Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving accuracy of detecting dangerous objects with deep learning

In this article, the problem of detecting dangerous objects with deep learning is presented. Convolutional Neural Networks are created with Python language ecosystem (Theano and Keras libraries), and then trained with different number of layers and different parameters. Accuracy of detection dangerous objects for artificial Neural Network with smaller number of layers is computed and obtained result is improved with deep learning. CIFAR-10 dataset is used due to useful classes included.
A Weighted Score Matching Algorithm for a Multimodal Biometric System Based on Fingerprint and Hand Geometry.

This paper presents a multi-modal biometric system implemented using MATLAB language. The system fused fingerprint and hand geometry at matching score level by applying a proposed modified weighted sum rule. The fingerprint system was tested using five FVC databases and the hand geometry system was tested using COEP database. Hence, the multi-modal system was tested by merging each FVC database with COEP database. The experimental results show significant improvement in the multi-modal system with an average EER of 3.27%; while it is 8.86% and 8.89% for fingerprint and hand geometry systems, respectively.
Enhancement algorithm for low-illumination color image based on neural network

Because of the low illumination color images have the characteristics of low brightness,low contrast,dark colors and low signal noise ratio(SNR),classic image enhancement algorithms are very limited on their enhancement effects.A color image enhancement algorithm based on BP neural network is proposed.In order to prevent color distortion during the image enhancement,the RGB image is transformed to HSI image.Experiments show that the algorithm improves the low-illumination images' visual effect on enhancing the brightness of the image and the image SNR,adjusting the image dynamic range,enhancing the image contrast and detail,and increasing entropy of the image.
Comparing Personalization Approaches for Inclusion in Learning Management System

Personalization is one of the important functions of modern Learning Management Systems. The basic idea of personalization in education is an incorporation of the person’s individual characteristics. This thesis is directed to study the main aspects of personalization and how personalization can be applied in LMS, in order to achieve the best results during learning process and make learning more convenient and flexible. The main objective of our study is to identify the personalization approach or a mix of approaches among Personalized Guidance, Personalized Learning Activities and Personalized Communication that can be more useful for inclusion in the LMS. We have found that a mix of Personalized Learning Activities and Personalized Communication Approaches is most useful for inclusion in the LMS. This mix can contribute to solving some of the problems of students, such as the difficulty of access and retrieval of desired educational materials and a communication problem. We have also examined the basis on which these approaches can provide personalization. Based on our research and analysis, we determined that the Personalized Learning Activities Aproach should include features such as: Study topic and Technical level; Personalized Communication Approach should be based on the Study topic.
A Meta Path-Based Link Prediction Model for Heterogeneous Information Networks

Real-world,multiple-typed objects are often interconnected,forming heterogeneous information networks.The problem of predicting links or interactions between objects in a network is an important task of network analysis.Different from traditional link prediction tasks in homogeneous networks,heterogeneous information networks have multiple distinct types of interrelated objects and relationships,and the objects in heterogeneous networks can be connected via different relationships.In this paper,we use meta-path,a kind of path that connects object types via a sequence of relations,to describe distinct semantics of different types of nodes and relationships,and propose a meta path-based link prediction model for heterogeneous information networks(MPLP). In this model we perform link prediction by combining the link probabilities between nodes on different meta-paths.The method is evaluated with two different genres of datasets:DBLP and Last.fm. Experimental results show that when predicting the formation of seven types of relationships,the AUC value of our method is on average improved 5.93%compared with the most accurate method.Furthermore,distinguishing different types of nodes and relationships by using meta-paths can improve the prediction accuracy significantly.At last, in order to balance the prediction accuracy and the scalability of our model,an experimental analysis demonstrates that considering meta-paths whose lengths are less than 5is enough to produce good results in terms of link prediction.
DATA MINING AND TEXT MINING: EFFICIENT TEXT CLASSIFICATION USING SVMS FOR LARGE DATASETS

The Text mining and Data mining supports different kinds of algorithms for classification of large data sets. The Text Categorization is traditionally done by using the Term Frequency and Inverse Document Frequency. This method does not satisfy elimination of unimportant words in the document. For reducing the error classifying of documents in wrong category, efficient classification algorithms are needed. Support Vector Machines (SVM) is used based on the large margin data sets for classification algorithms that give good generalization, compactness and performance. Support Vector Machines (SVM) provides low accuracy and to solve large data sets, it typically needs large number of support vectors. We introduce a new learning algorithm, which is comfortable to solve the dual problem, by adding the support vectors incrementally. It majorly involves a classification algorithm by solving the primal problem instead of the dual problem. By using this, we are able to reduce the resultant classifier complexity by comparing with the existing works. Experimental results done and produce comparable classification accuracy with existing works.
Two-variable logics with some betweenness relations: Expressiveness, satisfiability and membership

We study two extensions of FO2[<], first-order logic interpreted in finite words, in which formulas are restricted to use only two variables. We adjoin to this language two-variable atomic formulas that say, `the letter a appears between positions x and y' and `the factor u appears between positions x and y'. These are, in a sense, the simplest properties that are not expressible using only two variables. We present several logics, both first-order and temporal, that have the same expressive power, and find matching lower and upper bounds for the complexity of satisfiability for each of these formulations. We give effective conditions, in terms of the syntactic monoid of a regular language, for a property to be expressible in these logics. This algebraic analysis allows us to prove, among other things, that our new logics have strictly less expressive power than full first-order logic FO[<]. Our proofs required the development of novel techniques concerning factorizations of words.
A Novel Catalytic Hypervalent Iodine Oxidation of p-Alkoxyphenols to p-Quinones Using 4-Iodophenoxyacetic Acid and Oxone®

A novel catalytic hypervalent iodine oxidation of phenol derivatives using 4-iodophenoxyacetic acid and Oxone ® was developed. Reaction of P-alkoxyphenols with a catalytic amount of 4-iodophenoxyacetic acid in the presence of Oxone ® as a co-oxidant in acetonitrile-water (2:1) gave the corresponding P-quinones in -excellent yields without purification.
A heterogeneous computing approach to simulation of the Heston Stochastic Volatility Model

Stochastic volatility models are of fundamental importance to the pricing of derivatives. One of the most commonly used models of stochastic volatility is the Heston Model in which the price and volatility of an asset evolve as a pair of coupled stochastic differential equations. The computation of asset prices and volatilities involves the simulation of many sample trajectories with conditioning. The problem is treated using the method of particle filtering. While the simulation of a shower of particles is computationally expensive, each particle behaves independently making such simulations ideal for massively parallel heterogeneous computing platforms. In this paper, we present our portable Opencl implementation of the Heston model and discuss its performance and efficiency characteristics on a range of architectures including Intel cpus, Nvidia gpus, and Intel Many-Integrated-Core (mic) accelerators.
Low Complexity Sparse Channel Estimation Based on Compressed Sensing

In wireless communication, channel estimation is a key technology to receive signal precisely. Recently, a new method named compressed sensing (CS) has been proposed to estimate sparse channel, which improves spectrum efficiency greatly. However, it is difficult to realize it due to its high computational complexity. Although the proposed Orthogonal Matching Pursuit (OMP) can reduce the complexity of CS, the efficiency of OMP is still low because only one index is identified per iteration. Therefore, to solve this problem, more efficient schemes are proposed. At first, we apply Generalized Orthogonal Matching Pursuit (GOMP) to channel estimation, which lower computational complexity by selecting multiple indices in each iteration. Then a more effective scheme that selects indices by least squares (LS) method is proposed to significantly reduce the computational complexity, which is a modified method of GOMP. Simulation results and theoretical analysis show the effectivity of the proposed algorithms.
ASHRAE Standard 140 Weather Drivers Test Suite: Results Analysis

ASHRAE Standard 140, “Method of Test for Evaluating Building Performance Simulation Software” provides test suites that allow software developers and users to evaluate the capabilities of a Building Performance Software (BPS) program versus analytical standards and/or other software programs. The standard has proven through the years to be a good tool for identifying weaknesses in BPS both in individual programs and in the software programs in general. Up to this point, it was assumed in the test cases that the programs could properly read standard weather files and process this information for the simulations. A new test suite is being developed to test this assumption. In this test suite, BPS programs are run with different weather files and the outputs are compared to the values in the files and against the output from the other programs. Statistical analyses are performed on the results to determine the level of agreement between the programs and the data file and between each other.
Bi-objective Test Case Selection using Abstract Snytax Tree and Tree Matching

Software testing is a vital activity during software production to detect faults and to guarantee that the software is working properly as per the requirements.Regression testing is an activity that is carried out during the maintenance phase to ensure the quality and reliability after altering the existing version of the software. There are many techniques for performing regression testing. They are re-test all, test case minimization, test case selection and test case prioritization.The aim of this paper is to select a subset of test cases which should be run when there are siginificant changes in the original program. To extract the subset of test cases, a novel method is adopted in this research which uses executable backward static slicing.The program slices are converted into
COMPARISON OF BEHAVIOR OF MOSFET TRANSISTORS DESCRIBED IN HARDWARE DESCRIPTION LANGUAGES

This thesis addresses different generations of MOSFET CMOS transistor models and examines the models that would be appropriate for various applications based on the number of parameters given. Choosing the right transistor model has always been the biggest challenge when transistor models are used in VLSI circuit simulation. This choice not only affects the simulation time but also the accuracy of the results obtained. In this thesis we give quantitative results to show how choosing the correct model for simulation will allow us to make appropriate trade-offs between simulation time and simulation values. We compare simulation results of Verilog-AMS models with simulation results in SPICE.
Qualitative Analysis of an Unreliable Hybrid Multicast Protocol (UHMP)

Protocol Independent multicast Sparse mode (PIM SM) and Protocol independent multicast Dense Mode (PIM DM) uses a best effort method of propagating data along a multicast distribution tree, but the difference between them is that in PIM SM, leafs interested sends join message to the stud which in turn sends to the Rendezvous Point source (RPS) or source and the distribution tree is created, while in PIM DM the RPS or source sends a flood message to the stub nodes which then forward it to leaf nodes and leaf nodes that are not interested sends a prune message. In the hybrid multicast protocol (UHMP) being proposed the stub nodes originates the flood message to the leaf and uninterested leaf sends prune message, any stub that has one or more interested leaf sends a join message to the RPS. A simulation model was developed to mimic the behaviour of PIM SM, PIM DM and UHMP in different network size using hierarchical network and the control bandwidth overhead (CBO) for each of the multicast protocols was calculated, the CBO was use as the cost metric. The result shows that the UHMP uses less CBO than PIM DM both in a sparsely and densely populated network. While the differences in CBO usage between UHMP and PIM SM was not noticeable in sparse mode, UHMP however uses less CBO then PIM in a dense mode scenario.
Dynamic Interval Graph Coloring for Channel Assignment in Wireless Cellular Networks

Wireless Cellular Networks are most often modeled as a graphs and Channel Assignment Problems (CAP) can be formulated as graph coloring problms. Assignment of channels to the calls in the networks is a discrete-time event process involving the correct mapping of the channels to the caller- receiver pairs. Efficient usage of limited channels adopt the principles of reusability of same channels for some calls at same time instance. In this paper, an efficient coloring technique is proposed and we apply it to the identified interval-graph dynamically.
Research of Inverter Efficiency Based on SPWM / SVPWM Modulation Strategies

Inverter efficiency is an important index in photovoltaic power generation system,it can be improved by the effective modulation strategy which you choose.In order to improve the inverter efficiency,based on SPWM and SVPWM modulation strategies,the inverter loss calculation method was analyzed and simulated by Matlab / Simulink.The influence on efficiency of two modulation strategies was compared.The experiment is carried out on experimental platform to verify the conclusion.According to the experimental data and simulation results,come to the conclusion that using SVPWM modulation strategy can improve the efficiency of the inverter and obtain higher modulation.
Design of high-speed asynchronous FIFO memory based on VHDL

It is presented the asynchronous FIFO memory structure and its applications.It was analysised the asynchronous FIFO memory flags logic design and eliminate metastability.It is proposed an approach based on FPGA chips by using Grey code to address coding solution asynchronous reading and writing of the ideas and methods clock,and show a code of VHDL.The method has portability strong,efficient features which can be widely used digital system design related.
The High Transverse Momentum Non-Photonic Electron Measurements in $Au+Au$ collisions at $\sqrt {S_{NN}} = 200$ GeV at RHIC/STAR

We present preliminary results of Non-Photonic Electron (NPE) production, NPE-hadron azimuthal correlation as well as NPE elliptic flow studies at midrapidity using a data set with high statistics and low photon conversion background from Au+Au collisions at √ sNN = 200 GeV collected at RHIC in Run 2010.
Media Synchronization in 3D tele-Immersion Applications: an Architecture

This article discusses the challenges ahead for assuring media synchronization in 3D tele-immersion applications. The discussion is based on an architecture and use cases that are defined in the European FP7 project REVERIE. The architecture allows capturing, transmission, and rendering, in real-time, various types of 3D media streams (e.g., geometry, movements of the participants, 3D audio), with the final objective of enabling immersive communication (and interactions) between remote users. For achieving the final goal, media synchronization is a key requirement. In particular, this article will focus on two types of synchronization: between real-time streams, and between downloaded and real-time streams. The first case refers to the synchronization needed between the different real-time captured media (e.g., 3D audio and visual streams). The second one aims at synchronizing downloaded content (e.g., 3D models) and the media captured in real-time. The solution reported in this article is implemented as a novel real-time streaming engine that can handle various types of 3D media streams. Moreover, based on global timestamps, the engine can
Low Noise and Low DC Offset Zero IF WLAN Receiving Front-end Design

A zero IF receiving front-end with merged LNA and mixer is proposed.Low flicker noise is achieved by decreasing bias current of switching pairs of folded architecture,with high gain as well.DC offset is improved by common and differential mode feedbacks,making the IF output level stable.High IIP2 is obtained by differential circuits' architecture and optimized layout.Spectre-RF simulations based on Chartered 0.18 μm CMOS process indicate that the presented receiving front-end behaviors good performance in noise,DC offset,and etc.Power consumption of the chip is 16.5 mW with size of that being 0.23 mm2.
Clustering Analysis in Wireless Sensor Networks: The Ambit of Performance Metrics and Schemes Taxonomy

Research on wireless sensor network (WSN) has increased tremendously throughout the years. In WSN, sensor nodes are deployed to operate autonomously in remote environments. Depending on the network orientation, WSN can be of two types: flat network and hierarchical or cluster-based network. Various advantages of cluster-based WSN are energy efficiency, better network communication, efficient topology management, minimized delay, and so forth. Consequently, clustering has become a key research area in WSN. Different approaches for WSN, using cluster concepts, have been proposed. The objective of this paper is to review and analyze the latest prominent cluster-based WSN algorithms using various measurement parameters. In this paper, unique performance metrics are designed which efficiently evaluate prominent clustering schemes. Moreover, we also develop taxonomy for the classification of the clustering schemes. Based on performance metrics, quantitative and qualitative analyses are performed to compare the advantages and disadvantages of the algorithms. Finally, we also put forward open research issues in the development of low cost, scalable, robust clustering schemes.
The Application of Multi-class SVM based Binary Tree in Web Text Categorization

A method of binary tree multi-class support vector machine algorithm has been proposed to solve the problems of the current multi-class support vector machine algorithm,which can effectively reduce the members of support vectors and training time.Compared with l-v-r and l-v-l algorithm,experiments shows that the binary tree multi-class support vector machine algorithm is more effective and feasible.
Scenario-Based Templates Supporting Usable Privacy Policy Authoring

Because of the sensitivity of information systems storing and processing health-records of patients,access control considerations are of utmost importance to establish a foundation for privacy. Privacy represents an individual concern, therefore we believe that in principle only the patient can be the sole author of privacy settings. Still in a multi-stakeholder domain like health-care, information systems have to properly provide access to required health-data. Patient’s privacy settings might therefore interfere with health-care domain activities, leading to problems regarding the eﬀective use of an information system. We see a potential solution to this problem by aligning typical working activities of the healthcare domain with corresponding access control settings. As health-care stakeholders as well as patients are typically not security experts, usability is a key factor in building policy authoring tools. In this context we emphasize the awareness of stakeholders regarding the implications of their settings. In this work we propose a scenario-based approach with integrated access control aspects to support the development of privacy policy authoring tools. In this way medical stakeholders and privacy-concerned patients do not have to directly deﬁne privacy policies (which we think is in feasible), but instead are provided with tools to generate these policies by taking health-care domain activities into consideration.
AND/OR Graph-based Human Pose Recovery from Depth Images

Problem definition Recovering human pose from static images has become a widely researched topic in the latest years, specially since the introduction of realtime depth cameras. Human action analysis and human body tracking are two of the main scopes of solving this problem where the biggest challenge is located in the huge variation in appearance (i.e. clothes, body shapes, sizes) and huge number of possible human poses. State of the art approaches like [1] have their main limitation when dealing with occluded parts of the body, producing mistaken body poses when parts are not seen by the camera. Proposal We propose an approach that combines two different algorithms to parse the human body. First, using Random Forests we perform a per-pixel classification in the depth image [1] to create part proposals, which are positions in the image where each body part has high probability of appearing. Second, proposals are used to infer the final human pose using an AND/OR graph [2]. Discussion The obtained results show that we are able to recover the human pose reducing the number of false positives detection of limb proposals using a bottom-up parsing of the AND/OR graph. As future work, we plan to deal with the problem of occlusion detection by extending the procedure with a spatial-coherence top-down parsing of the AND/OR graph.
On the probabilistic symbolic analysis of programs

Recently we have proposed symbolic execution techniques for the probabilistic analysis of programs. These techniques seek to quantify the probability of a program to satisfy a property of interest under a relevant usage profile. We describe recent advances in probabilistic symbolic analysis including handling of complex floatingpoint constraints and nondeterminism, and the use of statistical techniques for increased scalability.
Structural and Chemical Analysis of Well-Crystallized Hydroxyfluorapatites

X-ray diffraction (XRD) patterns of hydroxyfluorapatites with a nominal composition of Ca10(PO4)6(OH)2-xFx, where x = 0.0, 0.4, 0.8, 1.2, 1.6, and 2.0, were collected and analyzed using the Rietveld method. A multiphase Rietveld analysis performed on calcined samples reveals a range of Ca/P ratios from 1.56 for the sample with x = 0.4 to 1.67 for the sample with x = 2.0 on the synthesized powders. Calcined apatites exhibit a decrease in the lattice parameter a, a greater crystallite size, lower distortion in the phosphate tetrahedra, and greater stability of the apatite structure for higher fluoride contents.