Reviewerly/exHarmony huggingface dataset:
Voice-Based Intelligent Virtual Assistant

The Virtual Personal Assistant (VPA) is one of artificial intelligence's greatest breakthroughs, giving people a new method to conduct their work with the help of a machine. This article gives a quick rundown of the approaches and concepts used to construct a Virtual Personal Assistant (VPA) and then use it in a variety of software applications. Speech Recognition Systems, also known as Automatic Speech Recognition (ASR), play a crucial part in virtual assistants, allowing users to converse with the system. We want to make a “WAANI” which is a virtual personal assistant in this project, and it will have vital characteristics that will help you satisfy your demands. We'll make it as entertaining as possible, much like previous VPAs, with user experience in mind. Several Natural Language Understanding (NLU) technologies, like International Business Machines (IBM) Watson and Google Dialog Flow, were created with this goal in mind. For the implementation of the software application in our project, we selected Google Dialog Flow as the NLU platform. The application's user interface is built on the Flutter software platform. All of the models utilised in this VPA will be built to be as energy efficient as feasible. Some of the common characteristics seen in most VPAs will be included. WAANI will be implemented via a smartphone app, with the intention of implementing it in the desktop environment in the future. The approaches utilised in the development of apps are assured to be provided in the following paper. Provides the outcomes of the developed functions inside of the application. It demonstrates how to utilise existing natural language understanding platforms to decrease user demand and then create a reliable software application.
A New Authentication Scheme Based on Chaotic Maps and Factoring Problems

Users of public key cryptography systems reveal their public keys, but they keep their private keys private. A public key directory is where all of your public keys are kept. Public key cryptosystems place a high value on preventing their keys from being forgery or otherwise tampered with. A key authentication mechanism is therefore required to confirm that an intrusion has not happened. A key authentication technique is developed in this study by solving numerous issues, including chaotic maps and factoring. When compared to schemes based on a single problem, the proposed scheme has been mathematically demonstrated to be safer. An alternative to conventional key authentication systems, the proposed scheme can help to design a cryptography system that addresses a variety of issues. In contrast, the newly created authentication technique involves only minimal and low-complexity computations, making it incredibly efficient.
Content-Based Spam Classification of Academic E-mails: A Machine Learning Approach

In the academic environment, players have overstretched University faculty with less available time. The task of reading and deleting electronic mail (e-mail) spam tends to consume or steal the little available time they have at their disposal. Due to the spam issue, automated processes or methods for separating spam from valid emails are becoming important. Due to the unstructured nature of the material, additional features, and a vast number of documents, the process of automatically classifying spam email presents significant difficulties. Increasing usage of the e-mail spam directly affects the performance of these spam classifications with regards to the quality and speed based on the challenges stated above. Most of the recent algorithms consider only relevant features or characteristics for the classification of the e-mails as spam or legitimate. The main objective of this work was to use a machine-learning algorithm to detect and categorize e-mail messages of university faculty into spam and non-spam with the identification of the most occurring attributes that are contained in the messages. These attributes helped in the generation of a classification model based on the Random Forest algorithm. Exploratory analysis of the academic e-mails revealed that the most occurring attributes that are easily associated with spam messages included “your”, “open access” and “remove”. Five hundred decision trees were generated in the Random Forest Model, which had an excellent classification accuracy of 0.942 (94.2%). The performance of the model was compared with similar models based on the random forest algorithm. The comparative analysis revealed that classification accuracy differs depending on the type of e-mails used.
Unconventional to Automated Attendance Marking Using Image Processing

The traditional method of raising your hand in a classroom to say “present ma’am” or “yes ma’am” or whatever other things you would say is kind of fading away, Image processing is becoming increasingly important in the digital world. Magicians play an important function in today's information era. Visual processing is necessary in the area of biometrics to identify a individual whose biometric image has already been saved in a database. Image based biometrics, such as faces, biometrics, and eyes, need image processing and pattern recognition algorithms. To perform correctly, an image-based biometric system requires a sample image of the user's biometric that is highly clear and unadulterated. This study proposes the development of a system that automated stamps students’ attendance using automated image render ways such as facial detection and identification. Face recognition is challenging due to factors such as face pattern, orientation, shape, varied patterns, and the complexity of face expression. Using multiple datasets, the system is taught to identify the face-representing pattern (positive pattern of face) and set it apart from the surroundings (critical pattern of facial expression) environment. The major goal is to provide an autonomous platform for recognising faces in video, as well as documenting student attendance by identifying them from their various face patterns. This aids in the automatic maintenance and management of the attendance system.
Optimization Methods for Image Edge Detection Using Ant and Bee Colony Techniques

In a computer image analysis, the main aim is to produce the image with specified appearance that provides more convenience for society and machines to detect, identify, and understand the situation. Image processing is the technique from which we can get any digital information that is stored in the form of images. “Appearance Edges” are very important feature of image inquiry and clarification. “Edge Detection” technique can be applied for extraction of edges from the image. By applying these techniques, we can get every small details of an image and unwanted details of the image can be discarded resulting in fewer amounts of data to be processed. ACO and BCO are well known meta-heuristic search algorithms used in explaining various combinative optimization problems. In this paper we are hybridizing the ACO and BCO algorithms to optimize the feature selection. The simulation results show that our proposed hybrid algorithm provides promising and optimal selection of features. We used Python’s simply framework for virtual simulations.
Internet of Things (IoTs) in the Hospitality Sector: Challenges and Opportunities

This research examines and evaluates IoT applications in the hotel industry by looking at several sectors and evaluating the benefits (opportunities) and drawbacks (challenges) of using IoT. The hotel industry will experience tremendous growth because to the Internet of Things, which offers a wide range of characteristics that can be used to tailor service. Future corporate procedures will focus on increasing productivity while reducing turnaround times. Integrating it will be necessary to preserve a competitive advantage because it will change standards and the landscape of services.
Synthetic Animations Based Dictionary for Indian Sign Language

The Indian Sign Language (ISL) is the natural language of the Indian deaf community to convey their thoughts, feelings, ideas, etc. with other people. ISL has a bulk of real human video sign language dictionaries but our main motive is to create the ISL dictionary by using synthetic animations which use virtual character Avatar (computer generated cartoon) instead of human videos. Because synthetic animation has more benefits as compared to real human videos in the terms of uniformity, cost, memory usage, and adaptability. As part of this project, a dictionary of English text to ISL was constructed as part of a translation system. Using a third-party programme called eSign editor Software, the English words are translated into Hamburg Notation System (HamNoSys). The HamNoSys code is then converted into a SiGML script, which is then translated into an animated sign by a 3D computer-generated cartoon. The produced dictionary contains a total of 3051 words, making it suitable for use in a translation system that converts spoken or written sentences into sign language motions for use in a deaf school. It’s a time-consuming and challenging task, but it’s a vital stage in the machine translation of all English text into ISL with 3D animations.
Opinion Summarization from Online Mobile Phone Reviews Using Feature Based Association Rule Mining

In the modern period, sentiment analysis and opinion mining are expanding greatly. On the internet, there are numerous e-commerce sites where customers can leave evaluations for a specific item. The reviews filled by the users are of most interest to both the individual, who is interested by the same product as well as the business person who wants to upgrade their product based on client’s review. We might be able to extract opinions from the internet and forecast customer preferences if we had a reliable mechanism for forecasting sentiments. Sometimes only knowing about the polarity related to specific product or service is not sufficient, summarized representation of these opinions is also very important to know about opinion related to specific feature of the product. There are various representations available for opinion summarization. Feature extraction is very crucial step in opinion summarization because based on extracted features summary can be generated. Three levels of granularity exist in opinion mining: document level, sentence level, and aspect level. ARM is used to find features of product and then naive bayes algorithm is applied to find polarity of the reviews. And at the end, whole summary is generated from review dataset. In this paper association rule mining is used to extract the features from online reviews of mobile phone.
An Approach of Deep Learning Technique for Object Detection

The goal of this study is to detect object using Deep learning algorithm. Object recognition is a subset of computer vision tasks that includes tasks like object recognition in digital images. Predicting the class of a single object in a frame is required for image classification. Object detection incorporates these tasks and assigns a classification to one or more objects in an image. The proposed research would concentrate on image feature extraction and classification, followed by training a model with provided data of images with known classifications using deep learning techniques. The model would then be able to predict the classification of new images and detect entities. The Faster R-CNN is a more precise and efficient way of generating region proposal through region proposal network. The network can detect the positions of various items accurately and quickly.
A Comparative Analysis of Fault Detection Techniques for Software Efficiency

Software fault prediction is a crucial and useful strategy for enhancing the reliability and quality of software. The capability to estimate which components of a big software system are almost certainly to have a prime number of defects in the next release provides improved project management, with early prediction of probable release delays, and reasonably leads to remedial measures to enhance software quality. Difficulty in this field is accurately identifying the flawed source code. The task of developing a model for Fault Prediction (FP) is difficult, and throughout history, several solutions have been offered. Recent advancements in machine learning (ML) technology, particularly the development of deep learning (DL) techniques, have enabled these approaches to handle various issues. Nevertheless, building robust fault prediction models is a difficult problem, and several strategy have been presented in the literature. The area of software fault detection and correction is examined in this study. This research also examines many fault detection systems using a table, graph, and image.
The Contribution of the Internet of Things to Enhance the Brands of Small and Medium-Sized Enterprises in Iraq

Recent times have seen the Internet of Things (IoT) becoming one of the strategic resources of small and medium-sized enterprises (SMEs). The study’s main objective is to explore how the implementation of the IoT will affect the branding of Iraqi SMEs. In-depth research should be done on how clients view the brand, its value, and the reputation of SMEs that employ IoT in their operations. Iraqi SME brands must overcome barriers and difficulties to grow on international markets. The fundamental problem is that there isn't enough information available when it's needed to understand the market condition and make wise judgments. Because end users need more information, decision-making processes are hampered, which makes it challenging to satisfy key market demands. To explore the features of utilising the IoT on the brand, the researcher used a qualitative method that included case studies and semi-structured interviews in three cases of SMEs in Iraq. The findings illustrate that SMEs’ performance in their activities and services can be improved by increasing flexibility in managing activities via speed of reaction and ease of management. The outcomes also demonstrated the IoT's assistance in meeting the demands of end-users in SMEs by delivering timely information and reports. The IoT contributed to enhancing customers' loyalty by meeting their needs and responding quickly to their requests. These benefits were achieved through the handling of massive amounts of data acquired from internal and external sources and categorised and processed in real-time. This study will be of interest to other experts in this subject, particularly those from Iraq and the Middle East.
Use Cases of Smart Contracts in Decentralized Health Infrastructure

The decentralized, peer-to-peer, distributed consensus, and anonymity aspects of blockchain technology have made it a popular research topic over the past decade. The blockchain technology eclipses regulatory issues and technical difficulties. A smart contract is a collection of self-executing, self-verifying, and tamper-resistant computer programs. Smart contracts incorporating blockchain technology are capable of performing a task in real time at a low cost and with a higher level of security. This paper explains the smart contract’s various components and operating mechanism. Identify and evaluate the various use cases of smart contracts, as well as the benefits of implementing smart contracts in blockchain applications. The study finishes with a discussion of implementation issues in the future real-world situation for smart contracts.
Detection and Categorization of Machine Translation in Indian Languages

India is a land of diverse cultures and religions consisting of twenty-two different Indian languages that are morphologically rich. Translation of languages is required when people visit foreign lands and for communication of their ideas and thoughts to the people living in that country. Nowadays, when Information Technology rules the world, machine translation is a very crucial term. Today, many machine translators are available online but if a translation of one Indian language to another language is to be performed, it's not up to the mark. In this paper, we have surveyed the available machine translators that are available online today, we have also discussed how the existing translators produce poor quality of translation- the Named Entities are not translated properly, complex/lengthy sentences result in poor quality of translation. So, at this stage machine translation requires extensive training to be performed in terms of sentence rewriting or converting complex and lengthy sentences to simple sentences and named entity translation needs to be performed before conversion of input text into translated text.
Experience-Based Knowledge Management with a Conversational AI Chatbot: Taking Hand-Shaken Tea Service in Taiwan as an Example

We human beings accumulate experience through daily practice with memory, and, we grow our knowledge through concrete experiences with an experiential learning cycle. Experience-based knowledge management (EBKM), by virtual of information technologies especially artificial intelligence (AI), could be realized nowadays to support individual daily life in experiential learning to enhance their happiness and wellbeing. The goal of the research is to explore the benefit of EBKM system with conversational AI chatbot interface to facilitate experiential learning. In the research, we designed the framework of the conversational AI chatbot with RASA’s NLU, Neo4j, and LINE. Then, we adopted scenario-based survey to evaluate the perceived value from the interaction between a designated user and the prototyping chatbot. In the survey, we designed four interaction scenarios containing different dialogue episodes aiming to order drinks from hand-shaken tea shops. We recruited 214 participants and assigned them to experience these four scenarios according to their status of difficulties in choosing hand-shaken brands or drinks. From the results, we found that the conversational AI chatbot can really help people to reduce their decision-making time on choosing drinks with better purchase experience. Moreover, we can conclude that the conversational AI chatbot can realize the vendor relationship management (VRM) that customers could make good choices from a wide range of vendors. We anticipate that, in the future, EBKM can be realized with the conversational AI chatbot for VRM.
Conversion of Sign Language to Text and Audio Using Deep Learning Techniques

Languages like sign languages rely on manual gestures to communicate. As effective means of communication for the deaf, sign languages were devised. It will be of great help for deaf and dumb people and also for people who have no knowledge of sign language. This eliminates the need for an intermediary as a translation medium. The number of sign languages used worldwide is unknown. Typically, each nation has its own native multiple languages. The primary goal of this study is to recognize the poses and hand gestures of all kinds of sign languages available worldwide and convert them into text and audio and making the system bilingual. This system tries to build a communication bridge between people with hearing and speech impairments and the rest of society. So far, many researchers have studied this area to get a better result. In this research work, the conversion of sign language to text and audio is carried out using techniques such as Media Pipe Holistic, Drawing Landmarks, Open CV, LSTM Neural Network, Google Translator, and GTTS in order to achieve a good accuracy of 98%. This model can be further improved by adding various language conversions for better performance.
Language Techniques and Literary Devices for National Messaging in President Buhari’s Democracy Day Speech

That language is used to convey a broad sense of meanings and that the meanings that are conveyed with language are moulded by our immediate social, political and historical conditions are safe assumptions about the efficacy of language. This paper discusses language techniques and literary devices for national messaging to ascertain their functions in national discourse. To achieve this aim, the study examines purposively sampled excerpts from Nigeria’s President Muhammadu Buhari’s Democracy Day speech on June 12, 2019, using insights from practical stylistics. This is to determine whether the language and literary devices have been deployed effectively to the speech’s content. The language and literary devices observed were reference items, to signal solidarity and inclusivity; evidential clauses, to consolidate the argument raised; capitalisation, to foreground the issues discussed; name-calling, to ridicule political opponents; the praise tactic, for self-promotion and positive representation, the blame-game tactic, to evade responsibility and denigrate previous administration; hasty generalisation, to advance personal agenda; and allusion, to draw knowledge from historical events. The study submits that the aforementioned devices, depending on how they are used in communication, can either foster or jeopardise national integration. Consequently, it recommends that national discourses be cautiously constructed using pertinent linguistic and literary devices.
The Application of Diachronic Corpus Compilation Principles in a Pilot Study of Subjectivity

Researchers claim (see Egbert, 2018) that, irrespective of the growing amount of corpora, there is insufficient focus on the research and discussion of corpus creation and analysis challenges. The ongoing international project LEXECON (2021-2024) raises awareness about these kinds of issues. The goal of this study is twofold: firstly, to explore corpus creation stages in relation to compilation criteria; and secondly, to pilot the functionality of the created subcorpus by researching first-person pronoun variations to uncover the subjectivity across the subcorpus genres. The pronouns were explored by observing their relative frequency, context, and surplus-deficit index. Two corpus analysis tools—Sketch Engine and Hyperbase 10—were applied. The corpus creation results confirm that balance is the most challenging corpus criterion to fulfil, whereas corpus editing is the most time-consuming corpus creation stage. The results obtained via first-person pronoun extraction confirm that the context and surplus-deficit index contribute to the research results no less than the relative frequency data. The analysis of personal pronoun data variations shows that essays contain the fewest first-person singular pronouns; however, in other genres, they often do not convey an authorial stance. Moreover, a greater surplus of possessive case reflects a more active authorial stance as opposed to objective case.