Miracle-dz/UniFAR-FTUs huggingface dataset:
Robust Stability Analysis of an Uncertain Aircraft Model with Scalar Parametric Uncertainty

This paper analyzes the robust stability of an uncertain aircraft model in which a key parameter, the location of the aircraft's center of gravity, is modeled as a real parametric uncertainty. A robust controller is specified and the stability bounds of the uncertain closed-loop system are determined using the small gain, circle, positive real, and Popov criteria. A graphical approach is employed in order to demonstrate the ease with which the above robustness tests can be carried out on a problem of practical interest. A significant improvement in stability bounds is observed as the analysis moves from the small gain test to the circle, positive real, and Popov tests. In particular, small gain analysis results in the most conservative robust stability bounds, while Popov analysis yields significantly less conservative bounds. This is because traditional small gain type tests allow the uncertainty to be arbitrarily time-varying, whereas Popov analysis restricts the uncertainty to be constant, real parametric uncertainty. Therefore, the results reported here indicate the conservatism associated with small gain analysis, and the effectiveness of Popov analysis, in gauging robust stability in the presence of constant, real parametric uncertainty.
Robust Video Object Segmentation via Propagating Seams and Matching Superpixels

Video object segmentation aims at separating foreground object from background, and it is far from well solved for different challenges such as deformation, occlusion and motion blurs. This paper proposes a robust video object segmentation method by propagating patch seams and matching superpixels. First, we predict the initial object contour based on pixel-level target labels calculated by patch seam propagation and rough sets. By a patch seam, we map a current patch to its most similar patch from last frame and obtain its labels based on the labels of mapped patch. Second, we utilize superpixels as middle level cues to optimize predicted object contour. The bidirectional distance based on three brightness channels is provided to match superpixels between adjacent frames. Using the boundaries of matched results and initialized object contour, many candidates of object contours are constructed. Third, we define an energy function based on multi-features to measure contour candidates, and the contour with minimum energy is the final segmented result of current frame. Finally, by propagating patch seams and matching superpixels, we compute video object segmentation results frame by frame. Fourteen videos of SegTrack-v2 data are used to evaluate our method. The quantitative and qualitative evaluations show that our method performs better than most present methods especially in dealing with occlusion, deformation and motion blurs.
Using Coalitional Game for Bandwidth-aware Fast Failover in Distributed SDN Environment

With the programmability of software defined networking (SDN), it is convenient to manage network through the master and slave controllers that also provide the failover mechanism. To achieve load balancing among SDN controllers and low response time, we should dynamically assign controllers under the bandwidth constraint, so the bandwidth consumption is important. In this paper, we formulate the bandwidth consumption as mix integer linear programming model and figure out that it is NP-hard. Thus, we propose a two-phase algorithm to handle it. In the first phase, we use k-means clustering algorithm to avoid achieving local minimum and try to maximize isolating failure effect. In the second phase, we use the output of first phase as initial assignment and utilize coalitional game to further improve the result. The simulation result shows that our proposed algorithm can not only get closer to optimal result but also isolate failure effect.
SMFL Integrating Spectrum and Mutation for Fault Localization

At present, spectrum-based fault localization and mutation-based fault localization are the most popular fault localization methods. The two methods suffer from the problems that multiple statements are ranked the same and there are high time costs. To improve the performance of fault localization, this paper proposes a novel approach for fault localization based on integrating spectrum and mutation. In addition to decreasing the time consumption overhead of the mutation-based fault localization method, the approach improves the accuracy of the spectrum-based fault localization method. The experimental results show that the approach improves the accuracy of fault localization in comparison with the spectrum-based fault localization method.
Collaborative Content Distribution With an End-to-End Caching Framework

While the mobile networks are evolving to a content-centric paradigm, the surging traffic demands keep stressing the ever-increasing network capacities. In-network caching and particularly edge caching offer an effective means to alleviate the traffic pressure by saving bandwidth resources and balancing traffic loads. In this paper, we consider an integrated content distribution model with universal in-network caching in an end-to-end scope. Based on this model, we formulate two key problems to enable collaborative content distribution across domains, i.e., a request screening problem at the mobile edge and a request routing problem at the integrated edge-core. We propose effective solutions for the two problems, which include a joint device caching and matching algorithm for request screening, and a joint source selection and flow routing algorithm for request routing. The simulation results demonstrate that the proposed solutions achieve significant performance gain over traditional approaches in relieving network congestion by exploiting network dynamics and user contexts.
An Initial Attitude Determination Method for Geomagnetic Field and Gyro Data Fusion with Kalman Filter

This paper presents an initial attitude determination algorithm using a magnetometer and gyroscopes. Firstly, it measures the geomagnetic field vectors in spacecraft's body-fixed frame (BFF) at two different points (point a and b with the corresponding BBF described as BFFA and BFFB) on orbit track and calculates their theoretical projections in reference frame (RF) with the international geomagnetic field model (IGRF). Secondly, the algorithm utilizes gyro rate integration to calculate the relative quaternion that rotates the spacecraft's BBF from BFFA to BFFB. Thirdly, it converts the coordinate of the first measured vector from BBFA to BBFB with the relative quaternion. Finally, utilizing two unparalleled vectors with their projections in RF and BFFB, the spacecraft's attitude at point b is estimated. The estimation results are directly influenced by measurement noise and by gyro drift but is still accurate enough as initial input for Kalman Filter (KF), which is sensitive to initial estimation errors. Simulations are carried out that demonstrate the efficiency of the algorithm in the particular case of high initial estimation errors.
A Supervised Learning Algorithm for Spiking Neurons Using Spike Train Kernel Based on a Unit of Pair-Spike

In recent years, neuroscientists have discovered that the neural information is encoded by spike trains with precise times. Supervised learning algorithm based on the precise times for spiking neurons becomes an important research field. Although many existing algorithms have the excellent learning ability, most of their mechanisms still have some complex computations and certain limitations. Moreover, the discontinuity of spiking process also makes it very difficult to build an efficient algorithm. This paper proposes a supervised learning algorithm for spiking neurons using the kernel function of spike trains based on a unit of pair-spike. Firstly, we comprehensively divide the intervals of spike trains. Then, we construct an optimal selection and computation method of spikes based on the unit of pair-spike. This method avoids some wrong computations and reduces the computational cost by using each effective input spike only once in every epoch. Finally, we use the kernel function defined by an inner product operator to solve the computing problem of discontinue spike process and multiple output spikes. The proposed algorithm is successfully applied to many learning tasks of spike trains, where the effect of our optimal selection and computation method is verified and the influence of learning factors such as learning kernel, learning rate, and learning epoch is analyzed. Moreover, compared with other algorithms, all experimental results show that our proposed algorithm has the higher learning accuracy and good learning efficiency.
Register before Publishing with Smart Forwarding, Mitigate Content Poisoning Attack in ICN

When NDN is deployed with IoT, this combination achieves more scalability and better mobility support. NDN immunes to most of the attacks which exist in today's Internet since its data-centric security. However, this newborn network architecture may still subject to Distributed Denial of Service (DDOS) attacks (e.g., interest flooding attack and content poisoning attack). In this paper, we mainly focus on the content poisoning attack (CPA) and discuss the state of art works on CPA firstly. After this, our mitigation, a name-space and key binding rule is proposed along with a smart forwarding strategy and results from simulations show that with the implementation of our mitigation in NDN, the interest satisfaction rate (ISR) of all consumers recovers from less than 30% to over 95% in real topology even when the network is under CPA with 25% of attackers.
Bursty Events Detection Approach on Chinese Microblog Based on Splay Tree Optimization

As a new media, Microblog plays an important role in people's daily life. People can gain, publish and share information in time, and interact with other users. With the characteristics of small text, spreading fast and great impact, an increasing number of scholars choose Microblog as their research object. Since Microblog generates a huge amount of data, bursty events detection seems to be very important. An algorithm based on the splay tree optimization is proposed so as to reduce the burst words clustering by recording the existing results. This paper adopts the method of trade space for time to optimize the algorithm, which changes the original complexity of the burst words clustering algorithm O(SN^3) into O(SN^2 log(N). The worst space complexity becomes O(N^2), however, it turns to be O(LN) by merely recording the effective results combined with the actual situation. Value L is related to the scale of cluster in the clustering result.
Optimal Cooperative Offloading Scheme for Energy Efficient Multi-Access Edge Computation

The distributed cooperative offloading technique with wireless setting and power transmission provides a possible solution to meet the requirements of next-generation Multi-access Edge Computation (MEC). MEC is a model which avails cloud computing the aptitude to smoothly compute data at the edge of a largely dense network and in nearness to smart communicating devices (SCDs). This paper presents a cooperative offloading technique based on the Lagrangian Suboptimal Convergent Computation Offloading Algorithm (LSCCOA) for multi-access MEC in a distributed Internet of Things (IoT) network. A computational competition of the SCDs for limited resources which tends to obstructs smooth task offloading for MEC in an IoT high demand network is considered. The proposed suboptimal computational algorithm is implemented to perform task offloading which is optimized at the cloud edge server without relocating it to the centralized network. These resulted in a minimized weighted sum of transmit power consumption and outputs as a mixed-integer optimization problem. Also, the derived fast-convergent suboptimal algorithm is implemented to resolve the non-deterministic polynomial-time (NP)-hard problem. In conclusion, simulation results are performed to prove that the proposed algorithm substantially outperforms recent techniques with regards to energy efficiency, energy consumption reduction, throughput, and transmission delay performance.
Facial-Based Personality Prediction Models for Estimating Individuals Private Traits

The essence of images users post and share at their social media platform is motivated and elucidated by their individual psychological constructs which are designated as personality traits. In this research, we investigate how social media profile pictures differ based on the personality of the users posting them at their social networking sites. In our experiment, we use profile images from Twitter platform whose personality we predicted based on 1.7 million data points. We conducted our analysis on users faces by extracting unique 50 facial features in order to examine the relationship between personality and profile picture. Our results reveal notable variations in profile picture selection between different personality traits. For example, high to openness males have a high surprise emotion in their profile pictures and a low smile value, while high in openness females have a high happiness emotion in their profile pictures and a high smile value. Finally, various machine learning approaches were investigated to test the effectiveness of these facial features in predicting users' psychological traits. Our results show that training personality models on a granularity based on gender gains higher accuracy. To our knowledge, this work is the first attempt of using ensemble learning methods for personality prediction task from users profile picture.
Public Key Encryption with Wildcards Keyword Search

A new searchable encryption scheme that support queries for keywords containing wildcards is proposed. The scheme reveals trivial information about keywords. In the scheme the number of charactors included in the trapdoor keywords does not have to be the same with the searched keywords. Consequently it is more practical and flexible than the existing scheme.The scheme can be proven to be IND-CPA Security in the Selective-Set Model.
Formulasi Sediaan Nano Herbal Tempuyung (Sonchus arvensis L.) dalam Bentuk Self Nano-Emulsifying Drug Delivery System (SNEDDS)

Tempuyung (Sonchus arvensis L.) is a plant native to Indonesia with a diuretic and antioxidant effect. To obtain the optimal therapeutic effect need innovation to make it the nano herbs preparations. This research aims to preparation and characterization of nano herbal tempuyung in the form of Nano Self-emulsifying Drug Delivery System (SNEDDS). The active substance of tempuyung was extracted with ethanol and the solvent evaporated to obtain a viscous extract. Extracts were standardized following the Indonesian Pharmacopeia Herbal. Next, the SNEDDS of tempuyung were made using Capryol-90 as oils, tween 20 as surfactants and PEG 400 as co-surfactant. SNEDDS was characterized include particle size and zeta potential. The viscous extract obtained from extraction was 77.52 g. This result has been eligible if compared with Indonesian Pharmacopoeia Herbal that mentions the acquisition of thick leaf extract tempuyung yield is not less than 7.5%. Tempuyung formulation in dosage forms SNEDDS obtained a clear nanoemulsion with a particle size of 16.2 ± 1.06 nm and zeta potential value of -37.48 ± 0.74 mV. It can be concluded that the tempuyung extract can produce a nano herbs in SNEDDS preparation.
Optical Trapping of Nanoparticles Through Artificially-Engineered Flat Materials

Day to day advancements in technology requires new materials and new technologies to be developed. Researchers nowadays are focused towards developing compact, portable, efficient, and, on-chip realizable devices that are easy to fabricate. Metamaterials have proved to be useful for the development of such devices due to their compactness and on-chip realization as compared to the bulky optical instruments. They have found their applications in almost every field of science and technology. In this paper, we have demonstrated the design of an optical tweezer that is capable of trapping nanoparticles in the visible regime. Hydrogenated amorphous silicon (a-Si:H) is used as the basic building block of this metalens along with glass as the substrate. Due to its ability to trap particles in the visible domain and its ease of manufacturing, it can be replaced in many commercial applications such as cameras, microscopes, and other devices that require high-resolution imaging. For the simulation of this metalens full-wave solution of Finite Difference Time Domain solutions is used, and optical force calculations are performed using Maxwell Stress Tensor technique. The proposed design could prove to be the foundation stone towards the development of highly compact, efficient, and commercially realizable on-chip devices.
A Double Non-Negative Matrix Factorization Model for Signed Network Analysis

As one of the most important characteristics of complex networks, community structure have been studied extensively in real cases. However, in signed networks that include both positive and negative edges, the development of community discovery is still limited now. Because the sign information on edges poses a challenge to modeling the signed network. Most existing methods are based on heuristics, so these methods tend to have high computational complexity and ignore the generation of the networks. Here, we propose a Double Non-negative Matrix Factorization (DouNMF) model from the perspective of generative model to detect communities in the signed network. This algorithm skillfully applies the Non-negative matrix Factorization algorithm to the signed network. In addition, the algorithm integrates indegree information into the process of matrix factorization. Large amounts of experiments on several artificial and real-world signed networks validate that the effectiveness and accuracy of our proposed approaches both in community discovery and link prediction.
Tactile P300 Brian-Computer Interface Paradigm for Robot Arm Control

This paper presents a tactile BCI for movement control of a three degree-of-freedom(DOF) robot arm. Six physical stimulus modules were placed on the left arm of the participant to stimulate event-related potentials with time-domain characteristics. Six different commands could be exported by classifying the Electroencephalogram(EEG) signal, which could control the robot arm movement in the corresponding position. To test the practicality of our BCI system, 5 subjects took part in the experiment with their appropriate stimulation time, electrode channel and corresponding classifier parameters. In the online experiment, the average of classification accuracy was 84.7%, with an average information transfer rate at 12.2 bits/min. In the robot arm control experiment, all subjects could complete a reach-to-grasp task. The results show that the tactile BCI paradigm is feasible and practical for some multi-DOF control applications.
Overlap Aware Compressed Signal Classification

Compressed signal processing (CSP) is a branch of compressive sensing (CS), which gives a direction to solve a class of signal processing problems directly from the compressive measurements of a signal. CSP utilizes the information preserved in the compressive measurements of a signal to solve certain inference problems like: classification, detection, and estimation, without reconstructing the original signal. It further simplifies the signal processing compared to conventional CS by omitting their complex reconstruction stage. This, in turn, reduces the implementation complexity of signal processing systems. This paper investigates the performance of CSP for classification application. After extracting the features from compressive measurements, these features or the data instances are used for classification purpose. Through experimental analysis, it has been found that as the CS undersampling factor is increased, the overlapping among the data instances predominates. This results in a complex decision boundary, which in turn degrades the classification accuracy at higher undersampling factors. To overcome the above issue, this paper proposes the use of a machine learning method known as overlap aware learning along with CSP. This generates a smoother decision boundary and hence improves the classification accuracy at higher undersampling factors. The simulation results show the trend of improved classification accuracy using the proposed method. An analysis of the proposed method has been done on different datasets and based on run-time complexity and complexity vs gain analysis to verify the effectiveness of proposed method.
Distributed Scheduling for Time-Critical Tasks in a Two-layer Vehicular Fog Computing Architecture

Ahstract— With the rapid development of vehicular applications and mobile devices, demands for resources to process time-critical and computation-intensive tasks are increasingly prominent. In this paper, we propose a two-layer Vehicular Fog Computing (VFC) architecture, including the client layer and the fog layer. Vehicles may generate tasks as clients, which are further assigned to the nodes in the fog layer for processing. The fog layer aggregates available resources of vehicles and infrastructures by exploiting their communication, computation and storage capabilities. Each task requires certain amount of resources for processing at the fog nodes. We formulate a distributed task allocation (DTA) problem, which takes deadline, vehicle mobility and fog capacity into consideration, and aims at maximizing the overall resource utilization of system, via the cooperation of vehicles and fog nodes. We linearize DTA into a 0–1 integer linear programming (ILP) problem to obtain the optimal solution. Further, we design a heuristic algorithm to obtain near-optimal performance with low computational overhead, which decomposes DTA into two subprocess and schedules tasks in each fog node independently. Finally, we build the simulation model and conduct a series of experiments based on real-world vehicle trajectories, which demonstrate the effectiveness and scalability of the proposed algorithm.
Automatic Football Match Event Detection from the Scoreboard using a Single-Shot MultiBox Detector

During a football match, the information is manually collected by humans. However, the correctness of the football match data is difficult to check because of the game’s speed, and thus, human errors can occur. This paper presents an automatic football match event detection from the scoreboard using a deep learning algorithm. The proposed method can reduce human error and performs the detection faster. In this study, the detection was trained with 30,000 data of Goals, Substitutions and Cards scoreboard from 68 matches of English Premier League 2017-2018 broadcast videos. The detection was tested with 80 sub-testing videos. These videos were prepared from 20 full matches broadcast videos, which consisted of 12 full matches from the year 2017-2018 and 8 full matches from the year 2018-2019. The proposed method contains three main steps: data gathering and augmentation, object detection for scoreboard visualization forms, and the event classification. The scoreboard detection is performed with a Single-Shot MultiBox Detector. The event classification employs the majority vote and time frame technique. The experimental results show an accuracy rate of 1.00 with the expected event scoreboards, comprised of Goal, Substitution, and Card events.