Reviewerly/exHarmony huggingface dataset:
Feature extraction using CMIM for sentiment analysis

Recently, a lot of attention paid to the domain of sentiment analysis (SA), with experts acknowledging the scientific trials as well as possible applications of the processing of subjective language. SA is the computational analysis of opinions or sentiments conveyed in a body of text. The aim of SA is the detection of subjective data present in several sources and figure out the attitudes of the author regarding the topic. Features extraction looks after the identification of the features used for opinion mining, features selection is used for choosing best features for opinion classification, features weighting method is used for weighting features for good recommendations, reduction methods are used for optimisation of classification procedure. In the current study, the feature extraction is carried out term frequency/inverse document frequency and features selection through conditional mutual information maximisation (CMIM). Feature classification is done through LogitBoost, chi-squared automatic interaction detector (CHAID) as well as K-nearest neighbour (KNN) classifiers. The experimental results were contrasted with one another.
A Hypersequent Calculus with Clusters for Data Logic over Ordinals

We study freeze tense logic over well-founded data streams. The logic features past- and future-navigating modalities along with freeze quantifiers, which store the datum of the current position and test data (in)equality later in the formula. We introduce a decidable fragment of that logic, and present a proof system that is sound for the whole logic, and complete for this fragment. Technically, this is a hypersequent system enriched with an ordering, clusters, and annotations. The proof system is tailored for proof search, and yields an optimal complexity for validity and a small model property for our fragment.
Semantic Perceptual Image Compression with a Laplacian Pyramid of Convolutional Networks

Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. These methods generally use thumbnail images or crop small patches from high-resolution images to train their networks. Instead of using patch-based training mode, we propose a novel DNN-based image compression framework in this paper. We apply the Laplacian pyramid to construct a multi-scale image representation. By learning the increasingly detailed representations, the proposed method is able to progressively restore an image. Furthermore, we use the adversarial networks for training to encourage the perceptual quality of the reconstructed image. Particularly at low bitrates, our model can only store the global semantics of an image and automatically synthesize the texture to achieve high subjective quality. Experimental results on demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in terms of visual quality.
School Now, Pay Later

Along with the time and technological development those are increasing rapidly, computer is one form of technology that is very rapid development and even the development can be calculated by day.Computer is an electronic media that plays a very important role in the development of technology today, and continues to dominate various work processes in order to be easier, more effective and efficient.MTS Guppi Airnaningan is an educational institution that does not miss information technology.But the student tuition payment service is still using manual method so that the activities related to the administration is still slow and still lining up to make payments.The objective of this research was to overcome tuition fee payment that still use manual systems and make tuition payment information system on computer-based.It will make MTS Guppi Airnaningan tuition payment become more efficient and more accurate.Using the data collection method and direct procedures to obtain accurate data which then continuing the system design step.After analyzing and observing directly the object of research, the researchers can draw the conclusion that the payment information system application can improve payment services for MTS Guppi Airnaningan student.
Integrating Sign Prediction With Behavior Prediction for Signed Heterogeneous Information Networks

People often use online social networks not only to express attitudes towards others, but also to make decisions, which forms signed heterogeneous information networks. Both sign prediction and behavior prediction can provide useful information for networks analysis, each of which has been a hot topic. However, existing methods for sign prediction mainly rely on the features from labeled links but ignore users' behavior and the features from unlabeled links, which often leads to dumb results. Similarly, inferring users' behavior without considering links' signs is dull as well. In order to solve this issue, in this paper, we present a novel model called SPBP to integrate Sign Prediction with Behavior Prediction in the context of signed heterogeneous information networks. It simultaneously captures users' social links (including both labeled links and unlabeled links) and users' behavior to improve the accuracy of prediction. First, due to the lack of labeled links in main stream social networks, we propose correlation estimation methods to estimate social correlation and behavioral correlation between users respectively. Then we encode structural balance-based features and status-based features according to social psychology theories. With the extracted features, we propose a sign prediction algorithm based on transfer learning to use knowledge extracted from related source networks to train the target network, which can effectively make up for the incompleteness of target samples. Finally, we propose a behavior prediction algorithm based on the predicted signs of links. Extensive experiments conducted on real-world signed heterogeneous information networks, Epinions, Slashdot and Wiki-RfA, demonstrate that SPBP can effectively solve both the sign prediction problem and the behavior problem.
VFX: A VISION-BASED APPROACH TO FORUM DATA EXTRACTION

Rapid development of the Internet has dramatically increased information available on the World Wide Web.Amongst these vast sources of information, discussion forums may be useful for businesses and organizations to get a glimpse of customer opinions or to extract product information.Little existing work reported in the literature has systemically investigated the problem of extracting user posts from forum sites.Extracting forum posts accurately raises a few challenges.First, forum comes in a variety of templates and this makes it hard to formalize general rules to extract forum posts.Second, each post record might appear relatively different from each other.This introduces inconsistency in the Document Object Model (DOM) for comparisons.Third, each post in the forum can consist of complicated subtrees rather than a single node in the DOM tree.To tackle these challenges, a vision-based approach was introduced to automatically extract posts from a web forum page based on its visual cues.In this paper, we propose a visual-based forum extraction (VFX) algorithm that can extract user posts in any types of forum without the need to inspect its template structure in advance.
A New Distribution on the Simplex with Auto-Encoding Applications

We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process. We explore and develop the theoretical properties of this new distribution and prove that it exhibits symmetry (exchangeability) under the same conditions as the well-known Dirichlet. Like the Dirichlet, the new distribution is adept at capturing sparsity but, unlike the Dirichlet, has an exact and closed form reparameterization--making it well suited for deep variational Bayesian modeling. We demonstrate the distribution's utility in a variety of semi-supervised auto-encoding tasks. In all cases, the resulting models achieve competitive performance commensurate with their simplicity, use of explicit probability models, and abstinence from adversarial training.
Radon and tectonic activity of the faults at the Central Mongolia

The relationship between the radon and tectonic activity of the faults for two geodynamically active regions of Central Mongolia is shown. Radon fault activity grew with increased seismic activity and with an increase in the contribution of extension in the dynamic framework of their formation. It is higher in concentrated faults of late stages of development compared with wide zones of fractures in the early stages of the formation. The established patterns will help increase the effectiveness of the use of radon surveys in studies of the seismic hazard of faults of the Earth’s crust.
Design and Development of A Single Page and Web-based Responsive E-learning System for Higher Education Institutions

Students now use not only computers or laptops, but also small devices such as mobile phones. The students at higher education institutions spent a lot of time on the internet to find the course materials. Sometimes the teacher sends the materials, but the students who are willing to take a specific course of a semester cannot get a brief concept of the course on before starting the semester. There is now a lot of online education systems on the Internet, but few are a single page and responsive. The purpose of this research is to develop a single page, responsive and web-based e-learning system. The method used in this research is the Rapid Application Development approach which has six stages of the development process, namely Planning, Analysis, Design, Implementation, Testing, and Maintenance. This research was conducted in the Faculty of Technology and Vocational Education, Universitas Pendidikan Indonesia. The students and teachers from the Department of Mechanical Engineering Education participated in this research. The results of this research indicate that with fully functional live chat, the OpenCourseWare system has a high usability test score, highly responsive and awesome features. The OpenCourseWare system is responsive and ajax-based mobile-friendly system that can be used by higher education institutions as an e-learning system based on the results obtained.
An overview of speaker recognition

Speaker recognition has been studied for many years and has been a hot topic. This paper presents an overview of speaker recognition methods, which include the classical and the state-of-art methods. According to the modular components of speaker recognition system, we fi rstly introduced the fundamentals of speaker recognition, which are mainly divided into two parts: feature extraction and speaker modeling. The most commonly speech features used in speaker recognition were elaborated fi rstly. In particular, the recent progress of deep neural network proposes a new approach of feature extraction and has become the technology trend. Secondly, the classical approaches of speaker recognition model were introduced, and elaborated the recent progress of deep learning speaker recognition. This paper especially provides an in-depth analysis on end-to-end model which consists of a training component to extract features, an enrollment component to training the speaker model, and an evaluation component with appropriate loss function for optimization. The fi nal part concludes the paper with discussion on future trends.
Conceptualizing inter-household energy exchanges: An anthropology-through-design approach

With the growth of decentralized, off-grid, and distributed renewable energy systems across the globe, an arena for energy exchanges between households is opening up. As compared to traditional ‘centralized’ energy supply systems, in these emerging energy systems households are imagined to acquire agency by having choice and control over inter-household energy exchanges within neighborhoods or villages. The existing literature on such scenarios of energy exchanges is mostly rooted in a techno-economic analysis built upon visions of rational choice approaches and lacks discussion on the sociocultural dimensions of energy exchanges. This research utilizes theoretical perspectives from economic anthropology to study the phenomenon of inter-household energy exchange. The methodological approach followed takes inspiration from discourses of design anthropology, research through design, and ethnography. This approach is instantiated in the form of a longitudinal multi-method study conducted at two off-grid villages in rural India. This interdisciplinary research makes knowledge contribution to the fields of energy studies and design anthropology. This dissertation develops conceptual knowledge of inter-household energy exchanges by investigating the social and cultural embeddedness of energy exchanges in a system where householders can decide with whom to exchange locally produced energy. Overall, the dissertation showcases that when people get to structure energy exchanges, they do so by employing a range of social, cultural, moral and economic notions, and demonstrates that there is more to energy exchanges than what the dominant rational choice perspective describes. This work proposes a novel approach called Anthropology-through-Design (AtD), which facilitates generating anthropological knowledge about a sociocultural phenomenon through a design intervention. The AtD approach takes a strategic step in relocating 'design' from being an object of anthropology to becoming an instrument for doing anthropology.
Aggregation functions based on the Choquet integral applied to image resizing

The rising volume of data and its high complexity has brought the need of developing increasingly efficient knowledge extraction techniques, which demands efficiency both in computational cost and in accuracy.Most of problems that are handled by these techniques has complex information to be identified.So, machine learning methods are frequently used, where a variety of functions can be applied in the different steps that are employed in their architecture.One of them is the use of aggregation functions aiming at resizing images.In this context, we introduce a study of aggregation functions based on the Choquet integral, whose main characteristic in comparison with other aggregation functions is that it considers, through fuzzy measure, the interaction between the elements to be aggregated.Thus, our main goal is to present an evaluation study of the performance of the standard Choquet integral the and copula-based generalization of the Choquet integral in relation to the maximum and mean functions, looking for results that may be better than the aggregation functions commonly applied.The results of such comparisons are promising, when evaluated through image quality metrics.
Reduction of marker-body matching work in activity recognition using motion capture

In this paper, activity recognition is performed using an optical motion capture system that can measure three-dimensional position information of reflective markers attached to the body. The individual markers detected by motion capture are automatically associated with which part of the body they are attached to. However, due to the overlapping of obstacles and other body parts and misplacement of the markers, these may be hidden from the camera and enter a blind spot, which may frequently cause a marker to be associated to different body parts erroneously. Usually, these errors need to be corrected manually after measurement, but this work is very time consuming, cumbersome and requires some skill. In this research, it is thought that there is no problem in recognizing the activity even if the process of spending the effort of correcting the correspondence between the marker after measurement and the body is omitted in the activity recognition using the motion capture. Because feature quantities are extracted from activity data when performing action recognition, even if an error occurs in part of the marker data, the effect is small because the correct feature quantities are selected and other marker data can compensate for an error. In addition, in this paper, we proposed a method to recognize the activity using the data when the human body template preparation required before Mocap data measurement is omitted, which is one of marker body matching work. The verification showed that even if the marker body matching operation was omitted, it was possible to recognize the action with high accuracy.
Error Analysis for the Particle Filter

The particle filter is a popular Bayesian filtering algorithm for use in cases where the state-space model is nonlinear and/or the random terms (initial state or noises) are non-Gaussian distributed. We study the behavior of the error in the particle filter algorithm as the number of particles gets large. After a decomposition of the error into two terms, we show that the difference between the estimator and the conditional mean is asymptotically normal when the resampling is done at every step in the filtering process. Two nonlinear/non-Gaussian examples are tested to verify this conclusion.
Real-time Moving Object Video Tracking using Support Vector Machines for Visual Servo Application

Visual servo refers to a process of moving a robot to fulfill certain specific purpose by using visual feedback. For a 2 DOF platform such as pan-tilt camera, the main purpose is to maintain the target object position in the camera field of view, specifically in the center of the image plane. This problem of automatically tracking a moving target has become a research topic worthy of further investigation because of the increasing use in video surveillance and security system. Nonetheless, the success of this problem in real time condition lies primarily on the performance of the visual feedback system used. This paper addresses the visual tracking control of an unknown target that may move arbitrarily in the scene. We proposed a system consist of a SONY FCB-EV7520 camera placed on pan-tilt manipulator platform, and the so-called structured output tracking with kernel (STRUCK) to track a moving target based on the consecutive image acquired. For visual servo control, a simple Proportional-Integral-Derivative controller is used for reducing the system error. Simulations on different conditions have verified the performance of the proposed system.
Meteorological Sensor Data Storage Mechanism Based on TimescaleDB and Kafka

The scale of meteorological sensor data increases at TB level every week. Traditional relational database is inefficient in storing and processing such data and cannot satisfy many soft requirements. However, the heterogeneity and diversity of the numerous existing NoSQL systems impede the well-informed comparison and selection of a data store appropriate for a given application context. Implementing a meteorological sensor data storage mechanism is a key challenge. Therefore, a meteorological sensor data storage mechanism based on TimescaleDB and Kafka is proposed. In this solution, meteorological sensor data was acquired and transmitted by Kafka and was sent to TimescaleDB for storage and analysis. Based on simulated meteorological sensor dataset, it compared the solution with other NoSQL stores such as Redis, MongoDB, Cassandra, HBase and Riak TS. The experimental results show that the storage mechanism proposed is superior in the storage and processing of massive meteorological sensor data.
Non-Gaussian filter based on the Method of Characteristics for Nonlinear Dynamical Systems

This paper deals with the development of a non-Gaussian filter for nonlinear systems with discrete time measurements. Specifically, for systems with no process noise, the evolution of the state probability density function is governed by the Liouville equation. In general, solving the Liouville equation is computationally challenging. To this end, we leverage the method of characteristics to propagate probabilities along the characteristics solutions of the Liouville equation. Further, a convex optimization procedure is proposed to reconstruct the state probability density function from these characteristic solutions. Numerical examples of capturing the non-Gaussian nature of the uncertainty in the duffing oscillator and the two body problem are illustrated.
Open Innovations as a Tool of Interaction Between Universities and Business Structures in the Digital Economy

The article is devoted to the study of the problems of finding the most preferred forms of interaction between universities and the business community. Universities are able to concentrate the intellectual potential of the region, and the business environment is able to realize this potential, so that any form of interaction will contribute to its economic and social development. The article provides an overview of the forms of interaction between higher education institutions and enterprises. The most effective forms of cooperation are identified. At the end of the work the model of interaction between the University and enterprises is given. The necessity of development for each University of the General system of management of interaction with the enterprises is proved.
Composing a Picture Book by Automatic Story Understanding and Visualization

Pictures can enrich storytelling experiences.We propose a framework that can automatically compose a picture book by understanding story text and visualizing it with painting elements, i.e., characters and backgrounds.For story understanding, we extract key information from a story on both sentence level and paragraph level, including characters, scenes and actions.These concepts are organized and visualized in a way that depicts the development of a story.We collect a set of Chinese stories for children and apply our approach to compose pictures for stories.Extensive experiments are conducted towards story event extraction for visualization to demonstrate the effectiveness of our method.
Toward Testing Self-organizations in Multi-Embedded-Agent Systems

This paper presents a testing approach for validating global adaptation in multi-embedded-agent systems. Those systems are gaining increasing attention due to their high adaptability and resilience. They differ from software multi-agent systems because embedded agents have additional constraints, like energy management that software agents don’t. Those constraints and other specificities, like the tight link with the physical environment, require the use of specific methods and tools for testing these systems. The proposed approach aims at validating at run-time the adaptation of those systems when the entities composing them, the agents, are able to change their global behaviors with self-organization processes. Self-organization processes are not specific to multi-agent systems but in their case, they allow agents to change their organization, i.e. their way of interacting, at runtime. The proposed approach and tool are designed to support lifelong monitoring of multi-embedded-agent systems. In such systems, agents have self-organization behaviors resulting in complex and ever adapting systems, which are challenging to test and monitor.