Reviewerly/exHarmony huggingface dataset:
[DC]Using Multimodal Input in Augmented Virtual Teleportation

Augmented (AR) and Virtual Reality (VR) can create compelling emotional collaborative experiences, but very few studies have explored the importance of sharing a user&#x0027;s live environment and their physiological cues. In this PhD thesis, I am investigating how to use scene reconstruction and emotion recognition to enhance shared collaborative AR/VR experiences. I have developed a framework that can be broadly classified into two sections: 1) Live scene capturing for real-time environment reconstruction, 2) Sharing multimodal input such as gaze, gesture, and physiological cues. The main novelty of the research is that it is one of the first systems for real-time sharing of environment and emotion cues. It provides significant insight into how to create, measure, and share remote collaborative experiences. The research will be helpful in multiple application domains such as remote assistance, tourism, training and entertainment. It will also enable the creation of interfaces that automatically adapt to the user&#x0027;s emotional needs and environment and provide a better collaborative experience.
Unmanned Transportation System In Future Cities

In the future, the driving occupations of pilots and drivers will become the past. Unmanned cars, unmanned airliners, unmanned high-speed railways, and unmanned ships will be widely used. As the center of command and control of these unmanned transportation units, unmanned transportation system will play a more and more important role. The unmanned transportation system shall at least include decision control subsystem, equipment management subsystem, encrypted communication subsystem, intelligent life-saving subsystem, unmanned vehicle and other subsystems. In addition, unmanned transportation system as a new concept and new model also brings some notential risks.
Polynomial Wiener LQG Controllers based on Toeplitz Matrices

Abstract This paper re-examines the discrete-time Linear Quadratic Gaussian (LQG) regulator problem. The normal approach to this problem is to use a Kalman filter state estimator and Kalman control state feedback. Though quite successful, an alternative approach in the frequency domain was employed later. That method used z-transfer functions or polynomials in the z-domain. The transfer function approach is similar to the method used in Wiener filtering and requires the use of Diophantine equations (sometimes bilateral) to find the optimal controller. The contribution here uses a similar approach but uses lower triangular Toeplitz matrices instead of polynomials to gain advantage of eliminating the use of Diophantine equations. This is because the single Diophantine equation approach fails when the system has non-relative prime polynomials and the need for bilateral Diophantine equations is computationally far more complex.
Behaviour Privacy: Non-verbal Threats in Avatar-based VR Systems

The current move towards highly-expressive, user-controlled avatars in VR creates unprecedented possibilities for realistic collaboration, approaching face-to-face experiences. This precise tracking of VR user behaviours also provides rich data sets for machine learning and deep learning algorithms to use to accurately identify user be-haviours, such as emotional states. However, the availability, type and amount of data also presents threats to user privacy, such as unobservability and undetectability. The current study highlights the privacy threats these avatar systems can present to users.
Does Deep Learning-Based Super-Resolution Help Humans With Face Recognition?

The last decade witnessed a renaissance of machine learning for image processing. Super-resolution (SR) is one of the areas where deep learning techniques have achieved impressive results, with a specific focus on the SR of facial images. Examining and comparing facial images is one of the critical activities in forensic video analysis; a compelling question is thus whether recent SR techniques could help face recognition (FR) made by a human operator, especially in the challenging scenario where very low resolution images are available, which is typical of surveillance recordings. This paper addresses such a question through a simple yet insightful experiment: we used two state-of-the-art deep learning-based SR algorithms to enhance some very low-resolution faces of 30 worldwide celebrities. We then asked a heterogeneous group of more than 130 individuals to recognize them and compared the recognition accuracy against the one achieved by presenting a simple bicubic-interpolated version of the same faces. Results are somehow surprising: despite an undisputed general superiority of SR-enhanced images in terms of visual appearance, SR techniques brought no considerable advantage in overall recognition accuracy.
Decentralised &amp; Collaborative DocuPad Using Blockchain

Document editor is one of the most used applications in day-to-day operations. The data entered in these documents must be secured. Thus, the users accessing them must be correctly authenticated and provided with privacy. For document editors based on the centralized system, this might not be feasible as they are dependent on third parties to provide a secure environment. Thus, the emergence of blockchain has brought many changes like more efficiency by eliminating third-party involvement; other benefits include real-time payment settlements, reduced costs, tokenization, and real-time payment settlements, which help give a solution. Since blockchain as a package provides security, anonymity, integrity, authenticity, and decentralization, the study proposed a Decentralized & Collaborative DocuPad application that can provide all the features left out by the centralized system-based processors like security and privacy issues, single-point failure issues, and scalability limitations. The proposed DocuPad application integrates with block stacks, a decentralized Internet platform. It provides a secure sign-in process, with each user having their private keys. The proposed application is not connected to any cloud-based system for storing the data. It works on a decentralized storage system known as Gaia, eliminating securing the storage systems. Also, this decentralized storage system helps us create a URL for the document when the status of its existence is changed from private to public. When shared, other users can access the information/data stored in the document.
ScaledYOLOv4 Based Person Re-identification System

Despite the fact that facial verification and tracking are a solved problem that is used in many fields and has many product solutions, it is not easy to find an open-source implementation of a human face tracking system with its description. In this paper, we introduce a person tracking system, describe its architecture and functionality. The proposed system is modular and scalable. It includes an IP-camera data stream reader module, face detection module based on state-of-the-art ScaledYOLOv4 model, face identification module with Dlib, and database reader-writer module for storing and autoscaling embeddings and features. The proposed system is easily integrated into existing software and hardware and easy to implement and repeat. Moreover, due to its modularity, it is easy to keep up with the state-of-the-art solutions in the face detection and identification tasks and, if necessary, implement them in the system.
VR Edufication on Historic Lunar Roving Missions

This work presents the design and evaluation of an educational VR game to teach historic Apollo lunar roving missions. The game consists of three gameplay scenes, and each scene aims to convey one type of knowledge. The game adheres with the learning objectives, presents the contents, and conveys knowledge in both active and passive interaction modes. We conducted a user study focusing on the understanding of the influence of different interaction modes on learning outcomes and learning engagement.
Overview of Technologies for Transmitting Audio Streams over Low-Speed and Unstable Communication Channels

The article presents the conducted research of intelligent means of encoding and restoring audio information with improved quality indicators in the conditions of using unstable low-speed and unstable communication channels. In the article we examine the existing technologies used for encoding and decoding audio information, and evaluate their applicability to low-speed and unstable communication channels. The technologies of restoring and improving the quality of audio information in the event of transmission losses are also considered.
Opinion Mining Approach: To Understand Public Reactions towards Reopening of Schools and Colleges/Universities in India

The psychological, developmental, and educational consequences of the prolonged shutdown and digital shift to e-learning methods are being felt in every family throughout India. Amid the COVID-19 pandemic, the use of social media platforms such as Twitter has increased as people turn to it to express their thoughts, anxieties, and concerns. As a result, I conducted sentiment analysis on two sets of data related to the reopening of schools in India and the reopening of colleges/universities in India. The tweets were retrieved from the social media site Twitter using 9 keywords relating to the reopening of schools and colleges/universities. The Tweepy module in Python was used for the extraction process. A total of 2,494 tweets over the course of 16 days, from December 8th, 2021 to December 23 rd, 2021 were gathered. Later a total of 1,510 tweets were evaluated once all of the pre-processing steps were done. The sentimental analysis was carried out using 2 unsupervised Lexicon-based models - the Vader Lexicon and the SentiWordNet Lexicon method. In this study, the outcomes of both techniques are highlighted. The main aim of this research is to gauge the sentiments of the public reactions expressed towards the reopening of schools and colleges/universities. These findings may be a beneficial aspect to consider while the policy-making process. The results of this study suggest that by using both approaches there was a higher number of positive tweets which may indicate that citizens in India are in the favor of schools and colleges reopening. This study can further be expanded by incorporating public viewpoints expressed on various other social networking platforms and can be extended to create a comprehensive dashboard with state-wise catered guidance, instructions, and resources for safely reopening schools and colleges in India.
E-LPDAE: An Edge-Assisted Lightweight Power Data Aggregation and Encryption Scheme

In smart grid systems, electric utilities require real-time access to customer electricity data; however, these data might reveal users’ private information, presenting opportunities for edge computing to encrypt the information while also posing new challenges. In this paper, we propose an Edge-assisted Lightweight Power Data Aggregation Encryption (E-LPDAE) scheme for secure communication in a smart grid. First, in the edge privacy aggregation model, the data of smart meters are rationally divided and stored in a distributed manner using simulated annealing region division, and the edge servers of trusted organizations perform key one-time settings. The model encrypts the data using Paillier homomorphic encryption. It then runs a virtual name-based verification algorithm to achieve identity anonymization and verifiability of the encrypted data. The experimental results indicate that the E-LPDAE scheme reduces overall system power consumption and has significantly lower computation and communication overhead than existing aggregation schemes.
An Analog-to-information Converter Using Non-Uniform Sampling Architecture and SAR ADC

An analog-information converter with a non-uniform sampling architecture SAR ADC based was developed using the CMOS 180 nm process, having a maximum reference frequency of 40 MHz. Mathematical modeling of the device and numerical modeling of the schematic solution were carried out, taking into account the extraction of parasitic elements from the topology. The simulation results showed that this design is capable of recovering an input signal in a frequency band up to 20 MHz from 500 samples, although the ADC used in the AIС has a signal bandwidth of up to 500 KHz. The presented design allows the device to be used in AIC or ADC mode.
The Analysis of Commercial Pattern of Cloud Computing Platform

For solving the problem which there have not suitable commercial pattern of cloud computing platform yet, a commercial pattern of cloud computing platform was proposed and constructed. The solving scheme are provided as follows. After explicitly demand surveyed, function analyzed and framework designed, an standard cloud computing platform with local characteristics and IoT think was constructed within 6 months successfully. After a period of time for operating and managing, for better operating the cloud computing platform, pursuing the continous income and excess profit, a creative and practical commercial pattern of cloud computing platform was presented and applied by this paper, that included in Data Operation (DO) and Joint Credit Reference (JCR), which could solve the problem that the income of many cloud computing platform are not continuous. Then the performance of cloud computing platform was tentatively evaluated by Markov Random Field theory and Belief Propagation algorithm. Moreover, Cumulative Vote Scheme (CVS) which was also presented by this paper was used to analyze and evaluate the performance of commercial pattern of cloud computing platform. Three different random group samples were used to test and evaluate the performance of commercial pattern by independent practical experiments. Finally, the commercial pattern of cloud computing platform was proved to be very effectiveness by experiment results, and the performance of cloud computing platform was very robustness.
A Framework for Enhancing the Supply of Scientific Research Projects Using Blockchain

Today&#x0027;s the scientific projects and corporate environment is more demanding than ever before. to satisfy the increasing demands of scientific research projects procurements by e-commerce applications are increasing their markets and functionality. Hackers can use security weaknesses in most applications to launch attacks. Information integrity, confidentiality, and availability have all been compromised as a result of the attacks. This paper described the current state the scientific research projects procurements and proposed a framework to enhance the procurement of scientific research projects to rationalize government spending by increase security and transparency of transactions using blockchain technology. A proposed framework consists two architectures. First architecture contains a conceptual architecture discuss a life cycle workflow for scientific research projects procurement application and a payment gateway for financial transactions. In the second architecture declare the layer&#x0027;s architecture to explain the role of each element in the layers and represent the blockchain architectures and transactions between researcher, supplier, and system admin using Smart contracts and cryptocurrency to overcome of weak security in financial transactions. and improving satisfaction, trust through transparency, preventing transactions counterfeit and sharing and reusing knowledge related to the project procurements with other systems.
Product Recommendation System using MLP Algorithm

DATA mining is a vital analysis domain today that focuses on data discovery in databases. It’s wherever knowledge from the database are mined in order that informative knowledge will be made and used effectively and with efficiency by humans. Prediction and description are its objectives. One amongst the aspects of information mining is that the Association Rule mining. It consists of two procedures: First, finding the frequent item set within the info employing a minimum support and constructing the association rule from the frequent item set with such confidence. It relates to the association of things whereby for each prevalence of A, there exists an occasion of B. This mining is additional applicable within the market basket analysis. Purchasers who buy certain items will benefit from that application. What is the attainable item that matches every item that they purchase? Among the most widely used association rule mining algorithms are MLPs and genetic algorithms.
Wheel Tread Reconstruction Based on Improved Stoilov Algorithm

With the development of rail transit in terms of speed and carrying capacity, train safety problems caused by wheel tread defects and wear have become more prominent. The wheel is an important part of the train, and the wear and defects of the wheel tread are directly related to the safety of the train; therefore, wheel tread testing is a key element of train testing. In phase measuring profilometry (PMP), the virtual sine grating generated by the computer is projected onto the measured wheel tread by a digital projector, and then a camera is used to obtain the modulated deformed grating on the surface of the wheel tread. Next, the wrapped phase is obtained by the improved Stoilov algorithm, and the unwrapped phase is obtained by the phase unwrapped algorithm. Finally, the three-dimensional (3D) profile of the wheel tread is reconstructed. This paper presents an improved Stoilov algorithm based on probability and statistics. Supposing that the probability of real data was the highest, we chose the cosine square matrix value of the phase shift for processing. After ruling out the singular points of large error, we obtained the closest value to the true phase shift using the method of probability and statistics. The experimental results show that this method can effectively restrain the singular phenomenon, and the 3D profile of wheel tread can be reconstructed successfully.
Pedestrian’s Abnormal Behaviour Detection Based on Machine Vision

Abstract It is significant to automatically analyse video sequences for timely and accurate detection of abnormal pedestrian behaviour by using computer vision and analysis technology in public places. To realize the real-time monitoring of abnormal behaviour, this study uses a hybrid behaviour detection model based on the colour characteristics of the pixels and the gradient characteristics of the pixels mathematically. According to the hybrid behaviour detection model, the criterion of abnormal behaviour of the same pedestrian based on two frames of video images is proposed. This is conducive to realizing early awareness of emergencies caused by abnormal behaviours and taking early warning measures, reducing casualties, and maintaining public safety and social stability.
BRGraph: An efficient graph neural network training system by reusing batch data on GPU

Summary With the increasing adoption of graph neural networks (GNNs) in the community, various GPU‐based graph programming systems have been developed to improve the productivity of GNNs. However, sampling‐based GNN training is still inefficient, and we observe that the main bottleneck comes from the data transferring, where vertex features are transferred from host memory to GPU through limited bandwidth. In this article, we propose BRGraph, a sampling‐based GNN training system that supports efficient data transferring. BRGraph leverages the duplicate vertices between mini‐batches by batch reusing (BR) strategy to avoid duplicate data transmission. Furthermore, to reduce the overhead of detecting duplicate vertices, we design an efficient parallel batch reusing algorithm based on GPU. BRGraph also exploits the data reusing potential of the non‐duplicate vertex features by the two‐level batch reusing (two‐level BR) strategy. Comprehensive evaluations on three representative GNN models show that BRGraph reduces data transferring time by up to 60% and delivers up to 1.79 GNN training speedup over the state‐of‐the‐art baselines. Besides, it can save GPU memory by up to 40% while reaching the same training time compared with the static cache strategy. When applying the two‐level BR, BRGraph further reduces 20% of the data transferring time compared with the BR.
Flick Typing: Toward A New XR Text Input System Based on 3D Gestures and Machine Learning

We propose a new text entry input method in Extended Reality that we call Flick Typing. Flick Typing utilizes the user&#x0027;s knowledge of a QWERTY keyboard layout, but does not explicitly provide visualization of the keys, and is agnostic to user posture or keyboard position. To type with Flick Typing, users will move their controller to where they think the target key is with respect to the controller&#x0027;s starting position and orientation, often with a simple flick of their wrists. Machine learning model is trained and used to adapt to the user&#x0027;s mental map of the keys in 3D space.
Enabling mechano-intelligence in adaptive structures utilizing physical computing

With advances in new technologies, researchers are attempting to develop the next generation of adaptive structures with intelligence directly embedded within their mechanical domain, the so-called mechano-intelligence. These attempts will enable mechanical systems to perform intelligent tasks, such as sensing the environment, changing geometries, making decisions, and executing computation in an even more proactive and autonomous manner, as compared to traditional mechatronic systems. However, there is no systematic foundation in constructing and integrating different aspects of mechano-intelligence. To advance the state of art, this research proposes to enhance the mechano-intelligence in adaptive structures through a machine learning framework called Physical Reservoir Computing (PRC). We show that a tunable modular metastructure can learn from its own wave dynamics and adaptively tune its own band structure via PRC. In other words, the metastructure can sense different input waves, make decisions and output appropriate control commands to alter its own wave characteristics without digital signal processors and controllers, i.e., achieve autonomous and integrated mechano-intelligence. Overall, this research provides a novel method to achieve intelligent and adaptive vibration/wave control based on the concepts of physical computing and learning and forms the basis for multi-faceted functional-relevant mechano-intelligence to be embedded in future adaptive structures and material systems.
Attention-Based Dual-Branch Cascade Network for Multi-Label Image Recognition

Abstract Multi-label image recognition is a practical task which aims to predict all concerned objects in an image, and the correlation between labels is considered as the key to solve multilabel problem. Previous approaches focus on exploring and exploiting the underlying relations between labels, by which the performance of most labels is improved significantly. But small object and label tail problems are ignored, and negatively influences the overall results. For ameliorating above problems, we propose a unified deep neural network named Attention-Based Dual-Branch Cascade Network (ADC Net), which contains Main Branch and Auxiliary Branch. ADC Net cascades to predict labels, and obtains the final result by element-wise adding. Attention modules in each branch contribute to recognizing small objects. Top-Down-Attention Module (TDAM) utilizes the preceding prediction map to guide the Auxiliary Branch. Besides, expanding training dataset is applied for learning the feature representation of tail labels. Our proposed methods are evaluated on two benchmark datasets: MS-COCO and VOC PASCAL 2007 datasets, and achieves state-of-the-art performance. Results of small objects, tail labels and visualization also prove the effectiveness of our method.