Reviewerly/exHarmony huggingface dataset:
Alzheimer's Disease Clinical Scores Prediction based on the Label Distribution Learning using Brain Structural MRI

Predicting Alzheimer's disease (AD) clinical scores offers a useful tool to monitor dementia progression at different time points. Previous machine learning methods usually focused on regressing the clinical scores directly but ignored the ambigu-ous information among score labels. In this study, we introduce a novel AD clinical scores prediction framework based on label distribution learning (LDL), named CSP-LDL. Notably, we first turn the clinical scores into a normal probability distribution of discrete labels to exploit uncertainty among the dementia scores. Then we learn the distribution of discrete labels by optimizing Kullback-Leibler (KL) divergence between the estimated and ground-truth distributions using a 3D CNN. Moreover, we further employ an expectation regression layer to regress clinical score value at the fine-grained level based on the predicted label distribution. Experiments on ADNI-1 and ADNI-2 datasets show that our CSP-LDL model outperforms existing state-of-the-art methods in terms of dementia regression accuracy at multiple time points using baseline structural magnetic resonance imaging (sMRI) data, demonstrating its effectiveness in early clinical diagnosis of AD.
Graph Collaborative Filtering for Recommendation in Complex and Quaternion Spaces

With the development of graph neural network, researchers begin to use bipartite graph to model user-item interactions for recommendation. It is worth noting that most of graph recommendation models represent users and items in the real-valued space, which ignore the rich representational capacity of the non-real space. Besides, the simplicity and symmetry of the inner product make it ineffectively capture the intricate antisymmetric relations between users and items in interaction modeling. In this paper, we propose Graph Collaborative Filtering for recommendation in Complex and Quaternion space (GCFC and GCFQ respectively). Specifically, we first use complex embeddings or quaternion embeddings to initialize users and items. Then, the Hermitian product (for GCFC) or Hamilton product (for GCFQ) and embedding propagation layers are used to further enrich the embeddings of users and items. As such, we can obtain both latent inter-dependencies and intra-dependencies between components of users and items. Finally, we aggregate the embeddings of different propagation layers and use the Hermitian or Hamilton product to obtain the intricate antisymmetric relations between users and items. We have carried out extensive experiments on three real-world datasets to verify the effectiveness of GCFC and GCFQ.
A Novel CNN, Bidirectional Long-Short Term Memory, and Gated Recurrent Unit-Based Hybrid Approach for Human Activity Recognition

Human activity recognition (HAR) is a crucial and challenging classification task for a range of applications from surveillance to assistance. Existing sensor-based HAR systems have limited training data availability and lack fast and accurate methods for robust and rapid activity recognition. In this paper, a novel hybrid HAR technique based on CNN, bi-directional long short-term memory, and gated recurrent units is proposed that can accurately and quickly recognize new human activities with a limited training set and high accuracy. The experiment was conducted on UCI Machine Learning Repository's MHEALTH dataset to analyze the effectiveness of the proposed method. The confusion matrix and accuracy score are utilized to gauge the performance of the presented model. Experiments indicate that the proposed hybrid approach for human activity recognition integrating CNN, bi-directional LSTM, and gated recurrent outperforms computing complexity and efficiency. The overall findings demonstrate that the proposed hybrid model performs exceptionally well, with enhanced accuracy of 94.68%.
Chaotic Based Image Steganography Using Polygonal Method

Information security is the most concerning factor in the time of tremendous innovative headway. Immense specialized information cleared a way for the development of information taking methods. Steganographic methods help to veil the mysterious data with some other media that goes about as a spread to the data. This strategy makes it hard for programmers to see the data since it shows up as a media instead of as data itself. Image steganography assists with concealing mystery data in any dimension and worth of images. These pictures can be traded without looking for any consideration of programmers to the mystery data inside it. Here the information is encoded into the image through an arbitrary rectangular region, to make it more secure. Using chaotic map we have generated random number series through which data embedded in rectangular region with uneven interval. This can be unscrambled at the beneficiary side with the best possible calculation alongside the secret key. We have scrambling the data and concealing it in the image utilizing the chaotic map. Removing the data and unscrambling it is done at the beneficiary. Embedding capacity of the proposed methodology is also comparable with other well known existing methodologies. Performance analysis is also measured and the proposed methodology generates enhanced result than the others.
Decentralized Smart City of Things: A Blockchain Tokenization-enabled Architecture for Digitization and Authentication of Assets in Smart Cities

IoT-enabled smart devices have become an essential part of the smart city architectures, which establishes all the underlying architectures to operate altogether, such as the Internet of Things (IoT), Cyber-Physical Systems (CPSs), Internet of Cyber-Physical Things (IoCPT), and Internet of Everything (IoE). Similarly, these underlying architectures constitute a system to realize the concept of smart cities and, ultimately, a smart planet. A private blockchain-based architecture, Decentralized Smart City of Things (DSCoT), has been proposed in this study which utilizes Blockchain tokenization (i.e., Non-fungible tokens-NFTs) for the representation and authentication of user and IoT assets by defining smart device attributes. Through NFTs, the uniqueness of the IoT assets and users has been realized, which helps digitize these assets as non-interchangeable units of data stored on a digital ledger. The proposed architecture ensures this functionality of unique asset representation by deploying smart contracts and further for IoT assets and user authentication. The mechanism provides security services such as confidentiality, integrity (using SHA-III one-way encryption), availability, and authorization (CIA). The evaluation of the proposed functions and components has been provided in terms of Gas consumption and time complexity, showing promising results. An innovative approach of functions to query the smart contract for the status of assets in the NFT registry offers no transaction cost (in Ether/Gewi), making the proposed extension efficient in terms of time complexity. This architecture aims to provide a smart city solution that may ensure robust security features utilizing Blockchain, NFTs, and SHA-III encryption mechanisms.
PoseBuddy : Pose Estimation Workout Mobile Application

The sedentary lifestyle of modern people is becoming more and more common, and a lack of physical activity is a risk factor for many diseases. Therefore, this project introduces a mobile application that works on iOS and Android, PoseBuddy, through computer vision, provides interactive experiences, personalized workout plans, and competition through connections with others. It allows users to exercise anytime and anywhere with fragmented time without the limitation of venues. PoseBuddy forms an interactive mode that obtains users’ pose data through the mobile device’s front camera, inputting to a high-accuracy human pose estimation model provided by TensorFlow, MoveNet. Afterwards, it provides real-time audio feedback during exercise after being verified by the accuracy validation algorithm. The real-time inputs from the camera feed can be captured and processed asynchronously by the system, allowing users to know in real-time if they are carrying out the workout correctly. Additionally, PoseBuddy is also a sports platform that allows users to post their experiences after exercising and making friends.
Interpolation of Sparely-Sampled Time-Variant Channel Response in 1D Trajectory Utilizing Ensemble Kalman Filter

Stored channel measurement can be utilized for developing site-specific radio channel emulator with high accuracy. The channel emulator is expected to synthesize temporal fading characteristic in terms of a time-variant channel impulse response (CIR) of the mobile station (MS) at any position in space. However, such continuous characteristics is hard to acquire directly from the measurement because only a set of discrete CIR samples could be sparely-captured. In this work, interpolation technique based on ensemble Kalman filter (EnKF) is proposed to reconstruct the continuous CIR profile given a sequence of undersampled measured CIRs. The proposed EnKF model was utilized to estimate and track fading parameters between two adjacent measured CIR. The interpolated CIR is then computed from those estimated parameters. Through experiment, performance of EnKF-based interpolation is evaluated in the simulation using 3GPP channel model. The advantage and limitation of the proposed interpolation scheme is discussed based on the simulation result.
Sentiment Analysis of Local Tourism in Thailand from YouTube Comments Using BiLSTM

Currently, social networks, where people can express their opinion through content and comments, are fast developing and affect various areas of daily life; Particularly, some research on YouTube travel channels found that almost tourists and audiences leave comments about their attitudes to that place. Thus, mining the emotional recognition of comments through artificial intelligence can bring knowledge about the tourists' general view. This article analyzes the relationship(s) between social media use and its effect on community-based tourism in Thailand using the Social Media Sensing framework (S-Sense) as sentiment analysis and the Bidirectional Long Short-Term Memory (BiLSTM) methods to analyze the text comments. This research collected 51,280 comments on 114 Youtube Videos, which are tourist attractions in various provinces in Thailand. The approach categorizes attractions based on sentiment analysis of 60% or more, including restaurants, markets, historical sites, temples, or natural attractions. The results show that 67.51% of the 19,391 clean-processed comments were satisfied with those attraction places. Therefore S-Sense and BiLSTM models can be sufficient to analyze the attitude of comments about attraction places with from 43 to remain 33 keywords of 1,603 comments. Furthermore, the offered sentiment analysis method has higher precision, recall, and F1 scores.
Evaluation and Comparison of Brute-Force Search and Constrained Optimization Algorithms to Solve the N-Queens Problem

The N-Queens problem is relevant in Artificial Intelligence (AI); the solution methodology has been used in different computational intelligent approaches. Max Bezzel proposed the problem in 1848 for eight queens in 8 $$\times$$ 8 chessboard. After that, the formulation was modified to an N-Queens problem in a chessboard. There are several ways of posing the problem and algorithms to solve it. We describe two commonly used mathematical models that handle the position of queens and restrictions. The first and easiest way is to find one combination that satisfies the solution. The second model uses a more compact notation to represent the queen’s potions. This generic problem has been solved with many different algorithms. However, there is no comparison of the performance among the methods. In this work, a comparison of performance for different problem sizes is presented. We tested the Backtracking, Branch and Bound, and Linear Programming algorithms for a different number of queens, reaching 17. In addition, we present statistical comparative experimental results of the different methods.
Fully Convolutional Transformer with Local-Global Attention

In an attempt to imitate the success of transformers in the field of natural language processing into computer vision tasks, vision transformers (ViTs) have recently gained attention. Performance breakthroughs have been achieved in coarse-grained tasks like classification. However, dense prediction tasks, such as detection, segmentation, and depth estimation, require additional modifications and have been tackled only in an ad-hoc manner, by replacing the convolutional neural network encoder backbone of an existing architecture with a ViT. This study proposes a fully convolutional transformer that can perform both coarse and dense prediction tasks. The proposed architecture is, to the best of our knowledge, the first architecture composed of attention layers, even in the decoder part of the network. This is because our newly proposed local-global attention (LGA) can flexibly perform both downsampling and upsampling of spatial features, which are key operations required for dense prediction. Against existing ViTs on classification tasks, our architecture shows a reasonable trade-off between performance and efficiency. In the depth estimation task, our architecture achieves performance comparable to that of state-of-the-art transformer-based methods.
Exponential Decay Function-Based Time-Aware Recommender System for e-Commerce Applications

Unlike traditional recommendation systems that rely only on the user's preferences, context-aware recommendation systems (CARS) consider the user's contextual information such as (time, weather, and geographical location).These data are used to create more intelligent and effective recommendation systems.Time is one of the most important and influential factors that affect users' preferences and purchasing behavior.Thus, in this paper, time-aware recommendation systems are investigated using two common methods (Bias and Decay) to incorporate the time parameter with three different recommendation algorithms known as Matrix Factorization, K-Nearest Neighbor (KNN), and Sparse Linear Method (SLIM).The performance study is based on an e-commerce database that includes basic user purchasing actions such as add to cart and buy.Results are compared in terms of precision, recall, and Mean Average Precision (MAP) parameters.Results show that Decay-MF and Decay-SLIM outperform the Bias CAMF and CA-SLIM.On the other hand, Decay-KNN reduced the accuracy of the RS compared to the context-unaware KNN.
FurryGAN: High Quality Foreground-Aware Image Synthesis

Foreground-aware image synthesis aims to generate images as well as their foreground masks. A common approach is to formulate an image as a masked blending of a foreground image and a background image. It is a challenging problem because it is prone to reach the trivial solution where either image overwhelms the other, i.e., the masks become completely full or empty, and the foreground and background are not meaningfully separated. We present FurryGAN with three key components: 1) imposing both the foreground image and the composite image to be realistic, 2) designing a mask as a combination of coarse and fine masks, and 3) guiding the generator by an auxiliary mask predictor in the discriminator. Our method produces realistic images with remarkably detailed alpha masks which cover hair, fur, and whiskers in a fully unsupervised manner. Project page: https://jeongminb.github.io/FurryGAN/ .
Poses Classification in a Taekwondo Lesson Using Skeleton Data Extracted from Videos with Shallow and Deep Learning Architectures

Sports is an important activity to help maintain and improve human health, help fight diseases, create flexibility for the body, and contribute to training the spirit of competition, spirit teammates, and increase soft human skills. The current sports practice has great support from technical technology, contributing to sports to achieve many high achievements. Currently, many technological techniques are applied to support sports for practicing and monitoring matches. For example, Video Assistant Referee re-examines videos to support the referee’s accurate decisions on a specific situation in football matches and applications in poses recognition of aerobics and martial arts sports. This study uses Fast Forward Moving Picture Experts Group (FFMPEG) technique to extract images from the video of Taekwondo and generate skeleton data from extracted frames with MoveNet. Then, we perform the poses classification tasks for TAEGEUK IN JANG lesson with deep learning architectures such as shallow convolutional neural networks, VGGNet, Inception, and Long Short-Term Memory networks. These architectures are modified to receive 1-Dimensional data as input, including key points of the skeleton. Poses recognition tasks in sports lessons use skeleton data to eliminate noise in the images, such as background behind practitioners and unrelated objects, and only focus on the movement/direction of the poses. Our proposed method has achieved good accuracy in the data, including 35 videos (more than 25,000 frames) distinguishing 20 poses in a basic Taekwondo lesson.
One day in a Roman Domus: Human Factors and Educational Properties Involved in a Virtual Heritage Application

As stated by Mosaker (2001), Virtual Reality (VR) environments that portray the past can be considered modern-day time machines. A substantial variety of Virtual Heritage (VH) applications have been developed recently, with the mission of using VR technologies for cultural preservation purposes. However, few of these projects focused on properly assessing these applications' educational value and goodness of interaction. In light of these considerations, a VR application reproducing an ancient Roman Domus has been developed to assess Human Factors variables and learning ratio of users. Therefore, 161 participants have been divided into three conditions in a between-subjects design: a Virtual Reality Experience (VRE), a First Person Experience (FPE), and a Multi-media Presentation Experience (MPE). Results showed an overall appreciation of the topic in all conditions, with comparable learning performances. However, we discovered a higher engagement and enjoyment of users with the VRE. Therefore, the Domus Romana application has been proven to be an effective complementary educational tool in explaining Roman houses.
Enhancement of aircraft flight data recording system (black box) using a new digital system

This paper presents a technique that can be used to record 256 measurements per second. Also it is measured and recorded separate parameters several times per second. This is not only facilitating the investigation of an accident or a serious incident, but also provides the opportunity to use the recorded data to predict aircraft behavior. It is believed that by analyzing the recorded data, one could identify the hazardous behavior and develop procedures to mitigate the problems before they actually occur. The flight data recording system (FDR) is an electronic device where place in the aircraft for facilitating the process of the investigation of aviation accidents and incidents also called (black box). The proposed technique is able to improve and increase the efficiency of the flight data recording system. The researchers used MATLAB/SIMULINK (program aircraft simulation) to record rapidly changing parameters with high accuracy. Therefore, the flexibility and adaptability of the proposed methodology can provide the results of high-resolution recording.
A Field-test of a Web Application for L2 Speaking Practice under an Uncontrolled Learning Environment for a Whole Semester

In second/foreign language speaking practice, there is limited time for students to practice, getting feedback from an experienced teacher in class. In order to remove the time constraint, computer assisted language learning systems are the first candidate, but few systems estimate learner's state, which includes their mental states and learning environments. For estimating it, it is natural to use specific peripherals or applications, but this leads to another constraint in learning environment. In addition, it is challenging to develop such a system because speaking practice, other than practice for other skills, essentially requires non-textual data. The distant goal of this study is to propose a system that generates feedback based on learners’ states, without any constraints on time and place. With this goal, the purpose of this paper is to identify the basic requirements of a system for speaking under uncontrolled learning environment by a long-term field test. In this field test, a prototype system we developed was used for 15 weeks (or 1 semester) as online assignments by 28 international students taking a Japanese language course at a university. Our prototype was a browser-based web application, and learners practiced speaking using their own devices, e.g., PCs and smartphones. This field test was conducted under uncontrolled learning environment of each learner participating in the long-term online course, without ever meeting face-to-face. These settings significantly differ from those of previous studies, which are well controlled under some specific environment. Our prototype has worked with many devices, and enabled students, including those who was not able to enter our country due to the coronavirus disease 2019 pandemic, to practice speaking. Through the field test, we identified some problems, which can be happened for similar systems of speaking, and summarized into three categories: operation devices, voice recording, and data transmission. We propose a set of methods to solve these problems, which are useful for developing similar systems in actual learning environments.
Using Artificial Intelligence for the Specification of m-Health and e-Health Systems

Artificial intelligence (AI) techniques such as machine learning (ML) have wide application in medical informatics systems. In this chapter, we employ AI techniques to assist in deriving software specifications of e-Health and m-Health systems from informal requirements statements. We use natural language processing (NLP), optical character recognition (OCR), and machine learning to identify required data and behaviour elements of systems from textual and graphical requirements documents. Heuristic rules are used to extract formal specification models of the systems from these documents. The extracted specifications can then be used as the starting point for automated software production using model-driven engineering (MDE). We illustrate the process using an example of a stroke recovery assistant app and evaluate the techniques on several representative systems.
Privacy Enhancement for Cloud-Based Few-Shot Learning

Requiring less data for accurate models, few-shot learning has shown robustness and generality in many application domains. However, deploying few-shot models in untrusted environments may inflict privacy concerns, e.g., attacks or adversaries that may breach the privacy of user-supplied data. This paper studies the privacy enhancement for the few-shot learning in an untrusted environment, e.g., the cloud, by establishing a novel privacy-preserved embedding space that preserves the privacy of data and maintains the accuracy of the model. We examine the impact of various image privacy methods such as blurring, pixelization, Gaussian noise, and differentially private pixelization (DP-Pix) on few-shot image classification and propose a method that learns privacy-preserved representation through the joint loss. The empirical results show how privacy-performance trade-off can be negotiated for privacy-enhanced few-shot learning.