Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Probabilistic Learning in Hidden Social Networks and Facsimile Detection

Many social processes such as applications for bank credits, insurances or social services are digitized as Big Data, hosting hidden social networks. While social learning is fundamental for human intelligence, convolutional neural networks and deep learning extend the foundation of artificial intelligence. Deep Probabilistic Learning is a multidisciplinary approach of probabilistic machine learning introduced for Big Data analysis and hidden social networks mining. It is presented in this paper along with experimental outcomes in frauds detection related to facsimile problems.
Performance Analysis of Direct-Drive PM Synchronous Wind Generator for Maximum Power Point Direct Battery Charging

In this paper the use of a direct-drive non-overlap winding permanent magnet synchronous wind generator is investigated for direct battery charging applications. A static finite element based analysis method is proposed to achieve maximum power point matching for a turbine-specific design and for battery charging to commence at the desired cut-in speed. This method makes use of an added external inductance and the performance effects of adding the inductance is investigated. It is shown, among other things, that the proposed finite element based calculation method is very sufficient and not computationally expensive. The required external inductance for maximum power point matching is found to be roughly four times that of the wind generator's internal inductance.
Augmenting Co-Located Social Play with Biofeedback: An Interactional Approach

Biofeedback holds great potential for augmenting game play, but research to date has focussed predominantly on single player games. This paper proposes an interactional approach, which emphasises how multiple players engage with biofeedback and one another to make sense of the feedback and to incorporate it into their game play. To explore this approach in the context of the dice game Mia, we designed AMIA (Augmented Mia), a prototype system that gives feedback on heart rate, skin conductance, and skin temperature on a player's hat or armband. A study with 21 participants showed that biofeedback was ambiguous, but nevertheless participants harnessed it as a hint about their opponents' strategies, as a means of distraction, as a handicap when players could not see their own feedback as it was presented on their hat, and as a point of connection with other players. We discuss the mechanisms underlying these interactions and present design opportunities along spatial, temporal, and compositional dimensions of biofeedback that encourage and heighten social interaction.
The Constrained Rayleigh Quotient With a General Orthogonality Constraint and an Eigen-Balanced Laplacian Matrix: The Greatest Lower Bound and Applications in Cooperative Control Problems

Mathematically, the Rayleigh quotient is defined as the quadratic function of a symmetric matrix and a nonzero (usually unconstrained) variable vector. In this paper, we consider the constrained Rayleigh quotient, in which the variable vector has the orthogonality constraint, i.e., it is constrained to be orthogonal to a nonzero vector, this nonzero vector is called the orthogonality-constraint vector (or abbreviated as the OC-vector) for the variable vector. The matrix for the Rayleigh quotient is an eigen-balanced (EB) Laplacian matrix. A tighter lower bound of the constrained Rayleigh quotient has many implications in mathematics as well as in cooperative control problems. The main contributions in this paper are as follows: First, we provide the greatest lower bound (or the infimum) of the constrained Rayleigh quotient with respect to a general OC-vector and the EB Laplacian matrix, whose results are novel and better than the existing results. Then, we interpret the physical meaning of our results in an insightful geometric form and characterize the properties of the results. Finally, as an example to illustrate the merit of the tighter lower bound of the constrained Rayleigh quotient, we consider the scale of the agents driving by a fundamental consensus protocol, and show that the convergence rate of the agents’ scale is characterized by the greatest lower bound of the constrained Rayleigh quotient.
Impact of the Thermal-Interface-Material Thickness on IGBT Module Reliability in the Modular Multilevel Converter

The reliability of the Modular Multilevel Converter (MMC) is of great interest in industrial applications, where the dominant failure mechanism of IGBT modules in an MMC system is temperature-related. In this regard, thermal modeling is critical to map the power losses to thermal profiles and then to predict the lifetime. Even though the Thermal Interface Materials (TIMs) in IGBT modules have a considerable influence on the thermal resistance, the thickness of TIMs is often spuriously considered as a constant according to the thermal conductivity or information in the datasheet. This may lead to misleading results in the lifetime prediction. Hence, this paper investigates the impact of the TIM thickness on the estimated lifetime of IGBT modules in an MMC system for offshore wind power applications, including the starting assembly thickness and the Bond-Line Thickness (BLT) of TIMs. In a 30-MW MMC case study, the lifetime of the IGBT modules is discussed with respect to two values of starting thickness and variable BLT from 20 µm to 60 µm. Experiments are also carried out on a scaled-down system to validate the impact of the TIM thicknesses on the reliability prediction.
A Fully Integrated 700MA Event-Driven Digital Low-Dropout Regulator with Residue-Tracking Loop for Fine-Grained Power Management Unit

This paper presents a fully integrated digital low-dropout regulator (LDO). A proposed event-driven digital control based on a residue-tracking loop provides not only a heavy load capacity of 700mA but also an accurate regulation. A latch-based shift register is adopted to improve the digital regulation against a large load current change. The proposed LDO embedding an on-chip 100pF output capacitor was fabricated in a 65nm LP CMOS process. The LDO provides a load regulation of 0.1mV/mA and an output voltage error below 1.1% across a range from 0.5 to 1V. The LDO achieves a figure-of-merit (FOM) of 6.74fs.
Passive Radar Target Detection Using Conjugate Symmetric Correlation Technique

Passive radar system (PRS), which employ illuminators of opportunity rather than a dedicated transmitter offer several advantages over the conventional active radar systems. This paper addresses the target detection problem in PRS that utilizes digital video broadcasting-terrestrial version 2 (DVB-T2) illuminator. We exploit the unique structure of P1-symbol that is pervasive in all DVB-T2 transmissions for target detection by proposing conjugate symmetric correlation-based algorithm. Using this approach, the location of the target can be estimated accurately given that the system is initialized by synchronizing with the transmitters (i.e., illuminators). Furthermore, we analyze the distributions of test statistics under Swerling I and Swerling III target fluctuation models. And performed Monte Carlo simulations. It is demonstrated that the proposed scheme is able to detect the target echo at very low signal-to-noise ratios (SNR) regime and is useful for passive radar systems.
Towards Less Generic Responses in Neural Conversation Models: A Statistical Re-weighting Method

Sequence-to-sequence neural generation models have achieved promising performance on short text conversation tasks. However, they tend to generate generic/dull responses, leading to unsatisfying dialogue experience. We observe that in the conversation tasks, each query could have multiple responses, which forms a 1-to-n or m-to-n relationship in the view of the total corpus. The objective function used in standard sequence-to-sequence models will be dominated by loss terms with generic patterns. Inspired by this observation, we introduce a statistical re-weighting method that assigns different weights for the multiple responses of the same query, and trains the common neural generation model with the weights. Experimental results on a large Chinese dialogue corpus show that our method improves the acceptance rate of generated responses compared with several baseline models and significantly reduces the number of generated generic responses.
Using Low-level Currents Measured during Lightning Events to Estimate Upward Leader Properties

Low-level currents measured prior to return strokes can potentially provide information about the properties of upward leaders during lightning flashes. However, these currents need to be properly analysed and interpreted in order to be useful for evaluating upward connecting leaders. In this paper, low-level currents measured before return strokes in two lightning events to two structures in Brazil are analysed and interpreted as case studies. The discharge current estimated from one of these events is used as input to a detailed thermohydrodynamic model with an extensive kinetic scheme for N2/O2 mixtures. The model allows the evaluation of the physical and chemical properties of upward connecting leaders. Estimates of the temperature, mass density, electric field and radius of the channel are presented for an upward connecting leader propagating in a lightning event. In addition, estimates of the axial density of electrons, ions and neutral particles (including NO and NO2) are also reported.
High-Speed Starter-Generator for Aerospace Applications: Design and Initial Testing

This paper deals with a new method of protecting the high-speed starter-generator intended for use in the More Electric Aircraft and More Electric Engine concepts from short circuit. The features of starter-generator with the new method of protection against short circuit, the materials used in machine, the results of thermal, electromagnetic and mechanical calculations of starter-generator are described in this paper and its effectiveness is proved. Particular attention was paid to the multidisciplinary design of starter-generator and to the choice of its optimal air and non-magnetic gap. In addition, initial tests of the proposed method of protection against short circuits are conducted; the results of the tests are discussed in the paper. Developed starter-generator is capable to provide the 200% short-term overload capacity of the rated power.
Sentiment Classification towards Question-Answering with Hierarchical Matching Network

In an e-commerce environment, user-oriented question-answering (QA) text pair could carry rich sentiment information. In this study, we propose a novel task/method to address QA sentiment analysis. In particular, we create a high-quality annotated corpus with specially-designed annotation guidelines for QA-style sentiment classification. On the basis, we propose a three-stage hierarchical matching network to explore deep sentiment information in a QA text pair. First, we segment both the question and answer text into sentences and construct a number of [Q-sentence, A-sentence] units in each QA text pair. Then, by leveraging a QA bidirectional matching layer, the proposed approach can learn the matching vectors of each [Q-sentence, A-sentence] unit. Finally, we characterize the importance of the generated matching vectors via a self-matching attention layer. Experimental results, comparing with a number of state-of-the-art baselines, demonstrate the impressive effectiveness of the proposed approach for QA-style sentiment classification.
Pulse-Injection-Based Sensorless Control Method with Improved Dynamic Current Response for PMSM

Sensorless control methods based on pulse injection are widely used in the standstill to low speed machine operation range. Generally, to achieve pulse-injection-based position estimation one or more switching periods for injection are dedicatedly needed, which results in reduced equivalent switching frequency, and consequently poor dynamic current response. In this paper, an improved pulse-injection-based method is proposed for sensorless drives of Permanent Magnet Synchronous Machine (PMSM) operating at standstill to low speed range. The proposed method can effectively increase the current dynamic response without increasing the control complexity. Meanwhile the current ripple could be reduced leading to lower torque ripple and acoustic noise. Experimental results are presented to validate the improved performance of the proposed sensorless method.
Clarifying Trust in Social Internet of Things (Extended Abstract)

Establishing trustworthy relationships among the objects greatly improves the effectiveness of node interaction in the social Internet of Things (IoT). It helps nodes overcome perceptions of uncertainty and risk. However, there are limitations in the existing trust models. In this paper, a comprehensive model of trust is proposed that is tailored to the social IoT. The model includes ingredients such as trustor, trustee, goal, trustworthiness evaluation, decision, action, result, and context. Building on this trust model, we clarify the concepts of trust in the social IoT in five aspects such as: (1) mutuality of trustor and trustee; (2) inferential transfer of trust; (3) transitivity of trust; (4) trustworthiness update; and (5) trustworthiness affected by dynamic environment. With network connectivities that are from real-world social networks, simulations are conducted to evaluate the performance of the social IoT operated with the proposed trust model. An experimental IoT network is used to further validate the proposed trust model.
Ring-shaped surface-mounted permanent magnet generators with modular stator for small wind turbines

The work compares the volume, and the copper and iron losses, of three topologies of a modular surface-mounted permanent magnet synchronous generator for a small wind turbine. These are connected to the grid by an AC-to-AC converter, formed from back-to-back PWM inverters. The modular construction reduces the manufacturing complexity since the generator has a ring shape and is installed around the turbine. The use of rare earth (NdFeB) and ferrite permanent magnets is assessed by considering the armature modules. The proposed topologies are evaluated with reference to the permanent magnet type and pole number in order to identify the arrangement that produces the lowest loss. Results show the use of ferrite permanent magnetics can achieve this.
PubSE: A Hierarchical Model for Publication Extraction from Academic Homepages

Publication information in a researcher’s academic homepage provides insights about the researcher’s expertise, research interests, and collaboration networks. We aim to extract all the publication strings from a given academic homepage. This is a challenging task because the publication strings in different academic homepages may be located at different positions with different structures. To capture the positional and structural diversity, we propose an end-to-end hierarchical model named PubSE based on Bi-LSTM-CRF. We further propose an alternating training method for training the model. Experiments on real data show that PubSE outperforms the state-of-the-art models by up to 11.8% in F1-score.
Frame- and Entity-Based Knowledge for Common-Sense Argumentative Reasoning

Common-sense argumentative reasoning is a challenging task that requires holistic understanding of the argumentation where external knowledge about the world is hypothesized to play a key role. We explore the idea of using event knowledge about prototypical situations from FrameNet and fact knowledge about concrete entities from Wikidata to solve the task. We find that both resources can contribute to an improvement over the non-enriched approach and point out two persisting challenges: first, integration of many annotations of the same type, and second, fusion of complementary annotations. After our explorations, we question the key role of external world knowledge with respect to the argumentative reasoning task and rather point towards a logic-based analysis of the chain of reasoning.
A Wireless Sensor Network-Based Defence Model against Selective Forwarding Attack

Due to low communication costs and convenient deployment, wireless sensor network has been widely applied in various fields. However, it still has some problems in the defence against selective forwarding attacks. To address these problems, this paper proposes a model against selective forwarding attacks, which is built on the threshold secret sharing mechanism and adopts the individualized path routing protocol. Through simulation test, this paper studies the effects of attack intensity on the successful transmission rate and communication load under the same network deployment and communication topology but at different node densities and average neighbourhood degrees. The results show that this model can effectively defend against forwarding attacks, also saves communication resource, offering a technical reference for similar studies.
Measurement of Two-Level Inverter Induced Current Slopes at High Switching Frequencies for Control and Identification Algorithms of Electrical Machines

Several modern control and online identification algorithms for electrical machines are based on fast current slope detection. This paper shows and compares several identification methods for the inverter induced current slopes at high switching frequencies and high bandwidth of the measured signal. Test bench measurements with a SiC-MOSFET-inverter with switching frequencies up to 60 kHz and an RL-load are used to compare the different identification methods. Best results among the investigated methods have been achieved with an easy implementable printed circuit board (PCB) design of a planar Rogowski coil.
From Interaction to Participation: The Role of the Imagined Audience in Social Media Community Detection and an Application to Political Communication on Twitter

In the context of community detection in online social media, a lot of effort has been put into the definition of sophisticated network clustering algorithms and much less on the equally crucial process of obtaining high-quality input data. User-interaction data explicitly provided by social media platforms has largely been used as the main source of data because of its easy accessibility. However, this data does not capture a fundamental and much more frequent type of participatory behavior where users do not explicitly mention others but direct their messages to an invisible audience following a common hashtag. In the context of multiplex community detection, we show how to construct an additional data layer about user participation not relying on explicit interactions between users, and how this layer can be used to find different types of communities in the context of Twitter political communication.
Parametric Assessment of Esophageal Speech in Post-Laryngectomy Patients

In this paper, we present an analysis of esophageal speech recorded for 27 patients after larynx excision. Classical time-frequency parameters were determined and compared with undeformed, natural speech. Next, we described voice source parameters using inverse filtering techniques. Also, an attempt was made to find out useful parameters for esophageal speech quality description. We discovered that some parameters responsible for speech intelligibility are similar to those in the natural human voice. Another conclusion drawn is the possibility of voice quality assessment by using glottal flow characteristics. The results are promising, showing the potential of the proposed methods in monitoring the voice rehabilitation process.
OpenFlow Controller-Switch Communication Overhead Reduction Scheme on Industrial Data Center Networks

This paper proposes a framework to reduce overhead of controller-switch communication for industrial SDN-based Data Center Networks (DCN). The proposal focuses on OpenFlow (OF) to reduce the number of control messages, consisting of both PACKET_IN and PACKET_OUT messages handled by the OF controller during rule installation on OF switches' flow tables and to selectively choose switches for rule installation. Extensive simulation shows significant results on reduced controller workload. The performance provides a mutual trade-off by considerably improving rule matching rate in the presence of slightly enhanced number of flow entries, conserving resources on both OF controller and OF switches for SDN-based DCN operation. As a consequence, network latency is reduced while throughput is enhanced, which offers a great promise in the future deployment of industrial DCN.
A Suppression Method of Harmonic Instability in Line-Commutated Converters Applying Active Harmonic Filters

Harmonic instability of line-commutated converters (LCC) may cause restriction in grid operations or increase of running costs. Recently, voltage-source converters (VSC) are applied to HVDC transmission system. Some of them are installed neighboring to the existing LCCs. In such case, VSC’s fast control characteristics can be utilized to contribute to LCC’s stable operation. This paper proposes a novel solution of harmonic instability in LCCs, where adjacent VSC is operated as an active harmonic filter. The VSC improves the high system impedance resulting from parallel resonance in the ac grid. The authors analyze and demonstrate its effectiveness to the harmonic instability based on a stability criterion and electromagnetic transient (EMT) simulations. The required compensation current for the active filter is much smaller than the rated current of the LCC. Moreover, it is confirmed that the active filter control can be applied to normal VSC based BTB, HVDC, or STATCOM systems as an additional function without interrupting their original functions.
Deep Attentive Sentence Ordering Network

In this paper, we propose a novel deep attentive sentence ordering network (referred as ATTOrderNet) which integrates self-attention mechanism with LSTMs in the encoding of input sentences. It enables us to capture global dependencies among sentences regardless of their input order and obtains a reliable representation of the sentence set. With this representation, a pointer network is exploited to generate an ordered sequence. The proposed model is evaluated on Sentence Ordering and Order Discrimination tasks. The extensive experimental results demonstrate its effectiveness and superiority to the state-of-the-art methods.
Coordinated Tuning of Graphene’s Chemical Potential With NIR Wavelength and Temperature Under Kubo Framework for Enhanced Performance of SPR-Based Chemical Sensor

The performance of a Kretschmann configuration-based surface plasmon resonance sensor with fluoride glass prism, Ag layer, and graphene monolayer is simulated and analyzed under the coordinated tuning of temperature (293–373 K), near infrared (NIR) wavelength (1310–1700 nm), and graphene’s chemical potential (0–1 eV). The simulation is carried out by considering the graphene’s conductivity variations with wavelength, temperature, and chemical potential (As per Kubo formulation). For Ag layer, the phenomena of phonon-electron scattering and electron-electron scattering are taken into account. Thermo-optic effect in dielectric media (ZBLAN prism and analyte samples, i.e., methanol and ethanol) is also considered. The simulation results indicate that a combination of shorter NIR wavelength, higher temperature, and larger chemical potential can provide considerably enhanced performance. A deeper analysis predicts that 1310 nm wavelength, greater than 0.6 eV chemical potential, and 325–330 K temperature can be used as a pragmatic combination in order to achieve significantly enhanced sensing performance.