Miracle-dz/UniFAR-FTUs huggingface dataset:
Geological Disaster Emergency Decision Support System based on Location-Based Service

Most Tibetan areas are extremely difficult in production and living conditions. With the rapid uplift of the Qinghai-Tibet Plateau, its geology, landform and climate have changed dramatically, resulting in frequent geological disasters such as collapse, landslide, debris flow, debris flow and glacial lake outburst floods. It is difficult for emergency rescue forces to reach on time and the emergency rescue teams also lack advanced technologies to support the emergency disposal. The health and life safety of the rescue personnel are often threatened. This paper discusses the construction of a geological disaster emergency decision support system based on WebGIS technology and supported by location-based service and satellite communication technology. It realizes business functions such as comprehensive management of geological disaster data, quick report of disaster and danger, three-dimensional desktop deduction of the emergency plan, monitoring and dispatching of rescue personnel, etc., to reduce the loss of people's lives and properties caused by geological disasters while improving the safety and security capacity of emergency rescue personnel, to achieve the purpose of emergency rescue.
Roller Bearing Failures Classification with Low Computational Cost Embedded Machine Learning

In this work, the authors present a Machine Learning (ML) algorithm which is able to detect incipient failures of roller bearings starting from data provided by accelerometers. Three typologies of signals coming from typical bearing failures are exploited to test the algorithm. Specifically, faults related to the bearing balls, the inner raceways, and the outer raceways are taken into account. Besides these, also a control set containing data related to bearings having no faults is exploited. Moreover, the ML algorithm is designed to be executed by a microprocessor, which can be used in a distributed sensor network that can be the base for an Internet of Things (IoT) monitoring system. In so doing, a predictive maintenance paradigm, relying on Artificial Intelligence (AI), for bearings is set up, enabling condition monitoring systems to actively predict faults thus timely halting machines.
Quasi-switched inverter using space vector pulse width modulation with triangular comparison for photovoltaic applications

This work analyzes a prototype of a quasi-switched boost inverter (qSBI) feeding an isolated resistive load from a DC source. The use of spatial vector pulse width modulation (SPWM) with triangular comparison is proposed to increase the qSBI gain factor, and its performance is contrasted with other types of spatial vector modulations, such as discontinuous modulations. To verify the validity of the method for voltage range extension in the qSBI converter, a semi-customized test platform was developed. This platform uses a DSP floating point card (Analog Devices ADSP-21369) for processing and control strategies and an interface card that includes a programmable logic array (FPGA) from Xilinx (Spartan-3), which allows to develop the synchronized modulation qSBI needs. The experimental results show improvements in the performance of the qSBI converter in terms of gain factor, voltage reduction in the capacitor, and input current profiles. Discontinuous space vector modulation strategies do not perform well when compared to continuous SVPWM or SPWM modulations, because the ripple levels in the currents taken from the PV module are approximately twice as great as in continuous modulation techniques. Finally, the usefulness of a qSBI as PV microinverter is confirmed by two practical experimental cases of a PV photovoltaic system with a maximum power point adjustment algorithm (MPPT).
Semantic-Aware Vulnerability Detection

Many vulnerability detection tools have been developed to detect vulnerabilities, but most of them only offer basic information for each detected vulnerability, which is insufficient for developers to reproduce and repair the vulnerability. We present an approach called SPDetect that uses safety properties to detect vulnerabilities and provides semantic information, including source code program expressions, about detected vulnerabilities. Each safety property defines the condition violated by one type of vulnerabilities. SPDetect uses symbolic execution to explore program paths and detect vulnerabilities by checking any violation of safety properties. To guide the symbolic execution to deep program paths that are more likely to contain vulnerabilities, we developed the novel technique of error path termination. We have designed and implemented a prototype of SPDetect for detecting vulnerabilities in C/C++ programs. Our evaluation of SPDetect on real-world programs shows that SPDetect can can detect vulnerabilities effectively and efficiently.
Cryptographic Algorithms for Security in Wireless Sensor Networks

Security in sensor network is a key concern because of the absence of temperature-safe equipment and the transmission idea if Wireless Sensor Networking (WSNs). A WSN is made up of a collection of sensor nodes and some sink nodes. Users that are interested, receive information on the state of the physical environment from storage nodes. It’s employed in a variety of applications, including wellbeing observing, natural surrounding monitoring, military monitoring and environmental monitoring. The compute power, battery capacity and data storage of sensor nodes all are constrained. Independent sensor hubs are connected to at least one base station in the wireless sensor network. As wireless sensor network become more powerful, they become much more susceptible to threats and necessitating the use of powerful safety measures. Recognizing proof of adequate cryptography for remote sensor networks is a critical difficulty because to the limits of energy, compute, capacity and capacity resources of the sensor nodes. When the number of sensor hubs grows, symmetric cryptographic plans do not scale effectively. Openkey based plans will be widely used in the future. Because of a computer network cannot be entirely trusted, privacy must be secured. Secret key, Cryptography key and key management are used in this security technique. A digital key is obtained for each user to secure the system’s integrity. We compare and contrast two public key-based computations RSA and ECC, conclude that ECC outperforms RSA in terms of calculation time and amount of data conveyed and stored.
Personalized Practice Exam Recommendation for Helping Students Prepare for Course Assessment

Quizzes, tests and exams are commonly used as important formative and summative assessment tools to test student learning towards learning objectives. Practice exams are an effective tool to help students better prepare for real exams in an in-person as well as online course environment. Personalized practice exams will be more effective for supporting individual students’ success in course assessments. In this paper, we present a personalized practice exam recommender system to help students prepare and do better in the real course exams. Our practice exam recommendation is based on the student-to-student collaborative filtering using course, exam and exam question grades. We compute a target student’s predicted grade on each exam question in a real exam, and based on the predicted grades, construct and recommend a personalized practice exam for the target student. Practice exam recommendation can be done at the beginning of a semester or progressively in course throughout a semester by incorporating the target student’s performance on the completed real exams.
Recovery Orientation in Methadone Maintenance: A Definitional Statement

In 2010, Lisa Mojer-Torres and I co-authored a monograph entitled Recovery-oriented Methadone Maintenance. Since its publication, the application of the concept of recovery to the methadone maintenance treatment (MMT) context has been both warmly embraced and at times hotly debated. Interestingly, proposals for recovery-oriented methadone maintenance (ROMM) have been criticized from both poles in the often vitriolic debates about MMT. Criticisms stem from differences in definitions of recovery and the resulting criteria for recovery status and fears that the ROMM concept will result in unanticipated harm to opioid treatment programs (OTPs), harm reduction programs, recovery mutual aid groups, and to those served by these organizations. Could a newly proposed concept produce such harm? Of course. There is a long tradition of harm in the name of help within the history of addiction treatment, and any concept, no matter how well-intended, may be hijacked and corrupted to support purposes for which it was not intended. Those possibilities underscore the importance of conceptual clarity and the rigorous testing of new ideas and service approaches. Although the ROMM philosophy and related changes in service practices were detailed in the 2010 monograph with extensive scientific citations, there appears to be the need for a short simple declaration of what ROMM is and is not. Such a declaration would ideally help ground continued discussion. This brief paper offers such a statement. The ROMM concept will continue to evolve through continued dialogue and evolving scientific findings on its central elements, but the statement below will provide a starting point for discussion reflecting the views of the authors who first proposed it.
SEGMENTATION NON SUPERVISEE D'IMAGES BASEE SUR LES MODELES DE MARKOV CACHES

En raison du grand nombre d'images, il y a un besoin evident pour des outils d'analyse efficaces et automatiques. Ce travail decrit la segmentation non supervisee d'images dans le cadre des modeles de Markov caches precisement les champs et les chaines de Markov cachees et l’estimation des parametres du modele. Particulierement les chaines de Markov cachees, basees sur le parcourt d’hilbert-peano de l'image, sont une alternative rapide et efficace que les champs de Markov cachees pour l’estimation des parametres et la segmentation non supervisee d’images. Des resultats obtenus sur des images reelles sont presentes.
Fractional discrete Laplacian versus discretized fractional Laplacian

We dene and study some properties of the fractional powers of the discrete Laplacian ( h) s ; on Zh = hZ; for h > 0 and 0 < s < 1. A comparison between our fractional discrete Laplacian and the discretized continuous fractional Laplacian as h! 0 is carried out. We get estimates in ' 1 for the error of the approximation in terms of h under minimal regularity assumptions. Moreover, we provide a pointwise formula with an explicit kernel and deduce Holder estimates for ( h) s . A study of the negative powers (or discrete fractional integral) ( h) s is also sketched. Our analysis is mainly performed in dimension one. Nevertheless, we show certain asymptotic estimates for the kernel in dimension two that can be extended to higher dimensions. Some examples are plotted to illustrate the comparison in both one and two dimensions.
Transforming Education System through Artificial Intelligence and Machine Learning

We are now living in the fourth industrial revolution, in which artificial intelligence (AI) is changing every aspect of our social interactions. AI has begun to generate new teaching and learning solutions in the education sector, which are being tested in various contexts. This paper will explain the extent to which artificial intelligence (AI) can be used in the education sector and its benefits. AI has progressed from simple rule-based systems to data-driven systems with sophisticated capabilities. This paper discusses the use of artificial intelligence (AI) in education to improve teaching and learning outcomes. AI, as a new technology, will bring about various changes in the 'learning experience' by creating an adjustable learning environment that will result in a 'personalized learning experience. ' We will see some examples of how AI is being used in education.
Real ant colony optimization as a tool for multi-criteria problems

This paper presents a population-based heuristic method - a real ant colony optimization (RACO) as a tool for multi-criteria optimization problems. The idea of multi-criteria optimization is discussed and the necessary modifications of RACO are proposed. These modifications made possible to use the method to simultaneously search many Pareto-optimal solutions. The method was numerically tested in problems of benchmark-type and used for solving simple engineering problems. This article presents and discusses all results obtained in tests, and two different approaches to multi-criteria optimization are additionally compared (search then decision and decision then search).
Performance Analysis of NOMA in Full-duplex Cooperative Spectrum Sharing Systems

This paper presents a non-orthogonal multiple access (NOMA)-assisted full-duplex (FD) cooperative spectrum sharing protocol to achieve full-rate at a cell-edge primary user, where the secondary receiver helps the primary network relay the information to the cell-edge primary user. Unlike other FD cooperative schemes, the FD relay in the proposed system is not transmitting while receiving symbols from the primary transmitter. Hence, the proposed scheme can achieve full-rate at a primary user without degrading its outage performance. Further, it guarantees every alternate time-slots to the secondary network for accessing the licensed spectrum by employing a secondary receiver as a cooperative relay. In this paper, we derive the analytical expression for the outage probability and ergodic capacity. Moreover, we validate the derived expression through simulation and asymptotic results. The numerical and simulation results substantiate that the proposed scheme significantly outperforms other existing protocols.
Social Networking in an Information-Centric System with Blockchain

for many users nowadays social media plays a big role in their world, as it helps to connect with new people while keeping the old bonds intact. The drawback is that we can’t able to see that, the data is being generated every minute of a second and that data is kept secure in the hands of a few tech giants, who are using it and selling it as they see fit and using it in a manner as they see profitable and with this paper I’d like to propose that shifting social media from host-centric to the information-centric network on the blockchain would not only be safer yet convenient and secured.In peer-to-peer system, cryptocurrency is used to pay for and secure content acquisition.The blockchain lets individuals acquire ownership over their Data, Property and Assets. In social networking, a decentralised approach to blockchain technology protects privacy and allows for e-commerce, ledger based crowd funding, smart apps, and smart contracts. Platforms like Facebook, LinkedIn, Instagram, Twitter, Reddit, and other digital social network media platforms offer free for all access, which is convenient in the name of connecting and socialising but they are collecting are data and using it to sell it to other third party buyers for the price of our privacySome of the most important difficulties confronting social media network business period, profit is focused on advertising and analytics. False propaganda based information on (Facebook), rampant bullying and harassment (Twitter), single party controlled censorship, and demonetization are some of the most serious issues facing social networking (YouTube).
2P1-E04 Unfolding a Rectangular Cloth by a Dual Arm Robot by Pinching-and-Slipping Motion

We proposed a method of unfolding a rectangular clothing by dual arm robot. Using the pinching and sliding motion, a robot has to recognize the shape pattern of the hem line. We classified the pattern into three groups: type (a), type (b) and type (c), and made an identification process using Laplacian filter. Using this system, we performed unfolding experiment and it was successful in 64 percent.
Attributed Labeled BTER-Based Generative Model for Benchmarking of Graph Neural Networks

Graph Neural Networks (GNNs) have become increasingly popular for tasks such as link prediction, node classification, and graph generation. However, a number of models show weak performance on graphs with low assortativity measure. At the same time, other graph characteristics may also influence GNN quality. Therefore, it is extremely important for benchmark datasets to cover a wide range of different graph properties, which can not be provided by real-world sources. In this paper, we present a generative model for attributed graphs based on Block Two-Level Erdős-Rényi model. Our model allows one to vary larger number of graph structural characteristics (namely, clustering coefficient, average degree, average shortest paths length, label and attribute assortativity) in a wider range. Our attribute generative method can be applied to any other non-attributed graph generative model with community structure and allows to control attribute assortativity corresponding to structure of graph. The experimental study shows that AL-BTER outperforms ADC-SBM and GenCAT under the assumption of equal importance of desired graph characteristics and provides wider ranges for attribute assortativity and average shortest paths and outperforms LFR in terms of clustering coefficient. GNN performance analysis confirms the sensitivity of the results to all topological properties except average degree and shows that benchmark graphs provided by AL-BTER are useful to discover new regimes of performance of graph convolutional networks.
Extending Feistel structure to DNA Cryptography

Abstract DNA cryptography is a novel field being taken up by the researcher community for research now days in order to have secure communication on a network. This technique is inspired from biological science, in which DNA is used as an information carrier from one generation to another. DNA cryptography is proposed for a secure end to end communication due to the vast parallelism and extra ordinary information density that are inherent in any DNA molecule. In this paper previous algorithm based on DNA cryptography is enhanced in terms of its security parameter by incurring feistel inspired structure in it. This adds some sort of confusion and diffusion, which makes it complex enough that it restricts the adversary to perform any kind of brute force attack. Encryption and decryption time is also compared on the basis of variable plaintext size.
Evaluation of Driver Car-Following Behavior Models for Cooperative Adaptive Cruise Control Systems

A cooperative adaptive cruise control (CACC) system targeted to obtain a high level of user acceptance must replicate the driving experience in each CACC vehicle without compromising the occupant’s comfort. “User acceptance” can be defined as the safety and comfort of the occupant in the CACC vehicle in terms of acceptable vehicle dynamics (i.e., the maximum acceleration or deceleration) and string stability (i.e., the fluctuations in the vehicle’s position, speed, and acceleration). The primary objective of this study was to develop an evaluation framework for the application of a driver car-following behavior model in CACC system design to ensure user acceptance in terms of vehicle dynamics and string stability. The authors adopted two widely used driver car-following behavior models, (a) the optimum velocity model (OVM) and (b) the intelligent driver model (IDM), to prove the efficacy of the evaluation framework developed in this research for CACC system design. A platoon of six vehicles was simulated for three traffic flow states (uniform speed, speed with constant acceleration, and speed with constant deceleration) with different acceleration and deceleration rates. The maximum acceleration or deceleration and the sum of the squares of the errors of the follower vehicle speed were measured to evaluate user acceptance in terms of vehicle dynamics and string stability. Analysis of the simulation results revealed that the OVM performed better at modeling a CACC system than did the IDM in terms of acceptable vehicle dynamics and string stability.
Editorial: Sedation and analgesia challenges in critically ill neonates and children

COPYRIGHT © 2022 Amigoni, Simons, De Hoog, De Wildt and Karam. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Sedation and analgesia challenges in critically ill neonates and children
Effects of propofol on macrophage activation and function in diseases

Macrophages work with monocytes and dendritic cells to form a monocyte immune system, which constitutes a powerful cornerstone of the immune system with their powerful antigen presentation and phagocytosis. Macrophages play an essential role in infection, inflammation, tumors and other pathological conditions, but these cells also have non-immune functions, such as regulating lipid metabolism and maintaining homeostasis. Propofol is a commonly used intravenous anesthetic in the clinic. Propofol has sedative, hypnotic, anti-inflammatory and anti-oxidation effects, and it participates in the body’s immunity. The regulation of propofol on immune cells, especially macrophages, has a profound effect on the occurrence and development of human diseases. We summarized the effects of propofol on macrophage migration, recruitment, differentiation, polarization, and pyroptosis, and the regulation of these propofol-regulated macrophage functions in inflammation, infection, tumor, and organ reperfusion injury. The influence of propofol on pathology and prognosis via macrophage regulation is also discussed. A better understanding of the effects of propofol on macrophage activation and function in human diseases will provide a new strategy for the application of clinical narcotic drugs and the treatment of diseases.
Host-microbiota interactions shaping T-cell response and tolerance in type 1 diabetes

Type-1 Diabetes (T1D) is a complex polygenic autoimmune disorder involving T-cell driven beta-cell destruction leading to hyperglycemia. There is no cure for T1D and patients rely on exogenous insulin administration for disease management. T1D is associated with specific disease susceptible alleles. However, the predisposition to disease development is not solely predicted by them. This is best exemplified by the observation that a monozygotic twin has just a 35% chance of developing T1D after their twin’s diagnosis. This makes a strong case for environmental triggers playing an important role in T1D incidence. Multiple studies indicate that commensal gut microbiota and environmental factors that alter their composition might exacerbate or protect against T1D onset. In this review, we discuss recent literature highlighting microbial species associated with T1D. We explore mechanistic studies which propose how some of these microbial species can modulate adaptive immune responses in T1D, with an emphasis on T-cell responses. We cover topics ranging from gut-thymus and gut-pancreas communication, microbial regulation of peripheral tolerance, to molecular mimicry of islet antigens by microbial peptides. In light of the accumulating evidence on commensal influences in neonatal thymocyte development, we also speculate on the link between molecular mimicry and thymic selection in the context of T1D pathogenesis. Finally, we explore how these observations could inform future therapeutic approaches in this disease.