Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid Approach to Identifying Key Factors in Environmental Health Studies

In recent years, the availability of data-driven analytics has become a key tool in discovery in public health and environmental science research. As a result, these communities have looked to leverage recent advances in machine learning algorithms. This class of algorithms are able to find hidden patterns and develop new knowledge in complex data, accelerating the rate of discovery in multiple research domains. In this paper, we present our methodology of applying machine learning algorithms to health outcomes, chemical exposures, and social behavior data from expectant mothers, as part of the NIEHS-supported PROTECT Center. The ultimate goal is to determine the dominant factors/features potentially responsible for the high rate of premature births in Puerto Rico.Many commonly-used machine learning algorithms can be used for feature selection. However, given the imbalance in our birth outcome data, with many more term (i.e., 37 weeks or longer) versus preterm pregnancies (i.e., less than 37 weeks), analysis of the PROTECT dataset presents many unique challenges. In addition to outcome imbalance, our database contains both quantitative and categorical data variables, adding some complexity to the analytical methods used. Applying straightforward correlation or regression analysis would be insufficient. Our datasets also contain a significant amount of missing data (incomplete records), providing noisy input to our algorithms. A further challenge is that we are working with a relatively limited set of complex data (only 2000 participants to date), so our models must be able to be built with a relatively small number of data samples.To overcome these challenges, we have implemented a cus-tomized end-to-end analytical toolchain which forms a pre-processing pipeline. Our framework performs general data filtering and handles missing data fields using a similarity-based approach. Next, we apply one of a number of different machine learning algorithms, including Linear Correlation, Normalized Mutual Information, Logistic Regression, and Decision Trees. We use these during both feature selection and model performance evaluation. Finally, we present top-ranked features produced by our model as potential key contributors of high preterm birth rates in Puerto Rico, and discuss results across these algorithms.
DEEP LEARNING ACTIVITIES ON REMOTE SENSED HYPERSPECTRAL IMAGES

In recent years, deep learning models have been widely used on remote sensing images. Deep learning is held on remote sensing images as well as in every area; is to be able to perform better performance classification than existing approaches and to perceive the feature inferences on its own. In remote sensing, more studies are made especially on hyperspectral images. The most important reason for this is that it can carry a large number of data features. The large number of data features means that there are a large number of attributes for that image. The most important disadvantage of hyperspectral images is; due to the influence of the environment of the device which is shooting the image, various noises may occur. There may be a variety of information loss on this image. Various algorithms techniques have been developed to prevent these losses, while hyperspectral images have been better classified by deep learning models. Recent advances in deep learning models in technological firms and the creation and development of their own deep learning model are evidence of how intense this interest is in this area. Our aim is to examine recent developments in deep learning activities on remote sensing images in this article; to compare the performances obtained by deep learning model and to give brief information about the methods used in this area.
Plasmon Coupling Within the Multifold Nanorod Metasurface for Sensing Applications

A novel design for improving the sensing performance of localized surface plasmon resonance based nanosensors is proposed. We analyze the Fano-like coupling interaction within the multifold gold nanorod metasurface. By means of such Fano-like coupling effects, we can enhance the figure of merit (FoM) of nanosensors based on gold nanorod array by nearly one order. The main idea of our design can be easily adjusted for other nanophotonic platforms, and provides new possibilities for improving the performance of a myriad of applications, such as slow light devices, and optical switches.
Optimal Complexity Architectures for Pipelined Distributed Arithmetic-Based LMS Adaptive Filter

This paper presents three optimal-complexity structures (I, II, III) for pipelined distributed arithmetic (DA) based least-mean-square (LMS) adaptive filter. The complexity of proposed structures is reduced by implementing offset-binary-coding (OBC) combinations of input samples on hardware. However, some non-OBC outputs are produced, and subsequently eliminated in the error computation during the initial clock cycles. For achieving more performance benefits, radix-4 OBC combinations of input samples are implemented with the proposed partial product generators. In addition, novel low-complexity implementations for the offset term, weight update block and shift-accumulate unit are also proposed. Analysis shows that byte-complexity of proposed structures vary linearly with the order of DA base unit, while their bit-complexity depends on the topology. All the structures show significant hardware savings, Structure-I has least critical-path and Structure-II, III offer superior convergence performance. Experimental results show that the Structure-I, II and III with 32 nd order filter provide savings 71.13%, 71.83% and 73.08% in area, 68.47%, 70.01% and 72.17% in power, 51.74%, 37.83% and 45.38% in area-per-throughput (APT), 47.33%, 33.72% and 43.19% in power-per-throughput (PPT), 55.11%, 59.66% and 64.20% in slice LUTs, 35.33%, 40.28% and 44.87% in flip-flops over the best existing scheme.
High-Performance Implementation of Matrix-Free Runge-Kutta Discontinuous Galerkin Method for Euler Equations

DG method is one of the mainstream high-order numerical discretization methods of CFD, and the RKDG which combines the Runge-Kutta explicit time stepping with DG space discretization plays an important role in the DG explicit numerical simulation. Even though the matrix-free has already been applied to improving the performance of the DG method, it has gained less attention relatively. In order to improve the RKDG's performance for Euler equations, we have implemented a matrix-free RKDG on the HopeFOAM, an open source CFD code. In this paper, we design and implement data structures to apply sum-factorization to the RKDG space discretization. During the simulation process, the arithmetic operations and memory cost decrease, which will improve the serial performance. For the data-level parallelism, we design the calculation strategy and vectorize the data to make it ideally suitable for the SIMD instructions, so that we make the most of the multiple processing elements of CPU. The experiment results show that matrix-free implementation can effectively speed up RKDG simulation on Euler equations and the performance improvement in three-dimensional space is much better than that in two-dimensions, whose maximum speed-up ratios are 26.287 and 8.278 respectively. Moreover, we test the parallel scalability of matrix-free RKDG implementation and the result indicates that matrix-free implementation does not lower the parallel speed-up ratio of HopeFOAM.
A Review on Collaborative Planning of Multiple Unmanned Surface Vehicles

The collaborative system of Multiple Unmanned Surface Vehicles (MUSVs) has shown broad prospects in the field of civil and military applications. Particularly, its military significance has become increasingly prominent. In complex and changing marine environments, surface unmanned vehicles will show a trend of teamwork. This paper details the current research status on technologies related to MUSVs, including MUSV Collaborative Sensing Technology, MUSV Information Stability Analysis, MUSV Collaborative Obstacle Avoidance Technology; second, this paper reviewed the problems in the research on MUSVs; finally, this paper put forward suggestions for future research through analyzing deficiencies of existing research.
Effective Policy Adjustment via Meta-Learning for Complex Manipulation Tasks

The ability of adjusting policy is the key to learning decision making when completing complex manipulation tasks for agents. To solve this problem with the consideration of both exploration and exploitation, we propose a novel deep reinforcement learning algorithm by combining the Hindsight Experience Replay (HER) with the Model-Agnostic Meta-Learning (MAML). To solve the complex manipulation tasks, HER could provide a relatively effective exploration by converting the single-goal task to the multiple goals in such an environment where rewards are sparse and binary, enhancing the ability to search better policies according to not only the successful the transition trajectories but also the failures, and the MAML could promote the ability of exploitation, which means the proposed algorithm could learn faster and adjust the policy model from limited experience within few iterations. Plenty of simulation results on the complex tasks of manipulating objects with a robotic arm have been done, and results show that HER integrated with MAML could accelerate fine-tuning for the original policy gradient reinforcement learning with neural network policy, and also improve the performance on the success rate.
Museum Interactive Information Broadcasting Using Indoor Positioning System and Bluetooth Low Energy: A Pilot Project on Trowulan Museum Indonesia

The tourism sector become a promising country income. This is because its nature which is not easily run out if managed properly. This sector can be a great support especially for developing countries such as Indonesia, because usually developing countries income only rely on natural and human resources income. Museums are one of the tourism sectors that have not been explored properly. Museums in Indonesia are still few that using the latest technology such as smartphone application, wifi, bluetooth, RFID. So, that it makes the museum a less attractive place to visit, especially for young generation. This research tries to create an interactive broadcasting museum information by utilizing applications on smartphones, indoor positioning systems, and bluetooth low energy beacons to provide information to museum visitors about artifacts around museum visitor location. As a pilot project a testing was conducted at the Trowulan Museum, Mojokerto, East Java, Indonesia, which is a museum that exhibits the relics of the Majapahit kingdom. The application has been able to provide interactive information about artifacts around the visitor's location so as to create an interesting museum to visit
Lifelong Learning Memory Networks for Aspect Sentiment Classification

Aspect sentiment classification (ASC) is a fundamental task in sentiment analysis. It aims at classifying the sentiment expressed on some target aspects/features of entities (e.g., products and services). Although a great deal of research has been done, this task remains to be very challenging. Recently, memory networks, a type of neural model, have been used for this task and have achieved state-of-the-art results. However, such neural models usually require a large amount of well-annotated training data for producing reasonably good results. Unfortunately, for the ASC task, the human-annotated data with aspect-level labels are scarce and costly to obtain. In this work, we aim to use big unlabeled data to help. The key idea is to make a memory network learn knowledge from the big unlabeled data (treated as past tasks) and use the learned knowledge to better guide its future task learning. To achieve this goal, we propose a novel lifelong learning approach that can automatically meta-mine knowledge from multiple past domains. In addition, a new model named lifelong learning memory network (L2MN) is proposed to incorporate the mined knowledge into its learning process, where two types of knowledge are involved, namely, aspect-sentiment attention and context-sentiment effect. Extensive experimental results using real-world review datasets demonstrate the effectiveness of our approach.
Unified Subspace Fitting Framework and Its Performance Analysis for Direct Position Determination in the Presence of Multipath Propagation

The cost functions and their performances of direct position determination (DPD) methods in the presence of multipath propagation are investigated. We first establish a general DPD (GDPD) model in the presence of multi-path propagation and point out that the existing cost functions cannot get the emitter positions correctly because of the singularity of the manifold matrix in a multipath propagation scenario. Eight cost functions are developed for the GDPD model and formulated in a unified subspace fitting (USF)-based framework, which provides insight into their algebraic and asymptotic relations. Moreover, we derive the closed-form expressions of the asymptotic distributions of the estimation errors, which are optimized by those cost functions. Besides, the optimal cost function for achieving an optimal asymptotic performance is derived based on the optimization theory. Finally, the numerical simulations and Cramér–Rao lower bound are provided to verify the analytical results and show that: 1) the cost functions which work well in the single-path DPD model cannot find the emitters correctly in a multipath scenario; 2) the signal subspace fitting cost functions and noise subspace fitting cost functions, which are proposed in this paper, find the emitters accurately in the multipath propagation scenarios; 3) the optimal-weighted-subspace-fitting cost function holds the best asymptotic performance under the USF framework; and 4) the asymptotic performance of a multiple dimension cost function is better than a 1D cost function.
Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices

An ultra-low power electrocardiogram (ECG) processing architecture with an adequate level of accuracy is a necessity for Internet of Things (IoT) medical wearable devices. This paper presents a novel real-time QRS detector and an ECG compression architecture for IoT healthcare devices. An absolute-value curve length transform (A-CLT) is proposed that effectively enhances the QRS complex detection with minimized hardware resources. The proposed architecture requires adders, shifters, and comparators only, and removes the need for any multipliers. QRS detection was accomplished by using adaptive thresholds in the A-CLT transformed ECG signal, and achieved a sensitivity of 99.37% and the predictivity of 99.38% when validated using Physionet ECG database. Furthermore, a lossless compression technique was incorporated into the proposed architecture that uses the ECG signal first derivative and entropy encoding. An average compression ratio of 2.05 was achieved when evaluated using MIT-BIH database. The proposed QRS detection architecture deals with almost all the ECG signal artifacts, such as low-frequency noise, baseline drift, and high-frequency interference with minimum hardware resources. The proposed QRS architecture was synthesized using 65-nm low-power process using standard-cell-based flow. The power consumption of the design was 6.5 nW while operating at a supply of 1 V and a frequency of 250 Hz. Moreover, the system could benefit from duty-cycling.
Online Spammer Detection using User-Neighbor Relationship

Today, online social media is being used as a primary means of interaction among people rather than any other communication medium. People use social media for socializing and collecting and sharing information, which presumes online social media is always reliable. However, there are a lot of advertisements or cyberattacks that take advantage of the impact of online social media. For example, spam, phishing, and advertising through influence manipulation. They are not only directly damaging users, but they are also giving indirect damages that will cause damage to the credibility of online social media to decline. We (1) propose measurements for online social status to identify the attackers in online social media and (2) analyze the difference in the manner in which a normal user and an attacker make a neighbor relationship.
Heterogeneous Information Network Based Clustering for Categorizations of Traditional Chinese Medicine Formula

Traditional Chinese medicine (TCM) is a highly important complement to modern medicine and is widely practiced in China and in many other countries. For TCM, herbal therapies are generally formula based, and individual herbs are rarely used. Unfortunately, due to the empirical nature of TCM, effective diagnosis and prescription methods are not well defined. In this paper, we propose a novel structured learning model to solve the problem of formula regularity, a pivotal task in prescription optimization. We integrate clustering with ranking in a heterogeneous information network. The results from experiments on the Pharmacopoeia of the People’s Republic of China (ChP) demonstrate the effectiveness and accuracy of the proposed model for discovering useful categorizations of formulas.
A Cache-based Approach to Dynamic Switching between Different Dataflows in Crowdsourcing

At times, a composite dataflow needs rerunning in crowdsourcing for various reasons, even when the dataflow may be half complete. Rerunning the dataflow requires more time and incurs monetary costs for the additional work that would need to be completed by crowd workers. This time and cost may be reduced by reusing complete or intermediate results in the previous run. However, at times, such results cannot be used as is (e.g., when the dataflow has been changed), and some additional tasks need to be completed in the old dataflow in order to make them reusable in the new dataflow. The benefit of reusing these results in the previous run may or may not be worth the cost of these additional tasks. This paper gives a general framework for formulating this problem, and proposed a method to estimate the additional costs. The simulation result shows that it is worth devising optimization techniques to identify feasible (namely, cost-effective) plans.
Improving Machine Learning Tools with Embeddings: Applications to Big Data Security

Considering the widespread diffusion of machine learning techniques to solve several issues, from network security to malware detection, in this paper we propose the adoption of word embeddings aimed to improve the machine learning-based classifiers. Three real-world experiment we perform in order to demonstrate that the proposed method overcomes in terms of performances the mostly used machine-learning algorithms.
Monocular Robot Tracking Scheme Based on Fully-Convolutional Siamese Networks

Tracking a specified target is a fundamental task of service robots. This paper studies and designs a monocular mobile robot tracking system based on Fully-Convolutional Siamese Networks. This tracking mobile robot system uses the Siamese convolution network as the tracker to lock the target. The bearing conversion algorithm and speed conversion algorithm are combined to implement following the target stably and accurately. The bearing conversion algorithm keeps the tracking target in the middle of the image and calculates the moving angle according to the distance from the center. The speed conversion algorithm sets different speeds according to the ratio of bounding box area. Implementation of the monocular mobile robot tracking system is grounded on the Robotic Operating System (ROS) platform. The experiment shows the effectiveness of the implemented the monocular robot tracking system which can achieve real-time following target at variable speeds according to the forward speed of the tracking target with safety distance even though having occlusion.
Twitter Health Surveillance (THS) System

We present the Twitter Health Surveillance (THS) application framework. THS is designed as an integrated platform to help health officials collect tweets, determine if they are related with a medical condition, extract metadata out of them, and create a big data warehouse that can be used to further analyze the data. THS is built atop open source tools and provides the following value added services: Data Acquisition, Tweet Classification, and Big Data Warehousing. In order to validate THS, we have created a collection of roughly twelve thousands labelled tweets. These tweets contain one or more target medical terms, and each label indicates if the tweet is related or not to a medical condition. We used this collection to test various models based on LSTM and GRU recurrent neural networks. Our experiments show that we can classify tweets with 96% precision, 89% recall, and 86% F1 score. These results compare favorably with recent research on this area, and show the promise of our THS system.
A Precise and Stable Segmentation Algorithm of SAR Images Based on Random Weighting Method and Modified Level Set

Level set methods have been widely used for image segmentation due to their good boundary detection accuracy. In the context of synthetic aperture radar (SAR) image segmentation, the presence of speckles and the distribution estimation of SAR image remain important issues that may hinder the accuracy of any segmentation method based on level set methods. In this paper, we propose a more accurate and a stable segmentation method based on the random weighting method and modified threshold-based level set energy functional. The proposed method uses a level set evolution that is based on the minimization of an objective energy functional, whose propagation function is based on the ${\mathcal{ G}}^{0}$ statistical model, whose parameters are estimated by random weighting estimators, and the estimator is not affected by the hypothesized model and sampling number. In addition, a new regularization item and length term, which maintain the regularity of the level set function and contour respectively, were employed. The experimental results demonstrate that the proposed methodology has a good and stable capability of segmentation, both in homogeneous, heterogeneous, and extremely heterogeneous regions in SAR image.
A Fault Identification Method Based on Multiresolution Generalized S- Transform *

In order to detect fault impulse feature of rotating machinery in operation of non-uniform rotation, a fault identification method based on multiresolution generalized S-transform (MGST) is proposed in this paper. In this method, generalized S-transform (GST) with different scales is employed to generate time-frequency spectrums for the fault vibration signal. Then, normalize these spectrums and calculate the shift factors, phase factors, frequency factors and scale factors of the maximum energy, thus, an impulse component with the maximum energy could be extracted. Repeat this iteration for several times, all the impulse components could be extracted, combining the rotating speed information and structural parameters, the fault could be identified. The simulation proves that the proposed method expresses fast decomposition speed and high precision.
Reducing State Overshoot of Switched Linear Systems

Switching among multiple controllers is potentially powerful in improving the transient performance of dynamical systems. In this work, we extend our previous work on minimum overshooting of switched linear systems through proper design of switching laws. For stabilizable discrete time switched linear systems, we propose a semi-decidable design algorithm to achieve stability while reducing the state overshoot. Numerical examples are provided to illustrate the effectiveness of the proposed schemes.