Miracle-dz/UniFAR-FTUs huggingface dataset:
Soft-Then-Hard Super-Resolution Mapping Based on Pansharpening Technique for Remote Sensing Image

Super-resolution mapping (SRM) technique can explore the spatial distribution information of land cover classes in mixed pixels for multispectral image (MSI) or hyspectral image (HSI). Soft-then-hard super-resolution mapping (STHSRM) is an important type of SRM technique. STHSRM first utilizes the subpixel sharpening to produce the high-resolution fractional images with the soft attribute values for each subpixel and then allocates the hard class labels to each subpixel. However, due to the low resolution in the original image, the fractional images are difficult to pick up the full spatial-spectral information from the original image. In this paper, pansharpening technique is utilized in STHSRM (STHSRM-PAN) to produce the fractional images with more spatial-spectral information, which improves the mapping results. First, the original low-resolution MSI or HSI and a panchromatic image (PAN) are fused by pansharpening technique to produce the improved resolution image with the high spectral resolution of MSI or HSI and the high spatial resolution of PAN. The high-resolution fractional images with more spatial-spectral information are then obtained by unmixing the improved resolution image. Finally, the class labels are assigned to each subpixel according to the soft attribute values from the high-resolution fractional images. Comparing with the state-of-the-art STHSRM algorithms, the STHSRM-PAN shows the best performance with the percentage correctly classified and Kappa coefficient (Kappa) in the three experimental results.
A Comparative Study Of Injection Locked Frequency Divider Using Harmonic Mixer In Weak And Strong Inversion

This paper presents a comparative study of a divide-by-4 injection locked frequency divider with the injection transistor operating in both the weak and strong inversion regions. The relation between the maximum lock range of the frequency divider and the nonlinearity of the injection transistor is investigated. We show that the strong nonlinear characteristics of the injection transistor induce the wider lock range. We further investigate the existence of the optimum biasing point in the weak inversion region, which leads to the maximum lock range given the same injection power. The injection locked frequency divider designed in GF 130 nm 1.2 V CMOS technology, is validated using the simulation results.
A PDE-based Model for Information Diffusion in Social Networks

Online social networks (OSNs), as a novel and effective channel, play an important role in sharing and spreading information. A great deal of research investigates information diffusion process along time dimension, while relatively little effort has been made on the spatial and temporal dynamics. In this paper, a Recursive Diffusive (RD) Model based on Partial Differential Equation (PDE) is proposed to describe temporal-spatial trend of information diffusion, which reflects the effects of the network topology on information propagation. Based on the data crawled from twitter, parameters of the model are estimated by Levenberg-Marquardt algorithm, where two different types of growth rate $r$, i.e., constant $r$ and time-varying $r$ are considered. The prediction errors are small compared with Diffusive Logistic model, which demonstrates the proposed model can well capture the trends of information diffusion for twitter.
RiSC: Quantifying change after natural disasters to estimate infrastructure damage with mobile phone data

Natural disasters have proven that governments, even in developed countries, have difficulties to get up-to-date data about not only affected people but also the location and intensity of infrastructure damage when a country is shook by the nature. Therefore, knowing how mobility patterns are changing, in the post disaster time-frame, is crucial in order to settle rescue centers and send help to the most affected areas. In this scenario, we analyze the relations between human mobility patterns and the effects of an earthquake that shook Ecuador on April 16th, 2016. We do so using more than 11 millions of aggregated call detail records provided by Telefonica. We propose a metric named Reach Score to build timeseries as a way to characterize the residents geographic reach according to their mobile activity. Next, we define the metric Reach Score change, RiSC to capture differences in mobility among two given dates. Our results show that these two metrics calculated on data from the day before and the day after the disaster reflect both the overall change in mobility at the province level and the intensity of infrastructure damage at canton level. In fact, we obtain a Pearson correlation coefficient of r = −0.819 between the metric RiSC and the infrastructure damage score taken from official data.
Scaling Collaborative Filtering with PETSc

Machine learning and recommendation systems have become extremely popular and widely used in recent years, with several major companies using recommenders to help their users sort through the vast array of products offered. Naturally, we want these recommender systems to give accurate predictions of products a user might like, but we also want these predictions quickly, based on the user’s past preferences and newer preferences they may have just expressed. Many recommendation algorithms have been created, including neural networks, nearest neighbor algorithms, and various latent factor models. While neural networks have received the most attention lately, these other algorithms can produce results that are just as accurate, and in many cases better understood. This paper explores how an optimized, highly scalable recommendation system can be written using scientific libraries and the scalability of such a the system by implementing implicit alternating least squares with PETSc.
Human-in-the-loop Multi-task Tracking Improved by Interactive Learning

It is a challenge to track a moving target with deformation, occlusion or motion blur in a video stream. Various machine learning algorithms have been proposed for target tracking such as TLD (Tracking-Learning-Detection) and MTT (Multi-task Tracking). However, these algorithms still have limitations to detect and recover from tracking failures. In this paper, we propose hMTT that introduces human into the tracking loop of MTT. Specifically, the tasks are represented and separated as related particles and outlier particles. We design a human-guided mechanism that keeps the related particles as transferable knowledge and replaces the outlier particles with more reliable ones whenever a tracking drift or a tracking failure happens. Meanwhile, the multi-task learning module keeps updating the shared features stored as templates in a dictionary. In addition, the overall confidence value of the tracking result can be calculated accordingly, and the confidence level can be learned on-line for result classification. Experimental results have demonstrated that hMTT outperforms TLD and MTT in various tracking tasks.
An Ensemble Feature Selection Method Based on Deep Forest for Microbiome-Wide Association Studies

With the rapid advancement of DNA sequencing, metagenomics and metatranscriptomics have made great progress, which deepen our understanding on the human microbiome and its impact on human health and diseases. The microbiome, which is characterized by small samples, high dimensions and complicated relationships with hosts, refers to the species, genes and genomes of the microbiota, as well as the products of the microbiota and the host environment. In fact, many machine learning methods have been used to conduct Microbiome-Wide Association Studies which can link the microbiome with the phenotypes, such as the status of human health and diseases. However, existing methods such as Support Vector Machines (SVMs) have some limitations on deep representation learning with deep architectures which can promote the reuse of features and potentially lead to progressively more abstract features at higher layers of representations. Recently, Deep Neural Networks (DNNs), a kind of deep learning models, are widely used for metagenomic data analysis and can perform well on representation learning. But they are considered as a black box and sufferring from criticisms due to theirs lacking of interpretability. Thus, it is interesting to explore other deep learning models for metagenomic data analysis. In this work, we introduce a deep learning model called Deep Forest to study the microbiome associations and we also present an ensemble method for feature selection. Experimental results show that Deep Forest outperforms the traditional machine learning methods. In addition, compared to DNNs, Deep Forest has better interpretability and less hyperparameters.
Face and Lip-Reading Authentication System Based on Android Smart Phones

In all biometrics, face features are widely used because of their advantages. Compared with other biological features, the recognition accuracy based on face features is not very high, and the system is easy to be deceived by photos. Identity authentication based on multi-modal biometric fusion has opened up a new field of development, which has higher technical requirements and better application performance than single biometric recognition and authentication system. As a dynamic feature, lip reading feature is not easy to imitate, and can be collected with face information at the same time, so it has high convenience. Fusion of lip reading and face information can improve the accuracy of the authentication system, at the same time, greatly enhance the anti-counterfeiting ability of the recognition system. In this paper, Linear Preserving Projection (LPP)is used to extract face features, and Projection Local Spatiotemporal Descriptor (PLSD)is used to extract lip reading features. In Android smart phone platform, face and lip reading are fused by Extreme Learning Machine, which improves the applicability of multi-biometric identity authentication system. The multimode authentication system improves the information security of mobile phone users.
Efficient Key Agreement Protocol for Smart Sensors

Information security is an important issue in any communication network, especially in Internet of Things in which many sensors are connected. Encryption is a basic tool to protect information security, and secret key is essential to encryption. In 2017, Guan and Tzeng proposed a key agreement protocol which is computationally lightweight and unconditionally secure. In their protocol, it was assumed that the two communicating nodes can receive random bit strings from a beacon node. With the advancement of hardware and software technologies, sensors may be able to generate good random strings. In this work, we assume that the sensor nodes can generate random bit strings for secret keys. Therefore, no beacon nodes are required. The simulation results show that key agreement protocol can be much more effective and efficient in establishing secret keys.
A Depth-Fused Adaptive Superpixel Approach for Visually Degradated Images

In the process of image generation, visual degradation often occurs. The current color-based superpixel methods are always unable to generate accurate superpixel in color degradation situation for the ambiguity of color information. In order to eliminate this ambiguity, we propose a novel approach designated as DFASP (Depth-Fused Adaptive Superpixel) to generate accurate superpixel under the condition of visual degradation. Furthermore, we design an adaptive mechanism to adjust the color and depth information automatically during the pixel clustering process. We compare our method with state-of-the-arts on public datasets and ours. In the visual degradation situation, this proposed method can, compared with color-based approaches, generate the more accurate contours of objects. The experimental results demonstrate that our method outperforms the popular methods on boundary adherence and regularity greatly.
Skew-Aware Collective Communication for MapReduce Shuffling

This paper proposes and examines the three in-memory shuffling methods designed to address problems in MapReduce shuffling caused by skewed data. Coupled Shuffle Architecture (CSA) employs a single pairwise all-to-all exchange to shuffle both blocks, units of shuffle transfer, and meta-blocks, which contain the metadata of corresponding blocks. Decoupled Shuffle Architecture (DSA) separates the shuffling of meta-blocks and blocks, and applies different all-to-all exchange algorithms to each shuffling process, attempting to mitigate the impact of stragglers in strongly skewed distributions. Decoupled Shuffle Architecture with Skew-Aware Meta-Shuffle (DSA w/ SMS) autonomously determines the proper placement of blocks based on the memory consumption of each worker process. This approach targets extremely skewed situations where some worker processes could exceed their node memory limitation. This study evaluates implementations of the three shuffling methods in our prototype in-memory MapReduce engine, which employs high performance interconnects such as InfiniBand and Intel Omni-Path. Our results suggest that DSA w/ SMS is the only viable solution for extremely skewed data distributions, but this solution is only valid on systems equipped with high performance interconnects. We also present a detailed investigation of the performance of CSA and DSA in various skew situations.
The Influences of Communication and Group Dynamics on Collaborative Problem Solving Task Performance

This study relates collaborative problem solving (CPS) behavior and background characteristics of three-person student teams completing tasks in an online electronics environment to task performance. Task performance was primarily predicted by classroom membership and minimally impacted by CPS communication types and group dynamics. The online environment and process data measuring CPS behavior substantially add to the field.
A Non-Invasive Characterization Method for MEMS Based Devices

In the MEMS industry, it is imperative to characterize the fabricated die and test the functionality of its mechanical subcomponents. This paper presents a novel noninvasive characterization method for MEMS devices using aerodynamic forces. These forces are applied onto a desired part of a MEMS sensor to stimuli the moveable components and subsequently analyze its functions. In this approach, we use a micro-fabricated cantilever array chip as a case study of our proposed method. In this paper, we outline the proposed method and then demonstrate and discuss its multiphysics simulation results. The research results and discussions in this paper pave the way towards the development of a noninvasive rapid and low complexity air-based characterization system for MEMS devices.
Toward the Generation of a Wind Geophysical Model Function for Spaceborne GNSS-R

This paper presents a comprehensive procedure to improve the wind geophysical model function (GMF) for the Global Navigation Satellite System Reflectometry (GNSS-R) instrument onboard the TechDemoSat-1 satellite. The observable used to define the GMF is extracted from the measured delay-Doppler maps (DDMs) by correcting for the nongeophysical effects within the measurements. Besides the instrument and the geometric effects as provided in the bistatic radar equation, a calibration term that accounts for the uncalibrated receiver antenna gain and the unknown transmitter antenna gain is proposed to optimize the calculation of GNSS-R observables. Such calibration term is presented as a function of observing elevation and azimuth angles and is shown to remarkably reduce the measurement uncertainties. First, an empirical wind-only GMF is developed using the collocated Advanced Scatterometer (ASCAT) winds and European Centre for Medium-Range Weather Forecasts (ECMWF) model wind output. This empirical GMF agrees well with the model output. Then, the sensitivity of the observable to waves is analyzed using the collocated ECMWF wave parameters. The results show that it is difficult to include mean square slope (MSS) in the development of an empirical GMF, since the difference between ECMWF MSS and the MSS sensed by GNSS-R varies with incidence angle and wind speed. However, it is relevant to take significant wave height ( $H_{s}$ ) in account, particularly for low wind conditions. Consequently, a wind/ $H_{s}$ approach is proposed for improved wind retrievals.
Efficient Dual-band Ultra-Low-Power RF Energy Harvesting Front-End for Wearable Devices

This paper presents a novel dual-band ultra-low power RF energy harvesting front-end for wearable devices, wireless sensor networks, and the Internet of things (IoT) designed in standard 130 nm CMOS technology. The proposed dual-band RF energy harvester operates at ISM bands of 915 MHz and 1.85 GHz and uses an efficient summation network to combine power from different frequency bands. A dual-band antenna receives signals from two different bands which are boosted by two different matching networks. Then, two self-compensated crosscoupled rectifiers convert the RF signals provided by the two different bands into DC output voltages. At the output of the two rectifiers, a summation network using switches and a control circuit combines power from different bands, charging the output capacitor. The post-layout simulation results demonstrate a sensitivity of −33 dBm for 1 V output at a capacitive load and the peak end-to-end efficiency is 43.2% at −18 dBm when two frequency bands are available. When only 915 MHz or 1.85 GHz is available, the results demonstrate a high peak efficiency of 42.3% and 44% at −16 dBm and −17 dBm, respectively.
Local Measurement-Based Ultra-High-Speed Main Protection for Long Distance VSC-MTDC

To ensure the continuous operation of the voltage source converter based multi-terminal HVDC system (VSC-MTDC), there has been much concern regarding how to isolate the single-pole earth fault by means of protective relaying. However, when the boundary components are not on the dc transmission lines, conventional main protections for the HVDC systems perform less satisfactorily. To solve this problem, a local measurement-based ultra-high-speed main protection is proposed, in this paper. The proposed method is achieved by the cooperation of the single-end directional start-up criterion and the surge arrival time difference between the 0-mode and 1-mode traveling waves. According to the simulation results, the proposed method has satisfactory performance against internal faults and is quite reliable in external fault and operating condition changes. Meanwhile, the reach is also wide enough to cover most of the protected line. Comparison studies are also conducted among the proposed protection and three types of existing protection schemes, highlighting the advantages of the proposed protection in terms of selectivity, operating speed, and resistive tolerance.
Improving Urdu Recognition Using Character-Based Artistic Features of Nastalique Calligraphy

The state-of-the-art Urdu recognition approaches for Nastalique use features along with the sequence of characters’ labels for classification and recognition. In Arabic-like cursive script, the characters are joined together to form a ligature. The conventional methods process the connected stroke of ligatures as a sequence of characters. However, connected stroke of a ligature image has a sequence of pairs of characters and their joiners, instead of a sequence of characters. The character has a distinctive shape that clearly distinguishes it from other characters. The joiner preserves the connecting stroke shape of a character with the next character. In this paper, an implicit Urdu character recognition technique is presented for the Nastalique writing style that is based on recognition of characters and joiners. The detailed analysis of the Nastalique calligraphy is carried out to extract the artistic features of characters and their joiners. The presented technique is tested on Dataset-1 of 1446 ligature classes covering 3309762 ligature instances and 91129 unique Urdu words. In addition, the system is also tested on 1600 text lines of UPTI dataset called Dataset-2. The character recognition accuracies are 95.58% and 98.37% on Dataset-1 and Dataset-2, respectively. The results reveal that the system outperforms the state-of-the-art hidden Markov models and deep learning-based Urdu recognition techniques.
AiProAnnotator: Low-rank Approximation with network side information for high-performance, large-scale human Protein abnormality Annotator

Annotating genes/proteins is a vital issue in biology. Particularly we focus on human proteins and medical annotation, which both are important. The most proper data for our annotation is human phenotype ontology (HPO), which are sparse but reliable (well-curated). Existing approaches for this problem are feature-based or network-based. The feature-based approach can incorporate a variety of information, by which this approach is more appropriate for noisy data than reliable data, while the network-based approach is not necessarily useful for sparse data. Low-rank approximation is very powerful for both sparse and reliable data. We thus propose to use matrix factorization to approximate the input annotation matrix (proteins × HPO terms) by factorized low-rank matrices. We further incorporate network information, i.e. protein-protein network (PPN) and network from HPO (NHPO), into the framework of matrix factorization as graph regularization over the two low-rank matrices. That is, the input annotation matrix is factorized into two low-rank factor matrices so that they can be smooth over PPN and NHPO. We call our software of implementing the above method “AiProAnnotator”, which in this paper has been empirically examined using the latest HPO data extensively under various experimental settings, including performance comparison under cross-validation, computation time and case studies, etc. Experimental results showed the high predictive performance and time efficiency of AiProAnnotator clearly.
Towards High Level Skill Learning: Learn to Return Table Tennis Ball Using Monte-Carlo Based Policy Gradient Method

Deep learning has achieved a great success in both visual and acoustic recognition and classification tasks. The accuracy of many state-of-the-art methods have surpassed that of human beings. However, in the field of robotics, it remains to be a big challenge for a real robot to master a high-level skill using deep learning methods, even though human can easily learn the task from demonstration, imitation and practice. Compared to Go and Atari games, this kind of tasks is usually continuous in both state space and action space, which makes value based reinforcement learning methods unavailable. Making a robot learn to return a ball to a desired point in table tennis is such a typical task. It would be a promising step if a robot can learn to play table tennis without the exact knowledge of the models in this sport just as human players do. In this paper, we consider such a kind of motion decision skill learning, a one-step decision making process, and give a Monte-Carlo based reinforcement learning method in the framework of Deep Deterministic Policy Gradient. Then we apply this method in robotic table tennis and test it on two tasks. The first one is to return balls to a desired point first, and the second one is to return balls to randomly selected landing points. The experimental results demonstrate that the trained policy can successfully return balls of random motion state to both a designated point and randomly selected landing points with high accuracy.