Reviewerly/exHarmony huggingface dataset:
Image Enhancement of Low Light Image using Deep Learning

Image enhancement of low light images is an important research area in computer vision and image processing. In recent years, deep learning has emerged as a powerful tool for enhancing low light images, with convolutional neural networks (CNNs) being the most used architecture. In this study, we propose a deep learning approach for enhancing low light images using a modified CNN. Our approach involves training the network on a large dataset of low light images and their corresponding enhanced images and using the trained model to enhance new low light images. We also propose a new loss function that encourages the network to enhance details and reduce noise in the images. Our experiments show that our approach outperforms existing state-of-the-art methods in terms of both objective metrics and visual quality, making it a promising technique for real-world low light image enhancement applications
Text and Image Plagiarism Detection

Today, much more than in the past are discussed of plagiarism in the research. Conditions of the Web and Possibility of complex and smart searches in a short time, is rated to this, and as a result has arrived significant damages to the research. Tools designed to deal with plagiarism act on the text and ignore images. On the other, an inseparable part of information transfer are images that transfer the large volume of information in an article or scientific research. Because of the images include a very wide range and especially found large amounts of images in the computer's texts, and as respects, flowcharts are carrying a lot of information, could be one of the options of plagiarism. The purpose of this project is examining the plagiarism rate of a paper in terms of images plagiarism using Histogram Model
Face Recognition Attendance System

Attendance management is a critical task in various organizations, including educational institutions, offices, and businesses. Traditional methods of attendance tracking, such as manual roll call or using physical time clocks, are often time-consuming. Our research paper presents a comprehensive study on an automated face recognition attendance system, proposing a novel approach that leverages deep learning-based face recognition techniques. Our system shows promising results in terms of accuracy, efficiency, and security, and has the potential to revolutionize attendance management in various organizations. Further research and development in this area can lead to widespread adoption of face recognition attendance systems, benefiting organisations and user alike.
Translation of an Excerpt from <i>Movable Horizon (Orizzonte mobile)</i> by Daniele Del Giudice

Click to increase image sizeClick to decrease image size Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsAnne Milano AppelAnne Milano Appel, Ph.D., has translated works by a number of leading Italian authors for a variety of US and UK publishers. Among the recent works she has published are M. Son of the Century by Antonio Scurati, Game of the Gods, A Devil Comes to Town, and Theory of Shadows by Paolo Maurensig, Heaven and Earth by Paolo Giordano, Snapshots, Journeying, and Blameless by Claudio Magris, and Deviation by Luce d’Eramo. Her awards include the Italian Prose in Translation Award, the John Florio Prize for Italian Translation, and the Northern California Book Award for Translation. Translating professionally since 1996, she is a former library administrator, and has a doctorate in Romance Languages and Literature.
Phishing Website Detection using Machine Learning Rules with Cryptography Technique

Phishing, a type of cybersecurity attack aimed at stealing personal information like passwords and credit card numbers, can be mitigated through the use of machine learning techniques for detecting phishing websites. In response, cybersecurity experts are actively seeking reliable and robust detection techniques to identify phishing websites. This research paper focuses on the implementation of machine learning technology for detecting phishing URLs by extracting and analyzing various features from both legitimate and phishing URLs. Support Vector Machine algorithms are employed for this purpose, to identify the most effective algorithm based on accuracy rate, false positive rate, and false negative rate, to enhance phishing detection measures. Cryptography Technique like the RSA algorithm is used to secure encryption method to encrypt the user search data before being stored on the server. The paper further discusses the reasons why ensemble machine learning methods are well-suited for binary phishing classification challenges in real-time detection scenarios, underscoring their efficacy in anti-phishing techniques.
Price Negotiating Chatbot on E-Commerce Website Using NPL

This proposes the development of a price negotiating chatbot on e-commerce websites using Natural Language Processing (NLP) technology. The chatbot aims to assist customers in negotiating prices with the sellers, enhancing their shopping experience. The proposed chatbot will be built using the Python programming language and the TensorFlow library for Natural Language Processing. The chatbot's architecture consists of several components, including a user interface, an NLP module, a price prediction module, and a negotiation module. The NLP module will enable the chatbot to understand and interpret the customer's messages, while the price prediction module will predict the minimum price that the seller is willing to accept. The negotiation module will use reinforcement learning algorithms to negotiate with the seller and come up with the best possible price for the customer. The proposed chatbot will provide customers with a convenient way to negotiate prices with sellers, enhancing their shopping experience. The chatbot's architecture, which includes a user interface, an NLP module, a price prediction module, and a negotiation module, will enable the chatbot to understand and interpret customer messages and negotiate prices with sellers on behalf of the customer. The chatbot will be trained and assessed on a substantial dataset of negotiation conversations, and its performance will be compared to other contemporary negotiation models. The results will demonstrate that the proposed chatbot significantly improves the shopping experience of customers, attaining higher success rates, shorter negotiation times, and increased customer.
Healthcare Data Security using Blockchain Technology

Blockchain in Healthcare is a significant field that has gained attention over the years which focuses on privacy and security of patient's information and its supporting data. The existing scenario is a cloud-based centralized healthcare data which is maintained by every organization that attends its patients, is recorded for clinical, patient care and research purposes. This centralized system can be tampered or manipulated by hackers or internal conflict of interest, that can be addressed by decentralized blockchain technology. An immutable permissioned blockchain which secures data from malicious attacks that is acquired using Healthcare 4.0 with Internet of Things (IOT) that comprises of sensor data, machine communication and automation systems. Medical institutions can utilize this technology to obtain insight and improve the analysis of medical records. This paper elaborates solutions for Healthcare data security using permissioned blockchain with authorized access of personal that provides secure data storage, sharing and accessing from secured blockchain technology. It also provides significant benefits in healthcare, reduces fear of data theft and by enabling smart contract-based storage design with best security benefits.
Techno-optimism and optimization in media architecture practice and theory

Media architecture community systematically explores the potentials of computation and digital media to intervene in form-finding, fabrication of buildings and urban data collection processes. Combining social media topic modelling techniques with the review of media architecture-related literature, I discuss methods to locate the media architecture community in social media, conduct initial discourse analysis and pursue a deeper investigation of the topics addressed by community. In the literature, media architecture is presented as an interactive set of technologies for a participative public life. And yet, while a dynamic facade increases possibilities for participation and creative expression, it also facilitates reframing participation as a technical problem. I position optimization and efficiency in media architecture discourse as a form of optimism and offer insights into its political implications. I propose to rethink the shortcut between optimism and optimization by tracing conceptual and professional relations that inform media architecture.
Compliance advice, ethics, and culture

This chapter discusses the provision of compliance advice by compliance officers, which is a central element of the compliance program. It first explains how compliance advice is the necessary supplement to training on the compliance policies and procedures. It then looks at the factors in the broker-dealer that both facilitate and discourage compliance advice. The chapter also considers the related topic of compliance officers advising on ethics, culture, and other social standards adopted by the broker-dealer. Finally, it briefly looks into what compliance officers should do if their advice is not followed.
Image Process Based Recommender System for Social Media Marketing

Social media has become one of the most powerful marketing tools, particularly comments, emojis, and stickers plays vital role in increasing the company and products branding in the market. The popularity of social media posts with images are getting increased steadily for the past few years, and social media posts with images receive more user attention than normal text posts. The purpose of this research is to devise a recommender system that can evaluate the effectiveness of images, emojis, and stickers in promoting products on social media and use different image processing techniques such as neural networks to process the pixel-level details of an image and filtering images based on image quality assessment. This research demonstrated the impact of emoji and text categorization on famous business Instagram accounts on cosmetics, personal care, and perfumes. CNN model was used in this research and it was found that images based social media postings are getting more comments and sales rather than text based postings.
Improving Recommendation System by using a knowledge Graph Database for Maintenance of Rolling Stock

Capitalization and sharing of knowledge play an essential role in the management of railway rolling stock maintenance. It is an activity often associated with collaborative decision-making systems. In this paper, we build a maintenance recommendation system on TigerGraph Cloud. We use the Failure tree graph constructed in previous work. We built a recommendation system that recommends the top-10 actions to a technician based on the rating prediction. We train the recommender model with a gradient descent algorithm. In the last part of this work, we compare the accuracy of this model based on Knowledge graph with the Collaborative filtering model. Preliminary results indicate that graph-based recommendation systems perform better than baseline methods. This study contributes to the sharing of knowledge on repair methods for rolling stock. The added value of our study is the use of a real dataset in the field of maintenance of railway rolling stock.
Fraud Classification In Financial Statements Using Machine Learning Techniques

With the blooming of technology, the financial sector has shown various number of improvements and has provided multiple e-financial services. Frauds can occur easily and quickly in data that lack safety levels. Although there are many laws that reduces the outcome of hacking, but unfortunately these laws cannot keep up with the enormous increase of hacking attacks level that are present. In this research, Bayes point machine, Random Forest, Cat Boost, Decision tree and Isolation Forest algorithms were applied on the credit card dataset, and it was found that Random Forest algorithm was outperforming well comparing to other algorithms.
Hybrid Resource Scheduling Algorithms in Heterogeneous Distributed Computing: a Comparative Study and Further Enhancements

In the context of heterogeneous distributed systems like modern High-Performnace Computing (HPC) that must respond to unpredictable requests of variable complexity with variable resource requirements (processing power as well as storage capacity), a classical scheduling algorithm would not be suitable. Therefore, hybrid dynamic scheduling approaches have been adopted. These later have the ability to adapt over time based on the knowledge gained from the results of previous work. Several techniques are thus used to optimize these algorithms such as resources clustering. In this paper, we propose a comparative study of some of most popular algorithms in order to highlight the situations in which each algorithm is more suitable. We evaluate their performance and evolution in a realistic setting of CloudSim tool without neglecting load-balancing, and measure these performance metrics at runtime.
Using Single Time of Quantum Computer for Shor's Factoring Algorithm

Peter Shor's in 1994 developed a factoring algorithm that can factor a composite number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{n}$</tex> in a polynomial time by using a quantum computer in one step of the algorithm. The algorithm may contain a loop that requires calling a quantum computer many times to find the order of selected number <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$a, a\in Z_{n}$</tex> . In this paper, the improvement to the Shor's factoring algorithm guarantees the single use of the quantum computer to find the order, where the rest of the algorithm can be implemented by a classical computer. A numerical example for the original and improved version is presented.
Semantic-based Job Recommendation Framework Using OWL Ontology

This paper proposes a new semantic framework for a job recommendation based on OWL ontology. The proposed framework will aid in the job search and recruitment process. The framework consists of a number of techniques: A web crawler is used to collect information from various web sites, the OWL job recommendation ontology provides a well-defined semantic model related to the job domain, SP ARQL constructs queries on OWL ontologies, EasyRDF allows sending SP ARQL queries to a third party server Virtuoso which will handle it and retrieve the results. The proposed framework recommends a suitable career opportunity for people based on their qualifications, skills, technological background, and so on. The OWL job recommendation ontology has been developed using Protégé 5.5.0 and the main components of the concepts and relations are discussed. The OWL ontology gives semantics to job information gathered from the web and enables more effective management, sharing, and recommendation of job information.
Baseline Estimation in Face Detection for AI Proctored Examinations through Convoluted Neural Networks

The COVID-19 pandemic took the world by surprise, and everything came to a halt. The education sector had to adjust accordingly by shifting to online learning. If the online delivery experience was overall successful, assessment integrity becomes questionable as examinees still manage to circumvent the anti-plagiarism mechanism put in place. In this paper, we propose an artificial intelligence solution using face and head pose detection to estimate the neutral position of the examinee which will form the basis to detect any suspicious behavior. The resulting implementation achieved a 97% accuracy when detecting the examinee in the frame and a 98% accuracy when there are multiple faces detected.
Exploring The Role of Big Data Algorithm Recommendation in Smart Cities- Taking Book recommendation As an Example

With the construction and development of smart cities and the increasing needs of people, big data algorithm recommendations in the public services of smart cities can better provide people with content or items that meet their hobby needs, and enterprises in innovative application services can upgrade their products and contents according to people's needs. To address the problems of low accuracy and large bias in today's big data recommendation algorithm. In this paper, we will take a book recommendation system as an example, aiming at solving the problems of lack of cold boot in old book recommendation algorithms, the broad classification of collaborative filtering algorithms, and inconspicuous preference bias. To find the improvement in the F1 measure after the improvement of the recommendation algorithm. In addition, put the experimental improved recommendation algorithm into the take-out recommendation on campus to find the feasibility of the recommendation algorithm.
Image Enhancement for Pedestrian Detection at Night Time

Present days people are often geographically dispersed unevenly in low-light photographs, making it challenging to differentiate them due to the few photons and inherent noise. Some person detecting models are unable to function in dimly lit environments. Within this project First, we will use gamma correction, a method for improving low-light images and storing the improved photos. Second step is to utilizing the enhanced and low-light pictures, the convolution neural network technique is applied.The data will be used to train the CNN model, which produces results for accuracy, precision, recall, and f1. The SVM HOG features will be applied during prediction to extract people identification from low-light photographs. Our proposed model provides more prediction results compare to existing project.
Impact of Machine Learning on Regional Languages Processing: A Survey

Machine Translation System refers to the process of automatically or with minimal human involvement converting text from one language form to another using computer technology (MTS). Rich data and increased processing speed by infrastructure have led to appealing solutions for complex issues in a variety of fields, including Speech Processing, Image Processing, Natural Language Processing, Machine Learning, and others languages.Regional languages of India are said to have limited resources.Traditional MT methodologies are challenging to use in the Indian context because to a lack of data and the rich morphological structure of Indian languages. In this study, several strategies and techniques for processing Indian language presented by researchers are examined.
Investigating the Value of Using Emotionally Intelligent Artificial Conversational Agents to Carry out Assessments in Higher Education: Review

The Viva (Oral Examination) is a crucial assessment method in higher education field to evaluate students on their work. In the typical setting of Viva, the learner and the assessor benefit from carrying out extremely adapted assessment taking opportunity to have a unique and deep insight into learner's knowledge and understanding. As the interaction with human examiners in Viva assessment can cause a considerable level of anxiety to the students, usage of conversational agents is emerging in the education domain. Hence, intelligent conversational agents will be an innovative, practical, and interactive tool which may help to reduce the level of tension caused by the traditional Viva mechanism. In this paper, we review the usage of artificial intelligent conversational agents in different businesses, looking at interaction modality, uses and recommended design rules. Additionally, we study the aspects: usability, interactivity, learnability, and reliability of the agents used in different domains to investigate the value of using them to carry out assessments in higher education. Furthermore, we compare between related frameworks in terms of utilizing web-based implementation, source code availability and use of Wizard-of-Oz techniques.
Determining Linguistic Features of Hate Speech from 2016 Philippine Election-Related Tweets

Hate speech is characterized as a deliberate attack directed towards a group of people motivated by aspects of the group, s identity. There is a growing interest in solutions involving automatic hate speech detection in response to the proliferation of hate speech. However., most automatic hate speech detection tools are designed for high-resource languages such as English which results in challenges in detecting hate speech in low-resource languages such as Filipino. Social media users within the Philippines predominantly use native language or a code-switched variation such as Taglish as the preferred linguistic style in online communication. This study seeks to determine linguistic features that characterize hate speech in the Philippine setting. The study characterizes hate speech using the following features: bilingual., part-of-speech., and psycho-linguistic features. Feature extraction was facilitated via fastText., NLTK (Natural Language Toolkit)., and LIWC (Linguistic Inquiry and Word Count) from an existing Filipino hate speech corpus collected during the 2016 Philippine Presidential Elections. Results show that hate speech from this dataset has significantly different features from non-hate speech. Specifically., the distinct features include language dominance., frequency of code-switching., frequency of parts-of-speech., and LIWC's summary variables and psychological process. These features which have been demonstrated to be statistically different between hate speech and non-hate speech can be leveraged to augment existing hate speech detection models., particularly within low-resource language contexts.
Improving The Security of E-Exam Systems

Though e-exams have their advantages like accuracy and speed reduced human intervention, there are some flaws as well. These flaws are security threats, the same way as traditional exams are exposed to them such as cheating or unauthorized access. Furthermore, e-exams are also exposed to threats like impersonation and tampering or accessing exam related data and information on servers. As a result, there is a need to establish a strong security framework within the e-exam system that universities adopt, such that smooth and timely conduction of exams can take place. The present study deals with such security threats of e-exam systems and provides a solution by proposing a new security framework, to deal with these threats. Based on the primary data, a security framework of e-exam system was proposed at university levels. This security model aimed to mitigate the threats determined during the survey, along with the strategies and system requirements suggested by the survey respondents. The proposed framework provides security at two levels. Firstly, at the university level using biometric authentication and user login authentication. The second level was the cloud level, where four security methods (encryption, anti-X, security system access and SQL injection) were added to secure the data in the cloud (application server side). Further it is suggested in the model to use Infrastructure as a service (IaaS) for cloud computing because of its various advantages and security options which have been discussed in the model.