Reviewerly/exHarmony huggingface dataset:
A deep learning model for Alzheimer’s disease diagnosis based on patient clinical records

Authors:
raw_author_name: Sebastián Ventura
raw_affiliation_string: Department of Computer Science and Numerical Analysis, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Cordoba, Córdoba, Spain

Abstract: Dementia, with Alzheimer's disease (AD) being the most common type of this neurodegenerative disease, is an under-diagnosed health problem in older people. The creation of classification models based on AD risk factors using Deep Learning is a promising tool to minimize the impact of under-diagnosis.To develop a Deep Learning model that uses clinical data from patients with dementia to classify whether they have AD.A Deep Learning model to identify AD in clinical records is proposed. In addition, several rebalancing methods have been used to preprocess the dataset and several studies have been carried out to tune up the model.Model has been tested against other well-established machine learning techniques, having better results than these in terms of AUC with alpha less than 0.05.The developed Neural Network Model has a good performance and can be an accurate assisting tool for AD diagnosis.
Natural Language Processing: Chances and Challenges in Dentistry

Authors:
raw_author_name: Martha Büttner
raw_affiliation_string: Department of Oral Diagnostics, Digital Health and Health Services Research, Charité – Universitätsmedizin Berlin, Germany
raw_author_name: Ulf Leser
raw_affiliation_string: Department of Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
raw_author_name: Lisa Schneider
raw_affiliation_string: Department of Oral Diagnostics, Digital Health and Health Services Research, Charité – Universitätsmedizin Berlin, Germany
raw_author_name: Falk Schwendicke
raw_affiliation_string: Clinic for Operative, Preventive and Pediatric Dentistry and Periodontology, Ludwig-Maximilians-University, Munich, Germany

Abstract: : Natural language processing (NLP) is an intersection between Computer Science and Linguistic which aims to enable machines to process and understand human language. We here summarized applications and limitations of NLP in dentistry. Narrative review. NLP has evolved increasingly fast. For the dental domain, relevant NLP applications are text classification (e.g., symptom classification) and natural language generation and understanding (e.g., clinical chatbots assisting professionals in office work and patient communication). Analyzing large quantities of text will allow understanding diseases and their trajectories and support a more precise and personalized care. Speech recognition systems may serve as virtual assistants and facilitate automated documentation. However, to date, NLP has rarely been applied in dentistry. Existing research focuses mainly on rule-based solutions for narrow tasks. Technologies such as Recurrent Neural Networks and Transformers have been shown to surpass the language processing capabilities of such rule-based solutions in many fields, but are data-hungry (i.e., rely on large amounts of training data), which limits their application in the dental domain at present. Technologies such as federated or transfer learning or data sharing concepts may allow to overcome this limitation, while challenges in terms of explainability, reproducibility, generalizability and evaluation of NLP in dentistry remain to be resolved for enabling approval of such technologies in medical devices and services. NLP will become a cornerstone of a number of applications in dentistry. The community is called to action to improve the current limitations and foster reliable, high-quality dental NLP. NLP for text classification (e.g., dental symptom classification) and language generation and understanding (e.g., clinical chatbots, speech recognition) will support administrative tasks in dentistry, provide deeper insights for clinicians and support research and education.
A hybrid facial features extraction-based classification framework for typhlotic people

Authors:
raw_author_name: SaiTeja Chopparapu
raw_affiliation_string: GITAM (Deemed to be University)
raw_author_name: Beatrice Seventline Joseph
raw_affiliation_string: GITAM (Deemed to be University)

Abstract: Facial features play a vital role in the real-time cloud-based applications. Since, most of the conventional models are difficult to detect heterogeneous facial features due to high computational memory and time for the internet of things (IoT) based video surveillance mechanisms. Video based facial features identification and extraction include a large number of candidates features which are difficult to detect the contextual similarity of the facial key points due to noise and computational memory. In order to resolve these issues, a hybrid multiple features extraction measures are implemented on the real-time video dataset to extract key points using the cloud-based classifier. In this work, a hybrid classifier is used to classify the key facial points in the cloud computing environment. Experimental results show that the proposed hybrid multiple feature extraction-based frameworks have better computational efficiency in terms of error rate, recall, precision, and accuracy than the conventional models.
Ultrasonic sensor decision-making algorithm for mobile robot motion in maze environment

Authors:
raw_author_name: Hind Zuhair Khaleel
raw_affiliation_string: University of Technology
raw_author_name: Bashra Kadhim Oleiwi
raw_affiliation_string: University of Technology

Abstract: An autonomous mobile robot is one that can move from one location to another without the intervention of a human. A maze environment is a complex environment since it contains many obstacles and the major problem is moving through it. To avoid obstacles while moving through the maze, the mobile robot must be designed with an algorithm. This work proposes a decision-making system with an ultrasonic sensor to allow the developed autonomous mobile robot to avoid obstacles in any maze setting through its movements. The maze was designed with a size of 100×200 cm2 for the case study. Due to the height dimension of the barrier (20 cm) and the height dimension of the mobile robot including the ultrasonic sensor (20 cm), a distance of 20 cm was taken between the wall (obstacle) and the sensor. The result distance between the (wood wall) object and the sensor indicates that it is a reasonable distance chosen for the mobile robot to move and turn with flexibility as it travels through the maze environment from 0 cm to 300 cm. This mobile robot path took 1 minute to finish at a speed of 5 cm/sec, indicating that it is a quick algorithm as compared with related work.
Implementing and developing multi-stage cryptography technique for low-cost long-range communication system

Authors:
raw_author_name: Eyad M. Hamad
raw_affiliation_string: German Jordanian University
raw_author_name: Amer Alsaraira
raw_affiliation_string: German Jordanian University
raw_author_name: Omar A. Saraereh
raw_affiliation_string: The Hashemite University

Abstract: The requirement for a secure emergency communication system has become imperative in tandem with the industrial revolution. Additionally, the development of technology has led to increasingly robust penetration techniques that pose a threat to communication system security, leaving data vulnerable to unwanted third parties. This paper introduces a novel, powerful security approach that ensures a secure emergency communication system. Moreover, this research focuses on several cryptographic techniques among various symmetric and asymmetric ciphers, including advanced encryption standards, substitution, and transposition. The article presents an affordable and secure communication system that can transmit data over long distances with low power consumption using long-range technology. This system features a unique function that transmits updated locations, directing rescuers to the designated location.
High capacity double precision image steganography based on chaotic maps

Authors:
raw_author_name: Maher K. Mahmood Al-Azawi
raw_affiliation_string: Al-Mustansiriyah University

Abstract: Steganography is the process of hiding confidential information within non-secret multimedia such that the 3rd party cannot distinguish if there is a secret message in it or not. Whereas cryptography is the technique of using mathematical concepts to convert information into unreadable codes via a key. This paper will propose two approaches, lossless and lossy image steganography. Both of them will use cryptography and steganography based on three different chaotic maps to ensure information security. In the cryptography part, two chaotic maps will be used to encrypt the secret information, while in the steganography section, one chaotic map is used to embed the message. The secret information will be concealed in the least significant bits (LSBs) of the double-precision image’s pixels. The double precision image is a high-quality image and can be represented in 64 bits per pixel for grayscale images, leading to a very high redundant bit. Simulation results show a high embedding capacity of 60.938% and 400% for lossless and lossy approaches respectively with a peak signal to noise ratio (PSNR) reach of 69.964 dB. Furthermore, this system is extremely secure due to the use of 3 chaotic maps with key space 2448.
Machine learning prediction for academic misconduct prediction: an analysis of binary classification metrics

Authors:
raw_author_name: Suraya Masrom
raw_affiliation_string: Universiti Teknologi MARA
raw_author_name: Nor Hafiza Abdul Samad
raw_affiliation_string: Universiti Poly-Tech Malaysia
raw_author_name: Ratna Septiyanti
raw_affiliation_string: University of Lampung
raw_author_name: Rahayu Abdul Rahman
raw_affiliation_string: Universiti Teknologi Mara

Abstract: Academic misconduct is unethical behavior in academic work. To sustain integrity culture and mitigating unethical conducts among higher education institutions community, the academic misconduct detection must be done at an earlier stage. Thus, this study attempted to provide a new empirical contribution with the analysis of binary classification performances metrics to describe the ability of machine learning in predicting academic misconduct. Four machine learning algorithms have been used namely generalized linear model (GLM), logistic regression (LR), decision tree (DT), and random forest (RF). Beside performances comparison, this paper presents the analysis of academic misconduct factors that were constructed based on demography and fraud triangle theory (FTT). The findings showed that all the four machine learning algorithms have obtained good ability in the prediction models with the accuracy at above 80% and below 20% of the classification errors. Rationalization from the FTT attributes has shown as the most important factor in GLM, LR, and DT. In RF, opportunity of FTT attributes have become the most important. Compared to FTT attributes, demography attributes were not providing much benefits to all the machine learning models but remain applicable at very low weight correlations.
IndoPolicyStats: sentiment analyzer for public policy issues

Authors:
raw_author_name: Muhammad Noor Fakhruzzaman
raw_affiliation_string: Universitas Airlangga
raw_author_name: Sa'idah Zahrotul Jannah
raw_affiliation_string: Universitas Airlangga
raw_author_name: Sie Wildan Gunawan
raw_affiliation_string: Universitas Airlangga

Abstract: The government requires some vaccination for public health. This has led to a debate in recent years, especially during the Covid-19 pandemic. This research aims to analyze the two sentiments of the public regarding the vaccination policy. This would be helpful to ensure the acceptance of the government campaign about vaccination. The data used was text data obtained from Twitter when Indonesia was facing the second wave of the Covid-19 pandemic. The data were pre-processed by removing noise data, case folding, stemming, and tokenizing. Then, the data were classified with random forest, Naïve Bayes, and XGBoost. The results showed that all classifiers exhibit satisfying performance but XGBoost performs slightly better in accuracy value. This method can be deployed to be an automatic sentiment analyzer to help the government understand public feedback about its policies. This would be given by proper pre-processing and enough datasets.
Dataset of user evaluations of prototypicality, aesthetics, usability and trustworthiness of homepages of banking, e-commerce and university websites

Authors:
raw_author_name: Aliaksei Miniukovich
raw_affiliation_string: Department of Design, Norwegian University of Science and Technology, Raufossvegen 40, 2821 Gjøvik; Department of Information Systems Production and Logistics Management, University of Innsbruck, Universitätsstraße 15, A-6020 Innsbruck
raw_author_name: Kathrin Figl
raw_affiliation_string: Department of Information Systems Production and Logistics Management, University of Innsbruck, Universitätsstraße 15, A-6020 Innsbruck

Abstract: The dataset contains full-page screenshots of homepages of commercial banking (N = 1033), online shopping (N = 1064), and university (N = 1059) websites, as well as the raw and aggregated user ratings of webpage design prototypicality, visual aesthetics, perceived usability and trustworthiness, and user demographic information. Design prototypicality was measured with three items, including typicality, exemplar goodness, and family resemblance, whereas the other design dimensions were measured with a single item each. Amazon Mechanical Turk crowdworkers (N = 3319 rating sessions) provided their demographic data and rated the homepages online. The demographic data have been anonymized, with generated unique participant IDs replacing MTurk crowdworker IDs. The screenshots are identified with generated IDs to provide partial anonymization for the websites, limiting their potential misuse outside design-related or user experience-related academic research. The raw rating data contain all collected ratings, whereas the aggregated data contain the per-webpage, per-dimension ratings derived solely from the ratings of study-compliant crowdworkers. The non-compliance among crowdworkers was detected based on several indicators, including rate-rerate consistency, seen-unseen webpage recognition, free-form feedback analyses, demographic data analyses, and other indicators. Future research could utilize the dataset either in user studies that require full-page webpages as stimuli, e.g., studies on the determinants of first impression, user preference, and user experience, or in computational research on web design, including computational aesthetics, as this type of research requires a large number of user-rated webpages, which this dataset provides.
How word semantics and phonology affect handwriting of Alzheimer’s patients: A machine learning based analysis

Authors:
raw_author_name: Nicole D. Cilia
raw_affiliation_string: Department of Computer Engineering, University of Enna "Kore", Italy; Institute for Computing and Information Sciences, Radboud University Nijmegen, The Netherlands
raw_author_name: Claudio De Stefano
raw_affiliation_string: Department of Electrical and Information Engineering Mathematics, University of Cassino and Southern Lazio, Italy
raw_author_name: Francesco Fontanella
raw_affiliation_string: Department of Electrical and Information Engineering Mathematics, University of Cassino and Southern Lazio, Italy
raw_author_name: Sabato Marco Siniscalchi
raw_affiliation_string: Department of Computer Engineering, University of Enna "Kore", Italy

Abstract: Using kinematic properties of handwriting to support the diagnosis of neurodegenerative disease is a real challenge: non-invasive detection techniques combined with machine learning approaches promise big steps forward in this research field. In literature, the tasks proposed focused on different cognitive skills to elicitate handwriting movements. In particular, the meaning and phonology of words to copy can compromise writing fluency. In this paper, we investigated how word semantics and phonology affect the handwriting of people affected by Alzheimer's disease. To this aim, we used the data from six handwriting tasks, each requiring copying a word belonging to one of the following categories: regular (have a predictable phoneme-grapheme correspondence, e.g., cat), non-regular (have atypical phoneme-grapheme correspondence, e.g., laugh), and non-word (non-meaningful pronounceable letter strings that conform to phoneme-grapheme conversion rules). We analyzed the data using a machine learning approach by implementing four well-known and widely-used classifiers and feature selection. The experimental results showed that the feature selection allowed us to derive a different set of highly distinctive features for each word type. Furthermore, non-regular words needed, on average, more features but achieved excellent classification performance: the best result was obtained on a non-regular, reaching an accuracy close to 90%.
Health-aware food recommendation system with dual attention in heterogeneous graphs

Authors:
raw_author_name: Saman Forouzandeh
raw_affiliation_string: Department of Statistics, School of Mathematics and Statistics, University of New South Wales, Sydney, NSW, Australia
raw_author_name: Mehrdad Rostami
raw_affiliation_string: Centre for Machine Vision and Signal Processing, Faculty of Information Technology, University of Oulu, Oulu, Finland
raw_author_name: Kamal Berahmand
raw_affiliation_string: Department of Science and Engineering, Queensland University of Technology, Brisbane, Australia
raw_author_name: Razieh Sheikhpour
raw_affiliation_string: Department of Computer Engineering, Faculty of Engineering, Ardakan University, Ardakan, Iran

Abstract: Recommender systems (RS) have been increasingly applied to food and health. However, challenges still remain, including the effective incorporation of heterogeneous information and the discovery of meaningful relationships among entities in the context of food and health recommendations. To address these challenges, we propose a novel framework, the Health-aware Food Recommendation System with Dual Attention in Heterogeneous Graphs (HFRS-DA), for unsupervised representation learning on heterogeneous graph-structured data. HFRS-DA utilizes an attention technique to reconstruct node features and edges and employs a dual hierarchical attention mechanism for enhanced unsupervised learning of attributed graph representations. HFRS-DA addresses the challenge of effectively leveraging the heterogeneous information in the graph and discovering meaningful semantic relationships between entities. The framework analyzes recipe components and their neighbours in the heterogeneous graph and can discover popular and healthy recipes, thereby promoting healthy eating habits. We compare HFRS-DA using the Allrecipes dataset and find that it outperforms all the related methods from the literature. Our study demonstrates that HFRS-DA enhances the unsupervised learning of attributed graph representations, which is important in scenarios where labelled data is scarce or unavailable. HFRS-DA can generate node embeddings for unused data effectively, enabling both inductive and transductive learning.
Learning with centered reproducing kernels

Authors:
raw_author_name: Xin Guo
raw_affiliation_string: School of Mathematics and Physics, The University of Queensland, Brisbane, QLD 4072, Australia
raw_author_name: Qiang Wu
raw_affiliation_string: Department of Mathematical Sciences, Middle Tennessee State University, Murfressboro, TN 37132, USA

Abstract: Kernel-based learning algorithms have been extensively studied over the past two decades for their successful applications in scientific research and industrial problem-solving. In classical kernel methods, such as kernel ridge regression and support vector machines, an unregularized offset term naturally appears. While its importance can be defended in some situations, it is arguable in others. However, it is commonly agreed that the offset term introduces essential challenges to the optimization and theoretical analysis of the algorithms. In this paper, we demonstrate that Kernel Ridge Regression (KRR) with an offset is closely connected to regularization schemes involving centered reproducing kernels. With the aid of this connection and the theory of centered reproducing kernels, we will establish generalization error bounds for KRR with an offset. These bounds indicate that the algorithm can achieve minimax optimal rates.
Accurate weight coefficient estimation of multi-camera light field PIV through backpropagation neural network

Authors:
raw_author_name: Xiaoyu Zhu
raw_affiliation_string: National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China
raw_author_name: Chuanlong Xu
raw_affiliation_string: National Engineering Research Center of Power Generation Control and Safety, School of Energy and Environment, Southeast University, Nanjing 210096, China
raw_author_name: Md. Moinul Hossain
raw_affiliation_string: School of Engineering, University of Kent, Canterbury, Kent CT2 7NT, UK
raw_author_name: Yan Liu
raw_affiliation_string: Key Laboratory of Advanced Energy and Power, Institute of Engineering Thermophysics, Chinese Academy of Sciences, Beijing 100190, China
raw_author_name: Boo Cheong Khoo
raw_affiliation_string: Department of Mechanical Engineering, National University of Singapore, 119260, Singapore

Abstract: Volumetric velocity measurement through multi-camera light field particle image velocimetry (LF-PIV) requires an accurate estimation of the weight coefficient (WC) of three-dimensional (3D) tracer particle distribution reconstruction. To achieve that, this study proposes a calibration method based on a backpropagation neural network (BP-NN) for the WC estimation of the multi-camera LF-PIV. The BP-NN model establishes a mapping relationship between the spatial voxels and pixels of the multi-cameras. The proposed method is compared with the direct ray tracing (DRT) method and it shows that the proposed method provides an accurate estimation of the WC. It also does not depend on the prior knowledge of angle separations of the multi-cameras as is required for the DRT method. The proposed method is initially evaluated by conducting synthetic tests of ring vortex field reconstruction and further verified by conducting experiments on a low-swirl injector (LSI) flow. Results show that the root mean square error of the ring vortex displacement field can be reduced from 0.71 voxels to 0.35 voxels by the proposed method. The relative errors of LSI flow axial and radial velocity components are smaller than 10%. Therefore, it demonstrates that the 3D flow velocity can be measured accurately by the multi-camera LF-PIV by incorporating the proposed BP-NN calibration method.