Reviewerly/exHarmony huggingface dataset:
The Effect of Using Video to Enhance Listening Ability

This research was design to examine whether videos could strengthen listening skill. It was a pre-experimental research. The participants were the Year-10 Students of Learning Group X Marketing 2 from Public Vocational High School 3 Pontianak in the Academic Year of 2022/2023. Based on the data analysis, the result showed that the video-based technique increased the student's achievement significantly in listening skill. The students showed enthusiasm during the teaching and learning process. They were very enthusiastic in applying the video technique when listening to a text. The t-test computation was higher than the t-critical value (10.36 &gt; 2.045). The score of effect size was 1,79, and it is &gt;1,00, categorized ‘strong’. To conclude, the video-based technique enhanced the students to listen texts better. With video, the students understood the media video helped students to understand the language even if they do not know what the words mean, by providing visual aspects of the communique. Thus, the writer suggested this technique be implemented in teaching listening ability.
Respon Siswa Menggunakan Media Pembelajaran Daring Berbasis Aplikasi WhatsApp pada Pembelajaran IPA di Masa Pandemi Covid-19

This study aims to determine student responses using online learning media based on WhatsApp applications in science learning during the covid-19 pandemic. This is a survey research by using quantitative and qualitative methods. The sample used is class V SD Negeri 1 Labuapi for the academic year 2020/2021 as many as 20 students. The instruments used are interview sheets and student response questionnaire sheets. Data analysis was carried out in three stages, namely data reduction, data presentation, and drawing conclusions or verification. The results of the study based on the research objectives, namely student attention in online learning based on WhatsApp applications from the results of the questionnaire 75% strongly agree, 48.75% Relevance, 55% Confidence, 39.28% Satisfied. Based on the objectives and results of the research, it can be concluded that students' attention in online learning based on WhatsApp applications obtained 75% questionnaire results in the positive category, 48.75% in the non-positive category, 55% confidence in the less positive category, and 39.28% satisfaction in the non-positive category.
Discussion on quality assurance of practical training teaching in higher vocational colleges

With the rapid development of higher vocational education in China, the teaching quality of Higher Vocational Education has been paid more and more attention. Practical Teaching, as the characteristic of higher vocational education, is not only the way to improve students' vocational skills, but also an effective means to cultivate students' good professional ethics and quality. The management of practical teaching process and the supervision of practical teaching quality are the important guarantee to promote the continuous improvement of practical teaching quality in higher vocational colleges.
Analysis on the Integrated Development of New Energy Industry and Vocational Education

With the continuous growth of global energy demand and the aggravation of environmental pollution, the new energy industry has become one of the important strategic directions for global economic development and environmental protection. Based on renewable energy sources such as solar, wind, hydropower, and geothermal energy, the new energy industry is a complete industrial chain covering multiple links including energy development, production, sales, and services. With the continuous advancement of technology and policy promotion, the new energy industry has rapidly developed globally and has become an important direction for economic transformation and industrial upgrading in various countries. At the same time, the development of the new energy industry also provides new opportunities and challenges for vocational education. Therefore, this article will explore the integration and development of the new energy industry and vocational education from several aspects such as the positioning of the new energy industry, future development trends, and strategic paths of integration, aiming to conduct in-depth research and provide useful reference and inspiration for the development of related fields.
Discussion on the Training of "Double Professional" Teachers in Clothing Specialty of Secondary Vocational Schools

With the continuous development of social economy, the increasing demand for technical talents and the country's emphasis on vocational education, vocational education has shown a good development trend in recent years. Due to the intensification of social competition, the requirements of various employers for the quality and professional skills of technical personnel have been significantly improved, which has prompted various vocational schools to constantly summarize, reform and practice, and explore a variety of effective methods for education, which is conducive to the continuous improvement of the "double-master" education industry. Through school-enterprise cooperation, teachers can learn about advanced new technologies, new knowledge, new processes and new methods from the garment industry and enterprises, and make full use of opportunities for industry-university cooperation to directly acquire advanced technologies and knowledge. From the production management experience to the realization of teaching objectives, it has laid a good foundation and cultivated high-quality talents needed by enterprises and institutions. Therefore, this article elaborates on the training of "double-qualified" teachers of clothing specialty in secondary vocational schools.
THE EFFECT OF EMPLOYEE PERFORMANCE APPRAISAL ON PERFORMANCE OF SAVINGS AND CREDIT COOPERATIVE SOCIETIES IN TANZANIA

This study examined the influence of performance appraisal on performance of Savings and Credit Co-operative Societies (SACCOS) in Tanzania. The study aimed at determining how performance appraisal tools, performance standards, and evaluation process affect SACCOS performance. Further, the study intended to examine the moderating effect of workforce diversity on the relationship between performance appraisal and SACCOS performance. Resource based view theory was used in the study. Quantitative approach was employed. Data was collected by using questionnaires from 317 HR managers or their equivalent (SACCOS Managers). Descriptive and correlation statistics were used for data analysis. Through correlation analysis, it was found that performance appraisal tools, performance standards, evaluation process and workforce diversity positively correlated with SACCOS performance. Further, hypothesis testing revealed that all the independent variables had significant linear relationship with SACCOS performance. Multiple linear regression analysis results showed that among all the three independent variables, only performance evaluation was found to be a significant predictor of SACCOS performance. When moderation effect of workforce diversity was tested, it was concluded that workforce diversity has a significant moderation effect on the relationship between performance appraisal and SACCOS performance. The study recommended SACCOS to have performance appraisal tools to enable them managing employee performance. Further, SACCOS has to establish well known standards to be used evaluating performance of their employees. Lastly, SACCOS should consider having a diverse workforce in terms of gender, age and education level. Keywords: Performance Appraisal, Performance Tools, Performance Standards, Performance Evaluation, SACCOS Performance, Organizational Performance. CITATION: Mwita, K. M., Nzulwa, J., &amp; Kaamara, M. (2023). The effect of employee performance appraisal on performance of Savings and Credit Cooperative Societies in Tanzania. The Strategic Journal of Business &amp; Change Management, 10 (1), 588 – 599.
Corporate Financial Performance and the Intervening Role of Energy Operating Costs: The Case of Jordanian Electricity Sector

This paper analyzes the impact of financial performance reported data on market value. The study was conducted on electricity companies for the period 2011 to 2021. The study also empirically analysis the intervening role for oil and gas as operating costs on this relationship. The empirical analysis involved testing the direct effect for company size (TA), profitability (ROE), debt (DR), liquidity (CR) and oil and gas price (OG-P) as independent variables on market value measured by price to book value (P-BV) as dependent variable. Several statistical test were used in the study comprise correlation, simple and multiple regression through Ordinary Least Square (OLS) in order to verify the effect of independent and mediating variables on the dependent variable. The main findings based on correlation, simple and multiple regression can be summarized as follows: independent variables namely ROE, CR and the mediating variable OG-P were found statistically significant and justified the change in market value; other factors TA and DR failed to prove any effect on market value of companies. The oil and gas costs confirmed their impact on market value as single variables effect and joint variable effect.
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive

Temporal graph representation learning aims to generate low-dimensional dynamic node embeddings to capture temporal information as well as structural and property information. Current representation learning methods for temporal networks often focus on capturing fine-grained information, which may lead to the model capturing random noise instead of essential semantic information. While graph contrastive learning has shown promise in dealing with noise, it only applies to static graphs or snapshots and may not be suitable for handling time-dependent noise. To alleviate the above challenge, we propose a novel Temporal Graph representation learning with Adaptive augmentation Contrastive (TGAC) model. The adaptive augmentation on the temporal graph is made by combining prior knowledge with temporal information, and the contrastive objective function is constructed by defining the augmented inter-view contrast and intra-view contrast. To complement TGAC, we propose three adaptive augmentation strategies that modify topological features to reduce noise from the network. Our extensive experiments on various real networks demonstrate that the proposed model outperforms other temporal graph representation learning methods.
Particle swarm optimization applications and implications

Among numerous optimization algorithms, particle swarm optimization (PSO) algorithm has obtained extensive attention among researchers in several years. It is very easy and simple to implement. This chapter supports the concept of Swarm Intelligence and particle swarm optimization. Moreover, multi-objective problems solution and various approached have been discussed. To overcome the limitations of PSO, it can be combined with many other algorithms so that better system efficiency, performance, quality, robustness and convergence can be greatly enhanced. PSO algorithms have extensive applications in almost all the fields, but this chapter explains its application for image processing and route-finding problem. Still PSO algorithm continue with many unresolved challenges of arbitrary convergence and convergence rate, determining easy and effective flexible algorithm parameters, and stabilizing global exploration with local exploitation.
Democratising Democracy: Votes-Weighted Representation

Democracy is in retreat around the world. To reduce the UK’s own democratic deficit, support is growing for some kind of proportional representation. We propose that existing versions of PR can be greatly simplified and improved on by giving each representative voting power in the assembly proportional to the number of votes they have been given by the electorate: “Votes-Weighted Representation”. Under VWR a country/region is divided into multi-member constituencies. Each voter has a single vote. Ballot papers list the candidates (each party can field several), whom voters then rank. The candidate with the fewest votes (top preferences) is eliminated, and their votes are transferred to next preferences, repeating until the number of candidates remaining equals the number to elect. Optionally, given sufficiently secure and accessible voting software, every voter can also have a 'dynamic' second vote, which they can arbitrarily split and delegate to any representatives from any constituencies, for however long and on whichever issues they wish; delegated votes are added to representatives’ vote weights. Any voter can override the delegation of their second vote on any issue, and remotely vote on it in the assembly themselves, directly. Compared with existing systems, these proposals will allow more accountable, more responsive and finer-grained coverage of the inherently multi-dimensional political space, including between elections. Fewer votes will be wasted, and better proportionality of voting power in the assembly should be achievable than under comparable Single Transferable Vote (STV) and highest averages or highest remainders methods for apportioning representatives. Also, fewer votes will be wasted compared with using electoral thresholds without vote transfers from eliminated candidates. Political fragmentation should be largely avoidable by using 6- or 7-member constituencies, without unduly compromising proportionality for bigger parties, or the diversity of elected representatives. Our proposals should help democratise democracy, improve its functioning, and reverse its global retreat.
A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Time

Large language models (LLMs) have played a pivotal role in revolutionizing various facets of our daily existence. Solving attention regression is a fundamental task in optimizing LLMs. In this work, we focus on giving a provable guarantee for the one-layer attention network objective function $L(X,Y) = \sum_{j_0 = 1}^n \sum_{i_0 = 1}^d ( \langle \langle \exp( \mathsf{A}_{j_0} x ) , {\bf 1}_n \rangle^{-1} \exp( \mathsf{A}_{j_0} x ), A_{3} Y_{*,i_0} \rangle - b_{j_0,i_0} )^2$. Here $\mathsf{A} \in \mathbb{R}^{n^2 \times d^2}$ is Kronecker product between $A_1 \in \mathbb{R}^{n \times d}$ and $A_2 \in \mathbb{R}^{n \times d}$. $A_3$ is a matrix in $\mathbb{R}^{n \times d}$, $\mathsf{A}_{j_0} \in \mathbb{R}^{n \times d^2}$ is the $j_0$-th block of $\mathsf{A}$. The $X, Y \in \mathbb{R}^{d \times d}$ are variables we want to learn. $B \in \mathbb{R}^{n \times d}$ and $b_{j_0,i_0} \in \mathbb{R}$ is one entry at $j_0$-th row and $i_0$-th column of $B$, $Y_{*,i_0} \in \mathbb{R}^d$ is the $i_0$-column vector of $Y$, and $x \in \mathbb{R}^{d^2}$ is the vectorization of $X$. In a multi-layer LLM network, the matrix $B \in \mathbb{R}^{n \times d}$ can be viewed as the output of a layer, and $A_1= A_2 = A_3 \in \mathbb{R}^{n \times d}$ can be viewed as the input of a layer. The matrix version of $x$ can be viewed as $QK^\top$ and $Y$ can be viewed as $V$. We provide an iterative greedy algorithm to train loss function $L(X,Y)$ up $\epsilon$ that runs in $\widetilde{O}( ({\cal T}_{\mathrm{mat}}(n,n,d) + {\cal T}_{\mathrm{mat}}(n,d,d) + d^{2\omega}) \log(1/\epsilon) )$ time. Here ${\cal T}_{\mathrm{mat}}(a,b,c)$ denotes the time of multiplying $a \times b$ matrix another $b \times c$ matrix, and $\omega\approx 2.37$ denotes the exponent of matrix multiplication.
Overcoming Catastrophic Forgetting for Fine-Tuning Pre-trained GANs

The great transferability of DNNs has induced a popular paradigm of “pre-training & fine-tuning”, by which a data-scarce task can be performed much more easily. However, compared to the existing efforts made in the context of supervised transfer learning, fewer explorations have been made on effectively fine-tuning pre-trained Generative Adversarial Networks (GANs). As reported in recent empirical studies, fine-tuning GANs faces the similar challenge of catastrophic forgetting as in supervised transfer learning. This causes a severe capacity loss of the pre-trained model when adapting it to downstream datasets. While most existing approaches suggest to directly interfere parameter updating, this paper introduces novel schemes from another perspective, i.e. inputs and features, thus essentially focuses on data aspect. Firstly, we adopt a trust-region method to smooth the adaptation dynamics by progressively adjusting input distributions, aiming to avoid dramatic parameter changes, especially when the pre-trained GAN has no information of target data. Secondly, we aim to avoid the loss of the diversity of the generated results of the fine-tuned GAN. This is achieved by explicitly encouraging generated images to encompass diversified spectral components in their deep features. We theoretically study the rationale of the proposed schemes, and conduct extensive experiments on popular transfer learning benchmarks to demonstrate the superiority of the schemes. The code and corresponding supplemental materials are available at https://github.com/zezeze97/Transfer-Pretrained-Gan .
Analysis of Real-Time Squiggle Analysis for Efficient Read Until

Analysis of Real-Time Squiggle Analysis for Efficient Read Until
GPU-Accelerated Long Read Alignment with Minimap2

GPU-Accelerated Long Read Alignment with Minimap2
Quantification of Localization Uncertainty in One-Stage Object Detection

Object detection is a multi-task learning problem consisting of target localization and target classification. Since traditional object detection algorithms (such as YOLOv5) do not take into account the uncertainty of bounding box coordinate localization when detecting targets, we do not know how much confidence the model has in its localization results, which is the purpose of the paper to study the uncertainty quantification of object detection models. In this paper, we sort out the mathematical theoretical framework of Bayesian deep networks and conclude that the dropout in deep learning can be used as uncertainty estimation in Bayesian deep neural networks. Next applying the above theory, we propose uncertainty Bayesian deep neural networks based on YOLOv5 to quantify the localization uncertainty of the targets. Then we propose Bayesian deep neural networks based on epistemic uncertainty and aleatoric uncertainty to capture the uncertainty of YOLOv5 on the localization regression task. Epistemic uncertainty quantifies uncertainty generated by the random optimization locating the model weights. Aleatoric uncertainty quantifies uncertainty generated because of the insufficient knowledge of the data.
Influence of prior knowledge on eye movements to scenes as revealed by hidden Markov models

Human visual experience usually provides ample opportunity to accumulate knowledge about events unfolding in the environment. In typical scene perception experiments, however, participants view images that are unrelated to each other and, therefore, they cannot accumulate knowledge relevant to the upcoming visual input. Consequently, the influence of such knowledge on how this input is processed remains underexplored. Here, we investigated this influence in the context of gaze control. We used sequences of static film frames arranged in a way that allowed us to compare eye movements to identical frames between two groups: a group that accumulated prior knowledge relevant to the situations depicted in these frames and a group that did not. We used a machine learning approach based on hidden Markov models fitted to individual scanpaths to demonstrate that the gaze patterns from the two groups differed systematically and, thereby, showed that recently accumulated prior knowledge contributes to gaze control. Next, we leveraged the interpretability of hidden Markov models to characterize these differences. Additionally, we report two unexpected and interesting caveats of our approach. Overall, our results highlight the importance of recently acquired prior knowledge for oculomotor control and the potential of hidden Markov models as a tool for investigating it.
The Impact of COVID-19 in Software Design Activities in the Global Software Engineering-Security Prospective

Abstract Background : Global Software Engineering (GSE) extends geographical, temporal, and cultural boundaries in distributed environments. Over the past two decades, GSE research has evolved to manage software development for distributed teams. The COVID- 19 pandemic highlights the need for comprehensive research, particularly during the software design phase, to support team collaboration in distributed development. Aim : This study systematically analyzes the evolution of research emphasis in the GSE field, specifically exploring on whether the research focuses increasing on software design due to the global pandemic. Method : We systematically analyzed the existing literature in two phases. In the first phase of our study, we mapped GSE research over the two decades leading to the pandemic (2000-2020). In the second phase, we used the forward snowballing approach to examine the literature on the software design phase published between 2020 and 2022. Results : The analysis of 592 research studies in the two phases reveals various trends in GSE research. Evaluation research is the most explored research type in methods and processes, and human aspects of development. Despite the paradigm shift caused by the COVID-19 pandemic that increased reliance on distributed teams, results show that while software organizations are extensively studied across all software engineering phases, the software design phase remains one of the least explored areas. Conclusion : This work highlights the evolving GSE research trends, emphasizing the rising significance of collaborative software design in distributed settings. Our findings address current research gaps and underscore the need for further research on software design activities. This contribution envisions a more collaborative, adaptable GSE field, guiding future research to support distributed teams.
GAN-Based Deblurring Algorithm for Drug Package Detection

With the development of Industry 4.0, many manufacturers are moving in the direction of automation and intelligence. Drug package inspection quality testing methods have also changed from traditional manual inspection to machine vision inspection. However, due to the long exposure time or vibration of the camera, the images are blurred, which will seriously affect the accuracy of the defect detection. We propose a deblur model with higher level of image restoration and more lightweight. Using reserving and merging blocks to replace the original reserving and merging blocks, which can be used in the inference phase to turn ResBlocks into equivalent straight structures using reserving and merging operations, making the network lighter. The pyramid squeeze attention module is also incorporated to enable the model to efficiently extract finer-grained multi-specimen scale spatial information, thus making more efficient use of feature space information. Finally, a drug package dataset was produced and the model was trained to obtain good deblurring results. The comparative experiments confirmed that the model has better image reduction and excellent reduction speed, and can be further cited in production inspection pipelines to improve detection accuracy.