Miracle-dz/UniFAR-FTUs huggingface dataset:
Approximate Cluster Heat Maps of Large High-Dimensional Data

The problem of determining whether clusters are present in numerical data (tendency assessment) is an important first step of cluster analysis. One tool for cluster tendency assessment is the visual assessment of tendency (VAT) algorithm. VAT and improved VAT (iVAT) produce an image that provides visual evidence about the number of clusters to seek in the original dataset. These methods have been successful in determining potential cluster structure in various datasets, but they can be computationally expensive for datasets with a very large number of samples. A scalable version of iVAT called siVAT approximates iVAT images, but siVAT can be computationally expensive for big datasets. In this article, we introduce a modification of siVAT called siVAT+ which approximates cluster heat maps for large volumes of high dimensional data much more rapidly than siVAT. We compare siVAT+ with siVAT on six large, high dimensional datasets. Experimental results confirm that siVAT+ obtains images similar to siVAT images in a few seconds, and is 8 - 55 times faster than siVAT.
On the Almost Global Stability of Invariant Sets

For a given invariant set of a dynamical system, it is known that the existence of a Lyapunov-type density function, called Lyapunov density or Rantzer's density function, may imply the convergence of almost all solutions to the invariant set, in other words, the almost global stability (also called almost everywhere stability) of the invariant set. For discrete-time systems, related results in literature assume that the state space is compact and the invariant set has a local basin of attraction. We show that these assumptions are redundant. Using the duality between Frobenius-Perron and Koopman operators, we provide a Lyapunov density theorem for discrete-time systems without assuming the compactness of the state space or any local attraction property of the invariant set. As a corollary to this new discrete-time Lyapunov density theorem, we provide a continuous-time Lyapunov density theorem which can be used as an alternative to Rantzer's original theorem, especially where the solutions are known to exist globally.
Analytic Physical Optics Solution to Bistatic 3D Scattering From Separated Dihedral

Constructing the mapping between the geometric object and its corresponding 3D scattering response is a fundamental but challenging task in the domain of electromagnetic scattering, which is crucial for radar target recognition and the 3D structure interpretation. In recent years, the analytic physical optics, as a typical analytic solution, has been successfully utilized to produce the parsimonious, physically relevant 3D bistatic scattering models for standard dihedral and obtuse dihedral. In this paper, the scattering mechanism of separated dihedral, as a canonical structure that often appears in the actual targets, is investigated and its corresponding bistatic 3D scattering model is derived and established by the combination of geometric optics and physical optics method. First, the single and double-bounce reflection, as well as the occlusion occurred under different incident wave angles for the separated dihedral are analyzed in detail. Consecutively, the bistatic 3D scattering responses for different reflection cases are derived by the physical optical integration. Finally, the closed-form solution to bistatic 3D scattering model of separated dihedral is presented. The experiments with the two state-of-the-art electromagnetic calculation methods, shooting and bouncing rays and method of moments, validate the effectiveness of the proposed model. Especially, the experimental results also manifest that the standard dihedral is the specific case of the proposed model.
Social Robot Selection: A Case Study in Education

In this paper the problem of selecting the appropriate social robot in education is being investigated for the first time. The social robot selection task is handled as a multi-criteria decision making problem and solved using the TOPSIS ranking method. Initially, a set of criteria is defined, based on the technical properties and human-interaction capabilities of the social robots. In a second stage some experts quantify a set of alternative social robots according to the predefined criteria and the robots are ranked by applying the TOPSIS method. The preliminary results are very promising and reveal the necessity of selecting the most suitable social robot subject to the educational needs and the technical requirements defined by the end user.
Mutual Inductance Calculation of Two Coaxial Solenoid Coils with Iron Core

This paper studies the mutual inductance between a pair of coaxial solenoid coils for wireless power transfer applied in implantable devices. This inductively coupled solenoid coil system, compared with the traditional antenna system, has an iron core introduced in the receiving coil for enhancing the inductive magnetic flux linking the coil. It allows transferring the power in the kHz range rather than the MHz range which normally requires a pure air core receiving coil, and hence it is a safer solution to human body. A simple analytical method for calculation of the mutual inductance as the key parameter in the wireless power transfer system is presented in this paper. The results are validated by Finite Element Method as well as experimental results.
Towards Theoretical Understanding of Large Batch Training in Stochastic Gradient Descent

Stochastic gradient descent (SGD) is almost ubiquitously used for training non-convex optimization tasks. Recently, a hypothesis proposed by Keskar et al. [2017] that large batch methods tend to converge to sharp minimizers has received increasing attention. We theoretically justify this hypothesis by providing new properties of SGD in both finite-time and asymptotic regimes. In particular, we give an explicit escaping time of SGD from a local minimum in the finite-time regime and prove that SGD tends to converge to flatter minima in the asymptotic regime (although may take exponential time to converge) regardless of the batch size. We also find that SGD with a larger ratio of learning rate to batch size tends to converge to a flat minimum faster, however, its generalization performance could be worse than the SGD with a smaller ratio of learning rate to batch size. We include numerical experiments to corroborate these theoretical findings.
Design and Comparison of High-Frequency Resonant and Non-Resonant Rotating Transformers

This paper concerns the design and comparison of resonant and non-resonant high-frequency rotating transformers. The transformers are modeled and designed using coupled magnetic, electrical, and thermal models. The models are experimentally verified. Two different design optimizations are conducted, first two standard core geometries are optimized such that, the efficiency is maximized for a required output power. Second, a geometrical optimization is performed, such that the core volume is minimized for the desired output power. The results of both optimizations have indicated large improvements in terms of output power and volume as a result of applying series-series resonant compensation.
BanglaMusicStylo: A Stylometric Dataset of Bangla Music Lyrics

With the rapid growth of Bangla music industry huge volume of Bangla songs are produced every day. Immense number of producers, lyricists, singers and artists are involved in production of songs from different genres. Among many genres of Bangla music; classical, folk, baul, modern music, Rabindra Sangeet, Nazrul Geeti, film music, rock music and fusion music has gained the highest popularity. Lyricists try to express their feelings and views towards any situation or subject through their writings. Therefore, each lyricist have their own dictionary of thoughts to put on music lyrics. In this paper, we have presented “BanglaMusicStylo”, the very first stylometric dataset of Bangla music lyrics. We have collected 2824 Bangla song lyrics of 211 lyricists in a digital form. All the lyrics are stored in text format for further use. This dataset could be used for stylometric analysis such as authorship attribution, linguistic forensics, gender identification from textual data, Bangla music genre classification, vandalism detection, emotion classification etc. Identifying the significant research opportunities in this area, we have formalized this dataset which could be used for stylometric analysis.
Retraining: A Simple Way to Improve the Ensemble Accuracy of Deep Neural Networks for Image Classification

In this paper, we propose a new heuristic training procedure to help a deep neural network (DNN) repeatedly escape from a local minimum and move to a better local minimum. Our method repeats the following processes multiple times: randomly reinitializing the weights of the last layer of a converged DNN while preserving the weights of the remaining layers, and then conducting a new round of training. The motivation is to make the training in the new round learn better parameters based on the “good” initial parameters learned in the previous round. With multiple randomly initialized DNNs trained based on our training procedure, we can obtain an ensemble of DNNs that are more accurate and diverse compared with the normal training procedure. We call this framework “retraining”. Experiments on eight DNN models show that our method generally outperforms the state-of-the-art ensemble learning methods. We also provide two variants of the retraining framework to tackle the tasks of ensemble learning in which 1) DNNs exhibit very high training accuracies (e.g., $> 95\%$) and 2) DNNs are too computationally expensive to train.
Wide-Band Meander Line MIMO Antenna For Wireless Applications

Multiple input multiple output (MIMO) is the solution of the lower capacity, reliability, and low data date of single input single output (SISO) antenna systems. MIMO has high capacity, high data rate, low latency, and high signal to noise ratio (SNR). Present and future generation of wireless applications rely on the MIMO antennas for the wide coverage of growing population of mobile handsets. Four ports hook shaped planar MIMO with meander line radiators and partial ground structure and perfect boundary conditions has been proposed for wireless applications. The proposed prototype with wideband characteristics is able to resonant in 2:1 voltage standing wave ratio (VSWR) and covers frequencies in 4.76 GHz-10.6 GHz band. The −10 dB return-loss bandwidth is 5.84 GHz. The MIMO antenna gain in the whole band is more than 3.6 dBi, whereas, the radiation efficiency is more than 66%. The proposed design work is carried out on the FR-4 dielectric substrate of size $45.9\times 45.9\ \mathbf{mm}^{2}$. The proposed design covers 5.15-5.85 GHz WLAN band, 7.1-7.76 GHz satellite communication band, and 5.9-8.5 GHz point to point high speed wireless communication band.
A Novel Enhanced Inverted PWM Driving Scheme for Three-Phase BLDC Motor Drive

In this paper, a bootstrap circuit operation for three-phase BLDC motor drive is analyzed theoretically to demonstrate/explain how bootstrap capacitor is refreshed/charged or discharged. There are two popular and widely used driving schemes for three-phase BLDC motor drive, conventional six-step and inverted PWM. However, these driving schemes have limited controllable revolution speed range since refreshing/charging of bootstrap is highly dependent on the switching frequency and suspending period. Therefore, a novel enhanced inverted PWM driving scheme is proposed to free the revolution speed range limit for three-phase BLDC motor drive and is verified experimentally.
Open-Domain Question Answering using Feature Encoded Dynamic Coattention Networks

With recent developments in Deep Learning, neural network methods have proved to be useful for QA (Question Answering) systems. Researchers have begun to tackle open-domain QA, in which the model is given a question and access to a large, diverse corpus (e.g. Wikipedia). Open-domain QA is complex, as it requires large-scale search for relevant passages by an Information Retrieval component, combined with a Reading Comprehension component that reads the passages to generate an answer to the question. In our paper, we propose a novel open-domain system with two components viz., Paragraph Retriever and Paragraph Reader. Traditionally, retrieval systems only retrieve the most relevant documents to the given question. Our Retriever component retrieves the most relevant paragraphs with respect to the question. In the Reader component, we propose a modification to the Dynamic Coattention Network model, by augmenting it with other useful linguistic features like Parts of Speech of words, Named Entities, Exact Match between question and paragraph, Wh question word (what, where, when, etc.) identification, for extracting the answer from a paragraph.
Real-Time Traffic Sign Recognition

Traffic sign recognition is a technology by which a vehicle is able to recognize the traffic signs on the road. In this paper, we propose a novel traffic sign recognition that can operate robustly and accurately for real scenes of Korean roads. The proposed method first detects a potential traffic sign and then recognizes the content of the potential traffic sign. With this approach, the proposed method can robustly recognize small traffic signs from long distances and reduce false alarm significantly. We employ Histogram of Oriented Gradient (HOG) and Support Vector Machine (SVM) in a two-step model. Compared with the original HOG and SVM method using three Hyundai data sequences with ground truth, our proposed method outperforms significantly and operates robustly in different conditions. The source code and datasets are available online at https://github.com/comvisdinh/realtimetrafficsignrecognition.
Achieving High Efficiency Static Low-Concentration Photovoltaic Module Using Hybrid Lens Arrays

We propose a new design paradigm of static low concentration photovoltaics for III-V or silicon solar cells. Although static/non-tracking low concentrator optics has been studied extensively, most of these designs are either difficult to implement or low optical efficiency. In this work, we present a design that uses lens array that mixes two different optics, giving more degrees of freedom to optimize the optics and the size of solar cells. This design approach brings the optical efficiency closer to the theoretical upper limit while maintaining manufacturability and scalability. Although the design example presented in this work is aimed to mount on sloped surface, this can be also applied to the building integrated photovoltaics or on car-roof.
Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert–Schmidt Independence Criterion

Multiple kernel learning (MKL) is a principled approach to kernel combination and selection for a variety of learning tasks, such as classification, clustering, and dimensionality reduction. In this paper, we develop a novel fuzzy multiple kernel learning model based on the Hilbert–Schmidt independence criterion (HSIC) for classification, which we call HSIC-FMKL. In this model, we first propose an HSIC Lasso-based MKL formulation, which not only has a clear statistical interpretation that minimum redundant kernels with maximum dependence on output labels are found and combined, but also enables the global optimal solution to be computed efficiently by solving a Lasso optimization problem. Since the traditional support vector machine (SVM) is sensitive to outliers or noises in the dataset, fuzzy SVM (FSVM) is used to select the prediction hypothesis once the optimal kernel has been obtained. The main advantage of FSVM is that we can associate a fuzzy membership with each data point such that these data points can have different effects on the training of the learning machine. We propose a new fuzzy membership function using a heuristic strategy based on the HSIC. The proposed HSIC-FMKL is a two-stage kernel learning approach and the HSIC is applied in both stages. We perform extensive experiments on real-world datasets from the UCI benchmark repository and the application domain of computational biology which validate the superiority of the proposed model in terms of prediction accuracy.
Performance Analysis of Priority-Based IEEE 802.15.6 Protocol in Saturated Traffic Conditions

Recent advancement in internet of medical things has enabled deployment of miniaturized, intelligent, and low-power medical devices in, on, or around a human body for unobtrusive and remote health monitoring. The IEEE 802.15. 6 standard facilitates such monitoring by enabling low-power and reliable wireless communication between the medical devices. The IEEE 802.15. 6 standard employs a carrier sense multiple access with collision avoidance protocol for resource allocation. It utilizes a priority-based backoff procedure by adjusting the contention window bounds of devices according to user requirements. As the performance of this protocol is considerably affected when the number of devices increases, we propose an accurate analytical model to estimate the saturation throughput, mean energy consumption, and mean delay over the number of devices. We assume an error-prone channel with saturated traffic conditions. We determine the optimal performance bounds for a fixed number of devices in different priority classes with different values of bit error ratio. We conclude that high-priority devices obtain quick and reliable access to the error-prone channel compared to low-priority devices. The proposed model is validated through extensive simulations. The performance bounds obtained in our analysis can be used to understand the tradeoffs between different priority levels and network performance.
Euler Clustering on Large-Scale Dataset

Our concern is nonlinear clustering on large-scale dataset. While existing popular kernels (RBF, Polynomials, Spatial Pyramid, etc.) are popularly used for implicitly mapping data into a high-dimensional or infinite dimensional space in order to generalise linear clustering methods, using these kernels cannot make kernel clustering approaches directly applicable for large scale dataset, since large scale kernel matrix or similarity matrix consumes a lot of memory (e.g., 7,450 GB memory over 1 million samples of data). To solve this problem, we introduce an Euler clustering approach. Euler clustering employs Euler kernels in order to intrinsically map the input data onto a complex space of the same dimension as the input or twice, so that Euler clustering can get rid of kernel trick and does not need to rely on any approximation or random sampling on kernel function/matrix, whilst performing a more robust nonlinear clustering against noise and outliers. Moreover, since the original Euler kernel cannot generate a non-negative similarity matrix and thus is inapplicable to spectral clustering, we introduce a positive Euler kernel, and more importantly we have proved when it can generate a non-negative similarity matrix. We apply Euler kernel and the proposed positive Euler kernel to kernel <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="zheng-ieq1-2742530.gif"/></alternatives></inline-formula>-means and spectral clustering so as to develop Euler <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="zheng-ieq2-2742530.gif"/></alternatives></inline-formula>-means and Euler spectral clustering, respectively. An efficient Stiefel-manifold-based gradient method and an equivalent weighted positive Euler <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="zheng-ieq3-2742530.gif"/></alternatives></inline-formula>-means are derived for fast computation of Euler spectral clustering and further alleviating the impact of discretization of the cluster membership indicators in Euler spectral clustering. The results show that the proposed Euler clustering approach achieves overall better clustering performance compared to using popular Mercer kernels and approximation models, whilst keeping the computational complexity of the same magnitude as the most popular linear clustering method <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="zheng-ieq4-2742530.gif"/></alternatives></inline-formula>-means.
Radar Classification for Traffic Intersection Surveillance based on Micro-Doppler Signatures

We propose a method for classifying objects within an intersection based on radar measurements. The complete processing chain beginning from raw data acquisition until target classification is elaborated. The range-Doppler processing for target detection, density-based spatial clustering of applications with noise (DBSCAN) clustering for associating the detections and a Kalman-filter based tracker for the multiple target scenario are implemented. As input for the classifier, features based on the micro-Doppler signatures were extracted and in a first step pedestrian and vehicles were discriminated by a support vector machine (SVM) classifier showing promising results from 300 recorded instances.
A Hybrid Control Method of 3-Level NPC Using PI and Predictive Current Control for Bipolar LVDC System

In recent years, research on bipolar LVDC(Low Voltage DC distribution) systems has been actively proceeding due to the increased interest in renewable energy. In this paper, discrete time modeling is performed to apply model predictive current control(MPCC) to 3-level NPC AC/DC PCS(Power Conversion System) with LCL filter, which is mainly used in bipolar LVDC systems. In order to improve steady-state stability and transient state dynamics of 3-level NPC AC/DC PCS and top and bottom capacitor voltage balancing control, we proposed an algorithm which combined application of conventional PI current control(PICC) and model predictive current control(MPCC). The control performance of the proposed algorithm is verified by PSIM simulation.
Boards of Directors: Assessing Their Functioning and Validation of a Multi-Dimensional Measure

Boards of Directors that function effectively have been shown to be associated with successful organizational performance. Although a number of measures of Board functioning have been proposed, very little research has been conducted to establish the validity and reliability of dimensions of Board performance. The aim of the current study was to validate the measurement properties of a widely-used model and measure of Board performance. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted on online survey data collected from 1,546 board members from a range of Australian organizations. The analyses yielded 11 reliable factors: (1) effective internal communication and teamworking (2) effective leadership by the Chair (3) effective committee leadership and management (4) effective meeting management and record keeping, (5) effective information management (6) effective self-assessment of board functioning (7) effective internal performance management of board members (8) clarity of board member roles and responsibilities, (9) risk and compliance management (10) oversight of strategic direction, and (11) remuneration management. These dimensions to a large extent correspond to previously suggested, but not widely tested, categories of effective Board performance. Despite self-reported data and a cross-sectional design, tests of common method variance did not suggest substantive method effects. The research makes significant contributions to the corporate governance literature through empirical validation of a measure shown to reliably assess 11 discrete dimensions of Board functioning and performance. Practical and theoretical implications, study limitations and future research considerations are presented.