Reviewerly/exHarmony huggingface dataset:
Automated Analysis of Job Market Demands using Large Language Model

This paper presents a comprehensive analysis of labor market demands for Myanmar workers in Japan, and Thailand, focusing on opportunities for individuals without higher education degrees. Leveraging ChatGPT’s text classification and summarization capabilities, we extracted vital insights from extensive job advertisements and social media groups. The dataset comprises 152 job advertisements from Thailand and 30 from Japan, collected in 2023. Our research provides a valuable snapshot of skill demands and job opportunities, offering insights for informed decision-making by both job seekers and international non-governmental organizations. The innovative approach of using ChatGPT highlights its efficacy in understanding labor market dynamics. These findings serve as a foundation for tailored interventions to bridge employment challenges faced by marginalized Myanmar youths.
Analysis of the use of Slang Language in Usage Indonesian Language Among Students of Vocational High School

Language is a tool used by humans to interact with each other. With human language, humans can interact anytime and anywhere according to their respective regional languages, especially Indonesian. Indonesian is the unifying language of the nation and must be upheld by its citizens. But in reality, there are still many citizens who experience obstacles in interacting due to a lack of language skills. Errors in language can happen to anyone, be it children, adults, or seniors; it depends on the person's understanding of the language according to the rules. This study aims to analyze the impact of using slang that often occurs among schoolchildren at SMK Miftahul Ihsan. This study uses a qualitative descriptive method that describes data on language errors that are often used by schoolchildren. The facts about the language errors were obtained by using the recording and listening methods in class. Besides the data collection techniques, this study used a recording device to record all the conversations of the informants in the class. The results of the study indicate that the impact of using slang that occurs in schoolchildren or adolescents is inherent and used in students' daily lives so that it becomes a habit. The language errors are: 1) The occurrence of deviant errors or slang; 2) Errors in the use of joining suffixes; 3) Errors in omitting one letter (phoneme) in a sentence; and 4) the replacement of diphthongs "au" and "ai" with "o" and "e.".
What's in a Domain? Anaylsis of URL Features

Many data science problems require processing log data derived from web pages, apis or other internet traffic sources. URLs are one of the few ubiquitous data fields that describe internet activity, hence they require effective processing for a wide variety of machine learning applications. While URLs are structurally rich, the structure can be both domain specific and subject to change over time, making feature engineering for URLs an ongoing challenge. In this research we outline the key structural components of URLs and discuss the information available within each. We describe methods for generating features on these URL components and share an open source implementation of these ideas. In addition, we describe a method for exploring URL feature importance that allows for comparison and analysis of the information available inside URLs. We experiment with a collection of URL classification datasets and demonstrate the utility of these tools. Package and source code is open on https://pypi.org/project/url2features.
Correlation between religious and military vocabulary in the Armenian text: A quantitative approach

The study aims to identify hidden trends and patterns in the distribution of lexical units of two thematic classes – religious and military vocabulary – in the poetic texts of Russian literature dedicated to Armenia. These themes represent important parameters in the Armenian text, they are realised in most works about Armenia and reflect important aspects of its history and culture. The analysis involves poems created in the XIX-XXI centuries. The scientific novelty of the study lies in the fact that it examines the issue of the lexical representation of two figurative-thematic parameters within the framework of a quantitative approach, which makes it possible to objectively determine the nature of the coverage of military and religious themes at various stages of the formation and development of the Armenian text. The study of the relations of lexical units in the texts of the works allows the researcher to examine both general trends in the perception of this country in Russian literature and the individual features of the poets’ construction of Armenia’s artistic image. The coefficients of variation, the Busemann coefficient, the estimation of statistical significance (chi-square) and the Euclidean distance are used for quantitative analysis. The results of the analysis made it possible to establish the interrelationships of two important themes for the Armenian text and the dynamics of their changes over time. The strongest changes were observed between the second (pre-revolutionary) and the third (Soviet) periods. A comparative analysis of the works of 8 poets who wrote about Armenia made it possible to classify them and determine differences in the frequency of military and religious vocabulary.
Unveiling the Power of TAG Using Statistical Parsing for Natural Languages

The Revolution of the Artificial Intelligence (AI) has started when machines could decipher enigmatic symbols concealed within messages. Subsequently, with the progress of Natural Language Processing (NLP), machines attained the capacity to understand and comprehend human language. Tree Adjoining Grammar (TAG) has become powerful grammatical formalism for processing Large-scale Grammar. However, TAG mostly rely on Grammar which is created by Languages expert and due to structural ambiguity in Natural Languages computation complexity of TAG is very high o(n^6). We observed that rules-based approach has many serious flaws, firstly, language evolves with time and it is impossible to create grammar which is extensive enough to represent every structure of language in real world. Secondly, it takes too much time and language resources to develop a practical solution. These difficulties motivated us to explore an alternative approach instead of completely rely on the rule-based method. In this paper, we proposed a Statistical Parsing algorithm for Natural Languages (NL) using TAG formalism where Parser makes crucial use of data driven model for identifying Syntactic dependencies of complex structure. We observed that using probabilistic model along with limited training data can significantly improve both the quality and performance of TAG Parser. We also demonstrate that the newer parser outperforms previous rule-based parser on given sample corpus. Our experiment for many Indian Languages, also provides further support for the claim that above mentioned approach might be an awaiting solution for problem that require rich structural analysis of corpus and constructing syntactic dependencies of any Natural Language without much depending on manual process of creating grammar for same. Finally, we present result of our on-going research where probability model will be applying to appropriate selection of adjunction of any given node of elementary trees and state chart representations are shared across derivation.
Detection and Differentiation of blood cancer cells using Edge Detection method

Medical imaging is an essential data source that has been leveraged worldwide in health- care systems. In pathology, histopathology images are used for cancer diagnosis, whereas these images are very complex and their analyses by pathologists require large amounts of time and effort. On the other hand, although convolutional neural networks (CNNs) have produced near-human results in image processing tasks, their processing time is becoming longer and they need higher computational power. In this paper, we implement a quantized ResNet model on two histopathology image datasets to optimize the inference power consumption
Real Time Face Recognition for Library Check in Check out System Using Deep Learning

Face recognition is a way to identify or verify someone’s identity by using their face. Face recognition systems may be used to pick out humans in photographs, video, or in actual-time. The main objective of the project is to authenticate users including students, faculty and staff using their faces to maintain the data of those who are logged in and logged out from the library database. The student or faculty library login and logout information can be maintained in a smart way i.e. the count of the students and faculty as library users can be easily available and data of users such as entry and exit time will be stored. Face Recognition Phase can be done through Live Streaming. In previous days all the library login and logout information is stored in records manually which is a time taking process. There is a chance of data loss by maintaining books and it is difficult to maintain multiple records. Nowadays users are entering their identification details for check in and checkout to use the library. and RFID also uses in many library systems which comes under manually inserting the cards which is time taking procedure So the project mainly focuses on proposing a smart way to maintain faculty/student login and logout library information by using Convolution Neural Network (CNN) and also AWS is used in the project to securely store all the data on the cloud so that Database can access them from anywhere.
Vision Companion AI Mate

This project acts as an important role in saving life of human beings and which is also its main aim. In order to complete day-to-day activities effectively and efficiently, Technology plays vital role in human life. Especially, Assistive Technology devices support to differently abled people, in order to live their life very comfortably. Realizing the importance of Assistive Technology, this product idea gives comprehensive solution for the blind people and provide them an end to end solution. With the power of embedded systems and advanced Artificial Intelligence techniques applied to the field of computer vision we aim to make a Hat/cap that can help the blind perceive the world around them. VISION COMPANION AI MATE the power of AI to describe people, text and objects. It can tell visually impaired persons, what is around them. The project titled “VISION COMPANION AI MATE “is an android application. This application provides a facility for OCR (Optical Character Recognition- It is also recognized as a text, then it will be converted to a voice message), Object Detection (To detect objects using Tensor Flow Lite for Android), Currency Detection (To detect the currency then it converted to voice) and Color detection (To pick the dominant colour of uploaded image or captured image) .Which are easily accessible to everyone and it is the power of AI to describe people, text and objects
Photochain: A Blockchain Based Secure Photo Sharing Framework for Cross-Social Network

Social Networks is one of the major technological phenomena on the Web 2.0. The evolution of social media has led to a trend of posting daily photos on online Social Network Platforms (SNPs). The privacy of online photos is often protected carefully by security mechanisms. However, these mechanisms will lose effectiveness when someone spreads the photos to other platforms. Photo Chain, a blockchain-based secure photo sharing framework that provides powerful dissemination control for cross-SNP photo sharing. In contrast to security mechanisms running separately in centralized servers that do not trust each other, our framework achieves consistent consensus on photo dissemination control through carefully designed smart contract-based protocols. These protocols to create platform-free dissemination trees for every image, providing users with complete sharing control and privacy protection. Considering the possible privacy conflicts between owners and subsequent re-posters in cross-SNP sharing, it design a dynamic privacy policy generation algorithm that maximizes the flexibility of re-posters without violating formers’ privacy. Moreover, Go-sharing also provides robust photo ownership identification mechanisms to avoid illegal reprinting. It introduces a random noise black box in a two-stage separable deep learning process to improve robustness against unpredictable manipulations. Through extensive real-world simulations, the results demonstrate the capability and effectiveness of the framework across a number of performance metrics.
Development of Counting Based Visual Question Answering System by Using Transformer and Pyramid Networks with Hybrid Deep Learning Model

Visual Question Answering (VQA) merges images and natural language processing, that enables machines to respond to queries about visual content with prowess by comprehending visual features and contextual cues in text. VQA aims to bridge the gap between human-like understanding and visual comprehension. Counting-based VQA is a specific subfield within VQA that focuses on answering questions related to counting objects or quantities in images. The objective of counting-based VQA is to develop algorithms and models capable of accurately answering questions that involve counting specific objects or quantities in visual data. Our Model consists of Bidirectional Encoder Representations from Transformers (BERT) to extract the texture features from the Question part and for the visual part, Feature Pyramid Network (FPN) is used to extract the deep features from images. Both the textual and visual features are integrated to form a combined set of features. These fused features are fed in to a hybrid model for answer prediction. This hybrid model is an integration of Gated Recurrent Unit (GRU) and One-Dimensional Convolutional Neural Network (1DCNN).
Highly Accurate Deep Learning PNN Classifier Based Skin Cancer and Depth Analysis

Melanoma cancer is a type of skin cancer and is the most dangerous one because it causes the most of skin cancer deaths. Melanoma comes from melanocyte cells, melanin producing cells, so that melanomas are generally brown or black colored. Melanomas are mostly caused by exposure to ultraviolet radiation that damages the DNA of skin cells. The diagnoses of melanoma cancer are often performed manually by using visuals of the skilled doctors, analyzing the result of Dermoscopy examination and match it with medical sciences. Manual detection weakness is highly influenced by human subjectivity that makes it inconsistent in certain conditions. Therefore, a computer assisted technology is needed to help classifying the results of Dermoscopy examination and to deduce the results more accurately with a relatively faster time. The making of this application starts with problem analysis, design, implementation, and testing. This application uses deep learning technology with Probabilistic Neural Network method and LeNet-5 architecture for classifying image data.
Kinship Measurement on Face Images by Structured Similarity Fusion

Kinship verification, which is a challenging problem in computer vision and pattern discovery. It has several applications, such as organizing photo albums, recognizing resemblances among humans, and finding missing children. A system for facial kinship verification based on several kinds of texture descriptors (local binary patterns, local ternary patterns, local directional patterns, local phase quantization, and binarized statistical image features) with pyramid multilevel (PML) face representation for feature extraction along with our proposed paired feature representation and our proposed robust feature selection to reduce the number of features. The proposed approach consists of the following three main stages: (1) face pre-processing, (2) feature extraction and selection, and (3) kinship verification. Extensive experiments are conducted on five publicly available databases (Cornell, UB KinFace, Family 101, KinFace W-I, and KinFace W-II). Additionally, a wide experiment for each stage to find the best and most suitable settings. Many comparisons with state-of-the-art methods and through these comparisons, it appears that our experiments show stable and good results.
Optimized Multi Support Vector Machine Based Approach for Fake News Detection

Fake News creates erroneous suspense information that can be identified. This spreads dishonesty about a country’s status or overstates the expense of special functions for a government, destroying democracy in certain countries. The project proposes an Multi Support Vector Machine (MSVM) -based approach for detecting fake news. The proposed model will be used to classify or detect the news as fake or real. Principal Component Analysis (PCA) is used for Feature Extraction. Principal Component Analysis (PCA) reduces the dimension of the data set comprising many related variables and recalls the maximum change in actual data. The proposed work will select the essential features with a Firefly-Optimized Algorithm (FA). The Firefly Optimized Algorithm (FA) is one of the various Evolutionary Algorithms (EAs) with various purposes. For the classification of fake news, an Multi Support Vector Machine (MSVM) classifier algorithm is implemented.
Feasibility Study of Implementation of Electronic Administration Systems in Hospitals

The manual approach is no longer the best option as healthcare services get more complex. As a result, several hospitals are beginning to implement IT to get around challenges and improve the efficacy and efficiency of their operations. This study aims to evaluate whether electronic administrative information systems can be implemented in hospitals. This study is a qualitative descriptive one that uses a literature review methodology. The author's choice of data for this study is secondary data. The collected data will undergo descriptive analysis. Based on the research findings, numerous advantages can be gained from installing electronic administration information systems at hospitals, notwithstanding any challenges or difficulties. Because they can improve the effectiveness and efficiency of hospital services in registering and storing patient data, registering, recording inpatient medical records, recording doctor data, recording room data, and producing administrative reports that management is required for decision-making and patient medical record information for insurance claims, electronic administrative information systems in hospitals are practical to implement. This study implies that hospital administrators who utilize electronic administration systems can use them as a reference source.
Analysis on Data Poisoning Attack Detection Using Machine Learning Techniques and Artificial Intelligence

One of the primary challenges of artificial intelligence in modern computing is providing privacy and security against adversarial opponents. This survey study covers the most representative poisoning attacks against supervised ML models. The major purpose of this survey is to highlight the most essential facts on security vulnerabilities in context of ML classifiers. Data poisoning attacks entail tampering with data samples provided to method during training stage, which may lead to a drop in the correctness and accuracy during inference stage. This research gathers most significant insights as well as discoveries from most recent existing literature on this topic. Furthermore, this work discusses several defence strategies that promise to provide feasible detection as well as mitigation procedures, as well as extra robustness against malicious attacks.
Harnessing Blockchain for Transparent and Efficient Land Asset Value Creation in India

Blockchain offers a decentralized and transparent ledger system that can be utilized to streamline land transactions, property management, and the overall land registry process. By implementing blockchain, the UAE aims to improve efficiency, reduce fraud, and enhance trust in the land market. Through the use of smart contracts, blockchain can automate and enforce the terms of land transactions, ensuring that all parties involved adhere to the agreed-upon conditions. This can lead to faster and more secure property transfers, as well as increased transparency in verifying ownership and property rights. Additionally, blockchain-based platforms can facilitate fractional ownership and tokenization of real estate assets. This means that properties can be divided into smaller units and represented as digital tokens on the blockchain. Investors can then purchase and trade these tokens, providing increased liquidity and accessibility to the real estate market. The UAE government, specifically the Dubai Land Department, has been at the forefront of exploring and implementing blockchain technology in land asset management. In 2017, they launched the Blockchain Strategy, which aims to transition all government transactions to blockchain platforms by 2020. The Dubai Land Department has also introduced the "Blockchain in Real Estate" initiative, focusing on utilizing blockchain for property-related processes. These initiatives and advancements in blockchain technology are expected to have a significant impact on land asset value creation in the UAE by increasing efficiency, reducing costs, and fostering greater trust in the real estate market. Keywords— Tokenization, asset, Blockchain
Research on Virtual Currency Trading Behavior under Financial Technology Innovation

In reality, there are very few identifiable virtual currency financial crime problems, this paper is studied from the micro level of the transaction subject and the macro level of the transaction currency, first of all, by building a machine learning algorithm to classify the elliptic data set node, the tree integration model has achieved better prediction effect in predicting abnormal Bitcoin transactions, which is more suitable for using this model to build an abnormal transaction detection system. Then, through the event research method, the impact of the collapse of larger transaction entities on the price fluctuation of currencies is studied, and it is found that some currencies are more affected by the platform failure event, and some currencies are less affected.