Miracle-dz/UniFAR-FTUs huggingface dataset:
The Joint Allocation of Spectrum Resources With Power Control in Ultra Dense Networks

Ultra-dense networks is an effective technology to increase the network capacity by deploying a large number of small base stations. But, in cellular downlink networks, the inter-cell interference deteriorates rapidly with the increase of small base station (SBS) density. How to manage and coordinate inter-cell interference is a hot research topic in 5G wireless communication system. In this paper, we assume that each user-equipment tagged with its closest SBS (the nearest SBS in distance) and all other SBSs using the same frequency spectrum resources simultaneously act as interferers. We propose our model based on Poisson point process and coordinate the interference to improve the signal-to- interference ratio by joint allocation of spectrum resources with power control between the first and second closest SBSs of each UE. The simulation results show the coverage probability is greatly improved with our allocation algorithm.
Analysis and Identification of Renal Calculi in Computed Tomography Images

The kidney stone, also known as renal calculus can be identified by Computed Tomography imaging. There are various problems associated with this imaging technique such as blurry image, the low resolution of CT scan image, the similarity of renal calculi with other parts near kidney and prediction of stone in the image of the kidney. Computed Tomography takes X-rays through the required slices from the body. The image first goes through various preprocessing stages, then it is segmented and finally, the number of stones and their respective area are identified to help the doctor to analyze the patient's condition. This study has developed an automatic renal calculi detection system using the digital image processing techniques in CT scan images.
Enrichment of Ontology Instances Using Linked Data and Supplemental String Data

Various emerging applications using IoT require frequent ontology re-construction in ways to enrich the knowledge map. We propose a hybrid approach for a new semi-automated ontology enrichment system which expands the present ontology by using both linked data extracted from other ontology of the same (or similar) application domain and string data crawled from the web. Our system enriches an ontology with new instances of concepts and relations in two phases. First, it extracts new instances and relations from a reference ontology of the same or similar domain. Second, it validates the possible relations between the original instances and the new ones using crawled data from the web search. Our system computes confidence value to check validity of those relations before adding them to the present ontology. Our experiment demonstrates that the proposed two-phase hybrid approach achieves improved efficiency and accuracy for enriching ontology instances.
First Responder Drones for Critical Situation Management

The rapid exponential increase in current urban area population has brought about numerous problems. Different kinds of accidents caused by natural or artificial reasons are some of the leading challenges experienced in the overcrowded cities. This calls for the use of rapid response techniques to save lives in such emergency situations. These emergency responses need not only be intelligent, but also have the ability to adapt to individual environments so as to manage an emergency situation more effectively. An appropriate and effective response can only be delivered if accurate and sufficient information about the critical situation is obtained. This information can be promptly acquired using airborne sensory systems known as First Responder Drones (FRDs). This paper proposes a paradigm for deploying drones as first responders to a disaster or an accident scene. The system comprises of three important sections. The first is the sensory system, which comprises of alert sensors deployed on unique places such as streetlights and traffic cameras. The second part is the intelligent drones, which have the ability to autonomously take off and maneuver through obstacles. The final section is the control station, where the drones send live data from the critical environment. This system is implemented using LoRa with GPS capabilities and ESP32 modules. A detailed study of the system was performed, and the results show that the response time for FRDs is much shorter compared to the conventional response systems. Moreover, the data collected by the drones proved to be extra valuable when analyzing the critical situation.
Design, Modeling and Analysis of a Novel Piezoactuated XYZ Compliant Mechanism for Large Workspace Nano-positioning

This paper shows a novel piezo-actuated three-degree-of-freedom (3-DOF) XYZ compliant mechanism for large workspace micro/nano positioning, actuated by three piezoelectric actuators (PEAs). By means of bridge-type mechanisms, large working range of nano-positioning mechanism in three directions are achieved. Meanwhile, the dual leaf parallelogram hinges mechanisms (DLPH) and mirror symmetric configuration of XY-direction are used to guide parallel motion and guarantee high motion accuracy. Accurate kinematics analysis considering input coupling indicates the proposed compliant mechanism can achieve a three-dimensional working range. The finite element analysis shows the working range of 83. 7μm*83. 7μm*50. 4μm in XYZ-direction can be realized. In general, the proposed mechanism can generate the precise position of large workspace in XYZ.
Research Collaboration Recommendation for Universities Based on Data Science

In this paper, we describe a methodology based on data science to suggest research areas for scientific collaborations between two universities, their research groups, and their researchers. Two different topics are analyzed: i) the strengths of both universities and ii) their complementarities. The analysis is based on research articles in the Scopus database. The methodology has been applied to seven collaboration scenarios for Tecnologico de Monterrey. The results of our methodology are illustrated via a case study with six Chinese universities. The methodology is shown to be relevant for establishing academic and research relationships.
Investigating Epipolar Plane Image Representations for Objective Quality Evaluation of Light Field Images

With the ongoing advances in Light Field(LF) technology, research in LF acquisition, compression, processing has gained momentum. This increased the need for objective quality evaluation of LF content. Many processing algorithms are still optimized against peak signal to noise ratio(PSNR). Lately, several attempts have been made to improve objective quality evaluation such as extending 2D metrics to 4D LF domain. However, there is still a great room for improvement. In this paper, we experiment with existing 2D image quality metrics on the Epipolar Plane Image representations of LF content to reveal characteristics of LF related distortions. We discuss the challenges and suggest possible directions towards a LF image quality evaluation on EPI representations.
A Hybrid Markov and LSTM Model for Indoor Location Prediction

Accurate and robust indoor location prediction plays an important role in indoor location services. Markov chains (MCs) have been widely adopted for location prediction due to their strong interpretability. However, multi-order Markov chains (<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-MCs) are not suitable for predicting long sequences due to problems of dimensionality. This study proposes a hybrid Markov model for location prediction that integrates a long short-term memory model (LSTM); this hybrid model is referred to as the Markov-LSTM. First, a multi-step Markov transition matrix is defined to decompose the <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-MC into multiple first-order MCs. The LSTM is then introduced to combine multiple first-order MCs to improve prediction performance. Extensive experiments are conducted using real indoor Wi-Fi positioning datasets collected in a shopping mall. The results show that the Markov-LSTM model significantly outperforms five existing baseline methods in terms of its predictive performance.
Abundance-Guided Superpixels and Recurrent Neural Network for Hyperspectral Image Classification

Mixed spectral responses from different ground materials often create confusions in complex remote sensing scenes and restrict classification performance. In this regard, unmixing approaches are being successfully carried out to decompose mixed pixels into a collection of spectral signatures. In this paper, we propose a method to integrate unmixing into a deep feature learning model in order to classify hyperspectral data. We propose to generate superpixels from the abundance estimations of the underlying materials of the image obtained from an unsupervised endmember extraction algorithm called vertex component analysis (VCA). The mean abundances of the superpixels are then used as features for a deep classifier. Our proposed deep model, formulated as a joint convolutional neural network and recurrent neural network, receives significant spectral-spatial information in the data to produce better and powerful features and achieve improved classification performance than several alternative methods.
A Novel Movie Recommendation System Based on Deep Reinforcement Learning with Prioritized Experience Replay

A recommendation system plays an important role in information overload case by recommending personalized services to improve user experience. In this paper, a novel movie recommendation system based on deep reinforcement learning (DRL) framework is proposed. In proposed system model, the state information is preprocessed to overcome the problems of data sparsity and cold start. Specially, user’s interest change is captured using cross entropy and used to prioritize experience replay in a replay memory. The experiments verify that the proposed model can speed up the network update and improve recommendation accuracy.
Evaluation Experiments for Lane Keeping Assistance System under Several Conditions

Automobile Technology, which includes driver assistance system, has improved. These days, accuracy and reliability of image processing using camera systems have also improved. On the other hand, in severe conditions, it is impossible to see the lane marker by using the image processing because of the effect of heavy rain or dark conditions. High reflection lane markers have been proposed in order to solve this problem, and it is presumed that image processing performance can be improved by using these types of lane markers. Experiments to evaluate two types of high reflection lane markers were performed under several conditions and with scenarios. The experimental results show the validity of high reflection tape under rainy weather and dark condition. In addition, analysis of the low lane recognition rate of ordinary lane markers is presented.
Low Computational Complexity Digital Predistortion Based on Independent Parameters Estimation

In wide-band digital predistortion linearizers, the number of coefficients of a simplified Volterra polynomial model required to model memory effects can increase dramatically, which causes large computational complexity, ill-conditioning or overfitting problems. We propose a novel digital predistortion (DPD) implementation approach called covariance matrix based independent parameters estimation (CM-IPE) method for a direct learning structure (DLA). In the approach, we use the constant transformation matrix to replace the time-varying transformation matrix because of the stationary and ergodic nature of input signals. And then the principal component analysis (PCA) method is applied for independent parameters estimation. The proposed method can reduce computational complexity. And by utilizing the PCA technique, the coefficients can be estimated independently which, at the same time, can prevent ill-conditioning or overfitting problems. Experimental results demonstrate that the proposed approach realizes the equivalent linearization performance as the traditional DLA method at lower computational complexity.
ActiMon: Unified JOP and ROP Detection With Active Function Lists on an SoC FPGA

Field programmable gate arrays (FPGAs) have been increasingly mounted on commodity systems. As a matter of fact, such an emerging adoption of FPGAs in the commodity systems is attributed to their versatility came from the programmable property. Accordingly many industrial and academic attempts have been performed to exploit FPGAs in a variety of applications. In this paper, we note that FPGAs also can be used to protect the host CPU from a nasty security threat, called code reuse attacks (CRAs). Code reuse attack (CRA) is a powerful technique that allows attackers to execute arbitrary code. Control-flow integrity (CFI) has been popularly employed to mitigate CRAs. CFI entails CRA monitoring that checks if a program runs as directed by its control-flow graph. However, as monitoring naturally incurs non-negligible runtime overhead to the host CPU, many studies proposed hardware techniques to lessen the monitoring overhead. To facilitate the immediate deployment of a hardware-based solution, we propose a CRA monitor, called ActiMon, that can be implemented on an SoC FPGA where the host CPU and FPGA are manufactured together in a single platform. However, implementing the CRA monitor operating on FPGA arouses a new challenge that has never been addressed in the previous solutions: the operating clock of FPGA is many times slower than the CPU. By overcoming this speed difference, we ultimately purpose to evince the feasibility of FPGA as a computing device in the field of CRA defense. For this purpose, we have developed a highly efficient algorithm designed to run on FPGA whose goal is to monitor the existence of CRAs on the host CPU residing in the same SoC FPGA platform. Empirical results show that ActiMon runs on our target SoC FPGA platform efficiently enough to catch up to the speed of host code execution and promptly detects two important types of CRAs, JOP (Jump-Oriented Programming) and ROP (Return-Oriented Programming), as soon as they occurred in the host system. We assert that such results are encouraging thanks to our unified, lightweight ROP/JOP detection mechanism based on a list of active functions, and also to additional optimizations to leverage the inherent capabilities of FPGA for parallel computation.
A First Evaluation of Thick Oxide 3C-SiC MOS Capacitors Reliability

Despite the recent advances in 3C-SiC technology, there is a lack of statistical analysis on the reliability of SiO2 layers on 3C-SiC, which is crucial in power MOS device developments. This article presents a comprehensive study of the medium- and long-term time-dependent dielectric breakdowns (TDDBs) of 65-nm-thick SiO2 layers thermally grown on a state-of-the-art 3C-SiC/Si wafer. Fowler–Nordheim (F-N) tunneling is observed above 7 MV/cm and an effective barrier height of 3.7 eV is obtained, which is the highest known for native SiO2 layers grown on the semiconductor substrate. The observed dependence of the oxide reliability on the gate active area suggests that the oxide quality has not reached the intrinsic level. Three failure mechanisms were identified and confirmed by both medium- and long-term results. Although two of them are likely due to extrinsic defects from material quality and fabrication steps, the one dominating the high field (>8.5 MV/cm) should be attributed to the electron impact ionization within SiO2. At room temperature, the field acceleration factor is found to be $\approx 0.906$ dec/(MV/cm) for high fields, and the projected lifetime exceeds 10 years at 4.5 MV/cm.
Needle in a Haystack: Attack Detection from Large-Scale System Audit

In this paper, we present a system NeedleHunter to detect Advanced and Persistent Threats (APTs) and reconstruction of attack scenarios. Due to the information asymmetry between attackers and defenders, detecting APT attacks remains to be a challenge. Instead of targeting individual exploits, correlating the various stages of the attack is a substantially more feasible strategy. In our approach, we first construct a version-based provenance graph by analyzing system audit logs. Then, we use the rule-based technique to detection a particular stage of the attack, and connect these attacks to generate an attack path by leveraging the correlation between information flows. Also, we implement the compaction technique to compress the scale of the graph for scalable forensic analysis. An evaluation of our approach indicates that NeedleHunter can capture the key stages of APT campaigns with high precision and reconstruct the details of the attacks.
A Study on Development of Wavelet Deep Learning

In recent years, deep learning that can learn features from a dataset has been remarkably developing in the field of face recognition and voice recognition and so on. However, it is difficult to pursue cause of misjudgment result because input-output relation of deep learning is a black box. Furthermore, the content has yet to be elucidated what the judgment is based on. Therefore, when introducing deep Learning into multiple fields, it is important to understand the reason. This study aims to pursue cause of misjudgment result by intervening in the preprocessing part of deep learning using 2-dimensional discrete wavelet packet transform.
Impact of Feature Selection Methods on Data Classification for IDS

The rapid increase of data has led to many security issues. Various new technologies and other sub-disciplines have been introduced into the research of intrusion detection system (IDS), which has become a hot topic in the current field of security. However, IDS utilizing traditional machine learning technology suffers from relatively low detection rate, large computational overhead, and high false positive rate, due to the large amount of redundancy and high correlation of network traffic data. Therefore, we select random forest (RF) to handle the classification of the network traffic data, and try the classic feature selection algorithms Extra-Trees (ET) and Chi-square (CHI) to reduce the detection time and improve the efficiency of IDS. The experimental results based on the NSL-KDD dataset indicate that ET-RF and CHI-RF outstand in accuracy and computational overhead. Meanwhile, RF with ET outperforms that with CHI.
Dynamic Pricing-Based Resilient Strategy Design for Cloud Control System Under Jamming Attack

In this article, resilient strategy design is investigated for a cloud control system (CCS) subject to jamming attack. In the presence of jammer, signals, including measurements and control inputs between cloud server and physical plant may be interfered, which degrades the signal-to-interference-plus-noise ratio and further leads to packet dropout of signals. A Stackelberg game is applied to depict the interaction of transmitter and jammer. On account of game equilibria of two players, controller is devised for CCS with time-varying delay. A novel cross-layer dynamic pricing mechanism is devised to drive the disturbance attention performance of CCS to a desired region under constrained information and time-varying networks. The simulation results are provided to verify the effectiveness of the proposed methodology.
Synthesis of an Algorithm for Angle Measurement Channel of the Information-Measuring System of a Maneuvering Aircraft Given its Dynamic and Kinematic Characteristics

To solve the problem of improving the accuracy of electronic tracking devices of the information-measuring system of air traffic control, it is necessary to use state and observation models that take into account not only kinematic, but also dynamic characteristics of aircraft. Kinematic and dynamic characteristics more adequately describe aircraft movement models during various maneuvers, compared with aircraft movement models where this information is not taken into account. In this paper, the algorithms for the functioning of the angle measurement channel of the radar station of the information-measuring system for air traffic control are developed taking into account the kinematic and dynamic characteristics of the aircraft.
A Proposal for Postpartum Support Based on Natural Language Generation Model

This article presents a Multifaceted Natural Language Generation Model to help women in a postpartum. This proposal supports speech and text interactions, developing responses through an ontology model and retrieval based-method techniques oriented to the welfare of women after the birth of their baby. We show a case study, introducing an architecture model of conversational agents, a scenario and a simulation of this proposal. Our results presented a better performance in response time generated from a retrieval based-method(two seconds), and similar response accuracy, slightly better for semantic queries (20% better).
A Multi-Density Clustering Algorithm Based on Similarity for Dataset With Density Variation

Clustering has been widely used in the fields of knowledge discovery, pattern recognition and artificial intelligence. However, discovering clusters in spatial databases is still a challenging task, especially when the shape, size, and density of clusters vary a lot. Existing algorithms have sensitive parameters, clusters must be separated far enough from each other and rich prior knowledge about datasets is required. In this paper, we propose algorithm DENSS, which performs clustering on the basis of the similarity of neighbour distribution and the number of shared neighbors for two objects. Algorithm DENSS can mine clusters that differ in densities, and within a cluster the local densities are reasonably homogeneous. Adjacent objects are separated into different clusters by significant change in densities. To verify the effectiveness of the algorithm DENSS, synthetic and real-world datasets are used for testing, and it has been compared with seven clustering algorithms. Experimental results show that the proposed algorithm has a relatively high efficiency, robustness and effectiveness, and is remarkably superior to the seven algorithms. This algorithm is universal and can rapidly and efficiently identify the clusters of different densities, shapes and sizes even in the presence of noise and outliers for any object feature types.
Rumor Spreading from Social Networks to E-commerce

E-commerce and social networks both have achieved great success. Recent studies suggest that the cooperation of Ecommerce and social networks can achieve better efficiency. Unfortunately, to avoid stricter security measures in Ecommerce, novel security problems are emerging when malicious users take advantage of this opportunity to spread rumor from social networks to E-commerce systems. In this paper, concerning the unidirectional coupled networks formed by information dissemination from social networks to Ecommerce, the S2I2R model is proposed to analyze the rumor spreading. Finally, numerical simulations show the performances of the dynamical processes with various control schemes.
IBFT Liveness Analysis

Istanbul Byzantine Fault Tolerance (IBFT) is one of a family of proof-of-authority (PoA) blockchain consensus protocols designed to ensure immediate finality. We partially analyse the correctness of IBFT and show that the protocol does not guarantee Byzantine-fault-tolerant liveness when operating on a partially synchronous network.
An Optimal Low-Complexity Detection Algorithm for Differential Spatial Modulation

Differential spatial modulation (DSM) is developed by spatial modulation (SM), which does not require the channel state information (CSI) at receiver. The optimally classic detection algorithm is maximum-likelihood (ML), but it has very high computational complexity. In this paper, a low-complexity detection algorithm, termed as time division combination (TDC) for DSM is firstly proposed. Simulation results show that the proposed TDC algorithm can significantly reduce the computational complexity compared with ML without loss of bit-error-ratio (BER) performance.