Reviewerly/exHarmony huggingface dataset:
Research on Fault Diagnosis of Battlefield Resource Scheduling System Based on Quantum BP Neural Network

In the context of the rapid development of technology and the current military situation which cannot be slacked off, the battlefield resource scheduling and planning system is an important prevention system. Performing fault diagnosis research on this system can reduce the waste of traditional troubleshooting and improve the efficiency of fault diagnosis. The traditional BP neural network is easy to fall into the local minimum value in fault diagnosis, and the convergence speed is slow. To solve the above problems, a fault diagnosis model of the battlefield resource scheduling and planning system is established, and quantum computing is added to improve the performance of the BP neural network. At the same time, we combine with the expert knowledge base to diagnose system failures, and this method has certain reference value for the virtual battlefield scheduling system.
Automatic Employability Test for Factory Workers using Collaborative Filtering

With the quick increase in world-wide population, need of automation is getting increased in every field. Employability tests are often used to check the ability of an experienced or fresher employee to work in a team and also their skill, to know how their actions can impact others. In this context, automated employability test for factory workers will motivate people to stay employable in the labor force of the future and help the organizations to perform recruitment process in an efficient manner. In this paper, we have proposed an automatic employability test platform using Collaborative filtering where similar and best matched activities have been recognized first by the use of item-based-collaborative filtering and then based on the performance of similar activities done by similar subjects, the ranking of employability has been determined for unknown workers using User-basedcollaborative filtering. If the ranking is higher than a previously defined threshold, the subject is said to be appropriate in the scenario and his/her employability is confirmed, but if the ranking is less than the threshold, then the subject is asked to practice more and take the next assessment of employability. To deal with the difference in body structure and habits of doing same action differently at first, we have considered the mean of the rankings of seven different activities and then weighted rank has been calculated to take the inter personal similarity into account. The proposed system is a novel work in this domain and outperforms the other existing works also.
Architecture of a Semantic WordCloud Visualization

A WordCloud by definition is a computer visualization technique used in the text mining methods of document summarization. WordClouds being simple and user-friendly are widely used in various real-life applications irrespective of the domain. The key design principle behind wordclouds is to project the most prominent keywords from a given raw text or document on a visual canvas. The prominence of each keyword is proportionately depicted using the size it occupies on the canvas. The traditional random wordclouds have the keywords being positioned at random locations on the canvas irrespective of the word semantic relationships. On the contrary, semantic wordclouds are an enhanced version of random wordclouds, where the words are positioned on the canvas by maintaining the appropriate relationships among them. In this article we provide a tutorial on the architecture and design components to generate such a system. Such an overview can be really helpful for aspiring researchers in the prerequisite analysis of semantic wordclouds.
A Subjective Network Approach for Cybersecurity Risk Assessment

We propose a subjective Bayesian network approach for cybersecurity risk assessment to address the limitations of traditional risk assessment models, which use precise values for the likelihoods of cyber-attacks. In many situations, it is often difficult to elicit accurate probabilities due to lack of knowledge, or insufficient historical data, making the evaluation of risk in existing approaches unreliable. With this approach, we seek to better reflect the reality underpinning the model and offer a better approach to decision-making via the modelling of uncertainty about the probability distributions in the form of subjective opinions, resulting in a model taking second-order uncertainty into account. We develop a subjective Bayesian network for cybersecurity risk, and then discuss the risk evaluation and decision analysis problem under the proposed model. Finally, our approach is evaluated against classical Bayesian networks using the scenario of wiper malware in an industrial control system. Our results show that taking uncertainty about the probabilities into account during security risk analysis can lead to different outcomes, and therefore different security decisions.
Energy Consumption Modeling for the PageRank Application in Spark

Energy consumption caused by processing massive data is growing explosively with the rapid growth of big data applications. PageRank is one of the typical algorithms that is frequently used by Google when evaluating the importance of web pages in search engine optimization. In this paper, an energy consumption model is proposed for the PageRank application to reduce energy consumption. We analyze the influence of some important features on the power consumption and run time of the PageRank application, including CPU frequency, Spark configuration parameters, and application characteristics such as the number of pages and iterations. Linear regression and multivariate polynomials regression are adopted as the main modeling algorithms. The experimental results show that the accuracy of energy consumption of PageRank applications can reach 87% in large-scale data. Based on the proposed energy consumption model, we can adjust the CPU frequency, the number of tasks and the number of computer nodes to optimize energy consumption.
Classification of Chinese Hotel Reviews Based on GCN

Abstract Text classification is a fundamental part of natural language processing and can help with many downstream tasks, such as emotion analysis, question and answer systems, and recommendation systems. The graph convolution neural network has the natural superiority in the non - Euclidean space data. For Chinese text data, there is a lot of correlation between the data, using the graph convolutional neural network for text classification can achieve good results. In our experiment, we use a simple one-hot encoding of the word vector to process our words and use of the graph convolutional neural network can achieve 94.24% accuracy in our data set.
TwinDNN: A Tale of Two Deep Neural Networks

Machine learning is one of the most popular fields in the current era. It is used in various areas, such as speech recognition, face recognition, medical diagnosis, etc. However, the problem is that the neural networks for machine learning applications are becoming too large and slow as they get more complicated and powerful. This problem gets especially serious when neural networks are used for edge devices with a small chip. As a result, researchers have proposed two major solutions to solve this problem.
A Pose Estimation Method for Multiple Identity Cards based on Corner Heatmaps and Part Affinity Fields

Automatic information extraction from identity cards is crucial in many applications such as eKYC, customer registration, and profile digitalization. Identity card detection and normalization are crucial steps before further information extraction. In practice, many identity cards of different types with both front and back sides may appear in an image or on the same page of a profile. This paper proposes a method to estimate the pose of multiple identity cards and classify the card into four categories: front/back sides of new/old identity cards. Particularly, we propose an EfficientNet-based model to detect the corners in an anchor-free manner and estimate the edges of identity cards using part affinity fields (PAF). According to the estimated PAF, the detected corners are then grouped in clusters, each of which corresponds to a single identity card. Experimental results show that our method yields promising results on our private dataset. We achieve a realtime speed of 24 fps on a machine with NVIDIA GPU GTX 1080 Ti.
Akıllı Ulaşımda İletişimin Geliştirilmesi için Blockchain Teknolojisinin Benimsenmesi

Today, IoT (Internet of Things) revolution and V2X (Vehicle-to-Everything) paradigm are collaboratively making a deep impact on a wide range of industrial areas, especially on transportation and automotive industry.Efficient and value added business models are highly appreciated to revolutionize transportation services and environments over the world; and to establish autonomous transport systems without the need of minimum possible human intervention.On the other hand; currently transportation ecosystem stakeholders are facing some problems including data integrity, non-repudiation, and trust.Data manipulation, missing information, and inconsistencies are examples of serious problems in the context of automated mobility and heterogeneity.This study aims to explore the potential of Blockchain technology in smart transportation.In this context, a communication model is presented for smart transportation ecosystems that provides solutions to problems, and also ensures data integrity, non-repudiation and trust by utilizing Blockchain technology.The proposed smart contract based model allows trustless users to negotiate on immutable and transparent data without the need for a trusted third party which can provide further innovative services.The proposed model is evaluated in aspects of appropriateness of technology, technical features, and adoption and diffusion of technology.Taking all this into account, it is possible to point out that the proposed model, not only have the potential of supporting development of V2X and smart transportation ecosystems, but also is feasible to other various smart environments having high heterogeneity and mobility.
The Travel of Privacy Standards and Regulations in Healthcare

Increasing technology dependence by individuals and organizations has resulted in a profusion of information privacy standards and regulations created to protect personal information.There are expectations of universality in the scope of standards and regulations but also, in most cases, some degree of flexibility that allows for adaptation and compliance with local requirements and influences.Our research into the privacy policy development at a health information exchange (HIE) finds that in practice, standards and regulations are subject to multiple translations that can result in policies and practices which inhibit the HIE's goal of facilitating data exchange.Translation must therefore be appropriately managed by the HIE to ensure data exchange is not constrained.This has important theoretical and practical implications for health information privacy in an increasingly technology pervasive world, by contrasting the global view with the local view of information privacy, through an application of healthcare standards setting and execution.
Development and Analysis of Simulation Virtual Online Guiding Resources based on VR Technology

Development and the analysis of simulation virtual online guiding resources based on VR technology is conducted in this paper. MR can easily complete human body modeling, as if it has a perspective eye, directly hitting the anatomical structure of the human body, and can interact with holographic images in real time through gestures, voice, peripherals, etc., and is in the theoretical study and practical exercise of general education both have positive and broad application prospects. In this research work, the face recognition, data mining and intelligent interaction models are combined to construct the efficient system. The experiment results have reflected the high accuracy and the core robustness. This proves the efficiency of the proposed model.
Image Encryption Algorithm Based on Dual Fingerprint Control

Biocryption system is a fast-developing field, which has been paid more and more attention by researchers. To solve the security problem of the biological key, we propose a new image encryption scheme combining fingerprint key and chaos system. Firstly, a chaotic coordinate pair is generated by using a high-dimensional chaotic system. Then, through the interaction between the encryptor fingerprint image and the decryptor fingerprint image, the fingerprint coordinate pair is obtained, and the fingerprint coordinate pair is involved in the control of the image encryption process, so that the information of fingerprint image is closely related to the encryption algorithm. At the same time, we also designed a new selective scrambling method (SSM) to improve scrambling efficiency. Finally, the scrambled image is dynamically diffused to improve the security of the cipher-image. Experimental simulation and performance analysis show that the encryption algorithm we designed has high encryption efficiency and security, and can resist common attacks.
Speech watermarking using Deep Neural Networks

Watermarking is a process in which both physical and digital media are marked using watermarks in order to protect ownership of the watermarked media. Digital watermarking is a technique where a watermark gets embedded into the carrier signal while preserving the quality of the original media. Embedding can happen in various domains and could be both hidden and plain, but the quality and the information carried by the signal should not deteriorate. This paper deals with hiding watermarks into speech audio signals using deep neural networks. We present an encoder-decoder architecture that achieved PSNR value greater than 57dB, which we used as a preservation measure of the original signal and message transmission accuracy of almost 100%. Audio data, used in this paper, consists of speeches from the Parliament of Montenegro.
Automatic Multi-label Classification for GDP Economic-phenomenon News

GDP is a measure of a country's economy. One of the variables used in the process of compiling GDP is the news analysis of economic phenomena. Briefly, the GDP figures are getting better if they are in line with the economic phenomena that occur. Analysis of this economic phenomenon can also be a supporting component of GDP publicity. This study aims to investigate the best classification model for economic phenomenon news to speed up the analysis process. We use a multi-label classification method because each news item has one or more categories. The label in this study corresponds to the GDP by Industry which refers to the Indonesian Standard Industrial Classification (KBLI). We use the Problem Transformation approach in combination with several single label classification algorithms. Label Power-set method combined with Linear SVC showed a better result among others, it reaches 75% for F-Measure and 0.021 for Hamming Loss in 5-fold cross-validation. However, the model performance increase to 84% in testing mode.
Bi-Rank: A New Bi-Directional Ranking Method for Goods Selection

Goods selection is a typical daily routine faced by e-commerce platforms, such as choosing the right goods for on-shelf and off-shelf . In this paper, we turned goods selection problem into a learning-to-rank task (LTR). Instead of ranking the head part and the tail part separately, we proposed a Bi-directional Ranking model, abbreviated as Bi-Rank, to solve this task. Bi-Rank relies on a customized loss function/metric named NDCG PLUS, which is an improved version of NDCG. NDCG PLUS incorporates the ranking loss of tail part in the total ranking loss. In addition, Bi-Rank model can choose different size and weight to balance head and tail. It can also downgrade from double-end to single-end by turn off a designed switch, avoiding the trivial process of manually changing items' label. The experiment shows that Bi-Rank model can achieve a good enough ranking result on collections' head part, while output a similar ranking result on tail part compared to the model that specifically optimizing the tail. In addition, this Bi-Rank model is also very flexible, efficient, and easy to use.
Construction and Application of Intelligent Terminal Perception Test Analysis System Based on Mobile Internet

With the rapid development of wireless communication industry, mobile phones have become an indispensable part of people's lives, so the requirements for the extended functions of its software are increasing day by day. Context-aware applications have changed the way of human-computer interaction in the past, and can provide appropriate services spontaneously and actively according to the current state of users. The user perception test and analysis system based on mobile phone business hall APP, the business event analysis function of the system is closer to the real user's business perception, and through rich data analysis, it can help the operation and maintenance personnel to accurately locate problems and improve the user perception analysis ability. Through comprehensive analysis of terminal equipment types, service types, service characteristics, user habits, etc., we can make reasonable responses to service calls from different terminals and users, so as to achieve the goal of scientifically allocating bandwidth resources and optimizing the mobile network, and greatly improve the timely rate of complaint handling and the accuracy of fault location.
Big Data Analytics: Schizophrenia Prediction on Apache Spark

Nowadays, the size of the dataset collected from medical record data increases dramatically, ranging from patient demographic data, clinical records, patient symptoms, and nursing diagnoses. No exception to the medical record data on schizophrenia patients, as revealed by WHO that the schizophrenia reaches 20 million in 2019, but the available data can be used for pre-diagnostic tasks regarding schizophrenia cases by adopting the concept of big data analytic. The main objective of this study is to design a prediction model to predict the type of schizophrenia (Paranoid, Catatonic, Residual, Hebephrenic, Symplex, and Undifferentiated) from the medical record dataset of schizophrenic patients. The dataset is then used in a comparative experiment with five machine learning classification algorithms, which are Artificial Neural Network (ANN), Random Forest (RF), Naïve Bayes (NB), Logistic Regression (LR), and Decision Tree (DT) under the apache spark system on MLlib package. Optimization experiments were also carried out through the L-BGS optimizer for ANN, and 10-folded Cross-validation for four other classification algorithms to obtain optimal results. The best results for the schizophrenia case prediction model were achieved by Random Forest by outperforming five other classification algorithms, with an accuracy of 0.93, a precision of 0.93, a recall of 0.93, and F1-measure of 0.92. This is followed by the performance of ANN, DT, LR, and NB
Enhanced Reliability of Mobile Robots with Sensor Data Estimation at Edge

The proliferation of sensing equipment serving an expansive range of applications has led the Internet of Things (loT) paradigm to cover technologies beyond Wireless Sensor Networks (WSN). Extensive advancement in electronics, communication methods and sensors has made it possible to leverage advanced technologies such as Machine Learning and Probabilistic Modeling in resource-constrained embedded systems. These techniques increase reliability and enhance interactions among physical elements of an loT-based system in which data loss or corruption seems inevitable. However, traditional data estimation and reconstruction methods cannot be directly applied considering the computational limitations at the edge of the network. Therefore, mobile robots would greatly benefit from a resource efficient sensor data recovery procedure, capable of generating near-accurate estimates at the resource-constrained Edge layer. In this paper, we introduce a novel Bayesian filtering-based data reconstruction scheme, with real-time performance and precision for incoming semantic and geometric information from a varied set of sensors to increase reliability of autonomous navigation of mobile robots. Afterwards, we corrupt each stream of observations to validate model performance against a baseline. Furthermore, we also provide benchmark on execution latency, CPU usage and current draw while running the models in a practical setup.
Removing Grids from Digital Image with Handwritten Chinese Text Based on Cycle-Consistent Adversarial Networks

The compositions of Chinese students and international students in China are usually written on paper with square grids, which is helpful for the neatness and standardization of writing. However, the existence of the kind of grid background makes it difficult to detect and recognize each character after scanning these compositions to obtain digital images. Therefore, these grids need to be preprocessed in some way. This paper proposes an approach based on Cycle-Consistent Generative Adversarial Networks (CycleGAN) to automatically remove these grid background. CycleGAN does not require paired data to train, and can complete the style conversion between the two image types. Through CycleGAN with UNet and L1 loss, it is convenient for us to prepare the unpaired training data and the image with grids can turn into image only with text by adversarial learning, which means the grid background of the original image can be removed, and characters will not be changed. In the paper, different styles of digital image with handwritten Chinese texts are tested, and the experimental results show that the proposed approach can effectively remove grids from these digital images. And the results of detecting and recognizing characters are more accurate.
Augmented reality in education

Augmented reality is part of technological used with the purpose of creating innovative applications that, among others, improve people's well-being. In this paper we describe the use of augmented reality in an application for mobile devices, aimed at improving the teaching and learning of healthy food consumption in the Panamanian population. For the development of the application, the food guides developed by the Ministry of Health of the Republic of Panama were considered, so that the application provides a new way of teaching, learning, and viewing the content of the food guides.
Design of 3D Printing RoboCup Rescue Robot

The Rapidly Manufactured Robot Challenge (RMRC) is a challenge of RoboCup Rescue Robot league that aims to develop just-in-time, custom purpose, and low-cost robots for responders to produce on the spot when disaster strikes. The RMRC robots are required be open-source hardware design using standard parts and/or 3D printed materials, and software that can be easily duplicated when needed. This paper aims to present the design of RMRC robot including hardware structure and software system. It specifically focuses on the robot design which contributed for the improvement of robot performance on complex terrain.