Reviewerly/exHarmony huggingface dataset:
CLR: Channel-wise Lightweight Reprogramming for Continual Learning

Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks. The main challenge is to maintain performance on previously learned tasks after learning new tasks, i.e., to avoid catastrophic forgetting. We propose a Channel-wise Lightweight Reprogramming (CLR) approach that helps convolutional neural networks (CNNs) overcome catastrophic forgetting during continual learning. We show that a CNN model trained on an old task (or self-supervised proxy task) could be ``reprogrammed" to solve a new task by using our proposed lightweight (very cheap) reprogramming parameter. With the help of CLR, we have a better stability-plasticity trade-off to solve continual learning problems: To maintain stability and retain previous task ability, we use a common task-agnostic immutable part as the shared ``anchor" parameter set. We then add task-specific lightweight reprogramming parameters to reinterpret the outputs of the immutable parts, to enable plasticity and integrate new knowledge. To learn sequential tasks, we only train the lightweight reprogramming parameters to learn each new task. Reprogramming parameters are task-specific and exclusive to each task, which makes our method immune to catastrophic forgetting. To minimize the parameter requirement of reprogramming to learn new tasks, we make reprogramming lightweight by only adjusting essential kernels and learning channel-wise linear mappings from anchor parameters to task-specific domain knowledge. We show that, for general CNNs, the CLR parameter increase is less than 0.6\% for any new task. Our method outperforms 13 state-of-the-art continual learning baselines on a new challenging sequence of 53 image classification datasets. Code and data are available at https://github.com/gyhandy/Channel-wise-Lightweight-Reprogramming
Generating multidimensional clusters with support lines

Synthetic data is essential for assessing clustering techniques, complementing and extending real data, and allowing for more complete coverage of a given problem’s space. In turn, synthetic data generators have the potential of creating vast amounts of data – a crucial activity when real-world data is at premium – while providing a well-understood generation procedure and an interpretable instrument for methodically investigating cluster analysis algorithms. Here, we present Clugen, a modular procedure for synthetic data generation, capable of creating multidimensional clusters supported by line segments using arbitrary distributions. Clugen is open source, comprehensively unit tested and documented, and is available for the Python, R, Julia, and MATLAB/Octave ecosystems. We demonstrate that our proposal can produce rich and varied results in various dimensions, is fit for use in the assessment of clustering algorithms, and has the potential to be a widely used framework in diverse clustering-related research tasks.
KLASTERISASI DESTINASI WISATA BERDASARKAN KOORDINAT GPS DENGAN METODE KERNEL K-MEANS

Although K-Means Clustering, a sort of non-hierarchical cluster analysis, is frequently employed, it has limitations when processing data with non-linearly separable characteristics (no distinct boundaries) and overlapping clusters, or when visually comparing the results of clusters to those of other clusters. When dealing with data that have overlapping and non-linearly separable cluster features, the Gaussian Kernel function in Kernel K-Means Clustering can be applied. The input data must be displayed using kernel functions in a new dimension, which is where Kernel K-Means Clustering and K-Means diverge.&#x0D; The purpose of this study is to determine the concept of steps and the results of Kernel K-Means Clustering analysis to classify tourist destinations in the Pesonajawa database based on GPS coordinates. The data used was obtained from the PesonaJava.com database, which consists of 28 tourist destinations located on the island of Java. The silhouette score is used in this study to evaluate the cluster results.&#x0D; The evaluation of the cluster results based on the research results revealed that the best number of clusters was K = 6, with a sigma value of = 5.
Validity of Lectora Inspire-Based Interactive Learning Media Integrated with Camtasia Studio

This study aims to analyze the process of developing interactive learning media based on Lectora Inspire, which is integrated with Camtasia Studio, for archiving subjects at SMKN 3 Bojonegoro. It also aims to assess the feasibility of the developed interactive learning media and examine student responses to it. The study utilizes the ADDIE R&amp;D model, excluding the Evaluation stage, and employs both quantitative and qualitative data analysis techniques. The participants in the study were 20 students from class X OTKP 1. The developed products were subjected to validation by material experts and media experts. The validation results indicate a material expert validation rate of 86.25% for Expert 1 and 82.5% for Expert 2, while the media expert validation rates were 84.28% and 88.57% for Expert 1 and 2, respectively. The average validation score from the four validators was 85.4%, indicating a high level of feasibility. The student responses revealed an average score of 84%, indicating a high level of interest. Therefore, it can be concluded that the interactive learning media based on Lectora Inspire, integrated with Camtasia Studio, can effectively support archiving learning
The Impact of Problem Based Learning Model with Android Multimedia Integrated on Student Learning Outcomes and Interest

The implementation of a learning model and learning media in the classroom has a significant impact on learning outcomes. This research aims to assess the degree to which different educational media can influence academic outcomes and interest learn. The selected research design is the Pretest-Posttest Design. This study employs a quasi-experimental research design utilizing a nonequivalent control group. The study employs a research design that utilizes a 3x2 Factorial Design. Using SPSS 25.0 for statistical analysis, it was determined that the academic performance of students instructed through various learning models resulted in a statistically significant outcome of 0.003 α (0.05). The alternative hypothesis (Ha) was deemed acceptable, whereas the null hypothesis (H0) was deemed unacceptable. The aforementioned suggests a significant disparity in the average academic performance outcome among XI students subjected to diverse educational materials about acid-base titration. The calculated average discrepancy in academic outcome among the two groups of students is 8.61. The first experimental group, which utilized Smart Apps Creator as a learning medium and catered to various learning styles, yielded an average student learning outcome of 88.61 ± 7.98. By comparison, the mean learning outcome score for students in the second experimental class (which utilized PowerPoint as a learning medium) is 80.00 ± 7.00.
Face detection and recognition of mask wearing in normal environment based on neural network

With the outbreak of COVID-19, wearing masks has become a hot topic again. In public places, Not wearing a mask correctly has almost the same harm as not wearing a mask. Mask detection is an extension of face detection. Therefore, it is of great practical signifi-cance to design a system that can correctly identify whether pedestrians wear masks cor-rectly. The face recognition technology based on neural network has been relatively ma-ture, but there is still a lack of work related to mask recognition, especially whether to wear masks correctly. This is not only the promotion of a two-classification problem to a three-classification problem, but also faces many practical problems, including data set acquisition, in this paper, mask recognition is divided into a multi-stage work. In the first link, Yolo network is used to recognize facial areas, and in the second link, RESNET is used to realize mask recognition. Finally, a layer of RESNET network is improved to achieve higher recognition accuracy.
A meta analysis of factors affecting non-face-to-face class satisfaction

Objectives The present study conducted meta-analysis with the goal of systematically arranging the relation between variables related to non-face-to-face class satisfaction.&#x0D; Methods To this end, the study collected data in the form of dissertations and academic papers published in Korea up to 2022. The final selection of papers numbered 215, including 52 master’s theses, 11 doctoral dissertations, and 152 academic papers. The coding method for the data followed the meta-analysis methods and coding procedures by Lipsy and Wilson(2001) as well as Cooper(2010). The effect size was calculated by converting the correlated data into Fisher’s Z transformation, which was then converted back to a correlation coefficient for interpretability.&#x0D; Results The analysis shows that the overall effect size of class satisfaction variables was high at .582, with effect sizes higher in the order of content, teacher, support model and system, and learner. In terms of effect size depending on moderator variable, the study found differences depending on year of issue, research subject, and non-face-to-face class type.&#x0D; Conclusions This study specifically confirmed the relationship between variables related to non-face-to-face class satisfaction by organizing research related to non-face-to-face class satisfaction through meta-analysis. In order to increase the satisfaction of learners in the educational scene, effective non-face-to-face class methods or strategies should be sought.
Arabic Extensive Listening and its Language Learning Benefits

Foreign Language learning is a complex process that requires considerable effort from both teachers and learners, particularly in developing effective listening skills in foreign language classrooms. Among the various approaches, Extensive Listening (EL) has emerged as a promising pedagogical method for enhancing language acquisition. This research investigates the utilization of Extensive Listening (EL) as a pedagogical approach for teaching Arabic listening skills, focusing on its efficacy in facilitating vocabulary and grammar acquisition, as well as exploring students' perceptions regarding the benefits of language learning through EL. Language teachers often face challenges in cultivating listening skills among foreign language learners due to its inherent difficulty. The study employed observation, documentation, and focus group discussions as data collection methods, while data analysis involved several stages: data description, data reduction, and drawing conclusions. The findings reveal that EL was effectively integrated into the Arabic listening course, aligning with the course objectives and learning materials. Students actively engaged in grammar analysis through audiobooks and demonstrated a positive attitude towards EL, acknowledging its role in enhancing their vocabulary, pronunciation, and grammar knowledge.
A survey on deep learning based face detection

The article has focused on surveying face detection models based on deep learning, specifically examining different one-stage models in order to determine how to choose the appropriate face detection model as well as propose a direction to enhance ourfacedetection model to match the actual requirements of computer vision application systems related to the face. The face detection models that were conducted survey include single shot detector, multi-task cascaded convolutionneural networks, RetinaNet, YuNet on the Wider Face dataset. Tasks during the survey are structural investigation of chosen models, conducting experimental surveys to evaluate the accuracy and performance of these models. To evaluate and provide criteria for choosing facedetection suitable for the requirements, two indicators are used, average precision to evaluate accuracy and frames-per-second toevaluate performance. Experientialresults were analyzed and used for making conclusions and suggestions for future work. For our real-time applications on face-related camera systems, such as driver monitoring system, supermarket security system (shoplifting warning, disorderly warning), attendance system, often require fast processing, but still ensures accuracy. The models currently appliedin our system such as Yolos, Single ShotDetector, MobileNetv1 guarantee real-time processing, but most of these models have difficulty in detecting small faces in the frame and cases containing contexts, which are easily mistaken for a human face. Meanwhile, the RetinaNet_ResNet50 model brings the highest accuracy, especially to ensure the detection of small faces in the frame, but theprocessing time is larger. Therefore, through this survey, we propose an enhancement direction of the face detection model based on the RetinaNet structure with the goal of ensuring accuracy and reducing processing time.
Center of darkness: Attacks and defensive strategies on blockchain consensus algorithm

ith the rapid development of blockchain technology, more and more attention has been paid to the core consensus algorithm, and the related security problems have followed. For example, due to the loopholes in the consensus algorithm, the Bitcoin Gold platform lost about 18 million USD. Therefore, how to avoid such attacks against the consensus algorithm is an unavoidable topic for everyone involved in the blockchain platform. Starting from the mainstream consensus algorithm, this paper introduces their evolution process and their respective principles. In addition, we also introduce the possible attack principles and harms from two perspectives. One is general, that is, attacks under this category are not limited to specific consensus algorithm and platforms. The other type is specific, that is, the vulnerability locates in specific consensus algorithm. At the end of this paper, we divide all the personnel involved in the blockchain into three categories, and put forward specific suggestions for each category of personnel to help them better avoid and respond to possible attacks on the consensus algorithm.
Robust feature mining transformer for occluded person re-identification

The person Re-identification (ReID) technology in real scenes is still a challenging task, especially when the target pedestrian is occluded. Existing occluded person ReID methods distinguish visible regions through pose information or semantic information. These methods focus on the body information in the visible region, which can perform well for ReID. However, since these approaches require the help of auxiliary networks, they are not easy to obtain efficient models. In addition, under the pose guidance, these networks focus only on the visible body parts and ignore other regional features. Therefore, we propose a Robust Feature Mining Transformer (RFMT) method to tackle the above problems of occluded person re-identification. Specifically, when dealing with occlusion, the proposed Residual Transformer (ResT) layer allows for a more efficient spread of image features through cross-layer connections, thereby reducing the loss of pedestrian information. Furthermore, obtaining complete pedestrian features becomes a big challenge in occluded scenes. Our proposed Global Attention (GAtt) enhances feature extraction by accurately focusing on the most representative part of images. And then, we design a hyperparametric centroid loss (Lctr) to catch stable features. Meanwhile, this loss can reduce the image search time during training and retrieval. Many experiments have demonstrated that our RFMT performs better than the state-of-the-art ReID methods on public occluded and holistic person re-identification datasets.
The History and Current Situation of China’s Undergraduate Major Evaluation and the Prospect of Intelligent Education Evaluation Based on Big Data Technology

The accreditation and evaluation of undergraduate majors is one of the most important elements in the monitoring of China’s higher education quality. with the combination of quantitative and qualitative research methods, this paper illustrates the stages of development, problems of undergraduate majors operation in China, and proposes an evaluation framework for undergraduate majors from the fourth paradigm perspective. The development of undergraduate major evaluation in China are divided into four stages: the budding period (1985–1998), the rising period (1998–2009), and the booming period (2010–2022). The forms of evaluation are divided into major accreditation, major ranking and major assessment. Major evaluation mode includes independent evaluation mode, comprehensive evaluation mode and appraisal mode. Continuous collection of sample data, customized indicators, multiple fusion calculation analysis, visual feedback are the typical features of big- data-based intelligent education evaluation.
On Convergence of Binary Trust-Region Steepest Descent

Binary trust-region steepest descent (BTR) and combinatorial integral approximation (CIA) are two recently investigated approaches for the solution of optimization problems with distributed binary-/discrete-valued variables (control functions). We show improved convergence results for BTR by imposing a compactness assumption that is similar to the convergence theory of CIA. As a corollary we conclude that BTR also constitutes a descent algorithm on the continuous relaxation and its iterates converge weakly-$^*$ to stationary points of the latter. We provide computational results that validate our findings. In addition, we observe a regularizing effect of BTR, which we explore by means of a hybridization of CIA and BTR.
New Type of Chaotic Attractors and Their Applications

We study the periodic solutions of a discrete neuron model for period two or period three of the parameter (internal decay rate) (βn)n=0∞. The novelty of this research is finding a chaotic attractor for certain interval, outside the defined interval the solution goes to positive infinity or to negative infinity. The investigation can be useful in the design of chaos-based neural networks architecture.
The Role of Knowledge Management: Organizational Culture and Leadership in Shaping Competitive Advantage

This study looks into the relationship between knowledge management and Organizational competitive advantage. It investigates how knowledge management practises might help to mitigate the impact of Organizational culture and leadership on attaining a competitive advantage in the marketplace. A quantitative research approach is used to obtain data from employees in various organisations via questionnaires. The findings emphasise the critical role of knowledge management in encouraging innovation, improving decision-making, and increasing operational efficiency, all of which contribute to competitive advantage. The research also highlights the role of Organizational culture and leadership in fostering knowledge sharing and use. The research's results emphasise cultivating a positive culture, investing in leadership skills, and dedicating resources to support knowledge management activities. While the study has limitations, such as its specific context and reliance on self-reported data, future research recommendations include longitudinal studies, comparative analyses, qualitative investigations, and investigating the impact of technology on knowledge management and competitive advantage. This study offers useful insights for organisations seeking to leverage knowledge management for long-term competitive advantage in today's volatile business market.
A Review on Recent Approaches," Methods of Video Quality Assessment

Generally the evaluation of video quality is classified into two methods.They are subjective and objective analysis of video quality and Subjective analysis is conducted based on human perception since it is concerned with how video is perceived by a viewer or subject and expresses subjects opinion on a particular video sequence in comparison with its original video sequence.The subject has to vote for the video sequences under certain test environment conditions for example the ITU-Recommendations.Human perception is considered as the true judgment of video quality and precise measurement of perceptual quality but it is quite expensive and tedious in terms of time such as preparation, running and human resources.Objective quality assessment is therefore essential.Objective Video Quality Metric should be designed based on HVS (Human Visualizing System) characteristics.Some aspects of HVS like contrast, orientation sensitivity, spatial and temporal masking effects, frequency selectivity and color perception are incorporated in the design of objective quality metrics.Even though it is computationally very expensive and complex to design a quality metric with above aspects.It is useful for a wide range of applications if it correlates well with human perception.The impairments visibility which is related to video processing system is subjected to spatial and temporal properties of video content, since subjective analysis is quite expensive and time conservative method, objective metric has been developed considering HVS.In our research work we have done experiment on both subjective and objective analysis.
Risk-Controlled Student Prescription Trees

Abstract Student Prescription Trees (SPTs) are interpretable policy trees which utilize a blackbox “teacher” model to predict counterfactuals on the basis of observed covariates and a interpretable decision tree (DT) “student” model which sorts datapoints into axis aligned partitions such that the sum over predicted coun-terfactuals outcome variables within each partition is optimized greedly. In this paper, we maintain this basic framework but experiment with a Bayesian hierarchical binomial regression model as the teacher, and require all gains acquired from DT splits to be statistically significant given the posterior distributions over outcome variables from any two disjoint partitions of the data when compared to their unity. This setup allows us to control the uncertainty (i.e., “risk”) associated with a deepening of the complexity (i.e., “depth”) of the student policy, and gives us natural tree growth stopping criteria. In general, fewer datapoints or greater variation are viewed as containing more uncertainty, and therefore greater uncertainty will result result in shallower tree policies, and vice versa.