Miracle-dz/UniFAR-FTUs huggingface dataset:
Motion Simulator Evaluation of a Flight Deck Cursor Control Device (CCD)

The purpose of this study was to understand pilots' interaction with a new Cursor Control Device (CCD) design, intended for use in a variety of aircraft, both fixed- and rotary-wing. The CCD is a flexible controller which replaces legacy devices to interface with increasingly complex interactive navigation displays. The intended use of the CCD on multiple aircraft types implies the device could be used in environments that experience high-vibration and turbulence; that pilots may or may not don flight gloves; and that flight deck installation may require pilots to use the device with their non-dominant hand. This evaluation assessed usability, pilot performance and pilot acceptance of the CCD across all these expected conditions. The goal was to ensure the design was capable of efficiently and effectively meeting its intended function, without placing undue strain or workload on pilots. A part-task evaluation of a prototype of the new device was conducted in a representative motion simulator with eight pilots. The study employed two task types: the ISO 9241–9 methodology as well as a test scenario using Honeywell's Interactive Navigation (INAV™) display. Task performance, selection accuracy and speed of selection were found to be acceptable in the simulated motion and vibration environments. Task performance as a function of time, accuracy and speed of selection were acceptable when the pilots used their non-dominant hand to control the CCD device, and while wearing flight gloves. The error rate for button presses was 5% across both ISO and INAV evaluation tasks. Pilot subjective ratings (6 on a scale of 1–7) indicated a strong acceptability for the CCD design. CCD usage had no significant effect on subjective workload; workload ratings were within acceptable bounds (< 4 on a scale of 1–10). The results of the study provided insight into the human factors assessment of a flight deck control use under complex conditions. The test procedure and results also provide an example of the application of the ISO 9241–9 methodology for flight deck applications.
Increasing Accuracy in Train Localization Exploiting Track-Geometry Constraints

Train-borne localization systems as a key component of future signalling systems are expected to offer huge economic and operational advances for the railway transportation sector. However, the reliable provision of a track-selective and constantly available location information is still unsolved and prevents the introduction of such systems so far. A contribution to overcome this issue is presented here. We show a recursive multistage filtering approach with an increased cross-track positioning accuracy, which is decisive to ensure track-selectivity. This is achieved by exploiting track-geometry constraints known in advance, as there are strict rules for the construction of railway tracks. Additionally, compact geometric track-maps can be extracted during the filtering process which are beneficial for existing train localization approaches. The filter was derived applying approximate Bayesian inference. The geometry constraints are directly incorporated in the filter design, utilizing an interacting multiple model (IMM) filter and extended Kalman filters (EKF). Throughout simulations the performance of the filter is analyzed and discussed thereafter.
Improving the Performance of Stock Trend Prediction by Applying GA to Feature Selection

Predicting stock trend by using machining learning is a hot research issue today. However, due to the non linearity and instability of the stock data, it is still very difficult to predict the stock trend with high accuracy. In order to improve the accuracy, most researchers focus on the models selection and features construction. A variety of feature construction methods have been proposed. However, not all features constructed in those paper are equally useful. Further more, many features of significant importance may not be selected in prediction. In order to improve the accuracy of stock trend prediction, this paper will focus on the features selection problem. Most feature selection methods employed in the stock trend prediction are based on filtration methods. Wrapper methods are rarely used. Compared with filtration methods, wrapper methods have better stability and accuracy. In this paper, we propose a feature selection algorithm by extending genetic algorithm (GA). Experiments are conducted on real-world stock price data set. The experiment results show that our GA-based feature selection algorithm is better in both stability and performance.
Spray and Locate Routing for Vehicular Delay-Tolerant Networks

Vehicular Delay-Tolerant Networks (VDTNs) are networks where there are no permanent end-to-end connections. VDTNs have a variable topology, with frequent partitions in the connections. Given the dynamic characteristics of these networks, routing protocols can take advantage of dynamic information, such as the node’s location, to route messages. Geolocation-based routing protocols choose the node that moves closer to the location of the message destination as the message carrier.In this article, the Spray and Locate geographical routing protocol was proposed. First, the protocol replicates a limited number of messages in the network, and then it uses the direction of movement of the nodes to route messages in the known destination’s direction. In order to obtain the locations of nodes in the network, a VDTN localization system, known as VDTN-Locate, in which each node in the network maintains a dictionary with the last location information known, including position, movement direction, speed and age, was also proposed.The performance of Spray and Locate was compared with geographic and non-geographic routing protocols. The results show that the Spray and Locate protocol has a higher delivery rate and lower latency than the other evaluated protocols.
Buccal Fat Pad Excision: Proceed with Caution

Background: Buccal fat pad excision is offered as a means of obtaining a more aesthetic midface. This procedure has been documented in the form of countless videos on Instagram and other social media platforms with no long-term patient follow-up. We performed a retrospective analysis of published data regarding buccal fat pad excision and sought to better elucidate pitfalls regarding this underreported procedure. Methods: A literature search was conducted in October 2017 through the PUBMED database regarding the utility of buccal fat pad excision in the setting of aesthetic improvement of the midface. Reference articles were screened to obtain relevant studies. A total of 121 citations were identified in the search but after eliminating duplicate studies and abstracts and utilizing predefined inclusion/exclusion criteria only 11 articles were satisfactory. None of these articles demonstrated any long-term patient follow-up. Results: Out of the 121 relevant citations identified, only 2 studies published describe a case series of > 5 patients regarding cheek or midface sculpturing with buccal fat pad excision for aesthetic purposes, the total sample size between these 2 studies was 53 patients. Neither of the studies had follow-up regarding patient satisfaction or related outcomes. Conclusions: Buccal fat pad resection as an aesthetic improvement of the midface has been described, but follow-up regarding loss of subcutaneous fat with aging and late secondary deformities have not been published in the literature. Further research in long-term patient follow-up including patient satisfaction and the encouragement of reporting postoperative complications is warranted.
Robust TDA-MAC for practical underwater sensor network deployment: lessons from USMART sea trials

In this paper, we present the results of deploying the first test prototype of the USMART low cost underwater sensor network in sea trials in Fort William, United Kingdom, on 29 June 2018 and 3 July 2018. We demonstrate the first ever hardware implementation of the Transmit Delay Allocation MAC (TDA-MAC) protocol for data gathering in underwater acoustic sensor networks (UASNs). The results show a successful application of TDA-MAC to remote environmental monitoring, integrating a range of sensor nodes developed by the universities of Heriot-Watt, York, Newcastle and Edinburgh. We focus on the practical challenges and their mitigation strategies related to TDA-MAC to increase its robustness in real-world deployments, compared with theoretical and simulation-based studies. The lessons learned from the sea trials reported in this paper prompted several crucial modifications to TDA-MAC which form a solid foundation for further work on the development of TDA-MAC based UASNs.
Single Sample Face Recognition: Discriminant Scaled Space vs Sparse Representation-Based Classification

Sparse Representation-based Classification (SRC) is an effective solution of face recognition as there have been many studies around it. However, classical SRC needs a large train data for the galley to produce an over-complete dictionary which result in high accuracy. This paper purposes to show that when there is only one sample per subject for the gallery, the simple linear Discriminant Scaled Space (DSS) can outperform classical SRC and is competitive with new single sample version of that along with significantly less runtime. In addition, it will be shown that SRC methods can be computed on the data proj ected to DSS which result in higher accuracy with less run time. To show the effectiveness of DSS, it is compared with different kinds of SRC on 11 public databases.
Droughts Over Amazonia in 2005, 2010, and 2015: A Cloud Cover Perspective

Amazon forests experienced recent severe droughts in an anomalous short period induced by different mechanisms and had different length periods and spatial patterns. Droughts of 2005 and 2010 were attributed to anomalous sea surface temperature over the Tropical Atlantic during the dry season, but the 2010 drought was more severe and remained for a longer period because it was also induced in late 2009 by a moderate to strong El Nino (EN). Drought in 2015 led to unprecedented warming and extreme soil moisture deficits over some regions, and it was attributed to a very strong EN. Several studies analyzed these drought events regarding different climatic factors such as anomalies in sea surface temperature, vegetation, temperature, precipitation, soil moisture deficits, solar radiation, etc. However, we have not identified a complete analysis of cloud cover (CC) over Amazonia during these drought events in the context of long-term trends and past strong EN events. This brief report aims to present a preliminary analysis of anomalies in CC over Amazon using reanalysis data with a focus on the last recent drought events into a long-term context. Results show a significant decreasing trend for total CC over Southern Amazonia during the dry season, in contrast to the increasing trend found over Northern Amazonia during this season and the widespread increasing trend during the wet season. CC anomalies are also indicative of the different West-East and North-South patterns linked to EN events or drought episodes driven by tropical Atlantic warm anomalies.
Deep Reinforcement Learning for Predictive Longitudinal Control of Automated Vehicles

This paper presents a predictive controller for longitudinal motion of automated vehicles based on Deep Reinforcement Learning. It uses advance information about future speed reference values and road grade changes. The incorporation of this information leads to a new design parameter with a high influence on learning speed: the selection of proper advance knowledge signals during training. We propose a design method which shows improved learning performance in our experiments. The performance of our controller is explored through simulation of a real world driving scenario in a parking garage. We demonstrate that our Reinforcement Learning agent can learn a behavior close to the optimal solution of a Nonlinear Model Predictive Controller, but at reduced computational costs.
Data Association for Grid-Based Object Tracking Using Particle Labeling

Estimating surrounding objects and obstacles by processing sensor data is essential for safe autonomous driving. Grid-based approaches discretize the environment into grid cells, which implicitly solves the data association between measurement data and the filtered state on this grid representation. Recent approaches estimate, in addition to occupancy probabilities, cell velocity distributions using a low-level particle filter. Measured occupancy can thus be classified as static or dynamic, whereby a subsequent tracking of moving objects can be limited to dynamic cells. However, the data association between those cells and multiple predicted objects that are close to each other remains a challenge. In this work, we propose a new association approach in that context. Our main idea is that particles of the underlying low-level particle filter are linked to those high-level objects, i.e., an object label is attached to each particle. Cells are thus associated to objects by evaluating the particle label distribution of that cell. In addition, a subsequent clustering is performed, in which multiple clusters of an object are extracted and finally checked for plausibility to further increase the robustness. Our approach is evaluated with real sensor data in challenging scenarios with occlusions and dense traffic.
Mining and Detection of Android Malware Based on Permissions

Due to the open app distribution and more than two billion active users, Android platform continues to serve as low-hanging fruit for malware developers. According to the McAfee threat report, the number of malware families found in the Google Play increased by 30% in 2017. Permission-based access control model is one of the most important mechanisms to protect Android apps against malware. In this paper, we propose a new permission-based model that enhances the efficiency and accuracy of Android malware analysis and detection, and has the capability of potentially detecting previously unknown malware. In this new model, we improve the feature selection by introducing a new weighting method, named TF-IDFCF, based on the class frequency (CF) of the feature. The results of our experiments show that our proposed method has a detection rate of greater than 95.3% with a low false positive rate, when tested with different classifiers.
The Gaps Between Current Management of Intracerebral Hemorrhage and Evidence-Based Practice Guidelines in Beijing, China

Background: The leading cause of death in China is stroke, a condition that also contributes heavily to the disease burden. Nontraumatic intracerebral hemorrhage (ICH) is the second most common cause of stroke. Compared to Western countries, in China the proportion of ICH is significantly higher. Standardized treatment based on evidence-based medicine can help reduce ICH's burden. In the present study we aimed to explore the agreement between the management strategies during ICH's acute phase and Class I recommendations in current international practice guidelines in Beijing (China), and to elucidate the reasons underlying any inconsistencies found. Method: We retrospectively collected in-hospital data from 1,355 ICH patients from 15 hospitals in Beijing between January and December 2012. Furthermore, a total of 75 standardized questionnaires focusing on ICH's clinical management were distributed to 15 cooperative hospitals. Each hospital randomly selected five doctors responsible for treating ICH patients to complete the questionnaires. Results: Numerous approaches were in line with Class I recommendations, as follows: upon admission, all patients underwent radiographic examination, about 93% of the survivors received health education and 84.5% of those diagnosed with hypertension were prescribed antihypertensive treatment at discharge, in-hospital antiepileptic drugs were administered to 91.8% of the patients presenting with seizures, and continuous monitoring was performed for 88% of the patients with hyperglycemia on admission. However, several aspects were inconsistent with the guidelines, as follows: only 14.2% of the patients were initially managed in the neurological intensive care unit and 22.3% of the bedridden patients received preventive treatment for deep vein thrombosis (DVT) within 48 h after onset. The questionnaire results showed that imaging examination, blood glucose monitoring, and secondary prevention of ICH were useful to more clinicians. However, the opposite occurred for the neurological intensive care unit requirement. Regarding the guidelines' recognition, no significant differences among the 3 education subgroups were observed (p > 0.05). Conclusions: Doctors have recognized most of ICH's evidence-based practice guidelines. However, there are still large gaps between the management of ICH and the evidence-based practice guidelines in Beijing (China). Retraining doctors is required, including focusing on preventing DVT providing a value from the National Institutes of Health Stroke Scale and Glasgow Coma Scalescores at the time of admission.
Driving Feature Extraction and Behavior Classification Using an Autoencoder to Reproduce the Velocity Styles of Experts

In this paper we present a work on encoding, clustering and modeling expert driver behaviors to be reproduced by an autonomous driving car agent. We collected speed, brake, steering wheel and acceleration data from an expert driving in typical Japanese suburban areas. We grouped together consecutive data in a sliding window as the input to a fully connected deep autoencoder for dimension reduction. We then clustered the encoded driving data into 9 different behaviors. For each behavior class, we modeled the speed using a fit of same clustered data and tested the models in a ROS car simulator. Results show that the models created from the clusters could represent different driving behaviors on various roads. Experimental simulations show that the proposed approach can reproduce speed behavior comparable to that of the expert drivers even in new environments
Persian Plagirisim Detection Using CNN s

The abundant and growing amount of scientific-research works and the ease of access to them has caused some abusive exploits from jobber people and illicit use of them in scientific and academic environments. “Plagiarism” refers to the use of scientific-research works by others without reference to them correctly. Due to the rapid growth of Persian electronic resources, this paper considers the plagiarism detection in Persian texts. Plagiarism detection consists of two distinct steps: Candidate Retrieval and Text Alignment. The focus of our proposed method is on both steps. In the first step, using a Convolutional Neural Network (CNN), a vector representation is created in document-level and then, the candidate documents are retrieved using the k-means clustering algorithm. In order to align text, the features are extracted at the sentence-level using a CNN. Finally, using the classification algorithms, the copied sentences are detected. Experiments were performed on the prepared corpus in the AAI competition and the prepared corpus in the PAN2015 competition. The achieved precision and recall are 0.843 and 0.806 for the first corpus and 0.833 and 0.826 for the second one respectively.
In-Flight Aircraft Smart Space Security using Multi-Entity Trust Evaluations

Smart spaces and smart cities allow humans to become more networked in physical spaces such as coffee shops, libraries, and shopping malls to enhance interactions such as custom advertising, navigation and other services. The air travel industry is also evolving through innovative projects like NextGen to become more connected. It is only a matter of time before airports and aircraft advance and become smart spaces to remain competitive. This will result in an increase of diverse passenger-provided computing platforms connecting to in-flight networks, facilitating seamless connectivity but also increasing the risk for cyber attack for all connected systems. Traditional cyber incident response often begins post-compromise, sometimes days or weeks after an attack has occurred. Given the volume and ubiquity of air travel, dedicated per-aircraft defenders are cost and computationally prohibitive, potentially extending this post-compromise detection and response delay. In the air travel use case, such a delay may result in life threatening consequences. Aviation Security Authorities (ASA) must be enabled to respond to new types of threats by deploying preventive risk-based computer network defense (CND) in these emerging smart spaces. We propose a hybrid of distributed and centralized multi-trust models specific to aircraft and in-flight use cases that evolves while the aircraft is in flight, fed by trust data from airline entities, flight crew observations, government records, network defender observations and other passengers. While each of these individual data sources by themselves lacks sufficient contextual information to detect cyber attacks, combining them into an air travel entity trust model to evaluate the risk of particular aircraft, passengers, airports, etc. can supply air travel security services with preliminary information needed to observe and respond to suspicious activity, and enable defenders to more efficiently target defensive resources and active preventative measures to the in-flight networks that most need them. If an untrusted passenger loses access to aircraft WiFi networks, or is not allowed to connect to on-board servers or the Internet due to untrustworthy activity, they may be denied the connectivity required to conduct their attack. Similarly, if expensive tools and human defenders can be engaged only when needed and on a per-passenger or per-aircraft basis, such efficiency gains may overcome prohibitive computation, bandwidth, and personnel costs enabling detection of network threats that appear while an aircraft is in flight.
A practical guide to chirp spread spectrum for acoustic underwater communication in shallow waters

Small and cheap micro AUVs enable diverse underwater monitoring applications in shallow inshore waters; e.g., inspection of underwater assets, observation of water quality, and identification of pollution sources. The formation and collaboration of swarm members yet requires communication and self-localization based on cheap, miniature acoustic devices. However, this is severely hampered by the effects of multi-path propagation and scattering in shallow waters. In general, we discovered improvements of chirp-based---i.e., frequency-sweep---synchronization in these scenarios. This paper presents strategies to find proper chirp parameters. First, we study the benefits and applicability of chirp-based modulation against FSK. Second, we discuss the impact of the chirp parameters in real-world scenarios and identify sweep spots w.r. t. bandwidth to improve the standard deviation of the position in the time domain up to 86 % above this point. Third, we present instructions to choose these parameters in an efficient and appropriate way for any shallow water scenario.