Miracle-dz/UniFAR-FTUs huggingface dataset:
A Convolutional Neural Network Aided Physical Model Improvement for AC Solenoid Valves Diagnosis

This paper focuses on the development of a physics-based diagnostic tool for alternating current (AC) solenoid valves which are categorized as critical components of many machines used in the process industry. Signal processing and machine learning based approaches have been proposed in the literature to diagnose the health state of solenoid valves. However, the approaches do not give a physical explanation of the failure modes. In this work, being capable of diagnosing failure modes while using a physically interpretable model is proposed. Feature attribution methods are applied to CNN on a large data set of the current signals acquired from accelerated life tests of several AC solenoid valves. The results reveal important regions of interest on current signals that guide the modeling of the main missing component of an existing physical model. Two model parameters, which are the shading ring and kinetic coulomb forces, are then identified using current measurements along the lifetime of valves. Consistent trends are found for both parameters allowing to diagnose the failure modes of the solenoid valves. Future work will consist of not only diagnosing the failure modes, but also of predicting the remaining useful life.
Inclusion of Phasor Measurement Units in Least Measurement Rejected State Estimator

State Estimation is the backbone of a modern electric power system and is employed by most of the Energy Management Systems (EMS) in the world to ensure the real-time monitoring and secure operation of the power system. Phasor Measurement Units (PMUs) are very popular metering equipment in the modern power industry because of its high accuracy and faster refresh rate of measurements. PMUs incorporated with conventional Supervisory control and data acquisition (SCADA) meters can improve the performance of the state estimation. In this paper, phasor measurements (voltage and current phasors) have been modeled into existing Least Measurement Rejected (LMR) estimator to simulate the incorporation of PMU meters. Our work also investigates the importance of locating PMUs at different buses in order to improve the accuracy of state estimation. The performance of proposed approach is investigated in terms of accuracy and computational efficiency in the presence of different bad-data scenarios at IEEE 30 and IEEE 118 bus systems.
New Trends in Speech Emotion Recognition

In this study, sound energy and characteristics of sound were investigated. Then, emotion recognition models built upon sound data in the literature were reviewed. Speech emotion recognition studies which adopt the most suitable machine-learning algorithms making feature extraction using both acoustic analysis methods and spectrogram analysis methods were investigated. In light of these studies, implementation has been carried out using EMO-DB data. Speech emotion recognition is a difficult problem for machine learning. The analysis of a sound signal is difficult to make as it includes various frequencies and features. Speech is digitized using signal processing methods and then sound characteristics are obtained through acoustic analysis. However, the overall success rate changes as the changes in these characteristics differ according to the emotions (sadness, fear, anger, happiness, neutral, displeasure, etc.). Although different methods are utilized in both feature extraction and emotion recognition, the success rate varies according to emotions and databases.
Handbook of theoretical computer science - Part A: Algorithms and complexity; Part B: Formal models and semantics

"Of all the books I have covered in the Forum to date, this set is the most unique and possibly the most useful to the SIGACT community, in support both of teaching and research. The books can be used by anyone wanting simply to gain an understanding of one of these areas, or by someone desiring to be in research in a topic, or by instructors wishing to find timely information on a subject they are teaching outside their major areas of expertise. " -- Rocky Ross, "SIGACT News" "This is a reference which has a place in every computer science library. " -- Raymond Lauzzana, "Languages of Design" The Handbook of Theoretical Computer Science provides professionals and students with a comprehensive overview of the main results and developments in this rapidly evolving field. Volume A covers models of computation, complexity theory, data structures, and efficient computation in many recognized subdisciplines of theoretical computer science. Volume B takes up the theory of automata and rewriting systems, the foundations of modern programming languages, and logics for program specification and verification, and presents several studies on the theoretic modeling of advanced information processing. The two volumes contain thirty-seven chapters, with extensive chapter references and individual tables of contents for each chapter. There are 5,387 entry subject indexes that include notational symbols, and a list of contributors and affiliations in each volume.
Optimal Load-Balancing Association Scheme for C-RANs with Mobile IoT Devices

In this paper, we study an optimal load balancing association scheme for a C-RAN (Cloud-based Radio Access Network) with mobile IoT (Internet of Things) devices. In C-RANs, short-range low-complex RRHs (Remote Radio Heads) are densely deployed to provide coverage. However, due to their limited processing power, it is critical to evenly distribute the traffic load. In addition, when user devices are mobile, frequent handovers can generate significant amount of control messages. In this regard, we propose an optimal load balancing association scheme between RRHs and mobile devices that minimizes the handovers as well. We first formulate an optimal load-balancing problem. Then, we revise the formulation such that an occurrence of handovers is penalized. We implement a discrete-time network simulator to evaluate and compare the performance of the proposed algorithm to conventional load balancing schemes. The evaluation results show that the proposed scheme can evenly distribute the traffic load, while minimizing the handovers.
Hierarchical Representation For Ct Prostate Segmentation

Traditional approaches for automatic CT prostate segmentation often guide feature representation learning directly based on manual delineation to deal with this challenging task (due to unclear boundaries and large shape variations), which does not fully exploit the prior information and leads to insufficient discriminability. In this paper, we propose a novel hierarchical representation learning method to segment the prostate in CT images. Specifically, one multi-task model under the supervision of a series of morphological masks transformed from the manual delineation aims to generate hierarchical feature representations for the prostate. Then, leveraging both these generated rich representations and intensity images, one fully convolutional network (FCN) carries out the accurate segmentation of the prostate. To evaluate the performance, a large and challenging CT dataset is adopted, and the experimental results show our method achieves significant improvement compared with conventional FCNs.
Segmentation And Modelling of Helanuclear Envelope

This paper describes an algorithm to segment the 3D nuclear envelope of HeLa cancer cells from electron microscopy images and model the volumetric shape of the nuclear envelope against an ellipsoid. The algorithm was trained on a single cell and then tested in six separate cells. To assess the algorithm, Jaccard similarity index and Hausdorff distance against a manually-delineated gold standard were calculated on two cells. The mean Jaccard value and Hausdorff distance that the segmentation achieved for central slices were 98% and 4 pixels for the first cell and 94% and 13 pixels for the second cell and outperformed segmentation with active contours. The modelling projects a 3D to a 2D surface that summarises the complexity of the shape in an intuitive result. Measurements extracted from the modelled surface may be useful to correlate shape with biological characteristics. The algorithm is unsupervised, fully automatic, fast and processes one image in less than 10 seconds. Code and data are freely available at https://github.com/reyesaldasoro/Hela-Cell-Segmentation and http://dx.doi. org/10.6019/EMPIAR-10094.
Reduced-Order Filtering of Fuzzy Dynamic Systems via an Event-Triggered Scheme

This paper is concerned with the generalized H2 reduced-order filtering problem for Takagi-Sugeno fuzzy systems employing an event-triggered scheme. For a continuous Takagi-Sugeno fuzzy dynamic system, we want to establish a reduced-order filter to transform the original model into a linear lower-order one. This filter can also approximate the original system with H2 performance, with a new event-triggered scheme used to decrease the communication loads within the network. By transforming the filtering problem to a convex optimization one, conditions are proposed to design the fuzzy reduced-order filter.
1.7 to 2.3 Gbps OOK LED VLC Transmission Based on 4 × 4 Color-Polarization-Multiplexing at Extremely Low Illumination

To achieve the high traffic rate of a GaN LED-based visible light communication (VLC), a 4 × 4 color-polarization-multiplexing system is demonstrated by using two blue and two green LEDs. In the measurement, an available bandwidth of blue and green LEDs can be greater than 465 MHz for VLC modulation traffic by using the suitable analogy front end circuit. Hence, the proposed VLC system can deliver 1.7 to 2.3 Gbit/s on–off keying traffic rates in the free space transmission length of 1–4 m under the extremely low illumination of 6.9–136.1 lux for the indoor application. Moreover, the measured BER of each LED is less than the forward error correction target of 3.8 × 10−3 to maintain the better signal performance.
Lesion Classification of Wireless Capsule Endoscopy Images

In this paper, we propose a scheme to classify different Wireless Capsule Endoscopy (WCE) lesion images for diagnosis. The main contribution is to quantify multi-scale pooled channel-wise information and merge multi-level features together by explicitly modeling interdependencies between all feature maps of different convolution layers. Firstly, feature maps are resized into multi-scale size with bicubic interpolation, and then down-sampling convolution method is adopted to obtain pooled feature maps of the same resolution, and finally one by one convolution kernels are utilized to fuse feature maps after quantization operation based on channel-wise attention mechanism in order to enhance feature extraction of the proposed architecture. Preliminary experimental result shows that our proposed scheme with less model parameters achieves competitive results compared to the state-of-the-art methods in WCE image classification task.
Implementation of a test system for advanced calibration and performance analyses of flickermeters

The paper presents an implementation of a test system for advanced calibration and performance verification of flickermeters. Test include not only the complete set of IEC standard test points, but also an array of different voltage fluctuations - related to common power quality issues - that may be encountered in actual use. This is done because it can be shown that different digital flickermeter implementations fully meeting the performance tests defined in IEC 61000-4-15 can still disagree significantly in some actual measurements. After the description and the performance analysis of the test system, a commercial instrument is tested and its response is presented.
A Neural Micro-Expression Recognizer

Recognizing micro-expressions underpins significant and critical research and significant application. We speculate that this problem requires the understanding of the subtle face movement, integration of face structures and a solution of limited training data. In this paper, we build an effective micro-expression recognition system that leverages techniques stemming from these speculations. First, we introduce an optical flow method based on the onset frame and the apex frame to encode the subtle face motion. This has already been validated by prior research. Second, to obtain discriminative representations from the rigid face structures, part-based average pooling is proposed to inject structure priors to the network. Finally, because the system suffers from small training sets, we propose to transfer domain knowledge from macro-expression recognition tasks to micro-expression recognition. Specifically, we adopt two domain adaptation techniques including adversarial training and expression magnification and reduction (EMR). Through experiment, we show that the proposed system achieves very competitive results on the 2nd Micro-Expression Grand Challenge (MEGC).
Analysis of Heat Transfer from a Heat Dissipating Device on a Substrate

Increasing trends of heat dissipation rate combined with high packaging density of heat dissipating devices in automotive electronics requires simple methods to estimate the heat transfer characteristics from the devices to the ambient. Especially, the heat dissipation rate of such devices is under serious considerations for the rigorous prediction of the operation temperature, which is a crucial parameter for the device reliability. As for the first step, this work aims at formulating the heat transfer from a heat dissipating device mounted on a flat plate, imitating an IC on a PCB (printed circuit board), to the ambient by using a simple heat transfer model. The model considers the heat transfer from the device to the ambient through two heat transfer paths: one directly from the device surface to the ambient, and the other through the solder into the PCB, and lastly from the PCB to the ambient. The heat transfer through the PCB is modeled by using a simple 1D fin model in cylindrical coordinates. The model prediction is evaluated with experimental results showing good agreement. Three-dimensional heat transfer analysis is also preformed to investigate the parameters essential to heat transfer in the described system.
Assessment of Photovoltaic Modules' Parameters Using the On-Site Measurements

Among various renewable energy sources, the photovoltaic technology is considered the proper technology for distributed power generation. The performance of photovoltaic panels depends on their electrical characteristics on various environmental conditions. The objective of this paper is to use the one-diode model together with on-site measurements, in order to evaluate the current-voltage and power-voltage curves and effects of irradiance and temperature on the performance the photovoltaic panel.
Proactive Policy for Efficiently Updating Join Views on Continuous Queries Over Data Streams and Linked Data

Modern data analytic systems benefit from the fusion of streaming data and linked data distributed on the Web. Accessing the linked data at query time is prohibited as usual due to its expensive cost. To reduce the high cost, most of the database systems have used a materialized view (a view) that stores local copies of the data. However, views by conventional maintenance policies such as immediate, deferred, and periodic fail to achieve high accuracy of answers to queries on data streams and linked data. To cope with the limitations, we propose a maintenance policy that releases expensive jobs of copying the latest version of linked data into views at the idle time. In other words, we pre-fetch a portion of linked data in advance according to their update pattern and query evaluation semantics. Our multiple maintenance policies that take into account changes of linked data alleviate the degradation of performance at run-time. Using real-world datasets we report that the proposed method has a significant improvement in terms of the response time, compared to the state-of-the-art methods.
A Twelve-Channel Digital Echo Canceler

We describe a recently constructed 12-channel digital echo canceler that interfaces directly with the 8-bit μ255 PCM now standard for digital transmission in the telephone plant. The four most interesting features of the canceler are time sharing of circuitry to reduce per channel costs, floating-point multiplication, loop-gain normalization, and the use of a test channel for fault detection. Extensive laboratory and field testing has shown the canceler to be working well.
Visual Scene-aware Hybrid Neural Network Architecture for Video-based Facial Expression Recognition

With rapid development of deep learning, facial expression recognition (FER) technology has made considerable progress recently. However, since conventional FER techniques are mainly designed and learned for videos which are artificially acquired in a limited environment, they may not operate robustly on videos acquired in a wild environment. To solve this problem, this paper proposes a scene-aware hybrid neural network (NN) having a novel combination of three-dimensional (3D) convolutional NN (CNN), 2D CNN and recurrent NN (RNN). The characteristics of the proposed network are as follows. First, we extract video-based global features and frame-based local features at the same time. In detail, the latent features containing the overall visual scene of a given video are extracted by 3D CNN with auxiliary classifier, and fine-tuned 2D CNN is adopted to extract latent features containing small details from each frame. Second, RNN not only performs temporal domain learning, but also feature-wise fuses two latent features extracted from the networks. For effective fusion, we also present three RNN schemes. Third, the proposed network, in which the above-mentioned methods collaborate, works very robust in a wild environment as well as in a limited environment. Extensive experiments show that the proposed network provides an average accuracy of 49.9% for AFEW dataset, i.e., a representative wild dataset, and an amazing accuracy of 98.2% for another CK+ dataset. We also show that the proposed network outperforms the state-of-the-art network(s).
Blockchain-based Smart Contracts: A Systematic Mapping Study of Academic Research (2018)

Blockchain based smart contracts are computer programs that encode an agreement between non-trusting participants. Smart contracts are executed on a blockchain system if specified conditions are met, without the need of a trusted third party. Blockchains and smart contracts have received increasing and booming attention in recent years, also in academic circles. We carry out a systematic mapping study of all peer-reviewed technology-oriented research in smart contracts. Our interest is twofold, namely to provide a survey of the scientific literature and to identify academic research trends and uptake. We only focus on peer-reviewed scientific publications, in order to identify how academic researchers have taken up smart contract technologies and established scientific outputs. We obtained all research papers from the main scientific databases, and using the systematic mapping method arrived at 188 relevant papers. We classified these papers into six categories, namely, security, privacy, software engineering, application, performance & scalability and other smart contract related topics. We found that the majority of the papers falls into the applications (about 64%) and software engineering (21%) categories. Compared to our 2017 survey [1], we observe that the number of relevant articles has increased about eightfold and shifted considerably towards applications of smart contracts.
Temporal Data Representation and Querying Based on RDF

With the explosive growth of temporal data, how to query and manage temporal data has become an important research issue. Resource description framework (RDF), as the standard data and knowledge description language of the semantic web, has been widely used to represent various domain data. Aiming at the representation and querying of temporal data, this paper proposes a temporal data representation model based on RDF and its corresponding querying method. First, a representation model called RDFt is proposed, which can represent temporal data with both the time information and the update count information, and the syntax and semantics of the RDFt model are given. Then, we propose a query language called SPARQL[t] for RDFt, and we give the query syntax and operations of SPARQL[t] in detail. In addition, a querying transformation algorithm from SPARQL[t] to SPARQL is proposed, in order to achieve compatibility with the existing RDF query engines. Finally, we implemented a prototype system that can support RDFt temporal data representation and querying, and the case studies and experimental results verify the feasibility of the proposed approach.
Characterization of BTI in SiC MOSFETs Using Third Quadrant Characteristics

Bias Temperature Instability (BTI) is a reliability concern for SiC MOSFETs which can have serious implications in the application if the true extent of the threshold voltage shift is underestimated. In this paper the third quadrant characteristics of SiC MOSFETs are used for characterizing the impact of accelerated gate stresses, evaluating the peak threshold voltage shift and tracking the recovery after stress removal. This method allows the evaluation of the impact of cumulative pulsed stresses of both long and short duration, which can be fundamental for characterizing the dynamics of BTI-induced threshold voltage shift in SiC MOSFETs under repetitive switching at the rated and accelerated gate voltage stresses.
Energy-Efficient Power Control for Ultra-Dense Networks with Distributed Antenna Arrays

We study the energy efficiency (EE) optimization problem of a ultra-dense small cell network. In each cell, based on the fog radio access network architecture, multiple distributed antenna arrays are connected to a mobile edge computing server to provide collaborative service to the users. We optimize the network EE by a novel distributed non-cooperative power control game with leakage interference-to-signal ratio constraints (NPG-LSC). Considering the backhaul power consumption, each cell independently optimizes its own EE while limiting the interference to other cells through the LSC. The proposed scheme enjoys lower complexity compared with the centralized method while achieving better network EE over the conventional game solution without leakage interference constraints. We prove that the formulated NPG-LSC scheme has a unique Nash equilibrium. Finally, simulation results show that the proposed scheme effectively improves the network EE.
MIMIC: a Cybersecurity Threat Turns into a Fog Computing Agent for IoT Systems

Fog and cloud computing are very attractive enabling technologies for Internet of Things applications. The virtualization of smart devices which compose the Internet of Things network is a promising field of research, leading to the design and development of new services and uses. To the best of our knowledge, this paper presents the first attempt to use a cybersecurity attack to realize a fog computing agent for Internet of Things systems. In particular the Man-In-The-Middle attack will be use to show the foundation of the proposed Man-In-the-Middle-Iot-Computing tool, namely MIMIC, and through the examples and tests performed we will show its potentialities.