Reviewerly/exHarmony huggingface dataset:
Towards Efficient Distributed Collision Avoidance for Heterogeneous Mobile Robots

We study the problem of distributed collision avoidance for mobile robotic systems, where a group of heterogeneous robots with different sizes and motion constraints avoid collisions with each other and static obstacles during the movements from their starting to goal locations. Existing methods mainly consider homogeneous robots and incur a high collision rate in environments with moving robots and static objects. Hence, we propose a distributed <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u> ollision <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> voidance for <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">heter</u> ogeneous mobile robots ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heter-CA</i> ), which allows each robot to independently avoid collisions considering the heterogeneity of robots and varying static obstacles. In <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heter-CA</i> , each robot predicts the trajectories of neighboring robots and estimates the varying size of static obstacles with the robots' range-finder sensors before motion planning, which enables each robot to avoid obstacles safely. Besides, we prove that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heter-CA</i> can guarantee collision-free movement between heterogeneous robots by satisfying sufficient conditions. We evaluate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heter-CA</i> in numerous simulated and real-world scenarios in which groups of heterogeneous robots perform navigation tasks. The experimental results demonstrate that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Heter-CA</i> takes <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$10\times$</tex-math></inline-formula> less computation time and achieves <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$5\%$</tex-math></inline-formula> less collision rate than baseline algorithms.
Studi Kandungan Asam Pada Air Hujan di Kota Pontianak

Study of Acid Rain in Rainwater in Pontianak City.Rain is naturally acidic, the more pollutant concentrations in the air might increase the value of acidity.Transportation activities play a major role in reducing air quality.Based on Central agency of Statistics (2021), the area of Pontianak city is 118.31 km 2 with a population of 658,685 people..The population growth rate in 2020 is 1.81%.The number of vehicles in the city of Pontianak in 2020 reached 31,853 units.The large number of residents in the city of Pontianak causes the need for transportation facilities to increase so that it may increase fuel consumption causing air pollution.SO 2 and NO 2 gas emissions originated from industrial and transportation activities are the cause of acid rain events when these gases emissions react with rainwater, and then acidic compounds increased.Naturally, the degree of acidity (pH) of normal rainwater is 5.6.Rainwater is a source of clean water in Pontianak so its quality should be monitored.This study aims to measure pH, nitrate (NO 3 -) and sulfate (SO 4 2-) in rainwater in Pontianak city with 24 samples in April and May 2022 and the relationship among pH and nitrate and sulfate acidity levels were analyzed using multiple linear regression equation.The measurement results showed that the average pH value is 6 with 15 samples did not meet the quality standards and 9 samples met the quality standards, while the results of all nitrate and sulfate samples met the quality standards of the Republic of
A Comparative Study of Deep Learning Models for Natural Language Processing (NLP)

Natural language processing (NLP) has become an indispensable tool across many disciplines, and deep learning models have shown promising early results in improving the accuracy and efficiency of NLP-related tasks.In order to get valuable insights into the strengths and weaknesses of different models and approaches, and to help determine which models are the most successful for fulfilling particular NLP tasks, a comparative study of deep learning models for NLP is invaluable.Several deep learning models for NLP tasks including sentiment analysis, named entity recognition, and machine translation are compared and contrasted in this article's literature review.This research takes a look at popular benchmarks and data sets for evaluating deep learning models for NLP comparisons.(NLP).The strengths and weaknesses of various models and approaches are also highlighted throughout the examination.In addition to a discussion of recent advancements in the field such pretrained language models and attention processes, the article also details the many challenges and limitations of comparing deep learning models for NLP and how they stack up against one another.(NLP).The report concludes with a discussion of directions in which further study of the topic may go.There is a need to construct more interpretable and multilingual deep learning models, and there is also a need to explore cross-modal learning and domain-specific models.When taken as a whole, a research comparing different deep learning models for NLP might have far-reaching effects on the creation of new NLP applications and the enhancement of current ones.This is due to its capacity to aid in the creation of more precise and efficient models for natural language processing and to provide light on the relative merits of existing approaches.
Restaurant recommendation based on heterogeneous attention model

Restaurant recommendation systems are able to alleviate the information overload problem substantially in the catering industry and facilitate customers to make decisions. Building an effective restaurant recommendation system can bring considerable profits and improve user experience. In this paper, we propose a neural restaurant recommendation model with heterogeneous attention (Restaurant Heterogeneous Attention Model). We use heterogeneous features that users may use as references to construct both restaurant representation and user representation of our model. In our model, the main approach to obtain restaurant and user representation is a restaurant encoder and a user encoder. In the restaurant encoder, we propose a heterogeneous attention model to learn unified restaurant representation. We consider the restaurant name, longitude, latitude, and categories as the encoder input and use both word-level attention and feature-level attention to assist the model to attend to significant words and features. In the user encoder, we learn the user representation based on the restaurants that the user has been to. Also, the different restaurants that the user has been to will provide different information for user encoder learning. In order to find which restaurant is more informative for user representation learning, we also apply an attention mechanism to build our user encoder. After conducting many experiments with a dataset from Yelp, the result shows our model can greatly improve the performance of restaurant recommendations.
Design and research of Chinese word segmentation method in architecture field

At present, one of the problems of Chinese word segmentation is the low efficiency of Out-Of-Vocabulary (OOV) detection in the field of expertise. Due to restrictions on the characteristics of the words of the profession itself, the word segmentation of architectural texts is not very effective in identifying OOV. This paper proposes a new method to recognize OOV, which is an unsupervised method based on improved algorithm and entropy. This paper uses algorithms to identify strings with relatively large interdependencies between texts, filters through the stop-words vocabulary and corpus to obtain candidate dictionaries, calculates the entropy between candidate dictionaries, and determine the final OOV by setting an accurate threshold, Add the recognized OOV as a professional dictionary for word segmentation. Experiments show that by using the algorithm proposed in this paper, the recognition effect of OOV in architectural text has been significantly improved. Compared with the algorithm, P ( precision ) increased by 15.92 %, R ( recall ) increased by 7.61 %. Therefore, the final word segmentation precision can reach 82.15% and recall can reach 80.45%.
Competitive deep reinforcement learning for robot control

This paper presents a competitive deep reinforcement learning (DRL) structure for the robot control, the basic method of which is to make the two actors learn from each other with competition. And the improvement of training efficiency and learning ability of the competitive DRL model is verified by the experiments under the Gym environments. For the task of robot formation, we construct a cooperation mechanism exerting robot to take the optimal action due to the state and the next reward of other robots within a certain radius around based on the Kuhn-Munkres algorithm, to avoid collisions and blocking among robots. Lastly, we conduct the simulations with the above algorithms, and the results illustrate that the trained robots are capable of self-navigation, obstacle avoidance and formation without collisions.
A video anomaly detection method with mask convolution and channel attention

Aiming at the problem of Insufficient feature extraction for data sets in video anomaly detection, an enhanced detection method combining masked convolution and channel attention is proposed. The auto-encoder structure is adapted, and a mask convolution module and a channel attention module are embedded in the memory model. The effectiveness of the proposed method is demonstrated by comparing the results of the method and the original method.
Understanding students’ continued use of electronic medical records in hospital: task technology to performance chain approach

One of the main goals of research on information systems is to help end users and organizations use information technology effectively. Fieldwork practice trains students to apply their knowledge and work skills based on the standards of the health ministry in electronic medical record regulations. This activity also prepares students to live in their health information management profession. This study examined the willingness and ability to use electronic medical records in hospitals to determine how well students understand electronic medical records and what influences their use. This study was conducted at a hospital in Mojokerto, East Java, where students practice fieldwork. An institutional-based crosssectional study was conducted to assess the acceptability of the EMR system among students at Mojokerto Hospital from July 1-31st, 2022. The sampling method used simple random sampling of 136. A structured questionnaire was adopted from the previous studies. The questionnaire consists of 16 questions from the TTF, the expected consequences of use (COU), Facilitating Condition (FC), utilization (UI), and Performance Impact (PI) constructs. Data were analyzed using SmartPLS version 3.0. The result is that Task-technology fit is associated with the expected consequences of use (P=0.00), Consequences of Use (COU) are associated with Utilization (P=0.000), and the Facilitating condition is related to Utilization (P=0.00). We can conclude that task technology fit is indirectly associated with Utilization because of the Consequences of Use (COU). Task-technology fit, and Utilization does not affect the performance impact. This study can continue by testing the construct variables by the students on the field trips and by healthcare providers such as nurses and doctors.
Distributed Database Systems for Large-Scale Data Management

Distributed database systems are essential to modern computing.Such infrastructures offer scalability, speed, fault tolerance, and security, enabling applications to analyse massive data volumes in real time.Some of the difficulties in designing and maintaining a distributed database system include data consistency, performance, security, scalability, complexity, and cost.A distributed database system must be carefully planned, constructed, and managed in order to address these problems.It also has to be constantly monitored and maintained in order to provide the highest levels of performance and security.Distributed databases provide a wide range of applications in these fields and more, despite the difficulties posed by sectors like e-commerce, social networking, healthcare, the Internet of Things (IoT), online gaming, financial services, and logistics.Distributed database systems will become more and more crucial in allowing real-time data processing and analytics as well as aiding the creation of new applications and services as the volume and complexity of data keep rising.In today's computer applications, the benefits of using a distributed database system for managing and analysing massive amounts of data far outweigh the drawbacks.
A distributed application high availability implementation

Application high availability (HA) has always been the goal pursued by various core businesses. At the practical level, multiple business systems are usually deployed based on two or more physically separated data centers, which are connected by BGP or dedicated lines to ensure data transmission efficiency and latency; and specifically for each data center, applications deployed through the industry's leading cloud-native technology development can provide a certain degree of single-cluster HA. This paper does not involve physical topology, like link layer and other hardware facility level HA implementation scheme, but mainly focuses on the software level HA, under the premise of hardware level HA, in order to achieve the HA effect of minute level RTO and second level RPO. The most important thing is to solve the synchronization efficiency problem. In this paper, we design a data synchronization algorithm with dependencies, and elaborate how to combine the algorithm to achieve automatic and efficient synchronization of workloads of distributed applications developed in cloud-native way and data of stateful applications in cloud-native scenario, and at the same time, it is convenient to perform switchover rehearsal between main and standby clusters in normal times, and when failure actually occurs. It can quickly switch the flow between clusters to achieve distributed applications when a failure actually occurs, effectively solving the problem of achieving HA of cloud-native applications across multiple data center clusters.
Fault-Tolerance and Reliability for Dependable Systems

In this analysis, we look into the factors that contribute to fault-tolerance and dependability in operational systems.Visual effects production and post-production processes rely significantly on technology and automation, making reliable systems essential to the operation of the industry.In order to improve system performance and dependability, this research examines the existing literature on fault-tolerance and reliability for dependable systems and identifies numerous strategies, techniques, and approaches.In order to guarantee the dependability of complex systems, the study concludes that fault-tolerant control systems, redundancy-based approaches, adaptive control methods, consensus algorithms, and failure detection algorithms are essential.Model-based approaches, fault-tree analysis, and Markov models are all highlighted as crucial to the examination of system dependability in this paper.The findings of this study have considerable consequences for the domains of visual effects and related disciplines, as they provide valuable insights and recommendations for enhancing the dependability and performance of such systems.The study concludes with several recommendations for further study, including the creation of new faulttolerant algorithms, the examination of system reliability in a variety of contexts, and the incorporation of machine learning and AI into such systems.
Consensus Algorithms and Distributed Ledger Technology for Decentralized Systems

Blockchain is an example of a decentralized system that relies heavily on two key technologies: consensus algorithms and distributed ledger technology.These technologies make it possible to conduct trustless and secure transactions without the requirement for a central authority to oversee them.In this article, we will discuss the history of decentralized systems, the function of consensus algorithms, as well as the positives and negatives associated with distributed ledger technology.In addition, we conduct an analysis of a selection of consensus algorithms and give case studies of distributed systems that make use of a variety of consensus algorithms.The purpose of this work is to contribute to the current body of research by providing a comprehensive review of consensus algorithms, distributed ledger technology, and the practical applications of these topics.In conclusion, we will examine the ramifications of this fast-advancing topic as well as the potential it presents for future research.
Blockchain-Based Distributed Systems for Trust and Transparency

The technology of distributed ledgers has the potential to transform a wide variety of different sectors by incorporating trust and transparency into the procedures of conducting transactions and exchanging data.In order to study the current status of research on blockchainbased distributed systems for trust and transparency, the objective of this literature review is to undertake a methodical analysis of research publications that have been published during the last five years.These articles were published in the preceding five years.This analysis is being conducted with the goal of determining the opportunities and challenges that may arise from the use of blockchain technology in a variety of sectors, including the healthcare industry, the management of supply chains, the provision of government services, and the financial sector.The publications that were reviewed provide evidence of both the breadth and complexity of the newly emerging field of study known as blockchain technology, as well as the ways in which blockchain-based distributed systems have the potential to improve features such as openness, accountability, and productivity in a range of settings.However, the papers also highlight the challenges associated with scalability, interoperability, regulation, and ethical considerations, as well as the necessity for additional research and engagement with stakeholder groups.This literature study, taken as a whole, offers helpful insights on the potential for blockchain technology to meet the growing need for accountability, traceability, and social responsibility in a range of settings.This finding also underscores the necessity of cross-disciplinary collaboration and the active participation of stakeholders in order to fully exploit the possibilities offered by blockchain technology.
Interactive Graphics and Gaming for Immersive Experiences

The combination of graphical user interfaces and video games has grown in importance as a means of providing truly immersive experiences for consumers.In order to provide consumers with a more interesting and participatory experience, developers have begun integrating hardware and software components to create immersive technologies and solutions.The article gives a brief history of interactive graphics and gaming, as well as a rundown of the necessary hardware and software for making immersive experiences.The benefits and drawbacks of these technology and tools are also covered, including issues like accessibility, ethics, and privacy.Finally, the ramifications of these technologies for culture and society, as well as the future of interactive graphics and games for immersive experiences, are discussed.We can develop immersive experiences that have a positive effect on society and culture if we challenge the status quo and find solutions to the problems that occur along the road.
Physically-Based Simulation for Fluid and Solid Dynamics

The potential for physical-based fluid and solid-dynamics simulation to transform many branches of engineering and science makes this an important field of study.In this study, we survey the state-of-the-art of physically-based simulation for fluid and solid dynamics, discussing the many methods, tools, and approaches currently in use.We also offer a methodology for simulating the behavior of fluids and solids that combines a particle-based approach with a continuum-based approach.The simulation framework, particle-and continuum-based simulation, and their coupling are all parts of our proposed technique.Future directions for research are also discussed, such as machine learning, multiscale modeling, real-time simulations, coupled simulations, validation and verification techniques, and computational resources for physically-based simulations.The discipline of physically-based modeling for fluid and solid dynamics is expanding rapidly, and there are many areas where new discoveries could be made.Many branches of engineering and research stand to benefit greatly by tackling these problems and looking in new ways.
Approximate error correction scheme for three-dimensional surface codes based reinforcement learning

Quantum error correction technology is an important method to eliminate errors during the operation of quantum computers. In order to solve the problem of influence of errors on physical qubits, we propose an approximate error correction scheme that performs dimension mapping operations on surface codes. This error correction scheme utilizes the topological properties of error correction codes to map the surface code dimension to three dimensions. Compared to previous error correction schemes, the present three-dimensional surface code exhibits good scalability due to its higher redundancy and more efficient error correction capabilities. By reducing the number of ancilla qubits required for error correction, this approach achieves savings in measurement space and reduces resource consumption costs. In order to improve the decoding efficiency and solve the problem of the correlation between the surface code stabilizer and the 3D space after dimension mapping, we employ a reinforcement learning (RL) decoder based on deep Q -learning, which enables faster identification of the optimal syndrome and achieves better thresholds through conditional optimization. Compared to the minimum weight perfect matching decoding, the threshold of the RL trained model reaches 0.78%, which is 56% higher and enables large-scale fault-tolerant quantum computation.