Reviewerly/exHarmony huggingface dataset:
Hybrid Multi Purpose Voice Assistant

In this modern world everyone is busy in their works and there are many people around us who feel it very hard to organize their tasks, so everyone is looking for an intelligent assistant which can organize their tasks and perform some simple tasks with just a single command using voice assistants like Siri, Alexa, Google Assistant, and Bixby. Similarly, a chatbot is a computer program that uses natural language processing and machine learning to simulate or mimic a conversation with human users. Chatbots can also be used for a variety of purposes, including customer services, marketing, and providing information. They can also be programmed to perform specific tasks such as making appointments, placing orders, or providing recommendations. The aim of the paper is to propose a hybrid system which combines the traditional voice assistance system with a chatbot. This is not only combining both the advantages of chatbots and voice assistants but also gives us the option of customize the software to users based on their needs. This Hybrid Multipurpose Voice Assistant uses Natural Language Processing (NLP) to convert voice commands to instructions that will be sent to the voice assistant based on which it will perform the tasks. It uses Long Short-term Memory to process the instructions. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) which is capable of learning and maintaining a record of information for long periods of time. Unlike traditional RNNs, which have a tendency to lose information over longer sequences, LSTMs use a special type of memory cell called a “memory gate” to control the flow of information into and out of the network. This allows LSTMs to selectively choose which information to keep and which to discard, making them well-suited for tasks such as language translation and speech recognition. The proposed Hybrid Multi-Purpose voice assistant will be a cost effective and efficient system which will understand and respond to a user's requests better and the interactions will be more human like. This project will also aim at helping people with a variety of disabilities and will be a step forward in the direction of making people, who suffer from different types of disabilities, lives easier. This voice assistant can be run both locally on a person's computer or externally using servers based on the requirement as the program is scalable and can work with existing technology and architecture.
Analysis of the Effectiveness of Self-Supervised-Learning for the Automation of Building Datasets

A method to train any models for object detection without manually annotated datasets has been earnestly desired. It naturally follows that the self-supervised learning (SSL) is a hopeful way to obtain features of target data with minimal human supervision in the future. There are well-known big datasets like COCO or ImageNet in the world. However, specific manual annotation tasks in the datasets have been required for training a model which can classify specific objects. Therefore, we have developed an application to make a dataset and present the efficiency of an SSL-based annotation which can learn features without annotations in advance and secure for the privacy to make a specific dataset which includes unique class names or objects for specific situations.
Damaged Image Repair using Masks with Computer Vision Inpaint Method

Image inpainting is the technique used to automatically fix damaged areas using data from sections that have been saved. With the development of deep learning in recent years, image drawing performance has substantially increased. This research study reviews the main methods used for automating image inpainting. This research study provides a brief overview of traditional techniques while concentrating on deep learning-based inpainting techniques, covering model categorization, strengths and drawbacks, scope of application, and performance comparison. Finally, the challenges and trends surrounding automated image inpainting are examined and foreseen. A tool called image inpainting uses the data from the remaining components to repair damaged areas. With the advancement of society, image inpainting has become a vital research area in the field of computer vision. It is extensively used in culture, daily life, and security, including object removal and the preservation of digital cultural assets. Conventional methods build geometric models based on geometric consistency and image content similarity, or they use texture generation to patch up small sections of damaged images. It partially solves the problem of loose coupling between high-level semantics and low-level image properties, enabling deep learning to gradually overtake traditional methods in computer vision.
Intelligent Transport System using Blockchain Technology

Blockchain is state-of-the-art technology that has multi-dimensional applications including smart vehicular transport system, in short, Intelligent Transport System (ITS). The number of vehicles, and hence the traffic, is ever-increasing. The V2X is reality with the help of IoT. With infusion of self-driving cars, the complexities will enhance. The complexities involve security, autonomy, decentralization, data integrity, collection and broadcasting information regarding the real-time traffic events etc. Most importantly, disguised vehicles with an intention to disturb, exploit and manipulate the ITS by transmitting wrong information should be identified and blocked to use the ITS. For this, the authentication of the vehicles is the primary concern. Here, blockchain comes to the rescue that ensures the confidentiality and privacy of the vehicle owner is maintained and creates a trust factor onto the ITS. In short, only those vehicles that are verified vehicles will be a part of ITS. This study also focuses on how blockchain can be leveraged to achieve secure, reliable ITS. More concisely, the traffic management aspects of the vehicular networks.
Development of Cloud based Smart Voice Assistant using Amazon Web Services

Voice assistants can interpret what the user is most likely to want by deciphering questions that have been framed in a variety of different ways. Natural language interfaces are the predominant emphasis of voice assistants using AWS. The product category has come to be defined by the personal assistants that can communicate in own language and on a human level. The distribution of assistants to various computer devices has been affected by this emphasis on natural language interface. Finally, there are countless opportunities to enhance the Software. The proposed work conducted thorough a review of recent literature for this paper. This study looked at a number of articles from the past three years that discuss the use of voice assistants in the healthcare industry. Finally, the paper presented a web application called “Medico” which is a smart voice assistant with its applications in the medical sciences. It is an application created with Neo4j and AWS services connected. The primary motivation for creating this application was to use a smart voice assistant to help patients with their questions. While doctors can inquire about patients' medical history, including past ICU visits, diagnoses, and treatments received, individuals can only access their own medical information and not those of others. In addition, using the Neo4j Graph Data Science Library, “Medico” may make basic treatment recommendations for specific patients.
AI-based Gender Identification using Facial Features

The identification of gender through facial images has gained significant importance in recent times. In the domain of human recognition through image processing, biometric features like facial structure, iris, voice, and other physical characteristics are employed to establish unique identification of individuals. As the quantity of image data on the internet continues to rise, the need for Artificial Intelligence (AI) algorithms capable of performing classification tasks like face and gender recognition has grown exponentially. With the increasing prevalence of machine-to-machine interaction, and the emphasis on ethical, efficient, and individual-centric systems becoming more pronounced, gender identification software has become an increasingly popular tool. Automated gender identification software has gained popularity in the era of exchange of information between two or more machines. The method for detecting the face involves using the Haar Cascade face detection technique, which employs the Viola-Jones face recognition algorithm. After detecting the face, the eyes, mouth, and nose can be identified as facial features using various computer vision techniques. Adaptive filters are utilized to decrease noise prior to detection process in order to increase gender identification accuracy. The gathered facial characteristics serve the neural network as input or test data, which is then trained on these features. The neural network is designed to learn gender-specific features and operate as a classifier for gender detection.
Hate Speech Detection using Multimodal Meme Analysis

Hateful memes, which often contain offensive language and derogatory images, have become increasingly prevalent on social media platforms, posing a significant challenge to content moderation efforts. To address this issue, the proposed approach explores the detection of hateful memes through a multimodal analysis that utilizes both visual and textual modalities. A novel lightweight architecture is proposed which involves generating object tags and image captions and utilising them to combine the visual and textual cues. In the final classification phase, various classification models, such as Support Vector Classifier and Gradient Boosted Decision Trees, are trained. This layer combines the results of concurrent processing branches (textual and visual) to provide predictions for the sample.
Research on Enhancing Accuracy in Vehicle Detection

Commutation is an important aspect of our everyday lives. Vision-based vehicle detection and classification has grown in prominence in the field of intelligent transportation systems research. Unmanned aerial vehicles (UAVs) for civilian remote sensing have sparked a lot of interest in recent years because they benefit the community in a variety of ways, such as recovering missing vehicles, avoiding traffic areas, and tracking vehicle quantity on road to plan the roads and establish new rules. In challenging traffic situations, vehicle detection is often used. Researchers have devised many approaches to these problems. Some of them produce decent results but are computationally costly and fail in some conditions. This detailed literature review delves into the specifics of the most accurate and efficient methods for recognizing and identifying cars. This paper focuses on many common techniques for vehicle detection. A few of the algorithms compared in the study include Convolutional Neural Network, You Only Look Once, Support Vector Machine, Faster R-CNN and Single Shot Detector. We can determine whether the approach provides improved precision for vehicle detection based on the comparison. From the contrast, we can analyse which method gives enhanced precision for vehicle detection.
Collision Avoidance Algorithm for a Quadrotor UAV in Dynamic Environments

The effectiveness of a quadrotor's mission is significantly determined by its ability to fly safely in its environment. Because quadrotors are frequently used in densely populated low-altitude airspace, collision avoidance algorithms are very important. If the anticipated trajectory can produce the least amount of jerk impact while still adhering to motion limit requirements, flight performance can be enhanced. Using a novel quadrotor collision avoidance algorithm, this study gives a trajectory with minimal jerk. The suggested method enables the quadrotor to follow a minimum jerk trajectory; however, when it encounters an obstacle, the quadrotor deviates from this trajectory and returns to the intended minimum jerk trajectory as the obstacle moves out of the way. This serves as the mission planning module for the quadrotor, and an autopilot proportional derivative controller then tracks the trajectory. The results of the flight simulation tests indicate that the quadrotor can successfully avoid linear and nonlinear moving objects. These simulations are conducted in the MATLAB environment.
Developing virtual and augmented reality applications for science, technology, engineering and math education

The Rhode Island IDeA Network of Biomedical Research Excellence Molecular Informatics Core at the University of Rhode Island Information Technology Services Innovative Learning Technologies developed virtual and augmented reality applications to teach concepts in biomedical science, including pharmacology, medicinal chemistry, cell culture and nanotechnology. The apps were developed as full virtual reality/augmented reality and 3D gaming versions, which do not require virtual reality headsets. Development challenges included creating intuitive user interfaces, text-to-voice functionality, visualization of molecules and implementing complex science concepts. In-app quizzes are used to assess the user's understanding of topics, and user feedback was collected for several apps to improve the experience. The apps were positively reviewed by users and are being implemented into the curriculum at the University of Rhode Island.
A novel neighbor selection scheme based on dynamic evaluation towards recommender systems

Collaborative filtering is a kind of widely used and efficient technique in various online environments, which generates recommendations based on the rating information of his/her similar-preference neighbors. However, existing collaborative filtering methods have some inadequacies in revealing the dynamic user preference change and evaluating the recommendation effectiveness. The sparsity of input data may further exacerbate this issue. Thus, this paper proposes a novel neighbor selection scheme constructed in the context of information attenuation to bridge these gaps. Firstly, the concept of the preference decay period is given to describe the pattern of user preference evolution and recommendation invalidation, and thus two types of dynamic decay factors are correspondingly defined to gradually weaken the impact of old data. Then, three dynamic evaluation modules are built to evaluate the user's trustworthiness and recommendation ability. Finally, A hybrid selection strategy combines these modules to construct two neighbor selection layers and adjust the neighbor key thresholds. Through this strategy, our scheme can more effectively select capable and trustworthy neighbors to provide recommendations. The experiments on three real datasets with different data sizes and data sparsity show that the proposed scheme provides excellent recommendation performance and is more suitable for real applications, compared to the state-of-the-art methods.
AR visualizations in laparoscopy: surgeon preferences and depth assessment of vascular anatomy

Introduction: This study compares five augmented reality (AR) vasculature visualization techniques in a mixed-reality laparoscopy simulator with 50 medical professionals and analyzes their impact on the surgeon. Material and methods: ​​The different visualization techniques' abilities to convey depth were measured using the participant's accuracy in an objective depth sorting task. Demographic data and subjective measures, such as the preference of each AR visualization technique and potential application areas, were collected with questionnaires. Results: Despite measuring differences in objective measurements across the visualization techniques, they were not statistically significant. In the subjective measures, however, 55% of the participants rated visualization technique II, 'Opaque with single-color Fresnel highlights', as their favorite. Participants felt that AR could be useful for various surgeries, especially complex surgeries (100%). Almost all participants agreed that AR could potentially improve surgical parameters, such as patient safety (88%), complication rate (84%), and identifying risk structures (96%). Conclusions: More studies are needed on the effect of different visualizations on task performance, as well as more sophisticated and effective visualization techniques for the operating room. With the findings of this study, we encourage the development of new study setups to advance surgical AR.
Bandwidth-Efficient Zero-Knowledge Proofs For Threshold ECDSA

Abstract In most threshold Elliptic Curve Digital Signature Algorithm (ECDSA) signatures using additively homomorphic encryption, the zero-knowledge (ZK) proofs related to the ciphertext or the message space are the bottleneck in terms of bandwidth as well as computation time. In this paper, we propose a compact ZK proof for relations related to the Castagnos–Laguillaumie (CL) encryption, which is 33% shorter and 29% faster than the existing work in PKC 2021. We also give new ZK proofs for relations related to homomorphic operations over the CL ciphertext. These new ZK proofs are useful to construct a bandwidth-efficient universal composable-secure threshold ECDSA without compromising the proactive security and the non-interactivity. In particular, we lowered the communication and computation cost of the key refresh algorithm in the Paillier-based counterpart from $O(n^3)$ to $O(n^2)$. Considering a 5-signer setting, the bandwidth is better than the Paillier-based counterpart for up to 99, 95 and 35% for key generation, key refreshment and pre-signing, respectively.
Teaching Reform of Object-oriented Programming Curriculum based on Contextual Teaching Method

Object-oriented programming is the core professional course offered by artificial intelligence major in most colleges and universities. As an important compulsory programming course, how to let the students quickly master complex abstract grammar knowledge, have good practical coding ability, and to apply their knowledge to analyze and solve basic problems in the field of artificial intelligence, need scientific and effective exploration and practice for the teaching mode. Combined with the practical teaching implementation, we take C++ programming course as an example to discuss the teaching reform ideas and practical effect of object-oriented programming curriculum under the guidance of contextual teaching method.
An Interoperable License Management Component for Educational Content

So far, it is the exception - at least in Germany - that digital school content from publishers is presented in the learning platforms used by schools. On the one hand, because contents are usually only available in proprietary formats. On the other hand, because the necessary license information and assignments to user IDs cannot be mapped. This paper presents an interoperable license management component that aims to bridge the gap between educational content providers and learners’ learning platforms, thus enabling seamless access to different educational content. The proposed solution enables different proprietary license definitions to be mapped to a standardized exchange format while allowing learners to access content from different providers’ target systems. The novel approach integrates known partial solutions, models an authorization mechanism, and provides a management interface for easy visualization and modeling of rights of use for digital content. The license management component is intended to meet the needs from media providers, real schools, realized on the example of the German state of Lower Saxony, as well as their students and must be as interoperable and flexible as possible for reusability. The uninterrupted perception by users of the publisher’s content integrated in this way for the first time in a school learning platform still must be proven by user tests. The work to be done next includes large-scale evaluations, assessment of the practicality of the management interface, and possible integration of payment processes. Overall, this work demonstrates a prototype of a license management component that adequately addresses the challenges of interoperability in education.
Economic Globalisation and Africa's Quest for Greener and More Inclusive Growth: The Missing Link

This study examines the contingency and threshold effects of economic freedom in the economic globalisation (EG) and inclusive green growth (IGG) relationship in Africa. Based on macro data for 22 African countries and the Driscoll-Kraay standard errors with fixed effects instrumental variable regression, the following findings are established. First, Africa’s mostly unfree economic setting, conditions EG to reduce IGG. Second, when we disaggregate EG into its financial and trade globalisation components, we find that the IGG-impeding net effect of the latter is rather striking. Third evidence from our threshold analysis suggests that by improving Africa’s mostly unfree economic architecture to 60% (moderately free) or 80% (free), the IGG-deteriorating net effects of EG are mitigated (but not nullified). We conclude that unless effort is made to improve Africa’s economic architecture level, the envisaged IGG gains of economic globalisation might prove elusive.
Blockchain-Based Self Sovereign Identity Systems: High-Level Processing and a Challenges-Based Comparative Analysis

Traditional identity management systems trust centralized certification authorities (CAs) to manage public keys and authenticate the mapping between users and their respective keys. This presents several security concerns as a CA is a single point of failure in the system. Besides, managing public keys by a centralized CA is becoming costly regarding the current growth in users and the distributed systems they use. Also, centralized identity management systems lack interoperability and present privacy concerns. Self-Sovereign Identity (SSI) systems aim to address these issues by providing decentralized identity ecosystems that facilitate the registration and exchange of identity attributes, and the propagation of trust between participating entities, without needing to rely on a central authority. Blockchain technologies improve security in the SSI systems by allowing control of the storage and disclosure of credentials and identity information. They improve the integrity, confidentiality, and interoperability of users’ information. This paper highlights the challenges that an Identity Management System (IdM) must overcome, and how blockchain-based IdMs leverage blockchain technology to meet these challenges. It also presents a comparative analysis of three SSI ecosystems based on the above-mentioned challenges. An SSI layered architecture model detailing the actors, objects, components, and processes is then proposed.
The Role of Adaptive Personalized Technologies in the Learning Process

This article is devoted to the development of an adaptive personalized online course in mathematics for students, which was implemented on the Stepik platform. The authors discuss a non-linear approach to online learning that adapts to the needs of the student as they progress through the course content, which leads to an individual learner's experience based on prior knowledge. Within the framework of this article, the authors consider the experience of Aktobe Higher Polytechnic College in the task of implementing an adaptive personalized online course, and also offer specific methods and technologies. For this reason, the authors are exploring Stepik adaptive personalized learning software as an educational platform and designer of open online courses and interactive training lessons, using videos, tests, and various tasks with automatic verification and instant feedback.