Reviewerly/exHarmony huggingface dataset:
A gentle introduction to theory (for non-theoreticians)

tutorial Share on A gentle introduction to theory (for non-theoreticians) Author: Benjamin Doerr École Polytechnique École PolytechniqueView Profile Authors Info & Claims GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2022 Pages 890–921https://doi.org/10.1145/3520304.3533628Published:19 July 2022Publication History 0citation40DownloadsMetricsTotal Citations0Total Downloads40Last 12 Months40Last 6 weeks6 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Dropout topology-assisted bidirectional learning particle swarm optimization for neural architecture search

The neural architecture search (NAS) is a new high-complexity optimization problem emerging in recent years. Solving NAS is challenging for optimization algorithms due to the following two issues. Firstly, besides the network architectures, there are also network hyperparameters that need to be optimized, which causes the search space of NAS to be complex and large and poses a great challenge to the optimization ability of the optimization algorithm. Secondly, NAS is an expensive optimization problem with expensive computational time to evaluate candidates, which poses a great challenge to the search speed and convergence of the optimization algorithm. Therefore, this paper proposes a novel dropout topology-assisted bidirectional learning particle swarm optimization (DBLPSO) algorithm for NAS to tackle these two issues. Firstly, inspired by the dropout technique in deep learning, a sorting-assisted dropout-based neighbor topology is proposed to enhance the population diversity and the optimization ability of PSO. Secondly, a bidirectional learning strategy is proposed to improve search speed and accelerate PSO convergence. In the experiments, the performance of the DBLPSO algorithm is evaluated on 10 tabular benchmarks based on NAS-bench 201, NATS-bench, and HPO-bench, by comparing with four NAS algorithms that have achieved state-of-the-art results on the NAS tabular benchmarks. The experimental results show that DBLPSO can obtain great performance for NAS and is superior to those NAS algorithms in comparison.
THE CARNEGIE CLASSIFICATION SYSTEM AND AMERICAN HIGHER EDUCATION FUNDING POLICIES: AN ANALYSIS OF INFORMATION RESOURCE COLLECTIONS AT SELECTED RESEARCH INSTITUTIONS

Information about the paper titled "THE CARNEGIE CLASSIFICATION SYSTEM AND AMERICAN HIGHER EDUCATION FUNDING POLICIES: AN ANALYSIS OF INFORMATION RESOURCE COLLECTIONS AT SELECTED RESEARCH INSTITUTIONS" at IATED Digital Library
ISSUES OF ICT SECURITY IN SECONDARY SCHOOLS AND ITS IMPACT ON EDUCATION

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 3115-3121ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.0775Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
STAY HOME, STAY SAFE: UNDERGRADUATE STUDENTS’ PERCEPTIONS OF ENGAGEMENT AND ADAPTABILITY TO ONLINE LEARNING AND ASSESSMENT DURING THE COVID-19 PANDEMIC

Information about the paper titled "STAY HOME, STAY SAFE: UNDERGRADUATE STUDENTS’ PERCEPTIONS OF ENGAGEMENT AND ADAPTABILITY TO ONLINE LEARNING AND ASSESSMENT DURING THE COVID-19 PANDEMIC" at IATED Digital Library
DETECTION AND TRACKING OF DRONES USING ARTIFICIAL INTELLIGENCE TECHNIQUES - A SURVEY

Enormous mechanisms and mediums are being employed to threaten the defense system and civilians. Drone strikes are one of them, which may refer to the unloading of explosions and supervision as such. So, detecting and tracking the Drone could be a viable solution for any organization to tackle the aerial threat challenges and secure the environment from malicious activities. Thus the present research discusses the current paradigm of Drone strikes, challenges and solutions to deal with such security concerns. The present article aims to examine the current status of Drone Detection critically, and the applicability and advancement of Artificial Intelligence enabled technology. The present research article also explores the working mechanism of the Object detection system and Convolutional Neural Network (CNN) on the ground level to help future researchers gain knowledge. The paper also explains the maximum reach of accuracy achieved by the various model and algorithms so that a new benchmark can be defined.
Virtual Machine selection and migration in Cloud Paradigm: A Comparative Analysis

Cloud Computing provides larger scale resources using virtualization that delivers computing services like databases, networking, hardware, software, intelligence and so on. It provides an alternative to on-premises resources. But there is a need of dynamic consolidation of VMs for energy consumption, load balancing, total number of migrations, QoS and improvement in Service Level Agreement Violation (SLA-V) which will further leads to enhanced utilization of resources. To optimize the migration time, energy consumption, SLA-V, it is essential to migrate the load of underloaded and overloaded resources for balancing the load of data centers. Author mentioned various techniques in the literature for improving the performance and resources used in load balancing, energy consumption, resource management and QoS.
Analysis on optimal ways to secure image encryption and decryption

In the last two decades chaos has been among the most important cryptology design methods to design modern encryption algorithms. Suggestions were found to be very vulner-able towards simple known attacks. However, these suggestions cannot be proven secure so a plan for security review of new ideas is required. So in this review paper we have addressed various techniques, security attacks, NPCR and UACI values, key size and the various key definitions, the advantages and disadvantages of various chaos based cryptosystems with special focus on designing, analyzing and implementing cryptographic algorithms that allow peoples and entities to interact securely with each other.
Monitoring and Controlling Data Through the Internet of Things (IOT) System: A Framework to Measure the Public Health

Associating and sharing information by means of the web between actual things, or “things,” coordinated with sensors, programming, and different advances are known as the Internet of Things (IoT). In order to improve technology through IoT, there have been a number of important studies and investigations. This study exhibits how the Internet of Things might be utilized to screen wellbeing. In this research work, with the help of IoT based human wellbeing checking framework the information circulatory strain, beat rate, internal heat level, pulse, and other crucial signs are providing to the internet. The use of IoT for the human health monitoring system in later on future, need a very accurate assessment of risk and this is required to provide a long term information to the device.
IoT Research on Healthcare Data to Control Illness of Oncology

Let us begin with the internet of things that empowers software programmers to start increasing prediction without being done with full to do so. With today's data availability, machine learning techniques are being developed to improve performance and maintenance prediction. Cancer, according the Health Organisation, is a global disease and the second leading cause of death worldwide. People with cancer have been identified as having an increased risk of dying in the current worldwide epidemic. With the disease's incidence growing decade after decade, the need for a continuously and then round surveillance apparatus has become crucial. A cancer monitoring system known as the internet of Things (IoT) has the potential to identify cancer-related indications in their initial phases, continue to monitor disease individuals, and screen someone who has been cured through post-treatment operations. This research presents a multi-layered architectural for an IoT-based disease monitoring and control system that might be utilised as a foundation for remotely cancer diagnosis and management.
Project Cost Management Information Solution Based on Data Mining Technology

research-article Project Cost Management Information Solution Based on Data Mining Technology Authors: Zeyang Li Hubei University of Science and Technology, China Hubei University of Science and Technology, ChinaView Profile , Ling Liu Wuhan University of Science and Technology, China Wuhan University of Science and Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 832–836https://doi.org/10.1145/3544109.3544363Published:18 July 2022Publication History 0citation17DownloadsMetricsTotal Citations0Total Downloads17Last 12 Months17Last 6 weeks4 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Research on classroom management system based on genetic algorithm

At present, the optimization of teaching strategies is mainly based on teachers' self-knowledge and policy promotion, which can not well meet the needs of teaching, and can not be adjusted objectively according to the actual situation of teaching. This paper designs a teaching strategy evaluation mechanism, and on this basis, using genetic algorithm to optimize the teaching strategy, to achieve the teaching strategy with the change of teaching content and teaching object and constantly evolve and optimize the purpose. At the same time, the teaching strategy optimization system based on genetic algorithm is designed and implemented, and it is applied in practice.
Research on the Current Status of Primary School English Online Attention Based on Big Data

With the modern development of education in China, the related data in the education field has rapidly expanded, and national education has entered the "big data era". Aiming at the characteristics of the large-capacity, multi-type, and high-growth data collection in the education field, the use of big data technology for aggregation, classification, analysis, and reorganization is the only way to promote the high-quality development of education and complete the modernization of education. Based on the big data mining of the National Bureau of Statistics and Baidu Index, using literature data, knowledge graphs, mathematical statistics and logical analysis, the time characteristics of primary school English online attention, attributes of concerned people and factors affecting the English learning needs of netizens are explored and analyzed. The results show that there is no seasonally significant difference in the time characteristics of primary school English online attention. "Learning" is the main demand for primary school English attention, but there is a group preference of "focusing on knowledge and neglecting skills". Therefore, in the context of the era of "big data + education", giving play to the guiding role of education policy and education evaluation, stimulating and leading netizens to correct and healthy online attention, is crucial to achieving high-quality development of primary school English education.
Application of Bayes algorithm in classroom management system

The difference classification teaching based on Bayes algorithm is on the basis of the needs of students, and through reasonable analysis, students are taught in accordance with their aptitude. In this paper, on account of the characteristics of students in a higher vocational college, multidimensional classification attributes are established, and naive Bayes classification algorithm is applied to some classes for classification teaching experiments. Through the analysis of the teaching effect, the application of difference classification teaching based on naive Bayes algorithm has achieved certain results, which effectively solves the problems of "not enough to eat", "too much to eat" and not reaching the standard in the current teaching, and also effectively stimulates the learning interest and improves the students' cooperation ability. This provides data support for the theoretical research and practical teaching of differential teaching in the future.
Mobile mask detection system based on YOLOv4-tinys

research-article Mobile mask detection system based on YOLOv4-tinys Author: Qiuchen Wang Huazhong University of Science and Technology, China Huazhong University of Science and Technology, ChinaView Profile Authors Info & Claims IPEC '22: Proceedings of the 3rd Asia-Pacific Conference on Image Processing, Electronics and ComputersApril 2022Pages 667–671https://doi.org/10.1145/3544109.3544331Published:18 July 2022Publication History 0citation14DownloadsMetricsTotal Citations0Total Downloads14Last 12 Months14Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Variational Adaptive LM-IEKF for Full State Navigation System of Wind Disturbance and Observability Analysis

Robust and accurate state estimation algorithms applied to the small UAVs are always promising depending on the multiple onboard local and global sensors. This paper proposes a variational adaptive Levenberg-Marquardt iterated extended Kalman filter (VA-LM-IEKF) full state estimation algorithm to calculate the reliable UAV flight state parameters in wind disturbance. The navigation system based on the LM-IEKF can provide an accurate state by expanding the optimization range of estimated points. An adaptive filter using the variational Bayesian approach is proposed to improve the filter robustness to the observation noise covariance matrix. Moreover, a judging criterion is introduced into the filter observation correction step to eliminate the observed abnormal values. In addition, observability analysis with Lie algebra for the navigation system is established to evaluate the system observability. Simulation and real-data experiments in the self-developed small UAVs platform demonstrate that the performance of the proposed algorithm is better than the state-of-art methods in solution accuracy and filter robustness.
Classification of Malicious Websites Using Feature Based Machine Learning Techniques

The start of the COVID-19 pandemic has revealed a huge increase in malware sites. The rising instances of phishing, spamming and malware have created a key requirement for a robust mechanism which can precisely detect and classify malicious URLs. Conventional classification methods such as blacklisting and signature-based detection are not adaptive due to enormous volume of data, innovations and technology changing over time, along with complicated features. In this work, we propose a feature-based machine learning system for the malicious URLs identification as a two-class problem and assess the performance of ML classifiers namely Random Forest classifier and Support Vector Machine. We have utilized a Kaggle dataset consisting of 72000 URLs for training our ML model. This research highlights various view-points related to the URL classification process which classifies the objective site as malicious or benign. Proposed method has wide applications in cyber security applications as well as cyber forensic investigations.
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition

A rich video data representation can be realized by means of spatio-temporal frequency analysis. In this research study we show that a video can be disentangled, following the learning of video characteristics according to their spatiotemporal properties, into two complementary information components, dubbed Busy and Quiet. The Busy information characterizes the boundaries of moving regions, moving objects, or regions of change in movement. Meanwhile, the Quiet information encodes global smooth spatio-temporal structures defined by substantial redundancy. We design a trainable Motion Band-Pass Module (MBPM) for separating Busy and Quiet-defined information, in raw video data. We model a Busy-Quiet Net (BQN) by embedding the MBPM into a two-pathway CNN architecture. The efficiency of BQN is determined by avoiding redundancy in the feature spaces defined by the two pathways. While one pathway processes the Busy features, the other processes Quiet features at lower spatio-temporal resolutions reducing both memory and computational costs. Through experiments we show that the proposed MBPM can be used as a plug-in module in various CNN backbone architectures, significantly boosting their performance. The proposed BQN is shown to outperform many recent video models on Something-Something V1, Kinetics400, UCF101 and HMDB51 datasets. The code for the implementation is available.
On the support t-designs of extremal Type III and IV codes

Let C be an extremal Type III or IV code and $$D_{w}$$ be the support design of C for weight w. We introduce the numbers, $$\delta (C)$$ and s(C), as follows: $$\delta (C)$$ is the largest integer t such that, for all weights, $$D_{w}$$ is a t-design; s(C) denotes the largest integer t such that w exists and $$D_{w}$$ is a t-design. Herein, we consider the possible values of $$\delta (C)$$ and s(C).
Reinforcement Learning for POMDP Environments Using State Representation with Reservoir Computing

One of the challenges in reinforcement learning is regarding the partially observable Markov decision process (POMDP). In this case, an agent cannot observe the true state of the environment and perceive different states to be the same. Our proposed method uses the agent’s time-series information to deal with this imperfect perception problem. In particular, the proposed method uses reservoir computing to transform the time-series of observation information into a non-linear state. A typical model of reservoir computing, the echo state network (ESN), transforms raw observations into reservoir states. The proposed method is named dual ESNs reinforcement learning, which uses two ESNs specialized for observation and action information. The experimental results show the effectiveness of the proposed method in environments where imperfect perception problems occur.
Adoption of Blockchain in IoT: Challenges and Solutions

Data is streamed from sensors, through fog devices, and onto a centralized Cloud server in traditional Internet of Things (IoT) ecosystems. Issues that arise include privacy concerns due to third-party management of Cloud servers, single points of failure, a bottleneck in data flows, and difficulties in regularly updating firmware for millions of smart devices from a point of security and maintenance perspective. Blockchain, the underlying technology of Bitcoin, was initially primarily intended for the transfer of monetary value. Nevertheless, researchers and security analysts from all over the world are focusing on the blockchain to address the security and privacy issues of IoT due to its decentralized architecture, fault tolerance, and cryptographic security benefits like pseudonymous identities, data integrity, and authentication. Blockchain technology protects users by avoiding reliable third parties. This has inspired researchers to investigate blockchain’s adoption into the IoT ecosystem. In this paper, let us understand more about blockchain, its application in IoT, challenges while handling IoT data on the blockchain, and its security solutions.
亚院二年级学生使用方位词“里”与“中”的偏误分析 KESALAHAN PENGGUNAAN KATA BENDA PENUNJUK ARAH “LI” DAN “ZHONG” PADA MAHASISWA STBA-PIA SEMESTER IV T.A. 2018/2019

This study aims to describe the types of errors and the factors that cause errors in the use of directional nouns “li” and “zhong” made by STBA-PIA students in the fourth semester of academic year 2018/2019. The research method used is descriptive qualitative, namely to describe the types of errors and the factors of errors. The instruments used in this research are elicitation techniques and interview techniques. The research subject was taken using snowball sampling techniques, which was as many as 25 subjects. The results of the research conducted by research on STBA-PIA students in fourth semester of academic year 2018/2019 are 10 omission errors, 6 addition errors, 14 misformations and 3 misordering. Meanwhile, the factors that cause errors are namely interlingual errors, which are 7 respondents and intralingual errors, which are 15 respondents. Keywords: Language Errors, Directional Nouns, “li”, and “zhong
NFMLTA/<i>MLJ</i> Roundtable Conference Grants

The Modern Language JournalEarly View Announcement NFMLTA/MLJ Roundtable Conference Grants Marta Anton, Marta AntonSearch for more papers by this author Marta Anton, Marta AntonSearch for more papers by this author First published: 20 July 2022 https://doi.org/10.1111/modl.12800Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
KTI-RNN: Recognition of Heart Failure from Clinical Notes

Although deep learning methods have recently attracted considerable attention in the medical field, analyzing large-scale electronic health record data is still a difficult task. In particular, the accurate recognition of heart failure is a key technology for doctors to make reasonable treatment decisions. This study uses data from the Medical Information Mart for Intensive Care database. Compared with structured data, unstructured data contain abundant patient information. However, this type of data has unsatisfactory characteristics, e.g., many colloquial vocabularies and sparse content. To solve these problems, we propose the KTI-RNN model for unstructured data recognition. The proposed model overcomes sparse content and obtains good classification results. The term frequency-inverse word frequency (TF-IWF) model is used to extract the keyword set. The latent dirichlet allocation (LDA) model is adopted to extract the topic word set. These models enable the expansion of the medical record text content. Finally, we embed the global attention mechanism and gating mechanism between the bidirectional recurrent neural network (BiRNN) model and the output layer. We call it gated-attention-BiRNN (GA-BiRNN) and use it to identify heart failure from extensive medical texts. Results show that the F1 score of the proposed KTI-RNN model is 85.57%, and the accuracy rate of the proposed KTI-RNN model is 85.59%.