Reviewerly/exHarmony huggingface dataset:
Research on Bank Personal Credit Evaluation Based on Decision Tree Model

With the rapid development and globalization of the economy, this article investigates the effectiveness of decision tree model for banks’ decision making in granting personal credit loans, unlike the previous traditional bank credit analysis model. By collecting user data of lenders and analyzing it through decision tree model, this paper finds that decision tree model can effectively process user information and analyze it, thus increasing prediction accuracy and reducing risk, and finally helping banks to make decisions.
Supporting Auditor Competence in the Digital Era 4.0.Through the Audit Learning System in Universities

Technological developments in the Digital Era 4.0 affect various professional fields, including independent auditors. Auditing processes that were done by manual methods in the past are now conducted with advanced technology, and this has increased the demand for auditor competence. Therefore, this study aims to examine the audit learning system in universities to support the competence of auditors in the Digital Era 4.0 and obtain information technology knowledge. Furthermore, it should be used as input for developing curriculum in the Accounting study program. The sample was 182 respondents consisting of lecturers and students from public and private universities in Bandung, Indonesia. The results showed that the use, knowledge, skills, and learning factors of information technology related to auditing are very important in the audit learning system to support the development of auditor competence. Furthermore, the information technology knowledge and skills required are in using Microsoft Excel, SAP, ACL, and ATLAS software. These four software are the most widely used in financial statements and are considered the best in Bandung based on respondents' perceptions.
Proceed From Known to Unknown: Jamming Pattern Recognition Under Open-Set Setting

In this letter, we study the problem of jamming pattern recognition (JPR) when some patterns have no training samples. Existing works were designed for recognition of known jamming patterns only, hence they failed to deal with unknown jamming patterns. To address this challenge, a zero-shot learning (ZSL) based JPR scheme is proposed. Firstly, a supervised training process is introduced to learn the latent feature representation of known jamming patterns. Then, an unsupervised classification approach is proposed to recognize different jamming patterns. Finally, both known and unknown jamming patterns are classified in latent feature space. Simulation results show that the proposed scheme achieves excellent performance when dealing with the open-set JPR tasks.
Illumination and Color Adjustment for Generating Professional Headshot

When making professional headshots, it has some standards, for example, illumination and color of face images should be adjusted well to show important features. In this paper, we propose a method to convert one image captured by smart phone into identification photo or professional headshot. It includes three stages. Firstly, face region is detected and the background is removed from image captured by some mobile device. The second stage is to enhance low illumination regions by estimating illumination map. Thirdly, skin regions are detected through the Cr channel in YCrCb color space. The skin regions with high illumination are adjusted to a specific domain, which aims to provide a comfortable visual perception. These strategies contribute to a pleasing adjustment. Experiments performed on random images indicate our method can obtain a good illumination and color adjustment for face images with a promising adaptability.
Smart Monitoring and Checking Technology of Substation Information based on Virtual Intelligent Electronic Device

Substation is the core node of power grid energy conversion and operation control. At present, digital and standardized smart substations have been extensively constructed. Data communication gateway machine information point table is the basis and key of information interaction between substation and master station. When building a new substation, it is necessary to manually compare the correctness of the information one by one between the station terminal and the dispatching master station. Thousands of information points make work efficiency very low, so A new method for automatic verification of substation monitoring information is proposed, which based on the total station configuration file and remote control information configuration file of smart substations. Besides, virtual IED(Intelligent Electronic Device, IED) are used instead of physical IED to build a system to realize the automatic verification system of new substation monitoring information. It provides a new effective means for checking the information point table of newly-built substations.
ReviewMT: Sentiment Preserved E-Commerce Review Translation System

We present a demonstration of our English–to–Indian languages (Hindi, Bengali, Gujarati, Marathi, Punjabi) product review translation system, ReviewMT1. The system is based on the neural machine translation (NMT) model which is used to translate the product reviews written by the users on the e-commerce platform, Flipkart. The objective of our translation system is to translate the user reviews, provide an interface to the translators/users to post-edit the output, rate the quality and to retrain the model from the corrected samples. The retraining using the post-edited samples provides better learning to the NMT model so that it does not repeat the same mistake during translation. ReviewMT also provides an additional functionality called ‘sentiment-preserved machine translation’ which preserves the sentiment of the source sentence into the target sentence during the translation. This is important as Machine Translation (MT) systems often fail to preserve different stylistic and pragmatic properties of the source text (e.g. sentiment, emotion, gender traits, etc.) to the target, especially in a low-resource scenario.
Formal Methods and Mathematical Intuition

The paper offers a retrospective on earlier developments and work in formal methods at The MITRE Corporation, emphasizing the leading work of Joshua Guttman and some of his colleagues. It then provides a short introduction to dimension theory and its history as a methodologically contrasting development in mathematics in which counterintuitive examples and counterexamples play a role in motivating mathematical growth by means of conjectures and refutations or proofs and refutations. The paper ends with a broad methodological comparison between developments in formal methods and developments in domains of mathematics like dimension theory in which mathematical intuition and proofs and refutations play a significant role.
Full-Viewpoint Depth Recovery of Light Field Image via Deep Neural Network

Recovery depth from a lenslet light field image can facilitate lots of applications including super-resolution and 3D reconstruction. However, current works mainly focus on the central sub-aperture image but pay little attention to full-viewpoint light field images. In this paper, we propose a deep learning-based method to recovery full-viewpoint depth by estimating the disparity map from the given light field image. We employ the ResNet to extract multi-dimensional features from the given light field image which is encoded as a 3D epipolar plane image, establish dense connections to enable the neural network to calculate the cost volume from extracted features, and use an AutoEncoder to convert the cost volume to a disparity map of the given light field image. We show several experimental results and two comparisons with the related works to demonstrate the effect and performance of our method.
Peer review declaration

All papers published in this volume of IOP Conference Series: Earth and Environmental Science have been peer reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review: Single-blind • Describe criteria used by Reviewers when accepting/declining papers. Was there the opportunity to resubmit articles after revisions? • International reviewers need to provide excellent feedback on the submitted articles after carefully analyzing merits of the paper with regards to scope, applications, research methodology, experimental techniques, data interpretation and grammar. • Authors were asked to revise their original manuscripts in alignment with the reviewers’ comments and suggestions for publication. • An opportunity will be given to the authors to resubmit their manuscripts after revisions. • Conference submission management system: http://www.academicconf.com/Identity/Account/Login?confName=wre2021 • Number of submissions received: 138 full paper submissions and 262 abstract submissions • Number of submissions sent for review: 78 full paper submissions • Number of submissions accepted: 25 full paper submissions • Acceptance Rate (Number of Submissions Accepted / Number of Submissions Received X 100): 32.05%. • Average number of reviews per paper: 3 reviewers • Total number of reviewers involved: about 150 reviewers, please check http://paper.academicconf.com/reviewers.aspx?confname=wre2021 • Any additional info on review process: We used Ithenticate to check the plagiarism issue during the review process • Contact person for queries: Prof. Dr. Chih-Huang Weng I-Shou University, Taiwan chweng@isu.edu.tw
A Novel GCN Architecture for Text Generation from Knowledge Graphs: Full Node Embedded Strategy and Context Gate with Copy and Penalty Mechanism

Text generation from knowledge graphs is a fundamental task, which aims to map triplets to description text. Previous research mostly adopts standard sequence-to-sequence methods, which would inevitably fail to capture graph structure information. In this paper, we propose a novel neural network architecture called GCN-FCCP, which is based on Graph Convolutional Network enabled by a Full node embedded strategy and Context gates with Copy and Penalty mechanism. The full node embedded strategy embeds each word in the input triplets as a new node to enhance the graph information, while a stacked multi-layer graph convolutional network is used as the encoder to directly exploit the input structure. For the decoder, we integrate a context gate into the LSTM network to retain the information of contexts during the hidden state updating process, which ensures the faithfulness to the original meaning. Meanwhile, we add copy attention and penalty mechanism to the decoder to solve the Out-of-vocabulary (OOV) problem and improve the quality of the generated sentences. Extensive experiments on the WebNLG dataset show that GCN-FCCP can effectively generate high-quality text from graph-structured input, which obtains high scores in four automatic metrics.
Design of Adaptive Controller for Robot Arm Manipulator Based on ANN with Optimized PID by IWO Algorithm

In this paper, a robot motion control system is adaptive controller based on the artificial Neural Network optimized PID by IWO. A proposed method for solving reverse kinematics used to determine the angle values of the arm joints when traced in any path. Forward kinematics derived according to the D-H method (Devavit - Hartenberg) representation. The design of the dynamic model based on the Lagrange method. Computing the dynamic model is a very important step in the robotics world. The adaptive controller based on ANN optimized PID used to improve system response. We can compute the forward kinematics and reverse kinematics and trajectory planning by design a graphical user interface by using MATLAB, computing the forward and reverse kinematics by hand is not easy, so by using this design it is an easier way. The dynamic model used with the adaptive controller based on ANN optimized PID, this method tries to select the PID parameters, without needing to the robot manipulator mathematical model. The system performance results show better response when using the proposed controller, the maximum overshot is equal to zero and the raise time is reduced to 0.1 in joint 3 also the optimal settling time 0.2 in joint 1 the delay time is reduced to 0.1 in joint 1 and joint 5. These results are much better when compared with traditional methods such as traditional PID controller. This proposed algorithm improved the system parameters to the optimal values.
A Study on Integration of Guidance System Using Real-Time PQ-RRT* Algorithm and a Trajectory Tracking Controller

Herein, the integrated design process of guidance and control systems required for the design of autonomous flight systems for aircraft is discussed. The system can generate a trajectory using real-time path planning, re-plan the path, and accurately track the created trajectory. The real-time path planning is implemented using a rapidly-exploring random tree-based algorithm. Furthermore, the problems with the real-time rapidly-exploring random tree-based algorithm and possible solutions are discussed accordingly. The control system is implemented by incremental backstepping control, which ensures trajectory tracking performance across the entire flight envelope. To verify the series of processes, simulations are run using the high-fidelity Bo-105 model. The effectiveness of the real-time PQ-RRT* and path optimization algorithm proposed in this work is verified by the simulation results.
Modeling Adaptation Processes in Mlearning

In recent years, the growth of mobile learning has been marked by the continued development of mobile computing and wireless technology. Mobile learning is a way of using mobile technologies to enhance traditional learning and expand the perspectives of the learning process itself one of the main objectives in a mobile learning environment is to provide the learner with appropriate content, i.e. provide the right resource at the right time and in the best way. In this paper we try to address the concept of adaptation in both processes: OnAction adaptation and OnChange adaptation.
A Survey on Anonymous Communication Systems Traffic Identification and Classification

research-article Share on A Survey on Anonymous Communication Systems Traffic Identification and Classification Authors: Ruonan Wang School of Physics and Electronics, Shandong Normal University, China School of Physics and Electronics, Shandong Normal University, ChinaView Profile , Yuefeng Zhao School of Physics and Electronics, Shandong Normal University, China School of Physics and Electronics, Shandong Normal University, ChinaView Profile Authors Info & Claims AISS 2021: 2021 3rd International Conference on Advanced Information Science and System (AISS 2021)November 2021 Article No.: 36Pages 1–5https://doi.org/10.1145/3503047.3503087Online:19 January 2022Publication History 0citation40DownloadsMetricsTotal Citations0Total Downloads40Last 12 Months40Last 6 weeks22 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
Joint Extraction of Entities and Relations by Adversarial Training and Mixup Data Augmentation

Joint extraction of entities and relations is an important task for building a knowledge graph and information extraction. However, small interference in the text can greatly change the semantics of words and sentences in natural language, thereby affecting the model’s prediction results. To solve the sensitivity of text to noise, in this paper, we present a method, called BERT of Adversarial Training and Sentence Mixup Data Augmentation (BERT-AT-SMDA), which use the BERT pre-training model to obtain the embedding vector representation of the text and then to incorporate the FGM adversarial training strategy into the fine-tuning of Bert to construct adversarial samples. Both the adversarial sample and the original sample into the encoder to training. Then, to prevent the over-fitting phenomenon and the additional interference caused by the instability of adversarial training, we introduce the mixup data augmentation method after obtaining the hidden layer of the encoder output, which performs a linear interpolation between the output hidden layer. In addition, we also study the effect of linear interpolation at different positions on the model’s joint extraction of entities and relations. We conduct experiments on a public dataset produced by the distant supervision method. The experimental results show that our method is effective for the joint extraction method based on tagging scheme. Not only improves the accuracy of entity and relation extraction, and ensures the robustness and generalization of the model.
Hdpoa: Honesty-Based Distributed Proof of Authority Via Scalable Work Consensus Protocol for Iot-Blockchain Applications

Applying blockchain technology to the Internet of Things (IoT) provides several advantages when compared to conventional systems, including improving the security by ensuring data integrity and accountability while enabling reliable control over many devices. However, integrating blockchain into IoT systems presents some challenges. A main challenges is designing a consensus protocol that is suitable for the IoT systems, where some devices may lack adequate resources, such as computation power. This paper introduces a novel consensus protocol called honesty-based distributed proof of authority (HDPoA) via scalable work. HDPoA is based on proof of authority (PoA) and proof of work (PoW), with the integration of PoW, HDPoA is able to realize the security advantages provided by PoW. This is achieved by utilizing the IoT devices’ collective computation power to mine and generate a new block.HDPoA was analyzed and then deployed and tested utilizing purposely built testbed using devices that are low-cost. A performance measurements and evaluation along with the security analyses of HDPoA was conducted using a total of 30 different IoT devices comprise of Raspberry Pis, ESP32, and ESP8266. These measurements included energy consumption, devices’ battery life, devices’ hash power, and the mining time. The measured values of hash per joule (h/J) for mining were 13.8Kh/J, 54Kh/J, and 22.4Kh/J when using the Raspberry Pis, the ESP32 devices, and the ESP8266 devices, respectively. The measured devices’ hash power, measured energy consumption, and the security analyses showed that HDPoA is secure and suitable for utilization into IoT-blockchain systems.
Online Signature Verification Systems on a Low-Cost FPGA

This paper describes three different approaches for the implementation of an online signature verification system on a low-cost FPGA. The system is based on an algorithm, which operates on real numbers using the double-precision floating-point IEEE 754 format. The double-precision computations are replaced by simpler formats, without affecting the biometrics performance, in order to permit efficient implementations on low-cost FPGA families. The first approach is an embedded system based on MicroBlaze, a 32-bit soft-core microprocessor designed for Xilinx FPGAs, which can be configured by including a single-precision floating-point unit (FPU). The second implementation attaches a hardware accelerator to the embedded system to reduce the execution time on floating-point vectors. The last approach is a custom computing system, which is built from a large set of arithmetic circuits that replace the floating-point data with a more efficient representation based on fixed-point format. The latter system provides a very high runtime acceleration factor at the expense of using a large number of FPGA resources, a complex development cycle and no flexibility since it cannot be adapted to other biometric algorithms. By contrast, the first system provides just the opposite features, while the second approach is a mixed solution between both of them. The experimental results show that both the hardware accelerator and the custom computing system reduce the execution time by a factor ×7.6 and ×201 but increase the logic FPGA resources by a factor ×2.3 and ×5.2, respectively, in comparison with the MicroBlaze embedded system.
Classification of Judicial Documents Based on Improved Bert Model

Bert model is the most comprehensive language model proposed in the past two years. It uses large-scale unlabeled prediction to train the representation of text with rich semantic information, that is, the semantic representation of text. Then, the semantic representation of the text is slightly tuned in a specific NLP task. Finally, it is applied to the NLP task, and has good performance in various natural language processing tasks Performance. For the classification of judicial documents, proposed a based on the improved Bert model way. The word vector is provided through the pre training model Bert. The semantic information is embedded into the LSTM model through the context of the Bert model. LSTM model is which is solves the problems of gradient disappearance and gradient explosion in long sequence Through the self attention mechanism, the important information in the text is given higher weight, and LSTM is used to encode the sequence and feature fusion contain category information. Through the Bert model and Bert-LSTM model, we can see that in accuracy improved 4.55% of the Bert-LSTM model.
Autonomous Foraging of Virtual Characters with a Constructivist Cognitive Architecture

Immersive experiences in virtual reality simulations require natural-looking virtual characters. Autonomy researchers argue that only the agent’s own experience can model their behavior. In this regard, the Constitutive Autonomy through Self-programming Hypothesis (CASH) is an effective approach to implement this model. In this paper, we contribute to the discussion of CASH within dynamic and continuous environments by developing mechanisms of memory decay, contradiction penalty, and relative valence. Such improvements aim to see how the agent might continuously reevaluate their learned schemas. The results show that our agents were able to develop autonomously into performing plausible behaviors, despite the changing environment.
Mathematical Model of the System for Student Progress Tracking

One of the main components of education is the control of the quality of education - an assessment of the activities of each educational institution from the level of knowledge and skills of students (both by the teaching staff and external, state bodies), and the quality of the activities of teachers. This article describes the implementation of the results of a universal system of recording progress, which allows for based on a generalized unified mathematical model, generate a point-rating system that can satisfy the needs of all participants in the educational process.
A One-stage Temporal Detector with Attentional LSTM for Video Object Detection

Temporal object detection is more challenging than static image detection because of the rich context information. Recently, state-of-the-art works mine context information to detect each frame by using LSTM-based modules. However, restricted by the low-exploration of temporal information, significant results in terms of accuracies and speeds are not reported by the existing methods. In this paper, we propose a new one-stage temporal detector for online video object detection. A new structure with an improved spatiotemporal LSTM (STLSTM) is proposed to suppress useless background information. Next, the SSD-based structure is improved to extract rich features and high-level semantic features. We evaluate the proposed model on the ImageNet benchmark and space human-robot interaction database. Extensive comparisons show that the proposed detector achieves state-of-the-art performance.