Reviewerly/exHarmony huggingface dataset:
Fuzziness Detection in Thai Law Texts Using Text Classification

Much research in machine understanding aims to construct an intelligence of machine. Certain concepts are necessary to allow the machine understand knowledges of human. However, there is a difficulty to allow the machine makes some decision on imprecise denotations in many domains such as a legal interpretation. Once, the machine will deal with vague information while pretending to be human-like, a fuzziness in knowledges must be detected before being constructed. This paper presents a methodology to classify a fuzziness in passages from four Thai codes of law, the four pillars of Thai law namely 1) The Criminal Code 2) The Criminal Procedure Code 3) The Civil and Commercial Code and 4) The Civil Procedure Code. We proposed three sources of a fuzziness in Thai law, i.e. 1) judge's opinion 2) proof of evidences and 3) relative section or laws. The experiments were designed to measure and compare the accuracy performance among five well-known text classification methods namely 1) Logistic Regression 2) Decision Tree 3) Random Forest 4) Support Vector Machine, and 5) Convolutional Neural Network. The experimental results show that a convolutional neural network significantly outperforms them with 96.49% accuracy.
Verification of the Digital Image Originality Using Fine Detail Level

In this paper proposes a verifying method of the originality of digital photos or video images based on the results of the detail coefficient calculations. The method is based on the algorithm for segmenting fine details in the normalized N-CIELAB colorimetric system and calculating their level (FDL). To check the originality of the image, the FDL value of the original and the resulting image is compared. The results of analysis of FDL change characteristics for the following image modifications are presented: additional compression in JPEG, JPEG2000 or MPEG standards in order to reduce file size and bit rate; zooming photo or video images to change resolution.
An Inexpensive Upgradation of Legacy Cameras Using Software and Hardware Architecture for Monitoring and Tracking of Live Threats

Surveillance through digital cameras is increasing exponentially. A majority of these cameras are not smart cameras; therefore, they send their video stream to a central server where it is processed and analyzed for any threats. Typically, human operators or machine learning algorithms at the cloud analyzed and processed the post-event videos to track and locate the perpetrator or victim. The centralized approach leads to two primary shortcomings: 1) the high cost of cloud infrastructure; 2) lack of instant tracking and detection of the threat. One solution is to replace these legacy cameras with the smart cameras so they can process information locally. Although the solution is costly, it could solve the real-time threat detection issues. However, the need for a central server remains there, to construct the path of threat, when threat moves from one camera view to another. The existing distributed architectures for threat tracking shifts the load of threat capturing and processing from a central server to the edge nodes, which in turn reduces the computational power but does not remove the role of the central server completely. These architectures don't equip each camera of processing and communicating with each other. Further, in the existing distributed architectures, the local cameras are not able to store the path of the threat individually and just transmit the captured trajectory to the central body. This research proposed a second alternative that makes use of legacy cameras through additional hardware and software components such that they can process information and collaborate locally. The research addresses the challenge by introducing a low cost distributed threat tracking framework that allows the single camera to identify the threat and communicate its information to other cameras without involving the central server. The framework stores the information in a lightweight architecture that is inspired by the blockchain storage algorithm. The system also allows querying the path traveled by the threat at any stage. To evaluate the system, we performed two simulated experiments: one with a central server and another with the proposed distributed system. The results of the experiments showed that the time to track the threat through the proposed system was lower than the existing centralized system. Moreover, the proposed system predicted the paths of threats with an accuracy of 85.49%. In the future, the technique may be improved with reinforcement learning and other machine learning techniques.
Evaluating Spatial Configuration Constrained CNNs for Localizing Facial and Body Pose Landmarks

Landmark localization is a widely used task required in medical image analysis and computer vision applications. Formulated in a heatmap regression framework, we have recently proposed a CNN architecture that learns on its own to split the localization task into two simpler sub-problems, dedicating one component to locally accurate but ambiguous predictions, while the other component improves robustness by incorporating the spatial configuration of landmarks to remove ambiguities. We learn this simplification in our SpatialConfiguration-Net (SCN) by multiplying the heatmap predictions of its two components and by training the network in and end-to-end manner, thus achieving regularization similar to e.g. a hand-crafted Markov Random Field model. While we have previously shown localization results solely on data from 2D and 3D medical imaging modalities, in this work our aim is to study the generalization capabilities of our SpatialConfiguration-Net to computer vision problems. Therefore, we evaluate our performance both in terms of accuracy and robustness on a facial alignment task, where we improve upon the state-of-the-art methods, as well as on a human body pose estimation task, where we demonstrate results in line with the recent state-of-the-art.
Personalized Ranking in Collaborative Filtering

The use of social information in collaborative filtering is highly encouraged, as it can improve the recommendation accuracy by handling the cold start issue. The intuition of social recommendation is to reflect one's personal choice by its social neighbors. Though there exists a considerable amount of studies in this domain, no attention is paid to incorporate the transitive relationships of social ties in the ranking problem. In this paper, we exploit the lth order transitive relations of a user and extend the popular Social Bayesian Personalized Ranking (SBPR) model. The use of transitive relation creates a more granular pairwise ranking of items for a particular user and levels the user's personal choice based on the order of its social neighbors. We implement the model and conduct experiments on two real-world recommendation datasets with different values of l. We show that our model outperforms state-of-the-art pairwise ranking techniques.
Parallel Queuing Model in a Dynamic Cloud Environment-Study of Impact on QoS: An Analytical Approach

Stabilizing the Quality of Service (QoS) currently being viewed as the main challenge in cloud computing environment and can be achieved either by employing an efficient queuing model or robust scheduling and allocation policies with load balancing features or combination of both. The QoS attributes are analyzed in a parallel queuing model featuring a pure analytical approach and extend enhancement to our previous work. The aim of the paper is to study behavior of hybrid parallel queuing model under conditions of three time varying input parameters: arrival rate, service rate, and number of servers on the response variables: queue length, waiting time, response time, and server utilization factor. With the change in these input parameters, the impact on response variables is analyzed to arrive at a valid conclusion.
Analyzing Longitudinal Development of Thematic Clusters Content in Scientific Texts

In this paper we present the results of a study devoted to identification of longitudinal changes that occur in a given research field. The approach that is employed is based on full text analysis. Extraction of terms and their relations, along with thematic clustering are performed by the use of the freely distributed VOSViewer software. The latter allows to detect terms in the form of noun phrases and to cluster these terms with the help of a modularity based algorithm. Longitudinal development of the constructed thematic clusters is analyzed through the use of directed graphs that are built to reflect significant changes in their content at the level of their formation and development over successive subperiods. An alluvial diagram is employed to show the overall transformation of the thematic clusters. The utilized approach is applied to the proceedings of “EuropaCat” catalysis conferences over a ten-year period. The conducted analysis shows that thematic clusters identified for the processed data are characterized by a low degree of stability. Even then, shifts of the researchers' interests from one theme to another can be clearly observed. Three most frequent types of cluster transformation are recognized: 1) continuance of a theme; 2) emergence of a new theme with its steady further growth; 3) emergence and discontinuance of a new theme. Main tendencies of temporal development of the detected thematic clusters are characterized in quantitative aspects.
Connecting Galaxies: Bridging the Gap Between Databases and Applications

An incompatibility of object-oriented application code and relational database engine often causes performance problems, known as Impedance Mismatch, which negatively affect business-critical application functions. The incompatibility can also over-complicate application design and increase the costs of development. We address these issues, applying a concept of the API contracts to the interaction between the application and the database. We introduce a new technique providing for the transfer of complex objects rather than low-level records. We describe the implementation of the proposed solution in industrial settings and show how suggested techniques streamline the application development, at the same time providing significant performance gains.
Research on Application of Emergency Text Classification Based on Combination Model

Abstract Based on the powerful learning ability of the deep learning model, the text uses convolutional memory network (CLSTM) to process the text information of the emergency. Firstly, the convolutional neural network is used to learn the local spatial feature information of the text, and then the text is extracted by the long and short memory neural network. The time feature information is used to scale the feature information by using the Softmax layer to finally obtain the event text category. Through experimental comparison, the precision, recall and comprehensive values of CLSTM model reached 0.879, 0.877 and 0.848, respectively, which were significantly higher than MLP and CNN models.
CONTENT OF THE PROFESSIONAL GIFTEDNESS OF A LAWYER

The paper deals with the problem of professional activity successfulness conditioned by genetic, intellectual and emotional characteristics of an individual. Its foundation of specialists productive activity is proved relying on B.M. Teplovs point of view about giftedness as a combination of abilities unique in qualities. Internal and external conditions are distinguished as professional giftedness factors. For defining the structure of the lawyer professional giftedness the principle of the giftedness and consciousness structures correlation is applied based on the principle of the consciousness and activity unity. The attention to the quality of maintenance of the students (future lawyers) professional giftedness development process is paid. The necessity for university lecturers preparedness to the students professional giftedness development is noted. The necessity demands integration of the forms and methods of educational and non-educational activity. The sample data is justified by the legal profession difficulties that require psychophysical efforts and tension for achieving aims and emotional stability to negative facts. The attempt to clarify the meaning of professional activity of a lawyer conditioned by the determinate relation to the world, the type of existence, which defines the essence of activity, is made.
The Prediction Model of Weekend Box Office Based on Big Data

The article was based on 56 films released from 2016 to 2017. Firstly, we got the movie related data through official release. Then using crawler software to get weibo data related to movies Secondly combining the movie data and weibo data, we analyzed the correlation between box office and many variables. Using linear regression model, BP neural network and grey model to predict the weekend box office. Finally, we combined the three models to get a combined model to forecast weekend box office. The results show that the relative error of the weekend box office of the combined model is the least, and the prediction method proposed in this paper is effective.
Beauty lies in the face of the beholder

Profile images play an important role in partner selection in a matrimony or dating site. The hypothesis of this paper is that perceived beauty of a profile image is a subjective opinion based on who is viewing the image. We validate this hypothesis by showing that this subjective bias for attractiveness can be learnt from the sender-receiver image pairs. We train a Bi-channel CNN based deep architecture that incorporates the visual features of both users and learns the attractiveness of sender as perceived by the receiver. This network was trained and tested on 3.5 million image pairs and achieved an accuracy of 69% with images alone, thus proving that rather than the eye, beauty lies in the face of the beholder. When this network was used in conjunction with other profile features such as age, city and caste, it further improved the accuracy of the system by a 5% relative number.
An Improved Visual Robot Moving Target Tracking Method

Moving target tracking has always been an important research direction in machine vision. Based on the careful study of the compression tracking algorithm, this paper proposes an improved algorithm for collecting a large number of samples in the tracking phase, using the positive and negative classifiers for judgment and large computational complexity: In the previous frame tracking result of the video, the window with the larger cross-correlation value of the target frame is used as the optimal tracking position in the corresponding region of the current frame, thereby reducing the calculation amount. The results of computer simulation experiments show that the algorithm improves the operation speed without affecting the tracking accuracy.
Expressibility in the Kleene Algebra of Partial Predicates with the Complement Composition

In the paper we investigate the expressibility of partial predicates in the Kleene algebra extended with the composition of predicate complement and give a necessary and sufficient condition of this expressibility in terms of the existence of an optimal solution of an optimization problem. We also investigate the expressibility in the first-order Kleene algebra with predicate complement. The obtained results may be useful for software verification using an extension of the Floyd-Hoare logic for partial pre- and postconditions.
Text sentiment classification based on the automatic expansion of sentiment dictionary

The sentiment dictionary-based text sentiment classification method is one of the main methods in the field of sentiment analysis, and the completeness of the sentiment dictionary is one of the key factors of the method. With the constant appearance of the new terms on Internet, the static sentiment dictionary cannot cover all the sentiment words. In this paper the automatic expansion methods of sentiment dictionary is explored, and a method of automatic recognition and orientation of sentiment words based on reverse back stepping is proposed, and the recognized sentiment words are used to expand the static sentiment dictionary. By applying the expanded sentiment dictionary to sentiment classification, we found that the expanded dictionary can greatly improve the recall rate of the sentiment classification without reducing the accuracy rate, so as to enhance the overall sentiment classification performance.
Examining the Efficacy of Commercial Cyber Security Certifications for Information Security Analysts

Numerous cyber security certifications are available both commercially and via institutes of higher learning. Hiring managers, recruiters and personnel responsible, and logically accountable are expected to make information backed, concrete, and sound, practical decisions when selecting personnel to fill positions. The role of an incident responder or security analyst requires near real-time decision making, pervasive knowledge of the environments they are tasked with protecting and functional situational awareness. Based on available statistics and hiring practices, can current commercial certifications offered in the cybersecurity realm, particularly that of incident response, provide effective indicators for a viable candidate? In this paper, we address this question and support our analysis with statistical data.
A Summary of Domestic and Foreign Education Big Data Literature

As a new technology, big data has been applied to various industries at a rapid speed due to its unique advantages. The education industry has also begun to use big data technology. Recently, domestic and foreign scholars have deepened their research on educational big data. Through the analysis of educational big data literature at home and abroad, this paper explores the concept and role of educational big data, and provides reference and suggestions for the better popularization of big data technology in education.
Implementation of Modified Gravitational Search Algorithm for Solar Panel Parameters Extraction

This paper proposes a non-linear problem for the extraction of parameters in a solar photovoltaic system. As many of the optimization algorithms used so far does not work at the low irradiance level a novel approach of parameter extraction using a gravitational search algorithm is presented. At different irradiance level, different parameters of various PV panels are evaluated. The results and critical evaluation of MGSA optimization approach are then compared with genetic and gravitational search algorithms. The simulation MATLAB output confirms the suitability of the MGSA for parameter extraction of solar PV panels.
A Study for S-generation of Children's Creative Thinking Ability

The 21st century is a generation of the knowledge economy that emphasizes technology and creativity. "Creativity" is seen as a new direction of education (Chen Longan, 1998; Shen Zhongwei, 2004). Time is slowly advancing. Nowadays, children receive education and accept the past. Multimedia teaching has added different novel teaching methods, such as video teaching and computer software-assisted teaching. When smart products are rapidly emerging, children are affected by the way these multimedia devices are affected.This study will use the school-age children in the scientific age as the research background, with the theme of "creative thinking and research", using Williams to create a power table, showing the innovative characteristics and causes of modern education. Suitable for school-age children. You can learn more about S-generation children to develop appropriate responses, such as job ability choices, weakness education guidance, and potential issues.
Application of Computer Aided Instruction in College Physical Education

Starting from the characteristics and laws of sports teaching in Colleges, the paper discusses the application of computer-aided teaching methods in sports teaching in Colleges by the method of documentation. It is believed that, with the rapid development of information technology, the traditional teaching mode of sports in Colleges can hardly meet the requirements of cultivating high-quality and skilled talents in higher education. The teachers in Colleges must speed up the transformation of their ideas and realize the transformation from traditional teaching In order to keep up with the trend of the times in the tide of higher education reform, teachers of education should change to those who master new computer teaching methods.
Smart Legal Contract Migration using Machine Learning

Most modern enterprise contract management systems deal with thousands of contracts. These applications are built to operate inclusively, i.e. preparing the contracts or editing the existing contracts within the application is quite easy. But, organizations also deal with various types of contracts outside such systems. Individuals working in the legal domain work on contracts stored mostly in word, pdf format. They usually work on these documents to generate various versions of the contracts. But sometimes, there exist physical contracts in the form of printed documents which needs to be digitized and fed into the system. If a contract management system provides the functionality of editing such contracts in the application, it poses a great challenge for the user to get them into the application, which might involve manual entry of contract from a physical document, copy/paste of content from a word/pdf file into the application. There exists no such intelligence to extract these clauses/content from physical documents, classify them with respect to many parameters such as applicable governing laws, the type of content present in the clauses such as Lease agreement, Sale deed, etc. and finally reuse them by having such clauses in a clause library. This smartness needs to be introduced into any CMS systems for seamless contract migration.
Visibility Metric for Visually Lossless Image Compression

Encoding images in a visually lossless manner helps to achieve the best trade-off between image compression performance and quality and so that compression artifacts are invisible to the majority of users. Visually lossless encoding can often be achieved by manually adjusting compression quality parameters of existing lossy compression methods, such as JPEG or WebP. But the required compression quality parameter can also be determined automatically using visibility metrics. However, creating an accurate visibility metric is challenging because of the complexity of the human visual system and the effort needed to collect the required data. In this paper, we investigate how to train an accurate visibility metric for visually lossless compression from a relatively small dataset. Our experiments show that prediction error can be reduced by 40% compared with the state-of-theart, and that our proposed method can save between 25%-75% of storage space compared with the default quality parameter used in commercial software. We demonstrate how the visibility metric can be used for visually lossless image compression and for benchmarking image compression encoders.