Miracle-dz/UniFAR-FTUs huggingface dataset:
Physical layer security performance study for Two-Hop wireless networks with Buffer-Aided relay selection

Physical Layer Security Performance Study for Two-Hop Wireless Networks with Buffer-Aided Relay Selection by Xuening Liao As wireless communication technologies continue to evolve rapidly, an unprecedented amount of sensitive information, such as financial data, physical health details and personal profile data, are transmitted through various wireless networks. However, the broadcast nature of wireless medium makes it difficult to shield these sensitive information from unauthorized users (eavesdroppers), and thus securing wireless communication is becoming an increasingly urgent demand. Physical layer (PHY) security has been proposed as one promising technology to provide security guarantee for wireless communications, owing to its unique advantages over traditional cryptography-based mechanisms, like an everlasting security guarantee and no need for costly secret key distribution/management and complex encryption algorithms. This thesis therefore focuses on the PHY security performance study for two-hop wireless networks with buffer-aided relay selection (a typical PHY security technique), where relay buffers will be adopted to help the transmission of the message. We first investigate the security-delay trade-off of the buffer-aided relay selection scheme in a two-hop wireless network with multiple randomize-and-forward (RF) re-
Comparison Of Text Summarizer In Indian Languages

Text summarization is the process of extracting the relevant information from a source text keeps the significant information. Mainly two types of text summarization methods such as abstractive and extractive. The extractive summarization ranks all sentences and high scored sentences are selected as summary. The abstractive summarization understands the content of a document and re-state in few words. This paper discusses about various text summarization methods followed by the Indian languages. The existing algorithms are explained and then the merits and demerits are discussed. This paper also investigates which method is suitable for summarizing documents in Indian languages.
Simulation combined model-based testing method for train control systems

A Train Control System (TCS) is utilised to guard the operational safety of the trains in railway systems. Therefore, functional testing is applied to verify consistency between the TCS and specification requirements. Traditional functional testing in TCSs is mainly based on manually designed test cases, which is becoming unsuitable for testing increasingly complex TCSs. Therefore, Model-Based Testing (MBT) methods have been introduced into TCS functional testing, to improve the efficiency and coverage of TCS testing, with application difficulties. To overcome the difficulties of applying MBT methods to test TCSs, the author introduces simulation combined MBT which combines an MBT method with simulation. Modelling method and implementation method for the proposed approach were explained in detail. Two case studies were undertaken to explore the effectiveness of the testing platform developed. The testing results obtained prove that the testing platform can be utilised to implement the functional testing of TCSs. To prove that the MBT platform is effective in detecting errors in the SUT, validation and verification was undertaken, which include validation of specification requirements and verification of the MBT platform. The testing performance is proven to be better than existing MBT methods in terms of coverage and efficiency.
Optimal Power Allocation for Mobile Users in Non-Orthogonal Multiple Access Visible Light Communication Networks

In this paper, we focus on the fundamental issues of non-orthogonal multiple access (NOMA) visible light communication (VLC) networks: achievable rates and optimal power allocation schemes for both static and mobile users. First, we derive both a lower bound and an upper bound of the achievable rates with closed-form expressions for static users in NOMA VLC networks. With the derived lower bound, we minimize transmit power under the minimum rate requirements and individual light emitting diodes (LED) power constraints, which turns out to be NP-hard. By exploiting the semidefinite relaxation (SDR) technique, the optimal power allocation scheme can be obtained by solving a convex semidefinite program (SDP). Second, we develop an optimal power allocation scheme for mobile users. Due to users’ movement, the estimated channel state information (CSI) may be inaccurate. We first characterize the CSI uncertainties as ellipsoidal regions, and derive a lower bound of the achievable rate expression. Then, we study the transmit power minimization problem for mobile users, which is non-convex. By applying $\mathcal {S}$ -lemma and SDR, the transmit power minimization problem can be reformulated as a convex SDP. Simulation results are presented to verify the effectiveness and robustness of the proposed power allocation schemes.
Improved HMM by Deep Learning for Ear Classification

Ear recognition is one of the most relevant applications of image analysis. It’s a true challenge to build an automated system which exceeds human ability to recognize ears. Humans do not identify the ears ordinarily, so we are not skilled when we must deal with a large number of unknown ears. The modern computers, with an almost limitless memory and computational speed, should overcome humans' limitations. This work uses Ear classification problem to improve Deviance Information Criterion- Structural Hidden Markov Model (DIC-SHMM) by Convolutional Neural Network (CNN). HMM is a strong model for large size of features. While the CNN, as a most important technology for deep learning, used for image classification, to recognize persons by their ears as a one of unique biometric physiological characteristics. Three systems will be used to classify ear images, deep learning for the original image directly, deep learning for eigenvector as Principle Components Analysis (PCA) of the original image to compare them with proposed combining convolution layers of CNN with improved HMM for the original Image. The proposed system shows the best correction rate as 97.5%.
The Role of Academic Libraries for Augmenting Self-Directed Learning in Higher Education

ABSTRACT Self-directed learning (SDL) has become a significant concept adopted by higher education institutions. In this context, academic libraries become an essential entity as they create these better learning environments. Therefore the present study aims to explore students’ perspective towards the usefulness of library resources for their learning approach, identify undergraduate satisfaction with library resources and services and their competency with some of the information literacy skills, and finally to investigate how academic libraries could support SDL in higher education.
Mobile Communication - Past, Present and Future: a Review

Abstract Communication is one of the most important things in human life, day by day the technology is changing, data rate becomes faster and equipment’s are also becoming compact, as the results of change in technology. The human beings have also adapted to the technology as well. In this article the revolution of wireless technologies like 2G, 3G, 4G and 5G (Future generation network) are explained. This article mainly focused on background of wireless communication system, models and technologies adapted for 4G and 5G. The key technologies, spectrum allocation and current projects are discussed and described; this work gives a detailed research to address the projects and developments for future generation wireless technology.
Adaptive SLAM with Synthetic Stereo Dataset Generation for Real-time Dense 3D Reconstruction

In robotic mapping and navigation, of prime importance today with the trend for autonomous cars, simultaneous localization and mapping (SLAM) algorithms often use stereo vision to extract 3D information of the surrounding world. Whereas the number of creative methods for stereo-based SLAM is continuously increasing, the variety of datasets is relatively poor and the size of their contents relatively small. This size issue is increasingly problematic, with the recent explosion of deep learning based approaches, several methods require an important amount of data. Those multiple techniques contribute to enhance the precision of both localization estimation and mapping estimation to a point where the accuracy of the sensors used to get the ground truth might be questioned. Finally, because today most of these technologies are embedded on on-board systems, the power consumption and real-time constraints turn to be key requirements. Our contribution is twofold: we propose an adaptive SLAM method that reduces the number of processed frame with minimum impact error, and we make available a synthetic flexible stereo dataset with absolute ground truth, which allows to run new benchmarks for visual odometry challenges. This dataset is available online at http://alastor.labri. fr/.
A Link Prediction Method Based on Learning Automata in Social Networks

Nowadays, online social networks are considered as one of the most important emerging phenomena of human societies. In these networks, prediction of link by relying on the knowledge existing of the interaction between network actors provides an estimation of the probability of creation of a new relationship in future. A wide range of applications can be found for link prediction such as electronic commerce and recommender systems or identification of terroristic relations in social networks. In this article, a new idea is presented for the prediction. It is an integration of the two methods of prediction of similarity score based link and prediction of probabilistic link, which is placed in a new category of link prediction methods. This idea acquires the similarity score between nodes from probabilistic techniques and through using learning automata, and provides better results compared to other criteria methods on standard datasets.
Systems for Synthesizing and Studying Robot Motion

We describe and detail two systems developed by the University of Wisconsin–Madison Graphics Group and HumanComputer Interaction lab designed to synthesize robot arm motion and evaluate human-robot collaborative performance. STrOBE is a general purpose robot trajectory optimizer. STrOBE optimizes a motion objective subject to a set of robot or environmental constraints using a nonlinear solver. RobotSim is an experimentation system implemented as a game. RobotSim measures the human-robot collaborative performance of robot arm motions. These systems have been developed with the intent of being released as open source software. We hope that releasing this software to the scientific community encourages further exploration of human-robot collaboration. This report serves as a resource for anyone looking to get started using these systems or looking for ideas for their own software.
Off the Grid: Visualizing a Numismatic Collection as Dynamic Piles and Streams

This research explores the merit of alternative arrangements of cultural artifacts to expose the aesthetic abundance of a cultural collection. The conventional display of museum objects in online interfaces tends to neglect the physical gestalt of the collection. For example, coins typically end up in tabular grids of thumbnail pages in online collections mimicking their rigid placement in storage drawers of a depot and glass cabinets in exhibitions. The overall aim behind this research was to devise visualizations that do justice to the material and semantic richness of an entire collection, while providing a casual mode of access that is inviting to people with no background in numismatics. To do this, we undertook an iterative design process, which involved a close collaboration with numismaticians and playful ideation with actual coins. Carefully negotiating expert knowledge and lay curiosity, the resulting visualization represents the collection’s dimensions using thousands of thumbnails as visual data points. The coins can be arranged into various layouts such as piles representing, for example, metal types, or streams visualizing the ebb and flow of coins over the centuries. In the interface, one can play with the coins in a manner that would be unthinkable in a physical exhibition and that has not been tried in a digital display. The article reports on the overall research and design process of this project, the resulting interface concept and prototype, and the feedback received during two evaluations.
$L_2$Boosting for Economic Applications

In the recent years more and more high-dimensional data sets, where the number of parameters $p$ is high compared to the number of observations $n$ or even larger, are available for applied researchers. Boosting algorithms represent one of the major advances in machine learning and statistics in recent years and are suitable for the analysis of such data sets. While Lasso has been applied very successfully for high-dimensional data sets in Economics, boosting has been underutilized in this field, although it has been proven very powerful in fields like Biostatistics and Pattern Recognition. We attribute this to missing theoretical results for boosting. The goal of this paper is to fill this gap and show that boosting is a competitive method for inference of a treatment effect or instrumental variable (IV) estimation in a high-dimensional setting. First, we present the $L_2$Boosting with componentwise least squares algorithm and variants which are tailored for regression problems which are the workhorse for most Econometric problems. Then we show how $L_2$Boosting can be used for estimation of treatment effects and IV estimation. We highlight the methods and illustrate them with simulations and empirical examples. For further results and technical details we refer to Luo and Spindler (2016, 2017) and to the online supplement of the paper.
Hand Motion and Gesture Control of Laboratory Test Equipment Using the Leap Motion Controller

 Abstract — In this paper, the design and development of a system to provide hand motion and gesture control of laboratory test equipment is considered and discussed. The Leap Motion controller is used to provide an input to control a laboratory power supply as part of an electronic circuit experiment. By suitable hand motions and gestures, control of the power supply is provided remotely and without the need to physically touch the equipment used. As such, it provides an alternative manner in which to control electronic equipment via a PC and is considered here within the field of human computer interaction (HCI).
Evaluation of Caching Strategies in Content-Centric Networking (CCN) for Mobile and Social Networking Environment

Users of the Internet are still using the basic network communication model that was created way back 1960s. The grand idea of migration from host-centric to information-centric has made Content-Centric Networking (CCN) one of the eminent candidates for the future internet. The extension of caching technology as one of the components in the networking itself require deeper thought than just plug and play of current web or server caching techniques. While most studies are focusing on new caching strategies, this study will highlight the gaps by comparing common caching strategies in different predicted scenario of the future. The evaluation was done using simulation tools known as SocialCCNSim focusing on six relevant caching strategies: Leave Copy Everywhere (LCE), Leave Copy Down (LCD), ProbCache, Cache “Less for More”, MAGIC and Randomly Copy One (RCOne) in different network topologies: Tree and Diamond. Rank is given based on metrics such as Cache Hit, Stretch, Diversity and Eviction operations that represented the most commonly used metrics in networking. Results show that all caching strategies have their own behavior toward different network topology. However, Cache “Less for More” considered the best with balanced result for both performance and resource utilization metrics.
AU (Action Unit) detection based on BEGAN data augmentation

Limited annotated training data is a challenging problem in Action Unit detection. Particularly, for micro-expression AU detection, more training data can help improve the performance of detection. For the purpose of data augmentation, this paper put to use the generative adversarial networks (GAN) which is able to generate High-quality pictures that as a supplementary to our limited database. In addition, we propose a sample and effective model for facial micro-expression action units (AU) detection based on 3D-CNNs and Gated Recurrent Unit (GRU) network. The network is composed of 6 layers including 3 convolutional layers, correspondingly, each convolution layer is followed by a pooling layer, and a single layer GRU unit with 15 hidden nodes. For the task of recognizing AUs, we have trained a network for the DISFA datasets, where the GAN applied on, so as to take full advantage of AU-tagged databases and enable the network convergence faster and easier. We show that our model and the method supplying labeled-AU database achieve competitive performance compared with state-of-the-art deep learning methods and traditional data expansion methods such as rotate angles and increase noise based on original drawings.
An efficient five-lncRNA signature for lung adenocarcinoma prognosis, with AL606489.1 showing sexual dimorphism

Background: Lung adenocarcinoma (LUAD) is a sex-biased and easily metastatic malignant disease. A signature based on 5 long non-coding RNAs (lncRNAs) has been established to promote the overall survival (OS) prediction effect on LUAD. Methods: The RNA expression profiles of LUAD patients were obtained from The Cancer Genome Atlas. OS-associated lncRNAs were identified based on the differential expression analysis between LUAD and normal samples followed by survival analysis, univariate and multivariate Cox proportional hazards regression analyses. OS-associated lncRNA with sex dimorphism was determined based on the analysis of expression between males and females. Functional enrichment analysis of the Gene Ontology (GO) terms and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways was performed to explore the possible mechanisms of 5-lncRNA signatures. Results: A 5-lncRNA signature (composed of AC068228.1, SATB2-AS1, LINC01843, AC026355.1, and AL606489.1) was found to be effective in predicting high-risk LUAD patients as well as applicable to female and male subgroups and <65-year and ≥65-year age subgroups. The forecasted effect of the 5-lncRNA signature was more efficient and stable than the TNM stage and other clinical risk factors (such as sex and age). Functional enrichment analysis revealed that the mRNA co-expressed with these five OS-related lncRNAs was associated with RNA regulation within the nucleus. AL606489.1 demonstrated a sexual dimorphism that may be associated with microtubule activity. Conclusion: Our 5-lncRNA signature could efficaciously predict the OS of LUAD patients. AL606489.1 demonstrated gender dimorphism, which provides a new direction for mechanistic studies on sexual dimorphism.
Statistical validation of utility of head-mounted display projection-based experimental impression evaluation for sequential streetscapes

This study compared the impression structure of streetscapes gained from field experiments with that from head-mounted display virtual reality experiments. The results were as follows: (1) the field experiment experience had a slight influence on the impressions reported from the head-mounted display experiment; (2) few items exhibited significant differences in impression values for the two experiments; (3) the impression structure comprised three factors—“Openness,” “Balance,” and “Activity”—in both experiments, and (4) no significant difference appeared between the two experiments relative to the subscale scores of these three factors. These results show that head-mounted display experiments can substitute field experiments.
A novel method of 3D motion object’s position perception

With the development of industrial automation, location measurement of 3D objects is becoming more and more important, especially as it can provide necessary positional parameters for the manipulator to grasp the object accurately. In view of the disabled object which is in widespread use currently, its image is captured to obtain positional parameters and transmitted to manipulators in industry. The above process is delayed, affecting the work efficiency of the manipulator. A method for calculating the position information of target object in motion is proposed. This method uses monocular vision technology to track 3D moving objects, then uses contour sorting method to extract the minimum constrained contour rectangle, and combines the video alignment technology to realize the tracking. Thus, the measurement error is reduced. The experimental results and analysis show that the adopted measurement method is effective.
Automatic Satellite Dish Positioning for Line of Sight Communication using Bluetooth Technology

Satellite dishes are used to receive beams of signals from satellites and other broadcasting sources which arethen focused onto an antenna. The dish needs to be adjusted to get the desired azimuth and elevation formaximum signal reception. To overcome the difficulty of adjusting it manually, it would be beneficial to have asystem that aligns the satellite receiver by mechanical means while allowing the user to interact with the systemremotely to achieve a line of sight communication with the satellite source of interest. This paper proposesthe design and development of a system which receives user specifications from an Android application viaBluetooth by either specifying the direction of orientation of the dish or selecting a satellite of interest. Acontrol system interacting with the developed user interface achieves this. It employs a microcontroller, a GPSdevice, a compass and two servo motors to manage the orientation of the dish on its horizontal and verticalaxes. The Smartphone utilizes its Bluetooth socket to communicate with the Bluetooth module interfacedto the microcontroller. A database containing information on available satellites is included in the Androidapplication which is transferred to the microcontroller for computation of azimuth and elevation angleswhen the GPS coordinates and compass headings are obtained from their respective devices. Tests carriedout showed positive results for control of the orientation of the satellite dish in various directions over a 50mradius. The automatic adjustment functionality provided precise direction for line of sight communicationwhen users chose their satellite of focus