Miracle-dz/UniFAR-FTUs huggingface dataset:
The Determination of Cluster Number at k-Mean Using Elbow Method and Purity Evaluation on Headline News

Information is one of the most important thing in our lives, while humans is naturally impatient when searching for information from the internet. Users want to get the right answer instantaneously with minimal effort. News headlines can be used to categorize news types, as appropriate. The appropriate type of news can make it easier for us to choose the particular topic we want. Similarity in a title can be used to clustering news based on news title. From those reason this dataset research contain the title of online news site. TFIDF used as Document Preprocessing method, K-Means as clustering method, and elbow method used to optimize number of cluster. Purity method applied to evaluate news title clustering as internal evaluation. SSE (Sum Square Error) of each cluster are calculate and compared to optimize number of cluster in the elbow method, the result of those comparison evaluate using internal evaluation called purity, purity value is conformity between cluster and ideal cluster. From the calculation of elbow method, the most optimal number of cluster are 8 cluster, there is 0.228 point between 7cluster and 8 cluster SSE value so the elbow form are made. Purity evaluation method generates value 0.514 in the number of cluster are 8, this is the highest value and the one closest to one rather than the other number of cluster which mean the most ideal. The conclusion is the elbow method can be used to optimize number of cluster on K-Mean clustering method.
OCA: Overlapping Clustering Application: Unsupervised Approach for Data Analysis

In this paper, a new data analysis tool called Overlapping Clustering Application (OCA) was presented. It was developed to identify overlapping clusters and outliers in an unsupervised manner. Python programming language was used for the development of the OCA. One of the methods used is the k-means algorithm, because of its simplicity to solve known clustering issues. The study also considered the use of median and median absolute deviation, it is known to be one of the most robust measures that are easy to use with the presence of outliers. Maxdist (maximum distance of data objects allowed in a cluster) is another method, it is used to identify data objects assigned to multi-cluster. The main function of OCA are composed of three phases. The first phase is to segment data objects into cluster using the k-means algorithm. The second phase is the detection of the abnormal values(outliers) in the datasets using median and median absolute deviation. Finally, the last phase is the identification of overlapping clusters, it uses maxdist as a predictor of data objects that can belong to multiple clusters. Based on the experimental results, the developed OCA demonstrated its capability in terms of detecting the abnormal values (outliers) and identification of clusters with overlaps. Experiments revealed that OCA is very useful data analysis tool for data clustering, outlier detection analysis and the detection of overlapping clusters.
Constrained Dynamic Surface Control of Electro-Hydrostatic Actuator Anti-Skid Braking System

The existing controller can hardly guarantee the operating stability of aircraft anti-skid braking system. To overcome the higher order nonlinearity and parameters variability of large passenger aircraft with electro-hydrostatic actuator (EHA) anti-skid braking system, a dynamic surface control approach, which based on barrier Lyapunov function (BLF), is proposed to design a slip ratio tracking control with an upper constraint. The purpose of the proposed BLF is to prevent constraint violation to ensure stability. A whole state space model of EHA anti-skid braking system is developed and simplified reasonably to a strict feedback form, whose model uncertainties are considered as unknown external disturbances. A nonlinear disturbance observer (NDO) is utilized to compensate this external disturbance. Through Lyapunov stability analysis, it is noticed that the proposed control strategy can converge to a small neighborhood of the origin by appropriately adjusting the controller parameters. Moreover, it can guarantee the tracking error is with semiglobal, uniform, and ultimate bounded. By using the employed control algorithm, the proposed controller enhanced braking performance and safety, which ensures the closed-loop system operating in the stable region of the friction curve. The proposed method eliminated the explosion problem of terms existing in traditional back-stepping approach and was easier to be implemented. The performance and the effectiveness of the proposed controller have been verified by carrying out simulation and real-time Hardware-in-Loop experiment.
Effect of Control in Electrical Drives on Image Registration Supporting Motion State Estimation

In modern electrical drives, more and more smart algorithms are used to support the control. One of those algorithms is image registration, allowing, in a graphical way, to estimate the motion states from an image, registered from multiple samples of the phase currents and its time gradients. By searching for deformations in these images, such methods allow to take into account physical nonlinear phenomena such as magnetic cross-saturation, known to affect the motion state estimation of the rotor. In this paper, the image registration is illustrated on a DTC drive for a salient pole synchronous machine and the effect of control on the distribution of current sample points within the image is studied.
ReRAM-Based Processing-in-Memory Architecture for Recurrent Neural Network Acceleration

We present a recurrent neural network (RNN) accelerator design with resistive random-access memory (ReRAM)-based processing-in-memory (PIM) architecture. Distinguished from prior ReRAM-based convolutional neural network accelerators, we redesign the system to make it suitable for RNN acceleration. We measure the system throughput and energy efficiency with the detailed circuit and device characterization. Reprogrammability is enabled with our design, and an RNN friendly pipeline is employed to increase the system throughput. We observe that on average the proposed system achieves <inline-formula> <tex-math notation="LaTeX">$79{\times}$ </tex-math></inline-formula> improvement of computing efficiency compared with graphics processing unit baseline. Our simulation also indicates that to maintain high accuracy and computing efficiency, the read noise standard deviation should be less than 0.2, the device resistance should be at least 1 <inline-formula> <tex-math notation="LaTeX">$\text{M}{\Omega }$ </tex-math></inline-formula>, and the device writes latency should be minimized.
A 2D Method For Acquiring The Radiation Pattern Of Two-Identical Vivaldi Antennas By Using A Stepped-Frequency Continue Wave (SFCW) Radar And A Rotation Stage

A simple measuring method for acquiring the radiation pattern of an ultra-wide band Vivaldi antenna is presented. The measuring is performed by combining two identical Vivaldi antennas and some of the intrinsic properties of a stepped-frequency continue wave radar (SFCW radar) in the range from 1.0 GHz to 6.0 GHz. A stepper-motor provided the azimuthal rotation for one of the antennas from 0° to 360°. The tests have been performed within the conventional environment (laboratory / office) without using an anechoic chamber or absorbing materials. Special measuring devices have not been used either. This method has been tested with different pairs of Vivaldi antennas and it can be also used for different ones (with little or no change in the system), as long as their operational bandwidth is within the frequency range of the SFCW radar.
Statistical Monitoring of Keyboard Handwriting for Continuous Authentication

A method of continuous authentication by keyboard handwriting is proposed, where differences in preferences of alternative keys are revealed, and there is a constant comparison of the reference and observed empirical distribution functions of the holding intervals of the most common keys. In addition, we calculate the rank correlations between the sequences of these intervals in frequently encountered terms and identify differences in the mean and standard deviations of the intervals. The experimental verification showed the closeness of the relative error frequencies of both types with predetermined theoretical probabilities.
When a Patch is Not Enough - HardFails: Software-Exploitable Hardware Bugs

Modern computer systems are becoming faster, more efficient, and increasingly interconnected with each generation. Consequently, these platforms also grow more complex, with continuously new features introducing the possibility of new bugs. Hence, the semiconductor industry employs a combination of different verification techniques to ensure the security of System-on-Chip (SoC) designs during the development life cycle. However, a growing number of increasingly sophisticated attacks are starting to leverage cross-layer bugs by exploiting subtle interactions between hardware and software, as recently demonstrated through a series of real-world exploits with significant security impact that affected all major hardware vendors.
DC-Link Variable Control of 2-Stage Plasma Generator for Improvement of Efficiency in Intermediate-Load and Light-Load Conditions

A control method of input-parallel and output-series connected inverters with a 3-phase PWM converter for a plasma generator is proposed in this paper. By controlling a DC-link voltage with PWM converter considering the load level, the proposed control method can improve an efficiency in intermediate-load and light-load conditions compared with adopting conventional control method. Through the simulation and experiment, the efficiency of the 2-stage AC-AC converter according to control methods are compared.
Anomaly Detection and Localization: A Novel Two-Phase Framework Based on Trajectory-Level Characteristics

Detecting and locating anomalies defined as unusual and irregular behaviors are important for public security in surveillance videos. In this paper, we propose a novel feature called Point Trajectory-based Histogram of Optical Flow (PT-HOF) to better capture the fine-grained spatial and temporal information along the point trajectory in crowd scenes. By encoding the extracted features through an unsupervised autoencoder network, the high-level representation features are used to build a Gaussian Mixture Model for estimating the anomaly likelihood of each trajectory. Furthermore, the consistency motion object (CMO) is constructed by clustering similar point trajectories in a local region to analyze the spatial structure of trajectories, which can improve the accuracy of anomaly localization. Experiments on two benchmark datasets demonstrate the advantage of the proposed algorithm by comparing with state-of-the-art methods.
Digital Poison " Approaching a Theory of Toxic Behavior in MOBA games

Negative behavior through computer-mediated-communication has become a major concern for players and developers of video games. Within the particularly successful game genre Multiplayer Online Battle Arena (MOBA), toxic behavior (TB) is a remarkably salient phenomenon that is gaining attention. TB is a behavior that generates anger and frustration, and contributes to an increasingly bad mood amongst players. The gaming industry has already tried to address TB with mixed results. However, looking at the status quo of research, a missing theoretical explanation of TB can be detected. With this study, I try to close this gap by approaching a theory for TB. Therefore, I plan to utilize a multilevel analysis using the levels of single MOBAs and aggregate the findings to the level of a game genre. Furthermore, I use the social cognitive theory, the theory of planned behavior, and the online disinhibition effect to propose an integrated theory of TB.
A Compact Meandered Ring Antenna Loaded With Parasitic Patches and a Slotted Ground for Global Navigation Satellite Systems

A compact meandered ring patch antenna loaded with four parasitic-grounded patches and a slotted ground is proposed. It has two orthogonal T-shaped coupling line feeds with different lengths which produce circularly polarized radiation. The ground is developed with an inner square patch with four symmetric slant corner cuts and an outer ring that further reduces the overall size of the proposed antenna. The measured frequency band with <inline-formula> <tex-math notation="LaTeX">$\vert S_{11}\vert < -10$ </tex-math></inline-formula> dB, the axial ratio (AR) band with AR < 3 dB and the gain are of 1561–1612 MHz, 1566–1594 MHz, and 3.13 dBi, respectively. The patch size is <inline-formula> <tex-math notation="LaTeX">$0.169 \times 0.169\,\,\lambda _{\mathrm {g}}^{2}$ </tex-math></inline-formula> and the overall size of the proposed antenna is <inline-formula> <tex-math notation="LaTeX">$0.285\,\,\lambda _{\mathrm {g}}\times 0.285\,\,\lambda _{\mathrm {g}}\times 0.035\,\,\lambda _{\mathrm {g}}$ </tex-math></inline-formula> (<inline-formula> <tex-math notation="LaTeX">$\lambda _{\mathrm {g}}$ </tex-math></inline-formula> denotes the guide wavelength at the center frequency). The proposed antenna is smaller than the existing antennas and can be an ideal compact antenna used for global navigation satellite system applications.
An Adaptive Leverage Sampling Scheme for Fingerprint-based Indoor Localization

The fingerprint-based localization technique has become a focus in indoor localization since it has the merits of strong anti-interference ability, good network stability, high quality data transmission and wide applicability. For fingerprint-based localization, however, offline site survey is usually time-consuming and labor-intensive. Therefore, offline site survey reduction becomes an important issue for fingerprint-based indoor localization. In this paper, we first model the fingerprints of all reference points (RPs) as a low-tubal-rank tensor and then propose a two-pass adaptive sampling scheme where under a given budget of samples, a fixed proportion of samples are firstly allocated uniformly at random, and then the remaining samples are assigned according to the leverage scores of the underlying tensor. Afterward, we apply a tensor completion algorithm such as ADMM, t-TNN and TenALS to solve the weighted nuclear norm minimization problem as above. The advantage is that the scheme has ability to identify the highly informative RPs that are conductive to fingerprint map reconstruction to mitigate the sample complexity for offline site survey. Simulation results show that the proposed adaptive sampling scheme can significantly improve the performance in terms of both reconstruction error and localization accuracy compared with the random uniform sampling method.
Recognition and Analysis of Emotion Types from Myanmar Movies

Recognizing emotion from speech has become the active research themes in speech processing and in applications based on human-computer interaction. The emotion recognition system is also intended to apply in criminal cases; one potential application is the detection of the emotional state in Automatic Teller Machine (ATM) machine to provide feedback to machine. Especially, if it detect fear emotion, it provide feedback to stop all of the money transition. In this domain, the image resolution of facial expressions captured by CCTV camera can be degraded at night due to the inadquate light. To overcome this problem, this paper conducts an experimental study on recognizing emotions from human speech. The emotions considered for the experiments include angry, disgust, happy, fear, sad and surprise. Feature selection method, which aims to extract effective acoustics features to improve the performance of emotion recognition is focused in this paper. The combination of MFCC (Mels Frequency Cepstral Coefficients) with Berouti Spectral Subtraction is used to obtain compressed feature vectors without losing much information. The standard SAVEES dataset and our self-created Myanmar emotional dataset are used for training and testing. After that, those emotions are identified with Random Forest classifier. Finally, the detail analysis results on enhanced features and only MFCCs features corresponding to different databases and different classifiers are compared and explained.
Mixed-Signal Transmitter Leakage Cancellation for PMCW MIMO Radar

PMCW (Phase-Modulated Continuous Wave) radar is an effective solution for large (Multiple Input Multiple Output) MIMO radars. But, as for every continuous wave radar, the performances of PMCW radars can be strongly degraded by the leakage of the transmitted signal to the receiver. This paper proposes a mixed-signal solution which aims at cancelling the transmitter leakage in the receiver chain, just after the mixer, in order to improve the radar sensitivity. For that purpose, we need, first, to estimate the spillover from each transmitter. Then, a cancellation signal is computed digitally for each receiver and injected, through a Digital-to-Analog Converter (DAC), in baseband. We show by simulation that a significant sensitivity gain is achievable.
Revision of the genus Lichtwardtia Enderlein in Southeast Asia, a tale of highly diverse male terminalia (Diptera, Dolichopodidae)

Abstract In the present paper the Oriental species of the genus Lichtwardtia Enderlein, 1912 are revised based on the type material of known species and new material from Singapore and Cambodia. A re-description and illustration of the holotype female of Lichtwardtiaziczac (Wiedemann, 1824) is given but since it has been described on the basis of a female only and its provenance India Orientalis is only a vague indication of its type locality, it is considered as a nomen dubium. All the species put as junior synonyms by Becker (1922) of L.ziczac are re-established to their original status with diagnosis: Lichtwardtiapolychroma (Loew, 1864) and Lichtwardtiaformosana Enderlein, 1912. However, L.coxalis is now also considered as a nomen dubium since the original description is too short to distinguish it from other species and the holotype female is lost. In addition a re-description and illustrations of L.hirsutiseta (de Meijere, 1916) are provided. Eight new species for science are described and illustrated: Lichtwardtiacambodiensis Tang & Grootaert, sp. n. (Cambodia), Lichtwardtiaconspicabilis Tang & Grootaert, sp. n. (Cambodia), Lichtwardtiainfuscata Tang & Grootaert, sp. n. (Cambodia), Lichtwardtiamonstruosa Tang & Grootaert, sp. n. (Cambodia), Lichtwardtianodulata Grootaert & Tang, sp. n. (Singapore), Lichtwardtiasemakau Grootaert & Tang, sp. n. (Singapore) and Lichtwardtiasingaporensis Grootaert & Tang, sp. n. (Singapore). Lichtwardtiazhangae Tang & Grootaert, sp. n. (Bali, Indonesia) is a new name for the species described by Zhang, Masunaga & Yang, 2009, as Lichtwardtiaziczac (Wiedemann, 1824). There are only a few good diagnostic non-genitalic characters for the species, but the male terminalia are distinctive, from simple to very complicated and armed structures. A key is given to the species of the Oriental region. Barcodes are provided for the Singaporean species. Although Lichtwardtia is a common genus in Southeast Asia it is generally not abundant locally. It is often found in anthropogenic disturbed habitats only. Four species are recorded from Singapore while eight species are sympatric and very abundant at the locality of Siem Reap in Cambodia.
Reconfigurable High-Resolution Microwave Photonic Filter Based on Dual-Ring-Assisted MZIs on the Si3N4 Platform

We report a reconfigurable optical filter chip based on the Si3N4 waveguide platform. Microwave filtering is realized using a device with optical-to-electrical mapping. The filter chip is based on a lattice structure with the dual-ring-coupled Mach–Zehnder interferometers (DR-MZIs) as the basic elements. Both the lattice and ring couplers are tunable, making the chip reconfigurable to various filter functions. A theoretical model is developed to analyze its optical filtering performance. Measurement reveals that the filter passband can reach hundreds of megahertz in the lower limit and several gigahertz in the upper limit. Various orders of infinite impulse response filters are implemented with a tailorable passband. The flexibility in tuning filter center frequency and passband makes it suitable in high-resolution adaptable microwave signal front-end processing.
Bandwidth Enhancement of a Broadband Dual-Polarized Antenna for 2G/3G/4G and IMT Base Stations

The bandwidth (BW) of a broadband dual-polarized antenna is enhanced for 2G/3G/4G systems and international mobile telecommunications services. The broadband dual-polarized element is a four-leaf clover antenna. By introducing a U-shaped slot to each leaf of the four-leaf clover and a parasitic element of four disks above the dual-polarized element, the BW enhancement is achieved. It is shown by simulation and experiment that the BW enhanced dual-polarized antenna achieves a BW of 67% (1.39–2.8 GHz) for reflection coefficients <−15 dB with an isolation (ISO) of 30 dB. An eight-element antenna array is developed for base station applications. The antenna array has a BW of about 70% (1.32–2.74 GHz) with an ISO of 25 dB. The antenna array achieves an average gain of ~17 dBi and a half-power beamwidth of 65 ± 5°, suitable for base station applications.
Discriminative Latent Visual Space For Zero-Shot Object Classification

In this paper We deal with the problem of zero-shot visual recognition. The standard zero-shot learning (ZSL) pipeline is based on the idea of learning a functional mapping from a visual embedding space to an auxiliary semantic space for a set of seen categories. In the testing phase, the task is to recognize a set of novel categories which are semantically linked to the already known ones. Although such a pipeline is inherently supervised, there exists very few endeavours in the context of ZSL that enforce discrimination in learning this mapping. In this work, we propose a novel encoder-decoder network to explore the possibility of learning an intermediate latent space for the visual features, which is deemed to be simultaneously reconstructive and discriminative. By reaching a trade-off between the joint (re)construction of the visual and the semantic embedding spaces, while ensuring separability among the known classes, the proposed model better generalizes to the unknown categories. Experimental results obtained on challenging datasets, such as AwA, CUB, and ImageNet-2, establish the efficacy of such a discriminative latent space for the standard ZSL setup.
FastFCA: Joint Diagonalization Based Acceleration of Audio Source Separation Using a Full-Rank Spatial Covariance Model

Here we propose an accelerated version of one of the most promising methods for audio source separation proposed by Duong et al. [“Under-determined reverberant audio source separation using a full-rank spatial covariance model,” IEEE Trans. ASLP, vol. 18, no. 7, pp. 1830–1840, Sep. 2010]. We refer to this conventional method as full-rank spatial covariance analysis (FCA), and the proposed method as FastFCA. A major drawback of the conventional FCA is computational complexity: inversion and multiplication of covariance matrices are required at each time-frequency point and each EM iteration. To overcome this drawback, the proposed FastFCA diagonalizes the covariance matrices jointly based on the generalized eigenvalue problem. This leads to significantly reduced computational complexity of the FastFCA, because the complexity of matrix inversion and matrix multiplication for diagonal matrices is O(M) instead of $O(M^{3})$ (M: matrix order). Furthermore, the FastFCA is rigorously equivalent to the FCA, and therefore the reduction in computational complexity is realized without degradation in source separation performance. An experiment showed that the FastFCA was over 250 times faster than the FCA with virtually no degradation in source separation performance. In this paper, we focus on the two-source case, while the case of more than two sources is treated in a separate paper.