Reviewerly/exHarmony huggingface dataset:
Bronchial Light Microscopy Image Segmentation Based on Boundary Attention

research-article Share on Bronchial Light Microscopy Image Segmentation Based on Boundary Attention Authors: Kunchen Li Northeastern University, China Northeastern University, ChinaView Profile , Zhexin Li Northeastern University, China Northeastern University, ChinaView Profile , Yicheng Liu Northeastern University, China Northeastern University, ChinaView Profile , Qinzhi Fang Northeastern University, China Northeastern University, ChinaView Profile , Bangwangke Tang Northeastern University, China Northeastern University, ChinaView Profile , Liping Huang Northeastern University, China Northeastern University, ChinaView Profile , Xinyu Xiong Sun Yat-sen University, China Sun Yat-sen University, ChinaView Profile Authors Info & Claims CCRIS '21: Proceedings of the 2021 2nd International Conference on Control, Robotics and Intelligent SystemAugust 2021 Pages 256–260https://doi.org/10.1145/3483845.3483890Published:22 October 2021Publication History 0citation34DownloadsMetricsTotal Citations0Total Downloads34Last 12 Months19Last 6 weeks3 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
The Mathematical Models of Lattice Functions in Modelling of Control System

Abstract The paper proposes an approach to constructing a mathematical model of lattice functions, which are mainly used in the study of discrete control systems in the time and domain of the Laplace transform. The proposed approach is based on the assumption of the physical absence of an impulse element. An alternative to the classical approach to the description of discrete data acquisition - the process of quantization in time, is considered. As a result, models of the lattice function in the time domain and the domain of the discrete Laplace transform are obtained. Based on the obtained mathematical models of lattice functions, a mathematical model of the time quantization element of the system is obtained. This will allow in the future to proceed to the construction of mathematical models of various discrete control systems, incl. expanding the proposed approaches to the construction of mathematical models of multi-cycle continuous-discrete automatic control systems
Utilizing Spatio-Temporal Redundancy to Maximize the Performance of Video Steganography: An Octree Approach

Utilizing data redundancy has been widely proposed as a practical solution to hide a substantial amount of secret data. For digital images, the distribution of pixels in a 2D matrix has increased the possibility of having redundancy since each pixel has a relation with the eight neighboring pixels. However, the 3D nature of video segments has introduced an additional source of data redundancy, namely temporal redundancy. Several recent video steganography schemes have suggested exploiting temporal redundancy to hide the secret data. This paper aims to fully capture the redundancy available in a video segment by incorporating both spatial and temporal redundancy using an Octree segmentation method. The input video signal is segmented into homogeneous variable-sized 3D cubes to maximize the redundancy in each region and thus, stronger energy compaction is expected in the 3D Discrete Cosine Transform (3D-DCT) domain. In other words, the hiding capacity is maximized since most of the signal will be concentrated within a few significant 3D-DCT coefficients leaving a large area with insignificant 3DDCT coefficients, which can be safely replaced by the secret data without noticeable degradations in the quality of the stego signal.
Improving the Generalization Capability of DNNs for Ultra-low Power Autonomous Nano-UAVs

Deep neural networks (DNNs) are becoming the first-class solution for autonomous unmanned aerial vehicles (UAVs) applications, especially for tiny, resource-constrained, nano-UAVs, with a few tens of grams in weight and subten centimeters in diameter. DNN visual pipelines have been proven capable of delivering high intelligence aboard nanoUAVs, efficiently exploiting novel multi-core microcontroller units. However, one severe limitation of this class of solutions is the generalization challenge, i.e., the visual cues learned on the specific training domain hardly predict with the same accuracy on different ones. Ultimately, it results in very limited applicability of State-of-the-Art (SoA) autonomous navigation DNNs outside controlled environments. In this work, we tackle this problem in the context of the human pose estimation task with a SoA vision-based DNN [1]. We propose a novel methodology that leverages synthetic domain randomization by applying a simple but effective image background replacement technique to augment our training dataset. Our results demonstrate how the augmentation forces the learning process to focus on what matters most: the pose of the human subject. Our approach reduces the DNN’s mean square error — vs. a non-augmented baseline — by up to 40%, on a never-seen-before testing environment. Since our methodology tackles the DNN’s training stage, the improved generalization capabilities come at zero-cost for the computational/memory burdens aboard the nano-UAV.
Retrieval Method of Poor Students Subsidy Resource Database Based on Improved Grey Correlation Degree

In the current retrieval methods, the Hamming distance measure is used to calculate the relevance of information resources, which leads to the lower average accuracy of retrieval text. Determine the number and density of key words of information resources in the resource database, adopt the technology of key word density to calculate the semantic score of information resources in the resource database, and assess the semantics of information resources; from the sequence of information resources in the resource database, design and improve the steps for calculating the relevance of information resources in the resource database based on grey relational degree, and determine the relevance of information resources in the resource database; adopt the DCMH method, set up the retrieval model, extract the characteristics of information resources in the resource database, design the retrieval objective function, and retrieve the information resources in the resource database of subsidies for poor students. The experimental results show that the average accuracy of retrieving the text information of the three sets of datasets is higher than that of the two comparison methods chosen in this experiment in the three dimensions of 16, 24 and 32, and 0.09, 0.12, 0.055, 0.065, 0.025 and 0.04 when the recall rate is 0.2%. Obviously, the retrieval method of this research has the strong retrieval performance.
Nonlocal implementation of multi-qubit controlled unitary quantum gates with quantum channel

Quantum computers are very efficient in terms of speed and security. Decoherence and architectural complexity restrict the control of sensitive quantum information as the number of qubits in a quantum computer increases. Therefore, it is more convenient to make a device with multiple quantum processors with small number of qubits instead of making a device with a large number of qubits quantum processors. The implementation of controlled unitary gates is a problem in such nonlocal systems. The methods in the literature for this problem use entangled qubit pairs, classical communication channels and classical bits. The existing methods perform some unitary operations on the target state for reconstruction after sending information through classical communication channels and applying quantum measurements on control states. In this study, a generalized method for nonlocal implementation of multi-qubit controlled unitary quantum gates with quantum channel is proposed. The proposed method can implement any controlled gate on the control and target qubits that are far from each other in terms of location. The method does not require classical channels and classical bits, any extra unitary operation for reconstruction. The proposed method is both more secure and uses less resources for operations than the other hybrid methods in the literature. Comparisons with existing studies are given in terms of required entangled qubit pairs, classical channels and bits, extra unitary operations for reconstruction the target state, and the advantages of the proposed method are revealed.
Blind Image Restoration with CNN Denoiser Prior

Repala, Hari Kishan Christopher, Aneeta Sudeep, P. V.Recently, convolutional neural networks (CNN) are used as a well-structured image prior for solving inverse problems. Deep image prior (DIP) has emerged as a popular blind image denoising approach that enhances given degraded image based on a CNN model with no prior heavy data-driven training. In this paper, we focus on further enhancing the effectiveness of the image denoising capability of the DIP by introducing the Kullback—Leibler (KL) Divergence regularization to the loss function. The noisy input image is assumed to be Gaussian distributed. For validation, we compared the performance of different versions of DIP method qualitatively and quantitatively using measures like PSNR, mean SSIM, and MS-SSIM. The results obtained indicate that the proposed method outplays the conventional deep image prior approach.
Genetic programming for iterative numerical methods

Abstract We introduce GPLS (Genetic Programming for Linear Systems) as a GP system that finds mathematical expressions defining an iteration matrix. Stationary iterative methods use this iteration matrix to solve a system of linear equations numerically. GPLS aims at finding iteration matrices with a low spectral radius and a high sparsity, since these properties ensure a fast error reduction of the numerical solution method and enable the efficient implementation of the methods on parallel computer architectures. We study GPLS for various types of system matrices and find that it easily outperforms classical approaches like the Gauss–Seidel and Jacobi methods. GPLS not only finds iteration matrices for linear systems with a much lower spectral radius, but also iteration matrices for problems where classical approaches fail. Additionally, solutions found by GPLS for small problem instances show also good performance for larger instances of the same problem.
Three-dimensional book data page segmentation and extraction method using Laplace equation

Our work aims to interpret historical documents in closed booklet form without physical unfolding through CT scanning techniques. X-ray based CT scans can transform booklets into physical data for analysis, and current research in this direction performs page segmentation at the two-dimensional level and does not analyze data from historical documents at the three-dimensional level. Our proposed method uses a self-developed web tool for 3D dimensional annotation and solves Laplace’s equation to construct the 3D spatial structure of historical documents and perform page segmentation. We experiment with data in booklet form and show how to segment and extract pages in three dimensions by using Laplace’s equation.
TRC-GCN: Two Residual Connections in Graph Convolution Networks for Node Classification Tasks

Recently, several variants of graph convolution networks (GCNs), which have shown awesome performance in node classification tasks, were investigated. This paper proposes a new GCN model by adding two residual connections (TRC), one is the initial residual connection, and the other is the residual connection to the previous layer. Moreover, a new adjacency matrix and a simpler method for realizing identity mapping are presented and integrated into the TRC-GCN model. The advantage of our TRC-GCN is that the representation of the target node integrates more information, which can learn node embeddings more outstandingly, so as to perform downstream tasks more effectively. Then, we use this model to perform node classification experiments on several datasets (Cora, Citeseer, Pubmed), and found that this model is more excellent than many other models.
Books and Events: Trends, Projects, Achievements in the Light of the 34th Moscow International Book Fair

The leading event for the library and publishing sector — the 34th Moscow International Book Fair (MIBF) — took place on September 24—27, 2021 at the Central Exhibition Complex “Expocentre”. The forum was held in a “hybrid format”, foreign participants joined the events online. There were announced the winners of the National competition “Book of the Year — 2021” and the 9th Competition of the Russian Library Association “The Best Professional Book of the Year — 2021”. The business program of the MIBF contained sectoral conferences and round tables devoted to the insight of state of the book industry. The 2nd International Conference “The World Book Industry in new realities: new technologies, new challenges, new prospects”, the sectoral conference “The Russian Book Market — 2021”, the round table “Books in the city” took place at the Fair. The MIBF outlined the vectors of the development of modern culture, creative industries and creative entrepreneurship in the context of books; prospects for the development of new formats for the reorganization of territories and the formation of a stable local identity; intersection points of interests of citizens, local and federal authorities, business and cultural institutions. The article summed up the overall results of the MIBF.
Albanian News Category Predictor System using a Multinomial Naïve Bayes and Logistic Regression Algorithms

In this paper, we proposed a system for the classification of different news in the Albanian language using the Multinomial Naïve Bayes and Logistic Regression algorithms. The system is web-based and developed with Python programing language and Flask web framework. For the classification of Albanian language news, we have used 70% of the dataset for training and 30% for tests. The result shows that our system can use for the classification of Albanian language news. The proposed Multinomial Naïve Bayes and Logistic Regression classifiers were analyzed and compared for the following evaluation parameters: Accuracy, Precision, Recall, F1-score, and Confusion Matrix. The obtained results showed that the Logistic Regression algorithm has higher accuracy compared to the Naïve Bayes Multinomial algorithm in the classification of news in the Albanian language.
SPLORT: Evaluation of a Web-based Approach for Descriptive and Exploratory Analysis of Multivariate Spatio-temporal Data

The multivariate Spatio-Temporal data challenges the analysts along the navigation and exploration of the information, using Data Analytics techniques. We present SPLORT, as a user-centered web-based solution for segmented Visual Analytics. The tool involves a “What-Where-When” pyramidal paradigm to drive Spatio-temporal analysis towards the findings of new insights. It features multi-layered coordinated views to help the analyst navigate and explore multivariate data simultaneously. We evaluated this web-based tool, and reported the findings and results, considering users’ performance and perceptions, basing our analysis on their Likert scale (0-5) responses, regarding their experience and the tool's usability, and the sentiment of their feedback, by using Natural Language Processing (NLP) techniques. Our findings from the pilot study held, suggest that SPLORT successfully allows the users to visually query, explore and analyze contextless multi-scale attributes within space and time, which quickly drives them to new insights and to support easier decision-making processes. Moreover, the tool's flexibility, due to its multi-layered GUI, supports better and more friendly visual analytics.
Dungeon's Room Generation Using Cellular Automata and Poisson Disk Sampling in Roguelike Game

Procedural Content Generation (PCG) allows game developers to create worlds in games easier compared with creating them manually. One of many PCG algorithms is Cellular Automata (CA). CA was used in this research together with Poisson Disk Sampling (PDS) to generate two-dimensional dungeons in a roguelike game. PDS was modified to adapt to a grid-based environment to increase computation's efficiency. Each room generated in the game was connected to each other. Together, all the rooms created a dungeon. The connections between the rooms were architected using Depth First Search (DFS) algorithm as commonly used in maze generation. The created dungeons became the world in the game. Players could explore the dungeons to conquer them and collect resources. Then, players could spend the resources to upgrade their character. According to the research evaluation, using only CA was not sufficient to generate dungeons with specific designs. Certain pipelines were demanded to adjust and to control the end results. The algorithm still run adequately fast, which was around 73 milliseconds, despite all the pipeline additions.
PENGARUH PENGGUNAAN LEMBAR KERJA MAHASISWA (LKM) BERBASIS MASALAH MELALUI GOOGLE CLASSROOM TERHADAP HASIL BELAJAR MAHASISWA PADA MATERI FISIOLOGI TUMBUHAN

This study aims to determine the effect of using problem-based student worksheets taught through google classroom on Plant Physiology material. This research is an experimental study (pre-experimental) with a one group pretest posttest design. The subjects of this study were students of Biology Education, semester VI as many as 19 students. The research instrument was in the form of student learning outcomes tests. Data collection techniques were carried out by tests, observations and documentation. The data analysis technique used paired t-test. The results showed that there was a significant effect of using problem-based student worksheets through google classroom on student learning outcomes on plant physiology material.
Semi-selective image dehazing

In this paper, we propose a new image dehazing method via the semi-selective method. Firstly, we obtain the effect range of the global atmospheric light according to the statistical prior method. Secondly, a relative total variation with adaptive boundary constraint is developed to optimize the transmission. Finally, a semi-selective method is used to select the image with the best visual sense. Experimental results show that our method outperforms state-of-the-art haze removal methods in dehazing visual effects.
Comparative Analysis of Bagging and Boosting Algorithms on the Classification of the Popularity of Educational-themed Youtube Videos

Youtube is a social media that is currently widely used by the public to access and share information in the form videos. In Indonesia, video content with the theme of entertainment on youtube is more popular than video content with an educational theme. So that not many content creators make educational videos with educational themes. The data used in this study consisted of video duration, type of content, availability of subtitles, number of views, likes, and comments. This data will be used to classify the popularity of videos on YouTube using the Bagging and Boosting algorithm. The stages used in this study consisted of collecting datasets, preprocessing, clustering, sampling, implementing Bagging and Boosting algorithms. This research resulted in a fairly good performance. The best performance is obtained from implementing the decision tree algorithm with boosting techniques on the dataset carried out by the SMOTE process with an accuracy value of 81.22%, a precision of 84.26%, a recall of 81.22%, and an F-Measure of 0.8272. This research is expected to provide information to content creators about content with educational themes that are preferred by the Indonesian people.
Information and cultural approach to the evaluation of educational literature

The article considers the place of the book in modern media information society, its changing role in the system of education. The book is represented as an information phenomenon (a source and means of transmitting information) as well as an artifact of culture, reflecting the values of the society of the current historical period. The essence of the information-cultural approach for the formation of scientific knowledge and its application in the educational environment in the form of educational literature is revealed. The modern definition of the book as a cultural phenomenon, a means of communication and scientific knowledge is given. The article analyzes the situation of creating and functioning the textbooks for bachelor and magister programmes, parallel existing of manuals in printed and electronic forms. The textbook as an academic manual is a unique phenomenon of educational environment represented simultaneously in accord to the sphere of spirituality and technology which is the essence of information and culturological approach.
Learning Transferable Policy in Reinforcement Learning for Vehicle Velocity Tracking Control

We propose the control system for driving robot using Hierarchical Reinforcement Learning. Driving Robots are playing an active role in test driving for evaluating fuel consumption and exhaust gas of automobiles. We can consider Reinforcement Learning as one of the control methods for driving robot. The control system using Reinforcement Learning has the advantage that there is no need to adjust parameters manually. However, Reinforcement Learning suffer from poor sample efficiency because it requires a lot of trials. In this research, we propose the control system for driving robot using the algorithm for learning hierarchical policy. Moreover, we introduce State Abstraction in Hierarchical Reinforcement Learning. By using abstract state, each low-level policy specialize in distinct behavior. The advantage of this method is that we can improve the sample efficiency by transferring low-level policies learned using multiple vehicles. The experimental result shows that the proposed method improve the sample efficiency in vehicle velocity tracking task.
Common Carotid Artery Ultrasound Image Enhancement Based on Fuzzy Set Theory and Phase Asymmetry Metric

Carotid ultrasound images can be used for computer-aided diagnosis of atherosclerosis. However, the low contrast and blurred edges in ultrasound images can reduce the accuracy of the diagnosis. In this study, an image enhancement algorithm is proposed for common carotid artery (CCA) ultrasound images. First, the CCA ultrasound images are normalized. Next, the normalized images are fuzzified by using the S-function. Then, the fuzzified images are enhanced based on non-linear transform. After enhancement, the images are transferred from the fuzzy domain to the spatial domain by using the inverse function. Finally, an edge enhancement method based on the phase asymmetry (PAS) metric is proposed for CCA ultrasound images. The final results indicate that the proposed method has outstanding performance as compared with other advanced methods.