Reviewerly/exHarmony huggingface dataset:
Finding Four-leaf Clovers while Supported by AI

It is said that a four-leaf clover brings good luck, but finding it is challenging because of the rarity and the difficulty distinguishing from common overlapped clovers with human eyes. We present a novel system that helps people to find four-leaf clovers with augmented reality (AR) and artificial intelligence (AI) technologies. This system tries to detect a four-leaf clover with the picture taken by the head-mounted camera and to pointed out where the four-leaf clover is in the real world by placing virtual sign. The goal of this study is to explore natural information representation without counteracting the user’s search and how much of technological intervention affects user’s perceived luckiness.
A Fine-grained Sentiment Analysis Method Based on Dependency Tree and Graph Attention Network

Abstract With the rapid development of the Internet, e-commerce and social media have continued to develop and grow. Merchants tend to better understand users’ attitudes and emotional tendencies through comments. As the number of user reviews increases, the previous sentiment analysis methods have highlighted the problems of high cost and high error rate. How to use more advanced methods to analyse comments has become an urgent problem to be solved. In order to solve the above problems, in this article, we propose a fine-grained sentiment analysis method based on dependency tree and graph neural network, which can help businesses and social network platforms to identify users’ sentiment tendencies and can be subsequently used in recommendation systems and public opinion analysis systems. The experimental results show that our scheme has achieved the best results.
Gradual value-dependent information flow control

Data confidentiality is critical but cumbersome to ensure. To help in the task, we propose a gradual, value-dependent approach to information flow control targeting a small imperative language similar to existing low-level languages. With a hybrid approach, we seek to lessen the flaws of standalone static and dynamic analyses and heighten their advantages.
Research and Practice of Association Rules Method to Solve the Problem of Allocating Places for Spring Major in Colleges and Universities——Taking Shanghai Jianqiao University as an Example

Abstract Aiming at the problem of the allocation of enrollment majors and the allocation of majors of students applying for admission in the spring recruitment of colleges and universities, through the FP-Growth algorithm in data mining, the relationship between the attributes, majors and reports of students applying for in-depth mining will be able to describe a class of students. The attributes and assignments of majors and reports are used as an item set, and its frequent patterns are tapped. Then, according to the set support degree, a set that can reflect the characteristics of students is selected, and a model is built based on this result. At the same time, this paper improves the evaluation criteria of the usual school registration situation. Through the calculation of variance, the overall professional distribution and registration rate are evaluated. Experimental results show that the model has good performance and can reasonably recommend students’ professional assignment.
Selection of Wavelet Based Optimal Denoising Method in fMRI Signals

Functional magnetic resonance images (fMRI) contains high amount of noise due to their structures. This high noise limits the correct interpretation of the information contained in the signal. In order to interpret the data correctly, it is necessary to eliminate the noise with effective and efficient noise reduction techniques while protecting the information in the signal. The aim of this study is to find an optimal methodology for denoising. For this purpose, Cubic Spline, Discrete Wavelet Transform (DWT) and Maximal Overlap Discrete Wavelet Transform (MODWT) are used as wavelet based noise reduction methods. These methods are applied on a real fMRI signal and compared with different parameters and their performance was evaluated.
Adaptive Generation Technology of Lightweight Cellular 3D Printing Path

With the continuous develop ment and progress of the economy and society, the demand for innovative high- tech development in all walks of life has gradually increased, and the corresponding requirements have also been raised in due course. Because the development and application of advanced and innovative technologies can cover a wide range, it is also conducive to vigorously promote the improvement of the fineness of each production product, and better meet the actual development needs of society. Therefore, as a representative of innovative advanced technology, 3D printing technology should be innovated and optimized by relevant technical developers in order to enhance its practical adaptability.
AS-ProCBE

The Internet of Things (IoT) is a Ubiquitous Computing materialization that is gaining prominence. Security has become potentialized as a necessary feature for a viable IoT. Promoting security over this dynamic and heterogeneous environment with pre-defined and static security mechanisms is an unfeasible task. Therefore, solutions for adaptive security are required. This paper has as main objective to present the conception of a proposal for adaptive security through progressive context-based enrichment considering the challenges of IoT scenarios, offering as a differential the contextual information acquisition in the opportune moment and contemplating heterogeneous sources. To evaluate this proposal was developed one case study to validate the progressive contextualization of security events. Finally, the strengths and limitations of our approach are discussed, followed by future works.
CULTURAL-PSYCHOLOGICAL ANALYSIS OF THE INFLUENCE OF EMOTIOGENIC PROPERTIES OF DIGITAL CONTENT ON ADOLESCENT USERS

The paper is dedicated to the analysis of the emotiogenic properties of digital content as a factor in the occurrence of impressing impact on an individual on the Internet. Impressing is considered as a sociocultural phenomenon - its results are the formation of a person’s sustainable desire for a certain type of activity. This is especially relevant in adolescence: for Internet users of this age category Internet is the second reality, while surfing the Internet is not only a significant part of social interactions, but also the actualization of individual’s abilities. Based on the sociological and psychological theory of perception, the emotiogenic properties of digital resources are represented as primary and secondary. The ability of each of the types of these properties to determine the occurrence of impressing in adolescent users is revealed. The corresponding assessment of the digital resources demanded by users of the considered age category is given on the basis of empirical data obtained by qualitatively-quantitative research methods: focus group, content analysis, interviews.
An approach to recommendation systems oriented towards the perspective of tourist experiences

The Community-Contributed Geotagged Photos has widely contributed to the construction of tourism recommendation systems that facilitate the task of choosing points of interest (POIs) to visit, organizing itineraries, managing activities and improving tourist experiences when they are visiting an unfamiliar city. Tourists have driven by their aspirations, desires, and preferences. There is a new kind of traveler who instead to explore the most popular places in a city; they would like to have contact with local people and culture, exploring areas where the local people usually visit considering temporal issues, such as: parts of day, day of week, vacations, events, holidays, etc. This work presents the new recommendation model, a tourist recommender system that makes POIs recommendations considering the different interaction of tourists and residents around a city over time. This new model was constructed using Naive Bayes classifier and Collaborative Filtering. The experiments used data of 83.302 photos taken in the city of Rio de Janeiro published on Flickr and 242 locations identified through OpenStreetMap. The results demonstrated that this approach could make predictions based on the temporal context and considering differences between resident and tourist perspective being this work one of the first to consider the data yielded by residents like relevant to the build of recommendations.
On the Graph Construction of Signal De-Noising Method Using Laplacian Matrix

In this paper, the graph construction of signal denoising method using Laplacian matrix is investigated. First, the four-step procedure to obtain a graph signal representation is described. Then, the details of graph signal de-noising method using Laplacain matrix are presented. Next, four kinds of graph constructions with different connectivities are studied including Hamiltonian cycle, spanning tree, Delaunay triangle and complete graph. Finally, the real temperature data is used to evaluate the performance of the graph signal de-noising method and to show which graph construction is a better choice according to the value of signal to noise ratio (SNR).
Analyzing the Composition of Academic Papers in Science and Technology : — a case study of highly cited academic research papers on authentication scheme

Research articles especially those in science and technology fields are verbal presentation of scientific and technological innovations. Their publication can help promote the application of science and technology. Highly cited research papers are regarded as the best and the most representative ones of the concerned research field. CGA model is quite mature in theory but not widely used at the moment. To explain and demystify the ways and reasons of the construction of research papers, a three-space model involving textual space, socio-pragmatic space and socio-cultural space operating at the four levels of discourse realization was proposed by Bhatia. It can be used to analyze professional academic research papers. In this paper, a critical genre analysis of 28 highly cited research papers on authentication schemes reveals problem-solution structure is adopted in these papers. The art tradition of cryptography forms the foundation of the way schemes are designed. Practicality and application requirement in daily life are the reasons that researchers advertise the superiority of their proposed schemes.
A Cooperative Path Planning Algorithm for Handling Robot

Abstract Path planning is one of the core research fields of the handling robot, which has the characteristics of complexity and constraint. Ant colony algorithm is a search algorithm developed in recent years, which has good performance and development potential in solving many complex problems. In this paper, ant colony algorithm and workspace modeling are used to determine the optimal path for the global path search algorithm of the handling robot. The results of spatial modeling and software simulation show that the ant colony algorithm has advantages in the path planning of the handling robot. This path planning algorithm can provide the corresponding solutions for the search path problem of the complex robot.
Authorship Attribution Using Regression Model

Authorship attribution has many important applications like text forensics. This paper proposes a new method for authorship attribution based on regression model coefficients derived from frequencies of common words in a text. The results of the proposed authorship attribution method compare favorably with that of the well-known Burrows method. Apart from producing accurate results, the proposed method has the merit of reducing the dimensionality of the problem. The results obtained in the paper have to be validated with a larger corpus of authorship data.
Upper Theoretical Estimate of Solving the Second Problem of Local a Posteriori Inference in Algebraic Bayesian Networks

The article presents the results of the study of the upper assessment of the sensitivity of solving the second problem of local posterior inference. The result is the found upper exponential estimate. The theoretical and practical significance of the study is to determine the permissible error in the data when working with algebraic Bayesian networks. Note that earlier this problem from a theoretical point of view was not considered.
Towards a Hybrid Layout for Complex Networks Visualization

The complex networks importance within Bioinformatics research has grown at a similar rate with biological data growth. Making sense of such a large data amount requires diverse tools to help with visual representation. Complex networks represented as graphs are a common approach to extracting intrinsic information out of the studied data. However, as underlying data grows in quantity, networks become more crowded and as a consequence, regular means of visualizing them start falling behind and become less efficient. This category of tools have multiple directions of improvement - either speed and efficiency, or eye-catching UI yet less performance. The results produced by them consist of 2D or 3D layouts, however, none of them convey a real sense of 3D, spatial correlation or stable positioning within a fixed reference system. This paper proposes a new hybrid approach (2D & 3D) to networks visualization, which aims to solve the 3D problem in a gridded manner, with clear reference points and by decluttering dense networks. All these could dramatically improve researchers' understanding of studied data.
Modeling Cloud Forensics Readiness using MetaAnalysis Approach

Cloud computing technologies are one of the most developed knowledge fields today. This has changed the industry in managing and providing business services. Increased use of cloud computing also increases security risks due to its open environment. Therefore, we need a security enhancement model that can be implemented in a cloud environment. Forensics readiness is a method that can be used to maximize an organization's potential in responding to violations. However, there is no standard forensics readiness model that can be applied properly to cloud environments. This study proposes a cloud forensics readiness model using meta-analysis. The study was conducted on 22 publications that produce classification matrix components that have extensions.
Geometric extending of divisible codes and construction of new linear codes

We introduce a new concept “geometric extending” for linear codes over finite fields and consider the extendability of divisible codes. As an application, we construct new Griesmer [n,5,d]q codes for 3q4−5q3+1≤d≤3q4−5q3+q2 with q≥3, combining the known geometric methods such as projective dual, geometric extending and geometric puncturing.
Domain Adaptive Cross-Modal Image Retrieval via Modality and Domain Translations

Various cross-modal retrieval methods that can retrieve images related to a query sentence without text annotations have been proposed. Although a high level of retrieval performance is achieved by these methods, they have been developed for a single domain retrieval setting. When retrieval candidate images come from various domains, the retrieval performance of these methods might be decreased. To deal with this problem, we propose a new domain adaptive cross-modal retrieval method. By translating a modality and domains of a query and candidate images, our method can retrieve desired images accurately in a different domain retrieval setting. Experimental results for clipart and painting datasets showed that the proposed method has better retrieval performance than that of other conventional and state-of-the-art methods.
Geo-Spatial Data Visualization and Critical Metrics Predictions for Canadian Elections

Open data published by various organizations is intended to make the data available to the public. All over the world, numerous organizations maintain a considerable number of open databases containing a lot of facts and numbers. However, most of them do not offer a concise and insightful data interpretation or visualization tool, which can help users to process all of the information in a consistently comparable way. Canadian Federal and Provincial Elections is an example of these databases. This information exists in numerous websites, as separate tables so that the user needs to traverse through a tree structure of scattered information on the site, and the user is left with the comparison, without providing proper tools, data-interpretation or visualizations. In this paper, we provide technical details of addressing this problem, by using the Canadian Elections data (since 1867) as a specific case study as it has numerous technical challenges. We hope that the methodology used here can help in developing similar tools to achieve some of the goals of publicly available datasets. The developed tool contains data visualization, trend analysis, and prediction components. The visualization enables the users to interact with the data through various techniques, including Geospatial visualization. To reproduce the results, we have open-sourced the tool.
Data-Efficient Ranking Distillation for Image Retrieval

Recent advances in deep learning has lead to rapid developments in the field of image retrieval. However, the best performing architectures incur significant computational cost. The paper addresses this issue using knowledge distillation for metric learning problems. Unlike previous approaches, our proposed method jointly addresses the following constraints: i) limited queries to teacher model, ii) black box teacher model with access to the final output representation, and iii) small fraction of original training data without any ground-truth labels. In addition, the distillation method does not require the student and teacher to have same dimensionality. The key idea is to augment the original training set with additional samples by performing linear interpolation in the final output representation space. In low training sample settings, our approach outperforms the fully supervised baseline approach on ROxford5k and RParis6k with the least possible teacher supervision.
Assisted Speech to Enable Second Language

Speaking a second language (L2) is a desired capability for billionsof people. Currently, the only way to achieve it naturally is througha lengthy and tedious training, which ends up various stages offluency. The process is far away from the natural acquisition of alanguage.In this paper, we propose a system that enables any person withsome basic understanding of L2 speak fluently through "Instant As-sistance" provided by digital conversational agents such as GoogleAssistant, Microsoft Cortana, or Apple Siri, which monitors thespeaker. It attends to provide assistance to continue to speak whenspeech is interrupted as it is not yet completely mastered. The notyet acquired elements of language can be missing words, unfa-miliarity with expressions, the implicit rules of articles, and thehabits of sayings. We can employ the hardware and software of theassistants to create an immersive, adaptive learning environmentto train the speaker online by a symbiotic interaction for implicit,unnoticeable correction.
Improved Multi-scale Opening Algorithm Using Fuzzy Distance Transform Based Geodesic Path Propagation

Vessel tree segmentation from CT scan angiogram images of the human brain is a challenging task. The complex geometry, interconnections, and fusion with soft tissues and bones make the segmentation process harder. The segmented cerebrovasculature plays a major role in the fast analysis of vascular geometry leading to an effective diagnosis of the diseased cerebrovascular segment. The present work proposes a geodesic path propagation based on fuzzy distance transform to improve the multi-scale opening algorithm for effective segmentation of carotid vessel with less user intervention. The geodesic path is estimated between a pair of vessel seeds given by the user. The points on the paths are used as the initial pure vessel seeds during the multi-scale opening of the vascular tree from other fused conjoint components (bone, soft tissue etc.) in a shared intensity space. We developed a 2D/3D user interface to mark user-specified vessel/bone seeds or separators on the input images. Experiments on three patients’ CTA images show significant qualitative improvement in segmentation results with much lesser user intervention.
Computer support for verification works on the theme "Prime numbers"

Computer support for mathematics teachers during verification (control, written, independent) work is one of the actual tasks of teaching methods. For such support, it is necessary to develop special computer programs. The article describes the method for developing such programs on the example of a program for conducting a test on the theme "Prime numbers" using the Pulot method. The program generates numerical data that are used in the tasks of the control work. Each student gets an individual variant of the work, and students cannot rewrite work results from each other. The program is independent of any databases and data tables. Variants of tasks (numerical data) are not saved anywhere and are not known in advance. Typically, testing programs have databases, tasks and their answers, and they can be obtained in advance and used as a cheat sheet. In the considered method and in the developed computer program, this is impossible. The application of the presented program greatly facilitates the teacher's work and ensures the objectivity of control of students' knowledge, and also increases the level and quality of teaching
Vehicle Brand Classification Method Based on PCA-NET Under Complex Background

At present, the classification of vehicle brands as an important unit in the urban intelligent transportation system has become a hot spot for researchers from various countries. The classification and recognition of videos and images have been effectively researched and applied. In order to achieve better classification effect and higher recognition efficiency, this paper uses Principal Component Analysis-Net (PCA-NET) to realize the classification of vehicle brand, and combined with Support Vector Machines (SVM) to achieve. From the experimental results, this method can effectively extract the vehicle front view and achieve classification. The classification accuracy is high, which can reach 93.2%. In addition, this method has flexible adaptability to complex background conditions.
Theoretical and Designing Framework of Constructivist Web-Based Learning Environment Model to Problem Solving

The purpose of this research was to synthesize theoretical framework of constructivist web-based learning environment for problem solving. The target groups used in study were 1) 3 contents experts for evaluation of the contents 2) 3 experts for evaluation of the instructional designers 3) experts for evaluation of the instructional media expert. 4) 3 experts for evaluation of the collecting tools and 5) 20 students of regional special education center 9, Khon Kaen in a course of intellectual disability unit in the 2st semester, 2019. The instruments used were 1) survey form for learning contextual 2) survey form for students characteristic and 3) The recording form for synthesis of the theoretical framework of constructivist web-based learning environment model to problem solving. Summarization, interpretation and analytical description were used to analyze the data. The result revealed that: Theoretical framework of constructivist web-based learning environment comprise of 5 components as following 1) Psychological base 2) Pedagogy base 3) Problems Solving base 4) Media theory and Technology base 5) Contextual base.