Reviewerly/exHarmony huggingface dataset:
Research on Issues Related to Lifelong Vocational Skills Training for Vocational Undergraduate Teachers

Undergraduate teacher vocational skills training is a systematic project that requires research and exploration from the combination of theory and practice. From the current situation of the construction of the teaching staff in Chinese universities, the traditional training model for university teachers in the past has played a good role in ensuring and promoting the improvement of teachers' teaching level, but it has gradually highlighted the significant limitations of the model itself. In this regard, this article conducts research on issues related to lifelong vocational skills training for vocational undergraduate teachers. This article also analyzes the current training mode analysis and existing problems. Due to the traditional emphasis on theoretical teaching and the neglect of educational practice, it seriously affects the cultivation of vocational skills for vocational university students. How to improve the vocational skills of vocational university students has become a major issue that major local undergraduate colleges in China need to explore. At a certain time period throughout the entire learning process, it is necessary to equip full-time and part-time guidance teachers, formulate assessment standards, and strictly implement them. We should clearly incorporate teacher vocational skill training as a task and indicator into the education and teaching system and student activities, and establish standardized and reasonable assessment standards.
A Study on Virtual Reality, Raspberry Pi and 3D Printing Technology towards the Interior Design Specialty Teaching in China

This study aimed to make a contribution to the research on the education and teaching of the Interior Design Specialty courses in local higher vocational colleges. And this study helps to try to find the possible solutions so as to improve the teaching effect, to improve students' understanding of professional knowledge, deepen students' cognition of professional knowledge. Virtual Reality, Raspberry Pi, and 3D printing technology are emerging fields, and applying these new technologies to teaching is also a teaching method that adapts to the development of the times, which can improve teaching effects and stimulate students' enthusiasm for learning. This study investigates the application of these technologies in teaching and related research literature at home and abroad, and explores the impact of these technologies on interior design course education and teaching. This research will benefit other teacher to understand the teaching effect and could be referenced or guideline for other researcher who is interested to study the teaching satisfaction in another organization.
Digital Archaeology. Promises and Impasses

Archaeology has gone digital for some time now! Topics such as GIS databases, 3D models, drone photography, meta- and para-data, semantic mapping, text mining, simulation, and social network analysis have become commonplace in archaeological discourse and practice. Digital and technological advancements seemingly offer limitless promises for data recording, analysis and dissemination. Yet, after several decades of innovation, we must ask ourselves which of these promises are actually fulfilled, and which persistent impasses are present. Today, some reflexive questions are more important than ever. In particular, when, how and why do our innovative archaeology tools fail? Do we approach our archaeological projects with a digital wand and (implicitly or explicitly) expect a magical solution? And when there is indeed a digital solution, at what expense does it come? In this volume, scholars and practitioners in the field discuss the state of the art, as well as the promises and impasses that digital approaches to archaeology entail. The authors discuss the current state of teaching digital archaeology, the societal impact of digital innovations, current issues in archaeological data management, promises and limitations of isotopic research and remote sensing techniques, and why subfields such as agent-based modelling and serious gaming struggle to keep momentum.
ReferPose: Distance Optimization-Based Reference Learning for Human Pose Estimation and Monitoring

Existing deep learning models for human pose estimation (HPE) have shown satisfactory performance in monitoring human actions. However, they usually face a dilemma between complexity and accuracy. To address this challenge, we propose an effective reference learning method for HPE (namely ReferPose), which is based on a new distance optimization strategy. Specifically, we utilize a reference model for pose learning and representation. The pose representation learned from the entire database is merged into the reference model, providing continuous reference learning guidance for an in-training model. In addition, we design a new cosine annealing-based reference guidance for temporal denoising and further develop a distance optimization strategy to provide joint guidance from pose knowledge, model representation, and temporal experience. Experimental results on two benchmark databases and a human fall monitoring system demonstrate that our ReferPose not only achieves promising accuracy improvement compared with several representative HPE models, but also offers low cost and high efficiency.
PENGEMBANGAN BUKU SAKU BERBASIS CERITA BERGAMBAR (KOMIK) PADA MATERI FLUIDA STATIS UNTUK KELAS XI SMA/MA

Abstract: Research and development (Reseacrh and Development) of Learning Media for Book Based on Storytelling Pictures (Comics) Has been done on Static Fluids. The subjects of the study were 20 students of class XIMA NurulQodiri in the academic year 2018/2019. The development model used is ADDIE (Analysis, Design, Development, Implementation, and Evaluation). The instruments used in the study were expert validation sheets, student response questionnaires and teacher response questionnaires. Based on the results of the study it was found that the percentage of scores from experts for the material component is 85.40% and graphics which is 78.43% each located in very decent criteria. While the response of students is 86.00% and the response of teachers by 87.78% obtained through product trials is in very good criteria. So it is seen from the average of the instruments developed 84.40% of the results it can be concluded that the Learning Media Based on Story-Based Pocket Books (comics) in the Static Fluorescent Material can be used in the study of Physics in SMA.Keywords: Development, Pocket Books, Teaching Materials, Picture Stories, Comics.
Spatial-angular Quality-aware Representation Learning for Blind Light Field Image Quality Assessment

Blind light field image quality assessment (BLFIQA) remains a challenging task in deep learning due to the unique spatial-angular structure of light field images (LFIs) and the lack of large-scale labeled data for training. In this work, we propose a novel BLFIQA method using spatial-angular quality-aware representation learning in a self-supervised learning manner. Visual content and distortion type are important factors affecting the perceived quality of LFIs. In our observation, the band-pass transform maps of LFIs with the same distortion type exhibit similar Gaussian distributions. Thus, we learn spatial-angular quality-aware representations by minimizing the distance in the embedding space between the luminance map and the band-pass transform map of the same LFI. To implement spatial-angular quality-aware representations of LFI, we also build a large-scale unlabeled dataset containing 40k distorted LFIs with different distortion types and visual content. Further, we propose a fusion-separation-fusion network (FSFNet) to extract features for representing the intrinsic spatial-angular structure of the LFI. After pre-training on the unlabeled dataset using the proposed self-supervised learning, the FSFNet is employed for downstream BLFIQA tasks and achieves good performance. Experimental results show that our proposed method outperforms seventeen state-of-the-art models on the Win5-LID, NBU-LF1.0 and LFDD datasets, and achieves 3.78%, 6.61% and 4.06% SRCC improvements, respectively. The code and dataset will be publicly available in https://github.com/JianjunXiang/SSL_and_FSFNet.
A Key Generation Scheme from Sensing Data for IoT Security : IEEE CNS 23 Poster

We propose a key generation scheme at the sensing layer of Internet of Things, which can protect huge amount of sensing data via utilizing the randomness of the data itself, as well as a few control parameters. The performance of this scheme, such as key generation rate and amount, is analyzed by simulation. Finally, the challenges in future research are pointed out.
PSYCHOLOGICAL PREVENTION OF PROFESSIONAL CRISIS LEARNING IN THE CONTEXT OF BEHAVIORAL

The purpose of the article. The purpose of the article was to find protection against the negative consequences of the professional crisis of education, which would be based on moral values and contribute to the integration of constructive ideas of past generations into the present. Methods. It is proposed to implement the idea of psychoprophylaxis using historical methods. The results. The factors of the crisis in the professional training of applicants for economic and military specialties are characterized. The peculiarities of the structure of mental phenomena and the concept of psychological support of the process of psychoprophylaxis are considered. The need to implement a systematic approach based on empirical data, including research aimed at harmonizing the psychological state, is emphasized. Scientific novelty. Empirical research has revealed the peculiarities of the social functioning of applicants of economic and military specialties. The practical significance. There are differences in the psychological well-being of specialists in economic and military specialties: they are characterized by a predominance of interest in history, which reflects interest in the profession, admiration for it; during a crisis, self-esteem and locus of control reach levels that indicate inadequate assessment and inability to use one's own strengths to achieve a goal. Psychological support of professional adaptation in this period involves determining the volume, quality and form of providing information about the enterprise, economy, division, specifics of activity, various services, about the team, its structure, traditions, group norms, social expectations. As a rule, such information comes to the adapting person in one form or another, in sufficient or excessive amounts, but without scientific justification. The conducted investigations allow us to draw preliminary conclusions. Indeed, the professional formation of students is accompanied by critical moments, the most difficult of which is the crisis of professional training, which falls on the 3rd year. Providing psycho-prophylactic help by applying the proposed historical-scientific method makes it possible to prevent extremely negative consequences that can deform the personality. Accordingly, further development and approval of the historical-scientific method of psychoprophylaxis against a wider background is necessary in order to recommend its use to teachers and students.
Fundamentals of Python Programming

Reading this book will help first-year B.E./B.Tech students become proficient in Python and problem-solving. This book will teach you how to use the Python language to solve real-world problems. One of the book’s many strengths is that it’s written at a level that’s accessible to undergraduates and Python newcomers. It’s simple to take up and immediately put to use in your life. This book includes a sample question paper from a prior year at Anna University in addition to several examples. Data, expressions, statements, control flow, functions, list tuples, dictionaries, files, modules, and packages may all be part of a primer on algorithms and problem-solving.
Dynamic MDETR: A Dynamic Multimodal Transformer Decoder for Visual Grounding

Multimodal transformer exhibits high capacity and flexibility to align image and text for visual grounding. However, the existing encoder-only grounding framework (e.g., TransVG) suffers from heavy computation due to the self-attention operation with quadratic time complexity. To address this issue, we present a new multimodal transformer architecture, coined as <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Dynamic</b> <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> utilmodal <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DETR</b> (Dynamic MDETR), by decoupling the whole grounding process into encoding and decoding phases. The key observation is that there exists high spatial redundancy in images. Thus, we devise a new dynamic multimodal transformer decoder by exploiting this sparsity prior to speed up the visual grounding process. Specifically, our dynamic decoder is composed of a 2D adaptive sampling module and a text guided decoding module. The sampling module aims to select these informative patches by predicting the offsets with respect to a reference point, while the decoding module works for extracting the grounded object information by performing cross attention between image features and text features. These two modules are stacked alternatively to gradually bridge the modality gap and iteratively refine the reference point of grounded object, eventually realizing the objective of visual grounding. Extensive experiments on five benchmarks demonstrate that our proposed Dynamic MDETR achieves competitive trade-offs between computation and accuracy. Notably, using only 9% feature points in the decoder, we can reduce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\sim$</tex-math></inline-formula> 44% GFLOPs of the multimodal transformer, but still get higher accuracy than the encoder-only counterpart. With the same number of encoder layers as TransVG, our Dynamic MDETR (ResNet-50) outperforms TransVG (ResNet-101) but only brings marginal extra computational cost relative to TransVG. In addition, to verify its generalization ability and scale up our Dynamic MDETR, we build the first one-stage CLIP empowered visual grounding framework, and achieve the state-of-the-art performance on these benchmarks.
Multi-Path Minimum Spanning Tree and Superpixel Based Cost Aggregation for Stereo Matching

Cost aggregation is a key step in stereo matching algorithms. Despite more than a decade of development, most algorithms still encounter challenges such as high error rates in low-texture regions and blurred edges. To improve matching accuracy, we propose a novel cost aggregation method based on multi-path minimum spanning tree (mPMST) and superpixel in this paper. The mPMST offers more optional paths for cost aggregation than the original MST by treating the reference image as an eight-connected graph. To improve both accuracy and computational efficiency, we innovatively run the mPMST for cost aggregation at the inside-superpixel level and superpixel level, which can obtain high accuracy in high-texture regions and low-texture regions respectively. In order to effectively fuse the two-level aggregated costs, we propose a novel adaptive weight based on calculating image entropy for each superpixel. This method can distinguish between regions with high and low texture and quantify texture complexity. Additionally, a novel disparity map refinement method is proposed to improve the quality of disparity maps using the novel cost aggregation structure proposed. In the experimental studies, we test our method on Middlebury and KITTI benchmarks. Average error rates of 5.94% for Middlebury 2006 and 24.51% for KITTI 2015 are achieved. Therefore, our method exhibits higher accuracy compared to other state-of-the-art approaches.
Sentiment ratio word graph using “local university” news articles and text mining

Unstructured data consisting of natural language such as SNS, papers, and questionnaire subjective question answers are generally compared or visualized using text mining. When analyzing text data by setting a random interval representing a time such as year, quarter, month, and day, it may be necessary to determine which interval has the most data, which sentiment words are used a lot for each interval, and how much text data in a particular interval is written negatively than positive. As a way to solve this problem, this study proposes an sentiment ratio word graph that can deliver the above three information at once. An sentiment ratio word graph is a graph created by dividing text data by a random interval standard representing time such as year, quarter, month, and day of the week and then assigning the sentiment score in the sentiment dictionary to the text data. When visualizing an sentiment ratio word graph, if you also use the pie coefficient to display words, you can further convey information about which words are most relevant to the sentiment word in a particular interval.
Triple Layered Security for Data Hiding Using Steganography and Visual Cryptography

The proposed system makes use of steganographic method along with visual cryptography for hiding data in images and to provide better security. Even though extensive research work has been done previously, but most of the research work gives no robust security for the image that has been encrypted. The method proposed in this paper is capable of hiding some secret message in least significant bits of the original image, thereby hiding the secret message in way to make its detection difficult. The secret image is then under went to visual cryptography algorithm. Thereby, creating shares of the secret image, which are noise like structures. Further, the method proposes to hide these shares in least significant bits of different images so as to create three levels of security for the message. The main objective of the proposed method is to combine the use of data hiding techniques, steganography and visual cryptography algorithms for designing a secure algorithm so that security, reliability and efficiency of secret message can be improved. The framework is implemented in matlab.
Improved Tuna Swarm Optimization Algorithm for Engineering Design Problems

Tuna Swarm Optimization (TSO) which is developed by being inspired by the hunting strategies of the tuna fish is a metaheuristic optimization algorithm (MHA). TSO is able to solve some optimization problems successfully. However, TSO has the handicap of having premature convergence and being caught by local minimum trap. This study proposes a mathematical model aiming to eliminate these disadvantages and to increase the performance of TSO. The basic philosophy of the proposed method is not to focus on the best solution but on the best ones. The Proposed algorithm has been compared to six current and popular MHAs in the literature. Using classical test functions to have a preliminary evaluation is a frequently preferred method in the field of optimization. Therefore, first, all the algorithms were applied to ten classical test functions and the results were interpreted through the Wilcoxon statistical test. The results indicate that the proposed algorithm is successful. Following that, all the algorithms were applied to three engineering design problems, which is the main purpose of this article. The original TSO has a weak performance on design problems. With optimal costs like 1.74 in welded beam design problem, 1581.47 in speed reducer design problem, and 38.455 in I-beam design problem, the proposed algorithm has been the most successful one. Such a case leads us to the idea that the proposed method of this article is successful for improving the performance of TSO.
Sistem Pendukung Keputusan dalam Mendiagnosa Gejala TBC dengan Metode WASPAS dan CPI

Mycobacterium bacteria is the cause of tuberculosis, an infectious disease that attacks the respiratory tract in humans and can easily spread through the air. In 2012, WHO data showed that tuberculosis is an infectious disease that causes the second largest health problem in the world. Tuberculosis is a disease that can attack other parts of the body as well as the lungs. The sooner a person finds out he has tuberculosis and gets tested, the more likely he will recover faster. There are many detection methods, but many are time consuming. A decision support system is needed using the WASPAS method and the CPI method which are capable of diagnosing tuberculosis. To overcome this problem. By forming a decision tree represented by rules, this decision support system implements Rank Order Centroids, one of the classification techniques in machine learning used in the data mining process. This study produces a system that is expected to make it easier for the general public to obtain timely and accurate information for diagnosing tuberculosis. A decision support system for diagnosing tuberculosis was developed as a result of this study. After being tested with 100 patient data, 50 as training data and 50 as testing data, the Confusion Matrix produces an accuracy value of 90%.
Penerapan Metode EUCS Untuk Evaluasi Tingkat Kepuasan Pengguna Aplikasi PNM Digi Karyawan

The PNM Digi Employee application was designed by PT. PNM Mekaar Persero to make it easier for employees to perform attendance and some administrative services for daily needs such as bill payments, purchase of credit, investments and many more. The purpose of this study was to determine the level of employee satisfaction in using the PNM Digi Employee application. User satisfaction was measured so that it became input for the company in managing and developing the PNM Digi Employee application. For this reason, this study was conducted to measure the level of user satisfaction of the Digi Employee PNM application by applying the End User Computing Satisfaction method. To obtain data as research material, research questionnaires were distributed to employees by adopting the dimensions of the End User Computing Satisfaction method, namely the dimensions of Content, Accuracy, Format, Easy of Use and Timeliness. It was found that the employees of PT. PNM Mekaar is very satisfied in using the PNM Digi Employee application based on the results of the tests that have been carried out showing the percentage of all dimensions is worth above 80%, namely the Content dimension 85.3%, Accuracy dimension 83.7%, Format dimension 86.6%, Easy of dimension. Use 85.3% and Timeliness dimensions 86% with dimensions that have a significant effect, namely the dimensions of format and dimensions of Timeliness.
Penerapan Artificial Intelligence Untuk Klasifikasi Penyakit Kulit Dengan Metode Convolutional Neural Network Berbasis Web

Skin is one of the human organs that functions to regulate body temperature in humans, as well as to protect all organs in the human body. There are many factors that affect skin health conditions that cause skin diseases. A system was developed to help people detect skin diseases. This system is Artificial Intelligence with Convolutional Neural Network method so that it will produce a very significant image. The network will be trained to find angles, edges, shapes, and also features. The results of system performance in this study using adam optimizer with a learning rate of 0.0001 get the highest value of data accuracy reaching a value of 97%. So that the identification of skin diseases is quite good.