Reviewerly/exHarmony huggingface dataset:
The Effectiveness of Cognitive Counseling with Cognitive Restructuring Techniques to Increase Students' Interest in Literacy

The lack of students’ literacy is required to be overcome in order to create well-informed and active human sources so it required an effective counselling technique. As a response of this current phenomenon, this research aimed at determining the effectiveness of Cognitive Counselling Theory with Cognitive Restructuring Techniques to Increase Students' Interest in Literacy. The type of quasi-experimental research is non-equivalent control group design with pre-test and post-test control group design. The involvement of students of class X, totalling 39 people as the experimental class and 39 as the control class were selected as the sample of this research. The data analysis method used is the t-test using the SPSS 20.0 Windows 10 program. The results of the t-count data analysis are evident from the t-hit value of 10,733 &gt; 0,227 t-table. So that Ho is rejected and Ha is accepted. The conclusion of this investigation was cognitive counselling with cognitive restructuring techniques is effectively used to increase literacy interest in SMA Negeri 2 Singaraja students. The implication of this study is on the change of the students cognitive and for the counsellor who wants to implement a new effective technique.
Large-scale distribution network load data cleaning research based on improved random forest

Some large-scale distribution network load data cleaning methods have the problem of excessive relative error. A load data cleaning method for large-scale distribution network based on improved random forest is designed. Based on the release characteristics of electrical loads, large-scale distribution network open source data is collected, and an anomaly data processing model is constructed based on improved random forests to describe the processing process of numerical error data, using the original sample set as a classifier and optimising the load data cleaning model. Test results: The mean relative errors of the large-scale distribution network load data cleaning method in this paper and the other two large-scale distribution network load data cleaning methods are 3.618%, 5.162% and 5.007% respectively, indicating that the performance of the designed large-scale distribution network load data cleaning method is better after incorporating the improved random forest algorithm.
Machine Learning and Social Media Harvesting for Wildfire Prevention

Social media can be exploited to ease or better understand many problems in science and technology. Social media sensing become one of the methods to get the update information from crowd. Text is the most unstructured information sources available on that media comes from their users. Taking useful information from the unstructured text however, still challenging. The conversion from unstructured data to a vector need to be carried out texts analysis. Machine learning offer an opportunity overcome the challenges. However, it needs massive amount of labelled data to train the machine learning. The research community pose the fact that the availability of the dataset for non - English languages are limited. Forest fire as one of topics discussed in many social media platforms in many languages including Indonesian language. This work fills the gap by providing a labelled dataset in forest fire topics. Moreover, an exercise of the dataset with some available machine learning techniques are compared. Due to the limited number of data rows, researchers perform data augmentation and observe the impact of the augmentation towards the classifier performance. According to our experiments, we found that Random oversampling (ROS), in general, improves the accuracy score for all models we employed. Bidirectional LSTM with ROS overperforms other classifier and achieves 91%, 91% and 90.7%of recall, precision and f1-score respectively.
How to Use this Book

This is a book with a single, specific purpose – to help you pass American Petroleum Institute (API) 510/570/653 Individual Certification Programme (ICP) examinations. It’s therefore a teaching book rather than a knowledge book. The knowledge book exists already, in the form of the package of codes and recommended practice (RP) documents that make up the bodies of knowledge (BoKs) of the three examinations this book covers. ICP exams are unique in the engineering inspection world in the size and complexity of the BoKs they use – up to 1000+ pages of text across a range of subjects.
Determining Meteorological Parameters Influencing Photovoltaic Solar Energy Generation in Quezon City Using Machine Learning Algorithms

One challenge in adapting to energy generation using solar photovoltaic (PV) modules is its variability with changing weather conditions. In this study, we aim to determine the effect of meteorological parameters that have the most effect on the variability of solar energy generation (SEG). Our study is conducted in Quezon City, part of the National Capital Region, Philippines. The maximum temperature, relative humidity, man temperature, and cloud opacity have the most effect on the variability of the SEG among the eight meteorological parameters that we consider in our study based on the principal component regressor (PCR) and random forest regressor (RFR) machine learning algorithms. The PCR model explains 55.5 and 49.2% variability in SEG of the training and test sets, respectively. On the other hand, the RFR model explains a 77.1% variation of the SEG in the training and 52.7% in the test set. Furthermore, the two models provided comparable predictions of SEG.
On the Perception of Frame Stalls in Remote VR for Task and Task-Free Subjective Tests

The performance of remotely rendered Virtual Re-ality (VR) is sensitive to temporal disturbances in communication channels. An earlier Quality of Experience (QoE) study of tem-poral impacts in the form of frame stalls has revealed difficulties with subjective disturbance ratings while performing a task in an interactive 6-degrees-of-freedom (DOF) VR environment. This study follows up on above observation by comparing QoE ratings in the presence and absence of a task. The exploratory findings show that the task-free subjective tests yield lower ratings compared to the subjective tests with task. This indicates that the participants became more sensitive to temporal impairments in the absence of a task. Also, the positive impact of reprojection on the QoE ratings decreased in the task-free environment. The simulator sickness results for individual symptoms were on similar low levels in both settings. The total score (TS) of sickness severity was higher after than before the subjective tests with task while the difference between the TS before and after the task-free subjective tests was insignificant,
The Construction of Cultural Translation Research System under Big Data Technology

Abstract In the context of big data development, one of the reasons for the ineffectiveness of Chinese cultural translation research is the lack of attention to the “system construction”, this paper focuses on the construction of the cultural translation research system under the big data technology, based on the MOOC recommendation system using the recommendation engine group for the hybrid recommendation, the recommendation engine group integrated the use of the mainstream collaborative filtering algorithm based on foreign translation literature in the recommendation algorithm. Through the experimental analysis, the model performance reaches the maximum when the number of cultural literature is 7, and the F_1 of the traditional method is improved by about 2.6%. Compared with the original recommendation algorithm, the improved recommendation algorithm of the MOOC implicit scoring model can get more accurate recommendation results to promote the construction of a Chinese cultural foreign translation discourse system. It is expected to promote the construction of Chinese cultural foreign translation discourse system, show the vitality, staying power, and influence of China’s excellent culture to the world, enhance the international discourse power and cultural soft power of Chinese culture in the new era, and contribute Chinese wisdom to global cultural exchange and governance.
Five stages of professional personality development: Comparative analysis

The purpose of the study is to determine the characteristic features of personal functioning at different stages of professional development.The survey involved 139 professionals from various fields (74 women and 65 men) aged 24 to 67 years.The sample is divided into 5 groups according to the stage of professional development.A comparative analysis of groups by parameters of professional self-realisation, emotional burnout and psychological well-being is carried out.The results obtained demonstrate the nonlinear, complex dynamics of self-realisation of the individual throughout life and clarify the internal mechanisms of professional development at each stage.The stages of primary and secondary professionalisation are accompanied by the greatest need for self-improvement and at the same time, exaggerated and unrealistic ideas about one's own professional competence.An increased symptomatology of emotional burnout has been identified, which accompanies the peak of professional excellence and determines the next stage of professional activity decline after 30 years of work experience.The coincidence of the normative age and professional crises entails a profound crisis of the pre-retirement age, which is characterised primarily by a loss of goal-setting.People who continue to work in the post-retirement age have the highest rates of self-fulfillment, which leads to overall satisfaction with life and self.The described patterns open new perspectives for the development of ways of psychological counselling and organisational support of specialists.
Analysis Of Raw 3D Images Of Stages Of Alzheimer’s Disease Using Deep Learning

Alzheimer’s disease is defined as a brain ailment that progressively impairs thinking, cognitive skills, and the capacity to complete most basic tasks. Memory loss, cognitive changes, and other neuronal brain disorders are all symptoms of AD, a degenerative condition. Since risk awareness encourages patients to take preventative measures even before the onset of irreversible brain damage, an absolute diagnosis of Alzheimer’s disease is vital. Total brain atrophy and hippocampal atrophy are considered to be the main diagnostic tests for the condition. For this condition, early identification is important, and automatic system design is required. Computer-assisted methods are implemented for the analysis of AD in several types of research and the outcomes are constrained due to the congenital findings. Early stages of AD can be diagnosed but not predicted because prediction is only useful before the disease manifests itself. Deep learning (DI) techniques are used to analyze the raw MRI 3D images to identify AD and its progressive stages. The efficiency of the deep learning networks is defined to be less, which is indefinite for diagnosing AD stages. Therefore, processing of the images is necessary for the detection and to predict the progression of AD.
An intelligent decision support approach for quantified assessment of innovation ability via an improved BP neural network

&lt;abstract&gt;&lt;p&gt;In today's competitive and changing social environment, innovation and entrepreneurial ability have become important factors for the successful development of college students. However, relying solely on traditional evaluation methods and indicators cannot comprehensively and accurately evaluate the innovation and entrepreneurial potential and ability of college students. Therefore, developing a comprehensive evaluation model is urgently needed. To address this issue, this article introduces machine learning methods to explore the learning ability of subjective evaluation processes and proposes an intelligent decision support method for quantitatively evaluating innovation capabilities using an improved BP (Back Propagation) neural network. This article first introduces the current research status of evaluating the innovation and entrepreneurship ability of college students, and based on previous research, it has been found that inconsistent evaluation standards are one of the important issues at present. Then, based on different BP models and combined with the actual situation of college student innovation and entrepreneurship evaluation, we selected an appropriate input layer setting for the BP neural network and improved the setting of the middle layer (hidden layer). The identification of output nodes was also optimized by combining the current situation. Subsequently, the conversion function, initial value and threshold were determined. Finally, evaluation indicators were determined and an improved BP model was established which was validated using examples. The research results indicate that the improved BP neural network model has a low error rate, strong generalization ability and ideal prediction effect which can be effectively used to analyze problems related to intelligent evaluation of innovation ability.&lt;/p&gt;&lt;/abstract&gt;
Can small and medium enterprises benefit from AR technology? Current challenges and trends

Albeit augmented reality (AR) technologies first have been discovered in the third quarter of the 20th century, their widespread use began just two decades ago. Existing paper trails show that AR has a wide range of industrial application: it simplifies human-machine communication, improves human-machine interfaces (HMI) for quick information exchange in training (including feedback to study the workflow), correction of errors, machine maintenance, assembly assistance etc. However, broader industrial acceptance of AR, prior to all by small and medium-sized enterprises (SMEs), recently faced considerable problems and the expansion of AR solutions does not match the high potential it has demonstrated. That results in a limited practical use, mainly for demonstration and advertising purposes. This short review is to present the state of the art of the industry, challenges that SMEs face in adopting AR technologies, and several practical examples of a (commercial) adoption of AR. Some prospects for further development of AR and its ongoing integration into industry are briefly discussed in the summary.
Digital Primer Implementation of Human-Machine Peer Learning for Reading Acquisition: Introducing Curriculum 2

The aim of the digital primer project is cognitive enrichment and fostering of acquisition of basic literacy and numeracy of 5 – 10 year old children. Here, we focus on Primer's ability to accurately process child speech which is fundamental to the acquisition of reading component of the Primer. We first note that automatic speech recognition (ASR) and speech-to-text of child speech is a challenging task even for large-scale, cloud-based ASR systems. Given that the Primer is an embedded AI artefact which aims to perform all computations on edge devices like RaspberryPi or Nvidia Jetson, the task is even more challenging and special tricks and hacks need to be implemented to execute all necessary inferences in quasi-real-time. One such trick explored in this article is transformation of a generic ASR problem into much more constrained multiclass-classification problem by means of task-specific language models / scorers. Another one relates to adoption of "human machine peer learning" (HMPL) strategy whereby the DeepSpeech model behind the ASR system is supposed to gradually adapt its parameters to particular characteristics of the child using it. In this article, we describe first, syllable-oriented exercise by means of which the Primer aimed to assist one 5-year-old pre-schooler in increase of her reading competence. The pupil went through sequence of exercises composed of evaluation and learning tasks. Consistently with previous HMPL study, we observe increase of both child's reading skill as well as of machine's ability to accurately process child's speech.
A Group Decision-Making Method Based on Reciprocal Preference Relations Created from Sentiment Analysis

Group decision-making is an event in which a group of experts have to decide between a limited set of alternatives. To share their opinions and ideas, this group of experts conducts a debate where they compare the alternatives with each other. Nevertheless, once the debate is over, the experts must provide their assessments by using reciprocal preference relations. This can be a problem, as an expert may not be consistent between what he expresses and his assessments. To create such consistency, in this work a group decision-making method is developed, which automatically creates the experts’ reciprocal preference relations from the comments they make in the debate. These comments are classified into positive and negative by using sentiment analysis techniques, specifically employing the majority membership in the bag of words of a given class. To calculate each value of the reciprocal preference relation, a new operator has been developed that uses the ranked comments and weights them. Nonetheless, the method offers the possibility to modify a reciprocal preference relation if the expert wishes to do so. Finally, this new group decision-making method develops a second operator that also uses the number of comments to adjust the weight of each expert. This operator quantifies the number of comments that an expert makes during the debate and gives him or her more weight based on the number of comments he or she makes. Consequently, the more comments the expert makes, the higher the weight he or she gets.
Blockchain-based Certificate-free Cross-domain Authentication Mechanism for Industrial Internet

In Industrial Internet, mutual authentication between enterprises is a prerequisite for establishing reliable upstream and downstream relationships. Existing authentication methods suffer from complicated certificate management and key escrow problems. Moreover, many authentication mechanisms cannot resist common security attacks and have high computational overhead and communication costs. Therefore, this paper proposes a blockchain-based certificate-free cross-domain authentication mechanism for Industrial Internet. By establishing an Ethereum consortium blockchain as the trusted cornerstone among different regions, industrial enterprises in each region generate the user’s private key with the key generation center in the region, thus avoiding the key escrow problem. This consortium blockchain adopts the proof of authority consensus mechanism for scalability and throughput. Industrial enterprises in different regions invoke smart contracts and query other industrial enterprises for mutual authentication and key negotiation. SVO logic proves the proposed scheme achieves the intended authentication goal, and the automated formal verification tool Scyther proves the scheme’s security. In addition, compared with seven related schemes in the last three years, the experimental results show that the proposed scheme has low communication overhead and computational cost in the authentication key negotiation phase. The experiments on the Ethereum consortium blockchain built by Raspberry Pi prove the effectiveness of the proposed scheme. Finally, the comparative analysis of common security properties proves the reliability of the scheme.
Conditional Generative Adversarial Network-Based Bilevel Evolutionary Multiobjective Optimization Algorithm

In bilevel multiobjective optimization problems (BLMOPs), the mapping from an upper-level vector to the corresponding lower-level optimal vectors is a complex set valued mapping. Existing methods require numerous surrogate models to fit such a set valued mapping by grouping the lower-level optimal vectors, and the effects are not satisfactory because the correlation among lower-level optimal vectors corresponding to the same upper-level vector is disregarded. In this paper, introducing conditional generative adversarial network (cGAN), we use only one surrogate model to effectively fit such a set valued mapping, which extracts knowledge from lower-level optimal vectors corresponding to the same upper-level vector. Then, a BLMOP is transformed into a single-level constraint multiobjective optimization problem (CMOP). By adaptively allocating computational resources to optimize the CMOP, promising upper-level vectors are obtained. Furthermore, a lower-level search is executed for these promising upper-level vectors, thus obtaining high-quality solutions. Because of the excellent performance of cGAN and the lower-level search conducted only for promising upper-level vectors, the computational overhead is greatly reduced. The proposed algorithm has achieved the best results in comparison with 5 state-of-the-art algorithms on benchmark problems and a real-world problem, whose effectiveness has been demonstrated.
Course Recommendation Based on Graph Convolutional Neural Network

Selecting the right learning content according to learners’ learning abilities and interests is the first and most important factor in achieving good learning performance. Based on the similarity between the course rating data in the Collaborative Filtering format (user, item, rating), and along with the development of Graph Neural Networks (GNN) in developing recommendation systems, we tried to develop a Collaborative Filtering (CF) model based on GNN architecture to recommend suitable courses to the learners. In this study, two CF models based on GNN, including Neural Graph Collaborative Filtering (NGCF) and Light Graph Convolutional Neural Networks (LightGCN), were experimentally compared with some traditional CF models such as Regularized Matrix Factorization (RMF), Light Matrix Factorization (LMF), and Neural Collaborative Filtering (NCF). The experimental results on the Coursera Course Review dataset using LightGCN give promising results in the precision, recall, and RMSE metrics.
Involving End Users in Risk-Based Co-design

The increasing availability of digital technologies in healthcare provides the means for increased participation of patients in their care path. This increased empowerment is becoming more widespread day by day. Nevertheless, still little or no inclusion in the design process is part of the common practice during the mHealth medical device design. This chapter proposes an approach based on the indications of international standards for medical device design and manufacturing to foster the adoption of co-design techniques and the inclusion of end-users in the decisional process. The adoption of the proposed method, in particular, is suitable with processes based on international standards for medical device management as per a compliant quality management system (ISO 13485), usability evaluation (IEC 62366) and risk management (ISO 14971), which are the methods used by manufacturers to prove compliance with the requirements of the European Regulations. Such a method ensures a high level of protection for all stakeholders while fostering innovation and participation.
Protecting Sensitive Data in Salesforce – A Multi-Layered Security Approach

This research paper aims to provide a comprehensive review of the security measures and best practices specifically tailored for Salesforce implementations. By delving into the intricacies of Salesforce's security capabilities and exploring advanced features, this paper intends to equip organizations and Salesforce administrators with the knowledge and tools necessary to establish robust security protocols and mitigate potential risks.