Miracle-dz/UniFAR-FTUs huggingface dataset:
On Flight Risk Analysis Method Based on Stability Region of Dynamic System Under Icing Conditions

The occurrence of loss of control in flight is often accompanied by deep coupling of flight parameters. In order to explore the mechanism of coupling induced flight risk, a method of flight risk assessment based on the stability region of a nonlinear dynamic system was proposed. The Monte Carlo method was improved to identify the boundary of the stability region, and the key parameters that can represent the flight safety were set. The risk quantification method and the colorized flight risk characterization method were proposed. Combined with the case of aircraft encountering icing, the variation trend of flight stability region and flight risk under different icing degrees was calculated. The results showed that with the aggravation of icing, the flight stability region was significantly reduced and the coupling of flight parameters was aggravated. And compared with the traditional angle of attack protection method, this method can find the potential flight risk earlier, and characterize the way of parameters’ coupling and the flight risk evolution process. The proposed method can improve the pilot’s situational awareness, and also provide theoretical support for the prevention of flight risk under adverse conditions, and provide reference for the further development of boundary protection control law.
CMOS Annealing Machine: A Domain-Specific Architecture for Combinatorial Optimization Problem

Domain-specific architectures are being studied to improve computer performance beyond the end of Moore’s Law. Here, we propose a new computing architecture, the CMOS annealing machine, which provides a fast means of solving combinatorial optimization problems. Our architecture is based on in-memory computing architecture through utilizing the locality of interactions in the Ising model. The prototype presented in 2019 has two processors on a business-card-sized board and solves problems 55 times faster than conventional computers.
Network-Side Task Allocation for Mobile Crowdsensing

Crowdsensing has been intensely studied to leverage the power of citizens for completing large-scale sensing tasks at a lower cost. It is considered as an efficient and economical approach on crowdsensing to develop unified platforms for various sensing applications. However, existing frameworks were vertically integrated systems, and thus the installation cost cannot be shared among multiple organizers. Another important issue of crowdsensing is to preserve the security and privacy of an individual and to maintain the integrity of sensor data. To address these problems, this paper proposes the network-side task allocation (NeSTA) framework for crowdsensing. In the proposed framework, the mobile network mediates the organizers and the participants. A sensing task is requested by the organizer to the mobile network, and then allocated to the participants by mobile edges. The installation cost of applications is significantly reduced with the horizontal integration of multiple applications. The privacy is ensured by obscuring the participants from the organizer. The size of task-allocation problem is reduced by dividing the original problem into subproblems. The validity of the proposed approach was confirmed through computer simulations using open dataset.
Intrusion Detection of Traffic Realizations Based on Maching Learning Using Fractal Properties

In this article we consider the problem of intrusion detection in computer networks by the realizations of network traffic. To solve this problem, time series analysis methods, fractal methods and data mining are used. The verification of anomaly detection methods was performed using experimental data sets. Using these data sets, realizations of multifractal stochastic binomial cascades were generated with a change of the multifractal characteristics. In the work, numerical experiments were conducted to detect intrusions using the example of detecting typical DDoS attacks, which showed that the Random Forest method using regression decision trees can effectively detect anomalies in network traffic.
Effects of Vehicular Blockage on Latency in 5G mm Wave Systems with Dynamic Point Selection

Ahstract-5G millimeter wave(mmWave) systems are prone to blockage from various objects in the typical propagation environment. Among these, human blockage and vehicular blockage are more prominent scenarios that can lead to link failure. In this paper, we analyze the effect of vehicular blockage on 5G mmWave systems using stochastic geometric models. Using this analysis, we derive the blockage probability of the link and provide a relation to latency in communication with vehicular blockage in various scenarios. Further, we show and quantify the fact that with dynamic point selection (DPS) scheme where user maintains simultaneous links with geographically separated transmit and receive points (TRP), the coverage probability can be improved significantly and the latency due to blockage can be reduced. We analyze the blockage probability and latency for DPS system in terms of blocker density, length and typical speed of the vehicle and number of simultaneous links maintained by the user. The proposed analytical model is validated using system level simulations. We show the trade off between the number of simultaneous links and latency using these results which helps in 5G system design for latency critical applications.
Evaluating the Effect of Audio Feedback on the Behavior of Automotive Fatigue and Distraction Detection System Users

Vehicular fatigue and distraction detection systems are important tools to avoid traffic accidents. Furthermore, there are many related papers proposing compositions using these techniques. Nevertheless, most of the validation tests performed with these devices happen in simulated conditions or environments, without a test with actual users on a real situation. Also, few works analyze behavioral features using these systems. In this work, we analyze the behavioral aspects of users from a fatigue and distraction detection system with and without audio feedback. Our results indicate that this feature has a positive effect on the drivers’ behavior.
Brazilian Mercosur License Plate Detection: a Deep Learning Approach Relying on Synthetic Imagery

Automated license plate recognition (ALPR) technology is a powerful technology enabling more efficient and effective law enforcement, security, payment collection, and research. A common license plate standard was adopted by the member states of the Mercosur trading bloc (Argentina, Brazil, Paraguay and Uruguay) and consequently requires an upgrade to the ALPR software used by law enforcement and industry. Due to the scarcity of real license plate images, training state-of-the-art supervised detectors is unfeasible unless data augmentation techniques and synthetic training data are used. This paper presents an accurate and efficient automated Mercosur license plate detector using a Convolutional Neural Network (CNN) trained exclusively with synthetic imagery. In order to obtain the synthetic training data, Mercosur license plates were faithfully reproduced. Digital image processing techniques were employed to reduce the domain gap and a CNN with basic image manipulation was used to embed the artificial licensed plates in to realistic contexts. The trained model was then validated on real images captured from a parking lot and a publicly available traffic monitoring video stream. The results of experiments suggest detection accuracy of about 95% and an average running time of 40 milliseconds.
Tolerating Retention Failures in Neuromorphic Fabric based on Emerging Resistive Memories

In recent years, computation is shifting from conventional high performance servers to Internet of Things (IoT) edge devices, most of which require the processing of cognitive tasks. Hence, a great effort is put in the realization of neural network (NN) edge devices and their efficiency in inferring a pretrained Neural Network. In this paper, we evaluate the retention issues of emerging resistive memories used as non-volatile weight storage for embedded NN. We exploit the asymmetric retention behavior of Spintronic based Magnetic Tunneling Junctions (MTJs), which is also present in other resistive memories like Phase-Change memory (PCM) and ReRAM, to optimize the retention of the NN accuracy over time. We propose mixed retention cell arrays and an adapted training scheme to achieve a trade-off between array size and the reliable long-term accuracy of NNs. The results of our proposed method save up to 24% of inference accuracy of an MNIST trained Multi-Layer-Perceptron on MTJ-based crossbars.
Graphene AMC Array As A Ground Plane for Beam-Switching at Terahertz Band

This paper introduces a graphene-based artificial magnetic conductor (AMC) surface backed dipole antenna for electronic beam-switching at terahertz (THz) applications. A graphene-based AMC array which consists of 17×17 unit-cell elements is used as a ground plane for dipole antenna. Each unit-cell element is consisting of SiO2 substrate sandwiched between graphene patches on the top and the bottom faces. The graphene patch at the bottom is surrounded by a graphene rectangular ring to control the back radiation from the dipole. All graphene-based components are firstly biased with DC voltage (µc= 2 eV) and unbiased (µc= 0) according to the required beam direction. The gain of the dipole is enhanced to 4.11 dBi. The radiation pattern of the dipole is switched vertically in different directions by changing the bias voltage of the graphene AMC unit-cells at the substrate bottom surface.
Remote Sensing Image Restoration Based on Wiener Filter and RBF Neural Network

During image acquisition, remote sensing images are subject to a variety of degradations. Wiener filter is widely used method for remote sensing image restoration. To overcome the power spectrum ratio's evaluation problem of traditional Wiener filter methods, a remote sensing image restoration algorithm based on Wiener filter and RBF (Radial Basis Function) neural network is proposed. The algorithm adopts Wiener filter to recover the degraded remote sensing image. And the power spectrum ratio of the image is calculated by the RBF neural network. To reduce the calculating time, this paper proposes a matrix factorization algorithm to realize matrix's parallel arithmetic. Experimentations demonstrate that the proposed algorithm could acquire satisfactory remote sensing image recovering results. And the processing time could be controlled in a relatively short time
OP73 Using Visualization In Scoping The Literature For A Prognostic Health Technology Assessment

INTRODUCTION: One of the challenges of large scale Health Technology Assessment (HTA) projects is managing the large volume of studies retrieved by the requisite comprehensive literature searches. At the scoping stage of the project, a pragmatic judgement needs to be made as to how sensitive the search strategy should be in order to find all the relevant papers without returning an overwhelming volume of irrelevant studies. METHODS: For this HTA (evaluating prognostic and predictive markers in rheumatoid arthritis), the research team already had prior knowledge of several key markers of interest, but wanted to ensure that no others had been missed. Advice from practising clinicians was obtained, but for additional validation, a broad scoping search was conducted for ‘rheumatoid arthritis’ using the sensitive Haynes filters for prognostic (1) and clinical prediction (2) studies. Unsurprisingly, this initial search retrieved too many studies for them all to be admitted to the full review; but once those dealing with known markers had been removed, a sample of the remaining records was loaded into a software visualization tool (3) to display “heat maps” of frequently occurring terms and phrases. RESULTS: On this occasion, no additional markers were identified, however this provided reassurance that the advice obtained from clinicians was comprehensive, enabling the HTA team to proceed confidently with its evaluation of the selected markers. CONCLUSIONS: Visualization offers an alternative means of exploring and interrogating large text archives, and has the potential to complement the role of traditional search methods in identifying literature for systematic reviews and health technology assessments. As processing power increases and more and more full-text papers become available open access, it may provide a solution to some of the limitations associated with comprehensive searching.
Product Mix Optimization Scenarios: A Case Study for Decision Support Using Linear Programming Approach

Determining the optimum product mix under the theory of constraints (TOC) has been considered by many researchers. In most cases, researches consider one scenario with one important aspect which is maximizing the profit by giving the best solution. This paper solves a product mix problem faced by a birds' food production facility. The problem is modeled and solved using two linear programing models. Several scenarios are presented for the same problem considering two important aspects, namely, resources' utilization and productivity. Data visualization is extensively used in this paper to facilitate the decision-making process. A software dashboard is developed for the decision maker to create, analyze and visualize different business scenarios.
Semantic Segmentation of Urban Scenes Using Spatial Contexts

This paper proposes a method for the joint inference of road layouts and the semantic segmentation of urban scenes by applying spatial contexts. The proposed method is based on the conjecture that a set of relevant elements in an urban environment contains a locational relationship among them. This relationship can be modeled as a location prior and label co-occurrences to help segment an image accurately. To apply these environmental characteristics, special coordinates referred to as road-normal coordinates are defined on the inferred road layout. These coordinates are determined by obtaining the most befitting road layout based on the marginal probability from the result of an existing segmentation algorithm. All possible segments in an image having depth information from the lidar sensor are projected into the road-normal coordinates, and the learned location prior and label co-occurrence statistics are applied to each segment as additional potentials of a conditional random field model. The proposed method is evaluated with the publicly available urban dataset including images and the corresponding point clouds.
Sensitive Devices and Phase Noise Degradation Mechanisms on all-NMOSFET RF VCO Aging

Device level reliability is introduced into simulations of an optimized all-NMOSFET, cross-coupled, radiofrequency Voltage Controlled Oscillator to trace the sensitive devices and parameters that reduce circuit performance over time. Parametric DC time dependent degradation is considered in all the devices of the circuit along with correlated flicker and thermal noise increase under hot carrier injection stress. Results show that, rather than the changes introduced by stress on DC parameters, the proposed topology is very sensitive to device level noise increase due to stress-induced traps, particularly at early stages of circuit life. This highlights the requirement of a unified simulation framework that takes into account not only DC/AC small signal degradation models but also device noise degradation in noise sensitive circuits.
Research on Web Service Selection Based on Improved Skyline Algorithm

With the rapid development of Internet technology, the Internet has entered people's daily lives, and people can do many things through the Internet, such as: online chat, online viewing of materials, online shopping, sending and receiving mail. The Internet has brought great convenience to our lives while also promoting social and economic development. Due to the wide application of the Internet, a large amount of Internet data is generated on the network. There are more and more web services with different functional attributes and different non-functional attributes on the network. It is more and more difficult for people to choose web services. The traditional web service selection method is When dealing with massive data, it faces great challenges. How to extract valuable services from massive web data has become an urgent problem to be solved. The traditional service selection method compares the QoS (quality of service) attributes of services to select the services with the best attributes. This method is time consuming. Therefore, this paper uses the database query technology Skyline to select services and extract the SP (Skyline point) services among the potential Web services. The Skyline algorithm is further improved, and the algorithm divides the entire service set into regions. The improved Skyline algorithm can effectively filter and reduce the dominance checks among regions without dominant relationships, which saves memory space and greatly improves the execution efficiency. Finally, the high accuracy and efficiency of the improved Skyline algorithm are verified based on simulation data and a real data set.
Task Decomposition and Parallelization Planning for Automotive Power-Train Applications

Since both the industry and the market have been driven by the multi-core processors, to parallelize the applications in the automotive industry is on huge demand. Two steps are required to parallelize a legacy program: parallelism discovery and parallelization planning. To discover the parallelism of a program is to identify the code regions where multiple procedures/ functions can be executed simultaneously, while parallelization planning is to find an optimal solution of assigning tasks on multi-cores based on the discovered parallelism. How to automate the parallelization in the Power-train domain remains a grand challenge due to the complexity of the program and the dynamics of the runtime environment. Many aspects should be considered including the speedup, computing resource bound, workload balance, etc. Considering all the above aspects, we used a directed acyclic graph to represent the decomposed program, then take the parallelization planning as a multi-objective optimization problem, where a Cobyla algorithm is deployed to search for the optimal solution by evaluating different parameters. We have tested our approach on the periodic tasks in the Power-train applications to validate its feasibility and efficiency.
Indoor Fingerprinting Positioning Based on the Least Anticipation Loss

the fingerprinting positioning is commonly used for indoor location estimation, since wireless signal outage, severe multipath propagation and interference when people walking can often lead to decrease ranging base positioning accuracy in indoor environments. Aiming at the situation that most of existing algorithms is not high in positioning accuracy and system robustness, this paper puts forward an optimal fingerprinting positioning algorithm based on the least risk Bayesian method. With the introduction of positioning loss, the algorithm makes each positioning result the least anticipation loss to minimize the positioning error. Theoretical analysis shows that the least anticipation loss positioning is optimal in positioning precision compared to the optimal positioning error probability methods. Experimental results demonstrate the better performance of proposed algorithm in precision and evaluation.
Enabling Relay Selection in Cooperative Networks by Supervised Machine Learning

In fifth generation (5G) networks, cooperative transmission assisted by relays is believed to be an essential element, which significantly improves the system reliability and enhances the network design flexibility. To coordinate multiple relays in cooperative networks and utilize them in an efficient manner, relay selection is required. In this paper, we enable a generic relay selection scenario by supervised machine learning techniques and propose a prototype framework for further investigation. The prototype framework is constructed by a relatively simple artificial neural network consisting of only one hidden layer and the number of neurons in the hidden layer is equal to the number of inputs/outputs. Numerical results show that any relay selection criteria that conform to a certain form can be implemented by such a simple prototype framework, which can reduce the required system complexity for performing complicated processing of relay selection by conventional algorithms. Furthermore, we also point out a number of potential research directions that are worth investigating as future work
Insulating Liquids, an Alternative to Silicone Gel for Power Electronic Devices

This paper studies the breakdown voltage for different insulating materials encapsulating power electronic devices. A comparison between some liquids and silicone gel is carried out. The 50 Hz AC voltage tests were performed according to the IEC 60156 specifications. The same process was applied to the DC voltage tests due to a lack of standard procedure concerning DC voltage tests on liquids. In each case, a ceramic substrate was placed in a container filled with the insulating material. A voltage was applied between two electrodes glued on the substrate. Mineral, both synthetic and natural ester oils and silicone gel were tested. The voltages of breakdown between oils were quite similar in AC and DC. The gel is more resistant to DC stress even though its self-healing is less than the liquids investigated.
Harvesting Social Media Sentiments for Stock Index Prediction

Stock index prediction is a complex problem as stock indices movement is influenced by many factors and events possibly affecting the economy. With the increasingly efficient algorithms and techniques in machine learning, there is a renewed interest in the use of machine learning to predict the flow of the stock index. In this paper, technical features of the New York Stock Exchange Composite (NYA) are derived. Content features from carefully curated Twitter accounts are also gathered so as to analyze and explore machine learning techniques to predict the movement of the NYA. Probabilistic model for sentiment features are derived and a simple recurrent neural network (Elman networks) with gated recurrent units was implemented for the prediction model. The results of the study showed that probabilistic sentiment analysis of twitter news applied to a simple recurrent neural network significantly improves the prediction performance.
Automatic Detection of Stack Overflow Attack in Canary

The existing buffer overflow vulnerability detection technology checks the control flow state of the program to determine the trigger point of the program and generate test cases. However, due to the limitation of the canary protection mechanism, it is difficult for the existing detection technology to determine whether the program satisfies the stack overflow attack condition in canary. In order to improve the security of the program and realize the detection of stack overflow attacks, this paper analyzes the existing stack overflow attack instances, summarizes the features of stack overflow attack, and builds an automatic stack overflow detection method in canary. The method uses taint analysis to monitor the input data of the program and the sensitive operation; uses the selective symbol execution technology to construct the path constraint and the data constraint of the stack overflow attack; generates a test case by solving the above constraints. Experiments show that this method can effectively achieve the detection of stack overflow attacks.