Reviewerly/exHarmony huggingface dataset:
MesoNet3: A Deepfakes Facial Video Detection Network Based on Object Behavior Analysis

Deepfake is the process of manipulating objects with images and video. As a result of the development of deep learning techniques such as GAN, Deepfake has become closer to the truth. Many researchers are based on discovering deep fakes that were created by traditional methods. These methods produce often generate artifacts that may be subtle to humans. This paper can detect deepfakes that are perfectly created. Through the use of the new MesoNet3 algorithm to analyze behavior, facial expressions, and the appearance of an object based on a dataset. This paper consists of two stages. The first stage is to build a new MesoNet3 network that is trained on a set of data using a deepfake dataset. The second stage is to test the videos through the extraction of the face. After that, enter it into MesoNet3 and discover whether it is fake or not. The new MesoNet3 algorithm has proven its ability and accuracy in detecting fake video, compared to the old Meso-4. The accuracy of the MesoNet3 in detecting and distinguishing fake and real videos is %99.54.
New Security Mechanism of Health Data Based on Blockchain–NFV

Health data security has permanently been essential trouble for humanity. In spite of its significant social position, modern approaches for handling health data are uneventful, complex, occasionally costly, and risky to human mistakes and misconceptions. On the health aspect, the Healthchain approach appears to own the possibility to evolve a unique measure for addressing health records utilizing virtual functions that are stable within blockchain technology. In this study, we provide a one-of-a-kind example that uses a licensed blockchain-NFV (Network Function Virtualization) to manage and store patient electronic health records (EHR). This method ensures transparency and, more precisely, immutability, which are required for secure administration and storage, ensuring a well-organized system that involves both doctors and patients, and, hopefully, restoring confidence in the overall health situation. Our profession may also present to obtain a speed of the blockchain system to EHR and promote different dialogues with health organizations to fully employ the potential of the stated technology, according to the requested purpose.
Estimating Blur Parameters to Reconstruct the Motion Blurred Images

One of the important topics of image restoration is the restoration of a motion-blurred image. The restored blurred images are based on estimating the two parameters of a point spread function (PSF) angle and length of the blur kernel. In this article, we suggested checking the quality of the input images before going further in processing when the image is not blurred, estimating the PSF parameters, and reconstructing the image based on the periodicity of motion-blurred in the frequency domain. Fourier transform is used to detect the blur in the input images, and the Hough transform is used to estimate the blurred image angle. Also, the cepstrum domain is used for blur length estimation by finding the lowest peak. After blur parameters are estimated, we determine a point spread function (PSF) to deblur the image using a wiener filter of non-blind deconvolution. The visual, as well as the peak signal-to-noise ratio (PSNR) of restored images, are compared with competent recent schemes. The experimental results of the suggested algorithm show good accuracy, robustness, and time efficiency for blur detection, estimation of the PSF parameters, and reconstruction of the image when compared with other works and methods.
Application of Shapley Additive Explanation Towards Determining Personalized Triage from Health Checkup Data

Machine learning has become a powerful tool to assist humans making decisions. In most cases, machine learning models act like a black box, a user can only view the outcome without knowing the decision-making process or the deciding factors. Explainable AI has shown good performance in interpreting prediction models and identifying the influential parameters behind the prediction/decision. Our previous works have been analyzing health checkup data collected by a digital healthcare system, called Portable Health Clinic (PHC), developed by us. The system uses a standard logic set based on WHO recommendations to triage the health status of a patient. The triage used in PHC is almost a static standard logic set that works for any patient at any age. We argue that the triage logic should vary from person to person. This paper attempts to use explainable AI to check whether triage could be personalized. An experiment has been carried out over a health check-up data set (N = 44,460), by applying XGBoost, a popular machine learning algorithm to predict a patient’s health status (risky or not risky). An eXplainable AI (XAI) technique called SHAP is used to explain the prediction results. The SHAP value clearly indicates that each health parameter (BMI, Blood Pressure, hemoglobin, etc.) has different cut-off points for different age groups, which suggests that the threshold to determine one’s health status is different and can be obtained. The results will be useful to improve the existing triage static logic. This paper demonstrates cut-off points for BMI and Blood Pressure (Systolic) for two age groups which is an indication of group triage. Our future work will search for the individual cut-off point for developing personalized triage. The obtained cut-off points need to be verified by health professionals.
Exploring the Design Space of Technological Interventions for Menopause: A Systematic Review

Menopause is a phase in a woman’s lifecycle, which is considered to have occurred when a woman does not have a menstrual period for 12 consecutive months. Despite being perceived just as a biological phenomenon, a woman experiences several psychosocial symptoms along the way as she transitions into menopause, which begin well before the cessation of menstruation and sometimes continue to exist even after the onset of menopause, making it a 2-to-7-year journey. Ongoing research on women’s health in the Human Computer Interaction (HCI) research community has led to an increasing number of works focusing on the intervention of technology in women’s health, including a rising interest in designing technological interventions for menopause. With approximately 6000 women reaching menopause daily in the US, there is a need to understand the design space of technological interventions for menopause by surveying prior studies, to eventually contribute towards designing technologies for menopausal women. This paper presents a systematic review of prior studies on technological interventions for menopause. The aim of the review is to (1) Understand how prior studies have approached the design of technological interventions for menopause (2) Identify the technological features and goals of the interventions proposed by prior studies (3) Identify the symptoms (physiological/psychosocial) being addressed by the proposed interventions. A systematic review of 12 papers collected from the ACM Digital Library highlights the characteristics of prior studies on technological interventions for menopause, such as type of study, study design and interventions that are discussed in the study. Based on the findings, we discuss aspects that were comprehensively studied, potential design implications for interventions for menopause along with limitations of the current study and opportunities for future research on technological interventions for menopause.
Peak Anomaly Detection from Environmental Sensor-Generated Watershed Time Series Data

Time series data generated by environmental sensors are typically “messy,” with unexpected anomalies that must be corrected prior to extracting useful information. This paper addresses automatic detection of such anomalies and discusses two lines of study for achieving efficient and accurate detection using AI techniques with a focus on peak anomalies. One study uses the classic knowledge-engineering process and the other uses a deep-learning method to mimic how a trained watershed scientist detects anomalies. These two approaches were applied to time series data collected from a research watershed in Vermont, U.S.A., and their performances were assessed with respect to detection accuracy and computational efficiency. The two approaches had different anomaly detection accuracy depending on the peak type. The knowledge engineering approach was readily tunable to achieve competitive or better detection accuracy while computationally far more efficient than the deep learning approach. Results indicate the advantage of using the two approaches in combination, while a more general study involving other watersheds’ time series data would be needed.
A Preliminary Analysis of Twitter’s LGBTQ+ Discussions

Social media platforms play a significant role in the lives of LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer, and others) individuals, where they have to tackle the challenge of managing their sexual and gender identities. In addition, social media have been leveraged as the go-to platform for a significant proportion of LGBTQ+ communities to come out and participate in discussions related to their rights and the discrimination faced. Twitter, in particular, has been analyzed to understand online behaviors towards LGBTQ+ communities, an example being how online Twitter discussions can reveal discriminatory behavior towards them. However, a macro-level analysis of LGBTQ+ tweets since the early days of Twitter has been understudied. In this research, we present a preliminary macro-analysis of users and tweets since 2006 to gather insights into queries such as: What emotions and toxicity levels are more prevalent in LGBTQ+ discussions? Do the emotions and toxicity levels in tweets change over time? What do we know about the users who have frequently been tweeting about LGBTQ+-related topics since 2006? Upon our analyses, we find that emotions such as joy and anger and toxicity such as identity attacks and threats are more prevalent in the negative user-bios and tweets. We also see a significant increase in activity on Twitter over the years for both overall and positive emotions.
Optimal Layer Selection on Deep Convolutional Neural Networks Using Backward Freezing and Binary Search

Transfer Learning in Deep Convolutional Neural Networks is a highly used method for image classification, the performance depends on the selection of the layers to be frozen; the search for the optimal layer where the backward freezing will be applied becomes increasingly complicated as the number of layers increases. We found an approximation of the optimal layer to apply backward freezing using a modified Binary Search method. We experimented with InceptionV3, Xception, DenseNet121, NasNetMobile, and ResNet50; the case of study is the classification of melanomas of benign and malignant class from Kaggle. We use partitions with four-folds and measure the performance with the ROC AUC score. Our results show an improvement in the ROC AUC score for ResNet50 and no significant change for InceptionV3. Code: https://www.kaggle.com/code/hherrera007/binary-search .
Image Generation from Sketches and Text-Guided Attribute Edition

Every day new Artificial Intelligence models are created, and existing ones are modified or combined to extend their range of applications and tasks they can solve. This paper presents a novel approach that combines Natural Language Processing and generative networks to generate images from sketches and descriptions in natural language. We present a pipeline that was followed to recondition the generative network. It includes the processed text that will give the context to the sketch used for the generation. Finally, the model and the generated images are evaluated and compared using benchmark data sets, and promising results are reported.
Using Features Based on Elongation to Enhance Sentiment Analysis

Elongated words such as “heellloo” or “heyyy” are a frequent feature of oral communication and are often used to underline or exaggerate the hidden message of the root word. Although elongated words are rarely found in written languages and dictionaries, they are common in social networks. They can be considered in the analysis of users’ sentiments. In this paper, we analyze the impact of elongation on the classification of sentiments, in addition to an in-depth study at the level of lexical forms of elongation. In this work we present a method to improve the accuracy of sentiment classification based on elongations of features. Experimental results conducted on Twitter data show that our model achieves an accuracy of 0.79 in 7-fold cross-validation experiments.
A personalised recommendation method of online educational resources on social media platform

Aiming at the problems of low recommendation accuracy and low user preference in traditional methods, a personalised recommendation method of online educational resources on social media platform is proposed. Firstly, the crawler technology is used to obtain the online educational resources data, and the resource data features are extracted. Then, the similarity of data features is calculated through cosine similarity, and the feature data with high similarity is fused to complete the feature preprocessing of educational resources data. Finally, the user's demand for resource data and preference degree are determined through the user interest model, so as to construct the online educational resources personalised recommendation model, and take the educational resources data and user preference degree as the input data to complete the educational resources personalised recommendation. The experimental results show that the proposed method has high accuracy and user preference.
Multiple evaluation methods of MOOC online English teaching quality based on social network

In order to solve the problem of low evaluation accuracy of English teaching quality evaluation methods, this paper designs a diversified evaluation method of MOOC online English teaching quality based on social network. Firstly, the characteristics of social networks and the information interaction process between users are analysed, and the data affecting the quality evaluation are collected for normalisation. Then, the data with high similarity is determined by cosine similarity calculation to realise data preprocessing. Finally, the diversified evaluation indicators are normalised, the weight of diversified evaluation indicators is calculated, the quality data evaluation model of diversified teaching indicators is constructed, and the diversified evaluation is completed. The experimental results show that the evaluation accuracy of this method is always higher than 90%, and the evaluation time is less than 2.6 s, which has a certain reliability.
A Smartphone Based Real-Time Object Recognition System for Visually Impaired People

Visual impairment is a major crisis for visually impaired people (VIP) and consequently, VIP require keen guidance to perform their regular activities. This paper develops a smartphone-based object (e.g. currency note, staircase, etc.) recognition system that can alleviate the monetary transactions, mobility issues, etc. for VIP. Due to the usage of a singular smartphone, the design of the system remains straightforward, and it requires no extra hardware. Alongside, it is convenient to adapt to the human body. Then, to recognize the object in real-time, it exploits Single Shot Detector (SSD), Convolutional Neural Network (CNN), and Tensorflow-lite (tflite) that classifies, train as well as test the object, and supports the platform, respectively for this purpose. First, it creates a dataset in the format of the COCO dataset. Then, it labels the recognized object by a text to speech conversion method and sends it to the VIP via Bluetooth technology. The experimental results show that for object recognition and detection, the accuracy of the developed system is 94.25% and 98.23%, respectively that outperforming the existing systems. Moreover, it uploads all processed data to a remote server. Overall, the proposed system uses audio messages to guide the VIP in recognizing currency notes and avoiding obstacles in their surroundings.
Mining association rules for classification using frequent generator itemsets in arules package

Mining frequent itemsets is an attractive research activity in data mining whose main aim is to provide useful relationships among data. Consequently, several open-source development platforms are continuously developed to facilitate the users' exploitation of new data mining tasks. Among these platforms, the R language is one of the most popular tools. In this paper, we propose an extension of arules package by adding the option of mining frequent generator itemsets. We discuss in detail how generators can be used for a classification task through an application example in relation with COVID-19.
Bangla Spelling Error Detection and Correction Using N-Gram Model

Spelling error detection and correction is an essential technique to ensure high quality writing in any language. A spell checker helps to identify possible misspelled words and provides most probable suggestions to improve the text. Developing a Bangla spell checker is quite challenging as the lexical resources in Bangla are limited as well as the language rules are hard to generalize. Bangla spelling errors can be broadly categorized as non-word error and real-word or semantic error. Real-word errors are context sensitive and more complex to correct. In this article, we present a Bangla text correction model for both non-word errors and real-word errors. The proposed model uses a combination of edit distance and n-gram language model to detect an erroneous target word and provides improvement suggestions. We have collected around 1,00,000 articles from online sources and compiled a proprietary dictionary of n-grams from the tokens of those articles. For our experiment, we have compiled 11,500 Bangla sentences to apprise our model and the performance rate of our proposed model is 96% on an average for error detection to handle both types of spelling errors.
Design a Quantum Cryptography Algorithm and Evaluate the Risks of Quantum-Based Nano Computing

Nanotechnology is a cutting-edge field that has gained trust in recent years. One area of research within nanotechnology is nanocomputers, which has opened up numerous possibilities for computer scientists and experts to create novel products in the realm of nano-based electronic manufacturing. There are many ways to implement the nano computing, like nanoelectronic computing, nanomechanical computing, nano chemical and DNA computing, and quantum computing which will provide the easy way to complete our task with very fast and accuracy. The importance of study on quantum computing is hypothetically a new technology, whereas quantum computing preserves a system of qubits. Qubits are the base and fundamental unit of quantum computing. There are several obstacles to developing a quantum computer-based teleportation system that could enable long-distance communication through the transmission of data or messages. Quantum-based cryptography is a fantastic mechanism for safeguarding the internetwork communication between sender and receiver. Here, the paper mainly focuses on designing a quantum cryptography distribution algorithm and analysis of risk factors between sender and receiver in quantum-based nano computing. The Qiskit Python library, created by IBM, was utilized to develop and implement the quantum cryptography algorithm.
Fake Profile Detection Using Image Processing and Machine Learning

In today’s technologically evolved society, almost everyone has a presence on social media. Making phony accounts is thus incredibly simple. One who may pose as someone else is referred to as having a “false profile.” These accounts are mostly used to malign somebody by impersonating them. Nevertheless, a phony profile can also be utilized for a number of purposes, such as inciting regional tensions, propagating false information, and publishing provocative material involving current sensitive issues. A model was suggested that can potentially help in the decrease of fraudulent profiles considering they pose such a major hazard to anyone and everyone. It can reliably pinpoint users which can be accused of being fraudulent, notably anyone without a personal image. In the suggested methodology, machine learning and image recognition were both employed to guarantee that each user has a distinct profile. The goal of this concept was to prevent users from opening accounts with someone else’s image or personally identifiable information. In hopes of preventing someone from using another user’s photograph or an image of any objects, this model additionally uses an image processing service for face recognition. In order to prevent fictitious individuals from being able to open an account by using someone else’s identify, One Time Password (OTP) technology was deployed. It is vital to define fake accounts using deep learning on a real dataset of respondents’ responses. The k-means method was applied to the dataset in order to determine the inaccurate results. The dataset was subjected to the k-means clustering algorithm, and it was discovered that the accuracy value was 75.30%.
Bornomala: A Deep Learning-Based Bangla Image Captioning Technique

Image captioning, giving a description of an image is becoming popular nowadays. Bangla is spoken by almost 260 million people on the planet. However, a little effort or research has been done on it in image captioning area. In English, just the opposite, there are numerous automatic photo captioning systems. Our motive is to create a Bangla automatic image captioning system that can correctly describe any image in Bengali. In image captioning, it is important to acknowledge the important objects of the image and how they are related to an image. Moreover, significant portions of the image are observed at first and then a caption is generated according to them. It is also important to generate semantically and syntactically right sentences. In this paper, we will try to present an attention based deep Bangla image captioning technique. Here, CNN is utilized to find out the features from the picture and their connections. Then LSTM is utilized to create a text description of the input image using this object and attention value. The result showed that, our model can predict the description of any image in Bangla very significantly. We also reported the BLEU score is between 35–44 which indicates that the model can generate the description for any image very rightfully.
Big data visual exploration as a recommendation problem

Big data visual exploration is believed to be considered as a recommendation problem. This proximity concerns essentially their purpose: it consists in selecting among huge amount of data those that are the most valuable according to specific criteria, to eventually present it to users. On the other hand, the recommendation systems are recently resolved mostly using neural networks (NNs). The present paper proposes three alternative solutions to improve the big data visual exploration based on recommendation using matrix factorisation (MF) namely: conventional, alternating least squares (ALS)-based and NN-based methods. It concerns generating the implicit data used to build recommendations, and providing the most valuable data patterns according to the user profiles. The first two solutions are developed using Apache Spark, while the third one was developed using TensorFlow2. A comparison based on results is done to show the most efficient one. The results show their applicability and effectiveness.