Reviewerly/exHarmony huggingface dataset:
ENAS-B: Combining ENAS With Bayesian Optimization for Automatic Design of Optimal CNN Architectures for Breast Lesion Classification From Ultrasound Images

Efficient Neural Architecture Search (ENAS) is a recent development in searching for optimal cell structures for Convolutional Neural Network (CNN) design. It has been successfully used in various applications including ultrasound image classification for breast lesions. However, the existing ENAS approach only optimizes cell structures rather than the whole CNN architecture nor its trainable hyperparameters. This paper presents a novel framework for automatic design of CNN architectures by combining strengths of ENAS and Bayesian Optimization in two-folds. Firstly, we use ENAS to search for optimal normal and reduction cells. Secondly, with the optimal cells and a suitable hyperparameter search space, we adopt Bayesian Optimization to find the optimal depth of the network and optimal configuration of the trainable hyperparameters. To test the validity of the proposed framework, a dataset of 1522 breast lesion ultrasound images is used for the searching and modeling. We then evaluate the robustness of the proposed approach by testing the optimized CNN model on three external datasets consisting of 727 benign and 506 malignant lesion images. We further compare the CNN model with the default ENAS-based CNN model, and then with CNN models based on the state-of-the-art architectures. The results (error rate of no more than 20.6% on internal tests and 17.3% on average of external tests) show that the proposed framework generates robust and light CNN models.
Pemanfatan Game Sebagai Media Belajar Pengenalan Ragam Rumah Adat Di Indonesia Berbasis Android

Technological developments that continue to move very quickly, giving birth to various kinds of innovations that can be useful for life. Game is one of the products of computer technology, games that are in great demand and use, such as entertainment, learning media, promotional media and many more. has 34 provinces with a variety of customs and culture in each province. Every province in Indonesia has traditional houses which have their own uniqueness, and therefore it is very unfortunate if Indonesian citizens, especially children, do not know about them. Therefore there must be media that can reach both adults and children in introducing Indonesian customs and culture, especially traditional houses. Therefore a game was made "Educational Game for Various Indonesian Traditional Houses", which aims to be a learning medium by using technology and can also attract children's interest in traditional houses.
C3Meta: A Context-Aware Cloud-Edge-End Collaboration Framework Toward Green Metaverse

The Metaverse aims to provide an immersive and interactive experience for people by digitizing the physical world to the virtual world using sensing, communication, and computation technology. However, the seamless transmission of massive data in the communication network and the intensive computation brought by large models in the Metaverse have raised concerns over energy consumption and carbon emissions. To address this issue, this article designs a Context-aware Cloud-edge-end Collaboration framework to establish an energy-efficient green Metaverse system (C3Me-ta) from the six aspects via the collaboration of end, edge, and cloud based on the system condition and information flow. We take the smart city as the case study of C3Meta and focus on a typical crowd counting task in the field of intelligent monitoring to demonstrate the effectiveness of the proposed framework. A series of key technologies, including key frame detection, adaptive partition, offloading location decision, and virtual scene reconstruction, are illustrated to achieve the trade-off between detection performance and energy consumption. An experimental platform is established to verify the performance of the proposed framework, and the results show C3Meta is an energy-saving method while maintaining low delay and high detection performance in the real-time system. The C3Meta framework has the potential to contribute to a sustainable and environmentally friendly Metaverse.
Design of Business Rule Model for Telecom Operator Support System

The polymorphism of customers and the variability of the market determine that changes in strategy and business logic are inevitably frequent. In order to meet this need, the idea of building a system structure based on business rule methods has emerged. Based on the analysis of various existing business rule modeling methods, as well as the core technologies and rule alerting related technologies of most business rule products applied in the market, this article proposes a research on the design of business rule models based on China Telecom supporting system, starting from the actual needs of the system. This article fully draws on the advanced experience of SID and extensively references the existing rules of various provinces of China Telecom. It designs an object model and data model for the business rule management of China Telecom support system, and based on this, defines a rule directory framework and sorts out the basic vocabulary of business rules. It provides effective support for achieving easy configuration, maintenance, understanding, troubleshooting, reusability, controllability, scalability, and customization of business rules.
Study of the Mechanism and Countermeasures for the Supply-Demand Coupling of Rural Planning Talent in Local Higher Education Institutions

The implementation of the national rural revitalization strategy has triggered an urgent demand for the training of rural planning talent at different levels. With local higher education institutions supplying talent for regional rural revitalization as a research object, based on the two major systems of professional talent training and regional rural development, this paper interpreted the new connotations of the supply-demand coupling of rural planning talent from the three perspectives of relationship, intensity and quality, analyzed the coupling mechanism on the basis of matching logic, local logic and flexible logic, and thus put forward countermeasures for coordinated supply-demand coupling of rural planning talent in local higher education institutions in the three aspects of structural, local and flexible adaptation. The study deepens the academic and theoretical research on talent supply and demand and professional talent training in local higher education institutions and provides important intellectual support for the formulation of relevant optimization and regulation policies by local governments and higher education institutions.
Noncyclic nonadiabatic geometric quantum gates in a superconducting circuit

Quantum gates based on geometric phases possess intrinsic noise-resilience features and attract much attention. However, the implementations of previous geometric quantum computation typically require a long pulse time of gates. As a result, their experimental control inevitably suffers from the cumulative disturbances of systematic errors due to excessive time consumption. Here, we experimentally implement noncyclic and nonadiabatic geometric quantum gates in a superconducting circuit, significantly shortening the gate time. Moreover, we experimentally verify that our universal single-qubit geometric gates are more robust to both the Rabi frequency and the qubit frequency shift-induced error, compared with the conventional dynamical gates, using the randomized benchmarking method. This scheme can also be utilized to construct two-qubit geometric operations while the generation of maximally entangled Bell states is demonstrated. Therefore, our results provide a promising routine to achieve fast, high-fidelity, and error-resilient quantum gates in superconducting quantum circuits.
Features, Importance, and Advantages of Knowledge Assessment at Database-Related Courses Via AcpSQL System

Although assessment at first glance seems like a simple process in which the teacher teaches, the student learns, and then the teacher checks this students’ learned knowledge and gives the learned knowledge a certain number, in modern education all stakeholders involved are aware of the insight that not everything can be expressed in numbers. The subject of this paper’s research is the system of automatic assessment and its role in the learning process. The aim of the research conducted in the summer semester of the academic year 2020/2021 among 49 students of the undergraduate study of computing was to examine how students at the Algebra University College database-related courses assess the role of the AcpSQL assessment system in their learning process. The research result showed that students consider all possibilities and features of the AcpSQL assessment system as highly important. However, they would not generally replace manual exam correction by teachers with the automatic exam correction system, but only partially. Although students believe that the system can contribute to the assessment and self-assessment of their knowledge, they estimate that the system can only partially replace student-teacher interaction as the most important relationship in the learning process.
Intra-school control - as part of the management system of an educational institution

This article provides an explanation of intra-school control as part of the management system of an educational institution. The pedagogical activity of any school is characterized by comprehension of the accumulated work experience, the search for new ideas, the achievement of a high level of pedagogical excellence in the organization of educational activities, the emergence and dissemination in the pedagogical, parental, and managerial environment. and a new understanding of the values of education, when education becomes a priority in the lives of the younger generation. And in every school, one of the real levers for such development is the apparatus of intra-school control. In-school observation should be motivated and stimulating, based on knowledge of the capabilities and interests of all participants in the educational process. On the one hand, its result is a qualitative improvement in relations within a certain group and between them, on the other hand, it contributes to the degree of professional growth and achievements of teachers and students.
Toward real-time and accurate dense 3D mapping of crop fields for combine harvesters using a stereo camera

Fast and accurate 3D scene perception is a crucial prerequisite for the autonomous navigation and harvesting of combine harvesters. However, crop field scenarios pose severe challenges for vision-based perception systems due to repetitive scenes, illumination changes and real-time constraints on embedded computing platforms. In this paper, we propose a feature-based, two-stage approach for real-time dense 3D mapping for combine harvesters. In the first stage, our approach constructs a sparse 3D map using reliable feature matching, which provides prior knowledge about the environment. In the second stage, our method formulates per-pixel disparity calculation as probabilistic inference. The key to our approach is the ability to compute dense 3D maps by combining Bayesian estimation with efficient and discriminative point cues from images, exhibiting tolerance against visual measurement uncertainties due to repetitive textures and uneven lighting in crop fields. We validate the performance of the proposed method using real crop field data, and the results demonstrate that our dense 3D maps provide detailed spatial metric information while maintaining a balance between accuracy and efficiency. This makes our approach highly valuable for online perception in combine harvesters operating with resource-limited systems.
Robust Structure from Motion observer: Input to State Stability approach

The authors present a novel nonlinear Thau-Luenberger observer for estimating Structure from Motion using a calibrated camera. Accurate reconstruction of the 3D structure of a scene relies on precise estimation of the camera's translational and angular velocities, which can be challenging for cameras on mobile platforms. The proposed observer aims to estimate these velocities robustly in the presence of measurement noise, with stability characterized through Input to State Stability analysis and Lyapunov theory. The stability conditions are determined using optimization techniques based on Linear Matrix Inequalities. The performance of the proposed approach is validated through simulation and experimental data, demonstrating its effectiveness in recovering the depth of tracked features and its robustness against disturbances.
Valorization of traditional ornaments in the classes of fine arts in extracurricular education

According to the provisions of the Education Code of the Republic of Moldova (Official Gazette No. 319-324 art. No: 634), Chapter VII. Article 36. (1) "Extracurricular education is carried out outside the school program and activity through activities complementary to the educational process carried out in educational institutions and aims to develop the cognitive, affective and action potential of children and young people, to respond to their interests and options for free time".
Virtual Home Staging: Inverse Rendering and Editing an Indoor Panorama under Natural Illumination

We propose a novel inverse rendering method that enables the transformation of existing indoor panoramas with new indoor furniture layouts under natural illumination. To achieve this, we captured indoor HDR panoramas along with real-time outdoor hemispherical HDR photographs. Indoor and outdoor HDR images were linearly calibrated with measured absolute luminance values for accurate scene relighting. Our method consists of three key components: (1) panoramic furniture detection and removal, (2) automatic floor layout design, and (3) global rendering with scene geometry, new furniture objects, and a real-time outdoor photograph. We demonstrate the effectiveness of our workflow in rendering indoor scenes under different outdoor illumination conditions. Additionally, we contribute a new calibrated HDR (Cali-HDR) dataset that consists of 137 calibrated indoor panoramas and their associated outdoor photographs.
Pengembangan Blog Berbasis STREAM (Science, Technology, Religion, Engineering, Art, And Mathematics) Sebagai Media Pada Pembelajaran Sistem Periodik Unsur

The research development of STREAM-based blog learning media on periodic system element material was conducted to determine the feasibility as a learning media product. It used Research and Development design with 4D Model. The development results were validated by two expert validators, one practitioner validator and ten students of State Senior High School 1 Tembilahan as students limited test validators. The feasibility validations of quantitative data were analyzed by using percentage and the qualitative data (responses and suggestions) for validators’ improvement were used as considerations for making revisions. Generally, the validators’ assessments of the development of STREAM-based blog learning media obtained that the mean percentage of validity results from material and media experts were 92.15%, the practicality test by the chemistry teacher was 94.33%, and the limited response test by the students were 87.6%. So it could be concluded that the STREAM-based blog learning media was feasible to use in learning activities.
Method of Application of Support Vector Regression In Predicting The Number of Visits of Foreign Tourists to The Province of Maluku

Maluku Province is one of the areas in Indonesia that has many tourist attractions to visit, both natural and cultural heritage. The high interest of foreign tourists who want to visit various tourist objects, makes the tourism industry able to bring benefits and profits for most of the people of Maluku. However, in the last two years, 2020-2021, all countries were faced with the Covid-19 pandemic. The Covid-19 pandemic has resulted in a decrease in the number of foreign tourist visits to Indonesia. To increase marketing activities in the midst of the Covid-19 pandemic that has hit Indonesia since 2020, foreign tourist information is increasingly needed as material for evaluation and planning for future development. One of the methods used to predict the number of foreign tourist visits to Maluku Province is Support Vector Regression (SVR). Based on forecasting using test data, the RMSE value is 1.334985 and the MAPE obtained is 1.256346%, so the prediction of the number of foreign tourist visits to Maluku Province in 2022 (January-June) states that in January the number of tourist visits was 999 hundred visits. and increased until June as many as 1121 thousand visits.
A Novel approach to denoise an image using CNN

Due to a variety of environmental and human factors, real-world photographs frequently contain noise. De-noising is a method for taking out noise from an image. De-noising images faces significant challenges because of the sources of noise. Gaussian, impulse, salt-and-pepper, and speckle noise are some examples of complicated sources of noise in imaging. In the realm of visual de-noising, Convolutional Neural Networks (CNN) have attracted an increasing amount of interest. Several CNN approaches can be used to denoise images. For the evaluation of these approaches, various datasets were used. The various CNN algorithms used for image de-noising are studied in this study. Many CNN architectures have been used over the years to denoise digital images. RIDNet and Autoencoder, two cutting-edge CNN architectures, are used here.
Automatically Discovering Novel Visual Categories With Adaptive Prototype Learning

This paper targets the task of novel category discovery (NCD), which aims to discover unknown categories when a certain number of classes are already known. The NCD task is challenging due to its closeness to real-world scenarios, where we have only encountered some partial classes and corresponding images. Unlike previous approaches to NCD, we propose a novel adaptive prototype learning method that leverages prototypes to emphasize category discrimination and alleviate the issue of missing annotations for novel classes. Concretely, the proposed method consists of two main stages: prototypical representation learning and prototypical self-training. In the first stage, we develop a robust feature extractor that could effectively handle images from both base and novel categories. This ability of instance and category discrimination of the feature extractor is boosted by self-supervised learning and adaptive prototypes. In the second stage, we utilize the prototypes again to rectify offline pseudo labels and train a final parametric classifier for category clustering. We conduct extensive experiments on four benchmark datasets, demonstrating our method's effectiveness and robustness with state-of-the-art performance. The source code and trained models will be made publicly.
DeepPose: An Integrated Deep Learning Model for Posture Detection Using Image and Skeletal Data

Identifying human actions and postures presents significant challenges for computerized systems. The categorization of these tasks holds particular relevance in the fields of health and robotics. Leveraging artificial intelligence technologies, it becomes feasible to define and classify recurring physical movements and postures accurately. Proper posture is further essential for the rehabilitation of patients because it affects the effectiveness of exercise training. Unfortunately, patients fail to follow the correct sequence when performing the exercises. To pursue the problem, a new method is proposed for posture recognition and pose estimation that does not require wearable devices. The proposed model utilizes 2D coordinates derived from the 2D poses as inputs with 18 joints of the human body as key points, along with an image dataset, to accurately classify various postures. This study involved training a custom CNN named DeepPose using both image and keypoint datasets and conducting a comparative analysis with the performance of two other pre-trained models. The result shows that the proposed model with the keypoint dataset outperforms over image dataset.
Access Control and Encryption Techniques during Big Data Accessing Stage

Privacy issues and data security are made worse by Big Data's velocity, volume, and variety. Being able to extract information from enormous amounts of data has gained relevance. The amount of data being produced is large. The key drivers of ongoing growth include the Internet's rapid spread, the Internet of Things (IoT), and another innovation. Since data generated by systems reflects the environment it was obtained from, user can use the data that acquire from them to comprehend how they function. When it comes to cybersecurity, where the primary objective is to protect assets, this has grown in importance. This paper will look at current big data and cybersecurity studies. We will focus on one of the crucial data security tactics, namely access control and encryption techniques, by highlighting the advantages and disadvantages of conventional techniques and providing a set of requirements that any access control solution for Big Data platforms may satisfy.
A Comprehensive Study of Sentiment Analysis on Covid19 Vaccine using ML and DL Based on Social Media Reaction of Bangladeshi People

Recently world is passing through a deadly pandemic called COVID-19. A global pandemic that was introduced or some say produced first in Wuhan, CHINA, and within some months spread around the world creating chaos in people’s minds because mankind had not seen such kind of virus in decades. Spending life in hard lockdown and keeping social distance affects people’s mental health and behavior. Within a year by the blessing of science, vaccines were introduced by different profitable and non-profitable organizations of different countries around the globe to fight back against the deadly virus. Due to mental health and behavior issue some peoples refuse to take the vaccine and also discoursed other people. Most of the activity happened on social media platforms like Facebook, and Twitter because of less monitoring. This effected the decision of people to take the vaccine as the vaccine is not taken as expected. On the other hand, some people express the benefit of talking about vaccines. In this paper, The Facebook comment that was posted related to covid-19 vaccine and vaccination campaign and tried to analyze the sentiment on covid-19 vaccine of people of Bangladesh has been examined. Due to the fact that Facebook comments allow users to voice their opinions, a dataset has been prepared from these comments. Dataset contained 1700 entries of Bangla comments and was labeled each entry with the corresponding sentiment from positive, negative, or neutral. Bangla comment was converted into numerical value by using the tokenizer method and performed various machine learning and deep learning algorithm on 1300 dataset which were where the LSTM algorithm appears as the best fit with an accuracy of 92.27%. LSTM works perfectly in the dataset containing Bangla comments.