Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Deformed Barcode Images with Scanpath Walkthrough

Most camera-based barcode reading systems are scanline-based and designed to work with barcodes attached on flat or smooth surfaces. These systems are likely not able to deal with barcodes on surfaces with high level of deformation, thus making it difficult to find a scanline passing through readable regions. In this paper, an approach that addresses the decoding of deformed barcode images is presented. Our method is based on constructing a scangraph that covers over the area of the barcode, and creating a scanpath that walks through the scangraph by avoiding the regions that are less readable. Barcode digit templates are then used to decode the data sampled along the scanpath. Preliminary results show that some challenging deformed barcode images can be correctly decoded with the presented technique.
Thermal Comfort and Stress Recognition in Office Environment

Work stress and thermal discomfort are some of the hurdles that office workers face every day. Office workers experience a periodic work stress because work is long and mentally challenging. At the same time, current thermal comfort provision technologies are inefficient and consume a large amount of energy. In our previous works, we proposed an efficient thermal comfort provision system that is based on a person's heart rate variability (HRV). However, because work stress can also affect the person's HRV, this paper investigates the possibility to distinguish HRV changes that are due to thermal discomfort from changes that emanate from work stress. We conducted experiments on subjects taking Advanced Trail Making Test (ATMT) and observed that stress alters HRV and that it is possible to distinguish stressed and non-stressed subjects with a 100% accuracy. We validated our method on the multimodal SWELL knowledge work (SWELL-KW) stress dataset and achieved similar results (99.25% accuracy and 99.75% average recall). Further analysis suggests that, although both thermal comfort and work stress affect HRV, their effect is perhaps non-overlapping, and that the two can be distinguished with a near-perfect accuracy. These results indicate that it could be possible to design an automatic and unobtrusive system that delivers thermal comfort and predicts work stress based on people's HRV
Anisotropic and temperature dependent mechanical properties of carbon honeycomb

Carbon honeycomb (CHC) is a newly synthesized carbon allotropy with promising applications in many fields of science and engineering. In this work, we investigate the mechanical properties of CHC with focus on the anisotropicity in terms of the tilt angle θ in zigzag-armchair (x-y) plane using molecular dynamics simulations. Results show that the tensile strength of CHC ranges from 15.0 to 23.7 GPa at room temperature, which is lower than that of graphene due to the weakness on the junction. Meanwhile, except in the armchair direction, the strength of CHC reduces as the stretching direction moves away from the zigzag direction, similar to that of graphene, while the Young’s modulus decreases with respect to tilt angle, opposite to that of graphene. Increasing the temperature will weaken CHC by reducing the strength, there is only a 16% reduction in the minimum strength in the x-y plane as the temperature increases from 100 to 900 K. In addition, the crack occurs first in cell axis direction then in the x-y plane, different from graphene which appears along the zigzag direction only.
A fuzzy cognitive maps model to develop a risk analysis model through the identification of critical human factors

Over the past years, technological developments have led to a decrease of accidents due to technical failures through the use of redundancy and protection. However, the “human factor” contributes significantly in accident dynamics, both statistically and in terms of severity of consequences. In fact, estimates agree that the errors committed by man are causes over 60% of accidents and for the remaining part the causes are due to technical deficiencies. This paper deals with various aspects of human behavior that can influence operator reliability, considering the environment in which operator is working. The aim of the research is to propose a fuzzy cognitive map capable to represent human knowledge where the relationships are difficult to describe in mathematical terms.
Evaluation of an Eco Audit tool - through an LCA of a novel car disc brake

Transport of goods and people is increasing and causing strains on the environment. Road vehicles emit exhaust and non-exhaust emissions. One significant contributor to non-exhaust emissions is particulates generated through wear from braking. The particulates originate from the contact surfaces of the pad and the disc. Particulate emission is a known issue with considerable impacts on plant, animal, and human health. In the EU Horizon 2020 LOWBRASYS (a LOW environmental impact BRAke SYStem) project(LOWBRASYS, 2017), one of the objectives was to design a novel disc brake that reduces particulate generation during braking. One of the results is a novel disc brake with disc and pad-materials that indicate a significant decrease in particulate formation during use. This is accomplished by changing the materials of the contact pair regarding composition and coatings (Wahlstrom, Lyu, Matjeka, & Soderberg, 2017). Materials used in the disc brakes cause environmental impacts during their life cycle. Some parts and processes need for example critical raw materials such as tungsten, cobalt, and more(European Commission, 2017). This paper evaluates a material selection tool with an environmental perspective for product developers called Eco Audit (Ashby et al. 2008). This tool is featured in the CES Edu Pack software provided by Granta Design, Cambridge University (Granta 2018). The purpose of this study is to evaluate if the Eco Audit tool can provide a fast and valid impact assessment from anLCAperspective. Results of the Eco Audit compared to the SimaPro results indicate that it is possible to make valid conclusions. The validity of the tool is connected to the purpose of the study. If the purpose is to identify critical life cycle phases and environmental impacts, then the tool can accurately aid the user. It could potentially be difficult to make valid conclusions when assessing a product with more complex processes or advanced materials. The tool’s strengths are the simplicity and easy accessibility for any user. The trade-off is precision, robustness, and representativeness of the target
Block Compressed Sensing of Self-adaptive Measurement and Combinatorial Optimization

The block compressed sensing has brought forth the problem that the reconstructed image is of lower quality compared with that of the compressed sensing. A new method is proposed in this paper, named as Block Compressed Sensing of Self-adaptive Measurement and Combinatorial Optimization, which capably solves the problem. According to different sparsity of each image block, we firstly measure the blocks by using different projections; then, we choose measurement with the optimal reconstruction as the final measurement. Eventually, reconstruct the original image using the optimal measurement we got. The proposed method outperforms the compressed sensing in terms of real-time and better reconstruction quality is achieved than the block compressed sensing. Our experimental results verify the superiority of the proposed method.
TID characterization of COTS parts using radiotherapy linear accelerators

Ionizing radiation can disturb electrical and electronic devices. These devices need to be tested and qualified to operate under special environment conditions such those present in nuclear or space applications. Radiation effects must be considered when designing systems that must have high mean time between failures and are exposed to radiation. This paper shows how common radiotherapy linear accelerators (LINAC) can be used to characterize the Total Ionizing Dose (TID) effects on electronic devices. This work presents the principal radiation-induced effects on a Raspberry Pi Compute Module board, as good representative of typical Commercial Off-The-Shelf components. The Monte Carlo method used and the procedure for TID characterization are shown.
Replication dynamics of individual loci in single living cells reveal changes in the degree of replication stochasticity through S phase

Abstract Eukaryotic genomes are replicated under the control of a highly sophisticated program during the restricted time period corresponding to S phase. The most widely used replication timing assays, which are performed on populations of millions of cells, suggest that most of the genome is synchronously replicated on homologous chromosomes. We investigated the stochastic nature of this temporal program, by comparing the precise replication times of allelic loci within single vertebrate cells progressing through S phase at six loci replicated from very early to very late. We show that replication timing is strictly controlled for the three loci replicated in the first half of S phase. Out of the three loci replicated in the second part of S phase, two present a significantly more stochastic pattern. Surprisingly, we find that the locus replicated at the very end of S phase, presents stochasticity similar to those replicated in early S phase. We suggest that the richness of loci in efficient origins of replication, which decreases from early- to late-replicating regions, and the strength of interaction with the nuclear lamina may underlie the variation of timing control during S phase.
Parameters estimation from remote sensing data for the generation of virtual 3D city models

Virtual 3D city generation is an essential element in various economical areas such as video games, virtual environments for the film industry, electro-magnetic propagation for the design of telecommunication infrastructures, pollution propagation estimation. Traditionally it must be constructed by hand in a 3D application by a human operator. While this approach gives the operator full control, it requires skills and a lot of time to complete an entire city. In the last years, several tools have been proposed for the automation of the process. However, many parameters such as the width and the length of the roads and the size and the height of the buildings are still set by the operator or randomly generated. The purpose of this project is to develop new tools for the estimation of these parameters from the automated analysis of remote sensing data. High resolution aerial imagery with vertical and oblique viewing angle are convenient sources to estimate the distribution of morphological parameters related to the shape of the terrain, the size and the height of the buildings, and the structure of the road network. In this document we will explain an approach for the estimation of some of these parameters and we will test if it’s possible to use this approach also for aerial raw images.
Discovering potential blood-based cytokine biomarkers for Alzheimer’s disease using Firth Logistic Regression

Background: Alzheimer’s disease (AD) is a neurodegenerative disorder where patients suffer from memory loss, cognitive impairment and progressive disability. Individual blood biomarkers have not been successful in defining the disease pathology, progression and diagnosis of AD. There is a need to identify multiplex panels of blood biomarkers for early diagnosis of AD with high sensitivity and specificity. This study focused on identification of cytokine biomarkers. The maximum likelihood estimates of the ordinary logistic regression model cannot be obtained when there is complete separation and the alternative is Firth logistic regression which uses a penalised Maximum Likelihood in parameter estimation. Methods: This paper reports a Firth logistic regression application in finding potential blood-based cytokine biomarkers for Alzheimer’s disease in a matched case control study. We used a principle component analysis to discriminate the correlated, completely separated covariates. Results: The Firth logistic regression results showed that nine individual biomarkers IL-1β, IL-6, IL-12, IFN-γ, IL-10, IL-13, IP-10, MCP-1 and MIP-1α had a significant relationshipwith elevated risk for AD as compared to the healthy control (HC). Principal component analysis with varimax rotation for the nine biomarkers revealed four factors (total variance explained=85.5%). The main principal component biomarkers were IL-1β, IL-6, IL-13 and MCP-1 (total variance explained=62.3%). Firth’s logistic regression model with the first principal component had accuracy of 78.2% with sensitivity and specificity of 71.8% and 75% respectively. Conclusion: Firth’s logistic regression is a useful technique in identification of significant biomarkers when there is an issue of data separation.
Machine Learning Methods for functional Near Infrared Spectroscopy

Identification of user state is of interest in a wide range of disciplines that fall under the umbrella of human machine interaction. Functional Near Infra-Red Spectroscopy (fNIRS) device is a relatively new device that enables inference of brain activity through non-invasively pulsing infra-red light into the brain. The fNIRS device is particularly useful as it has a better spatial resolution than the Electroencephalograph (EEG) device that is most commonly used in Human Computer Interaction studies under ecologically valid settings. But this key advantage of fNIRS device is underutilized in current literature in the fNIRS domain. We propose machine learning methods that capture this spatial nature of the human brain activity using a novel preprocessing method that uses ‘Region of Interest’ based feature extraction. Experiments show that this method out performs the F1 score achieved previously in classifying ‘low’ vs ‘high’ valence state of a user. We further our analysis by applying a Convolutional Neural Network (CNN) to the fNIRS data, thus preserving the spatial structure of the data and treating the data similar to a series of images to be classified. Going further, we use a combination of CNN and Long Short-Term Memory (LSTM) to capture the spatial and temporal behavior of the fNIRS data, thus treating it similar to a video classification problem. We show that this method improves upon the accuracy previously obtained by valence classification methods using EEG or fNIRS devices. Finally, we apply the above model to a problem in classifying combined task-load and performance in an across-subject, across-task scenario of a Human Machine Teaming environment in order to achieve optimal productivity of the system. MACHINE LEARNING METHODS FOR FUNCTIONAL NEAR INFRARED SPECTROSCOPY
Dexmedetomidine for the prevention of emergence delirium and postoperative behavioral changes in pediatric patients with sevoflurane anesthesia: a double-blind, randomized trial

Objectives Emergence delirium (ED) is a common neurologic complication that can not only distress children and their families in the early postanesthetic period, but can also have adverse effects on children in the long-term. This study aimed to investigate the effects of single-dose dexmedetomidine on ED in children with sevoflurane anesthesia and to observe postoperative behavioral changes through long-term follow-up. Methods Patients aged 2–7 years, American Society of Anesthesiologists class (ASA) I or II, scheduled for tonsillectomy with and without adenoidectomy were randomized to receive dexmedetomidine 0.5 μg/kg (Group D) or volume-matched normal saline (Group C) over 10 minutes after induction of anesthesia. The primary outcome was the incidence of ED within 30 minutes after extubation. Other outcomes were the incidence of pain, extubation time, post-anesthesia care unit (PACU) length of stay after extubation, adverse events, and the incidence of negative postoperative behavioral changes (NPOBCs). Results Ninety children completed the study. Compared with the control group (Group C), dexmedetomidine decreased the incidence of ED (31.1% vs 53.3%; P=0.033) and pain (28.9% vs 57.8%; P=0.006), but it prolonged extubation time (P⩽0.001). PACU length of stay after extubation and the percentage of adverse events were similar between groups. The incidence of NPOBCs in Group D was significantly lower at 1 and 7 days after discharge (33.3% vs 60.0%; P=0.011% and 24.4% vs 46.7%; P=0.028, respectively) than it was in Group C, but no significant difference was found at the 30th day. Conclusion Dexmedetomidine 0.5 μg/kg reduced the incidence of ED after sevoflurane anesthesia and might be used to prevent NPOBCs. Clinical trials registration ChiCTR1800016828.
A Practical Attribute-Based Document Collection Hierarchical Encryption Scheme in Cloud Computing

Ciphertext-policy attribute-based encryption can provide fine-grained access control and secure data sharing to the data users in cloud computing. However, the encryption/decryption efficiency of existing schemes can be further improved when encrypting a large document collection. In this paper, we propose a practical Ciphertext-Policy Attribute-Based Hierarchical document collection Encryption scheme named CP-ABHE. By practical, we mean that CP-ABHE is more efficient in both computation and storage space without sacrificing data security. In CP-ABHE, we first construct a set of integrated access trees based on the documents’ attribute sets. We employ the greedy strategy to build the trees incrementally and grow the trees dynamically by combining the small ones. Then, all the documents on an integrated access tree are encrypted together. Different to existing schemes, the leaves in different access trees with the same attribute share the same secret number, which is employed to encrypt the documents. This greatly improves the performance of CP-ABHE. The security of our scheme is theoretically proved based on the decisional bilinear Diffie–Hellman assumption. The simulation results illustrate that CP-ABHE performs very well in terms of security, efficiency, and the storage size of the ciphertext.
PERFORMANCE PREDICTIONS OF AIR-COOLED CONDENSERS HAVING CIRCULAR AND ELLIPTIC CROSS-SECTIONS WITH ARTIFICIAL NEURAL NETWORKS

In this study, mathematical models of air cooled condensers with circular and elliptic cross-sections were developed and performances were evaluated with artificial neural networks. Air velocity, orientation angle and ambient temperature were used as the input to the neural network structure while heat transfer rate to the air was used as the output. The data sets were generated from high fidelity, computationally inefficient expensive three dimensional computational fluid dynamics simulations. It was observed that artificial neural network model replaces computational fluid dynamics model and based on the mathematical model with artificial neural network, elliptic condensers perform better in terms of heat transfer compared to circular ones.
Compression-Based LMMSE Channel Estimation With Adaptive Sparsity for Massive MIMO in 5G Systems

Massive multi-input multi-output (MIMO) has been regarded as one of the key technologies for fifth generation (5G) mobile communication systems, as it can significantly enhance the system capacity with high spectrum and energy efficiency. For massive MIMO systems, accurate channel estimation is a challenging problem, especially when the number of parameters to be estimated is large and the number of pilots is limited. In this paper, a compression-based linear minimum mean square error (CLMMSE) channel estimation algorithm is proposed for massive MIMO in 5G systems. Compared with the traditional linear minimum mean square error (LMMSE) algorithm, the proposed approach calculates the channel autocorrelation matrix by investigating the channel prior information based on compressive sensing (CS) theory, utilizing the block sparsity of massive MIMO channels, to reduce the complexity for obtaining autocorrelation matrix. Then it substitutes matrix inverse operation by singular value decomposition to further reduce the computational complexity. In addition, a block sparsity adaptive matching pursuit (BSAMP) method is also proposed to adaptively estimate the block sparsity of the channel in the first step of the proposed CLMMSE algorithm, which can make it more efficient. The sparsity-adaptive processing is achieved by setting a threshold and finding the position of the maximum backward difference, then using the regularized method to solve channel estimation as a convex optimization problem. Analyses and simulations indicate that with slight performance degradation, the proposed algorithm reduces the computational complexity significantly compared with the traditional LMMSE algorithm. And compared with pure CS methods, CLMMSE has an obviously better performance, which is beneficial to solve the pilot pollution problem of massive MIMO in 5G systems. Furthermore, the BSAMP-based CLMMSE algorithm has better performance and lower time complexity than the algorithm based on other CS methods, which further improves the system performance.
Optimal Transport for Multi-source Domain Adaptation under Target Shift

In this paper, we propose to tackle the problem of reducing discrepancies between multiple domains referred to as multi-source domain adaptation and consider it under the target shift assumption: in all domains we aim to solve a classification problem with the same output classes, but with labels' proportions differing across them. This problem, generally ignored in the vast majority papers on domain adaptation papers, is nevertheless critical in real-world applications, and we theoretically show its impact on the adaptation success. To address this issue, we design a method based on optimal transport, a theory that has been successfully used to tackle adaptation problems in machine learning. Our method performs multi-source adaptation and target shift correction simultaneously by learning the class probabilities of the unlabeled target sample and the coupling allowing to align two (or more) probability distributions. Experiments on both synthetic and real-world data related to satellite image segmentation task show the superiority of the proposed method over the state-of-the-art.
Region Adaptive Single-image Super-resolution Using Wavelet Transform

An efficient single-image super-resolution based on the discrete wavelet transform is proposed. The low-resolution image to be enhanced is assumed to be the low-frequency subband of the high-resolution image to be reconstructed. A support vector machine that synthesizes the high-frequency subband based on the inter-subband correlation of the directional edges is designed. Subband coefficients are classified into one of homogeneous region, edge region, and textured region. The support vector machine is adaptively trained for each region. Experimental results of sample images show that the proposed system offers improvements in terms of both measured distortion and subjective appearance.
Generating Frequent Itemsets by RElim on Hadoop Clusters

Data mining is considered as the process of extracting the useful information by finding the hidden information out of large chunks of dataset. Frequent itemset mining is the popular data mining methods. MapReduce has turn out to be an important distributed processing model for large-scale data-intensive applications like data mining. MapReduce is an efficient, scalable, and easy programming model for large-scale distributed data processing on a huge cluster of commodity computers. In this paper, RElim algorithm is implemented on MapReduce framework.