Reviewerly/exHarmony huggingface dataset:
A secure and efficient DSSE scheme with constant storage costs in smart devices

With the continuous development of the Internet of Things (IOT) and cloud computing, smart devices are playing an increasingly important role in users' daily life. Dynamic searchable symmetric encryption (DSSE) schemes are popular on smart devices because of their efficient retrieval performance and low computational overhead. Traditional DSSE with forward update privacy and backward security can resist file injection attack and statistical inference attack. However, it is high cost and not suitable for smart devices due to large local storage and low storage capacity. To achieve forward update privacy, we design a novel index structure called RC-II (Inverted Index with Retrieve Control) for search control which improves the security of DSSE. Besides, we combine on and off-chain to decrease client's local storage to a constant. Specifically, we transfer a significant amount of local overhead to the service peers off blockchain. We solve the trust problem between the client and the service by putting authentication data on blockchain. Compared with the state-of-the-art schemes, our scheme has a constant client storage overhead and an excellent retrieval performance which provides guarantee for smart devices under IOT environment.
Phishing with Malicious QR Codes

The use of QR codes for malicious purposes was rather limited in the pre-COVID-19 world. That changed overnight, as the QR codes became a convenient go-between for sharing URLs, including malicious ones. This opens an attractive new way of phishing as QR codes are now widespread. To explore phishing with QR codes (i.e. quishing), we conducted a 173-participant study where we used a COVID-19 digital passport sign-up trial with a malicious QR code as a pretext. We found that 67 % of the participants were keen to sign-up with their Google or Facebook credentials, 18.5% to create a new account, and only 14.5% to skip on the sign-up. Convenience was the single most cited factor for the willingness to yield participants' credentials. Reluctance of using new services was the reason for creating a new account or skipping the registration. We also developed a Quishing Awareness Scale (QAS) and found a significant relationship between participants' QR code behavior and their sign-up choices: The ones choosing to sign-up with Facebook scored the lowest while the one choosing to skip the highest on average. We used our results to propose awareness training guidelines as well as develop and test usable security indicators for warning users about the threat of phishing with malicious QR codes.
(Retracted) Optimized path planning of drones for efficient logistics using turning point with evolutionary techniques

Mobile robots/drones are an important consideration in today’s society because several basic and hazardous activities of humans are replaced with robots. Moreover, developing an efficient strategy for a mobile robot to secure navigation is the major issue for autonomous robots. Recently, path planning is a major consideration and research on the topic of autonomous robots in the domain of logistics. Finding the safest and shortest path for mobile robots is the focus. The safe path is determined with the turning point algorithm from the moving robot’s starting position to the destination position without obstacles. The efficient path for the static and dynamic environment of the safe path is determined using reinforcement learning. Evolutionary particle swarm optimization (EPSO) has been used to enhance the path planning approach for mobile robots to make this path planning robust and efficient. The proposed evolutionary-based path planning approach for mobile robots satisfies three objectives, such as safe path, efficient path length, and also suitable for static and dynamic environments. Finally, the simulation results prove that the proposed method performs better in finding the safest and shortest path for the mobile robot in the static and dynamic environment and is useful for mobile robot robust tracking. The proposed TP-HL-EPSO secured the average cumulative reward of 167.
Autonomous Assistance System for Visually Impaired using Tesseract OCR &amp; gTTS

Abstract OCR makes machines to recognize text automatically. It not only saves retrieval times but also helps in digitalization of important documents. In this paper different techniques are used for converting textual matter from an paper document or an image into machine readable form (OCR text output) and then into audio output using gTTS (Google Text-to Speech, a Python library and CLI tool to interface with Google Translates text-to-speech API). In proposed model, the content writes spoken mp3 data to a file, a file-like object (byte string) for further audio manipulation with flexible pre-processing and tokenizing. The approach results into a graspable and comprehensible text output &amp; then fed into audio output for inference with 99 % accuracy. This paper outlines the stages of development, the major challenges with some interesting result comparison with different state of approaches.
Charbonnier-Marchaud Based Fractional Variational Model for Motion Estimation in Multispectral Vision System

Abstract As we are aware that motion estimation is an active and challenging area of vision system, which leads to the applications of computer vision. In general, motion detection and tracking in the image sequence (video) is carried out based on optical flow. In the recent-past, researchers have made a significant contribution to the estimation of optical flow through integer order-based variational models, but these are limited to integer order differentiation. In this paper, a nonlinear modeling of fractional order variational model in optical flow estimation is introduced using the Charbonnier norm, which can be scaled to integer order L 1 -norm. In particular, the variational functional is formulated by the inclusion of a non-quadratic penalty term, regularization term and the Marchaud’s fractional derivative. This non-quadratic penalty provides effective robustness against outliers, whereas the Marchaud’s fractional derivative possesses a non-local character, and therefore is capable to deal with discontinuous information about texture and edges, and yields a dense flow field. The numerical discretization of the Marchaud’s fractional derivative is employed with the help of Grünwald–Letnikov fractional derivative. The resulting nonlinear system is converted into a linear system and solved by an efficient numerical technique. Several experiments are performed over a spectrum of datasets. The robustness and accuracy of the proposed model are shown under different amounts of noise and through detailed comparisons with the recently published works.
Automated Posture Analysis for the Assessment of Sports Exercises

Numerous studies in the medical field correlate the maintenance of human posture in static and dynamic situations with the muscle-skeletal health. One of the most widely used methods for assessing human posture is through visual inspection by professionals. However, this observational assessment process requires the presence of a field expert performing a time-consuming manual analysis. Hence, a reliable automatic posture evaluation system would be of great help for professionals to detect postural misalignments. In the recent years, significant progress has been achieved in pose estimation through state-of-the-art deep learning techniques, competent to estimate human body landmarks fast and accurately from RGB images. In this paper, we describe a methodology scheme to estimate human posture and detect postural misalignments in static and dynamic exercises in real-time. The MediaPipe Pose algorithm is employed to detect human pose and the vector geometry of the pose is evaluated to detect postural misalignments. Furthermore, in order to not limit the applications of this work by preselecting rule parameters for only a certain set of exercises, the rule parameters of any form of exercise are automatically extracted from an example of a single correct execution through machine learning. The datasets of the videos utilized in this work were provided and annotated by a clinical exercise physiologist.
Domain-Specific Term Extraction: A Case Study on Greek Maritime Legal Texts

Preservation of cultural heritage has significantly attracted many research efforts. Amongst them, significant interest has been presented to sharing of cultural heritage content, whereas one major aspect of cultural content is the one concerning maritime heritage. In order to efficiently consume such content, high-quality descriptions are required. Towards this goal, we propose an approach for automatic term extraction of documents, where our methodology is based on the use of word embeddings along with a deep neural network architecture. We demonstrate its efficiency by using a large corpus of legal documents related with maritime.
Future of Artificial Intelligence and Machine Learning in Marketing 4.0

The ultimate aim of Marketing 4.0 is to use Machine-to-Machine learning for a superior Human-to-Human experience. Artificial Intelligence (AI) and Machine Learning (ML) play an important role in delivering this experience. This paper addresses the transformation in marketing from the traditional method to Marketing 4.0. using AI and ML. A systematic review has been conducted in this paper that brings together the diverse topics of AI and ML and marketing4.0. The literature has been classified based on the AI and ML applications and algorithms. Based on the review, a research gap has been identified and agenda for future work has also been given in this study. Findings reveal that digital transformation in marketing affects micro/small business enterprises adversely due to a lack of funds for such investments. Surviving in these times of technological advancement may prove to be a threat for SMEs in this competitive environment.
Analysing Elsevier Journal Metadata with a New Specialized Workbench inside ICSR Lab

In this white paper we introduce Elsevier's Peer Review Workbench which will be available via the computational platform ICSR Lab. The workbench offers a unique dataset to interested researchers who want to run research on journal evaluation and peer review processes. We describe its properties, advantages, and limitations as well as the process of proposal application. This is a living document and will be updated on a regular basis.
An Explore of Virtual Reality for Awareness of the Climate Change Crisis: A Simulation of Sea Level Rise

Virtual Reality (VR) technology has been shown to achieve remarkable results in multiple fields. Due to the nature of the immersive medium of Virtual Reality it logically follows that it can be used as a high-quality educational tool as it offers potentially a higher bandwidth than other mediums such as text, pictures and videos. This short paper illustrates the development of a climate change educational awareness application for virtual reality to simulate virtual scenes of local scenery and sea level rising until 2100 using prediction data. The paper also reports on the current in progress work of porting the system to Augmented Reality (AR) and future work to evaluate the system.
A Survey of Educational Augmented Reality in Academia and Practice: Effects on Cognition, Motivation, Collaboration, Pedagogy and Applications

Augmented Reality (AR) technology is gaining popularity and adoption by educational pioneers. While there is great excitement about this technology, there is also a lack of systematic understanding of what makes this technology effective or ineffective for education, and the practical and systemic challenges of implementing it at scale in classroom settings. In this research we provide a synthesis of empirical scientific findings about the factual benefits and detriments of this technology (from a dataset of 2023 academic papers, of which 39 were analyzed in depth), and of public opinions about augmented reality in education practice (gathered from a dataset of 86 websites and blog posts, of which 53 were analyzed in depth). We contribute a list of specific cognitive, motivational and social processes that are enhanced by AR technology. We also identify popular curriculum topics and popular AR applications, as well as summarize several factors that are important for educators to consider for integrating AR into classrooms.
Immersive Haptic Interface Simulating Skin Biopsy for Dermatological Skill Training

Dematological skill training is essential for medical students to perform successful skin tumor surgery. However, there is no exisitng system that can provide high-quality tactile simulation and training as the substitute of clinical training with patients. In this study, we developed immersive haptic simulation combined with virtual reality for simulating two important dermatological skills, outlining and incision, and investigated the effectiveness of the developed system to be used as a training tool at clinic or school. In the evaluation study, professional (dermatologists, residents, medical students) and non-professional (students in computer science) groups of people were invited to test the developed system in terms of usability and efficacy of multimodal feedback (virtual graphic and haptic feedback). The evaluation results suggest that the training system has potential for high adoption, especially with haptic feedback in a virtual reality envrionment. The quantitative results based on a proposed metrics prove the ability of the training that discriminate experts from non-experts as well as objectively measure the dermatological skills.
Multi-modal Contrastive Learning for Healthcare Data Analytics

Electronic Health Record (EHR) is a digital version of patient's medical charts. EHR consists of longitudinal multi-modal data including demographics, diagnosis, clinical notes and clinical features. Plenty of data analytics works have been performed on EHR data. Among them, predictive modeling has been widely explored and most researches use single modality to perform the prediction task. Comparing with previous researches using single modal data, utilizing the multi-modal data can boost the prediction performance for a variety of downstream tasks. In this paper, in order to maintain the hierarchy of diagnosis codes, we embed diagnosis codes in hyperbolic space, and we utilize a hyperbolic transformer to model the sequential diagnosis information in multiple admissions. Meanwhile, we use multi-modal contrastive loss to capture the relation between diagnosis and clinical features. And we propose supervised contrastive loss in the multi-label setting. We perform two downstream tasks including diagnosis prediction and mortality prediction on two public datasets. Experiments on real-world datasets demonstrate the effectiveness of multi-modal contrastive loss in healthcare.
License Plate Privacy in Collaborative Visual Analysis of Traffic Scenes

Traffic scene analysis is important for emerging technologies such as smart traffic management and autonomous vehicles. However, such analysis also poses potential privacy threats. For example, a system that can recognize license plates may construct patterns of behavior of the corresponding vehicles' owners and use that for various illegal purposes. In this paper we present a system that enables traffic scene analysis while at the same time preserving license plate privacy. The system is based on a multi-task model whose latent space is selectively compressed depending on the amount of information the specific features carry about analysis tasks and private information. Effectiveness of the proposed method is illustrated by experiments on the Cityscapes dataset, for which we also provide license plate annotations.
Detection-by-Simulation: Exposing DeepFake via Simulating Forgery using Face Reconstruction

This paper describes a new method to expose DeepFakes via simulating forgery using face reconstruction on real samples. Our method is motivated by that the DeepFake model introduces generation artifacts on synthesized faces, which can be simulated by similar CNN-based generative models. To simulate these forgery artifacts, we develop a simple auto-encoder network to reconstruct faces, as the generation process in face reconstruction shares some certain common properties with the generation process in the DeepFake model. Thus we can use the reconstructed faces as negative training samples. Then we develop a CNN network to fully take advantage of the simulation. Specifically, we design two components, an attention guided blending boundary prediction branch to predict blending boundary and a semantic feature enhancement to convey semantic information to deep layers. Then the proposed network is trained using the simulated faces and real faces. Extensive experiments are conducted on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$FF++$</tex> and Celeb-DF with comparison to several state-of-the-arts, which demonstrates the efficacy of our method.
Personalized Fashion Sequential Recommendation with Visual Feature Based on Conditional Hierarchical VAE

With the increase of online shopping services, there has been much research on fashion item recommendation. Unlike standard recommendation systems, a recommendation for fashion items needs to take into account the context of the item IDs in the user behavior and that of the fashion-specific visual features such as color and design. In this study, we propose the conditional hierarchical variational auto-encoder (CHVAE) for extracting fashion-specific visual features, and construct a fashion item recommendation system based on it. CHVAE is an extension of VAE to enable conditional and hierarchical learning. It can capture the continuous latent space of color and design using item images and labels, and extract visual features for fashion recommendations. In our experiments, we show that the proposed method outperforms an extensive list of state-of-the-art sequential recommendation models and achieves the same or better performance as human stylists.
Acute Kidney Injury Prediction with Gradient Boosting Decision Trees enriched with Temporal Features

This paper aims to predict the risk of Acute Kidney Injury (AKI) in intensive care units (ICUs) using machine learning techniques and statistical approaches. The data used in the study are derived from Medical Information Mart for Intensive Care (MIMIC) III, which is a freely accessible database of de-identified ICU-related data. The paper focuses on two different phases. The first one consists of a scrupulous phase of extraction and transformation of MIMIC data to retrieve all the criteria specified in the Kidney Disease Improving Global Outcomes (KDIGO) clinical practice guideline definition of AKI. The main features included are demographics, medications, comorbidities, charted vital signs, and laboratory events. In the second phase, we used several different techniques already used to predict AKI, and we also added a complex temporal feature, called Trend-Event Feature (TE-F). The prediction was performed using a rolling observational window design that includes the data collection window (length: 1 to 6 days) and the prediction window (7 days). The Gradient Boosting Decision Trees (GBDT) method was used, with different groups of features, to predict the risk of AKI. To evaluate the GBDT performances, we used the area under the ROC curve (AUROC), Recall, Precision, and F-score measures. We observed that lab parameters contribute the most to the prediction of the AKI risk. Moreover, adding TE-Fs to different groups of features leads to better performance results.
Wasserstein Metric Attack on Person Re-identification

Adversarial attacks in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{p}$</tex> ball have been recently investi-gated against person re-identification (ReID) models. How-ever, the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{p}$</tex> ball attacks disregard the geometry of the sam-ples. To this end, Wasserstein metric is a robust alternative as the attack incorporates a cost matrix for pixel mass movement. In our work, we propose the Wasserstein metric to perform adversarial attack on ReID system by projecting adversarial samples in the Wasserstein ball. We perform white-box and black-box attacks on state-of-the-art (SOTA) ReID models trained on Market-I 501, DukeMTMC-reID, and MSMTI7 datasets. The performance of best SOTA ReID models decreases drastically from 90.2% to as low as 0.4%. Our model outperforms the SOTA attack methods by 17.2% in white-box attacks and 14.4% in black-box at-tacks. To the best of our knowledge, our work is the first to propose the Wasserstein metric towards generating adversarial samples for ReID task.
Path Planning for Automobile Urban Parking Through Curve Parametrization and Genetic Algorithm Optimization

Parking a car is a difficult task and may be frustrating and stressful for the driver, while commonly causes a traffic jam. One way to mitigate such negative effects is to provide vehicles with self-driving capabilities. As a cornerstone of an automobile ability to move autonomously stands path planning, which despite many works in the literature, is still considered an open problem, especially with regard to nonholonomic vehicles. Based on this scenario, this work presents a path planning algorithm to parallel park an automobile based on polynomial parametrization and genetic algorithm optimization. The aim is to define a law of motion to lead the vehicle from an initial pose near a parking space to a final pose within the latter smoothly, with no interruption and avoiding any obstacles in the way. Simulation results in 3D physics simulation environment are presented to validate the feasibility of the proposed algorithm, which lay the foundation to broader studies.
Risks and Benefits of AI-generated Text Summarization for Expert Level Content in Graduate Health Informatics

AI-generated text summarization (AI-GTS) is now a popular topic in applied computer science education. It has proven helpful in various sectors, but its benefits and risks in education have not been thoroughly investigated. Few researchers have demonstrated the benefits of employing AI-generated text summaries in learning to generate ideas swiftly and to explore insights and hidden knowledge. AI-GTS has made it easier for students to understand electronically-available critical information. On the other hand, the risks linked with its implementation in education are understudied. Some anticipated risks include harming pupils' writing skills, overdependence, reduced critical thinking capacity, and increased plagiarism. This paper presents the application of AI-generated text summarization in a graduate health informatics course and discusses the risks and benefits to students. Furthermore, utilizing the Bidirectional Encoder Representations from Transformers (BERT) model, we demonstrate that the current state-of-the-art AI-generated text summarization has the potential to create expert knowledge content. We conducted a study with 58 health informatics graduate students in the Fall of 2019 to write annotated bibliography for 25 articles each, to which we also added the AI-generated article summaries. We then asked the students to peer grade and distinguish the AI-generated annotations from the student-written summary. Using the Kruskal-Wallis test, we found no significant difference in the peer grades between the two. The robustness of such AI-generated text summarization raises important questions for educators teaching in health informatics.