Reviewerly/exHarmony huggingface dataset:
Face Recognition Automated System for Visually Impaired Peoples Using Machine Learning

Recent studies and surveys are done by various researchers in the field of Machine Learning and the automation-based system say that the ratio of physically impaired (deaf/dumb) people has been drastically increased due to various health issues in the next generation. Despite of birth defects another reason for this increase in the ratio is an accident, natural health issues, or an oral disease. Victim of these types of health defects faces a lot of problems while communicating with other people in their daily routine life. All physically impaired people use a special language known as sign language to communicate with their colleagues, friend relatives but they are unable to communicate with other people who don’t know sign language. It is all known that communication plays a very important role in everyone’s day-to-day life. To help them, many automatic systems with specialized software have been developed by various researchers and the use of technology is increasing every day. Using the advanced technology, Machine Learning, in our research work, we are trying to design a Vision-based approach that will work by recognizing the face of a user. This approach is based on real-time data, this data is given as input (set of frames of a face from various angles of a physically impaired person) to the system. This set of frames will be processed, and segmented further and its various features will be extracted using a Machine Learning algorithm finally the output of the system is recognized in text format in any vision-based system.
Privacy Preservation in eLearning: Exploration and Analysis

Nowadays, a big amount of data is collected in eLearning environments, tracking students’ behavior and their performance of learning activities. Also, a part of educational data is used by third parties for statistical or research purposes. In many cases, the datasets are transferred and processed without any techniques for students’ identity protection and there are possibilities after attacks private and sensitive data to be revealed. The aim of the paper is to present the results from conducted explorations and analysis about applying privacy preserving algorithms k-anonymity and ($\varepsilon,\delta$)-differential privacy on data, collected in eLearning environment. The balance between students’ privacy protection and usefulness of output information is discussed considering several privacy parameters. Machine learning is used to predict the most suitable privacy models and in this way to support the decision making of data holder/owner.
GRABLOK: A Novel Graphical Password Authentication Utilising Blockchain Technology

One of the most important security issues is unauthorised access to computer systems. The number of leaked passwords and credentials grows exponentially each year, showing that current protection systems and authentication methods are insufficient. Attackers are bypassing the state-of-the-art systems and gaining access to corporate environments as well as our personal accounts comprising confidentiality and threatening privacy. This work focuses on a new password authentication scheme utilising 3D graphical passwords and Hyper Ledger Fabric. The initial implementation shows that this method is promising and can offer users and organisations better security minimising the risk of stolen credentials.
Video Face Anonymization for Preserving Privacy

This work scales with the development of preserving privacy on face anonymization in a video. The aim of this work is to identify, preserve and protect the privacy of individuals who appear in videos with sensitive contexts or wish to conceal their identities before sharing on social media platforms to avoid cyber bullying, cyber stalking and other problems caused by the trend of viral videos in today’s social climate. An automatic face detection is implemented and blurring for processing the video. The experimental results shows that person face privacy is preserved temporal in appearance across video frames.
Developing a GPT-3-Based Automated Victim for Advance Fee Fraud Disruption

Advance Fee Fraud (AFF) is amongst the most prevalent and destructive forms of cybercrime. Scammers typically commit AFF by tricking victims into making upfront payments for goods or services that are never provided. These payments are small compared to the alleged gains, and can thus be attractive for victims, particularly if they are vulnerable or in a heightened emotional state. Given that approximately three billion fraudulent emails are sent every day, the scale and impact of AFF demands innovative approaches. In this paper we document the development of an automated victim for AFF. The system leverages GPT-3, a large language model, in conjunction with deliberately engineered prompts to generate plausible responses to AFF emails, allowing fraud to be disrupted and actionable information relating to perpetrators to be obtained.
HERB+: Evolving an Industrial-Strength Privacy-Preserving Machine Learning Framework

Supervised machine learning does not hold without data. However, the needed data can be distributed in different locations and are non-shareable under privacy constraints. Methods to circumvent disclosure restrictions in collaborative machine learning are in strong demand. Thus, we propose HERB+ (Homomorphic Encryption for Random forest and gradient Boosting plus), a confidential learning framework for tree-based models under the scenario of vertically dispersed data. While previous related work focused on a specific algorithm, this work presents a wide variety of privacy-preserved and distributed tree-based algorithms (i.e., Decision Tree, Random Forest, and Gradient Boosting Decision Trees for both classification and regression tasks). HERB+ provides the most detailed and general discussions on using Fully Homomorphic Encryption for computing distributed tree-based algorithms during the training process. Our experiments show that although the learning protocols' efficiencies are not optimal, the predictive performance and privacy are preserved. The results imply that practitioners can overcome the barrier of data sharing and produce tree-based models for data-heavy domains with strict privacy requirements, such as Health Prediction, Fraud Detection, and Risk Evaluation.
An Investigative Study of Shallow, Deep and Dense Learning Models for Breast Cancer Detection based on Microcalcifications

Early cancer diagnosis, detection and treatment continues to be a mammoth task in because of many challenges such as socio and cultural myths, economic conditions, access to healthcare services, healthcare practices, availability of expert oncologists etc. Mammography is a successful screening method for the breast cancer detection. Mammography captures multiple features like masses, microcalcifications etc. Microcalcifications may indicate breast cancer in its early stages and are considered to play a crucial role in early breast cancer diagnosis. In this paper, we have undertaken an investigative study for breast cancer classification by automated learning from mammography images with microcalcifications. Three types of convolutional neural architectures – shallow (ResNet101), deep (VGG101) and dense (DenseNet101) learning models are employed in this investigative study towards contributing to the objective of rapid and early breast cancer diagnosis. To improve the accuracies of the learning models, the features extracted from microcalcifications have been fed to the learning models. We have experimented with varying hyperparameter setup and have recorded the optimal performances of the three models. It has been observed that among the three models, ResNet101 model demonstrated best performance of 94.2% in benign and malicious cancer classification and also demonstrated best performance in terms of time complexity. The dense model DenseNet101 was more sensitive and specific towards the classification of breast cancer using the microcalcifications. VGG101 performed well and has worked with nearly optimal results as that of ResNet 101 with a value of 93.6%.
ELMO2EDS: Transforming Educational Credentials into Self-Sovereign Identity Paradigm

Digital credentials in education make it easier for students to apply for a course of study, a new job, or change a higher education institute. Academic networks, such as EMREX, support the exchange of digital credentials between students and education institutes. Students can fetch results from one educational institute and apply for a course of study at another educational institute. Digital signatures of the issuing institution can verify the authenticity of digital credentials. Each institution must provide the integration of EMREX using its identity management system. In this paper, we investigate how digital credentials can be integrated into the Self-Sovereign Identity ecosystem to overcome the known issues of academic networks. We examine known issues such as the authentication of students. Self-Sovereign Identity is a paradigm that gives individuals control of their digital identities. Based on our findings, we propose ELMO2EDS, a solution that 1) converts digital credentials from EMREX to a suitable Self-Sovereign Identy data format, 2) enables authenticating a student, and 3) enables issuing, storing, and verification of achieved study.
Examining the Utility of Differentially Private Synthetic Data Generated using Variational Autoencoder with TensorFlow Privacy

With the emergence of AI(artificial intelligence), it is becoming more and more critical for organizations to utilize it to their advantage. However, organizations that possess a decent amount of data might not have the technical competence to perform machine learning, and vice versa. Hence, it is reasonable for the two kinds of organizations to work together to realize the value of the data. With the increasing concern over data privacy, regulations such as GDPR(General Data Protection Regulation) prevent an organization from sharing data with another unless the data is processed to the point that the individuals in the data are not identifiable. Various ways of data anonymization have been proposed and developed, including the ones that utilize neural networks to achieve the goal, like AE, VAE, and GAN. With the addition of a differential privacy framework like TensorFlow Privacy, privacy can be guaranteed, but data still needs to be usable after privacy protection measures are deployed. The present study aims to integrate TensorFlow Privacy into the synthetic data generation process and evaluate its usefulness for daily use in the industries. Since TensorFlow Privacy brings a provable privacy guarantee to synthetic data, the present study focuses on the evaluation of data utility. TensorFlow is widely used for machine learning in the industry and academically. TensorFlow Privacy, which is also developed by Google, can prove to be a valuable addition to the synthetic data generation pipeline. The result shows that VAE with TensorFlow Privacy 1) generates synthetic data with good data utility in most cases in terms of descriptive statistics and machine learning classification tasks, and 2) The customizable TensorFlow Privacy parameters work as intended in terms of privacy-utility trade-off.
How Data Diversification Benefits the Reliability of Three-version Image Classification Systems

Recently, we have witnessed an increased use of systems employing Machine learning (ML) models. The dependability of such systems is significantly impacted by the inference results of ML models that may not always be correct. Nversion ML systems can be adopted for improving output reliability by detecting or correcting errors by combining multiple inference results. In this study, we focus on a three-version ML system that combines the inference results of an ML model on diversified input data to make the system reliable for ML image classification tasks. The three-version image classification system uses only one deep-learning classifier while generating three inference results in response to diversified inputs. We use several image transformation methods to generate diversified input data for inferences. The system reliability is evaluated by the coverage of errors and the certainty of accurate predictions, as those metrics are decision method agnostic. We confirm that appropriately combined inference results from diversified data can increase the coverage of errors and improve reliability while maintaining the certainty of accurate predictions. In order to search for such effective combinations of diversification methods, we propose the neuron coverage improvement rate (NCIR) as an indicator of data diversity. Through the experiments, we show that the NCIR tends to have correlations with the coverage of errors and the certainty of accurate predictions, indicating the usefulness of the indicator.
Understanding Public Sentiment Towards a Public Rally Using Text and Social Media Analytic

The use of social media for analysing behaviours and trends of the public is a growing research area. In 2021, the #LAWAN social movement emerged in Malaysia as a result of discontentment of the people towards the government. This study intends to study and discover the public perception and sentiment towards the #LAWAN rally. The investigation also questions whether is the civil society supportive of such social movement and rally, especially when rally, protests and demonstration are traditionally and often seen as a negative and non-constructive approach in the country. Tweets with the hashtag #LAWAN over a day on and before the rally day on 31st July 2021 have been collected and analysed. Sentiment analysis helped to identify the public sentiments towards the rally, and topic modelling helped to discover common topics from the 5000+ tweets scrapped from the social media.
Foundations for Complete Exercise Generation

This paper introduces a new approach to exercise generation for university courses, that aims for high domain coverage, variability as well as adaptability. For this, we utilize a domain-specific language (DSL) which allows the creation of models describing subject matter. These models are further aggregated with a topic-oriented representation of a course and serve as the basis for an exercise generator, which then uses various templates to process different types of knowledge into different types of exercises. This novel approach is initially developed and tested by applying it to two engineering-related university courses.
Monitoring of the Social Distance between Passengers in Real-time through Video Analytics and Deep Learning in Railway Stations for Developing the Highest Efficiency

Near the end of December 2019, the globe was hit with a major crisis, which is nothing but the coronavirus-based pandemic. The authorities at the train station should also keep in mind the need to limit the spread of the covid virus in the event of a global pandemic. When it comes to controlling the COVID-19 epidemic, public transportation facilities like train stations play a pivotal role because of the proximity of so many people who may be exposed to the virus. Using common place CCTV cameras and deep learning with simple online and real-time (DeepSORT) methods, this study develops social distance monitoring using a YOLOv4 identification of a Surveillance Object Model. Based on experiments conducted with a minicomputer equipped with an Intel 11th Gen Intel(R) Core(TM) i3-1115G4 at 3.00GHz, 2995 Mhz, two Core(s), four Logical processor, four gigabytes of random-access memory (RAM), this paper makes use of CCTV surveillance, which was put into practice at the Guindy railway station, Chennai, Tamilnadu in India in order to detect the violation of social distancing.
An Analysis of Deep Learning Models for Breast Cancer Mammography Image Classification

Breast cancer is the most common and fastest-growing cancer on the planet. Breast cancer is more common in women than in men. Breast cancer can be managed if it is detected early and treated properly. Mammography is one of the procedures for detecting breast cancer. A mammogram is an X-ray scan of the breast that can help a radiologist detect early signs of breast cancer. Advances in the field of machine learning have resulted in increasingly intelligent and self-sufficient computer-aided diagnostic (CAD) systems as the learning ability of ML approaches has improved. Recent improvements in machine learning (ML) have a considerable impact on enhancing diagnostic skills, with deeper and more extensive representation approaches, usually referred to as deep learning (DL) approaches. Several studies have been conducted to identify the breast cancer. This research compares six optimized deep learning algorithms for identifying breast cancer in mammography pictures, namely InceptionV3, VGG16, ResNet50, ResNet101, DenseNet169, and Mobile Net, and finds that InceptionV3 beats the others.
Improving Class Imbalance Detection And Classification Performance: A New Potential of Combination Resample and Random Forest

Data mining is a knowledge discovery of the data that extracts and discovers patterns and relationships to predict outcomes. Class imbalance is one of the obstacles that can drive misclassification. The class imbalance affected the result of classification machine learning. The classification technique can divide the data into the given class target. This research focuses on four pre-processing methods: SMOTE, Spread Subsample, Class Balancer, and Resample. These methods can help to clean the data before undergoing the classification techniques. Resample shows the best result for solving the imbalance problem with 41.321 for Mean and Standard Deviation, 64.101. Besides, this research involves six classification techniques: Naïve Bayes, BayesNet, Random Forest, Random Tree, Logistics, and Multilayer Perceptron. Indeed, the combination of Resample and Random Forest has the best result of Precision, 0.941, and ROC Area is 0.983.
Analysis of Time and Space Complexity of Array, Linked List and Linked Array(hybrid) in Linear Search Operation

This paper focuses on Dynamic Implementation using Array and Linked list. In the data structure concept, the list plays a major role in the allocation of data where it is a general overview of creating a node in a linked list using an array with the initialization of size. The node contains data and is interconnected to the next node in the sequence via their links which perform the search operation where the space and time complexity are not wasted in these analyses. Linked lists are a great way to store a theoretically infinite amount of data with a small and versatile amount of code. The amount of data stored increases linearly with the number of nodes in the list (space-o(n)) where linked lists are more efficient than arrays. It is observed that considering the time complexity when there is a variation in data set linked array is almost reaches close to array whenever dataset is high also linked array has better time complexity than linked list when the searching element is in middle and last. Considering the space complexity linked array is almost 2 times and 4 times less space is needed than array and linked list respectively.
Solidity Vulnerability Scanner

The Ethereum smart contract language Solidity has caused a breakthrough in the field of blockchain technology with the introduction of logic in contract-form via these smart contracts. Since these contracts are immutable, there is a serious requirement to audit and test these contracts multiple times before they are deployed. There are several cases of financial and reputation damage due to unaudited smart contracts as seen in the cases of the Axie Infinity hack or Wormhole bridge hack which resulted in hundreds of millions of dollar losses. For a developer just getting into web3 development, these hacks might intimidate and drive them away from learning due to the fear of incurring such losses themselves or due to not understanding the reason for hacks. This tool allows a developer to simply paste their contract into a textbox and obtain a list of the contract-based vulnerabilities found via parsing and pattern searching. Mitigation techniques are also suggested line by line to aid the developer in securing the contract. The key difference between this tool and existing technologies is that it is compact and very precise in identifying known contract-based vulnerabilities. Providing developers with a quick and simple interface was the rationale behind the motivation. Tools like parsers and regular expression libraries were used to look for keyword detection methodology. The tool is to be used supplementarily and not as the sole identifier of vulnerabilities since there are multiple layers to web3 security and this tool only identifies smart contract-based vulnerabilities, not off-chain ones. Future additions to this tool may include example vulnerable code snippets and also explanations for the reason they are not safe for deployment as well as mitigations which can be used by any user who is interested in learning web3 security and how hacks happen.
Devanagri Character Image Recognition and Conversion into Text using Long Short Term Memory

The goal of this project effort is to identify handwritten characters. Devanagri Lipi still struggles to accurately and simply recognise human handwriting. The discipline of deep learning has extensively studied the recognition of handwritten characters. Character recognition software is crucial for everyday tasks like interpreting bank checks and postal information. The difficulty in creating machine-readable characters is still greatly hampered by several different handwriting approaches and the inferior quality of handwriting when compared to printed text. Neural networks operate similarly to the human brain; algorithms spot patterns and address universal issues. Convolution neural networks receive varied input and use filters to learn from multiple visual properties. Our deep learning model, which will operate on a grid data format and include convolution and pooling layers, will be developed. Later, we will concentrate on developing a GUI where we can write characters for automatic character recognition.
TP-NET: Training Privacy-Preserving Deep Neural Networks under Side-Channel Power Attacks

Privacy in deep learning is receiving tremendous attention with its wide applications in industry and academics. Recent studies have shown the internal structure of a deep neural network is easily inferred via side-channel power attacks in the training process. To address this pressing privacy issue, we propose TP-NET, a novel solution for training privacy-preserving deep neural networks under side-channel power attacks. The key contribution of TP-NET is the introduction of randomness into the internal structure of a deep neural network and the training process. Specifically, the workflow of TP-NET includes three steps: First, Independent Sub-network Construction, which generates multiple independent sub-networks via randomly se-lecting nodes in each hidden layer. Second, Sub-network Random Training, which randomly trains multiple sub-networks such that power traces keep random in the temporal domain. Third, Prediction, which outputs the predictions made by the most accu-rate sub-network to achieve high classification performance. The performance of TP-NET is evaluated under side-channel power attacks. The experimental results on two benchmark datasets demonstrate that TP-NET decreases the inference accuracy on the number of hidden nodes by at least 38.07% while maintaining competitive classification accuracy compared with traditional deep neural networks. Finally, a theoretical analysis shows that the power consumption of TP-NET depends on the number of sub-networks, the structure of each sub-network, and atomic operations in the training process.