Miracle-dz/UniFAR-FTUs huggingface dataset:
Pyrolytic Carbon Coating Effects on Oxide and Carbide Kernels Intended for Nuclear Fuel Applications

Abstract The coating of nuclear fuel kernels with pyrolytic carbon (PyC) is a well-understood practice dating back over half a century. In spite of decades of studies related to these coatings, no study has yet investigated the effect of the PyC deposition coating process on the kernels themselves. In this study, the composition and crystallographic phase of kernel materials were observed to change after exposure to the thermal and chemical environment of the PyC coating process. Specifically, the coating process increased the fraction of high carbon content phase within carbide microsphere kernels, with W2C containing microspheres driven toward WC, and UC containing microspheres driven toward UC2. Oxide microspheres consisted of a mixture of two crystalline phases. The monoclinic phase within yttria-stabilized zirconia microspheres was eliminated by the coating process resulting in a purely tetragonal phase. Hafnium oxide microspheres were more stable showing no detectable change in composition or crystal structure after coating.
Face Recognition Using Multi-level Histogram Sequence Local Binary Pattern

Face recognition is an active research area in the artificial intelligence.A face recognition algorithm using the RBF network is proposed based on wavelet analysis and multi-level histogram sequence local Binary pattern(M-HSLBP). Since wavelet analysis is insensitive to changes in expression,it can express the principal features of the face image by compressing data.LBP is an efficient local texture description operator.The wavelet transformed images were scanned with multi-degree changeable Sub-windows.Sub-images were transformed by an enhanced LBP,and then the LBP features are concatenated into an enhanced feature vector,which can express both local and holistic features of the face image.RBF network with high generalization is a good classifier,especially for larger number of samples.Experimental results on ORL and YALE face show that the proposed algorithm,which achieves recognition accuracy of above 98% is more effective and faster than the traditional method.
Color self-similarity feature based real-time pedestrian detection

Pedestrian detection has wide applications in intelligent surveillance and driver assistant systems.The most commonly used feature in pedestrian detection algorithms is a histogram of the oriented gradient(HOG),which is computationally intensive and results in slow detection speed.This analysis uses a color self-similarity feature(CSSF) that calculates the ratio of two rectangles to measure the self-similarity on the color channels.First,when extracting the human structure information,CSSF avoids the time-consuming gradient calculation which increases the speed.Secondly,CSSF uses a scalar feature which can be efficiently integrated with the AdaBoost based cascaded classifiers learning framework for training the human detector.Thirdly,CSSF is scale invariant,resulting in fast multi-scale detection.Tests show that the CSSF based detector gives improved accuracy and 7 times speedup compared with HOG detectors and can achieve real-time processing with 640×480 images.
Bitcoin network simulator data explotation

This project starts with a brief introduction to the concepts of Bitcoin and blockchain, followed by the description of the different known attacks to the Bitcoin network. Once reached this point, the basic structure of the Bitcoin Network Simulator is presented. The main objective of this project is to help in the security assessment of the Bitcoin network. To accomplish that, we try to identify useful metrics, explain them and implement them in the corresponding simulator modules, aiming to extract useful information from the simulations and display it on the GUI.
File Multi-analyses for Real-time Attack Source and Spread Site Trace

Recently, the illegal users with malicious intention utilize the file sharing site by making normal user's computer a zombie computer, which is a preliminary process for network intrusion attack. The propose scheme is divided into the method for real-time analysis for real-time tracking and the method of cooperative analysis method for non-real time analysis. By allowing the supervisors to choose the relevant analysis method selectively, we can conduct variable analysis depending on the network threat.
Implementation of the Agent using Universal On-line Q-learning by Balancing Exploration and Exploitation in Reinforcement Learning

A shopbot is a software agent whose goal is to maximize buyer´s satisfaction through automatically gathering the price and quality information of goods as well as the services from on-line sellers. In the response to shopbots´ activities, sellers on the Internet need the agents called pricebots that can help them maximize their own profits. In this paper we adopts Q-learning, one of the model-free reinforcement learning methods as a price-setting algorithm of pricebots. A Q-learned agent increases profitability and eliminates the cyclic price wars when compared with the agents using the myoptimal (myopically optimal) pricing strategy Q-teaming needs to select a sequence of state-action fairs for the convergence of Q-teaming. When the uniform random method in selecting state-action pairs is used, the number of accesses to the Q-tables to obtain the optimal Q-values is quite large. Therefore, it is not appropriate for universal on-line learning in a real world environment. This phenomenon occurs because the uniform random selection reflects the uncertainty of exploitation for the optimal policy. In this paper, we propose a Mixed Nonstationary Policy (MNP), which consists of both the auxiliary Markov process and the original Markov process. MNP tries to keep balance of exploration and exploitation in reinforcement learning. Our experiment results show that the Q-learning agent using MNP converges to the optimal Q-values about 2.6 time faster than the uniform random selection on the average.
Spatial Named Entity Recognition in LBS

The paper presents a method of spatial named entity(SNE) recognition for on-line location-based services(LBS). This paper discusses the establishment of spatial semantic lexicon combined Chinese word segmentation with geocoding,the match algorithm,the extraction of spatial semantic rules and SNE disambiguation.The experiment shows this algorithm can reduce computer memory consumption and system response time,and the recognition precision is also improved.It can meet the requirement of on-line LBS on the whole.
Fmoc amino acid fluorides : convenient reagents for the solid-phase assembly of peptides incorporating sterically hindered residues

Fmoc amino acid fluorides, recently shown to be a new class of rapid- acting acylating agents in peptide synthesis are well suited for the solid-phase synthesis of medium-sized peptides such as ACP(65-74), ma- gainin-II-amide, and h-CRF. The most important advantage of these rea- gents is their high reactivity in the coupling of sterically hindered amino acid residues, such as α-aminoisobutyric acid (Aib), results whi- ch are at least partly due to the small size of the fluoride leaving group. Both h-(Aib 32-35 )-CRF(1-41), bearing four consecutive Aib- residues, and alamethicin acid, neither previously accessible by solid- phase synthesis, were successfully synthesized via acid fluorides using unusually short coupling times. I n contrast, attempted syntheses via UNCA's and PyBroP activation, both reported to be well suited for steri- cally hindered systems, failed to give the desired peptides
Establishing Secure And Efficient Data Sharing For Dynamic Groups In The Cloud

Cloud Computing is an emerging technology, which provides a scalable service delivery model where we can obtain computing resources, networking and storage space on demand based on service level agreements. Cloud services in real world application provide data sharing with low-cost preserving characteristics. In this paper, a framework called Cryptographic Multilinear Data Sharing for Dynamic Group (CMDS-DG) is presented to minimize the encryption computation cost and storage overhead in a cloud environment. To assess the performance, this work compares the proposed framework with existing data sharing methods. The results show that the CMDS-DG framework can significantly reduce the encryption computation cost by minimizing the storage overhead when compared to other data sharing methods.
Effective ARP Based Call Admission Control (CAC) Scheme for LTE Systems

In wireless communication technologies like GSM, WCDMA, LTE, etc. Radio is stressed by the various traffic conditions, in terms of radio resources to provide the admission of new calls and required QoS. There are situations and scenarios, where the load takes unprecedented conditions and as part of services the requirements may change, like to improve on non-blocking calls though recessing on Quality of Service (QoS). Though such situations and scenarios are intermittent and last for limited periods of time, the importance of services becomes operator's credentials. We propose a top-up algorithm, which does not interfere with existing admission control mechanism in direct way, but provide them with effective Allocation and Retention Priority (E-ARP) for each call. This algorithm is based on "inferences and intensions based combinational logic". It works on the system specific inferences of certain parameters and intentional classification of existing and new demands based on weightage-based combinational logic, which results in modification of ARPs into effective ARPs. For evaluation of proposed Admission Control (AC) scheme blocking and outage probability of calls is used as performance metrics.
HEADING PREDICTION CONTROL ALGORITHM FOR WHEELED MOBILE ROBOT

A heading prediction control algorithm for wheeled mobile robot is presented in this paper. Heading prediction can be obtained from steering angle and velocity of the robot in real time, and also summed with measured heading of the robot to be used for controlling the feedback heading. In heading control simulation and experiments, a PID controller with heading prediction is tested. Compared with regular control method it can adapt wider velocity range and obviously improve dynamic characteristics of the controller.
Towards optimization techniques for dynamic load balancing of parallel gate level simulation

As a consequence of Moore's law, the size of integrated circuits has grown extensively, resulting in simulation becoming the major bottleneck in the circuit design process. Consequently, parallel simulation has emerged as an approach which can be both fast and cost effective. In this thesis, we examine the performance of a parallel Verilog simulator, VXTW, on four large, real designs using an optimistic synchronization scheme named Time Warp. As previous work has made use of either relatively small benchmarks or synthetic circuits, the use of these circuits is far more realistic. Because of the low computational granularity of a gate level simulation and because the computational and communication loads vary throughout the course of the simulation, the performance of Time Warp can be severely degraded or can even be unstable. Dynamic load balancing algorithms for balancing the computational and communication loads during the simulation are described in this thesis. Like all load balancing algorithms, the proposed algorithms have some tuning parameters which must be optimized. In addition, in order to avoid the simulation from being too optimistic, we make use of a time window. In the thesis, we make use of learning techniques from artificial intelligence (N-armed Bandit, Multi-state Q-learning) and heuristic searches (Genetic Algorithm, Simulated Annealing) to tune the parameters of the dynamic load balancing algorithms and to determine the size of the time window. we evaluated the performance of these algorithms on open source Sparc and Leon processor designs and on two Viterbi decoder designs and observed up to a 70% improvement in simulation time using these approaches.
A rigorous review technique: using formal notations within conventional development methods

The introduction and use of formal (mathematically based) specification in substantial systems development has been hampered by the additional burdens it places on practitioners. BT and Leeds Metropolitan University, have developed a framework for the gradual introduction of formal specification starting with its use as a review technique, applied to a standard range of systems development deliverables such as data and process models. The benefits of this approach are immediate and can also be more extensive. The paper describes the philosophy behind the general approach; offers an example of applying the rigorous review technique; and summarises the benefits that can accrue. <<ETX>>
Multi-Core Embedded Processor Based on FPGA and Parallelization of SUSAN Algorithm

Structure of 4-core embedded parallel processor is presented and FPGA validation platform that is called FPEP is built.To evaluate performance of multi-core processor platform,three feasible parallel algorithms with OpenMP on SUSAN that is a classic image processing algorithm: direct parallel SUSAN,image block processing and parallel processing of multiple images are proposed,and performance testing of these three parallel algorithms execute on Intel 4-core platform and FPGA validation platform of FPEP.Experimental results demonstrate that speedup ratio of three parallel algorithms on both 4-Core platforms is approximate 3.0,and speedup ratio of parallel processing of multiple images on FPGA validation platform of FPEP is approximate 4.0.
Distributed Caching Methods in Small Cell Networks

This thesis explores one of the key enablers of 5G wireless networks leveraging small cell network deployments, namely proactive caching. Endowed with predictive capabilities and harnessing recent developments in storage, context-awareness and social networks, peak traffic demands can be substantially reduced by proactively serving predictable user demands, via caching at base stations and users' devices. In order to show the effectiveness of proactive caching techniques, we tackle the problem from two different perspectives, namely theoretical and practical ones. In the first part of this thesis, we use tools from stochastic geometry to model and analyse the theoretical gains of caching at base stations. In particular, we focus on 1) single-tier networks where small base stations with limited storage are deployed, 2) multi-tier networks with limited backhaul, and) multi-tier clustered networks with two different topologies, namely coverage-aided and capacity-aided deployments. Therein, we characterize the gains of caching in terms of average delivery rate and mean delay, and show several trade-offs as a function of the number of base stations, storage size, content popularity behaviour and target content bitrate. In the second part of the thesis, we take a more practical approach of proactive caching and focus on content popularity estimation and algorithmic aspects. In particular: 1) We first investigate the gains of proactive caching both at base stations and user terminals, by exploiting recent tools from machine learning and enabling social-network aware device-to-device (D2D) communications; 2) we propose a transfer learning approach by exploiting the rich contextual information extracted from D2D interactions (referred to as source domain) in order to better estimate the content popularity and cache strategic contents at the base stations (referred to as target domain); 3) finally, to estimate the content popularity in practice, we collect users' real mobile traffic data from a telecom operator from several base stations in hours of time interval. This amount of large data falls into the framework of big data and requires novel machine learning mechanisms to handle. Therein, we propose a parallelized architecture in which content popularity estimation from this data and caching at the base stations are done simultaneously. Our results and analysis provide key insights into the deployment of cache-enabled small base stations, which are seen as a promising solution for 5G heterogeneous cellular networks.
Performances of Adaptive Cuckoo Search Algorithm in Engineering Optimization

A new optimization algorithm, specifically, the cuckoo search algorithm (CSA), which inspired by the unique breeding strategy of cuckoos, has been developed recently. Preliminary studies demonstrated the comparative performances of the CSA as opposed to genetic algorithm and particle swarm optimization, however, with the competitive advantage of employing fewer control parameters. Given enough computation, the CSA is guaranteed to converge to the optimal solutions, albeit the search process associated to the random-walk behavior might be time-consuming. Moreover, the drawback from the fixed step size searching strategy in the inner computation of CSA still remain unsolved. The adaptive cuckoo search algorithm (ACSA), with the effort in the aspect of integrating an adaptive search strategy, was attached in this study. Its beneficial potential are analyzed in the benchmark test function optimization, as well as engineering optimization problem. Results showed that the proposed ACSA improved over the classical CSA.
Research of Experts Schedule Mode in Academic Reference Services System

This paper emphasizes experts schedule mode of academic reference services system,which belongs to a rising academic digital library operation,puts forward an experts schedule model based on AHP,and validates its consistency.This paper introduces enquiry guide to solve the lack of traditional reference services system,puts enquiring and searching together perfectly,and reduces the probability of repeating enquiry.And it can avoid careless omission in the process of cross discipline enquiry.
LISP-CONS: A Content distribution Overlay Network Service for LISP

The Content distribution Overlay Network Service for LISP (LISP-CONS) is a protocol for distributing identifier-to-locator mappings for the Locator/ID Separation Protocol (LISP). LISP-CONS is not a routing protocol. LISP-CONS is designed to scale by using a hierarchical content distribution system comprised of Tunnel Routers, Content Access Resources, and Content Distribution Resources.
LaySiChain:the improved layout-sensitive scan chain inserter

LaySiChain The improved layout-sensitive scan chain inserter P.J.H. van den Heuvel June 1997 LaySiChain Electronic Design & Tools One ofthe most popular structured Dff techniques is scan design. Scan design add scan chains to a circuit which greatly improve the testability ofacircuit. One of the Philips ED&Ttools is able to insert scan chains into acircuit on basis ofa netlist description ofthe circuit. This traditional way ofinserting scan chains does not take the physical positions of the flip flops on the layout into consideration. It generally introduces long scan chain wires. We show that enormous reductions of the total wire length can be achieved when the flip flops are ordered taking the layout information into account. This reduces the routing area overhead and improves the routability of a design. The work described in this report consists of two parts. In part one we describe a total new design ofa layout sensitive scan chain insertion technique called LaySiChain. We present a fully automated and user friendly LaySiChain tool based on the stripes algorithm. Experiments on industrial designs show an average wire length improvement of 60%. The second part describes approximation algorithms called local search towards further improvements of LaySiChain. We introduce two local search strategies, variable depth search and tabu search. Experimental results based on these strategies show additional wire length improvements of 35% compared to the improvements obtained by the stripes algorithm.
Discovering Archetypes to Interpret Evolution of Individual Behavior

In this paper, we aim to discover archetypical patterns of individual evolution in large social networks. In our work, an archetype comprises of \emph{progressive stages} of distinct behavior. We introduce a novel Gaussian Hidden Markov Model (G-HMM) Cluster to identify archetypes of evolutionary patterns. G-HMMs allow for: near limitless behavioral variation; imposing constraints on how individuals can evolve; different evolutionary rates; and are parsimonious. Our experiments with Academic and StackExchange dataset discover insightful archetypes. We identify four archetypes for researchers : \emph{Steady}, \emph{Diverse}, \emph{Evolving} and \emph{Diffuse}. We observe clear differences in the evolution of male and female researchers within the same archetype. Specifically, women and men differ within an archetype (e.g. \emph{Diverse}) in how they start, how they transition and the time spent in mid-career. We also found that the differences in grant income are better explained by the differences in archetype than by differences in gender. For StackOverflow, discovered archetypes could be labeled as: \emph{Experts}, \emph{Seekers}, \emph{Enthusiasts} and \emph{Facilitators}. We have strong quantitative results with competing baselines for activity prediction and perplexity. For future session prediction, the proposed G-HMM cluster model improves by an average of 32\% for different Stack Exchanges and 24\% for Academic dataset. Our model also exhibits lower perplexity than the baselines.
Support vector machine in computer aided clinical electromyography

Motor unit action potentials (MUAPs) recorded during routine electromyography (EMG) examination provides important information for the assessment of neuromuscular disorders. In this preliminary study, support vector machines (SVMs) based on multi-class classifier is activated for the identification of normal subjects and patients suffering from motor neuron diseases (MND) and myopathies (MVO). The results in experiments prove the classification validity of SVMs which guarantee high generalization ability on the testing samples. Furthermore, its performance is compared with a back-propagation (BP) neural network. More excellent recognition accuracy indicates the potential of the SVMs techniques in clinical neuromuscular disorders evaluation.
Can Intelligent Hyperparameter Selection Improve Resistance to Adversarial Examples?

Convolutional Neural Networks and Deep Learning classification systems in general have been shown to be vulnerable to attack by specially crafted data samples that appear to belong to one class but are instead classified as another, commonly known as adversarial examples. A variety of attack strategies have been proposed to craft these samples; however, there is no standard model that is used to compare the success of each type of attack. Furthermore, there is no literature currently available that evaluates how common hyperparameters and optimization strategies may impact a model's ability to resist these samples. This research bridges that lack of awareness and provides a means for the selection of training and model parameters in future research on evasion attacks against convolutional neural networks. The findings of this work indicate that the selection of model hyperparameters does impact the ability of a model to resist attack, although they alone cannot prevent the existence of adversarial examples.