Reviewerly/exHarmony huggingface dataset:
IoT based Ambient Assisted Living Technologies for Healthcare: Concepts and Design Challenges

A great influence is imposed on our society as a result of the demographic shift toward the elderly population. This research study devises novel strategies to help the elderly population to maintain their independence and avoid social exclusion. Information and communication technologies provide a number of options to assist older individuals to live longer, be healthier, and live independently. Ambient Assisted Living (AAL) is a discipline that studies cutting-edge medical and rehabilitative technology that can help elderly people with disabilities. There has been a significant and growing amount of interest in the creation of AAL systems to assist independent living during the past decade. AAL can offer a range of solutions for enhancing people’s quality of life, enabling people to live healthier, more independent lives for longer, assisting those with impairments, and assisting caregivers and medical personnel. This paper presents an overview of the research and developments linked to AAL systems. The literature study presented in this work analyzes the existing AAL approaches along with their advantages and limitations.
Sign Language Conversion to Speech with the Application of KNN Algorithm

Sign language is mainly used by those who are unable to speak, to communicate. The vast majority of people are not able to understand the Universal Sign Language (unless they have learned it) and due to this lack of knowledge about the language, they have difficulty communicating with mute people. This paper focuses on developing an application that bridges the gap between mute people and the rest of society. It captures gestures made by a person using American sign language (ASL), and converts them into corresponding text and speech in real time. The proposed study will acquire translations of sign language gestures in text, which will be later transformed into audio. In this way, a sign language translator will be developed. Further, Convolutional neural networks (CNNs) are utilized for the detection of different gestures. CNNs are highly effective for solving computer vision problems and can detect the desired features with high degrees of accuracy upon exercising sufficient training. K-NN classification is used to recognize sign language.
Real Time Conversion of American Sign Language to text with Emotion using Machine Learning

Auditory impairment or hearing loss is a major problem in today’s human population. Sign language helps hearing-impaired people to live their social life without much difficulty. The latest technology used in detecting sign language connects them to the rest of the world. Sign language recognition and conversion to the text based on the movement of hands and the shape formed by the fingers is a complex system. The solutions using machine learning give significant success for this complex system. This paper mainly focuses on developing a system for recognizing the different hand signs in American Sign Language and their emotions simultaneously in real-time and converting them into text. The system resolves the need for a translator to bridge the gap between a sign language based user and a non-sign language-based user. Most state-of-the-art technology involves CNN models with image pixels by identifying specific key point coordinates on the face/hand obtained. Using the latest technologies like MediaPipe, an improved CNN model is developed based on the distances between each unique identified vital point. The customized model able to achieve 80 percentage of accuracy for the live image.
Comprehensive Analysis of Content Defined De-Duplication Approaches for Big Data Storage

In day-to-day life, huge amounts of data are generated and storage of that data becomes a difficult task. Archive and backups are the storage medium used to store the data. As the data in the backup and archive are redundant, it needs more storage space. Data deduplication methodology is utilized to shrink the storage space. De-duplication is the technique to remove redundant data and increase the storage space in the storage medium. To store the data in the storage medium without redundancy various Chunking algorithms are used. These algorithms can improve the availability of storage space in the Warehouse. This paper presents the performance evaluation of Two Threshold Chunking (TTC) and Rapid Asymmetric Maximum (RM in Content Defined Chunking Algorithm. Also, the performance of those algorithms is measured by several parameters like De-duplication Elimination Ratio (DER), Throughput and Processing time, etc. Based on the analysis, the processing time of TTD is less and throughput is high compared to RAM, which is discussed in detail with an experimental analysis in this paper.
An Effective Possibilistic Fuzzy Clustering Method for Tumor Segmentation in MRI brain Images

The segmentation of tumors in magnetic resonance imaging (MRI) is a medical emergency operation. Weakened MR images of the brain are used to segment them using the fuzzy C-means (FCM) clustering technique. The run time is longer because of the need to continuously calculate the clustering parameters. Using the probabilistic fuzzy clustering (PFC) technique for brain MRI image segmentation is recommended by the authors of this article. Morphological reconstruction and computation of local spatial similarity factors are performed before commencing the clustering step. Integrating a local spatial similarity factor into the morphological reconstruction process reduces noise, while maintaining the information's structural integrity.
Rating-based Restaurant and Food Recommendation System using Nearest Neighbor Algorithm

Nowadays, People prefer restaurants more than for special occasions. Simultaneously, availability of many restaurants in many areas and online food ordering platforms increases the reach of restaurants and restaurant food for people. Proposing a recommendation system will help the people to identify preferred food and restaurants rather than searching a list of restaurants and food items. Recommending restaurants and food items based on people’s rating on food items shows the quality of that food item in that restaurant and increases the reliability to new users on this recommendation system as the ratings are given by public users.
A Proposed Approach to Analyze Insider Threat Detection Using Emails

As we know, now a days everything is more dependent on technology, also all the private and government organizations are using technology for accomplishing each and every task. This results into that they are more prone to threats. Threats can be originated from inside or outside the organization. As per the study, insider threats are increasing in number as compared to external threats and even more dangerous too. Due to more privacy factors and hierarchy system within the organization external threats can be easily detected as compared to insider threats. So, there is need to focus on detection of insider threats. As lot of research has been undertaking on insider threat detection using user activity logs for analyzing behavior but no such extensive research has carried out on behavior detection using emails and its content. So, the main focus of this research paper is to propose a methodology for analyzing behavioral changes using email content.
Hybrid Deep Learning Model for Copy Move Image Forgery Detection

Digital image processing software has progressed to the point where it is trivial to create forgeries by employing various manipulative tactics on the actual (real) images. Professions including law, health, and education are vulnerable to modified images. Hence, it is essential to be able to determine whether or not an image is fake. One form of image fraud is called “copy-move,” in which a key object or objects are hidden from view by copying and pasting them into the same image. Many Copy-Move Forgery Detection (CMFD) models are rendered useless when the copied section contains noise or is resized before being pasted. Our goal of this research was to develop a highly effective and efficient detection method for this type of image forgery by utilizing a hybrid Deep Learning (DL) architecture. Firstly, the sample images are taken from MICCF2000. Second, alter the size of the images, and a filtering technique is used to eliminate any noise that may have been present in the original image. Finally, we combine Convolutional Neural Networks (CNN) and Support Vector Machine (SVM) to build a hybrid DL model. Measures such as accuracy, True Positive (TPR) and Negative Rate (TNR), False Positive Rate (FPR) and Negative Rate (FNR), Precision, and F1-score are used to verify the developed hybrid DL model. Using the proposed hybrid DL model, we were able to detect fake images with an accuracy of 95%.
Programming Applications Suitable for Secure Multiparty Computation Based on Trusted Execution Environments

Secure Multiparty Computation enables secure processing of the data from multiple users without ever exposing the data or the computation code of one user to the other parties or computation hardware provider. Such an approach enables new applications and better data computation results through secure and private data sharing and making richer datasets for the analysis. This paper explains how Secure Multiparty Computation is made possible by recent development in the computer processor industry and the popularization of the Trusted Execution Environment functionality. Four problems that need to be addressed are highlighted and solutions for two of them are presented. Design for one of the key components of a Confidential Computing System for Artificial Intelligence is provided. It is the RESTful web application implemented in the Python programming language using Flask framework, which is running on a secure virtual machine.
Towards the Speech-driven Database Design

The paper presents an early prototype of an online tool named SpeeD, which is aimed at conversion of a recorded speech into the corresponding conceptual database model. SpeeD is the first tool that provides such functionality. The tool is currently able to process English speech and generate the model represented by the corresponding UML class diagram.
Honey Encryption and AES based Data Protection Against Brute Force Attack

The rapid evolution of wireless communication technologies involves tremendous data transfer and data management among devices. This evolution led to the development of machine-to-machine interaction (M2M), which generates a large amount of data. Cloud computing serves as a platform for data to travel, data manipulation, and space for data to rest. Its acuteness in making resources available involve various challenges, including data protection. Password-Based Encryption (PBE) is the traditional methodology for preventing unauthorized access to maintain the privacy of sensitive data. The current encryption techniques used are vulnerable to various attacks. In this paper, cryptanalysis of AES with honey encryption is done against brute force attack and the performance of honey encryption in resistance against denial-of-service attack is analyzed. This paper concludes with several directions to future research on data protection in cloud and constrained environment.
No-reference local image quality evaluation

The Image Quality Assessment (IQA) metrics are developed to measure the perceptual quality of images after distortion. In this paper several No-Reference (NR) metrics are tested for different distortion types. In order to see how each type of distortion affects the image quality measures, each image will have only one type of distortion. The experiments analysis is performed aiming to local image quality evaluation in agreement with both subjective and objective assessment. The obtained experimental results show the potential of considering local quality estimation for dealing with actual challenges in efficient IQA.
Trusted Vote: Reorienting eVoting using Blockchain

Voting is the essential element for each nation to express one’s choice. Nowadays, Electronic Voting Machines (EVMs) are used to poll which may be tampered resulting in incorrect election results. Therefore, an online voting system has been introduced in this research. An Electronic Voting (E-Voting) machine is a balloting machine wherein the election opinions are notated, saved, stored, and processed digitally, which makes the balloting control project complicated than the conventional paper based method. The Election Commission forms elections and enlist party candidates along with the parties for contesting the election. This paper works with the following ideas by having the two different sets of modules: election commission and the voters. An election’s REST API is arranged on Ethereum’s Blockchain, which is the front end. The votes are then stored on the blockchain architecture, to which the Election Commission grants the number of votes. However, there are some limitations due to the fact that the blockchain architecture cannot run on the main net as it must be hosted, and a separate web3 provider must be used for interacting with it due to the lack of public API availability.
Enhancing reading capability of young Thai students with augmented reality technology: Design-based research

The Thai language, which is part of the Indo-Iranian language family, carries on rituals and local knowledge that have been passed down from generation to generation. The Thai National Statistics Office found that 10% of elementary school students in Thailand cannot read. This means that 90% of elementary school students in Thailand can read. The numbers disagree with a report from an international assessment agency that said young Thai students’ reading skills were the 50&lt;sup&gt;th&lt;/sup&gt; best out of 65 countries. So, it is still hard for young Thai people to read well. The main goals of this research are to: (i) make an augmented reality platform that will help students improve their reading skills and (ii) figure out how well the platform works. This study uses a method called “research design” (R&amp;amp;D) to look at how students feel about an educational product or method that was designed and then used. Research and development can be thought of as going through three different stages (research phase, development phase, and implementation phase). According to the results, the picture word inductive model (PWIM) strategy was put into place because the professionals said it should be. The most popular method of teaching was called “precision teaching.” Reading out loud and reading the same thing over and over were two ways that vocabulary was taught. According to the results of the experiment, the students’ scores after finishing the reading are higher than their scores after the first reading. As part of the second part of the study, reading comprehension skills were tested. The students have also gotten a lot better at understanding what they read as a whole.
Dynamic Consolidation for Continual Learning

Training deep learning models from a stream of nonstationary data is a critical problem to be solved to achieve general artificial intelligence. As a promising solution, the continual learning (CL) technique aims to build intelligent systems that have the plasticity to learn from new information without forgetting the previously obtained knowledge. Unfortunately, existing CL methods face two nontrivial limitations. First, when updating a model with new data, existing CL methods usually constrain the model parameters within the vicinity of the parameters optimized for old data, limiting the exploration ability of the model; second, the important strength of each parameter (used to consolidate the previously learned knowledge) is fixed and thus is suboptimal for the dynamic parameter updates. To address these limitations, we first relax the vicinity constraints with a global definition of the important strength, which allows us to explore the full parameter space. Specifically, we define the important strength as the sensitivity of the global loss function to the model parameters. Moreover, we propose adjusting the important strength adaptively to align it with the dynamic parameter updates. Through extensive experiments on popular data sets, we demonstrate that our proposed method outperforms the strong baselines by up to 24% in terms of average accuracy.
SAResU-Net: Shuffle attention residual U-Net for brain tumor segmentation

Computer-aided segmentation technology is important for clinical treatment of brain tumors. In recent years, U-shaped networks have become mainstream for medical image segmentation, significantly improving the performance of brain tumor segmentation tasks. Since merits of the U -shaped architecture, we propose a new shuffle attention residual U-Net, i.e., SAResU-Net, for brain tumor segmentation application. SAResU-Net combines several shuffle attention (SA) blocks and residual modules with a basic 3D U-Net, where SA blocks are added to skip connection positions to capture the local spatial and channel information. In addition, a self-ensemble module is leveraged to further boost the model performance. Evaluation experimental results on the 2019 and 2020 Brain Tumor Segmentation (BraTS) datasets show that our SAResU-Net is superior to its baseline, especially on the tumor core segmentation task. Moreover, our model achieves DSC values of 79.17%, 90.02% and 82.00% for the enhancing tumor (ET), the whole tumor (WT), and tumor core(TC) on the BraTS 2020 validation dataset, respectively, while on the validation dataset of BraTS 2019, the values are 77.74%, 90.40% and 83.58%, respectively, proving its effectiveness in the application of brain tumor segmentation.
IANet: Important-Aware Network for Microscopic Hyperspectral Pathology Image Segmentation

Microscopic hyperspectral pathology image (MHPI) provides a wealth of reference information for medical diagnosis. However, the accompanying high-dimensional complex features bring great challenges to the task of pathology image segmentation. In this paper, a novel important-aware network (IANet) for MHPI segmentation is proposed. IANet builds an encoder with pre-trained ResNet34 and hierarchical fusion pyramid (HFP) modules to extract multiscale high-level features in MHPIs. Furthermore, an important-aware fusion (IAF) module is developed and embedded in the skip connection to simultaneously highlight task-relevant salient spatial and semantic features. In particular, a target-aware edge enhancement (TAEE) module is designed to improve the edge segmentation effect of the target regions. The proposed IANet realizes the full mining of the intrinsic information in MHPIs, and has efficient feature fusion and fine edge segmentation capabilities. The experimental results show that the proposed method outperforms other state-of-the-art methods on the MHPI segmentation task, providing an effective way for auxiliary medical diagnosis.
Student Dropout Prediction using 1D CNN-LSTM with Variational Autoencoder Oversampling

Student dropout represents a social, resource and time loss for everyone involved. By identifying students with the potential to evade, it is possible to take the necessary measures to prevent that from happening. This paper investigates different techniques for classification using a real students dataset that is multivariate, sequential and imbalanced. To address imbalance, an approach using weighted loss function and an approach using synthetic data generated through a combination of variational autoencoder and the Adaptive Synthetic (ADASYN) technique were investigated. In addition, we propose two models for predicting evasion using LSTM and 1D CNN networks to take advantage of the multivariate sequential property. The experiments showed that the approach only with LSTM architecture in the dynamic module using the preprocessed original dataset and without the use of weighted loss function obtained the best mean sensitivity (0.9676) among the investigated proposals. The LSTM architecture in the dynamic module using the preprocessed original dataset with weighted loss function had better specificity (0.9558) and area under curve (0.9735). Both had the same mean accuracy of 0.9549.
Disentangled Speaker Representation Learning via Mutual Information Minimization

Domain mismatch problem caused by speaker-unrelated feature has been a major topic in speaker recognition. In this paper, we propose an explicit disentanglement framework to unravel speaker-relevant features from speaker-unrelated features via mutual information (MI) minimization. To achieve our goal of minimizing MI between speaker-related and speaker-unrelated features, we adopt a contrastive log-ratio upper bound (CLUB), which exploits the upper bound of MI. Our framework is constructed in a 3-stage structure. First, in the front-end encoder, input speech is encoded into shared initial embedding. Next, in the decoupling block, shared initial embedding is split into separate speaker-related and speaker-unrelated embeddings. Finally, disentanglement is conducted by MI minimization in the last stage. Experiments on Far-Field Speaker Verification Challenge 2022 (FFSVC2022) demonstrate that our proposed framework is effective for disentanglement. Also, to utilize domain-unknown datasets containing numerous speakers, we pre-trained the front-end encoder with VoxCeleb datasets. We then fine-tuned the speaker embedding model in the disentanglement framework with FFSVC 2022 dataset. The experimental results show that fine-tuning with a disentanglement framework on a existing pre-trained model is valid and can further improve performance.
Multimodal Forgery Detection Using Ensemble Learning

The recent rapid revolution in Artificial Intelligence (AI) technology has enabled the creation of hyper-realistic deepfakes, and detecting deepfake videos (also known as AI-synthesized videos) has become a critical task. The existing systems generally do not fully consider the unified processing of audio and video data, so there is still room for further improvement. In this paper, we focus on the multimodal forgery detection task and propose a deep forgery detection method based on audiovisual ensemble learning. The proposed method consists of four parts, namely a Video Network, an Audio Network, an Audiovisual Network, and a Voting Module. Given a video, the proposed multimodal and ensemble learning system can identify whether it is fake or real. Experimental results on a recently released multimodal FakeAVCeleb dataset show that the proposed method achieves 89% accuracy, significantly outperforming existing models.
Continuous User Authentication using WiFi

As the need for information security protection continues to increase, contactless and continuous user authentication solutions are gaining attentions. However, most of the existing schemes require the user to perform specific actions, which makes them inconvenient for practical usage. In this paper, based on widely deployed WiFi devices, we propose a framework to capture the uniqueness of human breath to achieve continuous user authentication. To achieve this, we first extract breath pattern from WiFi signal through a weighted combination of signals on different antennas and subcarriers. Then, we obtain time-domain morphological features, frequency-domain wavelet features and dynamic differential features to capture the uniqueness of the breath pattern. Finally, we utilize Gaussian Mixtures Models to achieve continuous user authentication. Extensive experiments on 24 users in 4 environments demonstrate that the proposed framework can robustly authenticate legitimate users with over 90% accuracy.