Miracle-dz/UniFAR-FTUs huggingface dataset:
Vision-based Indoor Positioning Method By Joint Using 2D Images and 3D Point Cloud Map

2D image-based and 3D structure-based are the most two popular vision-based indoor positioning methods. Though 2D method has better efficiency for image retrieval, the number of stored images is usually too many to achieve fast 2D-2D matching, and it cannot obtain high positioning accuracy either. In contrast, 3D method has smaller database and can achieve much higher positioning accuracy with the help of 3D information. However, it needs to construct 3D point cloud map of the positioning environment and spend too much time on 2D-3D matching. Therefore, in order to improve the positioning accuracy and the retrieval efficiency, we propose a vision-based indoor positioning method by joint use 2D images and 3D point cloud map in this paper. In offline stage, we select key images into the offline database based on the 3D point cloud map that is built by RGB-D simultaneous localization and mapping (SLAM), and establish the mapping relation between 2D image local feature points and 3D points. In online stage, we utilize the rough-fine matching strategy to obtain 2D-3D matching pairs between local feature points of the user query image and 3D points in the 3D point cloud map. The user position is finally estimated by the efficient perspective-n-point (EPnP) algorithm. Performance analysis and simulation results indicate that our proposed method can not only achieve high positioning accuracy but also speed up the matching process.
A Novel Device-Free Tracking System Using WiFi: Turning Fading Channel From Foe to Friend

Since that phase error exists in Channel State Information (CSI) measured by commodity WiFi device, the existing device-free tracking systems, based on Doppler extracted from CSI, suffer from the ambiguity of moving direction, which results in tracking error. Therefore, in this paper, we propose the design and evaluation of a device-free tracking system to solve this challenge. The contributions of this paper are listed as follows. Firstly, we creatively propose to use signal fading phenomenon, which is considered to be enemy to communication system, to estimate moving direction of the target. Secondly, we introduce ray tracing model to analyze the influence of moving target on received signal power and then build the profiles of received power of two cases, including the target moving towards and away from the link between transmitter and receiver. Thirdly, we rely on short-time Fourier transform (STFT) to process CSI, and then extract the power of Doppler in a time window. Then, Derivative Dynamic Time Warping (DDTW) is utilized to identify the moving direction. Consequently, the target can be tracked by moving speed and direction. We implement the proposed system on the commodity WiFi device and the experimental results show that it can achieve median tracking error 0.84m.
Adaptive detection and accommodation of communication attacks on infinite dimensional systems with multiple interconnected actuator/sensor pairs

The work provides a general model of communication attacks on a networked infinite dimensional system. The system employs a network of inexpensive control units consisting of actuators, sensors and control processors. In an effort to replace a reduced number of expensive high-end actuating and sensing devices implementing an observer-based feedback, the alternate is to use multiple inexpensive actuators/sensors with static output feedback. In order to emulate the performance of the high-end devices, the controllers for the multiple actuator/sensors implement controllers which render the system networked. In doing so, they become prone to communication attacks either as accidental or deliberate actions on the connectivity of the control nodes. A single attack function is proposed which models all types of communication attacks and an adaptive detection scheme is proposed in order to (i) detect the presence of an attack, (ii) diagnose the attack and (iii) accommodate the attack via an appropriate control reconfiguration. The reconfiguration employs the adaptive estimates of the controller gains and restructure the controller adaptively in order to minimize the detrimental effects of the attack on closed-loop performance. Numerical studies on a 1D diffusion PDE employing networked actuator/sensor pairs are included in order to further convey the special architecture of detection and accommodation of networked systems under communication attacks.
On-line Sparse Recovery from Correlated Multiple Measurements Without Time Latency

To eliminate the time latency between the observations and estimations during the on-line recovery for time-varying sparse signals from correlated multiple measurements, an on-line recovery algorithm without time latency is proposed in this paper. A sequential scheme is constructed for the support set detection and significantly higher detection accuracy is achieved compared with the existing algorithms. Expectation-maximization inference is introduced for the online estimation of the signals and model parameters, where the temporal correlation among signals is sufficiently exploited by a linear dynamic system and linearly optimal estimation for the signals is achieved through RTS smoothing in the E-stage of the inference process. Experiments showed that compared with existing algorithms, significantly higher reconstruction accuracy is obtained with acceptable reduction of efficiency.
Automated Classification of Retinal Diseases in STARE Database

- Retinal diseases are identified by conducting different medical examinations primary of which are visual examinations. One of the key issues in visual diagnosis of diseases is the human error due to poor decision making, for that an upcoming inter-disciplinary technology named Computer Aided Medical Diagnostic System provides precise detection and prediction of disease. Automated image analysis methods are far more helpful for early identification and evaluation of disease as compared to cryptic and time taking manual techniques of digital medical imaging. This study aims to develop an automated method for identification of eye disorders that affect the human retina which if left unidentified may result in blindness due to delayed detection and analysis. Image data was acquired by publically available STARE Database having fundus images and by implementation of exclusion and inclusion criterion it was pre-processed on MATLAB. Initial pre-processing increased the significance of the data to be analyzed. From 186 images, 16 diseases and 22 features were deduced. A support vector machine classifier was used for automated identification and classification, resulting in an accuracy of 94% and specificity of 98%. In the chosen technique sensitivity, specificity and accuracy of the results was affected by the problem of one-sided data. For the reduction of dimensionality of data (redundancy reduction) principal component analysis was employed. 5, 10 and 22 Principal components were obtained to reduce the amount of variables. PCA was performed prior to training of the SVM, results for different data dimensionality was compared for completeness
Context-Aware RPL-Based Mobile Crowd Sensing and Routing Protocol for Smart City Networks

Several smart city monitoring applications use Mobile Crowd Sensing (MCS) technology to collect surrounding data events and send them to a central location for analysis. However, the majority of these applications use infrastructure networks that use direct communication (LTE, 5G) to transfer these data to the remote server. This leads to an increase in the load of the network bandwidth and comes at a higher data cost as well. This paper proposes an infrastructure-less routing protocol for MCS based on RPL (Routing Protocol for Low Power and Lossy Networks). However, RPL does not support mobility, and therefore, the proposed protocol is context-aware that adapts to the status and mobility of the nodes. The conducted performance evaluation reveals that the protocol performs better than the RPL in terms of packet delivery ratio (22% higher) and power consumption, due to the mobility management and the reduction in the control packet overhead (60% in low-medium mobility).
Pricing QoE With Reinforcement Learning For Intelligent Wireless Multimedia Communications

With the unification of modern wireless services of Mixed Reality and Tactile Internet, providing Quality of Experience (QoE) with ultra low latency become critical challenges in edge resource allocation. In this paper we propose a reinforcement learning-based economic pricing model for wireless multimedia QoE, leveraging economic theories and machine intelligence. In the proposed approach, the QoE pricing model considers the User Equipment (UE)’s perceived QoE, the amount of purchased data, the wireless channel conditions, and the user’s subjective multimedia content preference. In addition, the QoE gain of UE, cost of three entities in wireless networks Content Provider (CP), Wireless Carrier (WC), and UE, are integrated in the economic concept of social utility. The social utility would be affected by all system factors such as unit data price, multimedia quality requirement, and wireless channel conditions. The proposed reinforcement learning method improves the social utility performance by maximizing the accumulated utility through obtaining the optimal factors set up. At last, through numerical simulations we show the impacts of different system parameters on UE’s QoE gain and the improvement of social utility performance by using the proposed reinforcement learning approach.
Gamification-Supported Collaborative Learning: A Systematic Literature Review

The use of new motivational techniques in collaborative educational contexts has been growing in recent years. Among these strategies, Gamification begins to stand out thanks to its playful character, formalized through the use of game elements. In this context, the authors of this article developed a Systematic Review (between 2013 and 2018) to find answers to the following questionings: “What are the collaborative learning techniques most used in contexts using the concept of gamification? ”; “Which gamification technique stood out most in collaborative educational environments? ”; “At what educational level was the practice of gamification with collaborative learning most used? ”, and; “Which country developed more research in the domain of collaborative learning supported by the concept of gamification? ”. From a research protocol defined for this review, 43 primary studies were selected and fully read, allowing us to satisfactorily answer these questionings and suggest possible directions for this area of research.
In vitro culture, PCR , and nested PCR for the detection of Theileria equi in horses submitted to exercise

This study compared the usefulness of in vitro culture, PCR, and nested PCR for the diagnosis of Theileria equi in horses submitted to stress during exercise. Blood samples from 15 apparently healthy horses, previously conditioned to a high-speed equine treadmill, were taken prior to and after exercise. The animals were divided into two experimental groups: 30-day training schedule (G1) and 90-day training schedule (G2). Statistical analysis was performed using a chi-square test and kappa statistic was used in order to assess agreement. No significant difference was observed between samples collected at resting or after exercise. In G1, merozoites of T. equi were detected in the blood smears of four horses before in vitro culture, whereas 14 samples were positive, confirmed by culture. In G2, five and 11 horses were positive before and after culture, respectively. No PCR amplified product was observed in any of the tested animals although the PCR system based on the 16S rRNA gene of T. equi detected DNA in blood with an equivalent 8x10-5% parasitaemia. The nested PCR based on the T. equi merozoite antigen gene (EMA-1) allowed the visualization of amplified products in all the horses. Therefore, nested PCR should be considered as a means of detection of sub-clinical T. equi infections and in vitro culture could be used as a complement to other methods of diagnosis.
Reliable Underlay D2D Communications over Multiple Transmit Antenna Framework

Robust beamforming is an efficient technique to guarantee the desired receiver performance in the presence of erroneous channel state information (CSI). However, the application of robust beamforming in underlay device-to-device (D2D) communication still requires further investigation. In this paper, we investigate resource allocation problem for underlay D2D communications by considering multiple antennas at the base station (BS) and at the transmitters of D2D pairs. The proposed design problem aims at maximizing the aggregate rate of all D2D pairs and cellular users (CUs) in downlink spectrum. In addition, our objective is augmented to achieve a fair allocation of resources across the D2D pairs. Further, assuming elliptically bounded CSI errors, the formulation ensures maintaining signal to interference plus noise ratio (SINR) above a specified threshold. The derived optimization problem results in a mixed integer non-convex problem and requires exponential complexity to obtain the optimal solution. We perform a semi-definite relaxation (SDR) to handle the stochastic SINR constraints by using the S-Lemma, obtaining a number of linear matrix inequalities. The non-convexity is addressed by introducing slack variables and performing a quadratic transformation to obtain sub-optimal beamformers via alternating optimization. The solution for channel assignments to D2D pairs is obtained by convex relaxation of the integer constraints. Finally, we demonstrate the merit of the proposed approach by simulations in which we observe higher and more robust network throughput, as compared to previous state-of-the-art.
TPDD: A Two-Phase DDoS Detection System in Software-Defined Networking

Distributed Denial of Service (DDoS) attack is one of the most severe threats to the current network security. As a new network architecture, Software-Defined Networking (SDN) draws notable attention from both industry and academia. The characteristics of SDN such as centralized management and flow-based traffic monitoring make it an ideal platform to defend against DDoS attacks. When designing a network intrusion detection system (NIDS) in SDN, how to obtain fine-grained flow information with minimal overhead to the SDN architecture is a problem to be solved. In this paper, we propose TPDD, a two-phase DDoS detection system to detect DDoS attacks in SDN. In the first phase, we utilize the characteristics of SDN to collect coarse-grained flow information from the core switches and locate the potential victim. Then we monitor the edge switches located close to the potential victim to obtain finer-grained traffic information in the second phase. The collection method of each phase fully considers the impact on the bandwidth between the controller and switches. Without modifying the existing flow rules, the collection module can obtain sufficient information about traffic. By using entropy-based and machine learning-based methods, the detection module can effectively detect anomalies and identify whether the potential victim marked in the first phase is the target of attacks. Experimental results show that TPDD can effectively detect DDoS attacks with little overhead.
High-level image representation-based on Gestalt theory for image classification

Feature extraction and representation are crucial stages for image classification. However, the derived features might not be robust discriminators, which radically alter image classification results. In this paper, we present an enhanced Bag of visual Words representation for better image classification. We are inspired by the Gestalt laws of grouping.So, our design goals are:•Introduce the spatial information into Bag of Words representation by grouping the aligned visual words (high order features),•Preserve the spatial relationships among the visual words groups against different geometric transformations,•Achieve the high-level representation of visual words by extracting visual phrases. Theses phrases convey the semantic meaning of object parts.The proposed approach has been evaluated, in terms of classification accuracy, on Caltech101 image database. Experimental results indicate that the classification performances are improved by using visual phrases.
Fuzzy Extractor-Based Key Agreement for Internet of Things

The emergence of the Internet of Things with its constraints obliges researchers in this field to find light and accurate solutions to secure the data exchange. This document presents secure authentication using biometrics coupled with an effective key agreement scheme to save time and energy. In our scheme, the agreed key is used to encrypt transmission data between different IoT actors. While the fuzzy extractor based on the fuzzy vault principle, is used as authentication and as key agreement scheme. Besides, our system incorporates the Reed Solomon and Hamming codes to give some tolerance to errors. The experimental results have been discussed according to several recognition rates and computation times. Indeed, the recognition rate results have been compared to other works to validate our system. Also, we clarify how our system resists to specific transmission attacks without affecting lightness and accuracy.
Modeling the irradiance dependency of photovoltaic modules using Artificial Neural Network

This paper suggests an efficient approach to accurately model the irradiance dependency of photovoltaic (PV) modules by using the artificial neural network (ANN). In fact, a Feed-Forward Back Propagation (FFBP) neural network has been developed in this paper to deal with this concern. This network uses the Levenberg-Marquardt (LM) as the back-propagation training algorithm to tuning the network bias and weights. Furthermore, in order to show the merits of this strategy, a set of real experimental current-voltage (I-V) characteristics, measured from a commercialized PV module (Multi-crystalline Shell S75 PV module) are employed as the training and test datasets. These data are measured at nine irradiances (200, 300, 400, 500, 600, 700, 800, 900 and $1000\mathrm{W}/ \mathrm{m}^{2})$. Finally, the real measured I-V characteristics and the estimated ones (using the trained network) are compared. The obtained results demonstrate clearly the high accuracy of the developed model to mimic the irradiance dependency of PV modules.
Candidate genes and potential mechanisms for chemoradiotherapy sensitivity in locally advanced rectal cancer

The aim of the present study was to investigate candidate genes for chemoradiotherapy (CRT) sensitivity in patients with locally advanced rectal cancer (LARC), and the potential mechanisms of their action. A microarray dataset (GSE98959) was obtained from the Gene Expression Omnibus database that included microRNA (miRNA, miR) expression profiling of 22 samples from patients with LARC who had received preoperative radiotherapy and chemotherapy. Of these patients, 10 responded to the treatment and 12 did not. Differentially expressed miRNAs (DEMs) were identified, followed by the construction of an miRNA-gene network. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) function analyses were performed on the target genes in the miRNA-gene network. Furthermore, a protein-protein interaction (PPI) network was constructed on the basis of the target genes, followed by GO function enrichment and KEGG pathway analysis. A total of 30 DEMs were identified between the responder and non-responder groups. Thiamine metabolism (including miR-371a-3p) was the pathway with the highest enrichment of DEMs. The pathway that was most markedly enriched in the target genes of upregulated miRNAs was the pluripotency of stem cells pathway, as indicated by phosphoinositide-4,5-bisphosphate 3-kinase γ (PIK3CG) and anaphase-promoting complex subunit 2 (APC2). Pathways in cancer exhibited the highest enrichment in the set of target genes of downregulated miRNAs. KEGG pathway and GO function analysis indicated that target genes in the PPI network were enriched in the glioma pathway and assembled in the intracellular signaling cascade function, as indicated by the proto-oncogene NRAS. miR-371a-3p may be a candidate miRNA for CRT sensitivity in LARC via the thiamine metabolism pathway. PIK3CG and APC2 may contribute to CRT sensitivity via signaling pathways regulating the pluripotency of stem cells. Furthermore, NRAS may serve an important role in mediating CRT sensitivity via an intracellular signaling cascade.
Simplifying Transformations for a Family of Elastic Metrics on the Space of Surfaces

We define a new representation for immersed surfaces in R3 by combining the SRNF and the induced surface metric. Using the L2 metric on the space of SRNFs and the DeWitt metric on the space of surface metrics, we obtain a 3-parameter family of metrics that corresponds to the family of "elastic metrics" proposed by Jermyn et al. in [19] on the space of immersed surfaces. Similar to the original SRNF representation this new representation results in an extrinsic distance function on the space of immersed surfaces that is easy to compute as it is given by an explicit formula. In addition to avoiding the degeneracy of the SRNF it allows for a data-driven choice of the parameters of the metric, while still providing for fast and accurate registration of surfaces.
Data-Aided Channel Estimation for Poisson Channels With Inter-Symbol Interference

It has been shown that long pilots should be employed in a Poisson channel with inter-symbol interference to obtain accurate channel estimate, which may reduce the transmission efficiency. In this paper, an iterative data-aided channel estimation and signal detection algorithm is proposed with significant shortened overhead but can approach the bit-error rate performance under perfect channel state information. In this algorithm, periodic pilots are not required and only one short pilot sequence is utilized to obtain a rough initial channel estimate, which will be strengthened and updated by iteratively employing the blockwise data symbols as additional pilots. It is proved that treating the detected data symbols as pilots is a biased estimation, where the bias is proportional to the bit-error rate. Numerical results show that the proposed approach employing only 20 initial pilots can approach the optimal bound. The same bound needs to be achieved by assigning at least 500 pilots using periodic pilot based channel estimation.
Machine-learning-assisted DDoS attack detection with P4 language

While Software Defined Networking (SDN) provides well-known advantages in terms of network automation, flexibility and resources utilization, it has been observed that SDN controllers may represent critical points of failure for the entire network infrastructure, especially when they are targeted by malicious cyber attacks such as Distributed Denial of Service (DDoS). To address this issue, in this paper we exploit stateful data planes, as enabled by P4 programming language, where switches maintain persistent memory of handled packets to perform attack detection directly at the data plane, with only marginal involvement of the SDN controllers. As machine learning (ML) is recognized as primary anomaly detection methodology, we perform DDoS attack detection using a MLbased classification and compare different ML algorithms in terms of classification accuracy and train/test duration. Moreover, we combine ML and P4-enab1ed stateful data planes to design a real-time DDoS attack detection module, which we evaluate in terms of latency required for the detection. Three real-time scenarios are considered, where P4-enab1ed switches elaborate the received packets in different ways, namely, packet mirroring, header mirroring, and P4-metadata extraction. Numerical results show significant latency reduction when P4 is adopted.