Reviewerly/exHarmony huggingface dataset:
Analysis of the Impact of Blockchain and Net Technology on the Financial Governance of Internet Enterprises

Under the background of Internet, new management mode needs to be explored by people. Therefore, enterprises need to combine with new ideas and technologies and make corresponding adjustments. In the face of the changeable domestic and foreign economic situation, China's enterprises only constantly strengthen their own financial management ability, improve the comprehensive competitive strength of enterprises, in order to better meet the requirements of social development and progress. Based on the idea of mimicry defense, a scheme based on mimicry chain code and mimicry system endorsement chain code is proposed in this paper. This scheme can effectively solve the security threats of the project due to the characteristics of centralized architecture and alliance chain.
Monitoring and assessment of pedagogical competence development among bachelors majoring in Ecology

The paper is devoted to the problem of environmental education implementation in modern conditions through formation of ecological consciousness and ecological culture of students personality within the educational process and outside. To solve this problem it is necessary to develop new approaches to the training of prospective environmental educators and their professional competence development. Its also necessary to control and evaluate all the main components of professional competence. In the structure of the teachers professional competence the authors of the paper include a set of theoretical knowledge and their application in various pedagogical situations, as well as value orientations and a set of indicators of the teachers culture: speech, communication style, attitude to himself/herself and his/her activities and to other fields of knowledge, etc. At the same time, the system of professional education of modern teachers consists of subject training as well as psychological, pedagogical and methodological training, the purpose of which is to form the ability to organize the educational process on principles of personality-oriented interaction. In the structure of professional and pedagogical competence of bachelors majoring in Ecology, it is possible to distinguish meaningful, value-motivational and operational-activity components. The authors also consider possible ways of monitoring and assessment of pedagogical competence development among bachelors majoring in Ecology for pedagogical activity, their professional competence and training.
Fast, Lightweight, and Robust Path Planning for Low-Power Embedded Environments

A fast, lightweight, and robust path planning algorithm is proposed for single agent robot navigation environments varying every moments, and its feasibility for low-power embedded environments are verified. Main objective of this paper is to find path immediately avoiding obstacles in unstructured environments for safe robot navigation on low-power embedded environments. Several experiments are conducted to evaluate the path planning performance and the long-term operation and to verify the consistency of performance and the feasibility for low-power embedded environments.
KATN

Leveraging sensor data of mobile devices and wearables, activity detection is a critical task in various intelligent systems. Most recent work train deep models to improve the accuracy of recognizing specific human activities, which, however, rely on specially collected and accurately labeled sensor data. It is labor-intensive and time-consuming to collect and label large-scale sensor data that cover various people, mobile devices, and environments. In production scenarios, on the one hand, the lack of accurately labeled sensor data poses significant challenges to the detection of key activities; on the other hand, massive continuously generated sensor data attached with inexact information is severely underutilized. For example, in an on-demand food delivery system, detecting the key activity that the rider gets off his/her motorcycle to hand food over to the customer is essential for predicting the exact delivery time. Nevertheless, the system has only the raw sensor data and the clicking "finish delivery" events, which are highly relevant to the key activity but very inexact, since different riders may click "finish delivery" at any time in the last-mile delivery. Without exact labels of key activities, in this work, we propose a system, named KATN, to detect the exact regions of key activities based on inexact supervised learning. We design a novel siamese key activity attention network (SAN) to learn both discriminative and detailed sequential features of the key activity under the supervision of inexact labels. By interpreting the behaviors of SAN, an exact time estimation method is devised. We also provide a personal adaptation mechanism to cope with diverse habits of users. Extensive experiments on both public datasets and data from a real-world food delivery system testify the significant advantages of our design. Furthermore, based on KATN, we propose a novel user-friendly annotation mechanism to facilitate the annotation of large-scale sensor data for a wide range of applications.
A DQN-based Internet Financial Fraud Transaction Detection Method

The anti-fraud issue of Internet finance is a hot research topic in the industry. Aiming at the complex fraud problem of Internet finance, this paper proposes a fraudulent transaction detection method based on Deep Q Learning, and constructs a feasible electronic transaction fraud detection model. Based on reinforcement learning, this method makes the agent learn classification strategies, builds the environment with RFM model, and uses SmoothL1 as the loss function to improve the learning efficiency of the agent. The experiment uses a variety of evaluation metrics to verify the performance. The results demonstrated that the proposed DQN-based fraud detection method in this paper has improved some performance evaluation metrics compared with the traditional method.
Application Prototype for Scientific Paper Layout Inspection

Thesis is one of the scientific papers that undergraduate students should complete. In completing the thesis, it is necessary to comply with the thesis guideline provided. A supervisor will help students for completing the thesis, including the layout and the systematics of writing. However, the rules regarding the writing format are sometimes neglected to be checked. Observations have been done to analyze the system development requirement by format checking manually of 10 theses. Some layout errors are still found. Therefore, the study aims to develop layout inspection to help students produce a thesis that complies with the thesis guideline. Several approaches were performed in this research, such as word tokenization, stemming, feature rectangle algorithm, and floodfill algorithm. In this research, the system uses a thesis document in PDF format as the input to extract text and image from each page by using PDFBox. Then, data will be used to evaluate margins, page sizes, page number distances, logo sizes, word counts, and matching of each word to the Indonesian dictionary. The result of this research is system could evaluate the format layout in 97%.
Game-theoretic Simulations with Cognitive Agents

We propose a novel knowledge representation framework called COGNISIM that supports game theoretic simulation experiments using cognitive agents. The framework allows an experimenter to evolve a population of such agents with strategies expressed teleo-reactively as logic programs. When agents encounter each other, events take place in the environment, caused either by agent actions or by environment processes. Such events change the environment’s internal state, and these changes are then observed by agents that, in turn, decide to take new actions that affect the environment. This loop continues until the terminating conditions of the simulation are met. Using this framework, we show how to repeat experiments from the literature based on Axelrod’s tournament. We also evaluate our platform’s performance in efficiently supporting large simulations in game theoretic settings.
Autonomic Networking Integrated Model and Approach (ANIMA)

This chapter presents the work of the Autonomic Networking Integrated Model and Approach (ANIMA) working group of the Internet Engineering Task Force (IETF). It was formed to standardize protocols and procedures for an ANIMA autonomic network (AN) and first chartered to define the ANIMA secure autonomic network infrastructure (ANI). This chapter describes the technical history and goals leading to this working group. It then describes how the ANIMA approach provides an evolutionary approach to securing and automating networks and to provide a common infrastructure to evolve into future autonomic networks. Finally, this chapter compares this approach to adjacent standards technologies and discusses interesting next steps.
Evaluation of Scalability and Multi-tenancy: A Use-Case

Flexibility and affordability in modern computing systems can be achieved through scalability and multi-tenancy of all involved devices. This paper presents a use-case of a multi-tenant software solution, where each tenant generates a stream of data that is subsequently processed. The processing output contains metrics to be presented on an analytical dashboard. The tenants are web applications developed using Ruby on Rails, the data streams ingestion and processing platform are developed using a Kafka cluster and KSQL, the analytical dashboard is developed using NodeJS. The use-case is evaluated by measuring the scalability through the response time from data stream creation to metrics display. The experiments are specified by changing the number of tenants and the workload for each tenant. The results show that the system can scale with regard to the number of tenants. The response time increases by 10% to 20%, when the number of tenants increases by 100% to 150%. With regard to the number of data items within the data stream, the response time is stable.
Conceptual Model to Evaluate the Learning Process Effectiveness, Extended with Artificial Intelligence Technologies

A conceptual mathematical model for evaluating the effectiveness of a learning process extended with technologies based on artificial intelligence is proposed, which takes into account the effect of all the main factors influencing it. The model is dynamic and takes into account both static terminal deviations from the set certification requirements and trajectory variations from the optimal indicators of academic success, effort and reached satisfaction by the learner. An analysis of the advantages of the proposed mathematical model over the traditional approaches for evaluating the effectiveness of the learning process is made.
Password Attack Analysis Over Honeypot Using Machine Learning Password Attack Analysis

Developing information and technology has caused the digitization of data in all areas of our lives. While this digitization provides entirely new conveniences, speed, efficiency, and effectiveness in our current life, it also created a new environment, space, and ultimately a risk area for attackers. This new space is called cyberspace. There is a constant struggle between security experts and attackers in cyberspace. However, as in any environment, the attacker is always in an advantageous position. In this fight, the newest approach for security experts to catch attackers is to use technologies based on prediction and detection, such as artificial intelligence, machine learning, artificial neural networks. Only in this way will it be possible to fight tens of thousands of pests that appear every second. This study focuses on detecting password attack types (brute force attack, dictionary attack, and social engineering) on real systems using Cowrie Honeypot. The logs obtained during the said attacks were used in the machine learning algorithm, and subsequent similar attacks were classified with the help of artificial intelligence. Various machine learning algorithms such as Naive Bayes, Decision tree, Random Forest, and Support Vector Machine (SVM) have been used to classify these attacks. As a result of this research, it was determined that the password attacks carried out by the attacker were phishing attacks, dictionary attacks, or brute force attacks with high success rates. Determining the type of password attack will play a critical role in determining the measures to be taken by the target institution to close the vulnerabilities in which the attack can be carried out. It has been evaluated that the study will make significant contributions to cybersecurity and password attacks.
Integration Model for Learning Management Systems, Source Control Management, and Autograders using Service-Oriented Architecture Principle

Most learning management systems (LMS) use a file uploader that receives archived source code from a student for programming exercises and requires the teacher to grade it manually. In this approach, students do not learn to use standard professional tools to work on a source code, and the teachers also spend much time grading. There is an opportunity to use source control management (SCM), such as Git via GitHub or GitLab, as a submission method for students. This mechanism helps programming students practice a common process used in the professional world as early as possible. Rather than manual grading, autograders are widely used in Learning Management Systems to help instructors grade student works. Autograders work faster than humans and provide objective grading. This paper discusses an integration model for learning management systems, source control management, and autograders, each of these components is usually used separately. We write a reference implementation that uses Moodle as the LMS and GitLab as the SCM. We also build a minimally functional autograder in place for proof-of-concept in this implementation. Students can submit their work using the merge request feature provided by GitLab from the repository that they fork from the instructor’s original repository. The system captures the merge request event, and the autograder starts grading student works and updates student scores in the LMS. We also discuss how the system performs when dealing with many requests semi-simultaneously to simulate an exam situation. The system follows the Service-Oriented Architecture (SOA) principle to keep each component agnostic, and developers can use any LMS, SCM, and autograder they find suitable. In our experiment, the system can handle 200 submissions in a short period amount of time. The results are that the student learns SCM basic workflow using the system, and the teachers are helped by automated grading.
Adversarial attack detection on node classification by autoencoder-based analysis of hidden layers in graph convolutional networks

The vulnerability of Graph Convolutional Networks (GCNs) to adversarial attacks, such as injecting computational noise to the input data, has become an issue in recent years. Thus, it is recognized as an important task to detect these attacks before or during model building. In this paper, focusing on adversarial attacks on network data for node classification tasks, we propose to build an attack detection model using latent information obtained from intermediate or hidden layers in GCN using autoencoders. More specifically, by employing various autoencoders including a standard autoencoder with node vectors as input units, an ego-network-based autoencoder, and some combinations of them as feature extractors, we build attack detection models of supervised SVM, One class SVM, and isolation forest. The results of comparative experiments with baseline methods using real network data show the effectiveness of the proposed framework on the detection of adversarial attacks.
Resolving Inconsistencies in Probabilistic Knowledge Bases by Quantitative Modification

Resolving the inconsistency that appears in knowledge bases is an extremely important stage in the process of merging knowledge bases. With probabilistic context, many studies have solved the inconsistency of a probabilistic knowledge base (PKB) with different approaches. However, these previous focus on addressing the problem of restoring consistency in a PKB by employing the principles of maximum entropy or building the consistency restorer with complex creeping functions. We discover that it is really difficult to find the functions that are utilized in the creeping consistency restorers. Therefore, the process of finding these functions may lead to a poor performance. Based on that, we proposed the inconsistency solvers for a PKB, where we consider revising the functions involved in handling the inconsistency in a simpler direction. Two the inconsistency solvers are the equitable deformation inconsistency solver and the amerced deformation inconsistency solver. Corresponding to each solver, an algorithm to solve inconsistencies in a PKB is proposed. Moreover, the cost of each algorithm also are considered and proved.
HazeNet

The visibility of scene is much important in applications of Intelligent Video surveillance systems, Autonomous Vehicle Navigation. The presence of haze limits the visibility, thereby hindering the proper operation of computer vision algorithms. There are lot of algorithms to remove haze unconditionally. But the haze removal on clear images or frames will cause adverse effects. Unnecessary dehazing results in the loss of original information and also delays the following process. To address these challenges, there is a need to recognize the image as hazy and haze-free before applying dehazing on it. Hence, this paper presents three light-weight deep neural network models to automatically classify image as hazy and haze-free in the name of HazeNet, R-HazeNet and RSE-HazeNet. The HazeNet is Convolutional Neural Network having five convolutional blocks and results in classification accuracy of 97.89%. In order to improve the performance of HazeNet further, the Residual blocks have been included after each convolutional layer. The model is called R-HazeNet. The residual blocks help in producing hierarchical features. To strengthen the useful convolutional channel features alone, Squeeze and Excitation blocks have been included with Residual units in RSE-HazeNet model. Both R-HazeNet and RSE-HazeNet produced classification accuracy of 99.98% which demonstrate superior performance compared to the stat-of-the-art methods. The proposed models are light-weight models due to the percentage improvement in number of parameters as a minimum of 93% has been achieved with respect to the the baseline method.
Development of Political QA Systems aimed at Assembly Minutes based on Abstractive Summarization

The assembly minutes published on the Web by local assemblies in Japan are not fully utilized. They are very long and complex in structure, making it difficult for citizens to read, and they only have a simple search function. In this research, we propose a Question-Answering System that allows citizens to solve their questions about the local government without reading the meeting minutes. The system consists of a content selector and a module based on a summarization model, and was trained on the Tokyo Metropolitan Assembly dataset. We built a prototype of the model that outputs answers to questions by referring to the assembly minutes, and it significantly outperformed the baseline method.
Grammatical Evolution-Based Approach for Extracting Interpretable Glucose-Dynamics Models

The quality of life of diabetic patients can be enhanced by devising a personalized control algorithm, integrated within an artificial pancreas, capable of dosing the insulin. A key action in the building of this artificial device is to conceive an efficient algorithm for forecasting future glucose levels. Within this paper, an evolutionary-based strategy, i.e., a Grammatical Evolution algorithm, is devised to deduce a personalized forecasting model to evaluate blood glucose values in the future on the basis of the past glucose measurements, and the knowledge of the basal and infused insulin levels and of the food consumption. The aim is to discover models that are not only interpretable but also with low complexity to be used within a control algorithm that is the main element of the artificial pancreas. A real-world database composed by Type 1 diabetic patients has been employed to evaluate the proposed evolutionary automatic procedure.
Simulation of the Workplace Personnel Training Organization Process

The presented work proposes a toolkit for establishing an effective process of training personnel in the required competencies at enterprises. In connection with the transformation of public life, the relevance of methods for training personnel in the workplace, namely mentoring, is noted, since nothing can replace personal communication in the training process. For the implementation of the mentoring procedure at the enterprise, a clear algorithm of actions is proposed, with a description of their technology. The most detailed consideration is the management of the mentoring process, firstly, by organizing an effective procedure for stimulating mentors, and, secondly, by means of forming pairs or groups “mentor - student (s)”, taking into account their compatibility. Also, the work pays special attention to the personnel assessment procedure, which is the basis for various activities related to personnel management.
Evaluation of IBSI Education System Use ISO/IEC 9126 Quality Model: How is the Quality?

The purpose of this study was to measure the quality of the IBSI Education System called SIWESI. Measurements will be carried out using ISO 9126 with the characteristics of functionality, usability, efficiency, reliability, and portability. Based on the test results from two perspectives including technical and user, SIWESI has good to very good quality. In terms of functionality, 95% of SIWESI's menus function properly. In terms of efficiency, the quality is good with the score of 100 for the desktop version and 99 for the mobile version. In the reliability characteristics, it can be stated that the more virtual users, the more the number of successful request processes will increase the resulting load time. Likewise with the results of Peak RPS, the more virtual users, the better the value. Meanwhile, for AVG Response Time, the more virtual users, the smaller the value. Technically, both from the browser and device side, SIWESI is accessible and works well. Based on the user's perspective, the quality of SIWESI is very good with a score of 84.66%.
Dynamic Response Measurement of Elastic Structures Through Smart Digital Image Correlation Method and Calculation

Abstract Based on the proper improvement and development of the basic principle of digital image correlation method, a 3D-DIC measurement system is built. Its measurement principle and application process can be divided into four steps: camera calibration, image acquisition, image matching and three-dimensional reconstruction. The measurement system can realize non-contact and full-field measurement and has the advantages of not being affected by the test environment, not imposing additional mass on the test model, and high signal-to-noise ratio of the measured data. Taking the elastic plate model as an example, the accuracy and reliability of the measurement system in the measurement of static and dynamic displacement response are verified by using the measurement results of laser displacement meter as a reference. In addition, the tensile strain of the projectile aluminum alloy tail model and the ground mode of the elastic plate are tested to further verify the practicability of the test system.