (PCA) algorithm for image recognition.The effectiveness of the suggested facial recognition system is shown through experimental findings using the face dataset constituting of 100 subjects using VTU-BEC-DB multimodal biometrics database with constraints of pose and illumination in the face images, without the intermediate step of intensity equalisation, we had a recognition rate of 93.60%.However, when the intermediate step of intensity equalisation was included, a novelty was added to the hybrid algorithm, and we achieved a recognition rate of 98.20%, which is higher recognition rate than the state-of-the-art methods.
Epoch in a neural network for brain stroke

A neural network is a data processing system consisting of a large number of simple and highly interconnected processing elements in an architecture inspired by the structure of the cortical regions of the brain. Therefore, neural networks can often do things that humans or animals can do, but traditional computers are often lousy. This research discusses brain tumors that can be detected by artificial intelligence. Stroke includes the sudden death of brain cells due to lack of oxygen, blockage of the circulatory system, or severance of flexible pathways to the brain. Therefore the need for action that must be faster to be able to detect this deadly disease. The method used is a Neural Network which can collect knowledge by detecting patterns and relationships between data and learning experiences. So that the detection process is carried out more quickly and the patient can be given medical action as soon as possible. In the study I conducted brain stroke from the number of strokes with a value of 0 4733 and 1 out of 248. This research has a test conducted by conducting epoch training from 1 to 300, the highest score accuracy is in epoch 1 and 2 with more high scores.
A Visual Question Answering Network Merging High- and Low-Level Semantic Information

Visual Question Answering (VQA) usually uses deep attention mechanisms to learn fine-grained visual content of images and textual content of questions. However, the deep attention mechanism can only learn high-level semantic information while ignoring the impact of the low-level semantic information on answer prediction. For such, we design a High- and Low-Level Semantic Information Network (HLSIN), which employs two strategies to achieve the fusion of high-level semantic information and low-level semantic information. Adaptive weight learning is taken as the first strategy to allow different levels of semantic information to learn weights separately. The gate-sum mechanism is used as the second to suppress invalid information in various levels of information and fuse valid information. On the benchmark VQA-v2 dataset, we quantitatively and qualitatively evaluate HLSIN and conduct extensive ablation studies to explore the reasons behind HLSIN's effectiveness. Experimental results demonstrate that HLSIN significantly outperforms the previous state-of-the-art, with an overall accuracy of 70.93% on test-dev.
Ways of implementing information technologies in professional training of future specialists

Creation of modern effective technologies, automation, intensification of production, implementation of planning and management with the help of modern information and communication technologies are the main factors of acceleration of scientific and technical progress. The purpose of the article is to show the importance of using information technologies in the training of future specialists. Without the integration of the educational environment of the higher school into the informational educational environment, without the exit of the educational process into the global information space, significant development of modern educational systems is impossible. In order to substantiate the principles of improving the professional training of education seekers, a quantitative analysis of open educational resources is proposed, the most popular platforms on which the path to open educational resources is designed are determined. The article examines the conditions by which the effectiveness of pedagogical support is achieved in the distance learning process. The substantiation of the most essential criteria and indicators involves the assessment of the quality of personality education in the digital educational space. They offer the perfect use of digital information, readiness for communication, critical thinking, digital and social competence.
Semantic Path Planning for Indoor Navigation Tasks Using Multi-View Context and Prior Knowledge

Recently, deep reinforcement learning (DRL) methods have significantly improved the performance of target-driven indoor navigation tasks. However, the rich semantic information of environments is still not fully exploited in previous approaches. In addition, existing methods usually tend to overfit on training scenes or objects in target-driven navigation tasks, making it hard to generalize to unseen environments. Human beings can easily adapt to new scenes as they can recognize the objects they see and reason the possible locations of target objects using their experience. Inspired by this, we propose a DRL-based target-driven navigation model, termed MVC-PK, using Multi-View Context information and Prior semantic Knowledge. It relies only on the semantic label of target objects and allows the robot to find the target without using any geometry map. To perceive the semantic contextual information in the environment, object detectors are leveraged to detect the objects present in the multi-view observations. To enable the semantic reasoning ability of indoor mobile robots, a Graph Convolutional Network is also employed to incorporate prior knowledge. The proposed MVC-PK model is evaluated in the AI2-THOR simulation environment. The results show that MVC-PK (1) significantly improves the cross-scene and cross-target generalization ability, and (2) achieves state-of-the-art performance with 15.2% and 11.0% increase in Success Rate (SR) and Success weighted by Path Length (SPL), respectively.
Generating Popularity-Aware Reciprocal Recommendations Using Siamese Bi-Directional Gated Recurrent Units Network

Reciprocal Recommender Systems (RRS) use bilateral preferences to satisfy the needs of both the parties involved. Popularity bias is a critical problem in RRS, which arises when the RRS tend to prefer few popular users over others and thus exhibit biased behavior towards popular users. It can have adverse effect on both the parties involved. Popular users may become swamped by the requests received from a large number of users and cease to acknowledge, which can make it difficult for other users to make contact. To address this challenge, we propose Popularity-aware Siamese Bi-directional Gated Recurrent Units (PSBiGRU) with the proposed popularity-aware reciprocal score (ParS)-based re-ranking that uses semantic similarity between explicit user profiles. The proposed model is evaluated on two reciprocal environments, namely, online recruitment and online dating. Experimental findings demonstrate that PSBiGRU surpasses the compared state-of-the-art methodologies and illustrate its viability.
Classification of texts on emergency situations in Almaty

Text classification is a process that includes stages and approaches for the effective classification of texts that are diverse in their structure. In this article, machine learning algorithms are implemented, such as the support vector method, logistic regression, and the k nearest neighborhood method for classifying texts collected from emergency news sites in Almaty. During the experiment, a special role was played by the data collection stage, as well as their subsequent processing. Prior to the classification of the data set, preliminary data processing was performed, which includes such steps as the removal of stop words, tokenization, stemming, lemmatization, feature extraction, and the construction of feature vectors. The data was obtained by automated collection of information from open sources using a script. Experimental results show that the classifier based on logistic regression provides the best performance results compared to other types of algorithms. The performance indicators of each algorithm were obtained, which allows us to perform a comparative analysis between them.
Web-Based E-LKPD for the Indonesian History Subject for Grade XI Senior High School

Education is currently inseparable from technological developments. The integration of technology into the education field provides renewal in various aspects. One of them is the use of technology in the learning process. The problems found in the Indonesian History subject were the low interest and low learning outcomes of the students. This is due to the great influence of smartphone development. One way to increase the students' understanding and interest in learning history is through an electronic form of LKPD (E-LKPD) displayed via the web. This research aims to analyze the process, validity, practicality and effectiveness of the web-based E-LKPD development for Indonesian History subject for Grade XI Senior High School. This study used the development and research (R&amp;D) method with the ADDIE model. The data analysis technique in this research used a Likert scale 4 questionnaire to determine the validity and practicality of web-based E-LKPD product. For the product effectiveness, a pre-test and post-test were carried out to see the achievement of student learning outcomes. The validity test of the E-LKPD involved material, media and language experts. The practicality test involved two educators of Indonesian history and 71 students from class XI at Senior High School. The results of the validity test from material experts were 3.9, from media experts were 3.8 and from linguists were 3.8 in the very valid category. The results of the practicality test by educators were 3.8 and from students were 3.7 in the very practical category. The gain score results of the effectiveness test through the pre-test and post-test of XI MIPA 7 were 0.9 and XI MIPA 8 were 0.8 in the very high category.
Robust Graph Embedding Recommendation Against Data Poisoning Attack

With the development of recommendation system technology, more and more Internet services are applied to recommendation systems. In recommendation systems, matrix factoring is the most widely used technique. However, matrix factoring algorithms are very susceptible to shilling attacks (trust or espionage). The former defends methods against data poisoning attacks focused on detecting individual attack behaviors. But there are few detection methods for group data poisoning attacks. Therefore, we propose a detection method based on Graph Neural Network (GNN) and adversarial learning. We train user-item nodes and edges through a semi-supervised learning approach, improving the robustness of the GNN recommendation system. Our work can be divided into the following parts: Firstly, we review the former recommendation systems and the graph representation learning recommendation systems. Secondly, we analyze the main vulnerabilities of the graph representation learning recommendation systems. Furthermore, the detection methods of data poisoning attacks are analyzed, and the difference between individual data poisoning attacks and group data poisoning attacks are discussed. Finally, we propose a per-process Robust-GNN semi-supervised detection model to conduct group detection on different types of attacks. In addition, we also analyze the sensitivity of the proposed methods. From the experiments results, it can be concluded that we should apply the attention mechanism to the proposed methods which makes it more generalized.
Collision Provenance Using Decentralized Ledger

The coverage and communication lag has been worked on in swarms of drones with different formation patterns, however; decentralization of the participants in the swarm is little in consideration and research. A flying ad-hoc network might be a good solution if implemented on peer-to-peer basis, but the physical layer of the swarm by itself is not sufficient to ensure immutability, integrity, and validity of data within the swarm. Due to the lack of central leadership in decentralized networks, a consensus is required for all the participants to agree on an intent or an action to be executed. A shared data structure or distributed ledger can be introduced in the application layer with some known consensus mechanisms such as proof-of-work or proof-of-stake to overcome the leadership problem. A state change can only occur by the consensus of all participants and mirroring the change. The data emitted by the participants will be stored in smart contracts where the storage is practically immutable outside of consensus. Thus, the collision data carries a layer of trust within itself if stored on a greatly distributed ledger and becomes a good approach for collision provenance in shared airspace. In this paper, we will integrate distributed ledger such as Ethereum and Hedera Hashgraph with a swarm starting with a brief comparison between both.
The Application of Virtual Reality Technology in Ophthalmology

Virtual reality (VR) is changing the way we perceive and interact with various digital information, so that many scenes can place users in an ideal visual sensory environment through head mounted devices. In recent years, the research of VR in the field of ophthalmology is mainly reflected in clinical application and teaching. This paper analyzes the relevant research in the fields of vision training and amblyopia treatment, myopia prevention and control, eye adjustment and convergence function, strabismus diagnosis, ophthalmic surgery assistance and ophthalmic teaching, and introduces the application of VR technology in the field of Ophthalmology.
Security in Smart Home Using Blockchain

The interest in smart homes has grown exponentially in the last few years. A topic that has been treated with great attention is the security of the data generated by the Internet of Things Devices (IoTDs) within the smart home. Methods and architectures for the scenario of smart homes have also been researched as smart homes become more popular. Since IoTDs generate sensitive data about the smart home environment, data security and privacy are key points for smart homes to continue getting popular. Blockchain is a technology that offers security and privacy naturally due to its decentralized aspects and the high level of interaction between the blockchain’s members and the manner that data is stored in hashed blocks. A smart contract is a computer code that runs inside the blockchain to provide authentication for the blockchain’s members. The combination of blockchain and smart contracts provides the management of smart devices in a manner that their data is handled securely and protected from unauthorized parties. A smart home network architecture based on blockchain and smart contracts is proposed to simplify the way to manage smart devices and assign security to their data.
Paperless Circulation of Business Management Documents Based on Electronic Signature Technology

The research on Paperless circulation of enterprise management documents based on electronic signature technology aims to study the current trend and problems of paperless circulation of enterprise management documents, including the importance of electronic signature technology in Paperless circulation. It also studies various methods for the electronic circulation of commercial documents, such as electronic signatures, digital signatures and other related technologies. The research also focuses on how to apply these technologies to the paperless circulation of business management documents. Paperless circulation is a step towards the goal of completely eliminating paper from business management documents. Using electronic signature technology for document authentication and verification is a way for organizations to achieve this goal. Electronic signatures have not been widely accepted, but as a substitute for handwritten signatures, they are becoming more and more popular. Handwritten signatures are easy to be forged or changed.
A New Retraceable Clustering Algorithm

With the advent of the era of Internet of everything, building a smart city requires us to set off a technological revolution in various industries. Facing the current trend of geographic information data continuously developing to big data, this paper studies the suitable clustering algorithm for the characteristics of geographic information data, an efficient retraceable hierarchical clustering algorithm is proposed for two-dimensional spatial data, firstly, the region map is divided by a method similar to grid indexing, and the precision of Geohash is used as the grid size division criterion. The data density of each grid under different Geohash precision is calculated, and a suitable precision is selected as the basis of clustering, and the regions are classified as locally dense, locally uniform and locally dispersed according to the data density under this precision, and reasonable clustering is performed according to the region division. Subsequently, the clustering is divided into two parts: splitting phase and merging phase, adding markers to subclasses at the time of splitting so that they can be traced back to the clusters before splitting, and then testing whether the splitting results need to be merged at the end of splitting. Compared with coalescence or splitting methods, it can better delineate clusters of clusters. The marker detection method makes the splitting results traceable back, improves the fault tolerance, and the merging phase is less computationally intensive.
CAAS (Chatbot as a Service): Sector-Wise Survey

The worldwide chatbot and voice Bot market share were worth USD 526 million in 2021 even more than that.A chatbot system application uses conversational counterfeit insights (AI) innovation to re-enact a discussion (or chat) with a client using informing apps, websites, mobile apps, or the phone.Chatbots are not related as a single or specific category but they expanded along a more extensive range.Chatbot applications and systems have been expanded over a long period, well-known prime examples include ELIZA, ALICE, and HeX.However, human computation lacks the speed of information processing, and it is inevitable to process a large number of user requests.A comprehensive and systematic methodological review of the literature on this topic was performed using a two-step approach.First, relevant keywords, synonyms, and search strings are identified to search for relevant research papers on the chatbot.The second step is article selection and investigation of papers, classifying papers into sub-categories based on aspects of the chatbot system.For the review purpose, initially183 articles have been encompassed according to the eligibility criteria for inclusion.Afterward, the full text of articles deemed eligible was further assessed for inclusion.Based on IC and EC, we have identified 42 articles for inclusion in the collection; Following Sectors covered in this article, such as CAAS (Chatbot as a service) in Education Sector, CAAS (Chatbot as a service) in Health Sector, CAAS (Chatbot as a service) in Ecommerce Sector, CAAS (Chatbot as a service) in General Use.There are two main points in this area of research: how well chatbots can understand user messages and how well chatbots can provide context-correct answers.
RM: An Android Mobile Application for the Course Research Methodology – A New Way of Learning the Course

Mobile applications are used for improving and engaging the students as well as teacher in teaching-learning process.The effectiveness as well as the impact of the mobile application can be seen in various fields such as medical education, health care system, engineering education, etc. Keeping this in mind, the mobile application for the very important course Research methodology is developed which will be useful to all UG, PG and PhD students of any stream.The preliminary study related to the mobile app is considered using the previous version of the app.Using the feedback of previous version of the app, the changes are made in the old version of this mobile app and new version are made available to the students.The link for this app on Google Playstore is link -https://play.google.com/store/apps/details?id=in.smd.rmversion.This app contains the PPTs, Notes, Multiple Choice Questions as well as Subjective Questions, Revision Part for revising the various topics of the this course, Test Your Knowledge part for testing the knowledge about this course and Quiz covering all units.In present study, the research question "Whether an android mobile app engage the learners and help them to understand the topics of this course by practicing the quiz and testing the knowledge through Test Your Knowledge section of this app?".In current study, this app was given to the forty five PhD research scholars for studying purpose.One group post-test method was conducted to check the effectiveness of this app.It is found from result analysis that there is significant improvement in the result which is verified using statistical analysis t-test.Feedback for this new version is also considered and presented in this paper.
Unearthing the Lonar Crater Using Hyperspectral Remote Sensing and Validating Through Non-destructive Approach

Remote Sensing is a prominent technology, applied at all fields of earth science.It is useful in the identification and mapping of minerals, vegetation, different materials and backgrounds by capturing the electromagnetic energy reflected from them.The analysis of remote places or structures is possible with the help of remote sensing with economic and timely manner.So, the proposed work focuses on exploring the Lonar crater with the help of remote sensing.The earth surface is enriched with number of useful minerals.Minerals are very important resources contributing in the wealth of a country.So, this work focused on mineral mapping and mineral identification at Lonar Crater by using high resolution hyperspectral images acquired by Hyperion sensor.The validation of minerals identified with hyperspectral imaging was completed by the use of nondestructive approach of spectroradiometer.The various minerals are identified by imaging analysis and validated through non-destructive approach.The significant results of the proposed methodology are: i) the type of rock at the Lonar crater is identified as basaltic igneous rocks, ii) the origin of Lonar crater is due to the extrusive volcanic activity and iii) The Lonar lake is saline in nature.The Spectral Classification using SAM gave accuracy of 89.01% with 0.8753 kappa coefficient and SVM technique, gave the accuracy of 97.25% with 0.9688 kappa coefficient indicating more match with the ground truth.