Miracle-dz/UniFAR-FTUs huggingface dataset:
Action recognition application using artificial intelligence for smart social surveillance system

Computer vision is an important area of artificial intelligence that aims to help it gain the ability to activate similar to humans. In the past, we often classify fruit by hand. Today, it is performed by the development of image-processing technology. When the quantity of fruits is huge, we need machine learning for classifying them. There-fore, we propose the fruit classification using the Tensorflow and Keras model (high-level framework of Tensorflow) in the paper. This is a simple problem of computer vision since it solves the basis problems such as object detection or face recognition. In the paper, we focus on modifying the network architecture of the Tensorflow model. As a result, the accuracy of the proposed model achieves 99% with only five epochs. © The Authors.
Report of the Basic Energy Sciences Roundtable on Opportunities for Basic Research for Next-Generation Quantum Systems

and fundamental physical symmetries. Quantum-coherent systems have exhibited remarkable coherent phenomena, including the ever increasing coherence times necessary for QIS. more important than the absolute time scales of coherence are the greater than ten orders of magnitude by which coherence times can exceed the intrinsic dynamical time scales of such systems. This exceptional ratio of coherence times to evolution times suggests extensive regimes of coherent behavior during which complex entangled states can be established and evolve before they decay. Our understanding of the complexity of entanglement remains limited to the behavior of small numbers of quantum-coherent systems. Advancements in this field will come from new understandings of the scaling of coherence lengths and times with system size and complexity, from clarifying the distinction and interactions between the quantum-coherent system and its surrounding incoherent bath of excitations, and from identifying new signatures of quantum states in artificial quantum-coherent systems. The current focus of the field on slow changes in the parameters and features of quantum-coherent systems, in order to adiabatically change their properties, will expand to scenarios in which fast, non-adiabatic changes can be made that weakly couple to an incoherent bath, if at all. Researchers to discover, during these investigations, novel entangled excitations with properties, including surprisingly long coherence times or stable behavior when manipulated.
Optimizing Data-flow Processor Architecture for Efficient Implementation of DSP Algorithms

The FPGA prototype of the Hybrid Architecture of Recurrent Signal Processor has shown promising results on the isolated word recognition (IWR) problem. This task is a subset of DSP algorithms. In expanding trial on a wide range of DSP algorithms, work has begun to increase the versatility of architectural solutions. The proposed solutions aim to reduce memory costs for storing tagged input data and optimize a number of computational pipeline modules. The article covers the implementation results of the FFT and a series of FIR filter algorithms. Some redundant, highly specialized for the IWR tasks blocks were excluded in the course of this work. The proposed architectural solutions increase the scalability of the architecture and reuse the introduced blocks to optimize the solution of other DSP tasks.
GazeDock: Gaze-Only Menu Selection in Virtual Reality using Auto-Triggering Peripheral Menu

Gaze-only input techniques in VR face the challenge of avoiding false triggering due to continuous eye tracking while maintaining interaction performance. In this paper, we proposed GazeDock, a technique for enabling fast and robust gaze-based menu selection in VR. GazeDock features a view-fixed peripheral menu layout that automatically triggers appearing and selection when the user’s gaze approaches and leaves the menu zone, thus facilitating interaction speed and minimizing the false triggering rate. We built a dataset of 12 participants’ natural gaze movements in typical VR applications. By analyzing their gaze movement patterns, we designed the menu UI personalization and optimized selection detection algorithm of GazeDock. We also examined users’ gaze selection precision for targets on the peripheral menu and found that 4–8 menu items yield the highest throughput when considering both speed and accuracy. Finally, we validated the usability of GazeDock in a VR navigation game that contains both scene exploration and menu selection. Results showed that GazeDock achieved an average selection time of 471ms and a false triggering rate of 3.6%. And it received higher user preference ratings compared with dwell-based and pursuit-based techniques.
Multi-Touch Smartphone-Based Progressive Refinement VR Selection

We developed a progressive refinement technique for VR object selection using a smartphone as a controller. Our technique combines progressive refinement with the marking menu-based CountMarks, which uses multi-finger touch gestures to “short-circuit” multi-item marking menus. Users can indicate a specific item in a sub-menu by pressing a specific number of fingers on the screen while swiping in the desired menu's direction. This reduces the number of steps in progressive refinement selection.
An efficient twitter data collection and analytics framework for effective disaster management

The world has faced an exponential increase in disasters and it is very evident that losses incurred due to disasters and the risk of disasters are also increasing. The literature [1] states that India has faced 371 natural hazards resulting in considerable deaths, infrastructural, agricultural, and economic devastation. Social networking development has been expeditious and has been considered beneficial in disaster management. In culture, social media sites such as Twitter and Facebook have reached an irreplaceable stature that demonstrates the prominence of colossal data acquired. Social networking data can be gathered and used for disaster-specific data analysis, which can be used to extract disaster logistics, identify patterns, and provide public preparedness assistance. The proposed work establishes a data collection system that is capable of capturing disaster-related tweets such as landslides, rain and flood in focus, eliminating obsolete and irrelevant data, retaining related data such as media and location without breaching social media ethics privacy policies. Within the period from May 2020 to December 2021, the system was able to collect 9,21,897 tweets. The diversity of culture, language and expressions prevalent in different parts of the globe is reflected in the way knowledge is conveyed, which makes it relevant to collect tweets in Indian languages. So, for easier data analysis, this system gathers tweets that are in Indian languages and translates them into English for efficient data analysis.
Virtual Workspace Positioning Techniques during Teleportation for Co-located Collaboration in Virtual Reality using HMDs

In many collaborative virtual reality applications, co-located users often have their relative position in the virtual environment matching the one in the real world. The resulting spatial consistency facilitates the co-manipulation of shared tangible props and enables the users to have direct physical contact with each other. However, these applications usually exclude their individual virtual navigation capability, such as teleportation, as it may break the spatial configuration between the real and virtual world. As a result, the users can only explore the virtual environment of approximately similar size and shape compared to their physical workspace. Moreover, their individual tasks with unlimited virtual navigation capability, which often take part in a continuous workflow of a complex collaborative scenario, have to be removed due to this constraint. This work aims to help overcome these limits by allowing users to recover spatial consistency after individual teleportation in order to re-establish their position in the current context of the collaborative task. We use a virtual representation of the user’s shared physical workspace and develop two different techniques to position it in the virtual environment. The first technique allows one user to fully position the virtual workspace, and the second approach enables concurrent positioning by equally integrating the input from all the users. We compared these two techniques in a controlled experiment in a virtual assembly task. The results show that allowing two users to manipulate the workspace significantly reduced the time they spent negotiating the position of the future workspace. However, the inevitable conflicts in simultaneous co-manipulation were also a little confusing to them.
Automatic Identification of K-complex in Sleep EEG Signal for Clinical Practice

Sleep is one of the prime natural activities for human well-being in physical, emotional, and mental aspects. The assessment of sleep EEG requires in the diagnosis of some sleep-related disorders. Sleep transients (K-complex or sleep spindles) are one of the effective parameters in sleep studies. K-complex (KC) detection is a relentless task from the long-duration recorded sleep EEG signal. Manual visual scrutiny takes ample time, susceptible to errors, and needs a skilled person. A computer-aided automatic detection model has been designed to identify KC in the sleep EEG signal to conquer these difficulties. The overview of the chronological advancement in KC detection's automated methods is also enlightened in this paper. The demonstration of this study is carried out on the sleep-EDF EEG database. The proposed simulation model followed the process flow of signal template generation for KC using prior knowledge and matched filter based automatic KC identification using the defined template. This proposed model can give better performance even if the test signal is badly corrupted with noise, i.e., low SNR (signal to noise ratio). The sensitivity, specificity, and accuracy of the model using this intended method are 92.72%, 87.48%, and 91.2% respectively, with comparatively better results than reported in the literature. Hence, considering the achieved outcomes in the test signal assessment, this proposed method can be an alternative for automatic identification of K-complex that may help neurologists diagnose sleep-related problems for clinical practice and diminish neurologist's load to a greater extent.
Investigation on the Temperature Stabilization of the Laser Diode Based on PID Control

The most commonly used PI/PID controller is used in industry. Almost all feedback loops in the industry use a P, PI, or PID controller (later PID will be used to refer to all subset controllers). The investigation is designed to stabilize the temperature of laser diodes using PID controller to: (1) stabilize the temperature to a certain extent to prevent sudden shifts; (2) be able to control the temperature using PID algorithms for the successful functioning of temperature-stabilized diodes; and (3) to minimize the fluctuation of temperature. The schematic method is to build a closed-loop system that regulates the laser temperature by using PID algorithms to keep the laser temperature as close to the required set point as possible. The system measures the laser temperature through the thermocouple which sends the acquired temperature to the microcontroller for processing. The system runs with P, PI, and PID controller methods. The Integral gain causes some oscillations but lowers the measurement on the increasing time. The additional integral term to the unit helps lower the steady-state error that is triggered by the P controller, as the integral controller constructs. The rising time is faster than 50 seconds. Many types of PID controllers may be programmed online using machine learning algorithms which improve the efficiency of the controller systems. When it is needed to continuously control a system variable, the PID control is an excellent alternative.
Investing style and sensitivity analysis in U.S. stock market during Covid-19 based on multi-factor model

According to the unimaginable influence of Covid-19 and the essential of capital asset pricing in the market, this article analyzes the TV industry of the US stock market before and during the epidemic based on the Fama-French five-factor model. Fama-French five-factor model comprehensively considers the impact of market risk premium (Mkt-RF), market value scale factor, (SMB), book-to-market value ratio factor (HML), profit factor (RMW) and investment factor (CMA) on this industry. Meanwhile, it can conduct a comprehensive evaluation of the impact of Covid-19 on the TV industry. The data in this article was selected from Kenneth R. French's databases and used multiple linear regression to obtain the results. The performance of factors is different due to the outbreak of Covid-19. By analyzing the result, it found that Mkt-RF, SMB are not significant in the model, but HML, RMW, CMA have changed from insignificant to significant. It indicates that during the Covid-19, investors are recommended to pay more attention to the firms with high book-to-market ratios, stable profitability, and aggressive investment style in the USA TV industry. Therefore, research on the stock market of the TV industry plays an important role in the steady development of the economy, the creation of social wealth, and the improvement of people's living standards.
Distortion-free Mid-air Image Inside Refractive Surface and on Reflective Surface

In this study, we propose an approach to display a distortion-free mid-air image inside a transparent refractive object and on a curved reflective surface. We compensate for the distortion by generating a light source image that cancels the distortions in the mid-air image caused by refraction and reflection. The light source image is generated via ray-tracing simulation by transmitting the desired view image to the mid-air imaging system, which includes distortive surfaces, and by receiving the transmitted image at the light source position. The proposed methods can be applied to dynamic images using a light source image as a UV map in texture mapping. Finally, we present the results of an evaluation of our method performed in an actual optical system using the generated light source image, which visually demonstrate the effectiveness of the proposed approach.
Towards VR Simulation-Based Training in Brain Death Determination

Patient protection and university hospital specialisations render a systematic training of physician-related skills increasingly difficult. Substitutes such as manikins or actor-based role-playing simulations are expensive and cannot be used for all clinical situations. As a consequence, teaching practical clinical skills has become one of the key concerns of modern medical education. Here we present a VR-based training prototype which allows medical students to perform brain death examinations in a virtual intensive care environment. Brain death diagnosis is a prime example for the challenges of medical education since it can neither be trained in simulations nor involve real patients, so that our simulation - for the first time - enables practical training of this legally obligatory and medically crucial diagnostic procedure. In particular, we motivate the use-case and teaching goals and describe the implementation of our prototype which is evaluated by medical experts to ensure medico-didactic standards.
3DColAR: Exploring 3D Color Selection and Surface Painting for Head Worn Augmented Reality using Hand Gestures

Color selection and surface painting has been largely unexplored in head-worn Augmented Reality (AR) using hand gestures. In this demonstration we present 3DColAR: A system that implements several 2D and 3D techniques for color selection. We also implement two key approaches for painting a virtual 3D model using mid-air hand gestures. This includes a virtual pen which the user can grasp using their hand, akin to a real pen and the use of the user's fingertip directly on the virtual 3D model. We hope to explore how these various techniques effect user's efficiency and accuracy when performing surface painting of virtual objects using mid-air hand gestures via. several user studies.
Evaluating the Impact of Limited Physical Space on the Navigation Performance of Two Locomotion Methods in Immersive Virtual Environments

Consumer level virtual experiences almost always occur when physical space is limited, either by the constraints of an indoor space or of a tracked area. This observation coupled with the need for movement through large virtual spaces has resulted in a proliferation of research into locomotion interfaces that decouples movement through the virtual environment from movement in the real world. While many locomotion interfaces support movement of some kind in the real world, some do not. This paper examines the effect of the amount of physical space used in the real world on one popular locomotion interface, resetting, when compared to a locomotion interface that requires minimal physical space, walking in place. The metric used to compare the two locomotion interfaces was navigation performance, specifically, the acquisition of survey knowledge. We find that, while there are trade-offs between the two methods, walking in place is preferable in small spaces.
Priority-Dependent Display of Notifications in the Peripheral Field of View of Smart Glasses

We propose a concept for displaying notifications in the peripheral field of view of smart glasses aiming to achieve a balance between perception and distraction depending on the priority of the notification. We designed three different visualizations for notifications of low, medium, and high priority. To evaluate this concept, we conducted a study with 24 participants who reacted to the notifications while performing a primary task. Reaction times for the low-priority notification were significantly higher. The medium- and high-priority notifications did not show a clear difference.
Implications to the tourism of Zanzibar Stone Town , UNESCO World Heritage Site

This paper examines the impact of the closed House of Wonders Museum in the tourism industry of Stone Town, Zanzibar. The paper aims to propose the best practices taken into account due to the impact raised by the closure of the Museum. There has been no clear information on the overall situation which faces the site since 2012 when the Museum closed. Data collected through mixed methods, including the sample size of 105 tourists who visited the House of Wonders Museum, 8 Government Official, 6 Tour Guides and 8 Tour Operators. The findings show that the closed museum has affected the level of tourists’ satisfaction, tour operators, community and tour guides economically. The paper recommends quick rehabilitation and reconstruction of the Museum to save the integrity and authenticity of this World Heritage Site.
Two-Stage Cross-Modality Transfer Learning Method for Military-Civilian SAR Ship Recognition

Military-civilian attribute recognition of ships in synthetic aperture radar (SAR) imagery plays an important role in marine surveillance. However, high-quality labeled data are hard to obtain for SAR ships, which hinder the development of deep learning models. Considering that models directly transferred from labeled optical images cannot achieve satisfactory performance for SAR applications due to the great discrepancy of different modalities, we propose a two-stage transfer learning method by combining the data-level and feature-level knowledge transfer. First, CycleGAN is adopted in the first stage to transfer the labeled optical image domain to the intermediate SAR-like image domain with the attribute labels. Then, a novel network called Domain Transfer using Adversarial learning and Metric learning (DTAM) is proposed to realize the task of military-civilian ship recognition by the domain adaption of the intermediate domain and the target SAR domain with joint adversarial learning and metric learning. To validate the proposed method, we establish a high-resolution SAR ship recognition dataset (HRSSRD), containing SAR and optical images of military and civilian ships. The experimental results show that the proposed two-stage architecture exhibits promising performance on the problem of SAR military-civilian ship recognition.
Ultrasound sensing and hand gesture recognition for dexterous prosthetic devices

Traditional myoelectricity-based systems have inherent limitations and weaknesses to control dexterous prosthesis. The gap lies not in the methodologies that already extensively researched, but the means of signal extraction from the forearm. This thesis has explored and successfully tackled the practical control problems of dexterous prostheses using Sonomyography (SMG). Two hypotheses were made and supported by relevant literature to highlight the type of contraction that relevant to the amputees. The relevance of Fatigue-less Maximum Isotonic Contraction (FLMIC) phase is highlighted in SMG with experimental data. A novel wearable and portable SMG capturing system is presented with performances on a par with myoelectric methods. The Quasi-radial construction of the ultrasound transducer array allows reading A-mode signals from both anterior and posterior compartments of the forearm. The arrays comprised of transducers which are purpose designed to meet the requirements. The experiments with amputee and healthy subjects revealed that comparable gesture recognition accuracies can be achieved. Proportional control of prosthesis is a noted problem. Since the majority of morphological changes occur in isotonic or dynamic region, the tension produced by the muscle is low. Contrary to sEMG, where it requires a significant motor unit activation, a low level muscle activation is enough to provide proportional control using sonomyography. This phenomenon is investigated in this thesis. The cross correlation method has been employed to recognize gestures between test and training sets. The experimental results demonstrated the ability to utilize the system in underwater without significantly compromising the performance. The experiment also demonstrated that the effectiveness of oil based coupling medium in such conditions. In this study a novel wearable ultrasound hardware is presented with evidence to prove its comparable performances with myoelectric systems.
Role of Dynamic Affordance and Cognitive Load in the Design of Extended Reality based Simulation Environments for Surgical Contexts

In this paper, HCI-based design criteria for the Extended Reality (XR) based training environments. XR is an umbrella term used to describe Virtual Reality (VR), Augmented Reality (AR) and Mixed Reality (MR) training environments are presented. The design criteria explored in the paper help lay a foundation for the creation of Human Centric XR environments to train users in an orthopedic surgical procedure. The HCI-based perspective presented in the paper investigates the criteria such as affordance and cognitive load during the design. The paper focuses on the design of XR based environments based on the participatory design approach and information-centric modeling. Further, the paper delineates the implementation of the XR based environments and a set of attributes that guide and influence the content of the environments. Testing and assessment strategy has also been described integrating the design thinking to the assessment and providing insights into the impact of such HCI-based criteria on participants' acquisition of skills and knowledge during interactions with the XR environments.