Miracle-dz/UniFAR-FTUs huggingface dataset:
Intelligent Colorization for Thermal Infrared Image Based on CNN

Thermal infrared image coloring is an important issue to improve human perception in various fields, such as military, medical, security etc. This paper presents a novel algorithm to generate visual, i.e. perceptually realistic color(RGB) images from thermal infrared(TIR) images based on convolutional neural networks(CNN). The proposed approach uses an encoder-decoder architecture with skip connections. In the encoder-decoder framework, the input is passed through a series of layers that progressively downsample, until a bottleneck layer, at which point the process is reversed. There is an enormous low-level information shared between the input and output, and it would be desirable to shuttle this information directly across the net. To circumvent the bottleneck for information, we add the skip connections. We train our model on a KAIST-MS dataset and present illustrative qualitative and quantitative analysis of our results. Experimental results with a large set of original images provided the validity of the proposed approach.
PIM-GraphSCC: PIM-Based Graph Processing Using Graph’s Community Structures

Graphs are used to store relationships on a variety of topics, such as road map data and social media connections. Processing this data allows one to uncover insights from its structure. However, while analyzing graphs with traditional processors, the graph connectivity can result in irregular memory access patterns and thus poor data locality that can result in low performance. Processing-in-Memory (PIM) is an attractive alternative for graph processing, as it can reduce data movement by bringing the computation closer to the data itself. While PIM-based techniques have been shown to improve graph processing performance, there is still room for improvement, as critical bottlenecks exist when connecting multiple PIM-based accelerators into larger clusters. Although a number of recent proposals have aimed to reduce inter-accelerator data movement, their techniques have generally overlooked the potential to optimize how the graph’s connectivity can lead to a more efficient hardware mapping. In fact, many real-world graphs have a small percentage of high-degree nodes that connect widely to a large number of other nodes. By clustering these nodes into communities, one can more efficiently map them to hardware, minimizing expensive inter-accelerator communication, a key performance bottleneck in these accelerators. To capitalize on this observation, we propose PIM-GraphSCC, the first PIM-based graph processor that exploits a graph’s connectivity to significantly reduce communication over critical resources: the inter-accelerator links. By partitioning graphs into communities, PIM-GraphSCC provides a community-aware graph partitioning scheme that reduces inter-accelerator data movement by up to 93 percent compared to modern graph processing schemes.
Learning Continuous Control Actions for Robotic Grasping with Reinforcement Learning

Robots are nowadays increasingly required to deal with (partially) unknown tasks and situations. The robot has, therefore, to adapt its behavior to the specific working conditions. Classical control methods in robotics require manually programming all actions of a robot. While very effective in fixed conditions, such model-based approaches cannot handle variations, demanding tedious tuning of parameters for every new task. Reinforcement learning (RL) holds the promise of autonomously learning new control policies through trial-and-error. However, RL approaches are prone to learning with high samples, particularly for continuous control problems. In this paper, a learning-based method is presented that leverages simulation data to learn an object manipulation task through RL. The control policy is parameterized by a neural network and learned using modern Proximal Policy Optimization (PPO) algorithm. A dense reward function has been designed for the task to enable efficient learning of an agent. The proposed approach is trained entirely in simulation (exploiting the MuJoCo environment) from scratch without any demonstrations of the task. A grasping task involving a Franka Emika Panda manipulator has been considered as the reference task to be learned. The task requires the robot to reach the part, grasp it, and lift it off the contact surface. The proposed approach has been demonstrated to be generalizable across multiple object geometries and initial robot/parts configurations, having the robot able to learn and re-execute the target task.
Better Link Prediction for Protein-Protein Interaction Networks

In functional genomics, experimentally obtained protein-protein interaction (PPI) data is often incomplete. To deal with this issue, computational approaches are used to infer missing data and to evaluate confidence scores. Link prediction is one such approach that uses the structure of the network of PPIs known so far to find good candidates for missing PPIs. In a recent study by Kovács et al., a novel PPI-specific link predictor was proposed. Their link predictor is biologically motivated by the so-called L3 principle and it was shown to be superior to other general link predictors when applied to PPI data. However, the L3 link predictor is only an approximate implementation of the L3 principle. As such, not only is the full potential of the L3 principle not realized, it may even penalize candidate PPIs that otherwise fit the L3 principle. In this paper, we formulate an L3-based link predictor without approximation, coined ExactL3. We show computationally that ExactL3 is better than the previously proposed methods on four major PPI datasets (STRING, BioGRID, IntAct/HuRI, and MINT). The predicted PPIs are also shown to be much more functionally relevant. This confirms that ExactL3 is a better link predictor for PPI networks, and demonstrates its ability to characterize PPIs by only the topological features of binary PPI networks.
IoT-Based Home Care System with a FPGA Development Board by Using RS-485 Interface and Verilog HDL

This paper presents the packet processing and transmission of a field programmable gate array (FPGA) development board by using an RS-485 interface module and Verilog HDL. The proposed communication protocol was established between the sensors in the sensing layer and web server. In the sensing layer, the sensor system, which comprises a temperature sensor, warning light, and fan, controls the environment temperature. An attractive Internet of Things application system was proposed to simultaneously monitor real-time temperature information through wireless communication and the webpage. Node-RED software was used to develop the home care system because of its advantages in facilitating management and maintenance. The linkage function was written in the JavaScript language on Node-RED software. In the designed system, when the temperature reaches the preset value, the fan and warning light automatically turn on and a notification email is sent to the user. The measurement results showed that the throughput and conversion time were 0.00958 Mbps and 283.83 ms, respectively, at a clock frequency of 1 MHz and Baud rate of 9600 bps.
BP Neural Network Noiceprint Recognition is Affected by Nasal Congestion

Voice is an inherent characteristic of humans, and the cost of acquiring equipment is low, and it is not restricted by location. Therefore, the speaker recognition technology that uses voice to identify the speaker has many unique advantages and is even irreplaceable in many scenarios. However, the development of speaker recognition technology still faces many difficulties and challenges. Affected by the vocal organs, when the nasal cavity is blocked, nasal congestion with distorted voiceprints will be emitted, which will affect the recognition rate of the speaker recognition system. Therefore, this article will study the difference between nasal congestion and normal sounds, and analyze the influence of nasal congestion on voiceprint recognition of an integrated and optimized BP neural network.
Current brain activity is a predictor of longitudinal motor imagery performance

This study aimed to evaluate whether current electroencephalographic spectral measures can predict participant’s performance during future sessions of a motor imagery task. By investigating this point, we hope to understand which spectral components are related to MI "literacy". Twelve healthy subjects performed a neurofeedback task whereby a cursor was moved to one of two targets (left and right) using only motor imagery of the corresponding hands. To evaluate the effect of aptitude, we measured the Mahalanobis’ distances between whole-scalp spectral patterns in four frequency bands (theta, alpha, beta, and gamma) during the first session of left and right motor imagery. Later, we used these features as inputs in a Support Vector Regressor to predict performance during the following two sessions. The performance was measured as the percentage of trials where the cursor correctly reached the target. Since our sample was balanced, this approach predicted performance on sessions two and three with mean absolute errors of 15.07±12.94% and 11.98±11.40%, respectively. The most relevant feature in both cases was the Mahalanobis’ distance in alpha. These results suggest that participants who can not evoke different patterns of alpha power during left- and right-hand motor imagery during the first session, also are less likely to improve during the following training sessions. The investigation of whole-scalp differences allows a holistic comprehension of the neural basis of motor imagery. This method also characterizes a potential predictor of performance for future applications of MI-based neurofeedback and brain-computer interfaces.
Biosensing and Theranostics Using Functionalized Magnetic and Plasmonic Nanoparticles

Multiplex biosensing techniques have been developed for rapid measurements of ultra-low concentrations of small molecules, oligonucleotides and disease biomarkers based on gold and magnetic nanoparticles. The optimal recognition receptors were selected by low-coherent interferometry. The unique for express methods detection limits were achieved: for DNA and small molecules - up to 20 fM, for protein biomarkers - up to 6 pg/ml. New methods have been proposed for targeted drug delivery with nanoparticles to suppress cancer cells in vitro and tumors in animals in vivo, e.g., for significant inhibition of growth of lung metastases of aggressive melanoma.
Extracting triples from Vietnamese text to create knowledge graph

Knowledge graph (KG) plays an increasingly important role in the current technology era. It is very useful in many fields such as searching for information, supporting question answering systems and in other AI applications, etc. Besides the private Knowledge Graphs like Google's "Knowledge graph", we also have Open Knowledge graphs as DBpedia, YAGO, . But generally, these Open Knowledge graphs contain very little data in Vietnamese. Due to this practice, our team proposed a way to create Vietnamese Knowledge graph by automatically scratching the Vietnamese text on the website as input, then using Named-entity recognition (NER) to recognize entities as nouns and combined with POS tag identifies words as verbs to extract triple in the simple sentences of the paragraph. The triple was then loaded into Neo4j to visualize the Knowledge graph.
LSFB: A Low-cost and Scalable Framework for Building Large-Scale Localization Benchmark

With the rapid development of mobile sensor, network infrastructure and cloud computing, the scale of AR application scenario is expanding from small or medium scale to large-scale environments. Localization in the large-scale environment is a critical demand for the AR applications. Most of the commonly used localization techniques require quite a number of data with groundtruth localization for algorithm benchmarking or model training. The existed groundtruth collection methods can only be used in the outdoors, or require quite expensive equipments or special deployments in the environment, thus are not scalable to large-scale environments or to massively produce a large amount of groundtruth data. In this work, we propose LSFB, a novel low-cost and scalable frame-work to build localization benchmark in large-scale environments with groundtruth poses. The key is to build an accurate HD map of the environment. For each visual-inertial sequence captured in it, the groundtruth poses are obtained by joint optimization taking both the HD map and visual-inertial constraints. The experiments demonstrate the obtained groundtruth poses are accurate enough for AR applications. We use the proposed method to collect a dataset of both mobile phones and AR glass exploring in large-scale environments, and will release the dataset as a new localization benchmark for AR.
Hybridization of Grasshopper Optimization Algorithm With Genetic Algorithm for Solving System of Non-Linear Equations

A novel algorithm for optimization in this article, called hybrid grasshopper optimization algorithm (GOA) with genetic algorithm (GA): hybrid-GOA-GA, is proposed for solving the system of non-linear equations (SNLEs). First, the SNLEs are converted into an optimization problem. Then, the optimization problem is solved by hybrid-GOA-GA. In the hybrid-GOA-GA, a population of randomized solutions is initialized. These solutions, by GOA, are looking for an optimal solution for SNLE in the domain of optimization problem. During this process, the evolution of these solutions is carried out by GA. Hybrid-GOA-GA integrates the merits of both GOA and GA; where GOA’s exploitability and GOA’s exploration potential are combined. Furthermore, hybrid-GOA-GA has good capability for escaping from local optima with faster convergence. The hybrid-GOA-GA has been tested by eight benchmarks problems which include different applications. Additionally, the effect of changing the initial intervals of the variables on the efficiency of the proposed algorithm is discussed. Also, the computational cost of the proposed algorithm is studied and compared with other methods. The results show that the hybrid-GOA-GA algorithm is superior to other algorithms, and return the best solution of SNLEs. Finally, in terms of accuracy, the effect of changing initial intervals and computational cost, the proposed approach is competitive and better in most benchmark problems compared to other methods. So, we can say that hybrid-GOA-GA is effective to solve a SNLEs.
Automatical Guardrail Design of Subway Stations through Multi-objective Evolutionary Algorithm

In subway stations, elevators are one of the most narrowed areas that slow down the moving of crowds. A large number of passengers gather around the elevator entrances and may cause unexpected accidents such as stampede. An effective way to guide the flow of passengers is to use guardrails. So far, the arrangement of guardrails in most subway stations is still designed manually, which requires rich experience and expert knowledge. In this paper, we propose to use the multi-objective evolutionary algorithm to design the guardrails of the elevator entrance automatically. The transfer time of passengers and the flow rate are optimized concurrently. The proposed algorithm is tested in two scenarios with different complexities. Experimental results show that the proposed algorithm can provide promising guardrail arrangements, and reveal some instructive conclusions for guardrail design in subway stations.
Cranial measurements of jaguars (Panthera onca) from the State of Oaxaca, Mexico

We recorded five jaguar skulls (Figure 1); four were placed in the physiographic subprovince of the Sierra Madre of Oaxaca, and one in the Pacific Coastal Plain (Ortiz-Perez et al. 2004). Three individuals were hunted in cloud forest, one in pasture for livestock contiguous to oak forest, and the other in an area with a mixture of semi-deciduous tropical forest and coffee crops. Three skulls were recorded above 1000 masl, one was placed at 800 masl and the other at 300 masl. The individuals were killed due to jaguar-livestock conflicts.
ASAF: Android Static Analysis Framework

Android Operating System becomes a major target for malicious attacks. Static analysis approach is widely used to detect malicious applications. Most of existing studies on static analysis frameworks are limited to certain features. This paper presents an Android Static Analysis Framework (ASAF) which models the overall static analysis phases and approaches for Android applications. ASAF can be implemented for different purposes including Android malicious apps detection. The proposed framework utilizes a parsing tool, Android Static Parse (ASParse) which is also introduced in this paper. Through the extendibility of the ASParse tool, future research studies can easily extend the parsed features and the parsed files to perform parsing based on their specific requirements and goals. Moreover, a case study is conducted to illustrate the implementation of the proposed ASAF.
Model-Free Decentralized Reinforcement Learning Control of Distributed Energy Resources

This paper presents a model-free decentralized control on distributed energy resources (DERs) to improve the dynamic performance of the power grid. The optimal control algorithm has been developed using the ideas from reinforcement learning (RL). The decentralized control design does not need the model information of the DERs, and the optimal control gains can be computed using the measurements of the DER states and its terminal voltage variables. The DER terminal voltages act as interaction variables with the rest of the grid, and the algorithm computes an optimal gain taking these interaction variables into consideration. Assuming sufficient coupling of DERs to the synchronous machine states, the controller improves the dynamics of DER states, which in turn enhances the oscillation characteristics of the power grid. We demonstrate various details of the learning design and the performance of the controller by simulations on the IEEE benchmark 68-bus model with two wind farms.
Valley-Hall Topological Transport in Graphene Plasmonic Crystal Waveguides

Due to immunity to disorder and structural imperfections, topologically-protected plasmonic modes have recently attracted increasing attention. Here, we introduce two different mechanisms to construct valley-Hall domain-wall interface waveguides in graphene plasmonic crystal to mimic the quantum valley-Hall effect. In the first case, we break the in-plane spatial inversion symmetry of a single-layer graphene plasmonic crystal waveguide to achieve valley-Hall topological characteristics, whereas in the second case, we break the out-of-plane spatial inversion symmetry of a bi-layer graphene plasmonic crystal waveguide to implement the analog quantum valley-Hall effect. A molecular sensor based on this valley-Hall topological transport phenomenon is also be presented.
A Study on the Relation between the Maximum Loadability Point and Undervoltage Load Shedding Schemes

In Electrical Power Systems (EPS), traditional voltage stability assessment techniques are based on the solution of algebraic equations to identify the static voltage stability margin (VSM) and the maximum loadability point (MLP) of the system. The estimation of the VSM and the MLP depends on the load growth direction and also on possible events that may occur as the load increases. The main contribution of this paper is to highlight that the actions of undervoltage load shedding (ULS) schemes are important for correct VSM and MLP estimation, which is made through a detailed explanation of the dependence of the MLP on the ULS schemes under contingencies. The paper also shows scenarios where the MLP is reached before any ULS scheme is triggered. The shape of the PV curves in these scenarios where no ULS scheme is activated is shown for a reduced order model of the South-Southeastern part of the Brazilian Interconnected Power System.
Comparative Analysis of Tagging and Feature-Tracking Cardiac MRI Techniques for the Evaluation of Cardiac Deformation

Currently, there is a high prevalence of cardiovascular diseases, being the leading cause of mortality worldwide. Thus, a rapid and effective evaluation of the heart is increasingly relevant to diagnose the patient early and achieve appropriate treatment. For this reason, this study aims to compare myocardial deformation parameters such as circumferential strain (segmental, base, mid, apical and global) and global torsion values by the CMR tissue tagging reference technique in SPAMM cine images and the promising featuretracking technique in b-SSFP cine images, in 31 healthy subjects and 35 patients diagnosed with infarct. The results obtained showed that there is a high agreement in the inter-technique and inter-observer reproducibility for all myocardial parameters evaluated. Therefore, it can be determined the CMR featuretracking technique is highly reproducible for circumferential strain and torsion parameters and could currently be a reliable clinical alternative to assess myocardial function.
Determination of Vitamin A in Infant/Adult Formula by HPLC-Isotope Dilution Mass Spectrometry

Abstract Vitamin A is an essential nutrient. Accurate determination of vitamin A in formulae or serum is a challenging problem, due to the labile properties of vitamin A and complexity of the matrices. For accurate and precise determination of vitamin A in formulae, a method of high performance liquid chromatography-isotope dilution mass spectrometry (HPLC-IDMS) was established with D4-retinyl palmitate as an internal standard. This method was validated by a standard reference material. Coefficient of variation of this method is 2.85%, which is less than that of other literatures for determination of vitamin A in complex matrices (5–10%). This method was applied to participate in an international intercomparison. The result indicated that this method is an accurate and precise method for determination of vitamin A in complex matrices especially in formulae.
Interpretable Global-Local Dynamics for the Prediction of Eye Fixations in Autonomous Driving Scenarios

Human eye movements while driving reveal that visual attention largely depends on the context in which it occurs. Furthermore, an autonomous vehicle which performs this function would be more reliable if its outputs were understandable. Capsule Networks have been presented as a great opportunity to explore new horizons in the Computer Vision field, due to their capability to structure and relate latent information. In this article, we present a hierarchical approach for the prediction of eye fixations in autonomous driving scenarios. Context-driven visual attention can be modeled by considering different conditions which, in turn, are represented as combinations of several spatio-temporal features. With the aim of learning these conditions, we have built an encoder-decoder network which merges visual features’ information using a global-local definition of capsules. Two types of capsules are distinguished: representational capsules for features and discriminative capsules for conditions. The latter and the use of eye fixations recorded with wearable eye tracking glasses allow the model to learn both to predict contextual conditions and to estimate visual attention, by means of a multi-task loss function. Experiments show how our approach is able to express either frame-level (global) or pixel-wise (local) relationships between features and contextual conditions, allowing for interpretability while maintaining or improving the performance of black-box related systems in the literature. Indeed, our proposal offers an improvement of 29% in terms of information gain with respect to the best performance reported in the literature.