Miracle-dz/UniFAR-FTUs huggingface dataset:
A Flexible Mix-Zone Selection Scheme Towards Trajectory Privacy Protection

The wide application of trajectory analysis research greatly promotes the trajectory dataset publishing which in the meantime carries out tremendous privacy leakage problems, especially those come with long-term pseudonyms. Mix-zone is a typical privacy protection approach in road network that enables a group of users switch to new pseudonyms in certain area. However, to ensure unlinkability between all incoming and all outgoing sub-trajectories, strict restrictions are defined which limits the number of qualified mix-zones. Additionally, the privacy of sub-trajectories is not further considered. In this paper, we propose a flexible trajectory partition scheme called Mix-STC which can find out more candidate mix-zones by means of three-dimensional spatiotemporal cubes calculation. Mix-STC provides privacy protection by limiting the length of sub-trajectories and perturbing the precise location of its end points. Experiments show that Mix-STC performs much better than original data in two different types of de-anonymization attacks. Our performance experiments demonstrate that the datasets processed by Mix-STC can still support typical trajectory applications well.
Comparative Study of Big Data Classification Algorithm Based on SVM

Linear support vector machine (SVM) is one of the most effective machine learning methods to process large-scale data set. Based on the simple expatiation of the principle of support vector machine (SVM), linear support vector machine (SVM) and traditional support vector machine's training time and classification precision are compared and analyzed through the experiment. The results show that linear support vector machine has obvious advantages in terms of training speed and classification precision, which is a powerful tool to realize big data classification and regression. The experimental results show that for linearly separable data set, the LIBLINEAR toolkit is characterized by short training time and high classification precision, which is very suitable for large-scale data set classification.
Coordination of the Biarticular Actuators Based on Mechanical Output Power in an Explosive Jump Experiment

The natural actuation redundancy of the human leg is partly due to the existence of biarticular muscles. These muscles span two joints, bringing in substantial advantages for human locomotion. The physiological evidence suggests that the biarticular muscles can reduce the negative power production in the actuators and therefore contribute to the efficiency of the explosive movements. We perform the jump movement on the compliant robotic leg (CARL) to verify the role of biarticular elements in a physical platform. We further investigate how the actuator force distribution method can contribute to the economics of the explosive task. In our experiments, the biarticular actuators display similar behavior to their biological counterparts, with respect to energy transfer. A comparison study of two force distribution methods shows that the minimization of the instant power can improve the efficiency of the movements by up to 5 %. It is demonstrated that the deployment of the redundant biarticular actuators with instant power minimization criteria can increase the efficiency of the jump motion with up to 11 %, compared to the configuration with no actuation redundancy.
On $V_{{\text{ce}}}$ Method: In Situ Temperature Estimation and Aging Detection of High-Current IGBT Modules Used in Magnet Power Supplies for Particle Accelerators

Magnet power supplies used in particle accelerators are a specialized application of power electronics that require long lifetime operation with no unscheduled interruptions and high output current precision. In this paper, the <inline-formula><tex-math notation="LaTeX">${V_{{\rm{ce}}}}\ $</tex-math></inline-formula> method with sensing current is considered for the estimation of the junction temperature and the aging detection of high-current insulated gate bipolar transistor (IGBT) modules used in this specialized application. First, sensitivity analysis of the sensing current and the gate–emitter voltage are documented to quantify their impact on the precision of the <inline-formula><tex-math notation="LaTeX">${V_{{\rm{ce}}}}\ $</tex-math></inline-formula> method for high-current modules independently of the application of interest. Two measuring circuits offering ease of calibration and reduced number of components are proposed but their applicability is not limited to this application. Their performance is tested on a 1.6 kA IGBT module that exhibits the highest current ratings documented for the <inline-formula> <tex-math notation="LaTeX">${V_{{\rm{ce}}}}\ $</tex-math></inline-formula> method with sensing current. The test is carried out at a switching frequency of 6.5 kHz using an experimental set-up that emulates a phase leg of the considered power supply. The <inline-formula><tex-math notation="LaTeX">${V_{{\rm{ce}}}}\ $</tex-math></inline-formula> method with sensing current is evaluated for the aging detection of high-current IGBT modules in general and its effectiveness for the specialized application is verified experimentally.
A Simple Planar Pattern-Reconfigurable Antenna Based on Arc Dipoles

A simple compact planar pattern-reconfigurable antenna based on arc dipoles is proposed in this letter. The antenna consists of four identical arc dipoles and a broadband reconfigurable feeding network, which are printed on the bottom and top layers of the substrate, respectively. By switching the on/off states of the p-i-n diodes, which are integrated in the reconfigurable feeding network, four different modes with endfire radiation patterns can be obtained in the azimuthal plane. The antenna is simulated, fabricated, and measured. Experimental results indicate that the proposed antenna achieves a bandwidth of 33.6% (2.25–3.16 GHz) for impedance matching better than 10 dB. The measured average gain is 4.11 dBi. Furthermore, the radiation efficiency is up to 60%. The proposed antenna is suitable for WLAN applications in wireless communications.
A Broadband Dipole Antenna With Parasitic Patch Loading

By loading a parasitic patch, a novel broadband unidirectional dipole antenna is presented. The antenna fed by a coaxial balun consists of a folded dipole, a parasitic patch, and a ground plane. The folded dipole has two bowtie elements and a U-shaped structure. In the low operating band, the folded dipole works as an electric dipole and the parasitic patch works as a director. By using the director, the impedance matching performance is improved dramatically. In the high operating band, the two bowtie elements are as an electric dipole, and a magnetic dipole is formed by combining the parasitic patch and U-shaped structure. A new resonant frequency is generated and the impedance bandwidth is enhanced due to the magnetic dipole. Measured results show that an impedance bandwidth of 1.61–3.45 GHz (72.7%) for S11 ≤ −15 dB is obtained. Stable radiation patterns, low cross polarization, and a maximum gain 8.77 dBi are achieved.
Smad7 Promotes Healing of Radiotherapy-Induced Oral Mucositis without Compromising Oral Cancer Therapy in a Xenograft Mouse Model

Purpose: We previously reported preventive and therapeutic effects of Smad7, a multifunctional protein, on radiotherapy (RT)-induced mucositis in mice without promoting human oral cancer cell survival or migration in vitro. The current study aims to determine whether a Smad7-based biologic can treat existing oral mucositis during radiotherapy for oral cancer and whether this treatment compromises RT-induced cancer cell killing in neighboring oral cancer. Experimental Design: We transplanted human oral cancer cells into the tongues of mice and applied craniofacial irradiation to simultaneously kill tumor cells and induce oral mucositis, thus modeling RT and mucositis in oral cancer patients. We topically applied a recombinant human Smad7 protein fused with the cell-penetrating Tat tag (Tat-Smad7) to the oral mucosa of tumor-bearing mice post RT when oral mucositis began to develop. Results: Topically applied Tat-Smad7 penetrated cells in both the oral mucosa and oral cancer, attenuating TGFβ and NF-κB signaling as well as inflammation at both sites. Tat-Smad7 treatment alleviated oral mucositis with reductions in DNA damage and apoptosis in keratinocytes, but increased keratinocyte proliferation compared with vehicle-treated mucositis lesions. In contrast, adjacent oral cancer exposed to Tat-Smad7 did not show alterations in proliferation or direct DNA damage, but showed increased oxidative stress damage and apoptosis compared with tumors treated with vehicle. Conclusions: Our results suggest that short-course Tat-Smad7 application to oral mucositis promotes its healing but does not compromise the cytotoxic effect of RT on oral cancer and has context-specific effects on oral mucosa versus oral cancer.
Metabarcoding and metabolomics offer complementarity in deciphering marine eukaryotic biofouling community shifts

Abstract Metabarcoding and metabolomics were used to explore the taxonomic composition and functional diversity of eukaryotic biofouling communities on plates with antifouling paints at two French coastal sites: Lorient (North Eastern Atlantic Ocean; temperate and eutrophic) and Toulon (North-Western Mediterranean Sea; mesotrophic but highly contaminated). Four distinct coatings were tested at each site and season for one month. Metabarcoding showed biocidal coatings had less impact on eukaryotic assemblages compared to spatial and temporal effects. Ciliophora, Chlorophyceae or Cnidaria (mainly hydrozoans) were abundant at Lorient, whereas Arthropoda (especially crustaceans), Nematoda, and Ochrophyta dominated less diversified assemblages at Toulon. Seasonal shifts were observed at Lorient, but not Toulon. Metabolomics also showed clear site discrimination, but these were associated with a coating and not season dependent clustering. The meta-omics analysis enabled identifications of some associative patterns between metabolomic profiles and specific taxa, in particular those colonizing the plates with biocidal coatings at Lorient.
Actuator- Based Optimization Motion Cueing Algorithm

A Motion Cueing Algorithm (MCA) estimates driving simulator motions subject to the driver demands. An essential task consists in sticking to the simulator's workspace limits on position, velocity and acceleration level. In this paper the driving simulator comprises a hexapod which is a parallel robot with six degrees of freedom. In contrast to classical MCAs, which are mainly based on filtering and scaling techniques, this paper introduces a new optimization approach which is designed to estimate the simulator motion, subject to the simulator's limitations. Previous optimization based MCAs use a workspace-based prediction model to estimate the resulting reference motions at the driver position over a time horizon. Unlike these approaches, this work applies an actuator-based approach, using the direct kinematics to estimate the reference motions in the workspace. Advantages lie in the direct integration of the actuator constraints on position, velocity and acceleration level. Solving the direct kinematics of the parallel robot used in the optimization is computationally expensive. Thus, two approximations of the direct kinematics are introduced; this leads to significant reductions in the computational time, while showing only small deviations from the exact kinematics.
Analyzing HTTP-Based Information Exfiltration of Malicious Android Applications

Exfiltrating sensitive information from smartphones has become one of the most significant security threats. We have built a system to identify HTTP-based information exfiltration of malicious Android applications. In this paper, we discuss the method to track the propagation of sensitive information in Android applications using static taint analysis. We have studied the leaked information, destinations to which information is exfiltrated, and their correlations with types of sensitive information. The analysis results based on 578 malicious Android applications have revealed that a significant portion of these applications are interested in identity-related sensitive information. The vast majority of malicious applications leak multiple types of sensitive information. We have also identified servers associated with three country codes including CN, US, and SG are most active in collecting sensitive information. The analysis results have also demonstrated that a wide range of non-default ports are used by suspicious URLs.
VMPBL: Identifying Vulnerable Functions Based on Machine Learning Combining Patched Information and Binary Comparison Technique by LCS

Nowadays, most vendors apply the same open source code to their products, which is dangerous. In addition, when manufacturers release patches, they generally hide the exact location of the vulnerabilities. So, identifying vulnerabilities in binaries is crucial. However, just searching source program has a lower identifying accuracy of vulnerability, which requires operators further to differentiate searched results. Under this context, we propose VMPBL to enhance identifying the accuracy of vulnerability with the help of patch files. VMPBL, compared with other proposed schemes, uses patched functions according to its vulnerable functions in patch file to further distinguish results. We establish a prototype of VMPBL, which can effectively identify vulnerable function types and get rid of safe functions from results. Firstly, we get the potential vulnerable-patched functions by binary comparison technique based on K-Trace algorithm. Then we combine the functions with vulnerability and patch knowledge database to classify these function pairs and identify the possible vulnerable functions and the vulnerability types. Finally, we test some programs containing real-world CWE vulnerabilities, and one of the experimental results about CWE415 shows that the results returned from only searching source program are about twice as much as the results from VMPBL. We can see that using VMPBL can significantly reduce the false positive rate of discovering vulnerabilities compared with analyzing source files alone.
TCvBsISM: Texture Classification via B-Splines-Based Image Statistical Modeling

This paper presents an image statistical modeling-based texture classification (TC) approach via the Bayesian-driven B-splines probability density estimation of the image textural surface appearance (ITSA), termed TCvBsISM. It approximates the probability density functions (pdfs) of the marginal distribution and joint distribution, involving the global organization and the locally structural layout of local homogeneous patches in the texture surface, respectively, of both the image raw pixel space and the filter response space, by the linear combination of B-spline basis functions (BsBFs) for ITSA feature characterization. The corresponding linear weighting coefficients (LWCs) are determined by an entropy-based optimization criterion with a prior smooth constraint over the LWCs. By leveraging the B-spline-based pdf modeling, distinctive ITSA structural features of texture images are characterized by the LWCs of the pre-defined BsBFs, which are then embedded in an integrated statistical feature dictionary learning, texture pattern representation, and discrimination model to perform TC. Extensive confirmative and comparative experiments on three different texture databases and one natural environmental scene database demonstrate that the proposed TCvBsISM is very promising, especially when the images of different texture patterns appear to be quite similar with the limited training samples. The effects of various parameters on TCvBsISM, such as the choice of the filter bank, the size of the image statistical feature dictionary, as well as the number of BsBFs, are also discussed.
Ship Movement Prediction Using k-NN Method

Trajectories of ships travelling in the Gulf of Finland were predicted using the k-Nearest Neighbours (k-NNs) method. Automatic Identification System (AIS) data gathered via open interface of the Finnish Transport Agency were used. The results will be exploited in a route optimization task for an emission control boat. The task requires prediction several hours ahead with reasonable accuracy. The idea is to compare the trajectories of a new ship and historical ships within a comparison area. The future behaviour of the new ship was estimated with the k-nearest neighbours. The performance of the method as well as the hyper parameters (nearest neighbours, k, and a weighting parameter α) of the proposed model were optimized using nested leave-one-out crossvalidation approach. The method enables the prediction within minutes' accuracy in time and less than 2 km in location several hours ahead, which is more than satisfactory for the route optimization purposes.
Interior Radiation Noise Reduction Method Based on Multiframe Processing in Infrared Focal Plane Arrays Imaging System

Infrared focal plane arrays imaging system tend to be affected by the interior thermal radiation that drifts slowly over the time, resulting in the continuous spatial low-frequency nonuniformity noise in the image. In this paper, a scene-based nonuniformity correction method for interior thermal radiation is proposed. The gradient domain least mean square (LMS) algorithm is used to estimate the gradient caused by the interior radiation noise according to the fixed spatial low-frequency information in multiframe images. After that, the calibration parameters are obtained by fitting the derivatives of the polynomial-based correction model with the calculated gradient image. The performance of the proposed method is thoroughly studied with infrared image sequences with simulated nonuniformity and real nonuniformity. The results show that the proposed algorithm can remove interior radiation noise more completely without ghosting artifacts than the state-of-art algorithms and is more robust to different scenarios.
A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

Increasingly advanced technology allows the monitoring of complex systems from a wide variety of perspectives. But the exploration of such systems from a multichannel sensor information viewpoint remains a complicated challenge of ongoing interest. In this paper, first, based on a well-designed double-layer distributed-sector conductance (DLDSC) sensor, systematic oil–water and gas–liquid two-phase flow experiments are carried out to capture abundant spatiotemporal flow information. Second, well flow parameter measurement performance of the DLDSC sensor is effectively validated from the perspective of normalized conductance. Third, a novel multiplex network-based model is presented to implement data mining and characterize the evolution of flow dynamics. The results demonstrate that the model is powerful for the exploration of the spatial flow behaviors from heterogeneity to randomness in the studied two-phase flows.
Research on Industrial Control Anomaly Detection Based on FCM and SVM

In order to solve the problem of virus and Trojan attacking the application layer network protocol of industrial control system, the rule of Modbus/TCP communication protocol is analyzed. An intrusion detection method based on clustering and support vector machine is proposed. The method combines unsupervised fuzzy C-means clustering (FCM) with supervised support vector (SVM) machine to calculate the distance between industrial control network communication data and cluster center. Partial data satisfying the threshold condition is further classified by support vector machine. Experimental results show that compared with the traditional intrusion detection method, this method can effectively reduce the training time and improve the classification accuracy without needing to know the class label in advance.
Time-Domain Measurement of Continuous-Variable Entanglement Using Temporally Shaped Local Oscillator Pulses

We demonstrated time-domain measurement and frequency domain measurement of continuous-variable (CV) entanglement with pulsed light source and waveguides. The experiment with pulsed light source has difficulty in temporal mode matching between the entangled beams and local oscillator (LO) beams. To break through this difficulty, we shorten the duration of LO pulse by optical parametric amplification (OPA) in a waveguide. We generated entanglement that satisfied EPR-Reid criterion in the time domain measurement.
Adversarial Gait Detection on Mobile Devices Using Recurrent Neural Networks

This paper presents an implicit and continuous user verification service, called dCASTRA, for mobile devices based on walking patterns inferred from smart phone sensors. We use LSTM (Long Short Term Memory) neural networks for learning gait biometrics from raw accelerometer and gyroscope data and enable a device centric implementation of the deep learning models for faster predictions. One of the challenges in building a gait biometric model is to differentiate the sensor data pertaining to the walking activity from other activities such as sitting, standing, climbing, running and driving, etc. We design a multi-layer framework, where the initial layer relies on Google Activity Recognition Service to extract the segments corresponding to the walking activity with high confidence and feed extracted time series data to LSTM networks in the subsequent layer. The use of LSTMs eliminate the need for tedious feature engineering and further enable us to capture long-term dependencies within temporal sequences, often overlooked by existing efforts. We use Google TensorFlow to develop LSTM based gait biometrics and deploy on Android-based smart phones for real-time prediction and evaluation. We compare dCASTRA with our prior effort and with other deep network architectures such as Convolutional Neural Networks (CNNs). Our results manifest that LSTM and CNN based dCASTRA identifies users in an average 5-6 steps (using 50 Hz sensor sampling rate) with 99% detection accuracy. However, CNNs face significant training overhead as opposed to LSTMs which in turn limits its ability to be deployed in practice.
Power Flow Analysis Using Graph based Combination of Iterative Methods and Vertex Contraction Approach

Compared with relational database (RDB), graph database (GDB) is a more intuitive expression of the real world. Each node in the GDB is a both storage and logic unit. Since it is connected to its neighboring nodes through edges, and its neighboring information could be easily obtained in one-step graph traversal. It is able to conduct local computation independently and all nodes can do their local work in parallel. Then the whole system can be maximally analyzed and assessed in parallel to largely improve the computation performance without sacrificing the precision of final results. This paper firstly introduces graph database, power system graph modeling and potential graph computing applications in power systems. Two iterative methods based on graph database and PageRank are presented and their convergence are discussed. Vertex contraction is proposed to improve the performance by eliminating zero-impedance branch. A combination of the two iterative methods is proposed to make use of their advantages. Testing results based on a provincial 1425-bus system demonstrate that the proposed comprehensive approach is a good candidate for power flow analysis.