Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of Touchscreen Displays for the Gulfstream G500 and G600 Symmctry™’ Flight Deck

Touchscreen display technology has been used for decades, but has only recently been accepted as a user interface within aircraft avionics suites. The proliferation of the touch interface was driven by the consumer electronics industry with smartphones and tablets. Touch interfaces have become embedded in society to the point where people are confused or annoyed when they touch a screen and find that it does not respond. Touchscreens have not been common in aircraft design, but the landscape is transforming. Gulfstream has led the aviation industry with the use of 10 touchscreens in the all-new Symmetry™ flight deck onboard the G500 and G600 aircraft. This provides the crew with five multi-function touchscreens (TSCs), two touch-enabled standby flight displays (SFDs), and three overhead panel touchscreens (OHPTS) that substantially replaced the physical switches present in prior aircraft models. This paper explains what Gulfstream implemented, and the challenges and considerations for implementing a touch interface in the flight deck. Gulfstream's touchscreen development is characterized by three major design stakeholders: Study-derived conclusions are provided that characterize the function-first design influence of Human Factors and highlights where art met function with Industrial Design. Implementation aspects are also discussed where concept became reality with Design Engineering.
Predictive Monitoring of Business Processes: A Survey

Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field.
A Review of Millimeter Wave Communication for 5G

The millimeter wave (mmWave) bands give new facilities with tremendous amount of spectrum to fifth generation (5G) mobile communication network to supply mobile data demand, which is expanding out of control. Essential differences are considered between conventional systems and mmWave communications, regarding directivity, sensitivity to blockage and high propagation loss. mmWave brings various challenges in communication on some issues such as anti-blocking, interference management, spatial reuse, dynamic control and system design for fully utilizing. In this study, we surveyed on solutions and standards for these challenges and we proposed design principles in architecture and protocols for mmWave communications. And previous studies in the literature on whether the millimeter wave band can be tested in small cell access in 5G, cellular access in 5G and wireless backhaul in 5G or not have been investigated. Also, we described prospective using areas of mmWave in 5G.
Binary Neural Networks for Classification of Voice Commands From Throat Microphone

Multi-class pattern classification has many applications including speech recognition, and it is not easy to extend from two-class neural networks (NNs). This paper presents a study about using binary classifiers with NNs together with a perceptual linear prediction (PLP) method for feature extraction to increase the classification rate of voice commands captured using a throat microphone, comparing this method with a single NN. Because there is no other data set with voice commands captured using a throat microphone in the Brazilian Portuguese language in researched literature, we created a data set with isolated voice commands with utterances captured from 150 people (men and women). All the voice samples are captured in Brazilian Portuguese, and they are the digits “0” through “9” and the words “Ok” and “Cancel”. The results show that the throat microphone is robust in noise environment, achieving 95.4% of hit rate in our speech recognition system with multiple NNs using the one-against-all approach, better performance than a simple NN that reach 91.88%. This result is very representative, since both classifiers obtained high hit rates. But, it requires 535% more time for training the multiple NNs compared with simple NN. The best configuration on PLP extraction order is 9 or 10 for voice samples captured by the throat microphone, which was observed that poor stressed vowel and fricative-like words “3” and “7” in Portuguese confuses the classifier.
Underground Comprehensive Pipe Gallery Illegal Intrusion Wireless Sensing System

With the rapid development and wide deployment of wireless technology, Wi-Fi signals have no longer been confined to the Internet as a communication medium. When Wi-Fi signals propagate indoors, the wireless channel state will be affected by obstacles in this propagation path. Specially, when people perform physical actions in the Wi-Fi signals coverage, the channel state of reflected wireless signals from various parts of human body will be changing as actions, so that the channel state information data will carry rich human body actions information. In this paper, we proposed UCPG, an Underground Comprehensive Pipe Gallery illegal intrusion wireless sensing system. The UCPG can perceive human body actions by extracting actions information from the channel state information data to realize the illegal intrusion detection in the underground comprehensive pipe gallery.The main innovation of the UCPG is to use the common commercial Wi-Fi signal to sense human actions. In the experiment, the passive detection method that users are not required to wear any hardware devices allows users to be unaffected by system detection, and the system is non-intrusive so that the user’s private information will not be leaked. Based on the performance advantages of Wi-Fi signals, the system can realize through-wall detection in non-line-of-sight environments, whose performance will not be affected by some environmental factors such as light intensity, and the detection range is the coverage of Wi-Fi signals without blind spots.
Sampling-based Bayesian Inference with gradient uncertainty

Deep neural networks(NNs) have achieved impressive performance, often exceed human performance on many computer vision tasks. However, one of the most challenging issues that still remains is that NNs are overconfident in their predictions, which can be very harmful when this arises in safety critical applications. In this paper, we show that predictive uncertainty can be efficiently estimated when we incorporate the concept of gradients uncertainty into posterior sampling. The proposed method is tested on two different datasets, MNIST for in-distribution confusing examples and notMNIST for out-of-distribution data. We show that our method is able to efficiently represent predictive uncertainty on both datasets.
Influences of Canopy Nitrogen and Water Addition on AM Fungal Biodiversity and Community Composition in a Mixed Deciduous Forest of China

Nitrogen (N) deposition and precipitation could profoundly influence the structure and function of forest ecosystems. However, conventional studies with understory additions of nitrogen and water largely ignored canopy-associated ecological processes and may have not accurately reflected the natural situations. Additionally, most studies only made sampling at one time point, overlooked temporal dynamics of ecosystem response to environmental changes. Here we carried out a field trial in a mixed deciduous forest of China with canopy addition of N and water for 4 years to investigate the effects of increased N deposition and precipitation on the diversity and community composition of arbuscular mycorrhizal (AM) fungi, the ubiquitous symbiotic fungi for the majority of terrestrial plants. We found that (1) in the 1st year, N addition, water addition and their interactions all exhibited significant influences on AM fungal community composition; (2) in the 2nd year, only water addition significantly reduced AM fungal alpha-diversity (richness and Shannon index); (3) in the next 2 years, both N addition and water addition showed no significant effect on AM fungal community composition or alpha-diversity, with an exception that water addition significantly changed AM fungal community composition in the 4th year; (4) the increment of N or water tended to decrease the abundance and richness of the dominant genus Glomus and favored other AM fungi. (5) soil pH was marginally positively related with AM fungal community composition dissimilarity, soil NH4+-N and N/P showed significant/marginal positive correlation with AM fungal alpha-diversity. We concluded that the effect of increased N deposition and precipitation on AM fungal community composition was time-dependent, mediated by soil factors, and possibly related to the sensitivity and resilience of forest ecosystem to environmental changes.
Capsules Based Chinese Word Segmentation for Ancient Chinese Medical Books

Neural network models are popularly used in Chinese word segmentation task. The capsule architecture is proposed recently which has solved some defects of convolutional neural network. In this paper, we first introduce the capsule architecture to Chinese word segmentation. We utilize capsules as neural units. Before doing routing algorithm, we make a sliding capsule window to select the features which are extracted from the primary capsule layer. The sliding capsule window is proposed to adapt the capsule architecture to the sequence labeling task. The experiment results show that our proposed capsules based Chinese word segmentation model achieves competitive performances with the previous state-of-the-art methods. Ancient Chinese medical books record a lot of valuable experiences from the ancient medical workers. However, the research about the automatic text analysis on ancient Chinese medical documents is just a beginning. Due to the lack of the annotated data for Chinese medicine, we develop the word segmentation guideline for the ancient Chinese medical documents and select 10 genres, 30 ancient Chinese medical books to set up the annotation dataset. And with the annotated data, we develop the segmenter for the ancient Chinese medical text. Experiments show that the $F_{1}$ measures of our model on the two datasets are 94.9% and 81.4% on Chinese Treebank6.0 and Ancient Chinese Medical Books, respectively.
A Belief State Planner for Interactive Merge Maneuvers in Congested Traffic

Autonomous driving in urban environments requires the capability of merging into narrow gaps. In cases of high traffic density this becomes more complex since one must consider the interaction with other vehicles. We formulate the problem as a Partially Observable Markov Decision Process (POMDP) by including the surrounding drivers in the state space to realize interactive behavior. The problem is solved online by an anytime Monte Carlo sampling algorithm in combination with an efficient A* rollout heuristic. This combination makes a combined lateral and longitudinal optimization possible. The resulting policy is optimized regarding various future merge scenarios and approaches the most suitable gap while taking into account the uncertain behavior of the surrounding drivers. Therefore, we present a novel motion model representing the uncertain cooperation of other drivers. It is based on a logistic regression model estimating the probability for cooperative behavior of a human driver given a future scene. We demonstrate the performance of our algorithm by various simulated scenarios. The resulting behavior for approaching gaps, estimating the future cooperative behavior of surrounding drivers and performing merges in narrow gaps are discussed.
Optimal dispatching of electric and hybrid buses subject to scheduling and charging constraints

We consider the problem of optimally determining the sequence of electric and hybrid buses departing from a multi-line bus terminal, considering both service constraints (schedule adherence) and energy constraints (electric bus charging status, bus recharging scheduling in capacitated facilities). The problem is formulated as a Mixed Integer Linear Program, with the objective of minimizing the total operational cost for the bus lines in question. System dynamics are captured by practical sets of constraints, ranging from scheduling adherence to discharge-recharge dynamics. Individual operational costs at the bus level and at the trip level are fully parametrized, allowing for extensive sensitivity analysis. We investigate a real-life case study based on the city of Luxembourg, where two charging stations have been installed in the central station's bus terminal. Experimental results show that, while full electrification is indeed possible, the required fleet size depends strongly on the given line(s) timetable.
Distributed Coordination of Renewables and Storage Systems in an Energy-Neutral Community

With high penetration of renewables-based distributed generation sources (RDGs) and distributed storage systems (DSSs), such as electric vehicles (EVs), in distribution systems, several issues such as maintaining energy-neutrality and computation/communication bottlenecks arise. These can be efficiently handled with coordination of DSSs that act as buffer and absorb/inject necessary active power, and by using distributed computing approaches. Therefore, in this paper, a holistic strategy is proposed to optimize the costs and revenues of owners in a community while maintaining net energy neutrality using a fully distributed consensus-based algorithm. Simulation studies performed on modified IEEE 30-bus test system show that the proposed algorithm effectively optimizes the community's devices under dynamic conditions.
Social Acceptance of Microgrids Dedicated to Electric Vehicle Charging Stations

The electromobility requires installing recharging infrastructures for electric vehicles (EVs) in urban areas. However, social acceptance is an important issue to increase public awareness on EVs recharging infrastructures and have to be studied simultaneously with the technical project. This paper aims at presenting the social acceptance study of an innovative energy system based on renewable energy, i.e. EVs charging station powered by a microgrid. The proposed energy system consists of three components: an intelligent infrastructure for recharging EVs (IIREVs) powered by microgrid, a heterogeneous fleet of EVs, and a building with a connection to IIREVs. The microgrid optimizes the power flows in accordance with the requirements of the EVs users and the public power grid. This microgrid contains photovoltaic sources and takes into account the following strategies: vehicle to grid, vehicle to building, and IIREVs to building (energy generated by the IIREVs and not used by the EVs directly feeds the building). The social acceptance survey show that a large majority accepts the IIREVs while some imperatives have to be considered in certain urban cases implantations. According to respondents’ expectations, an action plan is highlighted in order to best fitting IIREVs in urban areas.
Spatial-Temporal Person Re-identification

Most of current person re-identification (ReID) methods neglect a spatial-temporal constraint. Given a query image, conventional methods compute the feature distances between the query image and all the gallery images and return a similarity ranked table. When the gallery database is very large in practice, these approaches fail to obtain a good performance due to appearance ambiguity across different camera views. In this paper, we propose a novel two-stream spatial-temporal person ReID (st-ReID) framework that mines both visual semantic information and spatial-temporal information. To this end, a joint similarity metric with Logistic Smoothing (LS) is introduced to integrate two kinds of heterogeneous information into a unified framework. To approximate a complex spatial-temporal probability distribution, we develop a fast Histogram-Parzen (HP) method. With the help of the spatial-temporal constraint, the st-ReID model eliminates lots of irrelevant images and thus narrows the gallery database. Without bells and whistles, our st-ReID method achieves rank-1 accuracy of 98.1% on Market-1501 and 94.4% on DukeMTMC-reID, improving from the baselines 91.2% and 83.8%, respectively, outperforming all previous state-of-theart methods by a large margin.
Multi-Beam Scanning Electron Microscopy for High-Throughput Imaging in Connectomics Research

Major progress has been achieved in recent years in three-dimensional microscopy techniques. This applies to the life sciences in general, but specifically the neuroscientific field has been a main driver for developments regarding volume imaging. In particular, scanning electron microscopy offers new insights into the organization of cells and tissues by volume imaging methods, such as serial section array tomography, serial block-face imaging or focused ion beam tomography. However, most of these techniques are restricted to relatively small tissue volumes due to the limited acquisition throughput of most standard imaging techniques. Recently, a novel multi-beam scanning electron microscope technology optimized to the imaging of large sample areas has been developed. Complemented by the commercialization of automated sample preparation robots, the mapping of larger, cubic millimeter range tissue volumes at high-resolution is now within reach. This Mini Review will provide a brief overview of the various approaches to electron microscopic volume imaging, with an emphasis on serial section array tomography and multi-beam scanning electron microscopic imaging.
Outage Probability Constrained MIMO-NOMA Designs Under Imperfect CSI

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access scheme to be used in fifth-generation wireless networks. In this paper, multiple-input and multiple-output (MIMO) techniques are applied to NOMA systems by considering two types of imperfect channel state information—channel distribution information (CDI) and channel estimation uncertainty. Based on the two considered channel models, the power allocation and beamforming vectors are jointly designed to maximize the system utility of MIMO-NOMA, subjected to probabilistic constraints. Due to the implementation of successive interference cancellation in NOMA, the power allocation coefficients of the users in each cluster become coupled, which complicates the rate outage probability constraints and results in two challenging non-convex problems. For the optimization problem under CDI, we propose an efficient successive convex approximation (SCA) algorithm based on first-order approximation and semidefinite programming (SDP). For the optimization problem under channel estimation uncertainty, a new algorithm for the joint power allocation and receive beamforming design is developed to maximize the system utility based on SCA and an efficient 1-D search. In addition, the convergence and the feasibility are discussed for the two formulated problems. Furthermore, an efficient method to find a feasible initial solution is provided. Finally, the presented simulation results validate that the proposed two algorithms outperform MIMO-orthogonal multiple access and MIMO fixed NOMA (MIMO F-NOMA) with fixed power allocation and beamforming.
Correlation of matrix metalloproteinase-9 expression with morphometric analysis of mucosal vasculature in oral squamous cell carcinoma, oral epithelial dysplasia, and normal oral mucosa

Objective Oral squamous cell carcinoma (OSCC) invades and metastasizes, by degrading the extracellular matrix (ECM) and is associated with poor prognosis. Matrix-metalloproteinase (MMP-9) can initiate ECM degradation and angiogenesis which brings a significant change in tumor microenvironment favoring tumor progression. A major thrust has been laid on understanding this key enzyme as it has significant implications for cancer therapy. Comprehending the association of vasculature with MMP-9 expression in precancerous lesions oral epithelial dysplasia (OED) and OSCC is essential since the data regarding the same are fewer. The aim is to evaluate and correlate MMP-9 expression with morphometric analysis of mucosal vasculature in the normal oral mucosa (NOM), OED, and OSCC. Methods A total of 60 histologically diagnosed cases of OED (n = 30); OSCC (n = 30); along with 10 NOM (n = 10) as control were included. Immunohistochemical staining of MMP-9 and vascular morphometric analysis was performed for all the cases. Results were analyzed using the Chi-square test, Fischer exact test, and Spearman correlation test. Results A statistically significant difference in MMP-9 was noted among the groups with P = 0.011* (epithelium); P = 0.001* (stroma) by the highest value in OSCC group. Morphometry also revealed a progressive increase from NOM to OED to OSCC. Spearman’s correlation of MMP-9 with vascular parameters illustrated a positive relation of MMP-9 with mean vascular density (MVD) and mean vascular area percentage (MVAP). Conclusion Positive correlation of MMP-9 with MVD and MVAP demonstrates this markers effect on angiogenesis. Henceforth, MMP-9 can be embattled as a potential therapeutic target in combating tumor progression.
Improving Neighbor Discovery by Operating at the Quantum Scale

Duty-cycling is generally adopted in existing sensor networks to reduce power consumption and these networks depend on neighbor discovery protocols to ensure that nodes wake up and discover each other. For different neighbor discovery protocols, the discovery latency is determined by two factors: the wake-sleep pattern and slot size. To the best of our knowledge, previous works on neighbor discovery have thus far been focused on improving the wake-sleep pattern. In this paper, we investigate the extent to which we can improve discovery latency by reducing the slot size. We found that by reducing the slot size, i.e., reducing the listening time in active slots, the collisions between beacons and synchronization between nodes become more severe, which can lead to discovery failures that are not predicted by existing theoretical models. We show that we can mitigate these effects by reducing the number of beacons and introducing randomization. We propose a new continuous-listening-based neighbor discovery algorithm called Spotlight. Our evaluations with a practical sensor testbed suggest that Spotlight can achieve a 50% reduction in discovery latency over existing state-of-the-art neighbor discovery protocols without increasing power consumption in existing sensor networks.