Reviewerly/exHarmony huggingface dataset:
Mathematical Modeling of the Image Processing in Television Systems

Abstract Modern vision systems include a television image processing system. Improving the technical characteristics and expanding the capabilities of modern devices for recording television images makes it necessary to develop and create highly efficient algorithms for processing television images. Such systems process large amounts of information, therefore, the requirements for the reliability of processing large amounts of data at limited time intervals are constantly increasing. The rapid development of high-performance computing systems has recently created the conditions for expanding the range of tasks that can be solved with the help of vision systems. The research for improving the efficiency of methods and algorithms for digital image processing significant. Currently, the processing of television images is divided into several steps. The first step is the formation of a digital representation of the image (discretization, quantization and input of the image into the computer memory). The second step is the pre-processing of images (restoration and filtering). The third step is the formation of a graphic preparation of the image (segmentation and contour detection). One of the most complex and relevant is the scientific and technical task of the contour detections of objects in television images on the background with additive noise. The purpose of this study is the development of the contour detection method on the image with impulse noise using mathematical modeling.
Software implementation details of the secure data communication protocols stack based on the dynamic network topology

Abstract Secure data communication protocols stack based on the dynamic network topology is a set of protocols designed for secure data communication over a network. It is based on the principles of moving target technology along with cryptography, avalanche routing and group broadcasting. The data transfer protocol implemented in the protocols stack allows to transfer data not only hiding the data itself, but also its sender and receiver. The paper covers the software implementation details of the secure data communication protocols stack.
The Protection Officer's Role in Critical Information Infrastructure Protection

The protection officer as well as most of the industrialized world depends on multiple infrastructures that are critical to the population. Protection officers have had and will continue to have an important role in providing security to the facilities of these infrastructures. The United States Department of Homeland Security (DHS) describes critical infrastructure as “the physical and cyber systems and assets that are so vital to the United States that their incapacity or destruction would have a debilitating impact on our physical or economic security or public health or safety…” The officers provide this service based on the infrastructure's security policies and procedures.
An Efficient Blacklistable Anonymous Credentials without TTP of Tracing Authority Using Pairing-Based Accumulator

In conventional ID-based user authentications, privacy issues may occur, since users' behavior histories are collected in Service Providers (SPs). Although anonymous authentications such as group signatures have been proposed, these schemes rely on a Trusted Third Party (TTP) capable of tracing misbehaving users. Thus, the privacy is not high, because the TTP of tracing authority can always trace users. Therefore, the anonymous credential system using a blacklist without the TTP of tracing authority has been proposed, where blacklisted anonymous users can be blocked. Recently, an RSA-based blacklistable anonymous credential system with efficiency improvement has been proposed. However, this system still has an efficiency problem: The data size in the authentication is O(K'), where K' is the maximum number of sessions in which the user can conduct. Furthermore, the O(K')-size data causes the user the computational cost of O(K') exponentiations. In this paper, a blacklistable anonymous credential system using a pairing-based accumulator is proposed. In the proposed system, the data size in the authentication is constant for parameters. Although the user's computational cost depends on parameters, the dependent cost is O(δBL·K) multiplications, instead of exponentiations, where δBL is the number of sessions added to the blacklist after the last authentication of the user, and K is the number of past sessions of the user. The demerit of the proposed system is O(n)-size public key, where n corresponds to the total number of all sessions of all users in the system. But, the user only has to download the public key once.
A Study on Debugging Method for Engineering Software Using Marshalling in Restricted Environment

Although such commercial products such as ANSYS, NASTRAN and PATRAN provide most-wanted and productive functions used for analysis and design, they do not provide particular functions required by a manufacturing company. In this case, most companies develop the required engineering software on their own, and use C++ or C Language to develop the required engineering software. Recently, as the preference for GUI increases, the demand for GUI development increases as well. However, it is difficult for CUI developers to adapt to GUI development in a short period of time. In this paper, a debugging method for cooperation between internal developers who develop engineering software and external developers who develop GUI was proposed. This debugging method is advantageous in that it makes possible to develop GUI-based software without making significant changes to the basic structure of CUI-based engineering software. In addition, this debugging method is advantageous in that the core technology of a company can be protected, since external GUI developers cannot know the internal properties of engineering software.
An Automated Software Size Measurement Tool based on Generation Model using COSMIC Function Size Measurement

At present, most of the software industries need the well-defined models and standard measurement methods to measure the size of software. They are useful in the estimation of effort and time that are extensively used at the early stage of software development. COSMIC Functional Size Measurement (FSM) is one of the recognized methods to estimate the size of software project. The several research initiatives have focused on COSMIC FSM procedure with specific design diagram notation by manually and automatically. However, manual measurement of the various diagrams is time-wasting and difficult to measure the accurate size of the software. Therefore, the automated measurement of function size which includes the generation model based on three different diagram notations such as UML, SysML and Petri net and general mapping rules between COSMIC FSM and generation model to measure the size of software is proposed in this paper. The manual measurement of cooker system by using the generation model and COSMIC concept is successfully done in our previous paper. This proposed automated tool can handle various types of diagramming and get the size of software correctly.
Research on Harmony Search BFGS Hybrid Parallel Algorithm Based on Cloud Computing

In order to solve the problems of traditional harmony search algorithm such as slow convergence speed and low solution precision, this paper proposes the harmony search BFGS (Broyden-Fletcher-Goldfarb -Shanno) hybrid parallel algorithm based on cloud computing. The generated evolutionary harmony solution vector is used as the initial vector of BFGS algorithm, which then goes through the refining and optimization computing by BFGS algorithm, thus the hybrid parallel algorithm combining the harmony search and BFGS algorithm is constructed, the Map Reduce parallel programming model is used to design the Map and Reduce parallel functions of optimization computing on the cloud platform to construct the hybrid search BFGS hybrid optimization algorithm based on the cloud platform. It performs the programming and comparative experiment on Hadoop platform. The experimental results show that the proposed algorithm outperforms the harmony search algorithm in optimization precision, convergence speed and acceleration ratio, and its parallel computing is close to the linear acceleration ratio.
Impulse Noise Removal of Digital Image Considering Local Line Structure

For the impulse noise removal from a digital image, most of existing methods cannot repair line structures in an input image. In this letter, a method which considers the local line structure is proposed. In order to judge the direction of the line structure, adjacent lines are considered. The effectiveness of the proposed filter is shown by experiments.
Adaptive stabilization of two complex-conjugate dominant poles and quality indices of oscillating transient process in systems with interval parametric uncertainty

Abstract The paper is dedicated to a development of a controller synthesis method for control systems with an interval parametric uncertainty. The method combines two approaches to controller synthesis: adaptive one and interval one; also it is based on a pole domination principle. Therefore, the method allows providing desired control quality by placing two complex-conjugate dominant poles as necessary and reducing an influence of all other poles on a transient process in conditions of an interval parametric uncertainty. Application example of the synthesis method is provided; results of simulation modelling of a system, synthesized with the help of the method, are also provided.
Serverless Framework for Efficient Resource Management in Docker Environment

APIs provided by the Docker is executed through the container engine. Thus, it is a reality that the speed of creation and deletion of containers or requests for information is very slow. The load is even worse when many containers sending API requests at once. In this paper, we propose a method to apply module related to resource management in a container to reduce the load on API request generated in serverless environment. And we propose a new framework that manages resources of several containers or multiple Docker serves.
Learning media of vehicle power Train systems

Abstract This study aims to develop a valid, practical and effective learning media for power train systems for vehicles. This learning media is developed consisting of a clutch, transmission, propeller shaft, differential and and wheel shaft. All of these systems are made with parts (cutting) so that the movements of the parts appear. The results of media validation by media experts stated that the media developed was valid. The results of user evaluations stated that the media developed was declared practical. The quasi-experimental results of the control group with the experimental group gave different results of 17.7% better than the control group in the experimental group. This indicates that the learning media of the vehicle power train system developed was effectively used for learning in the subject of the power train system in the Department of Automotive Engineering Education, Faculty of Engineering, Makassar State University.
PPGCN: A Message Selection Based Approach for Graph Classification

Recently, the Graph Convolutional Networks (GCNs) have achieved state-of-the-art performance in many graph data related tasks. However, traditional GCNs may generate redundant information in the message passing phase. In order to solve this problem, we propose a novel graph convolution named Push-and-Pull Convolution (PPC), which follows the message passing framework. On the one hand, for each star-shaped subgraph, PPC uses a node pair based message generation function to calculate the message pushed by each local node to the central node. On the other hand, in the message aggregation substep, each central node pulls valuable information from the messages pushed by its local nodes based on a gate network with pre-perceiving function. Based on the PPC, a new network named Push-and-Pull Graph Convolutional Network (PPGCN) is proposed for graph classification. PPGCN stacks multiple PPC layers to extend the receptive field of each node, then applies a global pooling layer to get the graph embedding based on the concatenation of all PPC layers' outputs. The new network is permutation invariant and can be trained end-to-end. We evaluate the performance of PPGCN in 6 graph classification datasets. Compared with state-of-the-art baselines, PPGCN achieves the top-1 accuracy on 4 of 6 datasets.
Acceleration Using Upper and Lower Smoothing Filters for Generating Oil-Film-Like Images

A non-photorealistic rendering method has been proposed for generating oil-film-like images from photographic images by bilateral infra-envelope filter. The conventional method has a disadvantage that it takes much time to process. We propose a method for generating oil-film-like images that can be processed faster than the conventional method. The proposed method uses an iterative process with upper and lower smoothing filters. To verify the effectiveness of the proposed method, we conduct experiments using Lenna image. As a result of the experiments, we show that the proposed method can process faster than the conventional method.
Mining Semantic Tags in a Content Analysis System for a Letter Database of Ethnic Koreans Living in China

In this paper, we present a content analysis system for the letter database for ethnic Koreans living in China using several machine learning techniques. First, letters from ethnic Koreans living in China to Korea Broadcasting System (KBS) to find separated families were digitized and constructed as a letter database. Second, to help retrieval of digitized letters, semantic tags were annotated to each letter manually. Third, to find the contents of the digitized letters, machine learning techniques were applied to those semantic tags. Tag-based topic modeling was performed and a tag network was constructed. Over 150,000 letters were scanned as image files and have been constructed as a letter database. Among them, 3,500 letters are randomly selected and analyzed. Our approach shows that the semantic tags play an important role in the digitized letter retrieval. We are currently enhancing algorithms for tag analysis, and the developed system will broaden the horizon of digital humanities.
Research on Sentiment Classification of Active Scene Images Based on DNN

With the development of computer and communication technology, more and more activity scene design images have been digitized. In order to solve the problem of automatic classification of such scene images, this paper proposes a multi-featured activity scene image sentiment classification method. This research analyzed the relationship between color features (artistic style) and emotional semantics and established the activity scene image data base. According to the characteristics of the scene image. It extracted the global and local color features based on the characteristics of the scene image and completed the sentiment classification of the activity scene image with classifier of deep neural network.
Reinforcement Learning: Using Newly Gained Knowledge for Insights

In reinforcement learning an action is executed for each data point. The action’s execution generates a reward signal that indicates how good the decision was.
Manufacturing PACS, Online Medical Consultation System and Designing Security DICOM Web Viewer Software

Nowadays, Digital Imaging and Communications in Medicine (DICOM), an international standard used worldwide to access and handle various medical images through Picture Archiving and Communication System (PACS). This helps eliminate completely the use of costly and harmful traditional films. Besides, the existing infrastructures of the Internet have allowed us to develop the telemedicine services. However, the usage of foreign PACS is not suitable to diagnosis and medical consultation over the distance in Vietnam. This paper researches to implement PACS, online medical video consultation system and security DICOM Web Viewer serving diagnosis in Vietnam. First, we mention our methods of implementation. Then we discuss the results of completing image compression technology, connection technology, load balance technology for manufacturing PACS and online medical video consultation system. Next, the result of designing DICOM Web Viewer is introduced and updated. In order to solving security issue over Internet, modern security techniques are integrated into software system. Specially, by exploiting different techniques of data encoding, cryptography and watermarking, we propose an efficient solution to enhance information security in DICOM images over the Internet. Finally, our completed system has also been implemented and evaluated in hospitals.
Improving Students' Qualification Level by Introducing Innovative Educational and Production Technologies

This paper describes modern educational and production technologies that are being introduced in Zaporizhzhia Polytechnic National University and their effects on student' skills and qualification level.
Method to Present Situation Aware Suitable Reply Candidate on Mobile Terminal

In recent years, the number of users using a mobile terminal such as a smart phone and a small mobile terminal such as a smart watch is increasing. In addition, SNS users are increasing year by year. Therefore, necessity for creating messages on a mobile terminal through SNS is increasing. Since the operation screen is small on a mobile terminal, keys for character input are small. Therefore, creating a message on a mobile terminal takes time and typos are common. In particular, when creating a reply after receiving a message via SNS on a small mobile terminal, it may be difficult to respond quickly. Smooth communication between users may not be possible.We have proposed a method to present multiple relevant situation-aware reply candidates to the user corresponding to different situations according to the topic of the received sentence. By the proposed method, the user can select an appropriate reply without performing character input and message revision. The purpose of our proposed method is to support smooth communication between users. The system collects tweets to present a reply candidate. The collected tweets are divided into sentences. Tweets are often written in spoken language on variety of topics. Since the collected tweets may contain noise that is not appropriate as a reply, only suitable tweets are presented to the user as reply candidates by clustering and scoring them. The sentences are divided into different situations by clustering. The sentences with the highest score from each class is presented as a candidate reply. Since the proposed reply indicates different situations, it is expected that most of the time a suitable reply candidate will be presented to the user.
Delving into Precise Attention in Image Captioning

Recent image captioning models usually directly use the output of the last convolutional layer from a pretrained CNN encoder. This intuitive design remains two weaknesses: the top layer feature is not position-sensitive which is harmful for the decoder to generate precise spatial attention for object of interest; irrelevant features will mislead the decoder into focusing irrelevant regions. To tackle these weaknesses, we propose Feature Selection and Fusion Network (FSFN). Specifically, to tackle the first weakness, Feature Fusion module is proposed to generate fine-grained and position-sensitive features by fusing multi-scale features. To handle the second weakness, Feature Selection module is proposed to select more informative features which will prevent the decoder from focusing on irrelevant regions. Extensive experiments demonstrate that our model has successfully addressed the above two weaknesses and can achieve comparable results with the state-of-the-art under cross entropy loss without any bells and whistles on MSCOCO dataset. Furthermore, our model can improve the performance under different encoders and decoders.
Performance Improvement on Object Detection for the Specific Domain Object Detecting

Currently, various studies are underway to improve the performance of Object Detection technology. In the proposed system, rather than classifying all fields as a whole, we want to check the performance of the domain by performing additional learning on the specific domain. In this paper, we propose the performance improvement in the object detection system for detecting human in the coastal image that shot by drone.
The Method of Construction Knowledge Triples Under Joint Extraction of Entity Relations Based on Distant Supervision

Aiming at the problems of cascading mistakes and overlap relation problems in the current methods of relation extraction. A joint entity relation extraction method based on Bert is proposed. In this method, Bert and bilstm are adopted, and attention mechanism is introduced to improve the existing HRL model. By replacing the traditional word2vec word vector with Bert, richer semantic information is considered. The attention mechanism is introduced to consider the dependence between words on the basis of bilstm. At last, entity relation is extracted and entity relation triples are output. The experimental result shows that this method can deal with the overlap relation well and improve the result obviously. Experiments on the NYT10 dataset show that the proposed method has higher accuracy and recall rate. Compared with the latest joint extraction method, the P, R, and F1 values are increased by 5.2%, 4.6%, and 4.86%.
DEVELOPING ENGLISH READING MATERIALS BASED ON LOCAL CONTENT FOR GRADE TENTH STUDENTS OF SMAN 10 MEDAN

This study dealt with the development of English Reading Materials of Senior High School in SMAN 10 Medan. The objectives of this study is to develop the English reading materials for students of SMAN 10 Medan which represent the characteristics of their sociocultural environment. The research was conducted at SMAN 10 Medan. The research procedures of this study were Research and Development (R &amp; D) that were done through three phases: 1) evaluation and need analysis, 2) materials design, and 3) materials Validation. The instruments used in this research were a documentary sheet, questionnaire sheet, and interview sheet. The data from evaluation and need analysis were analyzed by using descriptive technique and the data from expert judgment results were analyzed using quantitative data analysis. The result of this study was a coursebook of local content-based reading materials which consist of 3 genres: recount genres, descriptive genres, and narrative genres, in which each genre consists of 2 texts complete with the tasks or exercises. Based on the experts� judgment results, the score was 87.06 which belonged to good category based on Dirgeyasa (2011), since the range is (70-89).
Enhancements in Anomaly Detection in Body Sensor Networks

Anomaly detection in Body Sensor Networks (BSNs), have recently received much attention from the healthcare community. This is partly due to the development of sensor based real-time tracking and monitoring networks. These networks have been responsible not only for ensuring critical medical treatment at times of emergency, but have also made it easier for health-care personnel to administer critical treatment. In this paper we consider improvements to existing machine learning methods that detect anomalous sensor measurements. The improved methods are a step in the right direction in ensuring unduly overheads due to faulty sensors don't interfere while administering life-critical treatment in a limited resources scenario.