Reviewerly/exHarmony huggingface dataset:
Authoring Next-Generation XR Storytelling Experiences using Real-World Scene Data - From Everyday Environments to the Ocean

course Share on Authoring Next-Generation XR Storytelling Experiences using Real-World Scene Data - From Everyday Environments to the Ocean Authors: Lap-Fai (Craig) Yu GMU GMUView Profile , Sai-Kit Yeung HKUST HKUSTView Profile Authors Info & Claims SA '23: SIGGRAPH Asia 2023 CoursesDecember 2023Article No.: 3Pages 1–39https://doi.org/10.1145/3610538.3614623Published:06 December 2023Publication History 0citation59DownloadsMetricsTotal Citations0Total Downloads59Last 12 Months59Last 6 weeks21 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 SiteGet Access
Nonorthogonal coding in spectrally-entangled photons

Abstract Controlling and engineering continuous spectral modes of entangled photons represents one of the promising approaches toward secure quantum communications. By using the telecom bandwidth generated from a cascade-emitted biphoton in atomic ensembles, a fiber-based long-distance quantum communication can be feasible owing to its low transmission loss. With multiplexed photon pairs, we propose to implement a nonorthogonal coding scheme in their spectral modes and present an architecture of multiple channels enabling a high-capacity transfer of codewords. Using the measures of the second-order correlations and associated visibility and contrast, we further quantify the performance of the proposed nonorthogonal coding scheme. Our results demonstrate the capability to encode and decode quantum information beyond the orthogonal coding scheme, and in particular, the multi-channel setup manifests a resilience and an advantage in a design with multiple channel errors. The proposed scheme here can be applicable to a large-scale and multiuser quantum communication and pave the way toward an efficient and functional quantum information processing.
ASSESSMENT OF RAINFALL DISTRIBUTION AND IMPLICATIONS FOR RICE PRODUCTION: EFFECT OF CLIMATE CHANGE

This study examined the pattern and distribution of rainfall in the year, 2021 in Ikwuano local Government Area, of Abia State, Nigeria. Data were from secondary sources and analyzed with descriptive statistics. Results showed that the first four (4) months of the year recorded very poor amounts of rainfall. The highest peak of rainfall was recorded in the month of August. The result also showed absence of “August break”. There was absence of double maxima in the months of July and August and heavy downpours were experienced between the months of July and September. Recommendations, among others, include that farmers should shift the planting period of some crops, crops that do not require much water to thrive well should not be cultivated between the months of July and September and farmers should cultivate “edible cover crops as must crops.”
Entanglement dynamics in U(1) symmetric hybrid quantum automaton circuits

We study the entanglement dynamics of quantum automaton (QA) circuits in the presence of U(1) symmetry. We find that the second Rényi entropy grows diffusively with a logarithmic correction as <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msqrt><mml:mi>t</mml:mi><mml:mi>ln</mml:mi><mml:mo>&amp;#x2061;</mml:mo><mml:mrow class="MJX-TeXAtom-ORD"><mml:mi>t</mml:mi></mml:mrow></mml:msqrt></mml:math>, saturating the bound established by Huang \cite{Huang_2020}. Thanks to the special feature of QA circuits, we understand the entanglement dynamics in terms of a classical bit string model. Specifically, we argue that the diffusive dynamics stems from the rare slow modes containing extensively long domains of spin 0s or 1s. Additionally, we investigate the entanglement dynamics of monitored QA circuits by introducing a composite measurement that preserves both the U(1) symmetry and properties of QA circuits. We find that as the measurement rate increases, there is a transition from a volume-law phase where the second Rényi entropy persists the diffusive growth (up to a logarithmic correction) to a critical phase where it grows logarithmically in time. This interesting phenomenon distinguishes QA circuits from non-automaton circuits such as U(1)-symmetric Haar random circuits, where a volume-law to an area-law phase transition exists, and any non-zero rate of projective measurements in the volume-law phase leads to a ballistic growth of the Rényi entropy.
Restricted data management: the current practice and the future

Many restricted data managing organizations across the world have adapted the Five Safes framework (safe data, safe projects, safe people, safe setting, safe output) for their management of restricted and confidential data. While the Five Safes have been well integrated throughout the data life cycle, organizations observe several unintended challenges regarding data being FAIR (Findable, Accessible, Interoperable, Reusable). In the current study, we review the current practice on the restricted data management and discuss challenges and future directions, especially focusing on data use agreements, disclosure risks review, and training. In the future, restricted data managing organizations may need to proactively take into consideration reducing inequalities in access to scientific development, preventing unethical use of data in their management of restricted and confidential data, and managing various types of data.
AQUASENSE: Sensor Based Water Quality Monitoring Device

Water is an essential part of living organisms. The Industrialization has caused immense contamination of potable water. Hence water quality monitors play an important role in detection of contamination in these water bodies. There are many water quality monitors available in the market for the detection of level of contamination. This research study discusses about the effective usage of Arduino UNO board, sensors to measure temperature, TDS and pH to measure the water quality. Hence, the main aim of this research study is to find the TDS, temperature and pH of the water samples collected from various places in Bangalore City, Karnataka, and conclude regarding the quality of the water whether it is portable to use for drinking and domestic purpose.
Enhancing Photovoltaic Power Generation through a Microcontroller-Driven Single-Axis Solar Tracker

Aims: The principal aim of this study is to make an automatic single-axis solar panel tracking system according to the sun's movement. The purpose of this effort is to design an efficient microcontroller-based solar panel follower system to follow the trajectory of the sun. Study Design: This research initiative aims to design, simulate, and implement an automatic single-axis solar panel tracking system using Arduino Uno microcontroller and light sensors and thus to ensure that the environment is clean and safe to combat the climate change effect. This is especially significant in locations where the amount of sunshine varies during the day. Place and Duration of Study: Department of Electrical and Electronic Engineering, American International University-Bangladesh (AIUB), Dhaka, Bangladesh between January 2023 and July 2023. Methodology: In this research effort, we used an Arduino Uno microcontroller, servo motor, LDR sensor, LEDs, solar photovoltaic panel, etc. to make the proposed system. Arduino IDE was used for the program development. The microcontroller controls a servo motor to drive the solar panel from east to west to follow the sun’s path in the same direction. Results: The simulation results and hardware output results confirm its functionality. Conclusion: This design has the potential to drastically cut energy costs and increase the use of renewable energy sources by augmenting the efficacy of photovoltaic systems through the use of a microcontroller-driven single-axis solar tracker, thus contributing to a more sustainable future. This can be scaled up in the future using more sensors and axis.
Does financial sector development have a sustainable level of effects on inclusive growth in Sub-Saharan African countries? Evidence from dynamic panel threshold analysis

This study examines the threshold level of financial sector development on inclusive growth in sub-Saharan Africa (SSA) between 2000 and 2020 with the view to ascertain the level of sustainability in the relationship between financial sector development and inclusive growth in SSA countries. Data extracted from the World Development Indicator (WDI) and International Financial Statistics (IFS) were analysed using dynamic panel threshold (DPT) techniques. Findings revealed that there exists a positive and significant sustainable level of financial sector development of 0.098 (out of a scale of 0-1.0) that can stimulate inclusive growth in SSA. Likewise, findings at the sub-regional level showed that three out of four (Western, Eastern, and Central African sub-region) except the Southern African region, indicated the existence of a threshold level of financial sector development that can effectively impact inclusive growth. Therefore, this study concludes that the relationship between inclusive growth and financial sector development in most SSA countries is nonlinear and conditional on certain macroeconomic policies. It is recommended that policymakers in respective SSA sub-regions and countries implement policies that will promote relevant financial innovations, reforms, and efficiency in financial inclusion in other to promote financial development above the minimum threshold level in SSA countries.
IDENTITY THEFT DETECTION IN CREDIT CARD APPLICATION FORMS USING MULTILAYER ALGORITHMS BASED ON DATA MINING TECHNIQUES

Owing to the volatile nature of the market, the slowdown in economic growth, and the rapid upsurge of digital e-commerce, the issue of fraud has become increasingly prevalent. The continuous evolution of electronic commerce technology has led to a significant increase in credit card usage, establishing it as the favored method of payment for both online and offline transactions. Consequently, the rise in credit card fraud has become apparent, affecting individuals seeking smart cards and loans, who now have the choice of applying for credit cards through online channels or traditional paper forms. Unfortunately, these application processes have exposed instances of fraudulent activities, notably identity theft, which poses a significant concern for both credit cardholders and financial institutions. Dishonest actors are unlawfully obtaining customer’s identities and gaining unauthorized access to credit cards, thereby posing substantial risks for both customers and financial entities.However, the existing strategies that rely on business rules and scorecards for fraud detection, without utilizing data mining, have shown limitations. In response to these shortcomings, this study introduces an innovative method for real-time fraud detection during the application phase. This method involves implementing a novel multi-layer fraud tracking system based on data mining algorithms. This system comprises two distinct algorithms, namely communal tracing and spike tracing, working together to improve the accuracy, speed, and efficiency of fraud detection procedures. By validating applications in real-time upon submission, this system acts as a robust deterrent against the approval of fraudulent credit card applications before issuance.
Algorithmization of Automatic Parking Control of Self-Driving Car

Currently, the development of a self-driving car (SDC) is becoming increasingly popular, the full autonomy of which is achieved by automatic control of all its driving modes and maneuvers, including parking — the most common maneuver. The problem of parking automation is of particular relevance, as far as it allows not only to facilitate the process of safe parking, but also to increase the density of parked cars. The paper considers the control problem of automatic parking of SDC. The statement and formalization of the control problem of car parking taking into account the mechanical and spatial constraints ensuring the safety of the parking maneuver are given. Both classical and modern control methods of automatic car parking are considered. The classical control method of SDC parking is based on the utilization of widely used Dubins and Reeds-Shepp traffic models ensuring fast acting optimal car parking. At the same time, the algorithm of a fast-growing random tree RRT was used to construct a path between two points. Due to randomization, an important advantage of this algorithm is its independence from the geometric representation and dimension of the modeled environment of the car. The modern control methods of SDC parking are based on the use of intelligent methods and technologies. In present paper in contrast to the classical, "untrained" methods, the control method of parking based on machine learning is used. The problem of synthesis of control algorithm of SDC parking based on the machine learning method with reinforcement is posed and solved. A car parking algorithm implemented in Python using mathematical libraries Matplotlib and NumPy is synthesized. Computer verification of the synthesized algorithm was carried out and optimal values of machine learning parameters were determined.
Preventing Crime and Terrorist Activities with a New Anomaly Detection Approach Based on Outfit

Video surveillance systems play an important role in ensuring security indoors and outdoors and detecting suspicious persons due to the increasing violence and terrorist acts every year. In the proposed study, an artificial intelligence-based warning system has been developed, which enables the detection of potential suspects who may carry out criminal or terrorist activities by detecting anomalies in surveillance videos. In this developed system, an abnormality is detected by using the outfits of the people. The YOLOv7 object detection model is trained on our customized data sets, and suspicious person detection is made through outfit information. Especially in cases where biometric data is hidden, dress information makes it easier to obtain information about people. For this reason, the knowledge of outfits is the main point of this study in the detection of suspicious persons. Thanks to this study, security guards will be able to focus on this suspicious person before they pre-empt any crime or terrorist activity. If there are other data confirming the suspicious situation as a result of this follow-up; security personnel will have time to eliminate the crime or attack. The experimental results obtained have been promising in terms of the usability of a person's outfit anomalies to ensure public confidence or avoid risk to human life. Although there are various studies in the literature for the prevention of terrorist or criminal activities; there is no study in which people's outfit is used to identify suspects.
The Research on the Influence of Bilibili Videos upon the Receptions Towards Universities among Chinese High School Students

Bilibili is an international company which was set up in 2008. This company started out as a dedicated anime website. This paper will investigate the impact of Bilibili on high school students acceptance of universities. According to the papers we have collected, there have been many existing studies and papers on Bilibili, however, there are still some exist research gaps. All those papers above dont mention the students. With the increasing number of Bilibili School graduates, it shows a gratifying phenomenon to the society. Students are now overcoming many difficulties, with a positive learning attitude, to achieve the goal of the new generation of continuous progress. However, it also reveals that the gears of schooling are still stumbling. The point of going to college when so many college courses are available online. There are no papers on the effect of Bilibili on senior high school students. So this paper is very necessary in China. This was research was carried out online. There are then questions in the research. Each question is given five different degrees of choice.
A METHOD OF REORDERING LOSSLESS COMPRESSION OF HYPERSPECTRAL IMAGES

Abstract. An improved lossless compression method with adaptive band reordering and minimum mean square error prediction was proposed to address the problems of huge data volume of remote sensing images, great pressure on transmission and storage and a low compression ratio. This method may determine the optimal band ordering adaptively, and make full use of the ordering correlation to eliminate the image redundancy according to the minimum mean square error criterion. First, it adaptively grouped hyperspectral image bands, and used the minimum spanning tree algorithm for band ordering within each group to enhance the inter-spectral correlation of adjacent bands. Later, it selected the contexts for inter- and intra-spectral prediction adaptively for the bands within the group to remove the redundancy of hyperspectral images. Finally, it conducted binary arithmetic coding of the predicted residuals to remove the statistical redundancy, and complete the lossless compression of hyperspectral images. The test results based on the hyperspectral images of ZY1-02D show that the method in this paper effectively utilizes the intra- and inter-spectral correlations, improves the prediction performance, and outperforms the commonly used compression methods.
Writing Skills of Pre-Service Teachers: Basis for a Proposed Intervention Program

Writing is considered as the most difficult skill for learners because they need to have a certain amount of L2 background knowledge about their rhetorical organizations, appropriate language use or specific lexicon with which they want to communicate to their readers. To assist instructors to improve writing competency and produce good writing, this study aimed to determine the writing skills of 40 pre-service teachers of the University of Saint Louis-Tuguegarao along content, grammar, organization, and mechanics as basis on the proposed intervention program. Specifically, it aimed to give an analysis on the writing skills and errors committed along the different components of writing. This qualitative study employed content analysis. Data were analyzed to determine the level of competencies and errors in essay writing. The level of achievements was categorized into exceptional, good, below average, and poor. The results of the study revealed that majority of the pre-service teachers have good competency along content, organization, and mechanics. Considering this, it is necessary to determine the writing skills to address the existing problem of pre-service teachers along the different components of writing.
Digital metamorphosis: Unraveling the evolution of global language in the technological culture

Modern technology and culture have profoundly changed global language perspectives, as reflected in traditional norms of interaction and communication. This paper studied the recent updates and changes that happened due to the widespread of modern technology, focusing on the influence of digital tools, artificial intelligence, social media, and other new media platforms. Quantitative and qualitative data collection methods were used in this research to bring the impact of modern technology on new language development, language diversity, and communication designs. The data for this research was collected from those Indians who migrated to Western countries for higher studies and better job opportunities, as well as from their friends and family members. They regularly connect to friends and family members over digital platforms, which may result in the evolution of their traditional language changes. The findings of this research highlight the dynamic relationship between digital interactions, current language, and digital culture. Today, communication and interaction do not have any barriers because of the evolution of global language into a digital metamorphosis. More than syntax symbols and modern nuances communicate in digital platforms. The change in language leads to cultural changes, and it is an important area to study and evaluate the modern changes in our communication medium. This research also provides valuable information in the field of language and technology and paves the way for further research in this rapidly developing field.
Towards an Open-source Digital Investigation Platform

Humans generate lots of data in the cyberworlds by their behaviors on social networks, interactions with computers or smartphones (writing documents, capturing images,…). Analyzing digital traces from Internet, hard drives or computers is an important issue considering the amount of data to process and the associated applications (criminal investigation, recruitment,…). In this paper, we propose an open-source software platform for such a task. It embeds many high-level evaluated tools. These tools rely on recent advances in artificial intelligence and deep learning. Our goal is to facilitate digital investigations conducted by criminal experts, researchers in digital archives, or IT engineers. This platform embeds many tools designed to process a document in order to extract some knowledge (as for example, determining if the filetype has been corrupted). We illustrate its benefit through the analysis of a real memory dump from a hard drive.
UAV Coverage Path Planning Based on Deep Reinforcement Learning

This paper proposes a approach to the problem of low coverage rate in the area coverage path planning of unmanned aerial vehicle (UAV) under the traditional Double Deep Q Network (DDQN). Our approach involves introducing noise parameters into the DDQN and using exploratory noise for deep exploration, which improves the efficiency of exploration. Compared to the traditional DDQN algorithm, our improved method enhances the exploration ability and convergence speed of the trained network, ultimately achieving a higher level of periodic reward. This enables the UAV to better avoid obstacles or no-fly zones in coverage path planning, avoiding it from being stuck or repeating paths in local environments, thereby enabling the UAV to cover more target areas in a shorter number of steps.
Quantum Path Integral Approach for Vehicle Routing Optimization With Limited Qubit

The Vehicle Routing Problem (VRP) is a crucial combinatorial optimization problem in transportation, logistics, and supply chain management. Quantum computing has gained interest in solving complex optimization issues. This review paper proposes a novel approach that combines quantum computing principles with the Feynman path integral formulation to address the VRP. The paper focuses on limited qubits and how quantum algorithms can circumvent this limitation. Conventional methods, such as Lagrangian and combinatorial optimization, require computational time to solve complex atomic-scale problems. The path integral formulation with the first Born approximation method offers a more elegant explanation of quantum computing. The algorithm investigates quantum system dynamics using the Schrodinger wave equation and computes probability amplitude distributions for optimal vehicle routing techniques. This work will take advantage of entanglement and interference effects for heuristic sampling of high-quality solutions, making the Quantum Path Integral Approach (QPIA) a promising study environment for investigating quantum circuits and algorithms in routing problems with constrained qubits.