Reviewerly/exHarmony huggingface dataset:
Multi-Trigger-Key: Towards Multi-Task Privacy Preserving In Deep Learning

Deep learning-based Multi-Task Classification (MTC) is widely used in applications like facial attributes and healthcare that warrant strong privacy guarantees. In this work, we aim to protect sensitive information in the inference phase of MTC and propose a novel Multi-Trigger-Key (MTK) framework to achieve the privacy-preserving objective. MTK associates each secured task in the multi-task dataset with a specifically designed trigger-key. The true information can be revealed by adding the trigger-key if the user is authorized. We obtain such an MTK model by training it with a newly generated training set. To address the information leakage malaise resulting from correlations among different tasks, we generalize the training process by incorporating an MTK decoupling process with a controllable trade-off between the protective efficacy and the model performance. Theoretical guarantees and experimental results demonstrate the effectiveness of the privacy protection without appreciable hindering on the model performance.
Development of Teaching Props on Water Cycle in Fifth Class Madrasah Ibtidaiyah

The research and development of teaching aids was motivated by the fact that students in class V MIN 1 in Palangka Raya City were faced for the past 2 years with online learning, some of which had an impact on decreasing learning outcomes, lack of student interest in learning, and the need for additional creative teaching aids to arouse students' interest in learning. . Teaching aids are communication tools used by teachers in the learning process. Having valid teaching aids will make it easier for students to understand the material. On the other hand, if the props used are not valid then it will not facilitate understanding for the participants. This development aims to describe the development of teaching aids for class V water cycle material at MI, and to determine the feasibility of the results of developing teaching aids for class V water cycle material at MI. This research is a type of Research and Development (R&amp;D) research that uses the 4- D model. Data collection uses interview, observation, documentation and questionnaire techniques. The final results of this research showed that the validity test of media experts' teaching aids obtained a percentage score of 97.91%, and material experts obtained a percentage score of 98.21%, in the category of very valid and suitable for use. The student response to the results of developing teaching aids was 95.83%.
Problems and Improvement Direction of PSAT(Public Service Aptitude Test)

PSAT(Public Service Aptitude Test)), introduced in 2004, is an evaluation system that measures comprehensive thinking skills that can be acquired through normal school education to test the qualities and abilities necessary to perform public office. Rather than knowing against the changing times, the purpose of “problem-solving” is to solve real problems and achieve results in work for the purpose of recruiting talented people with the aptitude and competence required by the organization.&#x0D; However, more than 20 years later, questions are being raised as to whether the test faithfully fulfilled the purpose of selecting talented people with practical skills.&#x0D; First of all, it is necessary to reconsider the specificity and expertise of public officials because PSAT(Public Service Aptitude Test) is a public office qualification evaluation, and to reexamine whether there is a correlation with the core competencies of public officials in selecting talented people suitable for public office in the public office recruitment examination.&#x0D; To this end, public officials have some characteristics unlike other occupations, some administrative differences from other countries, and PSAT (Public Service Aptitude Test) in Korea has identified its advantages and disadvantages compared to similar tests.&#x0D; As a result, PSAT is currently achieving great results in terms of efficiency in evaluating civil service qualifications, but it still has the problems that were initially presented. Internally, passages and questions tend to be repeated or recycled with other tests, and externally, the lack of connection between the first and second tests and lack of connection with actual administrative work were identified as the biggest problems. The solution is to change the test time and passages compared to the College Scholastic Ability Test (CSAT), LEET (legal Education Eligibility test)) or NCS (National Competency Standards), diversify the test sources, and link it with actual work. It suggests ways to do so.
Pengembangan Media Kotak Berhitung Penjumlahan dan Pengurangan

This research aims to develop a product in the form of Addition and Subtraction Counting Box Media Development in Grade I Mathematics Learning at SD Negeri Nawangsasi which is valid, practical and effective for use in learning. This research is a development research with four-D development model. Data collection techniques used interviews, observations, questionnaires and tests. Based on the results of the assessment analysis by the three validity experts with an average score of 0.81, it shows that the Addition and Subtraction Counting Box Media in Mathematics Learning meets very valid criteria. While the results of the teacher and student practicality sheet research obtained an average score of 81% that the Addition and Subtraction Counting Box Media in Mathematics Learning practical criteria. And the results of the effectiveness research of all first grade students obtained that the Addition and Subtraction Counting Box Media in Mathematics Learning meets the High criteria with a final score of 0.72 so that it can be concluded that the Addition and Subtraction Counting Box Media in Mathematics Learning meets the valid, practical and effective criteria, and is suitable for use in learning.&#x0D; Keywords: Counting Box, Mathematics, Addition, Subtraction.
Benchmarking quantized LLaMa-based models on the Brazilian Secondary School Exam

Although Large Language Models (LLMs) represent a revolution in the way we interact with computers, allowing the construction of complex questions and the ability to reason over a sequence of statements, their use is restricted due to the need for dedicated hardware for execution. In this study, we evaluate the performance of LLMs based on the 7 and 13 billion LLaMA models, subjected to a quantization process and run on home hardware. The models considered were Alpaca, Koala, and Vicuna. To evaluate the effectiveness of these models, we developed a database containing 1,006 questions from the ENEM (Brazilian National Secondary School Exam). Our analysis revealed that the best performing models achieved an accuracy of approximately 46% for the original texts of the Portuguese questions and 49% on their English translations. In addition, we evaluated the computational efficiency of the models by measuring the time required for execution. On average, the 7 and 13 billion LLMs took approximately 20 and 50 seconds, respectively, to process the queries on a machine equipped with an AMD Ryzen 5 3600x processor.
Review of Automatic Speaker Profiling: Features, Methods, and Challenges

Automatic Speaker Profiling (ASP), is concerned with estimating the physical traits of a person from their voice. These traits include gender, age, ethnicity, and physical parameters. Reliable ASP has a wide range of applications such as mobile shopping, customer service, robotics, forensics, security, and surveillance systems. Research in ASP has gained interest in the last decade, however, it was focused on different tasks individually, such as age, height, or gender. In this work, a review of existing studies on different tasks of speaker profiling is performed. These tasks include age estimation and classification, gender detection, height, and weight estimation This study aims to provide insight into the work of ASP, available datasets, feature extraction techniques, and learning models. Further, the performance of current speaker profiling systems is investigated. Finally, the challenges of speaker profiling are presented at the end of this review.
A Review for Arabic Sentiment Analysis Using Deep Learning

Sentiment Analysis is a research field that studies human opinion, sentiment, evaluation, and emotions towards entities such as products, services, organizations, events, topics, and their attributes. It is also a task of natural language processing. However, sentiment analysis research has mainly been carried out for the English language. Although the Arabic language is one of the most used languages on the Internet, only a few studies have focused on Arabic language sentiment analysis. In this paper, a review of the most important research works in the field of Arabic text sentiment analysis using deep learning algorithms is presented. This review illustrates the main steps used in these studies, which include pre-processing, feature extraction and classification, as well as the datasets used. In the end, all the research works are compared in terms of their methodology and results. The findings demonstrated that the majority of deep learning models, including CNN and LSTM, outperformed many of the machine learning models analyzed, and that the size of the training datasets had a direct correlation with the model's performance. Where larger datasets resulted in more successful model training.
MCDNet: Multi Context Dense Network for multi-frame super resolution of satellite images

Satellite image super resolution is an important task that generates high resolution satellite images from low resolution inputs. Multi-frame super resolution utilizes multiple low-resolution images to generate a single high-resolution image. Multi-frame super resolution methods face difficulty in handling spatial and temporal dependencies of pixels. In this work, we proposed a novel architecture named Multi-context Dense Network (MCDNet) to handle spatial and temporal pixel dependencies using multiple approaches of global average pooling, multiple size kernels, and self-attention. The proposed approach improved the PSNR values by 0.29 % and 0.001 % for super resolution of NIR and RED bands on the benchmark PROBA-V dataset.
System-on-Chip Message Flow Mining with Masked-Language Models

High-quality system-level specifications are necessary to validate system-on-chip (SoC) designs comprehensively. However, manual development and maintenance of such specifications across the entire SoC design flow are daunting tasks. We propose a novel method that utilizes a masked-language model to infer comprehensive and accurate specifications from SoC communication traces. The proposed method can overcome the inherent complexity of SoC traces induced by the concurrent executions of system transactions that existing mining tools often find highly challenging. Furthermore, the mined specifications provide a high-level and abstract view of SoC communication behaviors, which can significantly facilitate design understanding and validation. Experiments on traces from a simple SoC model and a more detailed, close-to-reality model developed in gem5 show promising results. The proposed method outperforms several existing state-of-the-art trace mining tools.
Optimized Dataset Generation and Disturbance Rejection Methodology for Neural Network Controllers in Second-Order Systems

Performance of a Neural Networks is highly dependent on the dataset provided it to learn. Traditionally, Neural Networks were used as tools to aid existing controller’s performance or they were trained using existing controllers such as Proportional-Integral (PI) or Sliding Model Controller’s data hence limiting its performance and accuracy to that of the controller itself. In this paper, an ideal dataset that gives the best desirable response is derived theoretically and used to train the neural controllers thus enhancing its performance much better than traditional PI controllers. The paper also proposes a control methodology by using multiple neural networks to reject the disturbance coming into the system and mitigate it easily much faster than the PI controllers with no overshoots. Detailed theoretical derivations are provided for the dataset generation and proposed methodology. The viability of the proposed neural controller was simulated and trained in MATLAB/Simulink environment and its response to variation in reference input has been inferred. The performance comparisons are made with the PI controller data and presented.
Towards Greener Stochastic Derivative-Free Optimization with Trust Regions and Adaptive Sampling

Adaptive sampling-based trust-region optimization has emerged as an efficient solver for nonlinear and nonconvex problems in noisy derivative-free environments. This class of algorithms proceeds by iteratively constructing local models on objective function estimates that use a carefully chosen number of calls to the stochastic oracle. In this paper, we introduce a refined version of this class of algorithms that reuses the information from previous iterations. The advantage of this approach is reducing computational burden without sacrificing consistency or work complexity to attain the same level of optimality, which we demonstrate through numerical results using the SimOpt library.
Brain Functional Connectivity Classification Method for Voice Pitch Processing in the Deaf Based on One-Dimensional Convolutional Networks

This study aims to investigate the differences in brain functional connectivity between deaf children and hearing children in processing three different tones (first tone, second tone, third tone) using deep learning methods. Additionally, the study aims to identify the disparities in brain activation regions during tone processing between deaf and hearing children. Additionally, the goal is to identify the variations in activated brain regions during tone processing between deaf and typically developing children. Five deaf children and two typically developing children were selected as participants. Resting-state functional magnetic resonance imaging (fMRI) scans were conducted using a functional MRI scanner. Subsequently, data preprocessing and fMRI data analysis were performed. A one-dimensional convolutional network was employed to extract features and classify the input brain functional connectivity. Through a series of theoretical and comparative analyses, it was determined that the one-dimensional convolutional network is capable of capturing local patterns within sequential data. It can extract information related to connectivity patterns from the one-dimensional sequence of connection weights, making it more suitable for processing brain functional connectivity data in deaf children.
Knowledge Transfer Happen in Virtual Simulation Experiment Teaching: A Factor Study Based on Educational Experiment

The factors influencing the effect of virtual simulation experimental teaching on students' knowledge transfer are multidimensional, and the effect of virtual simulation experimental teaching on students' knowledge transfer varies depending on different factors. In order to study the influencing factors of virtual simulation experiment teaching on students' knowledge transfer effect, this article establishes a research model based on knowledge transfer and immersive learning theory. 52 learners who participated in virtual simulation experimental teaching were used as the research objects to analyze the factors influencing the effect of virtual simulation experimental teaching on students' knowledge transfer through structural equation modeling. Results indicated that immersion as a technical characteristic of virtual simulation positively influences students' presence and intrinsic motivation, and presence positively affects knowledge transfer; virtual simulation experimental learning supports positively influence students' knowledge structure and self-regulation, and self-regulation positively affects knowledge transfer effects. However, this article also found unexpected results that intrinsic motivation and knowledge structure did not have a positive effect on knowledge transfer. The findings can provide useful references for future educators to design virtual simulation experimental teaching, thus promoting the effect of knowledge transfer.
Learning Organizations and Innovative Work Behaviors: A Moderated Mediation Model of Creative Self-Efficacy and Self-Leadership from the Perspective of Social Cognitive Theory and Social Schema Theory

Purpose: The purpose of this study is to investigate the direct effect of learning organizations on innovative work behaviors. This study also examines the mediating effect of creative self-efficacy between learning organizations and innovative work behaviors. Additionally, this study investigates the moderating role of self-leadership between creative self-efficacy and innovative work behaviors.&#x0D; Study design/methodology/approach: A total of 487 permanent employees (361 subordinates and 126 immediate officers/supervisors) from the manufacturing sector voluntarily in a non-contrived setting participate in the data collection survey. Statistical analysis of this study is conducted using different statistical software (SPSS v.25, AMOS v.22, and Smart-PLS v.3) to test the direct, mediation, moderation, and moderated mediation hypothesized relationships.&#x0D; Findings: Results of this study reveal that learning organizations predict innovative work behaviors of employees. This study's findings also reveal that creative self-efficacy partially mediates the relationship between learning organizations and innovative work behaviors. Furthermore, results indicate that a higher level of self-leadership moderates the positive relationship between creative self-efficacy and innovative work behaviors. Finally, this study’s results confirm that a higher level of self-leadership indirectly influences the positive relationship between learning organizations and innovative work behaviors via creative self-efficacy.&#x0D; Research limitations/implications for Practice: The present study highlights the importance of learning organizations for the enhancement of innovative work behaviors of employees. The employees' creative self-efficacy helps the management to overcome the uncertain situations of rapid globalized change through innovative behaviors. Additionally, self-leadership is vital for individuals and organizations to support the learning organization's culture and creative self-efficacy for enhancing innovative work behaviors.&#x0D; Originality/value of the Results: The present study will be helpful for the management how to overcome the circumstances of unprecedented changes which occur rapidly around the world; also, the findings of this study be beneficial for the management how to increase innovative behaviors of their workforce, which are now mandatory for survival and competitive advantage. Moreover, this study adds knowledge to positive psychology and organizational behavior by explaining the mediating role of creative self-efficacy and moderating role of self-leadership.
Inspirational and Intellectual Leadership Style in Pearl Continental (PC) Hotels of Pakistan and its Effect on Employees Job Satisfaction

The purpose of this research is to find the effect of inspirational leadership style and intellectual leadership style on employee’s job satisfaction in pearl continental (PC) Hotels of Pakistan. A questionnaire consisting of 14 items based on a five-point Likert scale (from agreeing to strongly disagree) was used to measure leadership style and job satisfaction from a hotel perspective. Using convenience sampling, the researcher distributed 130 questionnaires, were distributed out of which 100 properly filled questionnaires were received forming a percentage of 76.92. The data were analyzed through SPSS statistical packaged software and Descriptive statistics, correlation analysis, and regression analysis were used. The result of the study shows that two types of leadership styles namely; intellectual leadership and inspirational leadership have direct positive and significant relationships with employee’s job satisfaction in the pearl continental Hotel of Pakistan.
Research on UAV Path Planning Based on Improved ACO algorithm

Aiming at the shortcomings of traditional ACO algorithm in UAV path planning with more bending times and larger cumulative bending angles, which also has the problems of slow convergence speed and easy to fall into local optimal solutions and deadlock state. This paper proposes an improved ACO algorithm. Firstly, the algorithm adds the valuation function of A * algorithm to the heuristic function and introduces the anti-bending weight coefficient to reduce the number of path bends and the larger cumulative bending angle; Secondly, it proposes to use the pheromone deployment factor to improve the pheromone update rule and enhance the convergence speed and global optimality of the algorithm; Finally, a fallback mechanism plus a pheromone concentration penalty mechanism is introduced to solve the traditional ACO algorithm's tendency to fall into deadlock. The simulation results show that the improved ACO algorithm reduces the number of corners by 73% and the number of iterations by 70% compared with the traditional ACO algorithm in more complex maps. The improved ACO algorithm converges faster and more efficiently, which is more practical guidance for UAV path planning.
Evaluation Method of English Teaching Quality Based on Improved Kernel Extreme Learning Machine of Analytical Hierarchy Process and Krill Herd

To realize the evaluation of English teaching standards and reflect the English teaching standard extend, a series of English teaching quality evaluation indicate approaches based on lecturers' quality, teaching attitude, teaching detail, teaching approach and teaching impact was constructed by using the hierarchical analysis method. The English teaching quality extent is distributed to three levels: normalization, standardization, and excellent, which is taken as the output of the Improved Kernel Extreme Learning Machine of Krill Herd (KH-KELM). Eighteen secondary indicators data of English teaching standard assessment were received and utilized as input for the KH-KELM.The KH-KELM is a criterion for estimating the standard of English teaching. Compared with KELM and SVM, KH-KELM could enhance the correctness of ELT quality assessment and offer a new approach and method to ELT quality assessment. It offers a new way and approach to the assessment of English teaching standards.
Electric carbon conversion analysis model based on big data

The power industry is speeding up the construction of big data centers for energy and initially forming high-value energy big data assets. This paper proposes an improved big data correlation analysis algorithm, builds a power-carbon analysis model based on big data, and makes full use of the real-time, accurate, global and multi-dimensional advantages of big data for annual and monthly calculation of carbon emissions in various regions and industries. It provides data support, auxiliary checking calculation and decision-making reference for government research and judgment trend and macro-control.