Miracle-dz/UniFAR-FTUs huggingface dataset:
A BioCapsule Based Authentication System with Secured Intrusive user Authentication

A large portion of system breaches are caused by authentication failure. These failures are themselves related to the limitations associated with existing authentication methods. The current proxy based or biometrics based authentication, are not user-centric or endanger users security and privacy. A central theme of authentication is to authenticate users using characteristics intrinsically linked with human users rather than some external factors. A central theme of authentication is to authenticate users using characteristics intrinsically linked with human users rather than some external factors. But current authentication methods, whether proxy based or biometrics based, are not user-centric and/or endanger users’ security and privacy. A promising direction emerging from this effort is biometrics. This work proposes a biometrics based user-centric authentication approach. The proposed method securely fuses the extracted user biometrics feature with the user data forming a BioCapsule(BC) and enabling these BCs for authentication. This BC is then encrypted using AES algorithm to provide high security to the data. The user data used in this approach consist of user name, date of birth, address and account number which combines together forming a Reference Byte (RB). Such an approach is user friendly, secure, identity bearing yet privacy-preserving, resilient, and revocable once a BC is compromised. The proposed model can be applied for biometric ATMs which replaces card system by biometric technology for operating ATMs. During authentication, the user is required to provide another sample of the users biometric characteristics. This is matched with the one in the database, and if the two samples are the same, then the user is considered to be a valid user. Proposed model provides high security in authentication which also protects service user from unauthorized access for authentication. Such an approach is secure, privacypreserving, user friendly, and revocable. By fusing the users biometrics with a distinct user details makes the reference subject different among different users.
A Novel Projective-Consistent Plane Based Image Stitching Method

When different target surfaces, in three-dimensional space, are mapped onto an image plane, they have different projections. These projections vary with the viewpoint. These local differences have influence on the accuracy of image stitching. Most of the existing image stitching methods divide an input image into a number of fixed-size cells, and the pixels within the same cell are then warped using the same local transformation model for the alignment. These methods are based on the hypothesis that the transformation models in one cell are consistent. However, this hypothesis does not hold in general. In this paper, we propose a novel projective-consistent plane based image stitching method (termed PCPS). It divides the overlapping regions of an input image into some projective-consistent planes according to the normal vectors’ orientations of local regions and the reprojection errors of aligned images. The local projective transformation model is estimated for each projective-consistent plane. And then, a hybrid warping model is estimated. For the pixels in overlapping regions, the local projective transformation models are adopted to achieve a better alignment. While for the pixels in non-overlapping regions, a global projective transformation model is estimated by using the inliers uniformly distributed in the projective-consistent planes to avoid distortion. Compared with the state-of-the-art image stitching methods, the experimental results on a number of challenging image sequences show that the projective transformation model estimated by the proposed PCPS method for each projective-consistent plane is more accurate, and the achieved stitching results have less seams and projective distortion.
Face recognition using linear sparse approximation with multi-modal feature fusion

Abstract In the past few years, face recognition (FR) based on sparse representation is providing very good classification accuracy which calls for further research to exploit its capabilities to the maximum. In this paper, a novel FR method is developed as linear sparse approximation with multi-modal feature fusion (SMF classifier). SMF classifier works in three steps; multi-modal feature extraction, weight calculation for linear sparse representation and contribution calculation for final classification. In multi-modal feature extraction, a novel feature vector is developed by combining a local texture feature and a global color feature for better classification accuracy. To highlight the competency of proposed method, experiments are conducted for SMF classifier and simple sparse classifier for all possible training sets. Results are compared with simple sparse classifier in terms of mean classification accuracy. An analysis of percentage improvement in classification accuracy is performed to showcase the reliability of proposed method.
An Extensible Deep Architecture for Action Recognition Problem

Human action Recognition has been extensively addressed by deep learning. However, the problem is still open and many deep learning architectures show some limits, such as extracting redundant spatio-temporal informations, using hand-crafted features, and instability of proposed networks on different datasets. In this paper, we present a general method of deep learning for the human action recognition. This model fits on any type of database and we apply it on CAD-120 which is a complex dataset. Our model thus clearly improves in two aspects. The first aspect is on the redundant informations and the second one is the generality and the multi-functionality application of our deep architecture. Our model uses only raw data for human action recognition and the approach achieves state-of-the-art action classification performance.
Side-Channel Attacks on Intel SGX: How SGX Amplifies The Power of Cache Attack

In modern computing environments, hardware resources are commonly shared, and parallel computation is more widely used. Users run their services in parallel on the same hardware and process information with different confidentiality levels every day. Running parallel tasks can cause privacy and security problems if proper isolation is not enforced. Computers need to rely on a trusted root to protect the data from malicious entities. Intel proposed the Software Guard eXtension (SGX) to create a trusted execution environment (TEE) within the processor. SGX allows developers to benefit from the hardware level isolation. SGX relies only on the hardware, and claims runtime protection even if the OS and other software components are malicious. However, SGX disregards any kind of side-channel attacks. Researchers have demonstrated that microarchitectural sidechannels are very effective in thwarting the hardware provided isolation. In scenarios that involve SGX as part of their defense mechanism, system adversaries become important threats, and they are capable of initiating these attacks. This work introduces a new and more powerful cache side-channel attack that provides system adversaries a high resolution channel. The developed attack is able to virtually track all memory accesses of SGX execution with temporal precision. As a proof of concept, we demonstrate our attack to recover cryptographic AES keys from the commonly used implementations including those that were believed to be resistant in previous attack scenarios. Our results show that SGX cannot protect critical data sensitive computations, and efficient AES key recovery is possible in a practical environment. In contrast to previous attacks which require hundreds of measurements, this is the first cache side-channel attack on a real system that can recover AES keys with a minimal number of measurements. We can successfully recover the AES key from T-Table based implementations in a known plaintext and ciphertext scenario with an average of 15 and 7 samples respectively.
Wood Defects Recognition Based on Fuzzy BP Neural Network

Firstly, we applied the X-ray non-destructive testing technology to detect wood defects for getting the images. After graying the images, we calculated their GLCMS(Gray Level Co-occurrence Matrixes), then we normalized GLCMS to obtain the joint probabilities of GLCMS. The feature vectors of images, which included 13 eigenvalues of images were calculated and extracted by the joint probability of GLCMS. The fuzzy BP neural network(abbreviated as FBP) was designed by combining fuzzy mathematics and BP neural network . And the FBP neural network was regarded as the membership function of feature vectors, the outputs of the network was regarded as the degree of membership to the feature vectors in each category. We use the maximum degree of membership method for the pattern recognition of feature vectors, so the automatic identification and classification for feature vectors were achieved , and then the automatic identification of wood defects was realized. By simulated study and training many times, the results shown that the average recognition success rate of the network was more than 90%, and some FBP networks had an extremely high recognition success rate to training samples and test samples.
Network Coding in Multihop Wireless Networks: Throughput Analysis and Protocol Design

Multi-hop wireless networks have been widely considered as promising approaches to provide more convenient Internet access for their easy deployment, extended coverage, and low deployment cost. However, providing high-speed and reliable services in these networks is challenging due to the unreliable wireless links, broadcast nature of wireless transmissions, and frequent topology changes. On the other hand, network coding (NC) is a technique that could significantly improve the network throughput and the transmission reliability by allowing intermediate nodes to combine received packets. More recently proposed symbol level network coding (SLNC), which combines packets at smaller symbol scale, is a more powerful technique to mitigate the impact of lossy links and packet collisions in wireless networks. NC, especially SLNC, is thus a particular effective approach to providing higher data rate and better transmission reliability for applications such as mobile content distribution in multihop wireless networks. This dissertation focuses on exploiting NC in multihop wireless networks. We studied the unique features of NC and designed a suite of distributed and localized algorithms and protocols for content distribution networks using NC and SLNC. We also carried out a theoretical study on the network capacity and performance bounds achievable by SLNC in mobile wireless networks. We proposed CodeOn and CodePlay for popular content distribution and live multimedia streaming (LMS) in vehicular ad hoc networks (VANETs), respectively, where many important practical factors are taken into consideration, including vehicle distribution, mobility pattern, channel fading and packet collision. Specifically, CodeOn is a novel pushbased popular content distribution scheme based on SLNC, where contents are actively broadcast to vehicles from road side access points and further distributed among vehicles using a cooperative VANET. In order to fully enjoy the benefits of SLNC, we proposed a suite of techniques to maximize the downloading rate, including a prioritized and localized relay selection mechanism where the selection criteria is based on the usefulness of contents possessed by vehicles, and a lightweight medium access protocol that naturally exploits the abundant concurrent transmission opportunities. CodePlay is designed for LMS applica-
A Survey on Applications of Recommendation System

Recommendation system is a successful application area in various fields. This paper is concerned with its application in four main fields. One of the main problems faced by users in various fields is "decision making" Hence recommendation system acts as a gateway for users to be suggested as to what decision would be right and predicts the future post decision. This paper gives the overview of its application in four areas along with the data mining techniques used together with the algorithms.
Research of Software Testing Model based on Correlation Defect

Analysis of existing research results of defect testing, software defects during testing should consider the relevance of the defect test software, the design of a Markov chain with the right to test the model, the design of a multi-objective algorithm weights given Markov chain with the right test strategy based on defects associated with the state transition strategy based on multi-objective test weight matrix. The experimental results show that this strategy compared with Controlled Markov Chain testing strategy that can significantly reduce test cases and improve the defect detection rate.
Vers une personnalisation des Services RéSeau à la demande. (Toward the Customization of on-demand Network Services)

In this HDR, we present our work on the deployment of on-demand network services in the context of Cloud Networking. Our research addresses the issues of dynamic usage changes within the context of permanent technological evolution. Both usage and technological perspectives are associated to specific constraints and requirements, which need to be considered when designing new network services and architectures. Here, "On demand", refers to the consumption model as seen by the end customer, but also by the application and network levels for services/resources provisioning within the infrastructure of provider/operator. Following the Cloud approach for application services, we introduce our proposal for dy- namic providing of Cloud Networking. The need of an agile design of network services to provide on demand services, and the dynamicity when deploying and managing network ser- vices, lead us to rethink networking and its design with more autonomous and intelligent mechanisms. For this purpose, we introduce our modeling of functional and non-functional (QoS) aspects of the architecture components. QoS is our enabler to provide a network cus- tomization service and to move towards convergence between the control and management. We present our perspective and ou analysis on the application of SOA, SDN and NFV paradigms for the definition of the network as a service. We also propose to integrate a QoS-aware approach to define the dynamic deployment of customized virtual networks. For this purpose, we define a new phase in the lifecycle of network services within virtualized environments: the Network Virtual Deployment. We complete our proposal with the description of the virtual network offer.
Multi-View Classification via Adaptive Discriminant Analysis

In many real applications, an object is usually represented with multiple views, providing compatible and complementary information to each other. Therefore, it is highly desirable to recognize the object from distinct and even heterogeneous views. In this paper, we propose a novel method, the named multi-view locality adaptively discriminant analysis (MvLADA), for multi-view classification. The MvLADA integrates subspace learning and weighted matrix learning into a uniform framework, where the weighted matrix is adaptively attained and shared by all views. Compared with the most existing LDA-based multi-view methods, the MvLADA adaptively assigns different weights to each sample, which enhances MvLADA’s flexibility in practical applications. Moreover, the learned weighted matrix shared by all views exploits the point’s neighbor relationship automatically without requiring a $k$ NN procedure. Besides, the MvLADA is a parameter-free method without imposing any additional parameters. We validate the proposed MvLADA on three real-world datasets, indicating a better performance than the state-of-the-art multi-view algorithms.
Modeling the Interdependences Between Voltage Fluctuation and BTI Aging

With technology scaling, the susceptibility of circuits to different reliability degradations is steadily increasing. Aging in transistors due to bias temperature instability (BTI) and voltage fluctuation in the power delivery network of circuits due to IR-drops are the most prominent. In this paper, we are reporting for the first time that there are interdependences between voltage fluctuation and BTI aging that are nonnegligible. Modeling and investigating the joint impact of voltage fluctuation and BTI aging on the delay of circuits, while remaining compatible with the existing standard design flow, is indispensable in order to answer the vital question, “what is an efficient (i.e., small, yet sufficient) timing guardband to sustain the reliability of circuit for the projected lifetime? ” This is, concisely, the key goal of this paper. Achieving that would not be possible without employing a physics-based BTI model that precisely describes the underlying generation and recovery mechanisms of defects under arbitrary stress waveforms. For this purpose, our model is validated against varied semiconductor measurements covering a wide range of voltage, temperature, frequency, and duty cycle conditions. To bring reliability awareness to existing EDA tool flows, we create standard cell libraries that contain the delay information of cells under the joint impact of aging and IR-drop. Our libraries can be directly deployed within the standard design flow because they are compatible with existing commercial tools (e.g., Synopsys and Cadence). Hence, designers can leverage the mature algorithms of these tools to accurately estimate the required timing guardbands for any circuit despite its complexity. Our investigation demonstrates that considering aging and IR-drop effects independently, as done in the state of the art, leads to employing insufficient and thus unreliable guardbands because of the nonnegligible (on average 15% and up to 25%) underestimations. Importantly, considering interdependences between aging and IR-drop does not only allow correct guardband estimations, but it also results in employing more efficient guardbands.
Secured Connection in Wireless AD-HOC Network with Multiple Relay Transmission Using Dynamic Source Routing Algorithm

-One of the decentralized types of wireless networks is a wireless ad hoc network (WANET). The network is so called as it does not rely on a pre-existing model, such as routers in the wired networks or AccessPoint(AP) in managed (infrastructure based) wireless networks. Instead, each of the nodes participates in routing by forwarding data to other nodes. In addition to the conventional routing, ad hoc networks use flooding for forwarding data. Here, we propose a wireless ad-hoc network with randomly generated eavesdroppers for analyzing the secrecy of connection between source and destination, in which we use dynamic source routing techniques to transfer the data between the source and destination nodes through the relay nodes. Keywords– Ad-Hoc Network, Relay Transmission, Dynamic Source Routing
HMSPKmerCounter: Hadoop based Parallel, Scalable, Distributed Kmer Counter for Large Datasets

Counting the frequency of every distinct substring of length k in sequence reads is an initial step in many bioinformatics applications such as genome assembly, correction of errors in sequencing reads, fast multiple sequence alignment, and detection of repeats. This problem is called as a k-mer counter problem. Although k-mer counting problem looks simple, when size of the input sequence reads dataset is massive and the number of k-mers increases, single node based k-mer counter tools would exhaust the memory and hard disk capacity of a single computer. Hadoop is identified as one of the scalable, parallel big data frameworks for data-intensive applications and to process large data sets in a cluster of computers with low-cost commodity hardware. In this paper, a Hadoop based k-mer counter with Minimum Substring Partitioning (HMSPKmerCounter) method is developed and compared with k-mer counting program of BioPig which is the first Hadoop based k-mer counter and KMC3, which is the recent single node multithreaded k-mer counter tool. Our results show that Hadoop based K-mer counter with Minimum Substring Partitioning outperforms k-mer counter program of Biopig for the k values = 28, 40, 55 and 65. Also, results show that our implementation outperforms KMC3 as k value increases.
TIO: A VLC-enabled hybrid data center network architecture

To satisfy the ever-increasing bandwidth demand of modern data centers, researchers have proposed hybrid Data Center Networks (DCNs), which employ high-bandwidth Optical Circuit Switches (OCSs) to compensate for Electrical Packet Switches (EPS). Existing designs, such as Helios and c-Through, mainly focus on reconfiguring optical devices to meet the estimated traffic requirements. However, these designs face two major challenges in their OCS-based networks, namely, the complex control mechanism and cabling problems. To solve these challenges, we propose TIO, a hybrid DCN that employs Visible Light Communication (VLC) instead of wired OCS design to connect racks. TIO integrates the wireless VLC-based Jellyfish and wired EPS-based Fat Tree seamlessly and combines the opposite and complementary characteristics, including wireless VLC direct connection and wired electrical packet switching, random graph, and Clos topology properties. To further exploit the merits of TIO, we design a hybrid routing scheme and congestion-aware flow scheduling method. Comprehensive evaluations indicate that TIO outperforms the Jellyfish and Fat Tree in both topology properties and network performance, and the flow scheduling method also evidently improves performance.
An Optimization Approach for High Quality Image Segmentation using Mean Shift Filtering

Analysis of image is to deal with image semantic, image is not represented in single pixels it is an meaningful image objects and their mutual relations. High-resolution remote Sensing image contain complex structures, it have common brightness but with week boundaries and fail to detect edges of image objects. Then boundaries posterior probabilities are predicted by the model and associated with weights in the mean-shift filtering iteration, finally filtered image is put in to segmentation methods instead of the original image. The method can smooth the inner pixels of objects and preserve the boundaries of complex image (Remote sensing image in spatial and spectral resolution). It produces a more accurate image and higher classification accuracy. Image analysis application is multi source data fusion integration of different data types plays an important role in the remote sensing area, it can also work in “PSYCHOPHYSICS”. In this RGB image is converted to HIS image then given as an input, in experiments the regression model is used with an aerial image, which is tested with an aerial image and a QuickBird image. For the evaluations of image two popular segmentation methods used, those are fusion method and multiresolution. The goal of this work is both quantitative and qualitative evaluations reveal that the presented procedure facilitates a high image segmentation result and higher classification accuracy. Key words— Image segmentation, classification, mean shift filtering, HIS image, segmentation accuracy.