Reviewerly/exHarmony huggingface dataset:
Smart Materials, Future Cities: Potential for Sustainable Ambient Urban Informatics

Media façades and digital billboards are becoming an increasingly common fixture in urban landscapes. However, these contemporary signage solutions bear a number of negative social and environmental effects. In addition to obscuring historic architecture and increasing light pollution, they also compete with municipal signage, traditional advertisements, and mobile media to increase our experience of information overload. Smart (environmentally responsive) materials represent an ambient, low-energy alternative to contemporary digital signage. Although smart materials are being used as a sustainable alternative in engineering, they are less frequently used as a form of visual communication in urban informatics. This article describes three artistic experiments that reveal how smart materials can be used to convey information in urban settings.
On the remote entanglement of MW qubits using hybrid Rydberg systems

Distributed quantum computing is a promising architecture for the realization of scalable quantum computers. The cornerstone of such a quantum architecture is the ability to establish quantum correlations between remote computation modules. Remote entanglement and quantum logic gates have been demonstrated in a few physical systems enabled by coupling matter qubits to photons. However, building such a quantum architecture with superconducting qubits seems very challenging due to the lack of efficient matter-light quantum interface. Following our previous work on hybrid microwave-optical quantum gate, we propose a protocol to perform a remote quantum logic gate between distant superconducting quantum modules. MW qubits in distant modules are connected with photonic ancillary qubits and a hybrid Rydberg-cavity system is employed as the quantum mediator for microwave-optical photon interaction. We perform thorough analysis and find high-fidelity remote quantum logic gate is achievable by integrating state of the art quantum systems.
Enhanced Biometric Attendance Management System using IoT &amp; Cloud

Attendance is a critical part of any organization or business, and it is tracked on a daily basis. Many organizations presently record group action on a chunk of paper on those students register front of their names. the most disadvantage of this system is that a student will pretend the signatures of his or her classmates notwithstanding they're not there. Keeping track of the amount and names of scholars within the category could be a long task that takes up heaps of the faculty's time. In this paper, we describe how our system, which includes customized report production, can help solve this challenge in this paper. Our hardware modules are integrated to the Raspberry Pi, which is utilized to transfer the data. To record group action unambiguously by checking pre-registered knowledge, the system consuming task that takes up heaps of the faculty's time. offers two options: the Fingerprint Module and the Facial Recognition Module. After recording attendance the data is stored on the cloud which will provide security and backup in case of any software failures. This system will provide the customized reports which can be viewed from the mobile app to those who are having authorization to view.
Combating the Phishing Attacks

The phishing attack targets the client's email and any other connection medium to illicitly get the user credentials of e-commerce websites, educational websites, banks, credit card information, and other crucial user information. Exploitations caused by different types of cyberattacks result in data loss, identity theft, financial loss, and various other adversaries on both human and infrastructure. Therefore, investigating the threats and vulnerabilities on web applications and analysis of recent cyberattacks on web applications can also provide a holistic scenario about the recent security standpoint. Therefore, in this chapter, phishing attack techniques and their current scenario will be discussed extensively. Moreover, recent phishing techniques will be discussed to understand the severity of this type of attack. Finally, this chapter will outline the proposed and existing countermeasures for protecting users' identities and credentials from the phishing technique.
[DC] Improving Presence of Virtual Humans through Paralinguistics

Social presence and plausibility are two of the most important constructs being studied in IEEE VR to improve user experience in virtual environments. However, virtual humans that are used in these virtual environments lack the capability of adaptive speech that directly influences social presence and plausibility of the virtual humans that are used since humans use adaptive speech during conversations. Virtual humans lack the adaptability of their speech in the manner that humans do. Research has been explored in how pitch, rate of speech, and intensity can be manipulated in human discourse. However, little research has been done in using adaptable speech to affect dialog with virtual humans. This dissertation attempts to create a framework and a system that enables adaptive virtual human speech. This includes using machine learning and user modeling to manipulate paralinguistic features such as pitch, rate of speech, pause duration, and intensity using Speech Synthesis Markup Language (SSML). Understanding the interplay of paralinguistic features between virtual human and human will improve the social presence of virtual humans. By achieving this, virtual interactions can be moved toward the dynamic level of human-to-human interactions, thus increasing the co-presence and plausibility of these virtual human characters.
Telepresence Robot with Novel Stereoscopic Camera Configuration

A novel stereoscopic camera configuration which has two movable wide-lens cameras and one stationary fisheye-lens camera is proposed to build "view-sharing" telepresence robot for multiple users. The camera system provide 180-deg monocular field-of-view obtained from the stationary fish-eye camera for more than two users, and provide additional stereoscopic information of parallax for at most two users independently. The parallax is obtained from the movable cameras which revolves around the fish-eye camera according to each user's head motion so that the inter-lens distance keeps constant length, and stereoscopic view of remote environment is provides for each user. We demonstrate some applications using the proposed system such as body-sharing experience, joint attention via shared-view, and improvement of sense of presence.
Localization of space-inhomogeneous three-state quantum walks

Abstract Mathematical analysis on the existence of eigenvalues is essential because it is deeply related to localization, which is an exceptionally crucial property of quantum walks (QWs). We construct the method for the eigenvalue problem via the transfer matrix for space-inhomogeneous three-state QWs in one dimension with a self-loop, which is an extension of the technique in a previous study (Kiumi and Saito 2021 Quantum Inf. Process. 20 171). This method reveals the necessary and sufficient condition for the eigenvalue problem of a two-phase three-state QW with one defect whose time evolution varies in the negative part, positive part, and at the origin.
Linear Variational Autoencoder for Top-N Recommendation

Top-N recommendation is significant in various service-based platforms. Variational Autoencoders (VAEs) have been used in top-N recommendation in recent years for its effectiveness in collaborative filtering. Mult-VAE is such a variant that achieves great success, by adopting the multinomial likelihood, and an additional hyperparameter &#x03B2; on the KL divergence term of ELBO of VAE. However, Mult-VAE uses non-linear neural networks as encoder and decoder, to encode and reconstruct the user-item interaction data, which we prove unnecessary in sparse datasets because it will degrade the prediction accuracy in our experiments. Moreover, most variants of VAE-based collaborative filtering methods use the unnormalized user-item interaction data to make recommendations, which will hinder the learning process of the interaction data. In this paper, we propose Linear Variational Autoencoder (LVA), a linear version of Mult-VAE, considering additional normalization on user-item interaction data, for collaborative filtering under the implicit feedback setting. We verify its effectiveness in the experiments and prove that LVA achieves better or competitive performance over current state-of-the-art collaborative filtering methods, e.g, LightGCN, on four public real-world datasets.
Candidate Models for Federated Learning with Blockchain

At present, distributed machine learning has been applied in many fields. Federated Learning is one of the most popular distributed machine learning methods. However, the large number of Federated Learning clients makes the learning process more vulnerable to malicious participants. In this paper, we propose a new Federated Learning architecture combined with Blockchain technology, which achieves good results in defending against attacks. First, we introduce the overall structure of the combined scheme. Then, we explain the design of this combination scheme in detail, especially candidate models generation. Finally, we perform simulation experiments on MNIST to verify the superiority of our scheme. Experimental results show that our scheme can resist attackers effectively when the proportion of attackers is 40%, and the accuracy of the global model is still around 99%.
SocialSlider: Changing the Transparency of Avatars

Due to the growing number of users, social virtual reality can get crowded. As we sometimes prefer to be only surrounded by friends or more enjoy empty galleries to have a better view, we designed SocialSlider: an interaction technique that allows us to manipulate other avatars' transparency. We implemented SocialSlider with two transparency control modes and four avatar selection techniques: (1) select a single avatar, (2) select all, (3) select a co-located group, or (4) select a semantic group, such as unknown users. Through a user study, we found that (A) The concept of SocialSlider is appreciated and promising to overcome issues of crowded VR, such as occlusion. (B) The avatar's level of transparency can affect their perceived co-presence. Thus, we show that fading avatars' visibility is a beneficial technique in multi-user VR with benefits beyond avoiding occlusion, such as controlling the felt privacy or the experience of being social.
Design and Evaluation of an Augmented Reality App for Learning Spatial Transformations and their Mathematical Representations

There is a close relation between spatial thinking and mathematical problem-solving. This paper presents a newly developed educational Augmented Reality (AR) mobile application, BRICKxAR/T, to help students intuitively learn spatial transformations and the related mathematics through play. A pilot study with 7 undergraduate students evaluates students learning gain through a mental rotation and a math test on transformation matrices. The results show most students performed better with a higher score after learning with the app. Students found the app interesting to play and useful for learning geometric transformations and matrices.
Memory Optimisation on AVR Microcontrollers for IoT Devices’ Minimalistic Displays

The minimalistic hardware of most Internet of things (IoT) devices and sensors, especially those based on microcontrollers (MCU), imposes severe limitations on the memory capacity and interfacing capabilities of the device. Nevertheless, many applications prescribe not only textual but also graphical display features as output interface. Due to the aforementioned limitations, the storage of graphical data is however highly problematic and existing solutions have even resorted to requiring external storage (e.g., a microSD card) for that purpose. In this paper, we present, evaluate and discuss two solutions that enable loading fullscreen, optimal 18-bit colour image data directly from the MCU, that is, without having to rely on additional hardware. Importantly, these solutions retain a very low footprint to suit the microcontroller architecture; the AVR architecture has been selected given its popularity. The obtained results show the feasibility and practicability of the proposal: in the worst case, 21 Kbytes of memory are required, in other words approximately 33% of the flash memory of a 32-Kbyte MCU remain available.
Multipath Transmission Control Protocol for Live Virtual Machine Migration in the Cloud Environment

For mobile cloud computing (MCC), a local virtual machine- (VM-) based cloudlet is proposed to improve the performance of real-time resource-intensive mobile applications. When a mobile device (MD) discovers a cloudlet nearby, it takes some time to build up a virtual machine (VM) inside the cloudlet before data offloading from the MD to the VM can begin. Live virtual machine migration refers to the process of transferring a running Virtual Machine (VM) from one host to another without interrupting its state. Theoretically, live migration process must not render the instance being migrated unavailable during its execution. However, in practice, there is always a service downtime associated with the process. This paper focuses on addressing the need to reduce the service downtime in case of live VM migration in cloud and providing a solution by implementing and optimizing the multipath transmission control protocol (MPTCP) ability within an Infrastructure as a service (IaaS) cloud to increase the efficiency of live migration. We have also introduced an algorithm, the α-best fit algorithm, to optimize the usage of bandwidth and to effectively streamline the MPTCP performance.
An Approach to Utilize E-commerce Product Reviews to Remove Irrelevant Recommendations

Information explosion in the digital era has affected e-commerce users' purchase experience as the users encounter a large number of choices for the item of interest. A recommender system enriches the customers' shopping experience by downsizing the number of alternatives suggested to the user. It suggests the right items to the right customers at the right time, thus, benefiting both the customer and the stakeholders involved. User reviews are a first-hand account of a user's encounter with an item and consist of positive, negative, and neutral feedback. Users' opinion for item(s) of interest can be gauged through this textual feedback. Through this paper, an approach to improve the recommendations has been provided by leveraging the product review functionality. Extracting the product features from user-provided feedback and not recommending products with uninteresting product features to improve the recommendation list is the main idea behind this approach.
Privacy Issues in Stylometric Methods

Stylometry is a well-known field, aiming to identify the author of a text, based only on the way she/he writes. Despite its obvious advantages in several areas, such as in historical research or for copyright purposes, it may also yield privacy and personal data protection issues if it is used in specific contexts, without the users being aware of it. It is, therefore, of importance to assess the potential use of stylometry methods, as well as the implications of their use for online privacy protection. This paper aims to present, through relevant experiments, the possibility of the automated identification of a person using stylometry. The ultimate goal is to analyse the risks regarding privacy and personal data protection stemming from the use of stylometric techniques to evaluate the effectiveness of a specific stylometric identification system, as well as to examine whether proper anonymisation techniques can be applied so as to ensure that the identity of an author of a text (e.g., a user in an anonymous social network) remains hidden, even if stylometric methods are to be applied for possible re-identification.
Camera-based Human Counting for COVID-19 Capacity Restriction

In this paper, a Human Counting system is implemented for COVID-19 capacity restrictions. It was implemented using the deep learning model You Only Look Once version 3(YOLOv3) to detect and count the people in a room. The system also can monitor the social distancing between the people in the room while labeling each person as &#x201C;safe&#x201D; or &#x201C;unsafe&#x201D; depending on whether they respect the social distancing protocols that the World Health Organization recommended or not. To make the project user friendly, a Graphical User Interface (GUI) was implemented to allow the user to choose the source of their images that will be used as input to be processed by the system. An experiment was carried out to evaluate the performance of the system under different conditions and in different scenarios where the evaluation was done according to some metrics such as accuracy, precision and recall. The output results from this experiment were demonstrated in details and compared to a similar algorithm as both algorithms focused on people detection using images from an inclined camera. The results show an accuracy of 96&#x0025; for detection and the number of people counted.
Consistent camera-invariant and noise-tolerant learning for unsupervised person re-identification

Unsupervised person re-identification (re-ID) is still a challenging task. Existing methods usually utilize an alternative manner of generating pseudo labels by clustering and optimizing the model based on pseudo labels. Although these methods achieve great accuracy, there remain two problems unsolved: (1) Noise labels caused by camera variations and other factors. (2) The training of the model is inconsistent or unstable. In this paper, we propose a cluster memory-based meta learning (CMML) strategy with a cluster memory-based additive margin (CMAM) loss to deal with noise labels caused by camera variations and the model training problem, and a cluster memory-based noise-tolerant (CMNT) loss to tackle the rest noise labels. Extensive experimental results on three re-ID datasets (i.e., DukeMTMC-reID, Market1501 and MSMT17) validate the effectiveness of our proposed method. Our method achieves 70.3%, 81.8%, and 39.2% mAP on these three datasets, yielding comparable performance against the state-of-the-art purely unsupervised re-ID methods.
Safeguarding E-Healthcare Documents Utilizing an Infrastructure for Advanced Keyless Signature Infrastructure Blockchain Within the Cloud

The decentralized nature of vulnerable health records can bring about cases where timely records are unavailable, worsening overall health results. Moreover, as patient participation in healthcare increases, there&#x0027;s a growing demand for individuals to control and access the data. Blockchain is a protected, decentralized online server which may be employed to handle electronic health records (EHRs) efficiently, therefore with the possibility to boost health outcomes by producing a stream for interoperability. Thus, it&#x0027;s of key-value to secure electronic overall health captures. Centralized storage space of comprehensive health information appeals to constant viewing, and cyber-attacks of affected person captures are complex. Thus, it&#x0027;s essential to develop a method while using the cloud, which enables you to guarantee authentication and offers the integrity of overall health captures. The keyless signature infrastructure utilized within the suggested method of ensuring electronic signatures&#x0027; secrecy also guarantees elements of authentication. Besides, information integrity is handled through the proposed blockchain technologies. The functionality of the suggested framework is examined by evaluating the variables such as typical period, sizing, and then the price of information storage space and retrieval on the blockchain know-how with traditional details storage space methods. The result reveals that the resulting period of the suggested process, together with blockchain engineering, is practically 55&#x0025; smaller than traditional strategies. Also, they voice the price of storage space is approximately 25&#x0025; less when it comes to the method with Blockchain in deep comparability with all the pre-existing strategies.
Research on AI-assisted grading of math questions based on deep learning

AI-assisted grading system saves great amount of time for teachers. Current application focus on grading techniques for Chinese or English composition, where high frequency features contains major feature for the output. In contrast, grading math problems requires sophisticated precision and rigorous logic. This paper illustrated an AI-assisted grading system for math questions. This system learns the mapping from students’ free response in math exams to output score, requiring little effort from teachers. The system extracts equations and reasoning and compares them to output a final score. We proposed a new network, CycleLatex, as an efficient solution to image-to-latex translation problems. It is capable of unsupervised learning and has shown state-of-art performance on handwriting and multi-line equations. The system also implements a parser which is capable of comparing most equations and reasoning at a reasonable speed.
Applications of Machine Learning Techniques for What-if Analysis and Network Overload Detection

Both Artificial Intelligence (AI) and Machine Learning (ML) techniques have undergone a spectacular growth in the last decades, promising to help in the problem solution of various research areas. This article focuses on the applicability of Machine Learning techniques in the identification and prevention of network overload. To do this, we have collected real traffic matrices from the Abilene American network, these have been labeled as risky or not, and then fed into a set of Supervised Learning algorithms to see if they have the ability to forecast network overload situations. Indeed we observe that Quinlan’s C5.0 classification tree outperforms at detecting risky situations in which possible network overload can occur upon link failure (what-if analysis), showing accuracy values above 99% and Cohen’s kappa coefficient above 94%. This way, network operators can anticipate problematic network situations and prepare preventive countermeasures to mitigate their effects before they actually happen.
Spatial Exploration with a WiM for Capturing 3D Dioramic Snapshots

Immersive Virtual Reality can be a memorable medium for storytelling and artistic expression. However, taking snapshots of VR applications is commonly only possible by taking a two-dimensional screenshot on a tethered PC. We propose to capture 3D snapshots using the interaction technique World-In-Miniature (WiM) by using virtual RGB-D cameras to create three-dimensional snapshots that can be exported and viewed outside the application. We further describe a novel interaction method to fine-tune the content shown inside the WiM with VR controllers. This project is open-source and can be accessed at https://github.com/kyuq/3DWiMSnapshot
Towards AR for Large-Scale Robotics

Today, robotic arms are widely used in industry and increasingly being adopted by small enterprises and startups where they perform tasks beyond the concept of mass fabrication that these machines were developed for. Facilitating the use of industrial robots through new, domain-specific interfaces is essential for fostering these applications. Augmented reality (AR) has got the potential to more closely connect the digital aspects of robot programming with the physical aspects of materiality and the synchronization of these workspaces. We present approaches that go beyond existing AR solutions, primarily using research-oriented or collaborative robots, towards integrating AR with large-scale industrial robots and their comparably dated software architecture. By integrating these strategies into a visual programming environment, they can be made available to people with domain-specific expertise but little programming knowledge, thus fostering innovation and digitization in these fields.