Miracle-dz/UniFAR-FTUs huggingface dataset:
A CFI Countermeasure Against GOT Overwrite Attacks

In the Unix-like system, the Global Offset Table (GOT) overwrite attack is a long-lasting control flow hijacking attack. The attack, by leveraging the dynamic symbol binding mechanism, overwrites any GOT entry into the attacker’s target address to take the execution flow on the library function call. Recently, Full Relro (Relocation Read only), which arranges the GOT section as read-only at program startup, is regarded as most useful against the threat. However, it entails nontrivial loading overhead and is not applicable to libraries. Furthermore, many software packages are currently distributed without Full Relro. As a result, programs are still exposed to the risk of GOT attacks. In this paper, we propose a CFI-based protection scheme against the GOT overwrite attack. Using dynamically bound function symbols as branch identifiers, the scheme secures inter-module function calls on PLT (Procedure Linkage Table) effectively with little performance overhead. Our LLVM based implementation and evaluation on binutils-gdb show that the branch protection scheme is difficult to bypass, fast, and compatible with existing library programs.
Research on STLF Method Based on One-Dimensional Convolution and Slope Feature

Short-term load forecasting is of great importance to how to efficiently utilize generating units, optimize resource allocation and ensure the normal transmission of power in power system. This paper mainly studies how to improve the accuracy of short-term load forecasting through feature engineering and deep learning technology. Specifically, we construct a feature based on the slope of load data according to the changing trend of load data. We applied the proposed method to two real datasets of Hunan power grid and obtained MAPE values of 0.5758 and 0.5745 respectively. The experimental results show the effectiveness of the proposed method.
Analysis of the Errors in Polarimetry with Full Poincarè Beams

The main error sources of a recently proposed Mueller matrix polarimetric method based on the use of full Poincaré beams (FPBs) are described and analysed in detail. Numerical simulations are performed under the presence of different sources of errors. Their influence in the determination of the Mueller matrix is studied for samples presenting either birefringence or diattenuation. The present study will allow to improve the experimental procedure for obtaining the Mueller matrix of a sample using a FPB.
Dielectric Relaxation in Clays in a Wide Range of Frequencies at Temperatures from −15°C to 25°C

Experimental data of measurement of complex dielectric permittivity of kaolin and bentonite clays are given at moistening with distilled water and saline solution. The measurements were carried out in the frequency range from 10 kHz to 8.5 GHz at temperatures from −15°C to +25°C. Experimental dependences were simulated by a model that takes into account relaxation of bound and free water and relaxation caused by polarization at the interface of bound water-air and the process of low-frequency relaxation. It is shown that the parameters of the model describing relaxation processes in kaolin and bentonite clays differ significantly.
Non-invasive Blood Glucose Measurement System

Non-invasive blood glucose measurement is advantageous as it reduces the healthcare cost and pain of taking blood sample through existing invasive methods. The proposed system uses near-infrared sensors of 940nm wavelength to detect glucose concentration in blood. The glucose concentration is calculated based on the variation in the voltage after passing through finger. The output voltage is converted into glucose concentration using the algorithm developed in Arduino UNO. An accuracy of 97.5% was obtained.
System Parameter Design of Multimodal Small Satellite SARs Operating in Scan Mode and Transmit Power Optimization for Marine Scenes

Multimodal small satellite synthetic aperture radar (SAR) is a new radar system under development that integrates an SAR, altimeter, scatterometer, and spectrometer. When applied to marine scenes, this system can be used to measure both marine targets and marine dynamic environments with high precision. This study addresses system parameter design for multimodal small satellite SAR operating in scan mode, and this design provides system parameters such as antenna size, signal bandwidth, pulse repetition frequency, scanning wavenumber and wave position for system simulation in scan mode. A method for optimizing the transmit power when illuminating a marine scene based on the wind speed and wind direction above the sea surface is studied. The goal is to fully use the characteristics of strong sea surface microwave scattering under a suitable wind speed and wind direction to reduce the required transmit power, thus improving the available data sampling time per orbit of a multimodal radar when working in SAR mode. Various simulation experiments were conducted, and the system parameter design results are given under scan mode. Furthermore, imaging simulation results of ocean scenes are also given under conventional and decreased power after optimization. The results show that good ocean scene imaging results are obtained when the designed system parameters are used for system simulation. In addition, the simulation results also verify that when the sea surface wind speed is relatively high and the wind direction is suitable, an acceptable ocean scene imaging result can still be obtained by using reduced transmit power.
ROS Architecture for Nonlinear Control in Singularly Perturbed Wheeled Mobile Robots: Dynamic Confined Space-Based Approach

The research reported here presents the development of an open architecture that enables the integration of actuators level controllers (e.g., Arduino-based platforms) with the ROS framework, which can be used as a starting point and easily generalized for a wide variety of robots. To this end, the architecture is based on three layers, the actuators level controllers, the high-level nonlinear controllers and the middle layer. This last one also assumed as being an interface, which is used to communicate actuators level controllers with high-level controllers aiming small delays in the communication. To validate such approach, it will be considered the classical control by using static state-feedback linearization whose robustness with respect to the deformability of the wheel will be based on singular perturbations methods (specifically the flexibility, which is represented by ε) and corrective control actions based on the approach in [22]. The results show that the performance of the controller is increased when the degree of the corrective control is also increased as well as the suitable distribution of the architecture into the nodes of the ROS approach.
A Machine Learning Approach for Traffic Flow Provisioning in Software Defined Networks

With the recent surge of machine learning and artificial intelligence, many research groups are applying these techniques to control, manage, and operate networks. Soft-ware Defined Networks (SDN) transform the distributed and hardware-centric legacy network into an integrated and dynamic network that provides a comprehensive solution for managing the network efficiently and effectively. The network-wide knowledge provided by SDN can be leveraged for efficient traffic routing in the network. In this work, we explore and illustrate the applicability of machine learning algorithms for selecting the least congested route for routing traffic in a SDN enabled network. The proposed method of route selection provides a list of possible routes based on the network statistics provided by the SDN controller dynamically. The proposed method is implemented and tested in Mininet using Ryu controller.
On Object Detection Method of Tank Base on SNMS

The detection of overlapping tank targets in visible battlefield reconnaissance image was mainly studied. Analyzed the problem of the target detection of the Faster RCNN based on NMS and proposed the method of SNMS to improve the precision of detection and positioning of tank target detection in overlapping cases. In the process of bounding box regression, the traditional non-maximum suppression[4] is difficult to detect the overlapping tank object by hard threshold decision. We used the soft threshold method based on the score-resetting function to preserve the overlapping region proposals as many as possible. The experimental results show that the proposed method can significantly improve the performance of the overlapping tank object without increasing the computational complexity and additional training.
Utilizing User Generated Contents to describe Tourism Areas of Interest

The use of available place databases (like GeoNames and traditional maps) to obtain descriptive keywords of a user-defined place is not possible because such data sources mainly maintain location definitions of the well-known places only. Traditional sources may not be updated dynamically and may not ensure diverse information. Additionally, they do not give any information on the popularity, e.g., which is more popular among the places indexed by the same keyword. A bottom-up approach, based on real user attention, can address these problems. We propose a method to describe tourism area of interest (TAOI) by aggregating user generated social media text. We match the co-occurrence of important keywords in a particular location and select such words to describe TAOIs. We applied the proposed method to data on micro blogging service Twitter and photo sharing service Flickr and confirmed that our method made it possible to extract TAOI description. The recommended bottom-up approach enables the extraction of valuable information that is not possible by using traditional top-down approaches.
Fusion-On-Field Security and Privacy Preservation for IoT Edge Devices: Concurrent Defense Against Multiple Types of Hardware Trojan Attacks

Internet of Things (IoT) devices have connected millions of houses around the globe via the internet. In the recent past, threats due to hardware Trojan (HT) in the integrated circuits (IC) have become a serious concern, which affects IoT edge devices (IoT-ED). In this paper, the possibility of the IoT-ED with embedded HT that can cause serious security, privacy, and availability problems to the IoT based Home Area Network (HAN) has been discussed. Conventional network attack detection techniques work at the network protocol layers, whereas IoT-ED with HT can lead to the peculiar manifestation of attack at the physical and/or firmware level. On the other hand, in the IC design, most of the HT-based attack detection techniques require design time intervention, which is expensive for many of the IoT-ED and cannot guarantee 100% immunity. The argument in this paper is that the health of modern IoT-ED requires a final line of defense against possible HT-based attacks that goes undetected during IC design and test. The approach is to utilize power profiling (PP) and network traffic (NT) data without intervening into the IC design to detect malicious activity in HAN. The proposed technique is to effectively identify multiple attacks concurrently and to differentiate between different types of attacks. The IoT-ED behaviors for five different types of random attacks have been studied, including covert channel, DoS, ARQ, power depletion, and impersonation attacks. Data fusion has been leveraged by combining the PP and NT data and is able to detect, without design time intervention, each of the five attacks individually with up to 99% accuracy. Moreover, the proposed technique can also detect all the attacks concurrently with 92% accuracy. To the best of authors’ knowledge, this is the first work where multiple HT based attacks are concurrently detected in IoT-ED without requiring any design time intervention.
Implementation of Sliding Mode Based-Direct Flux and Torque Control for Induction Motor Drive with Efficiency Optimization

This paper presents an implementation of an efficient direct flux and torque control for induction motor drive. In control design, the modeling inaccuracy and various uncertainties cannot be avoided when classical methods are conducted. Therefore, it is recommended apply the nonlinear-robust control approaches to cover these drawbacks. The sliding mode approach is proposed to achieve a decoupled control and improve its robustness versus different disturbances. Furthermore, an optimal control algorithm is joined for losses minimization and efficiency maximization. This technique consists of the computation of optimum flux reference according to the load value and improves the efficiency for no load or light loads state, that makes is very suitable for variable load applications. The effectiveness of the control techniques is verified using an experimental test bench based on dSpace 1104 real-time interface.
Thin Film Photosensor Integrated on Planar Waveguide for Lab-an-Chip Applications

We present a device based on amorphous silicon photosensors and glass-diffused waveguides to be used inside lab-on-chip systems, to perform biomolecular recognition by means of optical interaction with the analyte. A 150 × 300 µm2 p-i-n photosensor was fabricated on a 200 µm wide optical waveguide, by using standard thin film microelectronic techniques, such as: thermal diffusion processes, metals and dielectrics deposition, semiconductors growth, UV photolithography and wet-dry etching to define the structure geometries. The electro-optical characterization of the device shows the effectiveness of the optical coupling and suggests its use for on-chip detection in lab-on-chip applications.
OFDM With Differential Index Modulation for Visible Light Communication

In this paper, orthogonal frequency division multiplexing with differential index modulation (OFDM-DIM) is proposed for visible light communication. Compared with traditional OFDM with index modulation (OFDM-IM), the proposed scheme decreases the average number of used subcarriers when transmits the same amount of information bits, therefore higher spectral efficiency can be achieved. The simulation results present that OFDM-DIM can provide higher data transmission rate and lower bit error rate (BER) over additive white gaussian noise (AWGN) optical channel. Meanwhile, simulations over reference channels of short range optical wireless communication are implemented, where OFDM-DIM performances better than OFDM-IM when line-of-sight (LOS) components are dominant.
Using an innovative model of service delivery to identify children who are struggling in school

Introduction School-age children with motor coordination challenges typically require formal referral for occupational therapy services and often experience lengthy wait times for one-to-one intervention. In a new service delivery model called Partnering for Change, therapists work collaboratively with educators in classrooms to observe, identify, and support children. This study describes children identified through a traditional referral process and compares them with children identified by occupational therapists through classroom observation and dynamic performance analysis. Methods Participants included 246 children enrolled in a 2-year evaluative study of the Partnering for Change service delivery. Parents completed a demographic questionnaire, the Developmental Coordination Disorder Questionnaire, and the Strengths and Difficulties Questionnaire. Children’s educators completed the Strengths and Difficulties Questionnaire and the School Function Assessment. Children completed the Movement Assessment Battery for Children. Results Children identified were significantly younger and more likely to be girls than those referred under the traditional model. Using observation and dynamic performance analysis, occupational therapists identified children who had equally marked difficulties as those who came from the waitlist. In the Partnering for Change model, waitlists for service were eliminated for all children. Conclusions Occupational therapists can identify children who are experiencing significant challenges participating at school without the need for standardized assessment, formal referrals, and waitlists.
Sparse Representation-Based Denoising for High-Resolution Brain Activation and Functional Connectivity Modeling: A Task fMRI Study

In the field of neuroimaging and cognitive neuroscience, functional Magnetic Resonance Imaging (fMRI) has been widely used to study the functional localization and connectivity of the brain. However, the inherently low signal-to-noise ratio (SNR) of the fMRI signals greatly limits the accuracy and resolution of current studies. In addressing this fundamental challenge in fMRI analytics, in this work we develop and implement a denoising method for task fMRI (tfMRI) data in order to delineate the high-resolution spatial pattern of the brain activation and functional connectivity via dictionary learning and sparse coding (DLSC). In addition to the traditional unsupervised dictionary learning model which has shown success in image denoising, we further utilize the prior knowledge of task paradigm to learn a dictionary consisting of both data-driven and model-driven terms for a more stable sparse representation of the data. The proposed method is applied to preprocess the motor tfMRI dataset from Human Connectome Project (HCP) for the purpose of brain activation detection and functional connectivity estimation. Comparison between the results from original and denoised fMRI data shows that the disruptive brain activation and functional connectivity patterns can be recovered, and the prominence of such patterns is improved through denoising. The proposed method is then compared with the temporal non-local means (tNLM)-based denoising method and shows consistently superior performance in various experimental settings. The promising results show that the proposed DLSC-based fMRI denoising method can effectively reduce the noise level of the fMRI signals and increase the interpretability of the inferred results, therefore constituting a crucial part of the preprocessing pipeline and provide the foundation for further high-resolution functional analysis.
Intelligent Secure Communication for Cognitive Networks With Multiple Primary Transmit Power

In this paper, we study an intelligent secure communication scheme for cognitive networks with multiple primary transmit power, where a secondary Alice transmits its secrecy data to a secondary Bob threatened by a secondary attacker. The secondary nodes limit their transmit power among multiple levels, in order to maintain the quality of service of the primary networks. The attacker can work in an eavesdropping, spoofing, jamming or silent mode, which can be viewed as the action in the traditional Q-learning algorithm. On the other hand, the system can adaptively choose the transmit power level among multiple ones to suppress the intelligent attacker, which can be viewed as the status of Q-learning algorithm. Accordingly, we firstly formulate this secure communication problem as a static secure communication game with Nash equilibrium (NE) between the main links and attacker, and then employ the Q-learning algorithm to select the transmit power level. Simulation results are finally demonstrated to verify that the intelligent attacker can be effectively suppressed by the proposed studies in this paper.
Ablation Treatment of Dental Tissue by530 nm and 353nm Radiation

The dependences of the depth of the crater in the tooth enamel and in the tartar, depending on the energy density (530 nm) were obtained. The dependence of the depth of the crater in tooth enamel and in tartar was investigated depending on the energy density (353 nm). Ablation thresholds for dental tissue wert found (353 and 530 nm). The dependence of the specific energy of evaporation of tooth enamel and tartar on the energy density is determined (353 and 530 nm).
Computer Simulation of Magnetoelectric Microwave Isolator

The paper is devoted to magnetoelectric microwave isolator based on a microstrip line with stubs and with magnetoelectric structure made on the base of yttrium iron garnet with the gallium gadolinium garnet substrate and lead zirconate titanate. A computer simulation of the dependence of the dielectric constant influence of the substrate on the parameters of the transmission line has been carried out. For magnetoelectric microwave isolator with substrate dielectric constant of $\epsilon$ = 10 (FLAN-10), the isolator ratio was of 2.77, for $\epsilon$ = 20 (FLAN-20) the isolator ratio was of 5.75. Magnetoelectric structures allow to improve the characteristics of the microwave isolator, for example, to make it possible to adjust the isolator ratio by means of an electric potential.
Power Allocation in Wireless Energy Harvesting Based Relaying Sensor Networks

In this paper, the system throughput of a wireless energy harvesting (EH) based relay sensor network has been maximized. Decode and Forward (DF) relaying node first harvest the energy using Time Switching (TS) protocol and then harvested energy is used to transmit the information towards the destination. A new system model is adopted where harvested energy is used to charge the battery of common control unit and distributed among the relay nodes to transmit information towards destination. Three power allocation methods namely equal power, water-filling and channel gain based power distribution algorithms have been adopted to maximize system throughput. The simulation results authenticate that the channel gain based power allocation method is superior and water-filling is poorest among them. The EH time also plays a significant impact in achieveing the maximum system throughput.
Handover Mechanism in 5G mmwave Band

5G networks are the next generation of mobile internet connectivity. Mobile data traffic may reach 30 Exabyte's per month. Microwave bands may reach saturation state to deliver the increment of data rate. Millimeter(mmwave) band is a promising band between 30 to 300 GHz. The main advantages of mmwave band are its small antenna radius and high attenuation. Handover in 5G mmwave is challenging because of its short cell radius where user equipment may perform a greater number of handovers meanwhile increasing handover delay. This paper presents the work of modification of x2 handover mechanism in 5G mmwave network. It mainly deals with reducing handover delay in 5G mmwave network considering different trajectories such as Horizontal trajectory and combination of horizontal and vertical.
Research on Orthogonal Iteration Algorithm of Visual Pose Estimation for UAV Landing

Aimed at the problem that the performance of orthogonal iterative (OI) algorithm is affected by the initial value when applying it for UAV pose estimation, the solutions of different non-iterative algorithms are selected as the initial values. Algorithm performances are evaluated from three aspects in algorithm stability, accuracy and real-time performance. Computer and experimental simulation are built to verify that a better performance can be obtained by using a Robust O(n) solution to the Perspective-n-Point problem (RPnP) as the initial value. Furthermore, the main factors affecting accuracy are explored among the number of feature points, the distance between the camera and the cooperation target and the random noise.
Research on CSI 300 Index Volatility Based on GARCH Model

In this paper, daily closing price of csi 300 index is selected as the research object, logarithm is taken and then difference data is used to establish a model, and then backcheck and forecast. To back the model test, VaR risk measurement model is established for the fitting of a series GARCH class models for evaluate.