Miracle-dz/UniFAR-FTUs huggingface dataset:
Hardware-conscious Query Processing in GPU-accelerated Analytical Engines

In the last years, modern servers are adopting hardware accelerators, such as GPUs, in order to improve their power efﬁciency and computational capacity. Modern analytical query processing engines are highly optimized for multi-core multi-CPU query execution, but lack the necessary abstractions to support concurrent hardware-conscious query execution over multiple heterogeneous devices and exploit the available accelerators. This work presents a Heterogeneity-conscious Analytical query Processing Engine (HAPE), a blueprint for hardware-conscious analytical engines for efﬁcient and concurrent multi-CPU multi-GPU query execution. HAPE decomposes query execution on heterogeneous hardware into, 1) efﬁcient single-device and 2) concurrent multi-device query execution. It uses hardware-conscious algorithms designed for single-device execution and combines them into efﬁcient intra-device hardware-conscious execution modules, via code generation. HAPE combines these modules to achieve multi-device execution by handling data and control transfers. We validate our design by building a prototype and evaluating its performance using radix-join co-processing and the TPC-H benchmark. We show that it achieves up to 10x and 3.5x speed-up on the radix-join against CPU and GPU alternatives, respectively, and 1.6x-8x against state-of-the-art CPU-and GPU-based commercial DBMSs on the selected TPC-H queries.
Improving GPU Multitasking Efficiency Using Dynamic Resource Sharing

As GPUs have become essential components for embedded computing systems, a shared GPU with multiple CPU cores needs to efficiently support concurrent execution of multiple different applications. Spatial multitasking, which assigns a different amount of streaming multiprocessors (SMs) to multiple applications, is one of the most common solutions for this. However, this is not a panacea for maximizing total resource utilization. It is because an SM consists of many different sub-resources such as caches, execution units and scheduling units, and the requirements of the sub-resources per kernel are not well matched to their fixed sizes inside an SM. To solve the resource requirement mismatch problem, this paper proposes a GPU Weaver, a dynamic sub-resource management system of multitasking GPUs. GPU Weaver can maximize sub-resource utilization through a shared resource controller (SRC) that is added between neighboring SMs. The SRC dynamically identifies idle sub-resources of an SM and allows them to be used by the neighboring SM when possible. Experiments show that the combination of multiple sub-resource borrowing techniques enhances the total throughput by up to 26 and 9.5 percent on average over the baseline spatial multitasking GPU.
A Structured Deterministic Sampling Strategy for Array Diagnosis from Far-Field Measurements

The aim of this conference paper is to introduced a novel structured deterministic sampling strategy for diagnosis of defective array elements from far-field measurements. Different from the existing one called structured random sampling strategy, the proposed method is designed according to a polynomial-based approach by deterministic selection of sampling locations among angles by Discrete Fourier Transformation. The merits of our strategy is that sampling positions are fixed and easy to construct while the measurement matrix also satisfy Restricted Isometry Property. Several numerical simulations are carried out and the results show that a higher successful rate of diagnosis is achieved by utilizing our method.
A Pattern and Polarization Reconfigurable Liquid Metal Helical Antenna

A pattern and polarization reconfigurable helix antenna with mechanically actuated liquid metal in a helical channel is presented for L1 frequency band (1.575 GHz). The antenna is composed of a flexible polymer tube shaped into a helix which is filled in and out with a peristaltic micropump. The micropump is controlled using a Raspberry Pi computer via an H-bridge DC circuit which can continuously adjust direction and speed of the pump. The Antenna generates four types of beams i.e., linearly polarized semi-doughnut and axial beams; elliptically and circularly polarized axial beams. The corresponding maximum gains are 1.1 dBi, 5.9 dBi, 7.6 dBi and 8.5 dBi for the four beams.
Millimeter-Wave Broadband Multi-Beam End-Fire Dual Circularly Polarized Antenna Array

A millimeter-wave (mm Wave) broadband multibeam end-fire dual circularly polarized (CP) antenna array using dielectric loaded stepped slot elements is proposed. The proposed antenna element exhibits excellent performance, including broad bandwidth, wide beamwidth, and compact element size. The feeding network using substrate integrated waveguide (SIW) is designed for multi-beam array applications. A broadband 4×4 SIW Butler matrix, interconnections, and transitions are designed and applied in the array prototype. The fabricated array prototype achieves the impedance bandwidth of 29.3% and the 3-dB axial ratio bandwidth of 22.5%. The proposed multi-beam end-fire dual-CP antenna array would be an attractive candidate for 5G mm Wave applications.
A retrieval system to analyse dermatological lesions using feature ortho-normalisation

ABSTRACT An information retrieval system is proposed as an assistance tool for diagnosing the skin lesion using Content-Based Image Retrieval approach. Efficiency of the retrieval system is deliberated in terms of the most relevant retrieval of images from database. The proposed diagnostic assistive model retrieves the skin lesion images and its disease category, case history, symptoms and treatment plan. This retrieval process is made from a dermatology database by the way of visual features in the input image such as shape, texture and colour. The author’s proposed principal component analysis (PCA) feature projection technique is to discriminate the features by projecting them onto a feature subspace. While projecting the features onto a feature subspace features are normalised orthogonally. So the proposed methodology is used to improve the classification by the way of discriminate the features, in-turn it focus the retrieval of comprehensive reference sources, so that the diagnosis accuracy of the dermatologists are also improved. Receiver-operating characteristic curve is used to analyse the proposed computer-aided diagnosis (CAD) method, while analysis we attained high contribution to detect the skin lesions. Totally 1450 images are experimented and the system produced the 99.09% specificity, 96.69% sensitivity and 98.3% accuracy. When compared with other works this system of assessment shows high retrieval and diagnosis concert.
Performance Analysis of Data Mining Techniques in IoT

Internet of Things (IoT) accumulates bulk of data from heterogeneous devices implanted with sensors. This data is accumulated over a period of time from sensory devices and is maintained on a server. To take optimal decisions in real time, meaningful information need to be extracted out from the data accumulated. Numerous data mining (DM) techniques are available for analyzing the data and then to make future predictions based on the discovered information. The number of devices connections in IoT is expected to reach 25 to 30 billion by 2020 and so as the new applications of IoT are going to emerge. Therefore, an efficient and fast DM technique is required to make predictions and take decisions in real time to maintain the goodwill of IoT in society. This paper first discusses three different DM techniques: classification, clustering, and association based mining and their possible combinations. Later, this work highlights the applications of using a particular DM technique in IoT. Finally, a comparative analysis is made among each DM technique on the basis of its precision, accuracy and recall value. This will lead to identity the best DM technique that can be applied in IoT.
Understanding Land Cover Change in a Fragmented Forest Landscape in a Biodiversity Hotspot of Coastal Ecuador

Understanding forest cover changes is especially important in highly threatened and understudied tropical dry forest landscapes. This research uses Landsat images and a Random Forest classifier (RF) to map old-growth, secondary, and plantation forests and to evaluate changes in their coverage in Ecuador. We used 46 Landsat-derived predictors from the dry and wet seasons to map these forest types and to evaluate the importance of having seasonal variables in classifications. Initial RF models grouped old-growth and secondary forest as a single class because of a lack of secondary forest training data. The model accuracy was improved slightly from 92.8% for the wet season and 94.6% for the dry season to 95% overall by including variables from both seasons. Derived land cover maps indicate that the remaining forest in the landscape occurs mostly along the coastline in a matrix of pastureland, with less than 10% of the landscape covered by plantation forests. To obtain secondary forest training data and evaluate changes in forest cover, we conducted a change analysis between the 1990 and 2015 images. The results indicated that half of the forests present in 1990 were cleared during the 25-year study period and highlighted areas of forest regrowth. We used these areas to extract secondary forest training data and then re-classified the landscape with secondary forest as a class. Classification accuracies decreased with more forest classes, but having data from both seasons resulted in higher accuracy (87.9%) compared to having data from only the wet (85.8%) or dry (82.9%) seasons. The produced cover maps classified the majority of previously identified forest areas as secondary, but these areas likely correspond to forest regrowth and to degraded forests that structurally resemble secondary forests. Among the few areas classified as old-growth forests are known reserves. This research provides evidence of the importance of using bi-seasonal Landsat data to classify forest types and contributes to understanding changes in forest cover of tropical dry forests.
A New Class of Reconfigurable Origami Antennas Based on E-Textile Embroidery

We introduce a new class of Origami-based tessellated antennas whose RF characteristics can be self-tuned by reconfiguration of the antenna shape. To enable mechanical flexibility and robustness, the antennas are fabricated using automated embroidery of silver-coated polymer E-threads. As a proof-of-concept, an E-textile accordion-dipole is fabricated, and shown to vary its resonance frequency in the 627 MHz to 991 MHz range. The proposed tessellated, E-textile antennas can be easily integrated into clothing and mechanical structures, providing a non-invasive way of quantifying deformation for a wide range of applications.
Unidirectional Circularly Polarized Slot Antenna Backed with Miniaturized AMC Reflector

This paper investigates a low profile circularly polarized (CP) slot antenna with unidirectional radiation. The antenna includes a coplanar waveguide (CPW)-fed square slot radiator loaded by two L-shaped strips around two diagonal corners, underneath which is an artificial magnetic conductor (AMC) reflector with an air gap in between. The AMC reflector is polarization independent and miniaturized by using lumped capacitors to accommodate sufficient unit cells within restricted electrical dimension. The antenna was analyzed numerically, and the simulated 10-dB impedance bandwidth and 3-dB axial ratio (AR) bandwidth are 16.0% and 3.0%, respectively, relative to the center frequency of 2.68 GHz. The overall size of the antenna is 0. 54λ0 δ 0. 54λ 0×0. 08λ0 with a maximum gain of 6.7 dBic.
Model-Based Optimization of Spectral Sampling for the Retrieval of Crop Variables with the PROSAIL Model

Satellite hyperspectral Earth observation missions have strong potential to support sustainable agriculture by providing accurate spatial and temporal information of important vegetation biophysical and biochemical variables. To meet this goal, possible error sources in the modelling approaches should be minimized. Thus, first of all, the capability of a model to reproduce the measured spectral signals has to be tested before applying any retrieval algorithm. For an exemplary demonstration, the coupled PROSPECT-D and SAIL radiative transfer models (PROSAIL) were employed to emulate the setup of future hyperspectral sensors in the visible and near-infrared (VNIR) spectral regions with a 6.5 nm spectral sampling distance. Model uncertainties were determined to subsequently exclude those wavelengths with the highest mean absolute error (MAE) between model simulation and spectral measurement. The largest mismatch could be found in the green visible and red edge regions, which can be explained by complex interactions of several biochemical and structural variables in these spectral domains. For leaf area index (LAI, m2·m−2) retrieval, results indicated only a small improvement when using optimized spectral samplings. However, a significant increase in accuracy for leaf chlorophyll content (LCC, µg·cm−2) estimations could be obtained, with the relative root mean square error (RMSE) decreasing from 26% (full VNIR range) to 15% (optimized VNIR) for maize and from 77% to 29% for soybean, respectively. We therefore recommend applying a specific model-error threshold (MAE of ~0.01) to stabilize the retrieval of crop biochemical variables.
Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification

In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.
Exploiting Inverse Scattering Methodologies to Design Artificial Materials

The usual aim of an inverse scattering problem is to retrieve the location, the shape and the electromagnetic parameters of the scatterer by processing measurements of the scattered field. Interestingly, the inverse scattering problem can be turned into a synthesis problem, provided the starting data is no longer a collection of measurements but rather some field specifications arising from design constraints. In this contribution, some methodologies already available in the inverse scattering community for diagnosis applications are extended and modified for design purposes. In this respect, the synthesis of artificial materials is dealt.
Imputation of Missing Diagnosis of Diabetes in an Administrative EMR System

Administrative electronic medical records (EMRs) contain rich patient data and are an important data source for health informatics studies. Prevalent in such EMRs, poor/missing diagnosis coding is intractable while can be mitigated by imputation techniques. In this work, based on an administrative EMR database in Singapore, we adopted popular machine learning methods to model the relations between diseases and healthcare utilization features, and used the model to impute missing diagnosis of diabetes. Further, this was partially validated with supplementary clinical data. The structured method in this work can be easily extended to other diseases and would benefit other works in health services and research.
Automatic Mapping of Thermokarst Landforms from Remote Sensing Images Using Deep Learning: A Case Study in the Northeastern Tibetan Plateau

Thawing of ice-rich permafrost causes thermokarst landforms on the ground surface. Obtaining the distribution of thermokarst landforms is a prerequisite for understanding permafrost degradation and carbon exchange at local and regional scales. However, because of their diverse types and characteristics, it is challenging to map thermokarst landforms from remote sensing images. We conducted a case study towards automatically mapping a type of thermokarst landforms (i.e., thermo-erosion gullies) in a local area in the northeastern Tibetan Plateau from high-resolution images by the use of deep learning. In particular, we applied the DeepLab algorithm (based on Convolutional Neural Networks) to a 0.15-m-resolution Digital Orthophoto Map (created using aerial photographs taken by an Unmanned Aerial Vehicle). Here, we document the detailed processing flow with key steps including preparing training data, fine-tuning, inference, and post-processing. Validating against the field measurements and manual digitizing results, we obtained an F1 score of 0.74 (precision is 0.59 and recall is 1.0), showing that the proposed method can effectively map small and irregular thermokarst landforms. It is potentially viable to apply the designed method to mapping diverse thermokarst landforms in a larger area where high-resolution images and training data are available.
Force-Temporal Characteristics of EEG-EMG Coherence During Isometric Contraction of the Tibialis Anterior Muscle

The coherence between electroencephalogram (EEG) and electromyogram (EMG) of the tibialis anterior (TA) muscle during isometric contraction is observed in the beta band (15 to 35 Hz). When the contraction force becomes stronger, the peak coherence frequency shifts to the gamma band (35 to 60 Hz). In contrast, based on the maintenance of contraction with the weak force, the peak coherence value increases. However, the manner in which coherence changes according to the maintenance of contraction with strong force remains unknown. In this study, five healthy male university students were directed to execute isometric contraction by dorsiflexion of their right ankle joint for 1 min with forces of 10 to 60% of the maximum voluntary contraction. During dorsiflexion, EEG and EMG of the TA muscle were performed. Then, the changes in coherence between EEG and EMG were examined, corresponding to the changes in contraction force and time course. In the majority of subjects, the peak coherence during the weak contraction force was observed in the beta band and lasted for 12 to 60 s. However, the peak coherence during the strong contraction force was observed in the gamma band only for the first 12 s. Subsequently, no significant coherence was observed. Therefore, muscle fatigue due to strong contraction forces and/or maintenance of the weak contraction force may have been observed as a different property of the peak coherence.
A Correction for Free-space Method by Considering Dispersion of Gaussian Beam

Frequency dependence of beam waist radius is discussed based on the dispersion property of Gaussian beam, and cutoff frequency of Gaussian beam is deduced by an analogy with the metallic waveguide. For the free-space test system that using focusing antennas with a Gaussian beam illumination, the effect of this dispersion property on material electromagnetic parameters test is analyzed. Correction factor that introducing beam waist radius is proposed to improve the test precision of free-space method.
A Reliable and Practical Approach to Kernel Attack Surface Reduction of Commodity OS

Commodity OS kernels are known to have broad attack surfaces due to the large code base and the numerous features such as device drivers. For a real-world use case (e.g., an Apache Server), many kernel services are unused and only a small amount of kernel code is used. Within the used code, a certain part is invoked only at the runtime phase while the rest are executed at startup and/or shutdown phases in the kernel's lifetime run. In this paper, we propose a reliable and practical system, named KASR, which transparently reduces attack surfaces of commodity OS kernels at the runtime phase without requiring their source code. The KASR system, residing in a trusted hypervisor, achieves the attack surface reduction through two reliable steps: (1) deprives unused code of executable permissions, and (2) transparently segments used code and selectively activates them according to their phases. KASR also prevents unknown code from executing in the kernel space, and thus it is able to defend against all kernel code injection attacks. We implement a prototype of KASR on the Xen-4.8. 2 hypervisor and evaluate its security effectiveness on Linux kernel-4.4. 0-87-generic. Our evaluation shows that KASR reduces kernel attack surface by 64%, trims off 40% of in-memory CVE vulnerabilities and 66% of system calls, and prohibits 99% of on-disk device drivers (including their related CVEs) from executing. Besides, KASR successfully detects and blocks all 6 real-world kernel rootkits. We measure its performance overhead with three benchmark tools (i.e., SPECINT, httperf and bonnie++). The experimental results indicate that KASR imposes less than 1% averaged performance overhead compared to an unmodified Xen hypervisor.
The Use of Unmanned Aerial Systems to Map Intertidal Sediment

This paper describes a new methodology to map intertidal sediment using a commercially available unmanned aerial system (UAS). A fixed-wing UAS was flown with both thermal and multispectral cameras over three study sites comprising of sandy and muddy areas. Thermal signatures of sediment type were not observable in the recorded data and therefore only the multispectral results were used in the sediment classification. The multispectral camera consisted of a Red–Green–Blue (RGB) camera and four multispectral sensors covering the green, red, red edge and near-infrared bands. Statistically significant correlations (>99%) were noted between the multispectral reflectance and both moisture content and median grain size. The best correlation against median grain size was found with the near-infrared band. Three classification methodologies were tested to split the intertidal area into sand and mud: k-means clustering, artificial neural networks, and the random forest approach. Classification methodologies were tested with nine input subsets of the available data channels, including transforming the RGB colorspace to the Hue–Saturation–Value (HSV) colorspace. The classification approach that gave the best performance, based on the j-index, was when an artificial neural network was utilized with near-infrared reflectance and HSV color as input data. Classification performance ranged from good to excellent, with values of Youden’s j-index ranging from 0.6 to 0.97 depending on flight date and site.