Применение параллельных вычислений для аннотирования сенсорных данных

Sensor data annotation involves automated marking of a time series of readings taken from the sensor, which highlights various activities specified by the specified series. Activity marking has a wide range of practical applications: predictive maintenance, intelligent management of life support systems, climate modeling, etc. Previously, we developed a parallel PSF algorithm for annotating sensor data using a GPU based on the concept of snippets. Snippet is a subsequence that many other subsequences of a given series resemble in the sense of a specialized similarity measure based on Euclidean distance. This article describes two case studies performed using the PSF algorithm: annotation of the readings of a wearable vibration accelerometer mounted on a person and a stationary vibration accelerometer mounted on a small crusher. As part of the research, computational experiments were conducted to evaluate the speed and accuracy of the developed algorithm. Also there was the research on the dependence of the efficiency of the algorithm on the values of the input parameters: the number of the desired snippets and the length of the subsequence.Аннотирование сенсорных данных предполагает автоматизированную разметку временного ряда показаний, снятых с сенсора, которая выделяет различные активности, заданные указанным рядом. Разметка активностей имеет широкий спектр практического применения: предиктивное техническое обслуживание, умное управление системами жизнеобеспечения, моделирование климата и др. Ранее нами разработан параллельный алгоритм PSF для аннотирования данных сенсоров с помощью графического процессора на основе концепции сниппетов. Сниппет представляет собой подпоследовательность, на которую похожи многие другие подпоследовательности данного ряда в смысле специализированной меры схожести, основанной на евклидовом расстоянии. В данной статье описаны два тематических исследования, выполненные с помощью алгоритма PSF: аннотирование показаний носимого виброакселерометра, закрепленного на человеке, и стационарного виброакселерометра, установленного на малогабаритной дробильной установке. В рамках исследований были проведены вычислительные эксперименты для оценки быстродействия и точности разработанного алгоритма. Также была исследована зависимость эффективности работы алгоритма от значений входных параметров: количества искомых сниппетов и длины подпоследовательности

AD-Link: An adaptive approach for user identity linkage

National Research Foundation (NRF) Singapore under its International Research Centres in Singapore Funding Initiativ

Docker: Una herramienta para crear imágenes y lanzar múltiples contenedores con ROS OS

Docker is a tool that allows to create containers with everything needed to run an application. This feature makes it key to the process of transferring software products in different environments, allowing code to be ported faster, with better use of resources, and more reliably. ROS 2 (Robot Operating System 2) is an open source SDK for robotics applications that provides hardware abstraction for control, which can benefit from the use of containers. This article presents an introduction to Docker, creating images, launching multiple containers, and most importantly, how it can be used in conjunction with ROS OS for robotics applications.  Docker es una herramienta que permite crear contenedores con todo lo necesario para ejecutar una aplicación. Esta característica la convierte en clave para los procesos de transferencia de productos software en diferentes entornos, al permitir transferir código con mayor rapidez, mejor uso de recursos, y de forma más confiable. ROS 2 (Robot Operating System 2) es un SDK de código libre para aplicaciones en robótica que proporciona abstracción del hardware para el control, que puede verse beneficiado por el uso de contenedores. En este artículo se presenta una introducción a Docker, la creación de imágenes, el lanzamiento de múltiples contenedores, y sobre todo, cómo se puede utilizar en conjunto con ROS OS para aplicaciones de robótica. &nbsp

Wiktionary Matcher

In this paper, we introduce Wiktionary Matcher, an ontology matching tool that exploits Wiktionary as external background knowledge source. Wiktionary is a large lexical knowledge resource that is collaboratively built online. Multiple current language versions of Wiktionary are merged and used for monolingual ontology matching by exploiting synonymy relations and for multilingual matching by exploiting the translations given in the resource. We show that Wiktionary can be used as external background knowledge source for the task of ontology matching with reasonable matching and runtime performance

Trinity: Syncretizing Multi-/Long-tail/Long-term Interests All in One

Interest modeling in recommender system has been a constant topic for improving user experience, and typical interest modeling tasks (e.g. multi-interest, long-tail interest and long-term interest) have been investigated in many existing works. However, most of them only consider one interest in isolation, while neglecting their interrelationships. In this paper, we argue that these tasks suffer from a common "interest amnesia" problem, and a solution exists to mitigate it simultaneously. We figure that long-term cues can be the cornerstone since they reveal multi-interest and clarify long-tail interest. Inspired by the observation, we propose a novel and unified framework in the retrieval stage, "Trinity", to solve interest amnesia problem and improve multiple interest modeling tasks. We construct a real-time clustering system that enables us to project items into enumerable clusters, and calculate statistical interest histograms over these clusters. Based on these histograms, Trinity recognizes underdelivered themes and remains stable when facing emerging hot topics. Trinity is more appropriate for large-scale industry scenarios because of its modest computational overheads. Its derived retrievers have been deployed on the recommender system of Douyin, significantly improving user experience and retention. We believe that such practical experience can be well generalized to other scenarios

Deep sequential pattern mining for readability enhancement of Indonesian summarization

In text summarization research, readability is a great issue that must be addressed. Our hypothesis is readability can be accomplished by using text representations that keep the meaning of text documents intact. Therefore, this study aims to combine sequential pattern mining (SPM) in producing a sequence of a word as text representation with unsupervised deep learning to produce an Indonesian text summary called DeepSPM. This research uses PrefixSpan as an SPM algorithm and deep belief network (DBN) as an unsupervised deep learning method. This research uses 18,774 Indonesian news text from IndoSum. The readability aspect is evaluated by recall-oriented understudy for gisting evaluation (ROUGE) as a co-selection-based analysis; Dwiyanto Djoko Pranowo metrics, Gunning fog index (GFI), and Flesch-Kincaid grade level (FKGL) as content-based analysis; and human readability evaluation with two experts. The experiment result shows that DeepSPM yields better than DBN, with the F-measure value of ROUGE-1 enhanced to 0.462, ROUGE-2 is 0.37, and ROUGE-L is 0.41. The significance of ROUGE results also be tested using T-Test. The content-based analysis and human readability evaluation findings are conformable with the findings of co-selection-based analysis that generated summaries are only partially readable or have a medium level of readability aspect

Обнаружение аномалий временного ряда на основе технологий интеллектуального анализа данных и нейронных сетей

The article touches upon the problem of discovering subsequence anomalies in time series, which is currently in demand in a wide range of subject domains. We propose a new semi-supervised method to detect subsequence anomalies in time series. The method is based on the concepts of discord and snippet, which formalize, respectively, the concepts of anomalous and typical time series subsequences. The proposed method includes a neural network model that calculates the anomaly score of the input subsequence and an algorithm to automatically construct the model’s training set. The model is implemented as a Siamese neural network, where we employ a modification of ResNet as a subnet. To train the model, we proposed a modified contrast loss function. The training set is formed as a representative fragment of the time series from which discords, low-fraction snippets with their nearest neighbors, and outliers within each snippet are removed since they are interpreted as abnormal, atypical activity of the subject, and noise, respectively. Computational experiments over time series from various subject domains showed that the proposed model, compared with analogues, has on average the highest accuracy of anomaly detection with respect to the standard VUS-PR metric. The downside of the high accuracy of the method is the longer time spent on model training and anomaly detection compared to analogues. Nevertheless, in applications of intelligent building heating control, the method provides a speed sufficient to detect subsequence anomalies in real time.В статье рассмотрена задача поиска аномальных подпоследовательностей временного ряда, решение которой в настоящее время востребовано в широком спектре предметных областей. Предложен новый метод обнаружения аномальных подпоследовательностей временного ряда с частичным привлечением учителя. Метод базируется на концепциях диссонанса и сниппета, которые формализуют соответственно понятия аномальных и типичных подпоследовательностей временного ряда. Предложенный метод включает в себя нейросетевую модель, которая определяет степень аномальности входной подпоследовательности ряда, и алгоритм автоматизированного построения обучающей выборки для этой модели. Нейросетевая модель представляет собой сиамскую нейронную сеть, где в качестве подсети предложено использовать модификацию модели ResNet. Для обучения модели предложена модифицированная функция контрастных потерь. Формирование обучающей выборки выполняется на основе репрезентативного фрагмента ряда, из которого удаляются диссонансы, маломощные сниппеты со своими ближайшими соседями и выбросы в рамках каждого сниппета, трактуемые соответственно как аномальная, нетипичная деятельность субъекта и шумы. Вычислительные эксперименты на временных рядах из различных предметных областей показывают, что предложенная модель по сравнению с аналогами показывает в среднем наиболее высокую точность обнаружения аномалий по стандартной метрике VUS-PR. Обратной стороной высокой точности метода является большее по сравнению с аналогами время, которое затрачивается на обучение модели и распознавание аномалии. Тем не менее, в приложениях интеллектуального управления отоплением зданий метод обеспечивает быстродействие, достаточное для обнаружения аномальных подпоследовательностей в режиме реального времени

Survey on encode biometric data for transmission in wireless communication networks

The aim of this research survey is to review an enhanced model supported by artificial intelligence to encode biometric data for transmission in wireless communication networks can be tricky as performance decreases with increasing size due to interference, especially if channels and network topology are not selected carefully beforehand. Additionally, network dissociations may occur easily if crucial links fail as redundancy is neglected for signal transmission. Therefore, we present several algorithms and its implementation which addresses this problem by finding a network topology and channel assignment that minimizes interference and thus allows a deployment to increase its throughput performance by utilizing more bandwidth in the local spectrum by reducing coverage as well as connectivity issues in multiple AI-based techniques. Our evaluation survey shows an increase in throughput performance of up to multiple times or more compared to a baseline scenario where an optimization has not taken place and only one channel for the whole network is used with AI-based techniques. Furthermore, our solution also provides a robust signal transmission which tackles the issue of network partition for coverage and for single link failures by using airborne wireless network. The highest end-to-end connectivity stands at 10 Mbps data rate with a maximum propagation distance of several kilometers. The transmission in wireless network coverage depicted with several signal transmission data rate with 10 Mbps as it has lowest coverage issue with moderate range of propagation distance using enhanced model to encode biometric data for transmission in wireless communication