Reviewerly/exHarmony huggingface dataset:
The Comparison of Quality Snapper Selection Results Using WASPAS and WP Methods

The increasing consumption of Indonesian fish makes fishmonger to be selective in presenting quality fish to consumers. This research made to select fish, especially snappers, namely red snapper and white snapper quality using four parameters including fish price, weight, eye color and gill color, into a computer-based decision support system. By using two decision support systems that have been compared; the results are used to determine which methods are the most effective. The purpose of this study is to provide recommendations for the best quality snapper to increase the selling value of the fish. The method used in developing this decision support system is the user-centered design method that accommodates all the needs of the end-user. Modules developed in the decision support system are made so that the users feel comfortable using this decision support system. This study produced an outcome in the form of recommendations from two decision support system method used to select quality snapper. The recommendations are comparable, so that the decision makers know the results by using both the WP method and the WASPAS method.
Attributed Graph Modeling with Vertex Replacement Grammars

Recent work at the intersection of formal language theory and graph theory has explored graph grammars for graph modeling. However, existing models and formalisms can only operate on homogeneous (i.e., untyped or unattributed) graphs. We relax this restriction and introduce the Attributed Vertex Replacement Grammar (AVRG), which can be efficiently extracted from heterogeneous (i.e., typed, colored, or attributed) graphs. Unlike current state-of-the-art methods, which train enormous models over complicated deep neural architectures, the AVRG model is unsupervised and interpretable. It is based on context-free string grammars and works by encoding graph rewriting rules into a graph grammar containing graphlets and instructions on how they fit together. We show that the AVRG can encode succinct models of input graphs yet faithfully preserve their structure and assortativity properties. Experiments on large real-world datasets show that graphs generated from the AVRG model exhibit substructures and attribute configurations that match those found in the input networks.
The Barrier on Intercultural Communication of South Thailand Students in Medan

South Thailand Students who are studying in Medan face some barriers in intercultural communication. As we know that communication and interaction are the main factor on the successful of learning process and building good relations in the society. The present research objective is to analyze the barriers on intercultural communication and how to solve the barriers incurred. This research apply Anxiety or Uncertainty Management Theory (AUM) and  the  methodology employed is qualitative method by searching, analyzing and making interpretation of the data found that consist of documents, interview process and observations. The informants are South Thailand Students who are studying in Medan. The results findings showed that the barriers of intercultural communication that South Thailand Students face were perception, language and motivation. The main barrier of these students to make an interaction in society is language factor and in the learning process at the university
A Question Answering System for Unstructured Table Images

Question answering over tables is a very popular semantic parsing task in natural language processing (NLP). However, few existing methods focus on table images, even though there are usually large-scale unstructured tables in practice (e.g., table images). Table parsing from images is nontrivial since it is closely related to not only NLP but also computer vision (CV) to parse the tabular structure from an image. In this demo, we present a question answering system for unstructured table images. The proposed system mainly consists of 1) a table recognizer to recognize the tabular structure from an image and 2) a table parser to generate the answer to a natural language question over the table. In addition, to train the model, we further provide table images and structure annotations for two widely used semantic parsing datasets. Specifically, the test set is used for this demo, from where the users can either choose from default questions or enter a new custom question.
A Hybrid Cognitive System for Radar Monitoring and Control Using the Rasmussen Cognition Model

The long-term goal of artificial intelligence (AI) is to provide machines the capabilities to learn, think, and reason like humans. To achieve these long-term goals, it is necessary to introduce human cognitive-like abilities into AI systems to create truly self-adaptive artificially intelligent systems. This marriage of human cognitive skills with “machines” creates hybrid systems that have characteristics of both. The question becomes which human cognitive model is appropriate for hybrid artificial intelligent systems. The purpose of this paper is to discuss the development of cognitive models to be infused into a modern radar system to create a cognitive radar system (CRS). The notion of a hybrid artificially intelligent system can be divided into two main categories: (a) human-in-the-loop systems with hybrid augmented intelligence requiring human-AI communication/collaboration and (b) a cognitive computing-based AI in which a fully cognitive model is infused into the machine to allow fully autonomous operation. Here, we discuss the first type, human-in-the-loop cognitive radar systems that provide intelligent decision support and analysis for radar systems. The design of hybrid artificial intelligence methods and algorithms is presented with applications to improvement to modern radar systems, utilizing a Rasmussen Cognition Model (RCM), which we feel is appropriate for a hybrid cognitive system utilized to create a cognitive radar system (CRS).
Contrastive Learning for Machine Translation Quality Estimation

Machine translation quality estimation (QE) aims to evaluate the result of translation without reference. Existing approaches require large amounts of training data or model-related features, leading to impractical applications in real world. In this work, we propose a contrastive learning framework to train QE model with limited parallel data. Concretely, we use denoising autoencoder to create negative samples based on sentence reconstruction. Then the QE model is trained to distinguish the golden pair from the negative samples in a contrastive manner. To this end, we propose two contrastive learning architectures, namely Contrastive Classification and Contrastive Ranking. Experiments on four language pairs of MLQE dataset show that our method achieves strong results in both zero-shot and supervised settings. To the best of our knowledge, this is the first trial of contrastive learning on QE.
Text Generation in Dialogue System via Deep Neural Network

At present, the research on text generation focuses on the article, and there is still a large gap in the study of dialogue. At present, the task of generating sentence level has achieved good results. How to generate a complete dialogue is still challenging, and the semantic coherence involved in the dialogue is a very important issue. In view of the above problems, our research is to generate dialogue text according to dialogue elements, and construct a dialogue text generation model to realize the organization of known discrete questions and key words of answer elements into a smooth dialogue. We use the deep learning method to divide the whole dialogue text generation model into two parts. Firstly, the dialogue elements are organized into the correct dialogue process and process, and then the answer elements in the answer sentence are improved into fluent sentences. Finally, by connecting the two models, we can generate fluent multi round dialogue texts according to the dialogue elements. We test and verify the two models, and the results show that this method can organize the dialogue process, improve the dialogue answers and generate the dialogue text.
Salient Error Detection based Refinement for Wide-baseline Image Interpolation

Wide-baseline image interpolation is useful in many multimedia applications such as virtual street roaming and 3D TV. It is also a challenging problem because the large translations and rotations of image patches make it hard to estimate the motion fields between wide-baseline image pairs. We propose a refinement strategy based on salient error detection to improve the result of existing approaches of wide-baseline image interpolation, where we combine the advantages of methods based on piecewise-linear transformation and methods based on variational model. We first use a lightweight interpolation method to estimate the initial motion field between the input image pair, and synthesize the intermediate image as the initial result. Then we detect regions with noticeable artifacts in the initial image to find areas whose motion vectors should be refined. Finally, we refine the motion field of the detected regions using a variational model based method, and obtain the refined intermediate image. The refinement strategy of our method can be used as the post refinement step for many other image interpolation algorithms. We show the effectiveness and efficiency of our method through experiments on different datasets.
Extraction of optimum ratio parameters of steel fiber reinforced concrete based on BP neural network

In order to overcome the problems of low precision in the traditional extraction method of steel fiber reinforced concrete optimum ratio parameters, a new extraction method of steel fiber reinforced concrete optimum ratio parameters based on BP neural network is proposed in this paper. Based on the analysis of suspension state, virtual accumulation state and actual accumulation state of concrete solid particles, combined with the characteristic particle sizes of large particles and small particles, the packing density and constraint conditions of concrete solid particles are calculated. Based on the structure of BP neural network, the input of hidden layer is calculated, and the output of hidden layer is calculated according to s function. By comparing the expected output with the actual output, the threshold and weight of BP neural network are modified to construct the optimal ratio parameters of steel fiber reinforced concrete. The experimental results show that under the same experimental environment, the flexural strength of concrete is the best under the ratio parameters extracted by this method, and the extraction accuracy of the best ratio parameters is the highest, so this method can improve the rationality of the ratio of steel fiber reinforced concrete.
Compression and Pattern Matching

Abstract We introduce our research on compressed pattern matching technology that combines data compression and pattern matching. To show the results of this work, we explain the collage system proposed by Kida et al. in 2003 that is a unifying framework for compressed pattern matching, and we explain the Repair-VF method proposed by Yoshida and Kida in 2013 and the MR-Repair method proposed by Furuya et al. in 2019 as grammar compressions suitable for compressed pattern matching.
Hybrid Resource Scheduling Scheme for Video Surveillance in GPU-FPGA Accelerated Edge Computing System

Video surveillance system with object re-identification is cited as a challenge to address to enhance the safety and convenience of citizens. The system consists of a combination of complex tasks requiring a lot of computing workload. With these characteristics, efforts have continued to accelerate the system. Existing systems did not benefit from the service latency perspective to make good use of heterogeneous accelerated edge computing system. In this paper, the goal is to accelerate the system used in smart cities on limited heterogeneous edge servers, and the scheduling planning method considering them is proposed. We first identify the computational volume-based execution time model of the heterogeneous accelerators. Then, we propose a scheduling plan that distributes this task graph to resources. Finally, the planning method proposed in this paper is experimentally compared with the previous GPU-FPGA allocation scheme. We compare it to the previously proposed method, and show that queue latency can be reduced, with showing robustness to the deadline violation rate.
Discharge fault diagnosis model for transformers based on free characteristic gas

Transformers are the critical equipment in power systems. During the operation of transformers, there is a risk of discharging faults such as arc discharge. High-energy discharge fault occurs with a large amount of free gas, which causes transformer fire, explosion, and other accidents. Based on this, this paper designs a discharge fault simulation device in transformer oil. It conducts breakdown discharge, spark discharge, and arc discharge tests to collect free gas generated when a discharge fault occurs. This paper selects four typical characteristic gases to form characteristic gas histograms. It uses a support vector machine (SVM) and ant colony optimization support vector machine (ASVM) to establish a fault recognition model to compare and diagnose the characteristic parameters of the map. The results show that the ant colony optimization support vector function can effectively identify the free distinct gas map of the transformer. This model is suitable for diagnosing and analyzing discharging faults of transformers based on free characteristic gases.
Contact-Free Interaction with Computer Systems Using Computer Vision Techniques

Interaction with computer systems has evolved a long way. It started with buttons, and now, we have touch screens the most commonly used interface using which we can feed inputs to the systems. But the ongoing pandemic pushed humanity to find ways to minimize contact with anything and everything. Hence, this research is about finding an alternate way of interacting with systems with zero contact. Hand gesture recognition is a field which has been evolving a lot over the years but now is the immediate need of the hour for such techniques to be implemented in everyday life. In this paper, we have proposed to utilize and evaluate popular computer vision techniques to solve the above said objective. One among the popular object detection deep learning model is residual neural networks (ResNet). The model with transfer learning approach is utilized and evaluated using a standardized labeled dataset. Further comparisons are made to bench mark the solution with different models. Thus, the proposal is to find a suitable model to accomplish a simple contact-free inputs to a computer system. It is believed that this research would be a stepping stone to build more complex contact-free interface with digital systems.
A study on classification of the security controls for the effective implementation to nuclear power plant

As regulatory bodies require full implementation of security controls in nuclear power plants (NPPs), security functions for critical digital assets are currently being developed. For the ultimate introduction of security controls, not alternative measures, it is important to understand the relationship between possible cyber threats to NPPs and security controls to prevent them. To address the effectiveness of the security control implementation, this study investigated the types of cyber threats that can be prevented when the security controls are implemented through the mapping of the reorganized security controls in RS-015 to cyber threats on NPPs. Through this work, the cyber threat that each security control can prevent was confirmed, and the effectiveness of several strategies for implementing the security controls were compared. This study will be a useful reference for utilities or researchers who cannot use design basis threat (DBT) directly and be helpful when introducing security controls to NPPs that do not have actual security functions.
An Improved Dehazing and De-raining Technique for Haze and Rain Streaks Removal

De-raining and dehazing is a challenging problem. To overcome the challenge, this research work develops a novel de-raining and dehazing technique with a combination of dark channel prior technique for dehazing and L0 gradient minimization for de-raining. Almost all the works done in this field have performed either dehazing or de-raining, but this paper addresses both dehazing and de-raining. This method efficiently eliminates haze and rain from the images. The proposed method eliminates the haze and rain effectively than any other existing methods. The minimization approach can throughout control the gradients (non-zero), which are upshot in image. This technique is self-sufficient for local characteristics. DCP method is established to restore the haze-affected picture. The outcome shows the efficiency obtained by combining DCP and L0 gradient minimization in eliminating the haze and rain from images, and also this research work results in an acceptable method to achieve a better clarity loss and color loss.
Improvement Of School Image And Student Parents' Interest To Choose A School Influenced By Product Strategies, Promotions And Prices In Sd Muhammadiyah 5 Porong

The research objectives of this thesis are to find out and analyze products, promotions and prices that affect the school's image, parents' interest in choosing schools and to find out and analyze school image as a mediator variable capable of influencing the relationship between product, promotion, price and parents' interest in choosing schools at SD Muhammadiyah 5 Porong.&#x0D; This research belongs to the type of quantitative research and uses descriptive statistical analysis. Researching on a particular population or sample. Data collection using a limited questionnaire method. Respondents were the guardians of the students of SD Muhammadiyah 5 Porong as many as 125 people. Validity test analysis. To test the validity and reliability of the data, the researcher used the W-Start program.&#x0D; Hypothesis test analysis. Analysis of the relationship between variables will be carried out by path analysis assisted by the SPSS version 21 windows program.&#x0D; The results of the SPSS program analysis show that the influence of Product, Promotion, and Price Factors together in creating School Image is 65%. Meanwhile, the other 35% are influenced by other factors.
An Examination of Neural Networks on Cluster Computers

Deep learning models often require large computational resources during the training process, and due to the size and complexity of the model and training data, usually have long training times. As a result, methods to accelerate the training process, specifically the training time, are areas of active research. Training on distributed cluster computers using data parallelism, using hardware acceleration, or the combination of the two are increasingly effective and accessible ways to reduce the large deep learning training times. In this paper, we train neural networks across different size clusters with and without hardware acceleration. We examine the effects of multi-worker multilayer perceptron training on highly class-imbalanced Medicare Part B data. Our results show the effects of distributed training, on training time and model performance, for multilayer perceptrons trained on class-imbalanced data are greater than the effects on convolutional neural networks trained on balanced image data.
Depth from Shading Based on Online Illumination Estimation Under RAMIS Environment

Shape From Shading (SFS) algorithm has attracted many attentions in the Robot-Assisted Minimally Invasive Surgery (RAMIS) environment due to the superior texture-free characteristic. But this algorithm is limited to shape reconstruction rather than depth reconstruction. Unlike natural illumination environment, the main reason of this limitation in RAMIS environment is that the illumination condition does not calibrated. In this paper, the imaging principle, incorporating surface reflectance and camera parameter, is fully modeled. Based on the model and the practical environment of RAMIS, we present an online illumination estimation method. Without requiring additional equipment, our method only uses images captured from two positions to perform. The illumination estimation is accomplished based on a Gradient Descent formulation. The result and accuracy of the method is shown through synthetic image experiment.
R-gram: Inferring message formats of service protocols with relative positional n-grams

Automatically discovering message formats of unknown service or system protocols from network traces has become important for a variety of applications, such as emulating the behavior of an unknown protocol in service virtualization, or enabling deep packet inspection in network security. Among existing schemes, the keyword extraction based approaches have been shown to be effective. Inspired by the template structure of protocol messages, recent works leverage the positions of keywords to extract message keywords more accurately. However, these methods are deficient for messages with large variations in length. To address this problem, we propose R-gram, which exploits the relative positions of keywords in messages, allowing the keywords to be robustly detected in variable length messages. It first extracts the common template of the messages in a given message trace with a fast sampling technique, and segments each message into blocks according to the relative positions of the common keywords in the template. It then identifies message keywords in each block by using a new concept and technique — relative positional n-gram (r-gram in short). Finally, the message keywords are used to separate all the messages into type-specific clusters and consequently derive the message format for each cluster. We have implemented and evaluated R-gram on real-world service traces containing either textual or binary protocol messages. Our experimental results show that R-gram is more accurate and robust than existing state-of-the-art tools in protocol message format extraction. Furthermore, R-gram is efficient for processing large-scale message traces.
Multi-Class Liver Cancer Diseases Classification Using CT Images

Abstract Liver cancer is the fourth common cancer in the world and the third leading reason of cancer mortality. The conventional methods for detecting liver cancer are blood tests, biopsy and image tests. In this paper, we propose an automated computer-aided diagnosis technique for the classification of multi-class liver cancer i.e. primary, hepatocellular carcinoma, and secondary, metastases using computed tomography (CT) images. The proposed algorithm is a two-step process: enhancement of CT images using contrast limited adaptive histogram equalization algorithm and extraction of features for the detection and the classification of the different classes of the tumor. The overall achieved accuracy, sensitivity and specificity with the proposed method for the classification of multi-class tumors are 97%, 94.3% and 100% with experiment 1 and 84% all of them with experiment 2, respectively. By automatic feature selection scheme accuracy is deviated maximum by 10.5% from the overall and the ratio features accuracy decreases linearly by 5.5% with 20 to 5 selected features. The proposed methodology can help to assist radiologists in liver cancer diagnosis.
Re-Identification risk in anonymized data sets with parent-child information

We explore the risk of re-identification in anonymized data sets that preserve genealogical information (i.e. parent-child links). We consider attacks based on the number of children of an individual. We use part of a well known data set in our experiments, which show that a substantial part of the population involved in the set can be identified, even if additional anonymity protection measures based on graph trimming are taken. We have also found that the risk quickly increases with generatiou depth.