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

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

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

AD-Link: An adaptive approach for user identity linkage

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

Supervised ontology and instance matching with MELT

In this paper, we present MELT-ML, a machine learning extension to the Matching and EvaLuation Toolkit (MELT) which facilitates the application of supervised learning for ontology and instance matching. Our contributions are twofold: We present an open source machine learning extension to the matching toolkit as well as two supervised learning use cases demonstrating the capabilities of the new extension

Ontology model for zakat hadith knowledge based on causal relationship, semantic relatedness and suggestion extraction

Hadith is the second most important source used by all Muslims. However, semantic ambiguity in the hadith raises issues such as misinterpretation, misunderstanding, and misjudgement of the hadith’s content. How to tackle the semantic ambiguity will be focused on this research (RQ). The Zakat hadith data should be expressed semantically by changing the surface-level semantics to a deeper sense of the intended meaning. This can be achieved using an ontology model covering three main aspects (i.e., semantic relationship extraction, causal relationship representation, and suggestion extraction). This study aims to resolve the semantic ambiguity in hadith, particularly in the Zakat topic by proposing a semantic approach to resolve semantic ambiguity, representing causal relationships in the Zakat ontology model, proposing methods to extract suggestion polarity in hadith, and building the ontology model for Zakat topic. The selection of the Zakat topic is based on the survey findings that respondents still lack knowledge and understanding of the Zakat process. Four hadith book types (i.e., Sahih Bukhari, Sahih Muslim, Sunan Abu Dawud, and Sunan Ibn Majah) that was covering 334 concept words and 247 hadiths were analysed. The Zakat ontology modelling cover three phases which are Preliminary study, source selection and data collection, data pre-processing and analysis, and development and evaluation of ontology models. Domain experts in language, Zakat hadith, and ontology have evaluated the Zakat ontology and identified that 85% of Zakat concept was defined correctly. The Ontology Usability Scale was used to evaluate the final ontology model. An expert in ontology development evaluated the ontology that was developed in Protégé OWL, while 80 respondents evaluated the ontology concepts developed in PHP systems. The evaluation results show that the Zakat ontology has resolved the issue of ambiguity and misunderstanding of the Zakat process in the Zakat hadith. The Zakat ontology model also allows practitioners in Natural language processing (NLP), hadith, and ontology to extract Zakat hadith based on the representation of a reusable formal model, as well as causal relationships and the suggestion polarity of the Zakat hadith

Machine learning for managing structured and semi-structured data

As the digitalization of private, commercial, and public sectors advances rapidly, an increasing amount of data is becoming available. In order to gain insights or knowledge from these enormous amounts of raw data, a deep analysis is essential. The immense volume requires highly automated processes with minimal manual interaction. In recent years, machine learning methods have taken on a central role in this task. In addition to the individual data points, their interrelationships often play a decisive role, e.g. whether two patients are related to each other or whether they are treated by the same physician. Hence, relational learning is an important branch of research, which studies how to harness this explicitly available structural information between different data points. Recently, graph neural networks have gained importance. These can be considered an extension of convolutional neural networks from regular grids to general (irregular) graphs. Knowledge graphs play an essential role in representing facts about entities in a machine-readable way. While great efforts are made to store as many facts as possible in these graphs, they often remain incomplete, i.e., true facts are missing. Manual verification and expansion of the graphs is becoming increasingly difficult due to the large volume of data and must therefore be assisted or substituted by automated procedures which predict missing facts. The field of knowledge graph completion can be roughly divided into two categories: Link Prediction and Entity Alignment. In Link Prediction, machine learning models are trained to predict unknown facts between entities based on the known facts. Entity Alignment aims at identifying shared entities between graphs in order to link several such knowledge graphs based on some provided seed alignment pairs. In this thesis, we present important advances in the field of knowledge graph completion. For Entity Alignment, we show how to reduce the number of required seed alignments while maintaining performance by novel active learning techniques. We also discuss the power of textual features and show that graph-neural-network-based methods have difficulties with noisy alignment data. For Link Prediction, we demonstrate how to improve the prediction for unknown entities at training time by exploiting additional metadata on individual statements, often available in modern graphs. Supported with results from a large-scale experimental study, we present an analysis of the effect of individual components of machine learning models, e.g., the interaction function or loss criterion, on the task of link prediction. We also introduce a software library that simplifies the implementation and study of such components and makes them accessible to a wide research community, ranging from relational learning researchers to applied fields, such as life sciences. Finally, we propose a novel metric for evaluating ranking results, as used for both completion tasks. It allows for easier interpretation and comparison, especially in cases with different numbers of ranking candidates, as encountered in the de-facto standard evaluation protocols for both tasks.Mit der rasant fortschreitenden Digitalisierung des privaten, kommerziellen und öffentlichen Sektors werden immer größere Datenmengen verfügbar. Um aus diesen enormen Mengen an Rohdaten Erkenntnisse oder Wissen zu gewinnen, ist eine tiefgehende Analyse unerlässlich. Das immense Volumen erfordert hochautomatisierte Prozesse mit minimaler manueller Interaktion. In den letzten Jahren haben Methoden des maschinellen Lernens eine zentrale Rolle bei dieser Aufgabe eingenommen. Neben den einzelnen Datenpunkten spielen oft auch deren Zusammenhänge eine entscheidende Rolle, z.B. ob zwei Patienten miteinander verwandt sind oder ob sie vom selben Arzt behandelt werden. Daher ist das relationale Lernen ein wichtiger Forschungszweig, der untersucht, wie diese explizit verfügbaren strukturellen Informationen zwischen verschiedenen Datenpunkten nutzbar gemacht werden können. In letzter Zeit haben Graph Neural Networks an Bedeutung gewonnen. Diese können als eine Erweiterung von CNNs von regelmäßigen Gittern auf allgemeine (unregelmäßige) Graphen betrachtet werden. Wissensgraphen spielen eine wesentliche Rolle bei der Darstellung von Fakten über Entitäten in maschinenlesbaren Form. Obwohl große Anstrengungen unternommen werden, so viele Fakten wie möglich in diesen Graphen zu speichern, bleiben sie oft unvollständig, d. h. es fehlen Fakten. Die manuelle Überprüfung und Erweiterung der Graphen wird aufgrund der großen Datenmengen immer schwieriger und muss daher durch automatisierte Verfahren unterstützt oder ersetzt werden, die fehlende Fakten vorhersagen. Das Gebiet der Wissensgraphenvervollständigung lässt sich grob in zwei Kategorien einteilen: Link Prediction und Entity Alignment. Bei der Link Prediction werden maschinelle Lernmodelle trainiert, um unbekannte Fakten zwischen Entitäten auf der Grundlage der bekannten Fakten vorherzusagen. Entity Alignment zielt darauf ab, gemeinsame Entitäten zwischen Graphen zu identifizieren, um mehrere solcher Wissensgraphen auf der Grundlage einiger vorgegebener Paare zu verknüpfen. In dieser Arbeit stellen wir wichtige Fortschritte auf dem Gebiet der Vervollständigung von Wissensgraphen vor. Für das Entity Alignment zeigen wir, wie die Anzahl der benötigten Paare reduziert werden kann, während die Leistung durch neuartige aktive Lerntechniken erhalten bleibt. Wir erörtern auch die Leistungsfähigkeit von Textmerkmalen und zeigen, dass auf Graph-Neural-Networks basierende Methoden Schwierigkeiten mit verrauschten Paar-Daten haben. Für die Link Prediction demonstrieren wir, wie die Vorhersage für unbekannte Entitäten zur Trainingszeit verbessert werden kann, indem zusätzliche Metadaten zu einzelnen Aussagen genutzt werden, die oft in modernen Graphen verfügbar sind. Gestützt auf Ergebnisse einer groß angelegten experimentellen Studie präsentieren wir eine Analyse der Auswirkungen einzelner Komponenten von Modellen des maschinellen Lernens, z. B. der Interaktionsfunktion oder des Verlustkriteriums, auf die Aufgabe der Link Prediction. Außerdem stellen wir eine Softwarebibliothek vor, die die Implementierung und Untersuchung solcher Komponenten vereinfacht und sie einer breiten Forschungsgemeinschaft zugänglich macht, die von Forschern im Bereich des relationalen Lernens bis hin zu angewandten Bereichen wie den Biowissenschaften reicht. Schließlich schlagen wir eine neuartige Metrik für die Bewertung von Ranking-Ergebnissen vor, wie sie für beide Aufgaben verwendet wird. Sie ermöglicht eine einfachere Interpretation und einen leichteren Vergleich, insbesondere in Fällen mit einer unterschiedlichen Anzahl von Kandidaten, wie sie in den de-facto Standardbewertungsprotokollen für beide Aufgaben vorkommen

Past, Present, and Future of EEG-Based BCI Applications

An electroencephalography (EEG)-based brain–computer interface (BCI) is a system that provides a pathway between the brain and external devices by interpreting EEG. EEG-based BCI applications have initially been developed for medical purposes, with the aim of facilitating the return of patients to normal life. In addition to the initial aim, EEG-based BCI applications have also gained increasing significance in the non-medical domain, improving the life of healthy people, for instance, by making it more efficient, collaborative and helping develop themselves. The objective of this review is to give a systematic overview of the literature on EEG-based BCI applications from the period of 2009 until 2019. The systematic literature review has been prepared based on three databases PubMed, Web of Science and Scopus. This review was conducted following the PRISMA model. In this review, 202 publications were selected based on specific eligibility criteria. The distribution of the research between the medical and non-medical domain has been analyzed and further categorized into fields of research within the reviewed domains. In this review, the equipment used for gathering EEG data and signal processing methods have also been reviewed. Additionally, current challenges in the field and possibilities for the future have been analyzed

Hybrid Energy Storage Systems Based on Redox-Flow Batteries: Recent Developments, Challenges, and Future Perspectives

Recently, the appeal of Hybrid Energy Storage Systems (HESSs) has been growing in multiple application fields, such as charging stations, grid services, and microgrids. HESSs consist of an integration of two or more single Energy Storage Systems (ESSs) to combine the benefits of each ESS and improve the overall system performance, e.g., efficiency and lifespan. Most recent studies on HESS mainly focus on power management and coupling between the different ESSs without a particular interest in a specific type of ESS. Over the last decades, Redox-Flow Batteries (RFBs) have received significant attention due to their attractive features, especially for stationary storage applications, and hybridization can improve certain characteristics with respect to short-term duration and peak power availability. Presented in this paper is a comprehensive overview of the main concepts of HESSs based on RFBs. Starting with a brief description and a specification of the Key Performance Indicators (KPIs) of common electrochemical storage technologies suitable for hybridization with RFBs, HESS are classified based on battery-oriented and application-oriented KPIs. Furthermore, an optimal coupling architecture of HESS comprising the combination of an RFB and a Supercapacitor (SC) is proposed and evaluated via numerical simulation. Finally, an in-depth study of Energy Management Systems (EMS) is conducted. The general structure of an EMS as well as possible application scenarios are provided to identify commonly used control and optimization parameters. Therefore, the differentiation in system-oriented and application-oriented parameters is applied to literature data. Afterwards, state-of-the-art EMS optimization techniques are discussed. As an optimal EMS is characterized by the prediction of the system’s future behavior and the use of the suitable control technique, a detailed analysis of the previous implemented EMS prediction algorithms and control techniques is carried out. The study summarizes the key aspects and challenges of the electrical hybridization of RFBs and thus gives future perspectives on newly needed optimization and control algorithms for management systems

Using Gaze for Behavioural Biometrics

A principled approach to the analysis of eye movements for behavioural biometrics is laid down. The approach grounds in foraging theory, which provides a sound basis to capture the unique- ness of individual eye movement behaviour. We propose a composite Ornstein-Uhlenbeck process for quantifying the exploration/exploitation signature characterising the foraging eye behaviour. The rel- evant parameters of the composite model, inferred from eye-tracking data via Bayesian analysis, are shown to yield a suitable feature set for biometric identification; the latter is eventually accomplished via a classical classification technique. A proof of concept of the method is provided by measuring its identification performance on a publicly available dataset. Data and code for reproducing the analyses are made available. Overall, we argue that the approach offers a fresh view on either the analyses of eye-tracking data and prospective applications in this field