Reviewerly/exHarmony huggingface dataset:
ADS-B Reception Error Correction Based on the LSTM Neural-Network Model

Standard automatic dependent surveillance broadcast (ADS-B) reception algorithms offer considerable performance at high signal-to-noise ratios (SNRs).However, the performance of ADS-B algorithms in applications can be problematic at low SNRs and in high interference situations, as detecting and decoding techniques may not perform correctly in such circumstances.In addition, conventional error correction algorithms have limitations in their ability to correct errors in ADS-B messages, as the bit and confidence values may be declared inaccurately in the event of low SNRs and high interference.The principal goal of this paper is to deploy a Long Short-Term Memory (LSTM) recurrent neural network model for error correction in conjunction with a conventional algorithm.The data of various flights are collected and cleaned in an initial stage.The clean data is divided randomly into training and test sets.Next, the LSTM model is trained based on the training dataset, and then the model is evaluated based on the test dataset.The proposed model not only improves the ADS-B In packet error correction rate (PECR), but it also enhances the ADS-B In terms of sensitivity.The performance evaluation results reveal that the proposed scheme is achievable and efficient for the avionics industry.It is worth noting that the proposed algorithm is not dependent on conventional algorithms' prerequisites.
Distributed artificial intelligence: Taxonomy, review, framework, and reference architecture

Artificial intelligence (AI) research and market have grown rapidly in the last few years, and this trend is expected to continue with many potential advancements and innovations in this field. One of the emerging AI research directions is Distributed Artificial Intelligence (DAI). It has been motivated by technological advances in communication, networking, and hardware, together with the nature of data being generated from connected, distributed, and diverse objects. DAI is expected to create a fertile environment for innovative, advanced, robust, and scalable approaches for AI supporting the vision of smart societies. In this paper, we explore state of the art on DAI and identify the opportunities and challenges of provisioning distributed AI as a service (DAIaaS). We provide a taxonomy and a comprehensive review covering the literature from 2016 to 2022. It comprises various aspects of DAI, including AI workflow, distribution paradigms, supporting infrastructure, management techniques, and applications. Based on the gained insights from the conducted review, we propose Imtidad, a framework for provisioning DAIaaS over the cloud, fog, and edge layers. We refine this framework and propose the Imtidad software Reference Architecture (RA) for designing and deploying DAI services. In addition, we extended the framework and developed a future networking infrastructure transformation framework, as it is one of the main enablers for DAI. This framework and RA can be used as guidance facilitating the transition to the future DAI, where DAI is decoupled from the design and development of smart applications. This paper, including the proposed framework, RA, taxonomy, and detailed review, is expected to have an extensive impact on DAI research and accelerate innovations in this area.
Identifying environmental variables in potential flood hazard areas using machine learning approach at Musi Banyuasin Regency, South Sumatra

Abstract Meteorological natural disasters are related to climate. Anomaly conditions in warm sea surface temperatures cause the water vapor to overflow into rain-forming clouds, gradually forming high integrated rainfall in some areas of Indonesia. High or extreme rainfall causes a hydro-meteorological disaster in the form of a flood. Musi Banyuasin Regency, South Sumatra, has a concave to flat topography, a swamp area with abundant large and small rivers prone to flood disasters. Between 2012 and 2022, the National Disaster Management Agency (BNPB) recorded 38 locations had been flooded. This study aimed to identify environmental variables that affect the potential flood hazards and areas with a high flood hazard level. This study used a maximum entropy model approach based on machine learning techniques. The model analyzed all the findings in the sample data to produce predictive information on the contributing environmental variables. The sample data was the 38 flood areas with each preliminary fact and topographic characteristic. Threat components were arranged based on environmental variables (aspect, slope, elevation, land cover, rainfall, and distance from the river). The results indicate that contribution of the average rainfall was 58%, elevation was 26.4%, slope angle was 8.6%, slope aspect was 5.8%, land cover was 1%, and river width was 0.1%. Then, the areas with high flood hazard levels were indicated in eight districts, namely Lais, Sekayu, Babat Supat, Keluang, Sungaililin, Lawang Wetan, Babatoman, and Sangadesa.
Extraction of CD&amp;R Work Phases from Eye-Tracking and Simulator Logs: A Topic Modelling Approach

Automation in Air Traffic Control (ATC) is gaining an increasing interest. Possible relevant applications are in automated decision support tools leveraging the performance of the Air Traffic Controller (ATCO) when performing tasks such as Conflict Detection and Resolution (CD&amp;R). Another important area of application is in ATCOs’ training by aiding instructors to assess the trainees’ strategies. From this perspective, models that capture the cognitive processes and reveal ATCOs’ work strategies need to be built. In this work, we investigated a novel approach based on topic modelling to learn controllers’ work patterns from temporal event sequences obtained by merging eye movement data with data from simulation logs. A comparison of the work phases exhibited by the topic models and the Conflict Life Cycle (CLC) reference model, derived from post-simulation interviews with the ATCOs, indicated that there was a correspondence between the phases captured by the proposed method and the CLC framework. Another contribution of this work is a method to assess similarities between ATCOs’ work strategies. A first proof-of-concept application targeting the CD&amp;R task is also presented.
Personnel policy in the sphere of intellectual labor (review of the book: Gorshkov M. K., Sheregi F. E., Tyurina I. O. Reproduction of specialists of intellectual labor: a sociological analysis. Moscow: FNISC RAN, 2023. 384 p.)

The book under review is noteworthy because the analysis of the state personnel policy in the sphere of intellectual labor is carried out from the theoretical positions of reproduction of the socio-professional structure of society. The conclusions based on theoretical postulates of the authors of the book are verified on the basis of rich statistical and sociological material. The author reveals the structure of inequality of chances to receive higher education, the reasons for the formation of "professional ballast" and imbalance in the market of intellectual labor. The necessity of strengthening the coordinating role of the state in the reproduction of specialists of intellectual labor in the interests of personnel support of innovative production was substantiated.
Applying a wearable MR-based mobile learning system on museum learning activities for university students

This study developed a wearable mixed reality (MR)-based mobile learning system and applied it to museum learning activities to explore whether the proposed system can effectively improve university students’ learning outcomes, situational interest, and cognitive load. Four museums were selected as the informal learning sites, and 64 university students were randomly divided into the experimental group (using the wearable MR-based mobile learning system) and the control group (using the conventional mobile learning system). The results show that in the retention and comprehension dimensions of the learning outcome, the experimental group has significantly better learning performance than the control group. The experimental group showed improvements in almost all dimensions of situational interest, except for the challenge dimension, while their extraneous cognitive load was reduced. Furthermore, this study found that the proposed system has a positive influence on museum learning in higher education, and has good potential for improving the conventional mobile learning system and enhancing students’ learning performance. The findings of this study can provide a reference for future research on the possibilities of using wearable devices for museum education.
Blended Teaching Reform of Data Structure Course Based on OBE Teaching Concept

Nowadays the teaching contents of many colleges and universities in China still have contradictions with advanced scientific development and technologies to varying degrees.The outmoded teaching contents and solidified teaching methods seriously limit the students' initiative in learning and diversification of development in the new era.This paper discusses the reform methods and implementation steps of the teaching mode under the OBE teaching concept, which has played a positive role in promoting the teaching of the data structure course and exercised the students' practical ability.
Research or investigation: perspectives of the sociology of higher education in the conditions of bureaucratisation and commercialisation

The article presents an analysis of the problems that arise in connection with the definition of the prospects for the sociology of higher education in the context of the features of the current stage of development and crisis processes in sociological knowledge itself. Authors believe, even a deep “immersion” of researchers into the educational environment does not always allow to adequately represent and explain the processes developing within it, that is due not only to limitations associated with the specifics of the science itself and the attitudes of researchers, but also with the bureaucratisation of educational reality, that minimises and in a number of cases even excludes the possibility of its sociological interpretation. The empirical base of the work was: 1) studies conducted by the authors or with their participation in universities of the Belgorod region and other regions of Russia from 2005 to 2023, including those organised by the Center for Social Technologies of BelSU; 2) secondary analysis of research carried out by graduate students, doctoral students and applicants in the preparation of their work in the dissertation council in sociological sciences at the Belgorod State National Research University; 3) an expert survey of specialists in the field of sociology of education, conducted by the authors in February 2023 (N=38). Based on the analysis of theoretical and empirical sources, the authors identify several circumstances that determine the emergence of barriers in the study of processes in higher education by sociologists. They are the corporativisation of modern universities in its &amp;amp;quot;economic and managerial&amp;amp;quot; dimension; the specifics of the attitude to sociological diagnostics of both the university bureaucracy and other participants in the educational process - teachers and students. In many cases, their position is not complementary to attempts to clarify certain aspects of the university's self-organisation. The very possibility of discussing them is often initially perceived by the participants as undesirable. According to the authors, under such conditions, any research aimed at obtaining the most relevant to reality, and not the results set in advance by the customer, takes on a peculiar form of &amp;amp;quot;sociological investigation&amp;amp;quot;. At the same time, we are not talking about a formal replacement of terms, but about a significant adjustment of the approach, due to the characteristics of the object and subject of cognition, the capabilities of the subject and the situation in which the diagnosis is carried out.
General Entropic Constraints on Calderbank-Shor-Steane Codes within Magic Distillation Protocols

Magic states are fundamental building blocks on the road to fault-tolerant quantum computing. CSS codes play a crucial role in the construction of magic distillation protocols. Previous work has cast quantum computing with magic states for odd dimension $d$ within a phase space setting in which universal quantum computing is described by the statistical mechanics of quasiprobability distributions. Here we extend this framework to the important $d=2$ qubit case and show that we can exploit common structures in CSS circuits to obtain distillation bounds capable of out-performing previous monotone bounds in regimes of practical interest. Moreover, in the case of CSS code projections, we arrive at a novel cut-off result on the code length $n$ of the CSS code in terms of parameters characterising a desired distillation, which implies that for fixed target error rate and acceptance probability, one needs only consider CSS codes below a threshold number of qubits. These entropic constraints are not due simply to the data-processing inequality but rely explicitly on the stochastic representation of such protocols.
Perbandingan Certainty Factor dan Dempster Shafer dalam Sistem Pakar Diagnosis Penyakit Tanaman Padi Kerjasama dengan Petani

Rice is the staple food of the people in West Lombok. However, rice plants are susceptible to disease from pests, fungi and unfavorable environments. This study was constructed to compare the performance of the Dempster Shafer and Certainty Factor methods in an expert system for diagnosing rice plant diseases based on the knowledge of 3 experts. From the accuracy test performed, it was found that the accuracy value of the system using the Dempster Shafer method was 81.11% while using the Certainty Factor method an accuracy value was 69%, so it can be concluded that the expert system for diagnosing rice plant diseases using the Dempster Shafer method is more accurate than using Certainty Factor method. From the MOS test carried out, using the Dempster Shafer method and obtained an average result of 4.2 while using the Certainty Factor method obtained an average result of 4.63, so it can be concluded that the system with the Certainty Factor method is easier and more interesting to use even though the accuracy value is lower.
Navigating the Quantum Threat Landscape: Addressing Classical Cybersecurity Challenges

This research paper analyzes the urgent topic of quantum cybersecurity and the current federal quantum-cyber landscape.Quantum-safe implementations within existing and future Internet of Things infrastructure are discussed, along with quantum vulnerabilities in public key infrastructure and symmetric cryptographic algorithms.Other relevant non-encryption-specific areas within cybersecurity are similarly raised.The evolution and expansion of cyberwarfare as well as new developments in cyber defense beyond post-quantum cryptography and quantum key distribution are subsequently explored, with an emphasis on public and private sector awareness and vigilance in maintaining strong security posture.
Smart Floor Mats for a Health Monitoring System Based on Textile Pressure Sensing: Development and Usability Study

The rise in single-person households has resulted in social problems like loneliness and isolation, commonly known as "death by loneliness." Various factors contribute to this increase, including a desire for independent living and communication challenges within families due to societal changes. Older individuals living alone are particularly susceptible to loneliness and isolation due to limited family communication and a lack of social activities. Addressing these issues is crucial, and proactive solutions are needed. It is important to explore diverse measures to tackle the challenges of single-person households and prevent deaths due to loneliness in our society.Non-face-to-face health care service systems have gained widespread interest owing to the rapid development of smart home technology. Particularly, a health monitoring system must be developed to manage patients' health status and send alerts for dangerous situations based on their activity. Therefore, in this study, we present a novel health monitoring system based on the auto-mapping method, which uses real-time position sensing mats.The smart floor mats are operated as piezo-resistive devices, which are composed of a carbon nanotube-based conductive textile, electrodes, main processor circuit, and a mat. The developed smart floor system acquires real-time position information using a multiconnection method between the modules based on the auto-mapping algorithm, which automatically creates a spatial map. The auto-mapping algorithm allows the user to freely set various activity areas through floor mapping. Then, the monitoring system was evaluated in a room with an area of 41.3 m2, which is embedded with the manufactured floor mats and monitoring application.This monitoring system automatically acquires information on the total number, location, and direction of the mats and creates a spatial map. The position sensing mats can be easily configured with a simple structure by using a carbon nanotube-based piezo-resistive textile. The mats detect the activity in real time and record location information since they are connected through auto-mapping technology.This system allows for the analysis of patients' behavior patterns and the management of health care on the web by providing important basic information for activity patterns in the monitoring system. The proposed smart floor system can serve as the foundation for smart home applications in the future, which include health care, intelligent automation, and home security, owing to its advantages of low cost, large area, and high reliability.
Trajectory Optimization for Autonomous Berthing of a Twin-Propeller Twin-Rudder Ship

Autonomous berthing is a crucial technology for autonomous ships, requiring optimal trajectory planning to prevent collisions and minimize time and control efforts. This paper presents a two-phase, two-point boundary value problem (TPBVP) strategy for creating an optimal berthing trajectory for a twin-propeller, twin-rudder ship with autonomous berthing capabilities. The process is divided into two phases: the approach and the terminal. Tunnel thruster use is limited during the approach but fully employed during the terminal phase. This strategy permits concurrent optimization of the total trajectory duration, individual phase trajectories, and phase transition time. The efficacy of the proposed method is validated through two simulations. The first explores a scenario with phase transition, and the second generates a trajectory relying solely on the approach phase. The results affirm our algorithm's effectiveness in deciding transition necessity, identifying optimal transition timing, and optimizing the trajectory accordingly. The proposed two-phase TPBVP approach holds significant implications for advancements in autonomous ship navigation, enhancing safety and efficiency in berthing operations.
Recommendation System Algorithm Content-Based Filtering Method to Provide Drink Menu Recommendations

Overchoice is a cognitive disorder in which people have difficulty making decisions when faced with many choices, that make the problem in this study. This over-choice phenomenon often occurs in choosing drinks in cafes and restaurants. This research aims to create a Recommendation System (RS) to assist in choosing the drink you want to order. Making a non-personalized hospital at the Mubtada Kopi cafe uses the best-rated and content-based filtering methods. The content-based filtering method tries to retrieve user preferences explicitly, asking the user to choose the preferences the user wants from the six content made before calculating the match between the user's preferences and the six contents in each item using the dot matrix formula. The results will be converted into a rating to match the best-rated hospital approach, which is made on a non-personalized basis. This rating matches the user's preferences and the Mubtada Kopi menu list items. The higher the rating, the better it matches the user's preferences. The order RS recommends with the Content-based filtering method is rosella tea, chocolate, lemon tea, blossom tea, and spice tea.
A Trusted Reputation Management Scheme for Cross-Chain Transactions

Blockchain has become a well-known, secured, decentralized datastore in many domains, including medical, industrial, and especially the financial field. However, to meet the requirements of different fields, platforms that are built on blockchain technology must provide functions and characteristics with a wide variety of options. Although they may share similar technology at the fundamental level, the differences among them make data or transaction exchange challenging. Cross-chain transactions have become a commonly utilized function, while at the same time, some have pointed out its security loopholes. It is evident that a secure transaction scheme is desperately needed. However, what about those nodes that do not behave? It is clear that not only a secure transaction scheme is necessary, but also a system that can gradually eliminate malicious players is of dire need. At the same time, integrating different blockchain systems can be difficult due to their independent architectures, and cross-chain transactions can be at risk if malicious attackers try to control the nodes in the cross-chain system. In this paper, we propose a dynamic reputation management scheme based on the past transaction behaviors of nodes. These behaviors serve as the basis for evaluating a node's reputation to support the decision on malicious behavior and enable the system to intercept it in a timely manner. Furthermore, to establish a reputation index with high precision and flexibility, we integrate Particle Swarm Optimization (PSO) into our proposed scheme. This allows our system to meet the needs of a wide variety of blockchain platforms. Overall, the article highlights the importance of securing cross-chain transactions and proposes a method to prevent misbehavior by evaluating and managing node reputation.
An Empirical Study of Higher Vocational English Learning Efficiency Based on DEA Model

The evaluation of learning efficiency is an important part of teaching work.However, traditional evaluation methods mainly focus on learning results and ignore the input factors of students.In view of this, this paper considers the relationship between input and output of learning and constructs DEA model to study the efficiency of English learning in higher vocational colleges.The model is applied to evaluate the learning efficiency of five students in a higher vocational college in Yunnan Province.The results show that 75% of the students have low comprehensive learning efficiency in preparing for the AB-level exam.The main reason is that the input of training time and resource use is not optimized.At the same time, the learning efficiency evaluation method based on DEA model is effective, and this model can become an important tool for teachers to improve teaching quality.