Reviewerly/exHarmony huggingface dataset:
A PSO-enhanced Gauss pseudospectral method to solve trajectory planning for autonomous underwater vehicles

A fast optimization method based on the Gauss pseudospectral method (GPM) and particle swarm optimization (PSO) is studied for trajectory optimization of obstacle-avoidance navigation of autonomous underwater vehicles (AUVs). A multi-constraint trajectory planning model is established according to the dynamic constraints, boundary constraints, and path constraints. The trajectory optimization problem is converted into a non-linear programming (NLP) problem by means of the GPM, which is solved by the sequential quadratic programming (SQP) algorithm. Aiming at the initial values dependence of the SQP algorithm, a method combining PSO pre-planning with the GPM is proposed. The pre-planned trajectory points are configured on the Legendre-Gauss (LG) points of the GPM by fitting as the initial values for the SQP calculated trajectory planning problem. After simulation analysis, the convergence speed of the optimal solution can be accelerated by using the pretreated initial values. Compared to the linear interpolation and the cubic spline interpolation, the PSO pre-planning method improves computational efficiency by 82.3% and 88.6%, which verifies the effectiveness of the PSO-GPM to solve the trajectory optimization problem.
Navigating Education in the Age of Covid-19: From Rapid Reactions to Sustainable Solutions

The pandemic outbreak presented many challenges to higher education in the UAE, but the government and universities adapted quickly to ensure that students could continue their studies without interruption. The Ministry of Education launched a virtual learning initiative that provided universities and colleges with the necessary guidance and resources to deliver their courses and lectures remotely. The government offered financial assistance and launched several initiatives to help students access the necessary tools and technology to continue their studies remotely, such as providing laptops and tablets to students who needed them. The Department of Chemistry at UAEU shifted to online learning, provided faculty-wide support, and ensured that every student was able to participate in digital learning. The measures taken by the UAE government and the Department of Chemistry at UAEU ensured the safety of its students and staff while continuing to deliver quality education.
ABOUT THE INTEGRATION OF PSCAD AND ETAP PROGRAMS INTO THE EDUCATIONAL PROCESS

The possibility of using simulation modeling programs for educational purposes is considered, the possibilities of the programs are listed, the fundamental differences, strengths and weaknesses of the products under consideration are emphasized
Impartial games with decreasing Sprague–Grundy function and their hypergraph compound

Abstract The Sprague–Grundy (SG) theory reduces the disjunctive compound of impartial games to the classical game of NIM . We generalize this concept by introducing hypergraph compounds of impartial games. An impartial game is called SG-decreasing if its SG value is decreased by every move. Extending the SG theory, we reduce hypergraph compounds of SG-decreasing games to hypergraph compounds of single-pile NIM games. We show that this reduction works only if all games involved in the compound are SG-decreasing. A hypergraph is called SG-decreasing if the corresponding hypergraph compound of single-pile NIM games is an SG-decreasing game. We provide some necessary and some sufficient conditions for a hypergraph to be SG-decreasing. In particular, for hypergraphs with hyperedges of size at most 3 we obtain a necessary and sufficient condition verifiable in polynomial time.
Deciphering CryptoArchitecture: The architectural design studio as a vehicle for exploring the integration between blockchain and architectural design

In this paper we are using the outputs of research led architectural design studios, originally articulated as pedagogical projects at the Edinburgh School of Architecture and Landscape Architecture, University of Edinburgh and the Robert Gordon University in Aberdeen to explore and evaluate the possible transformative effects of blockchain/Decentralised Ledger technologies and decentralisation in the architecture discipline.After describing the pedagogical studio experiments, we frame a classification of relevant thematic clusters, where distributed ledger technologies can be applied to architectural design and thinking.The design studio, is treated as a methodological research tool, appropriate for the identification of existing knowledge gaps around the topic.The emerging classifications in terms of blockchain and decentralisation implementation is used to synthesise a map of future pedagogical and research activity.
The extended predicative Mahlo universe in Martin-Löf type theory

Abstract This paper addresses the long-standing question of the predicativity of the Mahlo universe. A solution, called the extended predicative Mahlo universe, has been proposed by Kahle and Setzer in the context of explicit mathematics. It makes use of the collection of untyped terms (denoting partial functions) which are directly available in explicit mathematics but not in Martin-Löf type theory. In this paper, we overcome the obstacle of not having direct access to untyped terms in Martin-Löf type theory by formalizing explicit mathematics with an extended predicative Mahlo universe in Martin-Löf type theory with certain indexed inductive-recursive definitions. In this way, we can relate the predicativity question to the fundamental semantics of Martin-Löf type theory in terms of computation to canonical form. As a result, we get the first extended predicative definition of a Mahlo universe in Martin-Löf type theory. To this end, we first define an external variant of Kahle and Setzer’s internal extended predicative universe in explicit mathematics. This is then formalized in Martin-Löf type theory, where it becomes an internal extended predicative Mahlo universe. Although we make use of indexed inductive-recursive definitions that go beyond the type theory $\mathbf {IIRD}$ of indexed inductive-recursive definitions defined in previous work by the authors, we argue that they are constructive and predicative in Martin-Löf’s sense. The model construction has been type-checked in the proof assistant Agda.
DeepDefense: A Steganalysis-Based Backdoor Detecting and Mitigating Protocol in Deep Neural Networks for AI Security

Backdoor attacks have been recognized as a major AI security threat in deep neural networks (DNNs) recently. The attackers inject backdoors into DNNs during the model training such as federated learning. The infected model behaves normally on the clean samples in AI applications while the backdoors are only activated by the predefined triggers and resulted in the specified results. Most of the existing defensing approaches assume that the trigger settings on different poisoned samples are visible and identical just like a white square in the corner of the image. Besides, the sample-specific triggers are always invisible and difficult to detect in DNNs, which also becomes a great challenge against the existing defensing protocols. In this paper, to address the above problems, we propose a backdoor detecting and mitigating protocol based on a wider separate-then-reunion network (WISERNet) equipped with a cryptographic deep steganalyzer for color images, which detects the backdoors hiding behind the poisoned samples even if the embedding algorithm is unknown and further feeds the poisoned samples into the infected model for backdoor unlearning and mitigation. The experimental results show that our work performs better in the backdoor defensing effect compared to state-of-the-art backdoor defensing methods such as fine-pruning and ABL against three typical backdoor attacks. Our protocol reduces the attack success rate close to 0% on the test data and slightly decreases the classification accuracy on the clean samples within 3%.
Platform for Learning and Virtual Reality in Animal Husbandry

Nowadays, digital technologies are extensively used in the field of education. Virtual and augmented reality and 3D technologies are entering the field of education at all educational levels. They are a prerequisite for the application of new approaches in the presentation of the educational content and its easier perception and assimilation by the learners. The paper presents an integrated platform for open science and educational resource sharing, as well as an environment for distance learning and data analysis in animal husbandry derived from the learning resources of a given course in the system. The research is aimed at developing a computer-aided framework in the field of digitized education and creating new educational resources for distance learning in animal husbandry.
Quantum-Enhanced Cluster Detection in Physical Images

Identifying clusters in data is an important task in many fields. In this paper, we consider situations in which data live in a physical world, so we first have to collect the images using sensors before clustering them. Using sensors enhanced by quantum entanglement, we can image surfaces more accurately than using purely classical strategies. However, it is not immediately obvious whether the advantage we gain is robust enough to survive data-processing steps such as clustering. It has previously been found that using quantum-enhanced sensors for imaging and pattern recognition can give an advantage for supervised learning tasks, and here we demonstrate that this advantage also holds for an unsupervised learning task, namely clustering.
COMPUTER-AIDED DESIGN SYSTEMS IN THE FIELD OF ELECTRONICS AND ELECTRICAL ENGINEERING

The analysis of the workplace software of a specialist in the field of electronics and electrical engineering is given
A lightweight multi‐branch network for low‐light image enhancement

Abstract The generation process of low‐light images is fundamentally complicated due to their multi‐factorial degradation. Many previous methods favorably developed a multi‐branch network for impressive performance. However, the complex structures of multi‐branch networks always incur large computational cost and hinders their applicability in those resource‐limited settings. To alleviate these issues, in this letter a novel lightweight multi‐branch network (LMBN) for low‐light image enhancement is proposed. Specifically, a multi‐branch module and a joint loss are framed to surmount the complex degradation factors of low‐light images. Furthermore, the multi‐branch network is implemented by several heterogeneous and shallow encoder‐decoder modules. The elaborately designed lightweight model facilitates lower computational complexity but maintains impressive enhancement quality. The experimental results performed on three popularly benchmark databases demonstrate that the proposed LMBN shows the superior performance over other state‐of‐the‐art methods, indicating a perfect balance between the model's performance and the computational efficiency.
A Hybrid Quantum-Classical Algorithm for Multichannel Quantum Scattering of Atoms and Molecules

We propose a hybrid quantum-classical algorithm for solving the time-independent Schrödinger equation for atomic and molecular collisions. The algorithm is based on the S-matrix version of the Kohn variational principle, which computes the fundamental scattering S-matrix by inverting the Hamiltonian matrix expressed in the basis of square-integrable functions. The computational bottleneck of the classical algorithm─symmetric matrix inversion─is addressed here using the variational quantum linear solver (VQLS), a recently developed noisy intermediate-scale quantum (NISQ) algorithm for solving systems of linear equations. We apply our algorithm to single- and multichannel quantum scattering problems, obtaining accurate vibrational relaxation probabilities in collinear atom-molecule collisions. We also show how the algorithm could be scaled up to simulate collisions of large polyatomic molecules. Our results demonstrate that it is possible to calculate scattering cross sections and rates for complex molecular collisions on NISQ quantum processors, opening up the possibility of scalable digital quantum computation of gas-phase bimolecular collisions and reactions of relevance to astrochemistry and ultracold chemistry.
Comparative Estimation of Global Solar Radiation over Two Nigerian Cities, Using Artificial Neural Network and Empirical Models

The estimation of solar radiation intensity has been a focus of many researchers due to the cost of setting up its actual measurements. While many of them employed empirical models, this study utilizes the artificial neural network for the analysis and estimation of global solar radiation over two Nigerian cities. The model developed using sunshine hours, temperatures and relative humidity were compared with the existing empirical models. Model performance indicators comparing the measured data and the computed data for the derived and selected models, using the same number of input meteorological parameters showed that ANN having average values of RMSE, MBE, and MPE of 0.0744 MJm-2day-1, -0.0020 MJm-2day-1, and -0.0043%, respectively, performed slightly better. When different number of input meteorological parameters were used, the ANN gave the following error indicators for RMSE, MBE, MPE of 0.0394MJm-2day-1, -0.0023MJm-2day-1 and -0.0144% respectively. Also, in the result of solar radiation in Abuja, using the same number of meteorological parameters, the model with the best performance in the estimation of solar radiation is the ANN model with average values of RMSE, MBE, MPE of 0.1301MJm-2day-1, 0.0053MJm-2day-1 and 0.0441% respectively. Hence, the models are versatile for predicting global solar radiation in locations in the same climatic zones as locations studied in this study, where direct measurements of solar radiation is scarce and widely separated but there is availability of commonly measured meteorological parameters such as sunshine duration, minimum temperature, maximum temperature and relative humidity.
Design and proof of concept of chatbot for people with dementia

Dementia is a chronic, degenerative, incapacitating disease that affects millions of people yearly. People with dementia require constant supervision and assistance, unfortunately, most caregivers are informal since professional healthcare can be unaffordable. We believe that this burden could be alleviated using a chatbot. Chatbots have seen an increase in use in healthcare over the past decade and they have been proven especially effective dealing with mental health disorders and chronic conditions. However, there is still room for improvement regarding their intelligence when compared to other cutting edge conversational agents and there are barely any bots that support Spanish. Furthermore, there are fewer chatbots specialized for people with any type of dementia. Alzheimer’s being the most common one, this research’s goal is to determine what features and characteristicsa chatbot for people with Alzheimer’s dementia should have. A comparative analysis of healthcare chatbots and development tools was conducted as groundwork for the design. An underdeveloped instance of said chatbot was implemented as a proof of concept, the architecture proposed allows for progress to be made on the chatbot without compromising its integrity. All the development was done using Azure tools, namely the bot framework composer, which was sufficient for the intended purposes but it poses serious limitations.
A Systematic Literature Review on English Language Teaching in the Indonesian Tertiary Education Settings Amidst Pandemic Era

The transformation of traditional education into hybrid and full online education in the pandemic era has affected the art of teaching and learning for all stakeholders, including teachers and students in the Indonesian tertiary education settings.Not only has the teaching-learning environment been different, but the technology use is also carefully determined due to the available access.This article aims to review the research studies on online English Language Teaching, mainly in the four language skills and language components, conducted between March 1, 2020, and January 31, 2022.To address the current research objective, the database search was focused on the articles indexed by Science and Technology Index (SINTA) ranging between levels 1-2.There were 909 articles in total and the data set included 24 articles.The results show that the topical foci of the works were the use of applications and methods for writing (42%), speaking (25%), grammar (13%), reading (8%), listening (8%), and combined skills (4%) respectively.This paper presents not only the identification, analysis, and critiques of the main streams in the published works, but also emphasizes the overview of each work.As a major finding of the review, the current Systematic Literature Review (SLR) suggests the need for more orientation on empirical studies in the areas of language skills teaching employing technology during this pandemic and subsequently post-pandemic era in the Indonesian tertiary education settings.
Secure, accurate and privacy-aware fully decentralized learning via co-utility

Fully decentralized learning is a setting in which each peer in a P2P network trains a machine learning model with the help of the other peers. Each peer acts as a model manager by periodically sending her current model to other peers, who answer by returning model updates they compute on their private data. This creates a tension among privacy, accuracy and security. The privacy risk is that model updates returned by a peer can leak some of the peer's private data. Unfortunately, distorting model updates to protect privacy works against the accuracy of the trained model. On the other hand, aggregating the updates of several peers and then sending the aggregate to the model manager may preserve privacy but it goes against security, because the model manager cannot filter out individual bad updates. Also, peers are autonomous and hence it cannot be taken for granted that they will honestly supply model updates to help the model manager train her model. To reconcile accuracy, privacy and security, we present a fully decentralized learning protocol such that: (i) it allows perfectly accurate individual updates to be returned by peers to the model manager in a privacy-preserving manner; (ii) it is co-utile by design, that is, it incentivizes rational peers to follow the protocol without deviating. The latter feature discourages rational attacks that might compromise security and it also deters free riding, thereby ensuring the sustainability of the protocol.
The Transcendentalists and Their World

Journal Article The Transcendentalists and Their World Get access The Transcendentalists and Their World. By Robert A Gross. (New York: Farrar, Straus and Giroux, 2021. xxii, 836 pp. $40.00.) Donald E Pease Donald E Pease Dartmouth College, Hanover, New Hampshire Search for other works by this author on: Oxford Academic Google Scholar Journal of American History, Volume 110, Issue 1, June 2023, Pages 137–138, https://doi.org/10.1093/jahist/jaad127 Published: 01 June 2023
“New Singing of Ancient Poems” in music education of primary and secondary schools

“The new singing of ancient poetry” refers to a musical innovation form that takes the ancient poetry culture as the source of lyrics and rearranges the melody with modern music style and lasting appeal. It makes ancient poetry discourse from the perspective of music and breaks the barriers to understanding the ancient poetry culture. In recent years, Chinese ancient poetry has attracted more and more people's attention and love because of its unique value of recreation in music, and has become an important source of poetry and music. Through the discussion of the form of “new singing of ancient poetry”, this paper hopes to arouse some music educators in primary and secondary schools to pay attention to how to guide students to learn the music culture of ancient poetry, so that the traditional culture of ancient poetry can play its charm in music education of primary and secondary schools.
Implementasi Sistem Reminder Jadwal pada eLearning Moodle Berbasis API Menggunakan Framework Flutter

This research aims to develop a Flutter-based mobile application that provides users with information regarding class schedules and assignments. The application includes a reminder feature that sends notifications about lectures and assignments through WhatsApp and in-app notifications. The development methodology employed is the waterfall model, while testing utilizes black-box testing and Firebase Test Lab. The test results demonstrate the successful development of the application. Additionally, this application facilitates efficient and easy access to class schedules and assignments. It is expected to enhance users' academic performance and be applicable across various educational institutions. Overall, the development of this application offers users the advantage of effectively and efficiently managing study time and completing tasks, thereby contributing to the improvement of educational quality within institutions.
Audit Keamanan Sistem Informasi Manajemen Rumah Sakit Dengan Framework COBIT 2019 Pada RSUD Palembang BARI

This study examines the implementation of information system at RSUD Palembang BARI with the aim of enhancing information system security. In this context, a security audit is conducted using the COBIT 2019 framework. The COBIT 2019 domains and processes utilizing include EDM03, APO12, APO13, APO14, and DSS05. The research involves the identification and evaluation of information security risks, determination of necessary security controls, and ensuring compliance with the information security standards established by COBIT 2019. The findings indicate that the level of information system security at RSUD Palembang BARI is at level 3 (Defined), with a gap analysis difference of 1 level below the expected target. Based on the above results, efforts to improve and enhance the information system security at RSUD Palembang BARI are still needed. The use of information system security techniques such as vulnerability scanning, penetration testing, WAF, IDS and IPS, and data encryption, as well as improving security in terms of server physical aspects such as installing CCTV and restricting user access with access cards or fingerprints, can be implemented to ensure compliance with relevant information security standards. Consideration for obtaining security certifications, like ISO 27001, should also be taken. Additionally, the quality of human resources in terms of policy-making and the ability of employees to address threats and attacks on information system security should be improved through training and strengthening coordination among employees.&#x0D;