Reviewerly/exHarmony huggingface dataset:
Retrieving Human Traits From Gesture In Sign Language

Virtual signers (or signing avatars) play an important role in the accesibility of information in sign languages. They have been developed notably for their capability to anonymize the signer's appearance and to enable dynamic or interactive scenarios. Recording real movements thanks to motion capture provides human-like, realistic and comprehensible signing animations. However, such accurate systems may also convey extralinguistic information such as identity, gender or emotional state. In the present work, we want to address the problem of gestural identity in the context of animated agents in French Sign Language (LSF). On the one hand, person identification from signing motion is assessed through psychophysical experiments, using point-light displays. On the other hand, a computational framework is developed for the analysis of LSF motion in order to investigate which features are critical for identification. For some applications, determining these movement parameters will enable controlling the gestural human traits of virtual signers.
Automatic analysis of video content in the process of monitoring of industrial facilities

Abstract In order to automate the monitoring process, a review of methods of object detection in pictures or video content has been carried out. The methods have been analyzed in terms of their application in the software design, which will be implemented for monitoring with the use of UAVs (unmanned aerial vehicles). The selected methods have been chosen to create prototype software for finding video frames depicting a specific object and archiving the results in relation to this object.
Efficient Implementation of Dynamic Array SystemUsing MapReduce Framework

In recent years managing Big Data is a big challenge. Big data has huge volume, variety of format and its volume increases at a very high velocity. Traditional data structures fail to handle these types of data. Moreover Big Data storage scheme is very expensive. So some efficient scheme is needed. In this paper we show the application of dynamic Extendible Array for Big data storage. It is an efficient scheme which has better performance over other approaches. We used MapReduce concept to distribute the data in heterogeneous environment. The data that belongs to different dimensions are distributed to different machines to do the operations efficiently. The basic operations including insertion, deletion, update and retrieval of various types namely point key query, single key query and range key query are performed. Moreover the dynamic extension of the structure without reorganizing the existing data is performed. Experimental results are well studied. We used cheap commodity machines for our implementation.
Design of Learning Software Based on GPS Mobile Terminal

The GPS-based mobile terminals provide available hardware for online learning. Some learning software can be applied on the GPS-based mobile terminals to help people learn at any place and any time. The data in the software is properly managed and intelligently processed to help the users including online learning and communication with people of the same interests. The software can be designed with a user-friendly interface. In this study, we briefly discuss the main functions of the designed learning software including on-line learning, data management and data association. In addition, the basic structure and procedure of the learning software is explained with a three-stage steps of login, learning, and communication. With the help of the designed learning software on the GPS mobile terminals, the overall learning efficiency and effectiveness can be improved.
Learning Image Information for eCommerce Queries

Computing similarity between a query and a document is fundamental in any information retrieval system. In search engines, computing query-document similarity is an essential step in both retrieval and ranking stages. In eBay search, document is an item and the query-item similarity can be computed by comparing different facets of the query-item pair. Query text can be compared with the text of the item title. Likewise, a category constraint applied on the query can be compared with the listing category of the item. However, images are one signal that are usually present in the items but are not present in the query. Images are one of the most intuitive signals used by users to determine the relevance of the item given a query. Including this signal in estimating similarity between the query-item pair is likely to improve the relevance of the search engine. We propose a novel way of deriving image information for queries. We attempt to learn image information for queries from item images instead of generating explicit image features or an image for queries. We use canonical correlation analysis (CCA) to learn a new subspace where projecting the original data will give us a new query and item representation. We hypothesize that this new query representation will also have image information about the query. We estimate the query-item similarity using a vector space model and report the performance of the proposed method on eBay's search data. We show 11.89% relevance improvement over the baseline using Area Under the Receiver Operating Characteristic curve (AUROC) as the evaluation metric. We also show 3.1% relevance improvement over the baseline with Area Under the Precision Recall Curve (AUPRC).
Multi-sensor Data Fusion Based on Weighted Credibility Interval

The Dempster-Shafter combination rule often get wrong results when dealing with severely conflicting information. The existing typical improvement methods are mostly based on the similarity of attributes such as evidence distance, similarity and information entropy attribute as evidence weight correction evidence itself. Ultimately, the final weights of the evidences are applied to adjust the bodies of the evidences before using the Dempster’s combination rule. The fusion results of these typical methods are not ideal for some complex conflict evidence. In this paper, we propose a new improved method of conflict evidence based on weighted credibility interval. The proposed method considers the credibility degree and the uncertainty measure of the evidences which respectively based on the Sum of Absolute Difference among the propositions and the credibility interval lengths. Then the original evidence is modified with the final weight before using the Dempster’s combination rule. The numerical fusion example has verified that the proposed method is feasible and improved, in which the basic probability assignment (BPAs) to identify the correct target is 99.21%.
Modeling of dynamics of harmful emissions from motor vehicles using a procedure of automated formation of structural elements of a model

The paper deals with a problem of modeling of dynamics of harmful emissions from motor vehicles using a mathematical model in the form of a difference equation. To build such models, a method of structural identification based on the bee colony behavioral models is widely used. It is shown that in order to reduce the time complexity of this method and simultaneously ensure the possibility of finding of a unified model that would be applicable for different points in the city, it is important to ensure the completeness of a set of structural elements. An example of building of such a model using the cluster analysis of experimental data to form the initial set of structural elements is considered.
Research and Application of Network Behavior Feature Detection Model Based on Knowledge Base

With the rapid increase of network information, the problem of network security has become increasingly prominent. Monitoring the specific online behavior of target groups is a measure to prevent network security awareness. In this paper, a network user behavior monitoring model based on knowledge base is proposed. This model combines fast multi-pattern matching technology, which can match the existing behavior features in the knowledge base quickly, efficiently and accurately in the network environment with large traffic. Its advantage is that the feature knowledge base can be flexibly extended and controlled. This model not only facilitates the management of managers, but also monitors the specific behavior of network users in high-speed network environment.
Design of web-based goods ordering information system in manufacturing companies

Abstract The purpose of this study is to describe the design of information systems for ordering goods in manufacturing companies and can facilitate the company’s operations, especially in marketing products to customers. The method used in this paper uses a descriptive method, a method that presents a complete picture of the situation related to several variable situations that are examined. The results of the design and research show that a web-based goods ordering system is useful to help customers order items without having to come directly to a manufacturing workshop. This research was conducted by discussing the system of procurement of goods, processing of goods and shipping of goods carried out by the company using its website service.
A Simple Online Method for Determining the Direction, Speed and the Height of Base of the Clouds by Processing Their Ground-Based Images

The paper proposes a technique that allows, with a minimum of hardware and software costs, to determine the main types of clouds and cloud cover parameters without involving information about the meteorological situation from official sources that are not always available for a particular place and time of observation.
Nonparametric development of the ranked tree structure for the accurate facial landmarking localization

Reliable face recognition starts with the analysis of accurate and robust facial localization component and poses an unresolved challenging computer vision problem in unconstrained settings in the backdrop of clutter, and large head poses variations. The landmark localizer needs to meet robust reasonable point coordinates in correspondence with the face alignment; despite to the extent the image in consideration is under scenarios with face variability. Correspondingly, in this paper; the task is viewed as non-parametric development of local estimator that produces learned features in the leaf node of a tree with an adopted ranking in a supervised clustering way. Then, the proposed approach helps to implement a sequence of estimators based on ensembles of regression trees. The trees use simple scale invariant, asymmetric Multi-Scale shape indexed pixel difference at each split in the internal nodes using adopted label ranking clustering methods, which make those trees able to rapid detection of the process in the region of interest (ROI). The developed system is then tested on several publicly available datasets and analyzed for the sensitivity of the experimental occlusion. The results show that the performance of our method has practical value for commercialization aspect overcoming slowness in the state-of-the-art methods and evaluates to significant accuracy compared to recent methods.
Binary Discrimination and Regression

This chapter examines support vector machines. To do that properly it includes background on binomial regression and discrimination. Much of the technical material on support vector machines is relegated to Appendix A.
Transformer-DW: A Transformer Network with Dynamic and Weighted Head

State-of-the-art results on computer vision often use the CNN model where the number of channels is variant with the depth of CNN layer. The weighted channel has been proposed recently and further improves the performance of CNN. In NLP field, the Transformer structure with multi-head attention is the preferred model to solve many problems. However, Transformer cannot explicitly distinguish the information from different layers and different heads. Inspired by the use of channels in CNN model, we propose a novel model, Transformer-DW, with the dynamic and weighted head mechanism. The dynamic head allows the number of heads to increase adaptively with the depth of network layer thus explicitly modelling differences between layers. The Weighted head assigns a separate value to each head so that each head contributes differently to the task. We apply Transformer-DW to the two different tasks, machine translation and dialogue generation. Various experiments show that Transformer-DW outperforms Transformer and other baselines.
Analytical Experiments on the Utilization of Data Visualizations

Visualizing data has been well known and widely used to facilitate better understanding of data. Using the right type of visualization enables people to interpret data in a more accurate and correct way and would support decision makers in taking the right decisions. Many researchers have proposed the best types of charts and graphs to visualize data. They recommend certain types of charts based on the data type and the purpose of the visualization. However and up to our knowledge, there has not been studies which would experiment the righteous of such selection of the visualization charts. The main purpose of this research is to survey a large group of people to study the effect of selecting certain chart types on people’s comprehension of the visualized data. We conduct a user study to validate the theoretical assumptions on the selection of the best chart types. We evaluated the use of column chart to visualize categorical single variable data and line chart to visualize temporal data against other charts. We analyzed the user study participants’ performance according to their response time, accuracy of the results and overall satisfaction.
Developing The Pointer Backward-Forward Algorithm In Token Of Test In Text To Know The Level Of Individual Depression

Abstract The way you move and sleep, interact with people around you, depression change everything. This even looks at the way you talk and express yourself in writing. Sometimes this “depression language” can have a strong effect to others. Natural Language Processing (NLP) is one of the fields of computer science, artificial intelligence, and language (linguistics) related to the interaction between computers and natural human languages, such as Indonesian or English. The main objective of the NLP study is to make a machine that is able to know and understand the meaning of language, lexical analysis has the role of reading input characters and generating output tokens and making spaces, tabs, newlines and connecting rows of programs. However, the problems will occur when lexical analyzers where lexical is not able to determine when there are misspellings on tokens. Therefore, with the development of the backward-forward pointer algorithm, it is expected that in the future it will be easier to analyze both the words in front, in the middle and behind, so that the machine can quickly determine the individual depression level of speech tokens and speech tones.
Optical classifier based on volume holographic filter with vector decomposition

A new implementation of the weight vector in a classifier based on an optical system using a volume holographic filter with a vector decomposition algorithm is proposed for high-speed data classification. Using a volume holographic filter, the correlation between the input and the decomposed weight vector is calculated for the classification score, which is the output from the decision function in the optical classifier. The authors experimentally demonstrate the core concept and confirm the effectiveness of the proposed method by executing a two-class classification problem using a volume hologram medium.
Running Reinforcement Learning Agents on GPU for Many Simulations of Two-Person Simultaneous Games

It is desirable for multi-agent simulation to be run in parallel; if many agents run simultaneously, the total run time is reduced. It is popular to use GPGPU technology as an inexpensive parallelizing approach in simulation, but the “agents” runnable on GPU were simple, rule-based ones like elements in a scientific simulation. This work implements more complicated, learning agents on GPU. We consider an environment where many reinforcement learning agents learning their behavior in an iterated two-person simultaneous game while changing peers. It is necessary to run many simulations in each of which a pair of agents play the game. In this work, we implement on GPU the simulations where the agents learn with reinforcement learning and compare two methods assigning the simulations to GPU cores.
SCR-Apriori for Mining ‘Sets of Contrasting Rules’

In this paper, we propose an efficient algorithm for mining novel ‘Set of Contrasting Rules’-pattern (SCR-pattern), which consists of several association rules. This pattern is of high interest due to the guaranteed quality of the rules forming it and its ability to discover useful knowledge. However, SCR-pattern has no efficient mining algorithm. We propose SCR-Apriori algorithm, which results in the same set of SCR-patterns as the state-of-the-art approach, but is less computationally expensive. We also show experimentally that by incorporating the knowledge about the pattern structure into Apriori algorithm, SCR-Apriori can significantly prune the search space of frequent itemsets to be analysed.
Online Fuzzy Clustering of Data Streams

Chapter considers new approaches based on computational intelligence methods for solving the tasks of fuzzy clustering-segmentation of data streams sequentially fed for processing. The known methods of probabilistic and potential clustering under conditions of overlapping classes are analyzed and their adaptive recurrent modifications designed for solving problems arising in the framework of Data Stream Mining are introduced. The reasonability of a parallel probabilistic-potential approach is shown, where centroid-prototype clusters are specified using the optimization gradient procedure. For situations where the processed data is “polluted” by abnormal outliers, adaptive fuzzy clustering methods based on robust target functions that suppress outliers are proposed. The close connection of the proposed procedures with self-learning algorithms of T. Kohonen self-organizing cards is shown.
Основное содержание работы клубов психолого-педагогического сопровождения родителей, воспитывающих детей с ограниченными возможностями здоровья

This article substantiates the idea of creating psychological clubs for parents raising children with disabilities who need psychological and pedagogical assistance. Considerable attention is paid to increasing parental competence in the matters of upbringing, development, and social adaptation of “special” children, through psychological and pedagogical education; attracting parents to cooperation, in terms of common approaches to raising and educating the child.
Geometric Mean Metric Learning for Label Distribution Learning

Label distribution learning is an extend multi-label learning paradigm, especially it can preserve the significance of the labels and the related information among the labels. Many studies have shown that label distribution learning has important applications in label ambiguity. However, some classification information in the labels is not effectively utilized. In this paper, we use the classification information in the labels, and combine with the geometric mean metric learning to learn a new metric in the feature space. Under the new metric, the similar samples of the label space are as close as possible, and dissimilar samples are as far as possible. Finally, the GMML-kLDL model is proposed by combining the classification information in the labels and the neighbor information in the features. The experimental results show that the model is effective in label distribution learning and can effectively improve the prediction performance.
Sistem Pelacakan Lokasi Petugas Survei RTLH Menggunakan GPS Android dan WebGIS

The feasibility of a home for family residence is the basis for growing and developing a better family life. The roof, floor, and walls are data on the indicator of the feasibility of the house at the time of the survey. The need for a system that can display RTLH locations in the Web Geographic Information System (WebGIS) accompanied by detailed RTLH data and photos. This study explains how the system tracks the location of RTLH survey data officers using Android GPS, then represents it on WebGIS. The tracking method starts when the clerk fills out the home data input form via the Android application. Location coordinates in the form of latitude and longitude will be detected automatically and become input parameters accompanied by photos and RTLH data details. Offline data will be stored in SQLite then sent to MySQL via database synchronization while online. The purpose of this research is to establish a location tracking system for RTLH survey officers to facilitate the validation process of data suitability between data and facts in the field. This system managed to track the location of RTLH survey officers by being marked by the appearance of markers on the Google Map on the WebGIS application. From a total of 14,949 houses in Kota Solok in December 2018, there were 938 RTLH. From 938 RTLH, the coordinates of the location of latitude and longitude are 100%.
THE MICROTONAL PIANO AND THE TUNED-IN INTERPRETER

Abstract This article forms part of ‘The Microtonal Piano – and the tuned-in interpreter’, an ongoing artistic research project at the Norwegian Academy of Music that seeks to demonstrate how microtonality can increase the expressive possibilities of the acoustic piano. Many different modes of playing can result in microtonal sounds, and this article presents a preliminary overview of these possibilities. For the project, new works have been commissioned from several composers, and different aspects of microtonal modes of playing are integrated into these works. Multiphonics is obviously one of these modes of playing, as it most often results in microtonality. At the end of the article, different ways that multiphonics can be modified when used in combination with some of the other microtonal modes of playing are suggested.
Analysis of infrastructure for integration of mobile learning and project based learning in technological and vocational education

Abstract The development of applications on smartphone devices might be potential to help in improving teaching and learning process. This research addresses IT-infrastructure and readiness in integration of mobile learning and project based learning (PjBL) for vocational education. The research data were collected by questionnaires from 100 students of the tenth grade of a computer and network engineering program of the vocational school. The results show that the IT-device owned by the school has been qualified for implementing mobile learning and project based learning (PjBL). The research also found that the students are ready to participate actively in learning process by using proposed model that will be carried out by online, so that teachers can implement learning scenarios by using the online system. The mobile application is accessed by using IT- devices such as smartphones, netbooks, personal computers through internet/intranet network media by data sources of various digital documents learning in documents files, pdfs, videos, presentations and images stored in the cloud suitable the syntax project based learning based on basic competencies.