A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.

A Bayesian perspective on the ring attractor for heading-direction tracking in the Drosophila central complex

Efficient navigation requires animals to track their position, velocity and heading direction (HD). Bayesian inference provides a principled framework for estimating these quantities from unreliable sensory observations, yet little is known about how and where Bayesian algorithms could be implemented in the brain's neural networks. Here, we propose a class of recurrent neural networks that track both a dynamic HD estimate and its associated uncertainty. They do so according to a circular Kalman filter, a statistically optimal algorithm for circular estimation. Our network generalizes standard ring attractor models by encoding uncertainty in the amplitude of a bump of neural activity. More generally, we show that near-Bayesian integration is inherent in ring attractor networks, as long as their connectivity strength allows them to sufficiently deviate from the attractor state. Furthermore, we identified the basic network motifs that are required to implement Bayesian inference, and show that these motifs are present in the Drosophila HD system connectome. Overall, our work demonstrates that the Drosophila HD system can in principle implement a dynamic Bayesian inference algorithm in a biologically plausible manner, consistent with recent findings that suggest ring-attractor dynamics underlie the Drosophila HD system.

Implementasi Model Problem Based Learning Untuk Meningkatkan Keaktifan dan Hasil Belajar Peserta Didik Pada Pelajaran Administrasi Jaringan

This study aimed to determine the increase in student activity and learning outcomes through the implementation of the Problem Based Learning (PBL) learning model. This research is classroom action research conducted in 2 cycles. The method of data collection was carried out through observation. Data analysis was carried out by comparing the test results in cycle 1 and cycle 2 with descriptive techniques after implementing the learning method using the Problem Based Learning model. Students' activeness can be seen from the aspect of students who give full attention to the material, dare to ask the teacher, answer questions, and have opinions. In addition, students also collaborate in groups, work on questions, learn to use resources, and make group presentations. This study indicates that the learning outcomes and learning activities of class XII students of SMK PUSTEK BSD, South Tangerang City, Banten Province, in the Basic Network Administration subject have increased.

HUMAN-GEOGRAPHICAL FEATURES OF FUNCTIONING OF THE BASIC NETWORK OF CULTURAL INSTITUTIONS OF TERNOPIL CITY TERRITORIAL COMMUNITY

The article is devoted to the human-geographical study of the basic network of cultural institutions of the Ternopil city territorial community. The parameters of the territory and settlement network of the community are considered, which are the determining conditions during the structuring of geospace. The legislative principles of creating a basic network of cultural institutions of the local level are analyzed. The modern basic network of cultural institutions of the Ternopil territorial community is a consequence of the reform of the cultural sphere and the implementation of the decentralization reform. It went through three stages of its organizational formation. The basic network of culture of the local level of the Ternopil territorial community includes 39 institutions. More than 56% of the network's facilities are located in the city of Ternopil. According to the form of ownership and organizational and legal form, they are divided into 9 communal institutions, 1 communal enterprise and 29 establishments that are directly in communal ownership. The component structure of the sphere of culture and art of the community is formed by the following types of institutions: club-type cultural institutions, libraries, art schools, orchestras and cinemas. Primary socio-cultural services of the basic network are provided by 13 club-type cultural institutions. They are represented by the palace of culture, houses of culture and clubs (branches). Library institutions are the most numerous in the system of the basic network, the share of which reaches 51%. Among all 20 institutions of the community, the main role in this area is given to the Ternopil city centralized library system. Primary art education is represented by 2 music schools and 1 art school. The only municipal enterprise in the field of cinematography is the Ternopil Film Commission. A special feature of the basic network of cultural institutions of the Ternopil community is the presence of two orchestras. A significant addition to the basic network of community cultural institutions are communal institutions engaged in similar or related activities. Governing bodies ensure the systematic functioning of institutions and the implementation of measures to implement a consistent cultural policy. The territorial organization of cultural institutions of the local level of the Ternopil city community is an orderly network, where the connections between them are manifested in the formation of various combinations. Within the community, the functioning of 6 cultural and artistic systems was identified, which are combined into three types of different hierarchical levels (1 urban, 5 basic and 5 primary). The geospatial specifics of the location of the elements of the basic network result in the indicators of providing the city and basic administrative-territorial units of the community with cultural and art institutions. They are sufficient to ensure the sustainable functioning of the network and the provision of socio-cultural services. In the context of the spread of innovation diffusion, the rural area of the community is cascaded into three suburban zones: near (up to 6 km), medium (7-17 km), remote (over 18 km). The first zone meets the criteria of the village of Kurivtsi, the second – Malashivtsi, Glyadky, Chernykhiv, Ivankivtsi, Pleskivtsi, Kobzarivka, the third – Horodyshche, Nosivts, Vertelka. Problems of the organization of rendering of cultural services are revealed: outdated material and technical base; outflow of highly qualified creative specialists; insufficient funding from the budget; conservative forms and methods of providing cultural services; the initial level of development of cultural and creative industries. An important feature of the network of institutions of the Ternopil community is the real prospects for its expansion and improvement of functioning through the opening of new modern institutions. An important area of constant activation of socio-cultural activities is the constant increase of various forms of cultural mobility and touring activities. Key words: institution, network, community, culture, art, city, geocultural space, decentralization.

A Remote Sensing Image Segmentation Method Based on Fusion Mechanism

A U-Net convolutional network structure is fully capable of completing the end-to-end training with extremely little data, and can achieve better results. When the convolutional network has a short link between a near input layer and a near output layer, it can implement training in a deeper, more accurate and effective way. This paper mainly proposes a high-resolution remote sensing image change detection algorithm based on dense convolutional channel attention mechanism. The detection algorithm uses U-Net network module as the basic network to extract features, combines Dense-Net dense module to enhance U-Net, and introduces dense convolution channel attention mechanism into the basic convolution unit to highlight important features, thus completing semantic segmentation of dense convolutional remote sensing images. Simulation results have verified the effectiveness and robustness of this study.

MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

An IOT and Blockchain Approach for Food Traceability System in Agriculture

The food holds a major role and at the worldwide level in human lives and societies, the agriculture sector is known to be a major employer. In all the developing countries, food supply chain is the major domains of research which need a growth. Nowadays, the world wide serious topic is a food safety and the food safety issues are tackled by the trusted food traceability system. It can track and monitor the food production whole lifespan in which the processes of food raw material cultivation/breeding, processing, transporting, and selling etc. are included. In this paper, food quality problems are discussed and the food traceability system is proposed which is based on the Internet of Things (IoT) and blockchain technique for agricultural products. The presented system architecture is detailed and other existing problems are also discussed. The consortium blockchain is utilized as the basic network and the traceability system can achieve more reliable and trustable devices.

Gene networks under circadian control exhibit diurnal organization in primate organs

Mammalian organs are individually controlled by autonomous circadian clocks. At the molecular level, this process is defined by the cyclical co-expression of both core transcription factors and off-target genes across time. While interactions between these molecular clocks are likely necessary for proper homeostasis, these features remain undefined. Here, we utilize integrative analysis of a baboon diurnal transcriptome atlas to characterize the properties of gene networks under circadian control. We found that 53.4% (8,120) of baboon genes are rhythmically expressed body-wide. In addition, >30% of gene-gene interactions exhibit periodic co-expression patterns, with core circadian genes more cyclically co-expressed than others. Moreover, two basic network modes were observed at the systems level: daytime and nighttime mode. Daytime networks were enriched for genes involved in metabolism, while nighttime networks were enriched for genes associated with growth and cellular signaling. A substantial number of diseases only form significant disease modules at either daytime or nighttime. In addition, we found that 216 of 313 genes encoding products that interact with SARS-CoV-2 are rhythmically expressed throughout the body. Importantly, more than 80% of SARS-CoV-2 related genes enriched modules are rhythmically expressed, and have significant network proximities with circadian regulators. Our data suggest that synchronization amongst circadian gene networks is necessary for proper homeostatic functions and circadian regulators have close interactions with SARS-CoV-2 infection.

Quantum Networks and Symmetries

Quantum networks are promising tools for the implementation of long-range quantum communication. The characterization of quantum correlations in networks and their usefulness for information processing is therefore central for the progress of the field, but so far only results for small basic network structures or pure quantum states are known. Here we show that symmetries provide a versatile tool for the analysis of correlations in quantum networks. We provide an analytical approach to characterize correlations in large network structures with arbitrary topologies. As examples, we show that entangled quantum states with a bosonic or fermionic symmetry can not be generated in networks; moreover, cluster and graph states are not accessible. Our methods can be used to design certification methods for the functionality of specific links in a network and have implications for the design of future network structures.