Reviewerly/exHarmony huggingface dataset:
TRAINING WRITING NONFICIAL STORIES IN STUDENTS OF THE PGSD STUDY PROGRAM WIDYA DHARMA KLATEN UNIVERSITY

Students of the Widya Dharma University Primary School Teacher Education (PGSD) Study Program are expected to have insight and knowledge in nonfiction story writing. News that is part of a nonfiction story favored by elementary school children. Writing news will familiarize PGSD students in writing activities and later they also have no difficulty in doing thesis. PGSD students who take part in the training are expected to excel in student-level writing competitions. The trainees were also reminded to maintain the authenticity of the news written. News must be reported by prospective elementary school teachers so that students, families, and communities can avoid Liar News / hoaxes.
Automation of Structural and Parametric Synthesis of Electronic Document Management Systems Based on Neural Network Architecture

The task of the structural-parametric synthesis automation of electronic document management systems (EDMS) is considered to increase the efficiency of the design process, to reduce the time of modernization, and to reduce the influence of the human factor during the development of EDMS software. This task remains unresolved due to its complexity and the impossibility of full automation due to the presence of many factors that require man direct participation. However, it is possible to increase the level of automation in solving the problem of structural-parametric synthesis by changing the architecture of electronic document management systems, its decomposition and ensuring the isolation of individual units. Based on the analysis of successful approaches to automate the process of structural-parametric synthesis in various subject areas, the original concept of EDMS architecture is formulated, providing the possibility of automated design of the system in the process of its structural-parametric synthesis. The proposed architecture should take into account the specific features of the domain where EDMS is implemented, while remaining fairly universal, should include all software and hardware components of EDMS, the structure of information flows, a list of necessary functions, the influence of the user and the characteristics of his equipment. Based on the proposed concept of architecture, it is planned to further develop the appropriate mathematical models, algorithms and software, combined by a common methodology, in order to deliver and to solve the problem of the automation of structural-parametric synthesis of EDMS.
Study on the Application of Neural Network in the Computer Network Security Evaluation

Research computer network security problem.There were nonlinear relations among the evaluation inde-xes, and it was difficult for an accurate mathematical model to describe the nonlinear relationship.In order to improve the evaluation accuracy of computer network security, this study put forward a combination model to evaluate the computer network security.The combination model used particle swarm optimization (PSO) to optimize the parameters of BP neural network, speed up the BP neural network's convergence speed, and enhanced its global optimization ability, which effectively improved the accuracy of the evaluation model.Simulation results showed that compared with traditional BP neural network model, the combined model's learning ability was faster and global search ability was stronger, which effectively improved the evaluation accuracy of computer network security.
A Study on the Paradigm Shift of Government Emergency Preparedness in the Fourth Industrial Revolution

본 연구는 4차 산업혁명기 정부 비상대비정책이 주요 정보통신기술과 융합하여 지능화할 것을 제언한 것이다. 한국과 주요 국가들이 4차 산업혁명과 관련하여 추진하는 주요 정책을 살펴보면서 정부 비상대비정책의 혁신적 전환을 모색하였다. 정부 기관들이 동일한 플랫폼에서 인공지능과 빅데이터를 활용한 상황분석을 통한 의사결정, 사물인터넷과 무인자율체계를 활용한 동원자원의 효율적 관리와 수송, 가상현실과 증강현실을 활용한 교육훈련, 클라우드 컴퓨팅에 의한 정보체계의 통합운용 과 데이터의 공유 등으로 정부 비상대비정책의 패러다임 전환의 전망과 대응 방안을 고찰하였다.This study suggested that the government’s emergency preparedness policy during the fourth industrial revolution should become more intelligent by being integrated with major information and communication technologies. This study sought to make an innovative shift in the government’s emergency preparedness policy by reviewing major policies in connection with the fourth industrial revolution in South Korea and other major countries. The prospects for a paradigm shift in the government’s emergency preparedness policy include decision-making by artificial intelligence and big data on the same platform, efficient management and transport of mobilized resources via IoT (Internet of Things) and unmanned self-regulation systems, training using virtual reality and augmented reality, and sharing data with integrated management of the information system by cloud computing.
Indoor Path Planning using Harmonic Functions via Half-Sweep Arithmetic Mean Method

This paper presents the application of a two-stage Half-Sweep Arithmetic Mean (HSAM) iterative method to obtain the Harmonic functions to solve the path planning problem in a 2D indoor environment.Several path planning simulations in a known indoor environment were conducted to examine the effectiveness of the proposed method.It is shown that the proposed path planning algorithm is capable of generating smooth paths from various start and goal positions.Also, numerical results show that the proposed HSAM method converges much faster than the existing iterative methods, thus it drastically improves the overall performance of the path planning algorithm.
Demonstrating the robustness of frequency-domain correlation filters for 3D object recognition applications

This paper proposes frequency-domain correlation filtering to solve object recognition of three-dimensional (3D) targets. We perform a linear correlation in the frequency domain between an input frame of the video sequence and a designed filter. This operation measures the correspondence between the two signals. In order to produce a high matching score, we design a bank of correlation filters, in which each filter contains unique information of the target in a single view and statistical parameters of the scene. In this paper, we demonstrate the feasibility of correlation filters used to solve 3D object recognition and their robustness to different image conditions such as noise, cluttered background, and geometrical distortions of the target. The evaluation performance presents a high accuracy in terms of quantitative metrics.
Spatially adaptive binary classifier using B-splines and total variation penalty

ABSTRACTThis paper reports on our study of a binary classifier based on B-splines and the total variation penalty. The decision boundary of the proposed classifier is obtained using a variant of th...
Towards Assessing Online Customer Reviews from the Product Designer’s Viewpoint

Product reviews are a type of user-generated content that can be beneficial for both customers and product designers. Without quantifying the design knowledge present in product reviews, however, it is hard for the companies to integrate reviews into the design process. Several studies investigated review helpfulness in general, but few works explored the problem of review quality from the perspective of product designers. In this study, a theoretical model is presented and a system is proposed to assess the quality of online product reviews from the viewpoint of product designers. The system involves an original similarity-based metric to quantify the design information content of reviews on a continuous scale. Experiments are performed on a large number of digital camera reviews, with results indicating that the proposed system is capable of recognizing high-quality content, and would potentially assist companies in product improvement and innovation.
INTERACTIVE EDUCATION AS A BASIS FOR IMPLEMENTATION OF COMPETENCY ORIENTED PROGRAMS ON CHEMISTRY DISCIPLINES

The transition in university education from a qualification approach to a competence-based one demanded a revision of the goals, content, structure and technology of the educational process, which has become subject-oriented. Interactive education currently forms a basis for implementation of competency oriented education programs. The efficiency of a small group method in forming intellectual, speaking and communicative competencies is demonstrated through the examples of training discussions during laboratory and practical lessons at “Methods of raw materials quality control and chemistry production products” course as well as of original technology of implementation of science information by online group study during lessons at “Introduction to the profession” course. Use of this approach during interim attestation for “Math processing and optimization of chemical and analytical processes” discipline lets solve the tasks for a short period of time and always leads to positive results. Questionnaire survey conducted among teachers and students of various chemistry specialization courses in universities of the Republic of Mordovia shows that work in small groups lets all the students participate in activities and practice cooperation and communicative skills; it raises responsibility level for the result achieved jointly and allows conquering the fear towards complexity of the discipline which often prevent the ability to acquire knowledge efficiently. Regular participation in public speaking, conducting negotiations skills, ability to think and take decisions promptly form self-confidence and serve as a basic premise for further self-development. Annual students test results during interim attestation evidence maturity of knowledge of topics learnt through the interactive approach.
The Data Dissemination Resistant Trust Management Scheme for Securing Vehicular Ad Hoc Networks

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Bedtime window

We present a design of an always-on system connecting long-distance couples at bedtime, a time and space partners normally share together. The system offers a novel, real-time shared inking space for creative interactivity, a slow photo stream to balance privacy and remote presence. It adapts to the local light level in order to stay in the background, but can be also configured to reflect the remote light level to provide an additional communication channel.
MENGANALISIS KEMUNGKINAN KETERLAMBATAN PEMBAYARAN SPP DENGAN ALGORITMA C4.5 (STUDI KASUS POLITEKNIK TEDC BANDUNG)

Payment of tuition as one of the sources of funds, plays an important role in the sustainability of the operations of higher education. The problem that arises is that students are not often late to make payments in a timely manner. One of the factors causing the many cases of late payment of tuition fees due to lack of policy and decisive action on the part of the campus when students are late in making payments, besides the factors of parents and students also have an influence on the delay. The purpose of this study is to classify students who are late and timely in making SPP payments using the C4.5 algorithm. From the total sample used then divided into 4 partitions, partition 1 for 90% training data and 10% testing data, partition 2 for 80% training data and 20% testing data, and partition 3 for 70% training data and 30% testing data , and partition 4 for 60% training data and 40% testing data. The classification results of the C4.5 algorithm are evaluated and validated with cross validation and confusion matrix to determine the accuracy of the C4.5 algorithm in predicting late SPP payments. Based on the comparison of the results of evaluations and validations conducted, it shows that data partition 2 has a better level of accuracy than the other partitions, which is 75%.&#x0D; Keywords: Data Mining, Decision Tree (C4.5), SPP.
OpenGL based A 3D Visual Technology of Digital Core

Abstract OpenGL is widely used in many industrial application fields as a powerful 3D graphics library. Especially, it plays important roles in displaying the visualization emulation model of digital cores. In this paper, according to the practical demands of projects, we conduct a thorough research on displaying 3D visual models of the pore structure and the skeleton structure of digital cores using OpenGL application library. Actual applications indicate that the idea of this paper is feasible and it holds the keys to scientific research of digital cores.
Geometry and Generation of a New Graph Planarity Game

We introduce a new abstract graph game, Swap Planarity, where the goal is to reach a state without edge intersections and a move consists of swapping the locations of two vertices connected by an edge. We analyze this puzzle game using concepts from graph theory and graph drawing, computational geometry, and complexity. Furthermore, we specify quality criteria for puzzle instances, and describe a method to generate high-quality instances. We also report on experiments that show how well this generation process works.
Improving Semantic Segmentation for Autonomous Vehicles using Synthetic Images

Author(s): Divecha, Mehul | Advisor(s): Newsam, Shawn | Abstract: With the prevalence of Advanced Driver’s Assistance Systems (ADAS) and a surge in interest in autonomous vehicles, it has become important that the computer vision modules that make up these systems understand their natural surroundings and react appropriately to changes. A key aspect to understanding such natural scenes is to identify the locations and bounds of the objects present in the scene. Semantic segmentation is one to way to approach this problem. With the rise of deep learning techniques, there has been a tremendous progress in semantic segmentation with great improvements in quality and performance. However, one down-side of most deep learning methods is the requirement of a large set of annotated data. This becomes very cumbersome when it comes to segmentation problems, since they require pixel level annotations. Another issue that arises is that of a domain gap introduced when deploying a model on data that is different from what it was trained on. In this thesis we tackle the first issue by leveraging a practically unlimited source of annotated in the form of game engines and virtual environments. We then transform the data thus derived to have a more photo-realistic look matching their real-world counterparts, thus aiming to solve the second issue. We describe the process we have employed to transform the synthetic looking images to look as close to the real-world images as possible and show that there are significant gains to be had by adopting such a method.
Inverse Regression for Extraction of Tumor Site from Cancer Pathology Reports

Pathology reports are the primary source of information for cancer diagnosis of millions of the cancer patients across the United States. Cancer registries label these reports every year. The coded labels incorporate pertinent information such as cancer location, behavior, and histology. This information when combined with clinical information, medical imaging and even genomic information have a great potential to fuel discoveries in cancer research. The coding process is manual and requires many human experts to label the large volume of pathology reports in a timely manner. In this study, we have developed a supervised inverse regression based auto-labeler to automate the task. The experiments were conducted on a set of 942 pathology reports with human expert labels as the ground truth. We observed that the inverse regression based auto-labeler consistently performed better than or comparable to the best performing state-of-the-art method. These results demonstrate the potential of inverse regression for reliable information extraction from the pathology reports.
The Route Planning for AMR Based on Combined Ant Colony and Genetic Algorithm

AMR(Autonomous Mobile Robots) have developed rapidly recently and have been implemented into various industrial fields. AMR have become more practical , systematic and intelligent. Navigation technology is the core of AMR. This paper studies the route planning for AMR in the wireless sensor networks based on the combined ant colony and genetic algorithm. Firstly the system model for the control design of AMR is described . secondly the application of Genetic algorithm for route planning is introduced .Thirdly ,the paper analyses the most popular method ACO(Ant Colony Optimization) .and combine the GA(Genetic Algorithm) and ACO in the specific phase to develop a new combined method .Simulation and experiment results both show the combined algorithm has the better performance.
Encoder-decoder models for latent phonological representations of words

We use sequence-to-sequence networks trained on sequential phonetic encoding tasks to construct compositional phonological representations of words.We show that the output of an encoder network can predict the phonetic durations of American English words better than a number of alternative forms.We also show that the model's learned representations map onto existing measures of words' phonological structure (phonological neighborhood density and phonotactic probability).
Dyslexic and private reader

We present a demonstration based on a MobileHCI 2019 paper to use eye gaze to selectively render or obscure text. Obscuring is used to "simulate" a specific kind of dyslexia. Selectively render text is to give users a more private reading experience in public spaces.
Target Search via Feature Cutting Strategy of Visual Attention Mechanism

Only part of the information and specific goals need to be paid attention to in the real image processing. Based on this a method using top-down visual attention mechanism is proposed in this paper to find a significant part and the given target with the learning of different features in different scales of sample images. The weights of the extracted features in different scales are calculated. Only the important features and optimal scales are selected in the real processing, that is the features and scales are cut when the relative weights are lower than the given threshold. The experimental results show that the presented method can localize the target accurately and quickly.
LightStress

Long-term stress is a leading cause of global health loss. Despite its clear influence on productivity, and overall social and economic development, mental health continues to be neglected in work environments. To help reduce this problem, we present LightStress, a tangible affective artifact that adapts itself to the user's needs in order to improve their mental well-being. Our prototype is inspired by existing techniques and coping mechanisms collected during the two ethnographic studies we conducted (a cultural probe and an online imagery research).
Models of interaction of cryptography and chaotic dynamics

Models of interaction of cryptography and chaotic dynamicsCryptography deals with the problem of information protection by its transformation, providing the protection of information (by means of computational techniques), i.e. a set of agreed encryptian tools.Under the cryptosystem in the narrow sense we will understand dynamical systems with a nonlinear function and spatial states, it is represented by a differential equation.Some conditions of the dynamic system, the Lyapunov exponent, as a measure of sensitivity are considered.Identification of the interconnection between objects of study in the theory of chaos and cryptography is revealed; the conclusions about the possibility of using the trajectory of dynamical systems with the chaos for the representation and the transmission of information.
Artificial Intelligence based Bank Cheque Signature Verification System

Signatures are one of the feature which distinguishes a person's identity.But to make it as a conventional method to emerge it with the current trends of technology we will try to automate the signature system in order to make it more reliable and efficient.In this project, we present a simple approach of offline signature verification, where the signature is done on paper and then it will be transferred to an image format by capturing it with camera of mobile phone or tablet.For identifying the signature, we will first do some statistical calculations aiming to extract the features from the signature and then we train the captured signature images using some classification algorithm on these features from different signers.And at last, the extracted features from the tested signature are compared with the previously trained features and we know the identity of the signer.
Pattern Classification on Complex System Using Modified Gustafson- Kessel Algorithm

This work has been focused in the application of Gustafson-Kessel Algorithm in a complex system through a methodology proposed.The complex system here considered will be the financial market.So, the main objective of this paper is to classify objects in two patterns: winner and loser.The methodology is based on application of a method of clustering called Modified Gustafson-Kessel (MGK) in some open companies of the transportation sector and energy sector.Results shows that the use of MGK can better separate the promising actions from the non-promising ones with more precision due to its covariance matrix that can be change for generate the best separability among clusters.This produces a new tool for analysis of the dynamic of stock market with the main aim of given support to investor in make decision.
Ellipsis in Chinese AMR Corpus

Ellipsis is very common in language. It’s necessary for natural language processing to restore the elided elements in a sentence. However, there’s only a few corpora annotating the ellipsis, which draws back the automatic detection and recovery of the ellipsis. This paper introduces the annotation of ellipsis in Chinese sentences, using a novel graph-based representation Abstract Meaning Representation (AMR), which has a good mechanism to restore the elided elements manually. We annotate 5,000 sentences selected from Chinese TreeBank (CTB). We find that 54.98% of sentences have ellipses. 92% of the ellipses are restored by copying the antecedents’ concepts. and 12.9% of them are the new added concepts. In addition, we find that the elided element is a word or phrase in most cases, but sometimes only the head of a phrase or parts of a phrase, which is rather hard for the automatic recovery of ellipsis.
Honeywords: The New Approach for Password Security

The traditional systems only use username and password hashes for security. The research in the area of security shows that most of the passwords hashes have possibility of being compromised by hackers. Honeywords (false passwords) generation system is appropriate to protect hashed password files. In our system Honeywords are stored along with the original user password in the password file of the database. If the database is compromised by the hacker the hacker will not be able to recognize the real password. When the hacker tries to login into the system with one of the Honeywords, the system will trigger and redirect the hacker to the fake website and notify the user (via text message and mail).