Reviewerly/exHarmony huggingface dataset:
Blog Feedback Prediction based on Ensemble Machine Learning Regression Model: Towards Data Fusion Analysis

The last decade lead to an unbelievable growth of the importance of social media. Due to the huge amounts of documents appearing in social media, there is an enormous need for the automatic analysis of such documents. In this work, we proposed various regression models for the blog feedback prediction to be used in the data fusion environment. These models include decision tree regressor, MLP regressor, SVR, random forest regressor, and K-Neighbors regressor. The models are enhanced by average ensemble and ensemble using K-Neighbors regressor. The Blog Feedback dataset is used for training and evaluating the proposed models. The results show that there is a decrease in RMSE, MAE, MBE, R, R2, RRMSE, NSE, and WI when compared to the traditional methods.
Domain Adversarial Transfer Learning for Bearing Fault Diagnosis based on Data Fusion and Attention

In the research of equipment health status estimation, transfer learning has been greatly used and developed. However, during the application of transfer learning, it is found that when the data quality is poor or the data distribution between the training and testing objects is significantly different, the transfer learning exhibits a good performance in the source domain, but a poor performance in The target domain. This means that the network does not learn the essential relationship between signal and fault characteristics. To overcome the above problems, the data fusion and attention mechanism are introduced in this research. By fusing the original data with the manually extracted feature data at the feature level, certain guidance can be introduced under the condition of ensuring the integrity of information. At the same time, the attention mechanism gives more weight to the signal fault location, which enables the network to reduce the interference of unimportant signals and learn more generalized fault feature recognition patterns. Then it's introduced the above method into the Domain Adversarial Neural Network. The results validate that compared with original neural network, the method of introducing the data fusion and attention mechanism has a significant improvement effect.
Importance of emotional quotient and spiritual quotient on employee engagement in IT/ITES industries during COVID-19 pandemic

The COVID-19 pandemic was a worldwide emergency, seen unrivalled rapid growth and has affected the numerous business areas. It has obligated numerous organisations to roll out revolutionary vicissitudes to the work-environment just as to their work-force, which made uncommon disturbances to their employees both personally and professionally. Researchers state that with the help of emotional quotient (EQ) as a central component, organisational behaviour subtleties can be understood and addressed to enhance employee engagement (EE). On the other hand, spiritual quotient (SQ) reinforces human consolidative impression of completeness and has the ability to think beyond materialistic life, which thrusts individuals to spiritual path and furthermore assists them to adapt to agonising conditions of life. This study revealed that the EQ and SQ are important aspects in service industries like IT and ITES in enhancing the EE during pandemic times, and correlation analysis showed a positive relationship among them.
Reinforcement learning - based adaptation and scheduling methods for multi-source DASH

Dynamic adaptive streaming over HTTP (DASH) has been widely used in video streaming recently. In DASH, the client downloads video chunks in order from a server. The rate adaptation function at the video client enhances the user?s quality-of-experience (QoE) by choosing a suitable quality level for each video chunk to download based on the network condition. Today networks such as content delivery networks, edge caching networks, contentcentric networks, etc. usually replicate video contents on multiple cache nodes. We study video streaming from multiple sources in this work. In multi-source streaming, video chunks may arrive out of order due to different conditions of the network paths. Hence, to guarantee a high QoE, the video client needs not only rate adaptation, but also chunk scheduling. Reinforcement learning (RL) has emerged as the state-of-the-art control method in various fields in recent years. This paper proposes two algorithms for streaming from multiple sources: RL-based adaptation with greedy scheduling (RLAGS) and RL-based adaptation and scheduling (RLAS). We also build a simulation environment for training and evaluation. The efficiency of the proposed algorithms is proved via extensive simulations with real-trace data.
Erroneous Analysis in English Writing Assessments among Undergraduates in a Public University

A number of studies on morphological error analysis has been carried out in different settings, contexts, and levels of study to observe, analyze and describe the errors related to English grammar rules. Using Surface Strategy Taxonomy by Dulay et al. (1982), this current study focused on the investigation of the types and factors of errors in English writing assessment specifically among undergraduates in a public university in Malaysia. This current study employed a qualitative research method by using case study. 30 samples of English writing assessment were analyzed based on the taxonomy and the framework of Sources of Errors by Richard (1974). The findings revealed In general, it was observed that the students made a total of 248 errors on 30 transcripts; 95 were omission errors, 91 were misformation errors, 54 were addition errors and only 8 were misordering errors. The results also highlighted that omission was recorded as the top error committed by learners followed by misformation, addition and the least common error reported to be misordering. As for the source of error, intralingual error marked as the most prevalent factor contributed to the errors. The findings of the study may provide some light in emphasizing the possible interventions to assist learners in their writing as well as developing remedial program to help learners to master English grammar.
Security at Its Core

Today most computing devices (laptop, smart phone, smart appliances, etc.) are connected to the Internet, which poses challenges for the device manufacturers because they have to think about platform security. For any device, platform security is a combination of hardware, software, and associated configurations. Users are always equipped with these devices, and they are used to perform financial and/or personal data transfers. Hence, the security specification demands continuous evolution. Firmware, being closest to the hardware, assumes the primary role of enforcing the security configuration because it is within the Trusted Compute Boundary (TCB). Firmware is also responsible for abstracting the operating system from the underlying hardware, which provides more reason to ensure that the communication channel is secure.
Sosialisasi dan Penerapan Kompetensi Literasi Digital di MA AL-MUSTHOFA

With the development of the internet today, of course there are a lot of positive and negative things that are obtained, especially with the covid-19 outbreak, of course all activities run online from learning, shopping and so on, therefore internet users are increasing rapidly, so the purpose of this task is to our tik tok volunteers are assigned to provide guidance and understanding about the internet well, with prepared materials about hate speech and also provide special services needed by the intended partner, and for our group from class A with the name arithmetic 1 group with The beneficiaries of MA Al-Musthofa and the special requests requested are material services regarding: Microsoft Office and Canva as well as reinstalling Ma Al-Musthofa's labcom from the results of the pre-test and post-test of all the materials presented, showing that the material can be delivered properly with all value of Sig.(2-tailed) &lt; 0.05.
Detection of intrusions into the Internet of things system

The Internet of Things (IoT) is an evolving technology in which computing devices and sensors exchange data over a network to solve various tasks and provide services. Medical care, remote control of devices, interaction between machines, etc. are services provided today for users without human intervention. Despite a number of advantages, this technology also has disadvantages, one of which is security. There are many methods used to protect the Internet of Things, one of them is the Intrusion detection System (IDS) - this is one of the most original and well-organized methods that can protect Internet of Things devices from intruders and detect their attack with high accuracy. The article discusses such types of attacks as DDoS/DoS, hello flood and Sybil attack, etc., as well as various types of approaches to IDS, such as machine learning, SDN and machine-based identifiers, which can be useful for preventing and detecting attacks on Internet of Things devices.
Students’ Voices on Literacy Enhancement at a Tertiary Level

The development of reading literacy at an early stage is a determining factor for students at the later stages. A review of studies that are related to this issue indicates that some students are still underperforming in reading English texts even at the tertiary level, hence unsuccessful to meet the expectancy of literacy skills needed to progress accordingly. This paper aims to investigate students’ literacy skills in reading at the tertiary level. Adapting an interpretivism paradigm approach, the students’ reading background before tertiary education was explored through a focus group interview. Ten undergraduates of non-English majors from the English for Social Sciences class were interviewed to elicit their attitudes and perspectives towards the course. The interview involved continuous questioning and comparing in reflecting on how these factors would cater for the needs of undergraduate students. The findings indicate students’ negative perceptions towards the course as most of them had difficulties coping with academic reading at the tertiary level. This study highlights the need in providing suitable course content and academic support for the students in catering sufficient reading literacy to suit the students’ needs. The findings suggest students’ voices should be given some consideration when planning to improve the course contents and the mode of delivery of reading materials, especially for the tertiary level.
SUPPORTING INTEGRATED DROUGHT SCIENCE WITH U.S. GEOLOGICAL SURVEY TOOLS, APPLICATIONS, AND COMMUNITIES

The U.S. Geological Survey (USGS) generates data and information products critical for integrated drought research. These include data and models covering the diverse topics of landcover, groundwater, surface-water, snowpack, vegetation, soil moisture, stream temperature, invasive species, and biogeochemical processes. Like most research organizations, USGS is encountering new challenges as scientists contend with the scale of these data products when transferring, storing, analyzing, and referencing them to meet research goals and to enable reproducible scientific workflows. To support these needs, USGS has been modernizing internal data systems, integrating new technical architecture, and building communities of practice to support contemporary research requirements. ScienceBase (www.sciencebase.gov), a USGS data repository, now uses Amazon Web Services (AWS) cloud storage to support large files. For two common file formats, Cloud Optimized GeoTiff (COG) and formatted NetCDF, cloud storage can also support direct access for viewing, analysis, and sub-setting purposes. ScienceBase has also recently integrated with the Globus platform (https://www.globus.org/) to help scientists move and retrieve large datasets. These advances to data publishing efforts support persistent citation, research access needs, and directives to modernize Federal IT infrastructure. Paired with code-based analysis and modeling, a catalog that links related model code, data, and traditional text-based manuscripts (https://data.usgs.gov/modelcatalog), and a USGS-wide community of practice for sharing advances in integrated data and science (https://usgs.gov/cdi), the USGS is working to improve how the organization delivers information and decision-making tools to stakeholders. These tools and resources are publicly available to support collaboration with non-USGS partners.
Strategies of Thinking Visualization Tools in Reading Teaching

The word "visualization" originates from the English word "visual", which means "visual and intuitive".Generally speaking, visualization is the method by which we can transform abstract things into directly observable graphics and images.In teaching, especially in reading teaching, the use of thinking visualization tools is very helpful to realize the ordering of disordered knowledge and the concretization of abstract knowledge.It can not only improve students' interest in learning, make classroom knowledge more specific, but also improve teaching efficiency and optimize teaching effect.
Pemanfaatan Multiteks Sebagai Upaya Menumbuhkan Minat Baca di Sekolah Dasar

Reading is a very important activity in today's modern times. Knowledge transfer can be done quickly and accurately through reading activities. Unfortunately, reading is not yet entrenched in Indonesian society, especially in elementary schools. This can be seen from the results of research which show the low reading ability of students. In order for reading to become a culture, efforts are needed to grow it. These efforts must be carried out systematically and continuously. One thing that can be done is the use of multitext as reading material. Multitext, besides providing benefits to increasing interest and reading ability, also provides a solution to the problem of the lack of library collections in elementary schools.
A Multi-branch Cascade Transformer Network (MBCT–Net) for Hand Gesture Segmentation in Cluttered Background

Hand gesture segmentation is an initial and essential step to classify hand gestures, which provides a simple, intuitive, concise and natural way for human–computer interaction, human–robot interaction. However, hand gestures segmentation with various hand shapes cluttered background is still a challenging problem. To solve the problem, a Multi-Branch Cascade Transformer Network (MBCT–Net) is proposed to segment hand regions from the cluttered background based on encoder-decoder convolutional neural networks, the encoder of the MBCT–Net consists of a deep convolutional neural network (DCNN) module and a multi-branch cascade Transformer (MBCT) module. Furthermore, the MBCT module is designed to represent local details and global semantic information of hand gestures. Moreover, to enhance semantical interaction between different windows and expand the receptive fields of MBCT-Net, we design a multi–window self-attention (MWSA) block in each branch of MBCT module to extract features of hand gestures. The MWSA block not only reduces the amount of calculation, but also enhances semantic interactions between different windows. To verify effectiveness of the proposed MBCT–Net, corresponding experiments have been conducted, and the experimental results prove correctness of the MBCT–Net.
Adaptive variational Hamiltonian Monte Carlo sampling with application to ocean acoustics

The Hamiltonian Monte Carlo (HMC) algorithm is a variant Markov Chain Monte Carlo (MCMC) excelling at sampling high dimensional target distributions. The HMC explores a distribution as a particle trajectory in a Hamiltonian system by augmenting distribution parameters with auxiliary momentum variables. A particle trajectory is numerically integrated from initial point to some distant low-correlation point. Projecting back to parameter space yields a proposal point with high acceptance probability leading to faster exploration of the target distribution. The Leap-frog integrator is commonly used for its volume preservation (symplecticity) and reversibility traits that maintain the detailed balance condition of MCMC. Although effective, Leap-frog has sensitivity to choice of integration step-size and number of integration steps. Poor choices cause slow exploration or bias when regions of the distribution are not explored. Adaptive step methods could remove step size issues and explore regions of a distribution where Leap-frog fails. Existing adaptive methods self-adjust step-size while maintaining symplecticity and reversibility. In this work, we apply adaptive step methods to simulated target distributions and real world ocean acoustic data and compare performance. [Work supported by Office of Naval Research.]
Lights, Camera, Action! News Program Fosters Technology Ambassadors

Libraries function as the central hub of the school, the place with the ability to inspire creativity, risk taking, acquisition of new ideas and skills, and camaraderie among students and throughout the school. Today, libraries are the centralized hub of all aspects of media, which now encompasses technology, an aspect that continues to grow in influence by the year.
A Weakly Supervised Time Series Analysis Framework for Anomaly Detection in Nuclear Power Plants

Abstract Condition monitoring is essential to the management and maintenance of Nuclear Power Plants (NPPs), as anomalies in the condition of components can affect the normal operation state of the entire plant. Therefore, timely and automatic detection of anomalies plays an important role and is in high demand. At present, deep learning is widely used for anomaly detection. Nevertheless, anomalies are difficult to define, sparsely occurring, and are accompanied by variable noise labels, which poses challenges to detection. Moreover, the problems such as loss of temporal features and gradient vanishing that exist in general deep learning models when dealing with time series data also increase the difficulty of anomaly detection. In response to these problems, a weakly supervised time series analysis framework for anomaly detection in NPPs is proposed, constituted of weakly supervised learning (WSL) and attention mechanism. The validation of the proposed framework was performed on the High Temperature Engineering Test Reactor (HTTR) anomaly cases dataset using the analytical code “ACCORD”, which distributed anomalies independently across multiple instruments and were recorded from the responding sensors to each anomaly. At this stage, 3 classes of anomalies were used as input data for the validation experiments. The experimental results demonstrate the effectiveness and feasibility of the proposed framework on anomaly tasks for the condition monitoring of NPPs.
Advances in quantum error correction based on superconducting quantum systems

Information processing technology based on the basic principle of quantum mechanics shows great potential applications in computing, sensing and other fields, and is far superior to classical technology. With the advance of experimental technology, quantum control technology develops rapidly. Compared with other quantum information processing platforms, the superconducting system based on solid materials has the advantages of accurate quantum controllability, excellent quantum coherence and the potential for large-scale integration. Therefore, superconducting quantum system is one of the most promising platforms for quantum information processing. The existing superconducting circuits, which can integrate about one hundred qubits, have already demonstrated the advantages of quantum systems, but further development is limited by system noise. In order to break through this bottleneck, quantum error correction technology, which is developed from the classical error correction technology, has attracted extensive attention. Here, we mainly summarize the research progress of quantum error correction in superconducting quantum systems including the basic principles of superconducting quantum systems, the quantum error correction codes, the related control techniques and the recent applications. At the end of the article, we summarize seven key problems in this field.
Brief Discussion on the Influence of Learning Support and Self-Management on the Results of Postgraduate Entrance Examination

In recent years, great changes have taken place in the social demand for talents.The advantages of college students in the job market no longer exist.If we do not continue to study, we will not be able to become talents with high professional quality and strong comprehensive ability, and lack stronger competitiveness in the market, which will have a great adverse impact on their future development.Starting from the overall employment and career development environment at home and abroad, it will be found that the postgraduate entrance examination is the first choice for college students, resulting in increasingly fierce competition for the postgraduate entrance examination.How to win in this competition is a problem that teachers should actively think about.This article will analyze it from two aspects: learning support and self-management.
CTFF: An Efficient Strategy for Continuously Targeting Fire Fighting with Quadrotor

This paper considers the optimal control problem of continuously targeting for extinguishing a moving fire source with a quadrotor. It aims to enhance computational time-efficiency, and real-time targeting performance compared with generic optimizers onboard, even suffering from severe nonlinearities. To this end, an efficient strategy is proposed for this optimal control problem. First, the Gauss Pseudospectral Method is utilized to transform the optimal control problem into a nonlinear constrained quadratic programming problem. Then, via the Sequential Quadratic Programming (SQP) algorithm, the control input can be generated analytically. To further improve the computational efficiency, the techniques, separation of variables and prior gradient calculation, are applied. Through comparative simulations, it is verified that the proposed method has excellent targeting performance and high computing efficiency. Further analysis demonstrate that the proposed efficient strategy can complete the control plan in approximately 0.5 ms, which is 20 times faster than the generic optimizers.
Digital image processing for idle time analysis of construction equipment

The Correct analyses of construction equipment idle time have great importance for the cost-effective energy having of construction equipment's in mega project. The higher productivity can achieve with the less idle time of equipment; however, by human usual observation, this is ideal time. This paper outlines an image-processing based methodology for calculating the idle time of hydraulic excavation automatically. Tracing algorithm and image segmentation are based on HSV. In terms of defining and tracing construction equipment, the HSV color space is beneficial than RGB color scheme. Experimental results demonstrate that given methodology ensures far more effective equipment management in the construction industry
Face Recognition Based on Adaptive Nearest Regularized Subspace

In this paper, Nearest Regularized Subspace (NRS) is introduced into the field of Face Recognition to address the problems of multicollinearity and lack of selectivity of LRC classifiers over data. The NRS-LDA classifier is also constructed through the LDA construction Tikhonov matrix. At the same time, the advantages and disadvantages of classifiers such as LRC, CRC-Pre, NRS, and NRS-LDA are elaborated through experiments and the reasons for its generation are analyzed. The Adaptive Nearest Regularized Subspace (ANRS) classifier is proposed by combining the advantages of LRC and NRS.