Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning-Based Statistical Hypothesis Testing for Fault Detection

This paper elaborates the development of machine learning approach merged with statistical hypothesis testing aimed at enhancing the operation of photovoltaic (PV) systems by developing intelligent PV fault detection framework. Fault detection in PV systems is important to ensure optimal energy harvesting and reliable power production because PV systems usually operate in a harsh outdoor environment and tend to suffer various faults. In this paper, therefore, special attention is paid to detection of various faults during different modes of operation. The proposed approach merges the benefits of machine learning technique (MLT) with statistical hypothesis testing to enhance the fault detection and monitoring of PV systems. The proposed technique will be effective in monitoring the PV faults under both normal and abnormal conditions. For the framework developed, the modeling phase is addressed using MLT and the faults are detected using the generalized likelihood ratio test (GLRT) chart. The MLT is used to compute the residuals monitored and the GLRT chart is applied to the monitored residuals evaluated for fault detection purposes. The developed MLT-based GLRT algorithm is implemented and validated using both simulated and real PV data. The results are evaluated in terms of false alarm rates (FAR), missed detection rates (MDR) and computation time.
Threat Analysis for Wearable Health Devices and Environment Monitoring Internet of Things Integration System

With the rapid development of Internet of Things (IoT) applications, heterogeneous device management issues tend to arise in architecture security due to hardware computing power, types of software, data transmission interfaces, and networking protocols. Even during data exchange between devices and systems, traditional IoT devices are prone to the disclosure of personal information, which compromises privacy. Thus, planning an effective information security management strategy has become an essential part of application development. This paper presents a strategy to achieve information security verification and risk assessment for an IoT-based personal health information system. Using several interfaces of IoT devices, including Wi-Fi and Bluetooth, we simulate possible attack hypotheses and define test methods and evaluation methods suitable for each device. In our application systems for information security analysis, we also consider and integrate weaknesses of the system architecture to achieve a more complete information security threat analysis.
Service Extraction and Sentiment Analysis to Indicate Hotel Service Quality in Yogyakarta based on User Opinion

Yogyakarta is one of the most popular tourist destinations in Indonesia. In Jogja, Malioboro is one of the most visited place for its nature, culinary, shopping, culture, and arts. Every year, in Malioboro, room occupancy rate has increased significantly. The promising tourism business had allured many online booking service provider, including Traveloka. Using Traveloka, users can reserve, give ratings and opinions on hotel services. The rating and opinion are employed to indicate hotel services quality. Later on, from the indicator, user satisfaction level can be obtained. In this study, a new approach, the rule-based method is utilized to sort out opinions containing services. Moreover, it is also employed to extract service words and opinion words. Afterward, it is utilized to identify sentiments from opinions. Because the result is still impractical, then, it is segmented according to the department and function of the hotel. From the summary, indicators of the hotel service quality can be obtained. Later, the indicators will be utilized to determine the user satisfaction level. From the experiment, it is shown that the rule-based method can be employed to sort service-based opinions and to calculate service quality indicators. The rule-based method can classify opinions based on services with precision of 0.97, recall of 1, and f-measure of 0.98. The rule-based method can classify opinions based on sentimen with precision of 0.99, recall of 1, and f-measure of 0.99. As comparison, the experiments are also performed with other methods, namely KNN, SVM, J48 and Naïve Bayes. The result shows that the rule based method achieves the best performance, compare to the other methods.
OFDM based Signal Detection Performance in Cognitive Radio Systems

Performance of energy detector is affected by many factors, such as fading, shadowing, malicious user attack, etc. Those things potentially decreases the performance of primary signal detection. This paper studies OFDM based signal detection in cognitive radio systems. This work uses two difference input signal. The used two signals are PSK (Phase-Shift Keying) modulated signal with distributed Gaussian noise and QAM (Quadrature Amplitude Modulation) modulated OFDM (Orthogonal Frequency-Division Multiplexing) as an input signal in cognitive radio system. Comparison of these two signal focuses on receiver operating curve (ROC) that is represented by probability of detection (Pd) as a function of probability of false alarm (Pf). Initially, PSK modulated with distributed Gaussian noise is used as an input signal. Then, we replace the input signal with OFDM signal. Comparison is further analyzed by changing period of sensing time. Through numerical simulation, input signal based on OFDM reveals better performance compared with distributed Gaussian signal. Furthermore, from simulation shows that performing sensing with longer time is able to improve the performance of energy detector.
Remote Receiver Control in MPTCP Networks in Uncertain Operating Conditions

Owing to the security policies imposed, TCP conveys data for diverse application types, yet not necessarily suitable for the stream-related transport services. While in many contexts high throughput is preferred, other, e.g., multimedia or control system related ones, demand short latencies, rarely obtainable in the TCP scenarios. While such requirement can be fulfilled using appropriate network administration, it is a costly solution. Hence, another approach gains importance - the simultaneous use of separate transmission channels under a common control framework. For that purpose, suitable versions of well-known protocols are being developed. One of them is multipath TCP - MPTCP. The use of parallel channels to transfer the data promises numerous advantages from the application point of view. It also opens new research perspectives concerning data flow optimization. In this paper, a new flow controller, designed for the communication of multihome systems that exchange data using MPTCP protocol is presented. The controller operates on the top of other protocol components and responds properly to the fluctuations observed beneath, e.g., uncertain throughput and delays on the paths and non-optimal stream split ratio. The control algorithm establishes a feasible (non-negative and bounded) input signal and prevents both exhausting and overflowing the receiver buffer despite fluctuating networking conditions. It is executed at the sender side and it does not require any changes at the receiver.
Comparative Analysis of GFS and HDFS: Technology and Architectural landscape

Distributed file system has evolved as the De facto file system to store and process Big Data. GFS and HDFS are considered to be the frontrunners and are becoming the favored frameworks options for big data storage and processing. This paper explores the architectural dimensions and support technology of both GFS and HDFS and lists the features comparing the similarities and differences between the two. Architectural exploration is performed with the focus on GFS master, GFS chunkserver and GFS client with respect to the HDFS NameNode, HDFS DataNode and HDFS client. The paper further explorers available support technologies and add-ons for GFS and HDFS.
Information Extraction based on Named Entity for Tourism Corpus

Tourism information is scattered around nowadays. To search for the information, it is usually time consuming to browse through the results from search engine, select and view the details of each accommodation. In this paper, we present a methodology to extract particular information from full text returned from the search engine to facilitate the users. The approach is based on name entity recognition (NER). The main steps are 1) building training data and 2) building the model. The key task is the building training data: First, the tourism data are gathered and the vocabularies are built. Several minor steps include sentence extraction, relation and name entity extraction for tagging purpose. Then, the recognition model of a given entity type can be built. From the experiments, given hotel description, the model can extract the desired entity,i.e, name, location, facility as well as relation type. The extracted data can further be stored as a structured information, e.g., in the ontology format, for future querying and inference. The model for automatic named entity identification, based on machine learning, yields the error ranging 5%-25%.
Slack-based Traffic Shaping for Real-time Ethernet Networks

Ethernet has been identified as the most promising technology to be used as the backbone for the communication infrastructure in future automotive networks. Several traffic shapers have been proposed to meet the requirements of low latency and high throughput in automotive applications. Safety-critical traffic typically has strict timing requirements, however, meeting those requirements well ahead of time often brings no benefits. These spare capacities, called slack, could be exploited for the benefit of less critical but still time-sensitive traffic. Existing traffic shaping policies do not offer efficient possibilities to reduce and/or exploit this slack. In this paper, we propose a novel, scalable approach for slack-based traffic shaping, which can significantly improve the performance of time-sensitive traffic, at the expense of the slack of safety-critical traffic, which still fulfils all timing requirements. The promising results of the experimental evaluation suggest that the proposed approach represents an efficient means for meeting multi-dimensional and versatile requirements of current- and next-zeneration automotive applications.
Encoding Multiple Sensor Data for Robotic Learning Skills From Multimodal Demonstration

Learning a task such as pushing something, where the constraints of both position and force have to be satisfied, is usually difficult for a collaborative robot. In this work, we propose a multimodal teaching-by-demonstration system which can enable the robot to perform this kind of tasks. The basic idea is to transfer the adaptation of multi-modal information from a human tutor to the robot by taking account of multiple sensor signals (i.e., motion trajectories, stiffness, and force profiles). The human tutor’s stiffness is estimated based on the limb surface electromyography (EMG) signals obtained from the demonstration phase. The force profiles in Cartesian space are collected from a force/torque sensor mounted between the robot endpoint and the tool. Subsequently, the hidden semi-Markov model (HSMM) is used to encode the multiple signals in a unified manner. The correlations between position and the other three control variables (i.e., velocity, stiffness and force) are encoded with separate HSMM models. Based on the estimated parameters of the HSMM model, the Gaussian mixture regression (GMR) is then utilized to generate the expected control variables. The learned variables are further mapped into an impedance controller in the joint space through inverse kinematics for the reproduction of the task. Comparative tests have been conducted to verify the effectiveness of our approach on a Baxter robot.
The Learned Multi-scale Deep Filters for Underwater Acoustic Target Modeling and Recognition

Underwater acoustic target recognition (UATR) via ship radiated noise faces a big challenge due to the complex ocean environment. On the basis of Auditory system inspired Deep Convolutional Neural Network (ADCNN), an improved ADCNN is proposed to better model and recognize the ship radiated noise. In the improved ADCNN model, deep convolution filter groups are designed to decompose the time domain ship radiated noise into time domain signals with different timescales. Then, the decomposed signals are merged by merging layer and the deep features are extracted by the following full connected layers. Finally, the softmax layer is utilized to obtain the prediction probability for each ship radiated noise sample. In this paper, we expound the decomposition mechanism of deep filters in improved ADCNN taking the view of the time structure of ship radiated noise. Further, the feature fusion method of decomposed signals is improved by weights sharing. In the experiment, the effect of number of deep filter groups is discussed. The experimental results show that setting more filter groups can achieve better classification performance to some extent and that improved ADCNN can achieve better or equivalent recognition results compared to some existing underwater acoustic target recognition methods.
Behavioral Analysis of Resource Allocation Systems in Cloud Infrastructure

This paper presents behavioral analysis of resource allocation systems in cloud infrastructure. This analysis is performed on the basis of these systems monitoring and machine learning. To do it, the system creates a model of normal system behavior using learning set. The constructed model is used as a reference. Recognition of an abnormal behavior of the system is based on the comparing of the normal behavior with the current one. The results represent the beginning of the research. In the continuation, the issues of anomaly detection based on virtual machine (instance) resource utilization metrics will be analyzed. Also, an algorithm for determining anomalies will be developed based on a comprehensive analysis of the CPU, RAM, HDD resources usage. For enhancing of the anomalies detection accuracy it is planned to increase the period of learning.
Practical Differential Privacy for Location Data Aggregation using a Hadamard Matrix

Location data are very valuable personal information. These private data are frequently sent to servers to query relevant information to the users. Often aggregated location statistics are made publicly available for public resource planning, behavioral studies, or commercial usages. This raises privacy issues. Working under the differential privacy paradigm, Chen, Li, Qin, Kasiviswanathan, and Jin [ICDE'16] proposed a count estimation protocol called PCEP which ensures є-local differential privacy and provides good frequency approximation based on the private succinct histogram protocol by Bassily and Smith [STOC'15]. We observe that, while providing an excellent theoretical guarantee, one of the key steps which essentially reduces location dimension, based on the Johnson-Lindenstrauss Lemma is not very helpful in practice as the actual dimension is even larger than the original dimension. We replace this step with the usage of Hadamard matrix. This results in more communication-efficient protocol (because the Hadamard matrix can be computed efficiently by the users) with theoretically improved approximation guarantee.
A Comparative Study for Shallow Water Match-field Inversion Using Surrogate Models

The inversion for geoacoustic parameters is an important task in the study of underwater acoustic propagation and the match-field method is the most popular approach. When considering a series of uncertain environmental parameters with different ranges, the solution of match-field inversion normally involves frequent computation of replica sound fields, which results in a significant computational cost. In order to alleviate the computational burden and efficiently solve the inversion optima, surrogate-based optimization can be applied by making use of surrogate modeling techniques for sound-field calculation. Three types of surrogate modeling techniques including Polynomial Chaos Expansion (PCE), Kriging and PCE-based Kriging are applied in this study to formulate a surrogate-based match-field inversion framework. The performance of proposed method is validated on a two-layer numerical submarine model. The results show that the proposed method can greatly accelerate the match-field geoacoustic inversion process and achieve a balance between the inversion efficiency and accuracy.
Detection of Anomalies in the Information Networks of Industrial Automation Systems Based on Artificial Immune Detectors

The actions of attackers are the main sources of anomalies in the information networks of industrial automation systems, which can lead to serious consequences up to the occurrence of emergency situations. Neutralizing software development, as well as the development of new network anomalous behavior detection methods is one of the most important tasks. This article proposes a heuristic anomalies detection method based on the use of immune detectors in the form of the Kohonen layer and the hidden Markov model. Conclusions contain the results obtained in comparison with other anomaly detection systems. This paper proves that the proposed method can serve as a basis for building effective anomalies detection systems.
Design of distributed robust control of 1D-spatially interconnected systems in the repetitive process setting

In this paper we consider the $\mathcal{H}_{\infty}$ controller design problem for uncertain differential linear repetitive processes via full order dynamic output feedback controllers. The existence condition of desired robust $\mathcal{H}_{\infty}$ controllers is expressed as a feasibility problem of a linear matrix inequality (LMI). Then it is shown that the developed results can be applied to robust distributed controller design for 1D-spatially interconnected systems that are composed of several linear continuous-time subsystems, where each directly interacts with neighbouring subsystems. A simulation based case study on the model of a vehicle platoons system is given to demonstrate the feasibility and effectiveness of the new designs.
A Data-driven Vessel Motion Model for Offshore Access Forecasting

Access forecasting for offshore wind farm operations is concerned with the prediction of conditions during transfer of personnel between offshore structures and vessels. Currently dispatch/scheduling decisions are typically made on the basis of single-valued forecasts of significant wave height from a numerical weather prediction model. The aim of this study is to move beyond the significant wave height metric using a data-driven methodology to estimate vessel motion during transfer. This is because turbine access is constrained by the behaviour of crew transfer vessels and the transition piece in the local wave climate. Using generalised additive models for location, scale, and shape, we map the relationship between measured vessel heave motion and measured wave conditions in terms of significant wave height, peak wave period, and peak wave direction. This is explored via a case study where measurements are collected via vessel telemetry and an on-site wave buoy during the construction phase of an east coast offshore wind farm in the UK. Different model formulations are explored and the best performing trained model, in terms of the Akaike Information Criterion, is defined. Operationally, this model is driven by temporal scenario forecasts of the input wave buoy measurements to estimate the vessel motion during transfer up to 5 days ahead.
Multiscale Gaussian Process Regression-Based GLRT for Water Quality Monitoring

This paper proposes a new contaminant detection and water quality monitoring approach. Firstly, we propose an enhanced water quality modeling technique based on machine learning (e.g Gaussian process regression (GPR)) that aims at improving the proper understanding of the behavior of water distribution systems. To improve the performances of the developed water quality model even further, multiscale representation of data will be used to develop multiscale extension of these method. Multiscale representation is a powerful data analysis way that presents efficient separation of deterministic characteristics from random noise. Thus, multiscale GPR method, that combines the advantages of the machine learning method with those of multiscale representation, will be developed to enhance the water quality modeling performance. Secondly, technique to detect contaminant in WDN using hypothesis testing chart will be developed. Generalized likelihood ratio test (GLRT) has shown a good detection performances when compared to the classical detection charts. Then, to further enhance the performance of contaminant detection, a multiscale GPR-based exponentially weighted moving average (EWMA) GLRT (EWMA-GLRT) chart is developed. Therefore, this paper aims at enhancing the performances of contaminant monitoring using multiscale GPR-based GLRT and MSGPR-based EWMA-GLRT approaches.
Features Matching based Merging of 3D Maps in Multi-Robot Systems

The paper focuses on the feature matching based merging of 3D maps in a multi-robot system. The presented approach works globally what means that an initial transformation is not necessary for a proper integration of maps. The only one assumption is that the maps have a common part that can be used during a features detection, description and a matching process to compute a transformation between them. Then the found initial solution is corrected by a variation of an ICP based method. The maps are stored in the octree based representation (octomaps) but during transformation estimation a point cloud representation is used as well. In addition, the presented method was verified in various experiments, both in a simulation, with Turtlebots robots and with publicly available datasets. The solution can be applied to many robotic applications such as underwater robots, aerial robots or robots equipped with manipulators. However, so far it was mostly tested in groups of wheeled robots.
Emotion Recognition and Dynamic Functional Connectivity Analysis Based on EEG

Although emotion recognition techniques have been well developed, the understanding of the neural mechanism remains rudimentary. The traditional static network approach cannot reflect the entire brain activity at the time scale. Instead, a newly introduced temporal brain network is an optimal approach which can be used to investigate the dynamic functional connectivity (FC) of the human brain in different emotion states considering the time-varying brain regional interaction. In this study, we focused on emotion recognition and dynamic FC analysis with SEED dataset. First, multiband static networks were computed by the phase lag index (PLI). Then, subject-independent discriminative connection features of such static networks were selected to recognize the positive, neutral, and negative emotion types. In addition, we constructed the temporal brain network by sorting the static network according to time sequence. The experimental results show that the beta band is the most suitable for emotion recognition due to the best accuracy of 87.03%. And, the frontal and the temporal lobes are more sensitive to brain emotion-related activities. Moreover, we find the spatio-temporal topology of dynamic FC shows the small-world structure. Notably, the positive emotion is more distinguishable in the temporal global efficiency, especially between positive and neutral emotion states. Our findings provide new insight into the emotion-related brain regional coordination evolution and show the potential of dynamic FC for the investigation of the emotion-related brain mechanism.