Miracle-dz/UniFAR-FTUs huggingface dataset:
Extension of Conventional Co-Training Learning Strategies to Three-View and Committee-Based Learning Strategies for Effective Automatic Sentence Segmentation

The objective of this work is to develop effective multiview semi-supervised machine learning strategies for sentence boundary classification problem when only small sets of sentence boundary labeled data are available. We propose three-view and committee-based learning strategies incorporating with co-training algorithms with agreement, disagreement, and self-combined learning strategies using prosodic, lexical and morphological information. We compare experimental results of proposed three-view and committee-based learning strategies to other semi-supervised learning strategies in the literature namely, self-training and co-training with agreement, disagreement, and self-combined strategies. The experiment results show that sentence segmentation performance can be highly improved using multi-view learning strategies that we propose since data sets can be represented by three redundantly sufficient and disjoint feature sets. We show that the proposed strategies substantially improve the average performance when only a small set of manually labeled data is available for Turkish and English spoken languages, respectively.
Distribution of CO2 in Western Pacific, Studied Using Isotope Data Made in Taiwan, OCO‐2 Satellite Retrievals, and CarbonTracker Products

To assess sources and processes that affect the variability of CO2 at local to regional scales, we have analyzed the mixing ratio [CO2] and stable isotopic compositions (δ13C and δ18O) of atmospheric CO2 for three years (2014–2016) in urban and sub‐urban areas in Taipei, Taiwan. The data are compared with those from some background sites, viz., Lulin, Mauna Loa, and Minamitorishima, to evaluate how local emissions affect CO2 level regionally. [CO2] over the urban and sub‐urban stations are significantly higher than that observed at the three aforementioned remote sites mainly due to local emissions, which partly mask the seasonal cycle caused by photosynthesis and respiration. Likewise, significantly low δ13C and δ18O values observed at two Taipei stations also point to anthropogenic emissions. The seasonal cycles in [CO2] and in the isotopic compositions are retrieved using the ensemble empirical mode decomposition method. Regional impact is assessed using CO2 products from the Orbiting Carbon Observatory‐2 satellite, the NOAA/EARL CarbonTracker project, and meteorological data from European Centre for Medium range Weather Forecast‐Interim. We found that besides local emissions, Taiwan is largely affected by external CO2 in winter and spring originated from north, west and southwest landmasses. In winter air masses with elevated CO2 concentrations, originated in eastern China influence Taipei. In spring season, about 2 ppmv enhancement in CO2 observed at the top of Lulin, a high mountain station (2.8 km), could be linked to CO2 produced by biomass burning in the southeast Asian countries and transported to the region by easterly winds.
Cascade-Dispatched Classifier Ensemble and Regressor for Pedestrian Detection

This paper focuses on ensemble classifiers for pedestrian detection. Ensemble learning is widely used in this field for context disambiguation or via a cascade-of-rejectors. However, applying the typical, parallel, instance of it remains disappointing in most cases. Our work studies the mechanisms that hinder the efficiency of ensemble classifiers for pedestrian detection, and, based on our findings, we introduce a structured classifier ensemble that improves performance without loss of speed. We also harness this principle for context disambiguation via the application of a regressor to pedestrian detection. Experiments on the INRIA and Caltech-USA datasets validate the approach.
Joint SAR Imaging and Multi-Feature Decomposition From 2-D Under-Sampled Data Via Low-Rankness Plus Sparsity Priors

In this paper, we introduce a multi-feature decomposition approach to the problem of synthetic aperture radar (SAR) image reconstruction from under-sampled data in both range and azimuth directions. Conventional SAR image formation methods may produce images that are not appropriate for interpretation tasks such as segmentation and automatic target recognition. We deal with this problem using an efficient joint SAR image reconstruction–decomposition framework in which features of interest are enhanced and decomposed simultaneously. Unlike conventional methods, our proposed framework provides multiple segment images along with a composite SAR image. In the composite image not only the resolution is improved but also both the speckle and sidelobe artifacts are reduced. In the decomposed images, different components can be roughly attributed to different potential segments, which facilitate the subsequent interpretation tasks such as shape-based recognition or region segmentation. Moreover, these decomposed images contain easier-to-segment regions rather than taking the entire scene for segmenting the feature of interest. By formulating the SAR image reconstruction as a low-rank plus multi-feature decomposition problem, the optimization problem is solved based on the alternating direction method of multipliers. Using combined dictionaries, multiple transform-sparse components are represented efficiently by a linear combination of multiple sparsifying matrices associated with the features of interest in the scene. Our proposed method jointly reconstructs and decomposes different pieces of the imaged SAR scene, in particular the low-rank part of the background and sparsely represented features of interest, from under-sampled observed data. Using extensive experimental results we show the effectiveness of the proposed method on both synthetic and real SAR images.
Exploring End-To-End Attention-Based Neural Networks For Native Language Identification

Automatic identification of speakers’ native language (L1) based on their speech in a second language (L2) is a challenging research problem that can aid several spoken language technologies such as automatic speech recognition (ASR), speaker recognition, and voice biometrics in interactive voice applications. End-to-end learning, in which the features and the classification model are learned jointly in a single system, is an emerging field in the areas of speech recognition, speaker verification and spoken language understanding. In this paper, we present our study on attention-based end-to-end modeling for native language identification on a database of 11 different L1s. Using this methodology, we can determine the native language of the speaker directly from the raw acoustic features. Experimental results from our study show that our best end-to-end model can achieve promising results by capturing speech commonalities across L1s using an attention mechanism. In addition, fusion of proposed systems with the baseline system leads to significant performance improvements.
A Comparative Study of Urban Mobility Patterns Using Large-Scale Spatio-Temporal Data

The large scale spatio-temporal data brought about by the ubiquitous wireless networks, mobile phones, and GPS devices present a fertile ground for studying human mobility. These data sources come with high coverage and resolution that enable studies of mobility patterns for human populations at large that other conventional methods such as surveys are not capable of. In this paper, we study anonymized spatio-temporal data from telco networks to understand the variability in human mobility behavior across different geographical regions. We present methodologies for extracting trips and other mobility features from large-scale spatio-temporal data. We also look into daily activity patterns of the populations in two specific cities, Singapore and Sydney. Our results include measures of distance and frequency of people's travel, as well as their purpose of travel, mode of transport, and route choice. We extract mobility patterns known as motifs. We also define a mobility index to assess the mobility level of individuals and compare it among different regions and demographic groups. This work contributes to a more comprehensive understanding of urban dynamics, supporting smart city development and sustainable urbanization.
Robust Computational Method for Identification of miRNA-mRNA Modules in Cervical Cancer

Cervical cancer is a leading severe malignancy throughout the world. Molecular processes and biomarkers leading to tumor progression in cervical cancer are either unknown or only partially understood. An increasing number of studies have shown that microRNAs play an important role in tumorigenesis so understanding the regulatory mechanism of miRNAs in gene-regulatory network will help elucidate the complex biological processes that occur during malignancy. Identification of microRNA-messengerRNA (miRNA-mRNA) regulatory modules will aid deciphering aberrant transcriptional regulatory network in cervical cancer but is computationally challenging. In this regard, an algorithm, termed as relevant and functionally consistent miRNA-mRNA modules (RFCM3), is proposed. It integrates miRNA and mRNA expression data of cervical cancer for identification of potential miRNA-mRNA modules. It selects a miRNA-mRNA module by maximizing relation of mRNAs with miRNA and functional similarity between selected mRNAs. Later using the knowledge of miRNA-miRNA synergistic network different modules are fused and finally a set of modules are generated containing several miRNAs as well as mRNAs. This type of module explains the underlying biological pathways containing multiple miRNAs and mRNAs. The effectiveness of the proposed approach over other existing methods has been demonstrated on a miRNA and mRNA expression data of cervical cancer with respect to enrichment analyses and other standard metrices. The proposed approach was found to generate more robust, integrated, and functionally enriched miRNA-mRNA modules in cervical cancer.
Eight-Element Dual-Polarized MIMO Slot Antenna System for 5G Smartphone Applications

In this paper, we propose an eight-port/four-resonator slot antenna array with a dual-polarized function for multiple-input-multiple-output (MIMO) 5G mobile terminals. The design is composed of four dual-polarized square-ring slot radiators fed by pairs of microstrip-line structures. The radiation elements are designed to operate at 3.6 GHz and are located on the corners of the smartphone PCB. The square-ring slot radiators provide good dual-polarization characteristic with similar performances in terms of fundamental radiation characteristics. In order to improve the isolation and also reduce the mutual coupling characteristic between the adjunct microstrip-line feeding ports of the dual-polarized radiators, a pair of circular-ring/open-ended parasitic structures is embedded across each square-ring slot radiator. The −10-dB impedance bandwidth of each antenna-element is 3.4–3.8 GHz. However, for −6-dB impedance bandwidth, this value is 600 MHz (3.3–3.9 GHz). The proposed MIMO antenna offers good S-parameters, high-gain radiation patterns, and sufficient total efficiencies, even though it is arranged on a high-loss FR-4 dielectric. The SAR function and the radiation characteristics of the proposed design in the vicinity of user-hand/user-head are studied. A prototype of the proposed smartphone antenna is fabricated, and good measurements are provided. The antenna provides good features with a potential application for use in the 5G mobile terminals.
Joint DOA and Clutter Covariance Matrix Estimation in Compressive Sensing MIMO Radar

We apply the technique of compressive sensing (CS) to multiple-input multiple-output (MIMO) radars to estimate the direction of arrival (DOA) of potential targets embedded in cluttered environments using far fewer samples than the Nyquist rate. Specifically, incorporating clutter into the sparse Bayesian learning (SBL) methodology, we devise a novel algorithm for joint estimation of targets’ DOAs, clutter covariance matrix, and noise variance. Furthermore, we develop a low-complexity and fast version of the proposed algorithm, which can be efficiently employed in practice.
Learning-based Adaptive Data Placement for Low Latency in Data Center Networks

Low-latency data access is an important challenge for data center networks. Proper placement of the data items can reduce the data travel time in the distributed storage systems, which contributes significantly to the latency reduction. Most existing data placement approaches have often assumed the prior distribution of data requests or discovered so through trace analysis. However, the traditional static model-based solutions are less effective to handle the system uncertainties in a dynamic environment. We present DataBot, a reinforcement learningbased adaptive framework, to learn the optimal data placement policies faced with the dynamic network conditions and timevarying request patterns. DataBot utilizes a neural network, trained with a variant of Q-learning, whose input is the realtime data flow measurements and whose output is a value function estimating the near-future latency. For rapid decision making, DataBot is divided into two decoupled production and training components, ensuring that the convergence time of the training will not introduce more overheads to serve the read/write requests. Evaluation results demonstrate that the average write and read latency of the whole system can be lowered by about 35% and 40%, respectively.
Actin Depolymerization in Dedifferentiated Liver Sinusoidal Endothelial Cells Promotes Fenestrae Re‐Formation

Liver sinusoidal endothelial cells (LSECs) possess fenestrae, which are key for the exchange between blood and hepatocytes. Alterations in their number or diameter have important implications for hepatic function in liver diseases. They are lost early in the development of hepatic fibrosis through a process called capillarization. In this study, we aimed to demonstrate whether in vitro dedifferentiated LSECs that have lost fenestrae are able to re‐form these structures. Using stimulated emission depletion super‐resolution microscopy in combination with transmission electron microscopy, we analyzed fenestrae formation in a model mimicking the capillarization process in vitro. Actin is known to be involved in fenestrae regulation in differentiated LSECs. Using cytochalasin D, an actin‐depolymerizing agent, we demonstrated that dedifferentiated LSECs remain capable of forming fenestrae. Conclusion: We provide a new insight into the complex role of actin in fenestrae formation and in the control of their size and show that LSEC fenestrae re‐formation is possible, suggesting that this process could be used during fibrosis regression to try to restore exchanges and hepatocyte functions.
Stereo visual odometry by combining points and lines

Combining Points and Lines Gomez-Ojeda, Ruben, and Gonzalez-Jimenez, Javier. University of Malaga {rubengooj}@gmail.com Abstract Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.
Phase Noise in FMCW Radar Systems

Phase noise is one of the fundamental performance parameters in modern radar, communication, spectroscopic, and metrological systems. In this paper, a phase noise theory has been developed for FMCW radar systems. A new design equation has been derived to specify the maximum bound on the allowable source phase noise level in radar systems. The nonlinear phase noise decorrelation function due to coherent mixing has been analyzed for propagation delays less than the coherence time of the reference oscillator, and the spectral broadening of target responses has been discussed for delay times greater than the coherence time. The effects of the subsystems in the transceiver chain are presented and a new model of phase noise in ADCs is discussed. Phase noise modeling techniques are presented, followed by a comparison of a PLL frequency synthesizer with a low-noise frequency synthesizer to demonstrate the reduction of phase noise sidebands for improved detection and tracking performance. Practical measurements from two millimeter wave FMCW radar systems utilizing the two frequency synthesizers have been presented to validate the developed theory.
Cost-effective microfluidic device for detection of psychoactive substances

This paper presents design and fabrication of cost-effective microfluidic chip using xurographic technique. Seven layers of PVC foils and gold electrodes as a plane of microfluidic device were laminated in order to obtain a compact chip. Syringe pump was used for introducing fluid in one inlet and when fluid went through the microchannel the electrical parameters of capacitive structure has been changed which was measured by means of Impedance analyser. Using this method, it is possible to detect concentration of psychoactive substances in low volume in aqueous or biological media (such as saliva), very efficiently.
A Privacy-Preserving Edge Computation-Based Face Verification System for User Authentication

The face recognition has become a common means of identity authentication because of the advantages of uniqueness, non-invasive and not easy to be stolen. The outsourcing of face recognition to the service provider is a typical manner nowadays. However, it raises vital concerns about the privacy of outsourcing server due to the sensitivity of face data. Therefore, a frame of identity authentication based on the technology of privacy-preserving face recognition is presented in this paper. The convolutional neural network is used for face feature extraction. To overcome the issue of privacy leaked, a method of secure nearest neighbor that can compute the cosine similarity over encrypted feature vectors is proposed. What’s more, the edge computing is introduced in our frame to increase the authentication efficiency by removing some operations from the cloud to the edge of the Internet. Moreover, we also propose a secret sharing homomorphism technology which is used for distributed computing to improve the fault-tolerance of our identity authentication system. The experimental results show that the proposed schemes are secure and effective.
Regular Expression Matching with Memristor TCAMs

Regular expression (RegEx)matching is a key function in network security, where matching of packet data against known malicious signatures filters and alerts against active network intrusions. RegExs are widely used in open source and commercial network security systems as they easily and concisely represent complex patterns like those malicious signatures. However, the latency and power required to perform RegEx matching is incredibly high and approaches to this problem struggle to achieve > 1 Gbps on real-world rulesets while internet wirespeeds continue to increase > 100 Gbps. We propose performing RegEx matching using memristor-based ternary content addressable memories (mTCAMs)with compressed finite automata (CFA)to meet this challenge. In this work, we show fabrication of mTCAM circuits with excellent device properties from 100nm to 20nm device sizes and validate mTCAM operation. SPICE simulations investigate mTCAM performance at scale and a mTCAM dynamic power model using 16nm mTCAM layout parameters demonstrates 0.173 fJ/bit/search energy for a $36\times 250$ mTCAM array. Using a tiled architecture to implement a Snort ruleset, we estimate performance of our mTCAM approach to be 47.2 Gbps at 1.21W dynamic search power (39 Gbps/W), compared to a state-of-the-art FPGA approach which achieves 3.9 Gbps at 630mW (6.2 Gbps/W). Preliminary error analysis shows the mTCAM approach allows for arbitrarily low false positive/negative rates using minimal and standard state refresh techniques. Dynamic search power is also calculated prior to applying standard TCAM power-reduction techniques capable of lowering power by $\sim\times 10$, further demonstrating the promise of mTCAM for wirespeed RegEx matching at low power.
Analysis of Dielectric Properties and Breakdown Characteristics of Vegetable Insulating Oil with Modified by ZnO Nanoparticles

In view of the higher requirements of electrical, physical and chemical properties of vegetable insulating oil in large power transformers, the method of improving insulation characteristics of vegetable insulating oil by using nanoparticles has become a research hotspot at home and abroad. In this paper, the preparation, surface modification of ZnO nanoparticles and the preparation process of the vegetable insulating oil based ZnO nanofluids (NF) were studied. The effects of different ZnO nanoparticles concentration on dielectric properties and breakdown characteristics of vegetable insulating oil were analyzed. The results shown that the power frequency breakdown voltage of ZnO NF similar to linear increased with the increasing of nanoparticles concentration, the maximum increase was 30.17%. The positive and negative polarity breakdown voltage of lightning impulse shown a trend of first rise and then decrease, and the positive and negative breakdown voltage increased by 24.62% and 5.46%, respectively. Both the positive and negative lightning impulse breakdown time of ZnO NF increasing with the nanoparticles concentration, the max value were 18.94%, 35.76%, respectively. However, ZnO nanoparticles would increase the dielectric loss of vegetable insulating oil and reduce its volume resistivity.
Self-optimizing Scanning Parameters for Seamless Handover in IEEE 802.11 WLAN

Within IEEE 802.11 Wireless Local Area Networks (WLANs), a handover between wireless Access Points (APs) is inevitable for the mobile stations to maintain network connectivity as they move. Handover occurs frequently due to the limited coverage range of APs and the mobility of stations (STAs). High handover latency causes interruption to data flow and degrades the quality of service (QoS) experienced by the mobile stations. Therefore, minimizing handover latency is critical for maintaining seamless communications and improved QoS. In this paper, we build upon recent advances in mobile networks whereby Self-Organizing Network (SON) has been shown to work well in handling mobility. Inspired by SON, we propose an algorithm that combines: (i) scanning timers optimized with Genetic Algorithm (GA) and (ii) self-adapting timers to reduce the handover latency for 802.11 WLANs. We use the IEEE 802.11k standard to retrieve radio parameters from measurement reports and feed these parameters to a GA to find the best fit values to reduce scanning delay. We compare the proposed algorithm performance with IEEE 802.11 standard and our previous work neighbor list mechanism (NLM). Evaluation results demonstrate that our proposed algorithm reduce the handover latency by 78.79% compared with standard 802.11, the packet loss is 3.37% lower than standard 802.11 while the throughput improves up to 3.93%.