Reviewerly/exHarmony huggingface dataset:
Personalized Privacy-Preserving Federated Learning: Optimized Trade-off Between Utility and Privacy

The emerging federated learning (FL) offers a feasible solution for the privacy preservation of users' sensitive data in training artificial intelligence (AI) models. Meanwhile, differential privacy (DP) is widely used in FL to ensure that data privacy is not disclosed during model training. However, in the practical deployment of DP in FL, a prominent challenge is that most existing FL solutions set the same privacy level for different users, resulting in over-protection for some users while insufficient protection for others. In this paper, we propose a novel federated learning framework with user-level personalized privacy protection (named FLUP) to meet the personalized privacy requirements of different users while maintaining high data utility. In this framework, we propose a user-level personalized DP mechanism that combines a personalized sampling algorithm and Gaussian perturbation to meet each user's personalized differential privacy corresponding to their privacy parameters. Then, we qualitatively analyze the impact of the sampling threshold on model performance. Furthermore, to balance user privacy requirements and AI model performance, we design a utility-aware game model to distributively determine the optimized sampling threshold and the users' differential privacy parameters. Finally, by conducting validation experiments, we demonstrate the feasibility and effectiveness of our proposed framework in terms of model performance as well as user privacy preservation.
Personalized QoE Enhancement for Adaptive Video Streaming: A Digital Twin-Assisted Scheme

In this paper, we present a digital twin (DT)-assisted adaptive video streaming scheme to enhance personalized quality-of-experience (PQoE). Since PQoE models are user-specific and time-varying, existing schemes based on universal and time-invariant PQoE models may suffer from performance degradation. To address this issue, we first propose a DT-assisted PQoE model construction method to obtain accurate user-specific PQoE models. Specifically, user DTs (UDTs) are respectively constructed for individual users, which can acquire and utilize users' data to accurately tune PQoE model parameters in real time. Next, given the obtained PQoE models, we formulate a resource management problem to maximize the overall long-term PQoE by taking the dynamics of users' locations, video requests, and buffer statuses into account. To solve this problem, a deep reinforcement learning algorithm is developed to jointly determine segment version selection, and communication and computing resource allocation. Simulation results on the real-world dataset demonstrate that the proposed scheme can effectively enhance PQoE compared with benchmark schemes.
ThumbAir

Typing while wearing a standalone Head Mounted Display (HMD)---systems without external input devices or sensors to support text entry---is hard. To address this issue, prior work has used external trackers to monitor finger movements to support in-air typing on virtual keyboards. While performance has been promising, current systems are practically infeasible: finger movements may be visually occluded from inside-out HMD based tracking systems or, otherwise, awkward and uncomfortable to perform. To address these issues, this paper explores an alternative approach. Taking inspiration from the prevalence of thumb-typing on mobile phones, we describe four studies exploring, defining and validating the performance of ThumbAir, an in-air thumb-typing system implemented on a commercial HMD. The first study explores viable target locations, ultimately recommending eight targets sites. The second study collects performance data for taps on pairs of these targets to both inform the design of a target selection procedure and also support a computational design process to select a keyboard layout. The final two studies validate the selected keyboard layout in word repetition and phrase entry tasks, ultimately achieving final WPMs of 27.1 and 13.73. Qualitative data captured in the final study indicate that the discreet movements required to operate ThumbAir, in comparison to the larger scale finger and hand motions used in a baseline design from prior work, lead to reduced levels of perceived exertion and physical demand and are rated as acceptable for use in a wider range of social situations.
Not Only for Contact Tracing

Many countries developed and deployed contact tracing apps to reduce the spread of the COVID-19 coronavirus. Prior research explored people's intent to install these apps, which is necessary to ensure effectiveness. However, adopting contact tracing apps is not enough on its own, and much less is known about how people actually use these apps. Exploring app use can help us identify additional failures or risk points in the app life cycle. In this study, we conducted 13 semi-structured interviews with young adult users of Belgium's contact-tracing app, Coronalert. The interviews were conducted approximately a year after the onset of the COVID-19 pandemic. Our findings offer potential design directions for addressing issues identified in prior work - such as methods for maintaining long-term use and better integrating with the local health systems - and offer insight into existing design tensions such as the trade-off between maintaining users' privacy (by minimizing the personal data collected) and users' desire to have more information about an exposure incident. We distill from our results and the results of prior work a framework of people's decision points in contact-tracing app use that can serve to motivate careful design of future contact tracing technology.
Reference Architecture for Blockchain-Native Distributed Information System

The blockchain paradigm and its application of Distributed Ledger Technology (DLT) are now enabling technologies for the worldwide development of trustworthy and decentralized solutions. DLT offers enormous potential to accommodate the intricate architectural patterns of individual Distributed Information Systems (DIS). Despite its immense potential, the current research effort is not focused on deploying DLT inside the DIS area. We identified sixteen essential DIS Architectural Properties (APs) that offer issues for DIS system architects using Grounded Theory. Then, we assessed the capabilities of the major DLT platform and highlighted its prospects and limits with regard to supporting DIS APs. Using the meta-analysis research technique, we demonstrated that DLT might serve as a development platform for a blockchain-native DIS (BN-DIS) that enables design and development processes. A BN-DIS utilizes DLT infrastructure and blockchain design principles to expand internationally, support thousands of distributed nodes, and employ DIS APs unique to the Blockchain. Lastly, we give a case study illustrating how DLT handles DIS APs in a real-world context, giving empirical proof of the DLT’s efficacy in servicing DIS APs.
Living Intimately with Machines

research-article Open Access Share on Living Intimately with Machines: Can AI Be Disabled? Author: Laura Forlano Illinois Institute of Technology Illinois Institute of TechnologyView Profile Authors Info & Claims InteractionsVolume 30Issue 1January - February 2023pp 24–29https://doi.org/10.1145/3572808Published:11 January 2023Publication History 2citation1,549DownloadsMetricsTotal Citations2Total Downloads1,549Last 12 Months1,549Last 6 weeks114 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteView all FormatsPDF
An active contour model reinforced by convolutional neural network and texture description

Active contour models (ACMs) are popular and widely used for many image segmentation applications and obtain promising results. However, these methods are unable to achieve the highest performance in the presence of intensity inhomogeneity. To address this issue, this paper presents an ACM based on the combination of a convolutional neural network (CNN) and a texture descriptor approach. This study uses a CNN model to generate parameter maps more effectively for ACM. Compared to conventional global techniques, these parameter maps increase the speed of movement of the contour into the target. In this approach, the Local Word Directional Pattern (LWDP) is applied as the texture descriptor. LWDP is a texture descriptor that uses the angle between two gradients for exploring the texture structure inside the image. In the proposed method both the original image and the new image obtained by the LWDP texture descriptor (encoded image) are provided as inputs to the CNN. The experimental outcomes show that the proposed strategy consistently outperforms the state-of-the-art in accuracy and robustness for segmenting images with fuzzy boundaries and intensity inhomogeneity.
Multi-scale image–text matching network for scene and spatio-temporal images

In recent years, with the development of deep learning technology, computer vision and natural language processing have made significant progress, and establishing the relationship between computer vision and natural language processing has attracted more and more attention. The spatio-temporal images taken by satellites or aircrafts and scene images with people and other things are the main focus area. Existing methods have yielded excellent results in image–text matching, but there is still room for improvement in effectively using coarse and fine-grained information. We propose a method to solve this problem using multi-scale graph convolutional neural networks. We extracted the multi-scale features of images and texts for matching separately. Global and local matching are used to calculate the overall image sentence and local image–word similarity. Local matching is divided into two stages, first, the node level matches the correspondence between the learning region and the word. Next, the structure level matches the correspondence between the learning region and the phrase to make the matching more comprehensive. Finally, we verified our model on Flickr30k, MSCOCO and RSICD datasets.
A Two-Stage Network for Zero-Shot Low-Illumination Image Restoration

Due to the influence of poor lighting conditions and the limitations of existing imaging equipment, captured low-illumination images produce noise, artifacts, darkening, and other unpleasant visual problems. Such problems will have an adverse impact on the following high-level image understanding tasks. To overcome this, a two-stage network is proposed in this paper for better restoring low-illumination images. Specifically, instead of manipulating the raw input directly, our network first decomposes the low-illumination image into three different maps (i.e., reflectance, illumination, and feature) via a Decom-Net. During the decomposition process, only reflectance and illumination are further denoised to suppress the effect of noise, while the feature is preserved to reduce the loss of image details. Subsequently, the illumination is deeply adjusted via another well-designed subnetwork called Enhance-Net. Finally, the three restored maps are fused together to generate the final enhanced output. The entire proposed network is optimized in a zero-shot fashion using a newly introduced loss function. Experimental results demonstrate that the proposed network achieves better performance in terms of both objective evaluation and visual quality.
User experiences of medical students with 360-degree virtual reality applications to prepare them for the clerkships

Abstract For medical students, the transition from the preclinical to the clinical phase of their curriculum (clerkships) can result in increased levels of stress and anxiety. This is partly caused by low self-perception of preparedness. By using 360° video-based virtual reality it is possible to provide learners virtual access to clinical situations ahead of time. This technique can provide active and contextual user experiences and offers opportunities to demonstrate both behavioral skills and subject knowledge. We developed two 360° video-based virtual reality applications for medical students transitioning to the clerkships. In this study, we describe the development and evaluated the user experiences. Two virtual reality applications were developed for use in a small group learning session. One of the applications is an interactive virtual tour of a hospital ward, in which learners explore the Internal Medicine ward and learn about the roles of different health care professionals and their mutual interactions. In each room, the learners listen to a voice-over and look at hotspots to gather additional information. The other application has been developed to train students in observing (un)professional behavior of healthcare providers in their daily activities. An evaluation was performed by an anonymous explorative questionnaire with open and closed questions (Likert scales) regarding the user experience and cybersickness symptoms. In our study, 171 students used the applications and completed the questionnaire. For 63% of the respondents, this was their first experience with a virtual reality headset. Qualitative analysis showed that students evaluated the learning method as realistic, informative and enjoyable. Most students evaluated virtual reality as a good (59%) or excellent (26%) tool for learning. Forty-five percent of the students experienced physical discomfort, such as nausea, dizziness, headache and disorientation. In most cases, these complaints were mild, although a small number experienced severe nausea ( n = 6) or severe headache ( n = 2). Students suggested several areas of improvement including increase of display resolution and decrease of ambient noise causing distraction. 360° video-based virtual reality can successfully be implemented in the medical curriculum to create a realistic learning experience to prepare students for the clerkships.
A High-Capacity Coverless Information Hiding Based on the Lowest and Highest Image Fragments

Coverless data hiding is resistant to steganalytical tool attacks because a stego image is not altered. On the other hand, one of its flaws is its limited hiding capacity. Recently, a coverless data-hiding method, known as the coverless information-hiding method based on the most significant bit of the cover image (CIHMSB), has been developed. This uses the most significant bit value in the cover image by calculating the average intensity value on the fragment and mapping it with a predefined sequence. As a result, CIHMBS is resistant to attack threats such as additive Gaussian white noise (AGWN), salt-and-pepper noise attacks, low-pass filtering attacks, and Joint Photographic Experts Group (JPEG) compression attacks. However, it only has a limited hiding capacity. This paper proposes a coverless information-hiding method based on the lowest and highest values of the fragment (CIHLHF) of the cover image. According to the experimental results, the hiding capacity of CIHLHF is twice that of CIHMSB.
Detection of Email Spam using Machine Learning Algorithms: A Comparative Study

In the digital world a lot of emails are received every day, and most of them are not of any relevance to us, some are containing suspicious links which can cause harm to our system in some way or other. This can be overcome by using spam detection. It is the process of classifying whether the email is a genuine one or if it is some kind of spam. The purpose of spam detection is to deliver relevant emails to the person and separate spam emails. Already every email service provider has spam detection but still, its accuracy is not that much, sometimes they classify useful emails as spam. This paper focuses on the comparative analysis approach, where various Machine Learning models are applied to the same dataset. The different machine learning models were compared based on accuracy and Precision. Support vector machine results in 98.09% accuracy.
Automatic Short Answer Grading (ASAG) using Attention-Based Deep Learning MODEL

In artificial intelligence, automatic short answer grading (ASAG) sparked the interest of many researchers. These Systems are used to evaluate the student's performance based on their intellectual and cognitive skills. Unfortunately, short answer grading poses various challenges to assess individual abilities. The first challenge, short sentences can be 10 to 20 words long. These short sentences include primary and secondary keywords, identifying such keywords is a challenge for syntactic processing. Furthermore, the order and relationship among the words affect the actual meaning of the answers. Answers provided by students may not be syntactically correct. The second challenge is different question types included in the short text: - factoid, descriptive, short, and long questions. Different question types influence the intent of the answer which affects the precision of grading accuracy. As a result, strategies for overcoming these problems in the assessment are required. In this study, we have proposed the attention-based deep learning model known as bidirectional encoder representation from a transformer (BERT) for the evaluation of short subjective answers. The measurement findings indicate that the BERT model is effective for automatic short answer grading.
Digital model reconstruction through 3D Stereo Depth camera: a faster method exploiting robot poses

In industrial robotic contact-based operations it is necessary to have a detailed geometrical knowledge of the work-piece to automate the generation of the working trajectory.In many cases, the digital model is not available or it differs from its as built actual conditions.Vision sensors, especially 3D vision sensors allows to scan the work-piece and reconstruct its digital copy that is used for the generation of the robotic working trajectory.In this paper we compare two algorithms for the generation of the 3D model of an unknown work-piece based on the use of the RGB-D images taken from different perspectives.The first technique, based on standard image reconstruction methods commonly used for reconstruction of indoor scenes where error in the order of few centimeters is negligible, is exploited in this work in order to be exploited in contact-based robotic operations where the geometrical error has to be limited to few millimeters.It is based on the analysis of the images to estimate the camera pose taking each image.Based on the pose, the algorithm integrates then the information contained in the images in a unique volume representing the scene.The second algorithm directly uses the known poses of the robot, stored while capturing each image, to integrate the information to create the model.The two algorithms are compared to evaluate the accuracy, acquisition time and elaboration time.The analysis is done considering two kinds of objects, the first one with regular shape, the second one corresponding to an actual industrial object.
Tracking and Data Fusion in Joint Sensing and Communication Networks

In this paper, we consider a beyond-5G multiple-input multiple-output (MIMO) joint sensing and communication (JSC) system where the base stations (BSs) act as monostatic radars by exploiting the signal transmitted to the user equipments (UEs) through a multi-beam radiation pattern. This generates a trade-off between the two functionalities, which should be investigated. This work aims to show the benefits of tracking algorithms to the root mean squared error (RMSE) of an object’s position estimation by reserving only a small fraction of the transmitted power for sensing. First, we compare the performance obtained with several tracking algorithms, such as the cubature Kalman filter (CKF), Gaussian mixture cardinalized probability hypothesis density (GMCPHD) and particle filter (PF), by varying the radar cross-section (RCS) of the object and the power devoted to sensing. Then, we consider a scenario where multiple monostatic JSC systems cooperate to improve the target position estimate accuracy via data fusion performed by tracking algorithms. Numerical results show that all tracking methods improve sensing performance under typical wireless communication scenarios and that cooperative sensing through data fusion boosts the whole system’s performance significantly, allowing the network designer to save resources for communication.
Improved YOLOv5 Method for Fall Detection

Aiming at the problem that the current human fall detection methods can not meet the high accuracy and detection speed at the same time, an improved YOLOv5 algorithm is proposed to detect fall or not. This method adds ECA to the backbone of YOLOv5s to improve the ability of the model to extract features, and increases precision without too much computation. PANet is modified to BiFPN for the weighted fusion of features of different dimensions extracted by network of neck. Experiments on the Le2i fall detection public fall dataset shows that the precision of the improved method has increased by 2.7 percentage points, and has a certain improvement compared with other mainstream algorithms, which proves the effectiveness of the algorithm.
An intelligent data capturing framework to improve condition monitoring and anomaly detection for industrial machines

Companies understand the need to monitor their machines when operational under real life conditions, since it provides valuable information on how these machines and their processes can be improved and why they might fail. Currently, due to bandwidth and storage constraints (costs), companies are limited by how much data they can send from their machines into the cloud, resulting in transmission of only a reduced set of aggregated features. Such a reduced set often misses the critical information required to analyse the machine's behaviour, such as possible defects, which is often only present in the high frequent raw data signals. Because sending all high frequency data to the cloud is not possible, the work in this paper proposes a hybrid approach where we do transmit high frequency data, resulting in a complete yet compact set, aiming to reduce the redundancy of the transmitted data as much as possible. As a result a valuable and up to date dataset will be available in the cloud for machine monitoring and anomaly detection purposes while restricting ourselves to feasible transmission and storage requirements. This hybrid approach has been implemented and applied on a grid monitoring application, focusing on grid disturbances.
Improving the distribution of covid-19 vaccines using the blockchain technology: the Italian case study

Vaccination is one of the most effective ways to prevent and control the outbreak of infectious diseases. The vaccine supply chain differs from the traditional supply chains because of the perishability of the products, which need strict transport and warehousing conditions to guarantee the health and safety of people. In addition, in case of pandemics, the big amount of doses requested for the implementation of a mass vaccination campaign forces governments to design a proper logistic network and plan a rapid and efficient distribution of vaccines. This paper studies the organization of allocation and distribution of the covid-19 vaccines in Italy. The main criticalities in managing the vaccine supply chain have been identified and, because of its peculiarities, the blockchain has been considered a suitable technology to solve them. A simulation model has been developed to reproduce the current distribution of vaccines in Italy, and a future scenario with blockchain has been studied. The findings show that it is possible to improve the performance of the vaccine supply chain and make it more resilient by implementing the blockchain technology.