Reviewerly/exHarmony huggingface dataset:
PENERAPAN MODEL PEMBELAJARAN CTL (CONTEXTUAL TEACHER AND LEARNING) BERBANTUAN MEDIA VISUAL TERHADAP HASIL BELAJAR SISWA PADA MATERI JARINGAN TUMBUHAN

This study aims to determine the application of the CTL (Contextual Teacher And Learning) learning model assisted by visual media to student learning outcomes in plant tissue material for class XI at Senior hight School 1 Sidamanik. The problem in this study is the low learning outcomes of students which are known from the results of interviews with one of the teachers in the field of biology studies at Senior hight School 1 Sidamanik. Therefore, the CTL (Contextual Teacher And Learning) learning model with the aid of visual media is applied to plant tissue material. The method in this research is quantitative, with the type of research Pre-Experimental Design and research design One Group Pret-test-Post-test design. The study population was all students of class XI semester 1 at Senior hight School 1 Sidamanik for the academic year 2022/2023, the sample of this study was class XI IPA II, which consisted of 33 students. This study uses a test instrument to measure learning outcomes before and after the study. The test used consists of 30 questions. After completing the validity test, there are 20 valid questions and 10 invalid questions. The results of the research show that the learning outcomes of students have increased. It can be seen before and after the treatment, the average value of the pre-test results an average value of 43.9. After treatment with the CTL learning model (contextualized teaching and learning), post-test scores increased by an average of 77.00. It is then analyzed using the N-Profit formula to obtain a percentage of 58.53, which falls into the "very efficient" category. Kata kunci: Hasil belajar,Model pembelajaran CTL (Contextual Teacher And Learning), Media Visual
Single-image piecewise planar reconstruction of urban buildings based on geometric priors

Single-image 3D urban building reconstruction is an important and challenging topic in computer vision. However, it is a highly ill-posed problem due to the intrinsic geometrical ambiguity in a 3D space. To address the problem, this paper presents a novel single-image piecewise planar reconstruction method based on geometric priors. The proposed method utilizes geometric priors (e.g., plane orientation and intersection angles between planes) learned through convolutional neural networks to handle three challenging subtasks sequentially: (1) detecting planar regions with structure information (e.g., each component edge is associated with two planes intersecting at a specified angle); (2) inferring the planes corresponding to the planar regions progressively by taking full advantage of diverse constraints (e.g., structure and proximity constraints) based on geometric priors; and (3) globally optimizing the resulting planes under a unified framework that incorporates image cues and geometric priors. Experimental results on two public datasets released by CASIA and our own dataset demonstrate that the proposed method significantly outperforms five state-of-the-art methods (including two geometry-based methods and three learning-based methods) by an approximately 20 % margin in terms of accuracy. Code and data are available at https://github.com/three-dimensional-computer-vision/single-image-reconstruction.
Enhancing Code Summarization with Graph Embedding and Pre-trained Model

Code summarization is a task that aims at automatically producing descriptions of source code. Recently many deep-learning-based approaches have been proposed to generate accurate code summaries, among which pre-trained models (PTMs) for programming languages have achieved promising results. It is well known that source code written in programming languages is highly structured and unambiguous. Though previous work pre-trained the model with well-design tasks to learn universal representation from a large scale of data, they have not considered structure information during the fine-tuning stage. To make full use of both the pre-trained programming language model and the structure information of source code, we utilize Flow-Augmented Abstract Syntax Tree (FA-AST) of source code for structure information and propose GraphPLBART — Graph-augmented Programming Language and Bi-directional Auto-Regressive Transformer, which can effectively introduce structure information to a well PTM through a cross attention layer. Compared with the best-performing baselines, GraphPLBART still improves by 3.2%, 7.1%, and 1.2% in terms of BLEU, METEOR, and ROUGE-L, respectively, on Java dataset, and also improves by 4.0%, 6.3%, and 2.1% on Python dataset. Further experiment shows that the structure information from FA-AST has significant benefits for the performance of GraphPLBART. In addition, our meticulous manual evaluation experiment further reinforces the superiority of our proposed approach. This demonstrates its remarkable abstract quality and solidifies its position as a promising solution in the field of code summarization.
ASFL: Adaptive Semi-asynchronous Federated Learning for Balancing Model Accuracy and Total Latency in Mobile Edge Networks

Federated learning (FL) is a new paradigm for privacy-preserving learning. This is particularly appealing in the mobile edge network (MEN), in which devices collectively train a global model with their own set of data. It is, however, routinely difficult for FL algorithms to satisfy different training task preferences in terms of the total latency and model accuracy due to a number of factors including the straggler effect, data heterogeneity, communication bottleneck and device mobility. To this end, we propose an Adaptive Semi-asynchronous Federated Learning (ASFL) framework, which adaptively balances the total latency and model accuracy according to the task preferences in MEN. Specifically, ASFL conducts a two-stage operation: i) Device selection stage. Each global round selects a set of devices that can maximize the model accuracy to eliminate data heterogeneity and communication bottlenecks; ii) Training stage. We first define a latency-accuracy objective value to model the balance between the latency and accuracy. Then in each global round, we use a deep reinforcement learning (DRL) algorithm based on soft actor-critic with discrete actions to intelligently derive the number of picked devices (i.e., participants in the current global aggregation) and the lag tolerance at each global round to maximize the latency-accuracy objective value. Extensive experiments show that ASFL can improve the latency-accuracy objective value by up to 94% compared with three state-of-the-art FL frameworks.
Mueller-Matrix Stress Mapping in TeO2 Crystals Under Dynamic Loading

Components of optoelectronic devices installed in aircrafts and space vehicles experience significant mechanical loads during their operation. Excessive and cyclic loads may lead to the defect growth or the fatigue failure. In this paper, we describe a non-destructive imaging technique for stress mapping in anisotropic crystalline materials during bench test. The technique is based on Mueller-matrix imaging and the material photoelasticity. The results of experimental studies for two observation directions coinciding with different crystallographic axes of TeO2 are presented. Main limitations and further potential development of the technique are discussed.
Video action recognition: A survey

In recent years, as computer vision technology advances quickly, video action recognition has become a research hotspot. However, the field of video action recognition still faces many problems and challenges, such as the complex temporal dynamics of video data and the high computation cost caused by the explosive growth of video data. This paper summarizes the algorithms of video action recognition, and introduces them in two parts, namely, traditional handcrafted representation methods and deep learning representation methods. Among them, the traditional handcrafted representation methods are divided into two categories: Holistic Representation Methods and Local Representation Methods; The deep learning representation methods can be divided into RGB data based methods and Kinect-Based methods according to the type of input data modality. This paper focuses on the methods based on RGB data. This paper introduces the representative achievements according to different research directions, compares the advantages and disadvantages of various algorithms, and finally puts forward the prospect of the future development direction.
Unveiling the potential of pre-processing and differentiable binarization thresholds for invoice text detection

Invoice text detection aims to automatically identify key information in invoices, which is one of the representative applications of Optical Character Recognition (OCR) technology and a research hotspot in the computer vision community. The classic traditional OCR method consists of three key steps: preprocessing stage, text detection and text recognition. In the feature extraction stage, it faces problems such as insufficient generalization ability and poor robustness. In this paper, we reveal the great potential of preprocessing strategies in improving OCR accuracy, and propose an invoice text detection method based on DBNet and differentiable binarization thresholds. Specifically, we first introduce color segmentation and local adaptive threshold to improve the preprocessing process, which can effectively suppress the influence of background information and context noise on detection results. In addition, a differentiable binarization threshold is introduced in the feature extraction, which improves the error correction speed in the whole deep learning process. Studies have shown significant improvements in test results after color segmentation or background removal compared to the original image. Therefore, we propose to properly consider the text detection of invoices in the preprocessing stage for improvement.
An improved demosaicing method based on Hibbard algorithm

As computer vision become more widespread and deeper, the requirements for accuracy and clarity of an image gradually increase, and more accurate image reconstruction algorithms urgently need to be developed and researched. Existing image reconstruction algorithms mainly use the conversion of images into RGB or use the difference to predict certain regions to build mathematical models and then revert to images. The existing bilinear interpolation algorithm may reduce the resolution and damage the high frequency part, thus blurring the edges and making it difficult to achieve the expected results. The colorimetric constant method is greatly affected by noise, and the boundary problem is still unsolved. The gradient edge interpolation method only considers the horizontal and vertical directions, which is a single idea and does not meet the needs of oblique edges, and there will be problems such as color overflow. And based on some features near the oblique boundary, this paper focus on the case of large or small edge slope, and select the horizontal difference and vertical difference for algorithm improvement respectively by comparing the color difference along all four directions. Experimental results show that the PSNR value increases slightlyby 0.0013. It effectively optimizes the image and lays the foundation for feature perception in the image.
BBRP22 based security technique for data security

Nowadays data plays major role in the current internet world. People generate more data through online media. The generated data are not secured properly and can easily get hacked by the hackers. To overcome from this data security issue, we have planned to apply BBRP25 technique to secure the data. BBRP22 technique has 6 phases. To apply this technique, we must follow the given below steps:1. To track down the great key S; 2. To get the Z1 &amp; Z2 values from indivisible numbers; 3. To view as the an and b values in lattice; to find the values with the assistance k; and trade x and y values. 4. To get the T-test values and match those numbers. 5. To find the n esteem by utilizing k. 6. Apply the n values in legitimate condition; and trade x and y values. The data will be fully secured by applying BBRP22 strategy while compared to ChaCha.
Pruning Convolutional Filters via Reinforcement Learning with Entropy Minimization

Structural pruning has become an integral part of neural network optimization, used to achieve architectural configurations which can be deployed and run more efficiently on embedded devices. Previous results showed that pruning is possible with minimum performance loss by utilizing a reinforcement learning agent which makes decisions about the sparsity level of each neural layer by maximizing as a reward the accuracy of the network. We introduce a novel information-theoretic reward function which minimizes the spatial entropy of convolutional activations. This minimization ultimately acts as a proxy for maintaining accuracy, although these two criteria are not related in any way. Our method shows that there is another possibility to preserve accuracy without the need to directly optimize it in the agent’s reward function. In our experiments, we were able to reduce the total number of FLOPS of multiple popular neural network architectures by 5–10 $$\times $$ , incurring minimal or no performance drop and being on par with the solution found by maximizing the accuracy.
On Speeding up the Levenberg-Marquardt Learning Algorithm

A new approach to the practical realizations of calculations to the Levenberg-Marquardt learning algorithm is presented. The proposed solutions aim to effectively reduce the high computational load of the LM algorithm. The detailed application of proposed methods in the process of learning neural networks is explicitly discussed. Experimental results have been obtained for all proposed methods and they confirm a very good performance of them.
Increasing the Solar Panel Efficiency after Production by Alternate Methods

The objective of this project is to increase the efficiency of the solar panel by using the alternative methods and to utilise the maximum energy from solar energy. Generally the efficiency of the solar panel is about 16%. The solar energy is being wasted in enormous amount without converting it into electricity. There are different ways to increase the efficiency of solar panel. Water or any medium is used to decrease the temperature of the solar panel and maintain at ambient temperature which will help to increase the efficiency of the solar panel. The second method is using mirror in an appropriate angel which will increase the intensity of the solar rays reflect on the solar panel and increases the efficiency. The other method is to use convergence lens which will help to concentrate the solar rays to the solar panel and the efficiency increases. The common method is to have periodic rotation of solar panel according to the sun movement which will increase the time period of production for the solar panel.
Research on two popular recommendation algorithms for anime

Anime is a popular eastern art form whose audience is mainly young people. In recent years, many people choose to watch anime on the websites. The recommendation system plays a very important part in improving the user experience and saving time. This paper focuses on two basic recommendation algorithms based on machine learning and deep learning methods, including the content-based and collaborative filtering method. The data used in this paper was downloaded from a public dataset on Kaggle. This paper shows how the two methods perform in the anime dataset and compares the results of the two methods. However, two methods have their own advantages in different conditions. The content-based method works when the user wants some related contents. The collaborative filtering method works better while considering more factors other than contents. These two methods can be combined under a new algorithm to form an even more reliable and reasonable recommendation system in the future studies.
PERANCANGAN USER INTERFACE EXPERIENCE (UIX) APLIKASI ANDROID SEBAGAI PENUNJANG SELF-DIRECTED LEARNING BAGI MAHASISWA

Self-Directed Learning (SDL) is a student learning process in which students take responsibility for directing learning goals until these goals are met. The results of the questionnaire held by the researcher stated that there were several problems that students felt in the learning process with the SDL approach, including the absence of a facilitator to help students with learning difficulties; limited library references; a failure to identify the learning styles that stood out according to the student's personality; and the absence of a learning map that provides students with information. The goal of this research is to produce a prototype Android-based user interface experience (UIX) application that can aid student learning, which is classified as acceptable. Design thinking is used in this research to facilitate the development of UIX designs for future designs if necessary, namely empathize, define, ideate, prototype, and test. A UIX design test with system usability scale (SUS) metrics yields a score of 88.5, which is a grade A and acceptable for a prototype UIX design. In the test results, five participants completed several tasks and provided feedback for the proposed UIX design application, so that users can maximize their learning potential by using the Seltern Space application.
The Relevance of Open Data Principles for the Web of Data

Open data has been improving both publishing platforms and the consumers-oriented process over the years, providing better openness policies and transparency. Although organizations have tried to open their data, the enrichment of their resources through the Web of Data has been decreasing. Linked data has been suffering from notable difficulties in different stages of its life cycle, becoming over the years less attractive to users. According to that, we decided to explore how the lack of some opening requirements affects the decline of the Web of Data. This paper presents the Web of Data radiography, analyzing the governmental domain as a case study. The results indicate that it is necessary to strengthen the data opening process to improve resource enrichment on the Web and have better datasets. These improvements describe that open data must be public, accessible (in machine-readable formats), described (use of robust, granular metadata), reusable (made available under an open license), complete (published in primary forms), and timely (preserve the value of the data). The implementation of these characteristics would enhance the availability and reuse of datasets. Besides, organizations must understand that opening and enriching their data require a completely new approach, and they have to pay special attention and control to this project, generally by putting money, the commitment by management at all levels, and lots of time. On the contrary, given the magnitude of availability and reuse problems identified in the opening and enrichment data process, it is believed that the Web of Data model would inevitably lose the interest it aroused at the beginning if not addressed immediately by data quality, openness, and enrichment issues. Besides, its use would be restricted to a few particular niches or would even disappear altogether.
Gradient Matching for Categorical Data Distillation in CTR Prediction

The cost of hardware and energy consumption on training a click-through rate (CTR) model is highly prohibitive. A recent promising direction for reducing such costs is data distillation with gradient matching, which aims to synthesize a small distilled dataset to guide the model to a similar parameter space as those trained on real data. However, there are two main challenges to implementing such a method in the recommendation field: (1) The categorical recommended data are high dimensional and sparse one- or multi-hot data which will block the gradient flow, causing backpropagation-based data distillation invalid. (2) The data distillation process with gradient matching is computationally expensive due to the bi-level optimization. To this end, we investigate efficient data distillation tailored for recommendation data with plenty of side information where we formulate the discrete data to the dense and continuous data format. Then, we further introduce a one-step gradient matching scheme, which performs gradient matching for only a single step to overcome the inefficient training process. The overall proposed method is called Categorical data distillation with Gradient Matching (CGM), which is capable of distilling a large dataset into a small of informative synthetic data for training CTR models from scratch. Experimental results show that our proposed method not only outperforms the state-of-the-art coreset selection and data distillation methods but also has remarkable cross-architecture performance. Moreover, we explore the application of CGM on model retraining and mitigate the effect of different random seeds on the training results.
Data-free Knowledge Distillation for Reusing Recommendation Models

A common practice to keep the freshness of an offline Recommender System (RS) is to train models that fit the user’s most recent behaviour while directly replacing the outdated historical model. However, many feature engineering and computing resources are used to train these historical models, but they are underutilized in the downstream RS model training. In this paper, to turn these historical models into treasures, we introduce a model inversed data synthesis framework, which can recover training data information from the historical model and use it for knowledge transfer. This framework synthesizes a new form of data from the historical model. Specifically, we ’invert’ an off-the-shield pretrained model to synthesize binary class user-item pairs beginning from random noise without requiring any additional information from the training dataset. To synthesize informative data from a pretrained model, we propose a new continuous data type rather than the original one- or multi-hot vectors. An additional statistical regularization is added to further improve the quality of the synthetic data inverted from the deep model with batch normalization. The experimental results show that our framework can generalize across different types of models. We can efficiently train different types of classical Click-Through-Rate (CTR) prediction models from scratch with significantly few inversed synthetic data (2 orders of magnitude). Moreover, our framework can also work well in the knowledge transfer scenarios such as model retraining and data-free knowledge distillation.