Miracle-dz/UniFAR-FTUs huggingface dataset:
Boosting the Deep Multidimensional Long-Short-Term Memory Network for Handwritten Recognition Systems

One of the main challenges in the handwriting recognition area lies in identifying complete lines of handwritten text. In this paper, we propose a handwriting recognition system based on a deep multidimensional long-short-term memory (MDLSTM) network within a hybrid hidden Markov model framework. The MDLSTM architecture was elaborated to enhance the recognition performance and decrease the recognition time. Accordingly, we present modifications regarding the layers order and the number of pooling layers compared to a standard MDLSTM model. Since the results reported in the literature for deeper MDLSTM architectures relies on optimizing the network width with a fixed depth, we investigate the trade-off between both these properties to obtain an optimal topology. The system was evaluated with English handwritten text lines from the IAM database and the experiments demonstrated that the proposed MDLSTM architecture was able to maintain a robust recognition performance (around 3.6% CER and 10.5% WER) and present significant speedups, approximately 48% and 32% faster than the state-of-the-art MDLSTM optical model, regarding the learning and classification times, respectively. The full system including a decoder with linguistic knowledge presents competitive results with the state-of-the-art.
An Automatic Multi-String Musical Instrument Tuner using One-to-Many Micro Actuating Mechanism

In this research, the one-to-many micro actuating mechanism is designed and implemented on a multi-string musical instrument as a part of an automatic string tuning system. The tuning process has been tested on the three highest treble strings of Thai hammered dulcimer. A pickup sensor is placed under each string to identify and monitor the signal amplitude when the string is hit. Goertzel algorithm is applied to calculate the initial frequency of the strings. Tuning time for each string is predicted in advance. The micro actuating modules, then adjust the tension of the strings to the target frequency for the predicted tuning time period. The process can be repeated until the target frequency is reached. The experimental results show that, by using a single motor drive, the proposed micro actuating modules can successfully tune three strings of the different initial frequencies to the same target frequency simultaneously and independently.
An HMI Concept to Improve Driver's Visual Behavior and Situation Awareness in Automated Vehicle

At a level-3 or higher level automation [1], a driver does not have to constantly monitor the vehicle and environment while driving, which enables the driver to conduct non-driving-related tasks (NDRTs) and be out of the control loop. This may influence a driver's visual behavior, cognitive states, which leads to loss of situation awareness (SA) and skills. This is dangerous if the automated system reaches its boundaries: the driver must take-over the driving task in a critical situation within a limited period of time. In this paper, a concise HMI concept of the LED ambient light positioned at the bottom of the windscreen is presented, which contains information about the status and intention of the automation, detected potential hazards and the warning for a take-over request (TOR) by varying the LED's color, frequency, lighting position and animation. The goal is to increase the SA during automated driving and improve the take-over quality while allowing the driver to perform NDRTs without distraction and annoyance. In this between-subject-design experiment in a static driving simulator, 50 participants performed a visual-motoric task on a smartphone during a 45-min automated drive with or without the new HMI. Compared to the baseline, results show significant improvements in the gaze behavior and take-over quality. The new HMI also shows a high acceptance and increases the trust in automation while avoiding overtrust.
Secrecy Analysis of Distributed CDD-Based Cooperative Systems With Deliberate Interference

In this paper, a cooperative cyclic-prefixed single carrier (CP-SC) system is studied and a scheme to improve its physical layer security is proposed. In particular, a distributed cyclic delay diversity (dCDD) scheme is employed and a deliberate interfering method is introduced, which degrades the signal-to-interference-plus-noise ratio (SINR) over the channels from a group of remote radio heads (RRHs) to an eavesdropper, while minimizing the signal-to-noise ratio loss over the channels from the RRHs to an intended user. This is obtained by selecting one RRH that acts as an interfering RRH and transmits an interfering artificial noise sequence to the eavesdropper. Through the use of the dCDD scheme, a channel that minimizes the receive SINR at the eavesdropper is selected for the interfering RRH. This choice enhances the secrecy rate of the CP-SC system. The system performance is evaluated by considering the secrecy outage probability and the probability of non-zero achievable secrecy rate, which are formulated in closed-form analytical expressions for the case of identically and non-identically distributed frequency selective fading channels. Based on the proposed analytical framework, the diversity order of the system is studied. Monte Carlo simulations are employed to verify the analytical derivations for numerous system scenarios.
Unilateral Chronic Lung Allograft Dysfunction Assessed by Biphasic Computed Tomographic Volumetry in Bilateral Living-donor Lobar Lung Transplantation

Background Early diagnosis of unilateral chronic lung allograft dysfunction (CLAD) is difficult because the unaffected contralateral lung functions as a reservoir in bilateral living-donor lobar lung transplantation (LDLLT). We previously reported the usefulness of 133Xe ventilation scintigraphy for detection of unilateral change, but the supply of 133Xe has been stopped globally. The present study aimed to examine the usefulness of inspiratory and expiratory computed tomography (I/E CT) volumetry for detection of unilateral change in CLAD patients. Methods This was a retrospective single-center, observational study using prospectively collected data. A total of 58 patients who underwent bilateral LDLLT from August 2008 to February 2017 were analyzed. Respiratory function tests, I/E CT were prospectively conducted. &Dgr;Lung volume was defined as the value obtained by subtracting expiratory lung volume from inspiratory lung volume. Results Fourteen (24%) cases were clinically diagnosed with CLAD, of which 10 (71%) were diagnosed as unilateral CLAD. &Dgr;Lung volume of bilateral lungs strongly correlated with forced vital capacity (r = 0.92, P < 0.01) and forced expiratory volume in 1 second (r = 0.80, P < 0.01). Regardless the phenotypes (bronchiolitis obliterans syndrome or restrictive allograft syndrome) of CLAD, &Dgr;lung volume onset/baseline significantly decreased compared with that in the non-CLAD group. Among the 10 unilateral CLAD patients, 3 with clinically suspected unilateral rejection yet did not show a 20% decline in forced expiratory volume in 1 second. In 2 of these, &Dgr;lung volume of unilateral lungs on the rejection side decreased by 20% or more. Conclusions Our findings suggest that I/E CT volumetry may be useful for assessment and early diagnosis of unilateral CLAD after bilateral LDLLT.
Ship Detection in Optical Satellite Images Using Haar-like Features and Periphery-Cropped Neural Networks

The ship detection field faces many challenges due to the large-scale and high complexity of optical remote sensing images. Therefore, an innovative ship detection method that is simple, accurate, and stable is proposed in this paper. The algorithm consists of the following two steps: 1) the AdaBoost classifier, combined with Haar-like features, is used to rapidly extract candidate area slices, and 2) according to the characteristics of ships, a periphery-cropped network is designed for ship verification. Furthermore, we analyze the characteristics of ocean images to improve the contrast between the target and the background. Thus, an RGB spectrum-stretching method is proposed. Finally, we evaluate our method using spaceborne optical images from the Jilin-1 satellite, Google satellites, and the public dataset NWPU VHR-10. Our experimental results indicate that the proposed algorithm achieves a high detection rate.
PD-1/PD-L1 Axis, Rather Than High-Mobility Group Alarmins or CD8+ Tumor-Infiltrating Lymphocytes, Is Associated With Survival in Head and Neck Squamous Cell Carcinoma Patients Who Received Surgical Resection

In current studies, the influence of tumor immune microenvironment on tumorigenesis and tumor progression has been widely explored. In the present study, we investigated the expression and significance of high mobility group box 1 (HMGB1), HMG nucleosome-binding protein 1 (HMGN1), the receptor programmed cell death 1 (PD-1) and its ligand programmed cell death ligand 1 (PD-L1) in head and neck squamous cell carcinoma (HNSCC). We explored whether HMGB1 and HMGN1 take part in recruiting T cells to HNSCC microenvironment. Furthermore, we assessed the prognostic value of HMG proteins, TILs, and PD-1/PD-L1 in postoperative patients. Tumor tissue sections were collected from 81 cases of patients with resectable HNSCC. All patients' information was integrated with clinical and pathological records, as well as follow-up data. We used immunohistochemistry to examine the subcellular localization and expression levels of HMGB1 and HMGN1, as well as tumor CD3+, CD8+, FOXP3+ lymphocyte infiltration, and the expression of immune inhibiting molecules PD-1/PD-L1. Results showed that there was no significant difference in the number of CD8+ and FOXP3+ T cells between the two groups with or without HMGB1 cytoplasmic expression in tumor tissues. The number of CD3+ T cells in HMGB1 cytoplasmic expression group (339.39 ± 230.76) was more than that in group without HMGB1 cytoplasmic expression (233.30 ± 230.91, P < 0.05). The number of CD3+, CD8+, and FOXP3+ T cells in HMGN1 cytoplasmic expression group [400.74 ± 224.04, 158.10 ± 112.10, 36.00(15.00, 69.00)] was more than that in group without HMGN1-cytoplasmic expression [222.84 ± 217.78, P < 0.01; 105.10 ± 108.25, P < 0.05; 13.00(6.75, 32.25), P < 0.01]. The positive rates of PD-1 and PD-L1 in tumor tissues were 29.6 and 67.9%, respectively. Multivariate analysis suggested that tumor expression of PD-L1 was an independent prognostic factor and PD-L1 overexpression indicated a poor overall survival (OS) and disease-free survival (DFS). Taken together, we concluded that HMGB1 and HMGN1 secreted by cancer cells may relate to recruitment of tumor infiltrating lymphocytes (TILs) in HNSCC. PD-1/PD-L1 axis, rather than HMG proteins or CD8+ tumor-infiltrating lymphocytes, has a critical role in tumor immune microenvironment and could predict the outcome of HNSCC patients who received surgical resection.
A Generic Approach towards Maneuver Coordination for Automated Vehicles

Increasing the predictability of traffic participants is the key for developing safe Automated Vehicles (AVs). Whereas most research focuses on enhanced environment perception and vehicle trajectory planning methodologies, this contribution proposes a generic concept employing inter-vehicle communications to coordinate maneuvers of AVs. Not targeting a specific application, the concept is based on a generic description of vehicle behaviors, leveraging the well-known concept of Frénet frames. By sharing a vehicle's planned maneuver with surrounding traffic participants, inter-vehicle communications reduces the uncertainty associated to the prediction of future vehicle maneuvers. In case of conflicting maneuvers, vehicles are given the opportunity to disseminate a desired trajectory in addition to its planned trajectory. Maneuver coordination is then established in a three-step implicit distributed fashion, based on common right-of-way rules and on both disseminated types of trajectories. Next to the introduction of the maneuver coordination process, this contribution elaborates on related research questions and provides an outlook on an extension to a well-known microscopic simulation environment to analyze effects of the maneuver coordination process on inter-vehicle communications and traffic efficiency. The proposed concept aims at providing a generic concept to reach coordination between Connected and Automated Vehicles (CAVs).
Disentangled Dynamic Representations from Unordered Data

We present a deep generative model that learns disentangled static and dynamic representations of data from unordered input. Our approach exploits regularities in sequential data that exist regardless of the order in which the data is viewed. The result of our factorized graphical model is a well-organized and coherent latent space for data dynamics. We demonstrate our method on several synthetic dynamic datasets and real video data featuring various facial expressions and head poses.
Creating Important Statement Type Comments in Autocomment: Automatic Comment Generation Framework

This study explains important statement comments generated by Autocomment, which is an automatic comment generation framework described in [1]. To create the comments, the source code is not expected to be runnable, but to conform to the syntax rules of the Java programming language. Two types of comments are created: summary comments and important statement comments. How the important statements are extracted in a given Java source, how they are classified, how comments are generated for each type of important statement have been explained in this work.
Factors Affecting the Successful Adoption of e-Health Cloud Based Health System From Healthcare Consumers’ Perspective

Cloud computing in healthcare has witnessed a major development in recent years due to its remote access capabilities among others. Studies have shown that it has attracted great attention in the field of healthcare. However, research studies show a number of healthcare consumers are yet to accept the technology, especially in developing countries due to reasons, such as the data security and the improper utilization of available information and communication technologies (ICTs) in healthcare. This paper therefore aims to identify factors affecting healthcare consumers’ attitude toward the adoption of the cloud-based health center. Questionnaires were administered to 465 respondents in four locations in Benue, Nigeria with 76.9% response rate. The analysis of the data was conducted with statistical package for social science (20.0), factor analysis, and LISREL (9.30), was used to determine the structural path model. Using the social technical design approach, we developed the cloud-based health center which will provide access to healthcare services remotely to rural communities and reduce the cost/time of medical healthcare delivery when implemented into the Nigerian healthcare system. The results show that performance expectancy (<inline-formula> <tex-math notation="LaTeX">$\beta =0.31$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t=5.80$ </tex-math></inline-formula>), effort expectancy (<inline-formula> <tex-math notation="LaTeX">$\beta = 0.19$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t= 3.90$ </tex-math></inline-formula>), social influence (<inline-formula> <tex-math notation="LaTeX">$\beta = 0.21$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t=3.95$ </tex-math></inline-formula>), facilitating conditions (<inline-formula> <tex-math notation="LaTeX">$\beta = 0.22$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t=4.82$ </tex-math></inline-formula>), data security (<inline-formula> <tex-math notation="LaTeX">$\beta = 0.15$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t=3.07$ </tex-math></inline-formula>), and information sharing (<inline-formula> <tex-math notation="LaTeX">$\beta =0.11$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t=2.53$ </tex-math></inline-formula>) had a significant impact on the behavioral intention of healthcare consumers. However, cloud-based health knowledge (<inline-formula> <tex-math notation="LaTeX">$\beta = 0.09$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$t=1.49$ </tex-math></inline-formula>) was found to be statistically not significant. With these findings, healthcare policy makers must think carefully before the implementation of cloud-based health center. Otherwise, the integration will continue to create a challenge.
Visual Quality Enhancement Of Images Under Adverse Weather Conditions

The visual quality of an image captured by vision systems can degrade significantly under adverse weather conditions. In this paper we propose a deep learning based solution to improve the visual quality of images captured under rainy and foggy circumstances, which are among the prominent and common weather conditions that attribute to bad image quality. Our convolutional neural network(CNN), NVDeHazenet learns to predict both the original signal as well as the atmospheric light to finally restore image quality. It outperforms the existing state of the art methods by evaluation on both synthetic data as well as real world hazy images. The deraining CNN, NVDeRainNet shows similar performance on existing rain datasets as the state of the art. On natural rain images NVDeRainNet shows better than state of the art performance. We show the use of perceptual loss to improve the visual quality of results. These networks require considerable amount of data under adverse weather conditions and their respective ground truth for training. For this purpose we use a weather simulation framework to simulate synthetic rainy and foggy environments. This data is augmented with existing rain datasets to train the networks.
An Implementation Analysis of Communications, Navigation, and Surveillance (CNS) Technologies for Unmanned Air Systems (UAS)

The aviation industry and government agencies face a rapidly-emerging need for integrating large-scale populations of Unmanned Air Systems (UAS) into the worldwide controlled and uncontrolled airspace. Critical components for integration include the Communications, Navigation, and Surveillance (CNS) technologies necessary for ensuring safe UAS operations. Under NASA program NNA16BD84C, our work on CNS architectural concepts for the safe operation of UAS in controlled and uncontrolled airspace has introduced CNS architectures which must be analyzed in terms of implementation readiness. Controlled airspace operations for UAS are consistent with the needs for manned aviation in the worldwide Air Traffic Management (ATM) service. Uncontrolled airspace operations are consistent with the NASA Unmanned (air) Traffic Management (UTM) concept of operations. Implementation readiness is based on the NASA concept of Technology Readiness Levels (TRLs) ranging from TRL1 (basic principles observed and reported) to TRL9 (actual system flight proven through successful mission operations). In the architecture concepts, we have introduced a number of new CNS architectural elements which need to be correlated with TRL levels. In this paper, we present our implementation analysis for communications networks, communications data links, navigation, and surveillance. Each area has been under active research and development during the course of the current NASA program which has produced studies on UAS CNS Requirements, UAS CNS Architecture for Controlled Airspace and UAS CNS Architecture for Uncontrolled Airspace. We have published our architecture concepts in major UAS-related conferences (including iCNS2017, IEEE Aerospace 2018, and iCNS2018) and will continue to seek additional publication opportunities. We look forward to continuing our work to realize a full integration testing scenario for both controlled and uncontrolled airspace operation.
From Coarse to Fine: Robust Hierarchical Localization at Large Scale

Robust and accurate visual localization is a fundamental capability for numerous applications, such as autonomous driving, mobile robotics, or augmented reality. It remains, however, a challenging task, particularly for large-scale environments and in presence of significant appearance changes. State-of-the-art methods not only struggle with such scenarios, but are often too resource intensive for certain real-time applications. In this paper we propose HF-Net, a hierarchical localization approach based on a monolithic CNN that simultaneously predicts local features and global descriptors for accurate 6-DoF localization. We exploit the coarse-to-fine localization paradigm: we first perform a global retrieval to obtain location hypotheses and only later match local features within those candidate places. This hierarchical approach incurs significant runtime savings and makes our system suitable for real-time operation. By leveraging learned descriptors, our method achieves remarkable localization robustness across large variations of appearance and sets a new state-of-the-art on two challenging benchmarks for large-scale localization.
Lack of sexual dimorphism on the inhibitory effect of alcohol on muscle protein synthesis in rats under basal conditions and after anabolic stimulation

Previous studies indicate women have a higher blood alcohol (i.e., ethanol) and acetaldehyde concentration after consuming an equivalent amount of alcohol, and that women are more susceptible to the long‐term negative health effects of alcohol. However, there is a paucity of data pertaining to whether there is a sexual dimorphic response in skeletal muscle to alcohol. Adult male and female Sprague–Dawley rats were used and the primary endpoint was in vivo determined muscle (gastrocnemius) protein synthesis (MPS). The initial study indicated MPS did not differ in female rats during proestrus, estrus, metestrus, or diestrus; hence, subsequent studies used female rats irrespective of estrus cycle phase. There was no difference in MPS between male and female rats under basal fasted conditions, and the time‐ and dose‐responsiveness of both groups to the inhibitory effect of acute alcohol did not differ. The ability of alcohol to suppress MPS was comparable in male and female rats pretreated with alcohol dehydrogenase inhibitor 4‐methylpyrazol. Chronic alcohol feeding for 6 weeks decreased MPS in male but not in female rats; however, MPS was reduced in both sexes at 14 weeks. Finally, oral gavage of leucine increased MPS similarly in male and female rats and chronic alcohol feeding for 14 weeks prevented the anabolic effect in both sexes. These data suggest normal fluctuations in ovarian hormones do not significantly alter MPS in female rats, and that there is no sexual dimorphic response to the effects of acute alcohol intoxication on MPS. While chronic alcohol consumption appeared to decrease MPS at an early time point in male compared to female rats, there was no sex difference in the suppressive effect of alcohol at a later time point. Overall, these data do not support the prevailing belief that females are more susceptible than males to alcohol's catabolic effect on MPS.