Miracle-dz/UniFAR-FTUs huggingface dataset:
Data Fusion and Speed Tagging Alignment Based on Millimeter Wave Radar Detection and Camera Images

A data fusion architecture is proposed for the fusion and speed tagging of the moving object in both camera images and two-dimensional radar detection results. After calibration between the camera and radar coordinate parameters, a Faster-Region Convolutional Neural Network (Faster-RCNN) is used to determine the tagging point of the moving object, while a simplified Recurrent Neural Network (RNN) is used to extract the target trajectory. Experiment results show that the proposed method is able to tag the range speed of moving targets in camera images in a fast and accurate manner.
Structured Allocation-Based Consistent Hashing With Improved Balancing for Cloud Infrastructure

Consistent hashing has played an indispensable role in cloud infrastructure, although its load balancing performance is not necessarily perfect. Consistent hashing has long remained the most widely used method despite many methods being proposed to improve load balancing because these methods trade off load balancing against consistency, memory usage, lookup performance, and/or fault-tolerance. This article presents Structured Allocation-based Consistent Hashing (SACH), a cloud-optimized consistent hashing algorithm that overcomes the trade-offs by taking advantage of the characteristics of cloud environments: scaling management and auto-healing. Since scaling can be distinguished from failures, SACH applies two different algorithms to update hashing functions: a fast-update algorithm for unmanaged backend failures to satisfy fault-tolerance with quick response and a slow-update algorithm for managed scaling. Hashing functions are initialized or slow-updated considering the characteristics of the fast-update algorithm to satisfy load balancing and the other properties as far as the number of failed backends is kept small by auto-healing. The experimental results show that SACH outperforms existing algorithms in each aspect. SACH will improve the load balancing of cloud infrastructure components, where the trade-offs have prevented the renewal of hashing functions.
iAnalysis V1.0: An Interactive Analysis Service System

With the development of scientific big data technology, problem-oriented analysis becomes normal case. iAnalysis V1.0, an interactive analysis cloud service system, gives a unified cloud resource management service for scientific data analysis. It can not only be used directly by end-user scientists through the service portal, but also be called by other existing data systems in the form of docker container. In this paper, there are presented the key technical methods of iAnalysis V1.0, including the interactive analysis service solution based on container technology, the interactive analysis components and storage & high-performance computing services for interactive analysis. There are also introduced the specific application services of iAnalysis V1.0 such as lightweight deployment service, management and monitoring services, external application programming interface services and so on. Some applications of iAnalysis V1.0 are also presented. The basic deployment images of iAnalysis V1.0 can be download from https://hub.docker. com/repository/docker/bioinf/ianalysis.
Study of Power Devices for Use in Phase-Leg at Cryogenic Temperature

As air traffic has been increasing in recent years, the environmental impact of aviation is more obvious, forcing governments to impose stringent regulations on emissions. In order to meet these regulations, and reduce the carbon footprint, research has been directed towards the all-electric and hybrid aircraft, where the use of cryogenic HTS machines and cables has been proposed to reduce the overall size of the aircraft. With the cryogenic system already in place, research has been exploring the use of power electronics at lower temperatures in order to obtain systems with higher power densities and lower losses. In this paper several power semiconductor devices are tested at room and cryogenic temperature in order to evaluate their performance at lower temperature. One of the tested devices, a “CoolMOS” superjunction MOSFET, is used in a single voltage source phase-leg which is experimented with at room and cryogenic temperature to evaluate its efficiency as a primary indication of its usefulness in the All-Electric Aircraft.
From Real Malicious Domains to Possible False Positives in DGA Domain Detection

Various families of malware use domain generation algorithms (DGAs) to generate a large number of pseudo-random domain names to connect to malicious command and control servers (C&Cs). These domain names are used to evade domain based security detection and mitigation controls such as firewall controls. Existing prevalent techniques to detect DGA domains such as reverse engineering malware samples and statistical analysis techniques are time consuming, can be easily circumvented by attackers, and need contextual information which is not easily or feasibly obtained. Due to this, the use of machine learning and deep learning techniques to detect DGA domains has picked up significant interest in the cyber security and analytics communities. The ultimate goal is to detect DGA domains on a per domain basis using the domain name only, with no additional information. As with all techniques, there is the possibility of false positives: valid domains being detected as DGA domains. This paper explores the possible use cases that can result in false positives for DGA domain detection using machine learning and deep learning techniques, and how such use cases, if not uniquely addressed within an automated system or model or technique, can also be used as attack vectors by attackers using DGA domains.
A Wideband Dual-Band Dual-Polarized Antenna for 5G Base Station Applications

A wideband dual-band dual-polarized antenna is presented in this paper for 5G base station applications. Two squared loop patches are utilized for lower band and upper band radiations, and also the size reduction. To excited the square loop patches, a linear and H-shaped slots are used to couple the energy to the top radiating patches. Due to the requirements of dual-band operation and very low reflection coefficients, balanced open-circuited stubs and high impedance line are elaborately introduced to match the dual-band operation. The proposed antenna was designed, fabricated, and measured for verification. The measured results show that the proposed antenna can work at 3.3-3.64 GHz and 4.8-5.02 GHz with the overlapped reflection coefficients lower than -15 dB and isolation higher than 33.3 dB. In addition, unidirectional radiation patterns are achieved at both lower and upper bands for 5G base station applications.
Spatio-Temporal Crime Predictions by Leveraging Artificial Intelligence for Citizens Security in Smart Cities

Smart city infrastructure has a significant impact on improving the quality of humans life. However, a substantial increase in the urban population from the last few years poses challenges related to resource management, safety, and security. To ensure the safety and security in the smart city environment, this paper presents a novel approach by empowering the authorities to better visualize the threats, by identifying and predicting the highly-reported crime zones in the smart city. To this end, it first investigates the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to detect the hot-spots that have a higher risk of crime occurrence. Second, for crime prediction, Seasonal Auto-Regressive Integrated Moving Average (SARIMA) is exploited in each dense crime region to predict the number of crime incidents in the future with spatial and temporal information. The proposed HDBSCAN and SARIMA based crime prediction model is evaluated on ten years of crime data (2008-2017) for New York City (NYC). The accuracy of the model is measured by considering different time scenarios such as the year-wise, (i.e., for each year), and for the total considered duration of ten years using an 80:20 ratio. The 80% of data was used for training and 20% for testing. The proposed approach outperforms with an average Mean Absolute Error (MAE) of 11.47 as compared to the highest scoring DBSCAN based method with MAE 27.03.
A Combined Classical Molecular Dynamics Simulations and AB Initio Calculations Approach to Study A-SI:H/C-SI Interfaces

In the silicon heterojunction solar cells, intrinsic hydrogenated amorphous silicon a-Si:H is used to passivate the crystal silicon c-Si surface to suppress the electrical losses at interfaces and to keep ultralow contact resistivity for the selective transport of one type of carrier only. We use ReaxFF (Reactive Force Field) molecular dynamics to efficiently simulate the thermalisation, quenching, and equilibration processes involving thousands of atoms forming realistic a-Si:H/c-Si interface structures. We generated snapshots of the equilibrated c-Si/a-Si:H interface atom configurations at room temperature. The ab initio characterization has been executed on selected configurations to monitor the electronic properties of the c-Si/a-Si:H interface. The evolution of the intragap states is monitored by analyzing density of states and charge density. This all will allow to design more efficient silicon solar cells belonging to the silicon heterojunction technology.
Cross-Lingual Voice Conversion With Controllable Speaker Individuality Using Variational Autoencoder and Star Generative Adversarial Network

This paper proposes a non-parallel cross-lingual voice conversion (CLVC) model that can mimic voice while continuously controlling speaker individuality on the basis of the variational autoencoder (VAE) and star generative adversarial network (StarGAN). Most studies on CLVC only focused on mimicking a particular speaker voice without being able to arbitrarily modify the speaker individuality. In practice, the ability to generate speaker individuality may be more useful than just mimicking voice. Therefore, the proposed model reliably extracts the speaker embedding from different languages using a VAE. An F0 injection method is also introduced into our model to enhance the F0 modeling in the cross-lingual setting. To avoid the over-smoothing degradation problem of the conventional VAE, the adversarial training scheme of the StarGAN is adopted to improve the training-objective function of the VAE in a CLVC task. Objective and subjective measurements confirm the effectiveness of the proposed model and F0 injection method. Furthermore, speaker-similarity measurement on fictitious voices reveal a strong linear relationship between speaker individuality and interpolated speaker embedding, which indicates that speaker individuality can be controlled with our proposed model.
ClaviNet: Generate Music With Different Musical Styles

Classically, the style of the generated music by deep learning models is usually governed by the training dataset. In this article, we improved this by proposing the continuous style embedding <inline-formula><tex-math notation="LaTeX">${z}_{s}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>z</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="chan-ieq1-3046491.gif"/></alternatives></inline-formula> to the general formulation of variational autoencoder (VAE) to allow users to be able to condition on the style of the generated music. For this purpose, we explored and compared two different methods to integrate <inline-formula><tex-math notation="LaTeX">${z}_{s}$</tex-math><alternatives><mml:math><mml:msub><mml:mi>z</mml:mi><mml:mi>s</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="chan-ieq2-3046491.gif"/></alternatives></inline-formula> into the VAE. In the literature of conditional generative modeling, disentanglement of attributes from the latent space is often associated with better generative performance. In our experiments, we find that this is not the case with our proposed model. Empirically and from a musical theory perspective, we show that our proposed model can generate better music samples than a baseline model that utilizes a discrete style label. The source code and generated samples are available at <ext-link ext-link-type="uri" xlink:href="https://github.com/daQuincy/DeepMusicvStyle">https://github.com/daQuincy/DeepMusicvStyle</ext-link>.
Robust Beam-forming Method Based on Conjugate Virtual Array

In order to improve the performance of antenna array beam-pattern, in this paper, we propose a method of signal vector expansion based on conjugate virtual array. By using conjugate virtual array elements to expand the array, the array aperture is increased without changing the actual number of array elements. Furthermore, the side-lobe level is reduced while the manufacturing cost of the antenna is not increased. The signal steering vector is extended on the basis of the virtual array to make the array beam-forming effect more robust. In this paper, we consider the antennas array with 11 elements which is simulated with or without interference, amplitude and phase errors. The effectiveness of the method is verified by comparing the simulation results of the original array beam-pattern, the virtual array beam-pattern and the beam-pattern after expanding the steering vector.
A Three-Pronged View on Organizational Agility

The ability of organizations to sense and respond to changes—defined as organizational agility—is considered by senior executives among their top information technology (IT) concerns as an important ability for organizations on their quest toward sustained competitive advantage. However, every transformation toward agility also comes at a cost, requiring resource commitment and IT landscape changes. We present examples of successful and unsuccessful attempts at achieving agility while leveraging IT. Our presented cases focus on information systems development agility, customer agility, and entrepreneurial agility. Our findings suggest that agility is neither achieved easily, nor is a guarantor for success. Depending on the context and implementation of organizational agility, however, it can significantly improve process and product performance. We develop a three-pronged view consisting of a functional, temporal, and ambidextrous view to resolve these challenges. We end with three recommendations for practitioners that seek to shape their organization's journey toward agility.
Active Disturbance Rejection Controller Design for Oxygen Excess Ratio of Proton Exchange Membrane Fuel Cell

Efficient oxygen excess ratio (OER) control has a vital impact on prolonging the lifetime and excavating the potential for net power improvement of proton exchange membrane fuel cell (PEMFC). However, unknown uncertainties such as dynamic load scenarios, system nonlinearity, and noise make OER control challenging. In this article, the active disturbance rejection controller (ADRC) is applied to control the ORE at its set value by manipulating the compressor voltage. It regards the uncertainties of nonlinearity and disturbance as the “lumped disturbance”, and then the extended state observer (ESO) is designed to estimate and compensate it in real-time. Next, according to the structure of ADRC, the final control law is derived. Finally, a high-fidelity model is used to verify the controller, and the simulation results demonstrate the effectiveness and superiority of the designed controller compared to the baseline controller.
Millimeter-Wave High-Gain Substrate Integrated Multi-Slot Antenna Array with A Low Cross Polarization Level

In this paper, a Q-band substrate-integrated waveguide (SIW) based multi-slot antenna array with a very low cross-polarization level is developed. The proposed antenna element is composed of a 2×2-slot subarray in the top substrate layer, fed through a transverse coupling slot from the feeding SIW in the bottom substrate layer. Following this, a two-layer antenna array with eight subarrays is fed with a star-shaped eight-way SIW power divider with a centrosymmetric geometry, which contributes to an enhanced low-cross polarization level. Good agreement is achieved between the simulation and the measurement, which shows that the proposed antenna array covers an impedance bandwidth (|S11|<–10 dB) of 4.9% from 44.1 to 46.3 GHz with a peak gain of 19.9 dBi and a co-to-cross polarization ratio more than 45 dB and 44 dB in E-and H-plane, respectively. The measured antenna efficiency at 45GHz is 85.1%.
Hyperspectral Image Denoising Using 3-D Geometrical Kernel With Local Similarity Prior

The majority of existing hyperspectral (HS) image denoising methods exploit local similarity in HS images by rearranging them into the matrix or vector forms. As the typical 3-D data, the inherent spatial and spectral properties in HS images should be simultaneously explored for denoising. Therefore, a 3-D geometrical kernel (3DGK) is developed in this article to describe the local structure. The proposed method assumes that the pixel can be represented by other pixels within a 3-D block efficiently owing to the local similarity with adjacent positions. Then, the HS image is modeled by the 3-D kernel regression with L1-norm constraint, in which the local similarity is captured by the proposed 3DGK. To efficiently compute the parameters in 3DGK, geometrical structures, such as scale, shape, and orientation in the 3-D block, are estimated from the gradient information approximately. Finally, the noises are effectively removed while preserving the structures in HS images. Moreover, experimental results on simulated and real datasets demonstrate that the performance of 3DGK is better than those of the methods based on local similarity prior.
Aircraft Assembly Ramp-Up Planning Using a Hybrid Simulation-Optimization Approach

Assembly processes have the most influencing and long-term impact on the production volume and cost in the aerospace industry. One of the most crucial factors in aircraft assembly lines design during the conceptual design phase is ramp-up planning that synchronizes the production rates at the globally dispersed facilities. Inspired by a pilot study performed with an aerospace company, this paper introduces a hybrid simulation-optimization approach for addressing an assembly production chain ramp-up problem that takes into account: (1) the interdependencies of the ramp-up profiles between final assembly lines and its upstream lines; (2) workforce planning with various learning curves; (3) inter-plant buffer and lead-time optimization, in the problem formulation. The approach supports the optimization of the ramp-up profile that minimizes the times the aircraft assemblies stay in the buffers and simultaneously attains zero backlog. It also generates the required simulation-optimization data for supporting the decision-making activities in the industrialization projects.
Multitemporal Relearning With Convolutional LSTM Models for Land Use Classification

In this article, we present a novel hybrid framework, which integrates spatial–temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial–temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial–temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images.
Abnormal Detection of Electricity Consumption of User Based on Particle Swarm Optimization and Long Short Term Memory With the Attention Mechanism

In the process of power transmission and distribution, non-technical losses are usually caused by users’ abnormal power consumption behavior. It will not only affect the dispatch and operation of the distribution network, bring hidden dangers to the security of the power grid, but also damage the operating costs of power companies and disrupt the operation of the power market. Aiming at users’ abnormal electricity consumption behavior, this paper proposes a model based on particle swarm optimization and long-short term memory with the attention mechanism (PSO-Attention-LSTM). Firstly, according to the actual electricity theft behavior, six typical electricity theft modes are summarized, and 4 composite modes are obtained by combining them, so as to comprehensively test the detection performance of the model for various electricity theft behaviors. Secondly, a detection model based on PSO-Attention-LSTM is proposed, and the model is built using the TensorFlow framework. The model uses the attention mechanism to give different weights to the hidden state of LSTM, which reduces the loss of historical information, strengthens important information and suppresses useless information. Use PSO to solve the difficult problem of model parameter selection, and optimize the hyperparameters to improve the model performance. Finally, the data set of the University of Massachusetts was used for simulation and compared with convolutional neural network-long short term memory (CNN-LSTM), attention mechanism-based long short term memory (Attention-LSTM), LSTM, gated recurrent unit (GRU), support vector regression (SVR), random forest (RF) and linear regression (LR) to verify the effectiveness and accuracy of the method used in this article. In this paper, Matlab software is used to analyze and visualize the detection result data.
Recognizing semantic relation in sentence pairs using Tree-RNNs and Typed dependencies

Recursive neural networks (Tree-RNNs) based on dependency trees are ubiquitous in modeling sentence meanings as they effectively capture semantic relationships between non-neighborhood words. However, recognizing semantically dissimilar sentences with the same words and syntax is still a challenge to Tree-RNNs. This work proposes an improvement to Dependency Tree-RNN (DT-RNN) using the grammatical relationship type identified in the dependency parse. Our experiments on semantic relatedness scoring (SRS) and recognizing textual entailment (RTE) in sentence pairs using SICK (Sentence Involving Compositional Knowledge) dataset show encouraging results. The model achieved a 2% improvement in classification accuracy for the RTE task over the DT-RNN model. The results show that Pearson's and Spearman's correlation measures between the model's predicted similarity scores and human ratings are higher than those of standard DT-RNNs.