Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Material Recognition for Estimating Reflectance and Illumination From a Single Image

In this paper, we propose a novel approach for recovering illumination and reflectance from a single image. Our approach relies on the assumption that the surface geometry has already been reconstructed and a-priori knowledge in form of a database of digital material models is available. The first step of our technique consists in recognizing the respective material in the image using synthesized training data based on the given material database. Subsequently, the illumination conditions are estimated based on the recognized material and the surface geometry. Using this novel strategy we demonstrate that reflectance and illumination can be estimated reliably for several materials that are beyond simple Lambertian surface reflectance behavior because of exhibiting mesoscopic effects such as interreflections and shadows.
Verification of Architectual Constraints on Interaction Protocols Among Modules

—The importance of adhering to an adopted architec- tural style throughout software development and maintenance has been long recognized. This paper introduces an approach to efﬁ- ciently checking the correspondence of architectural constraints on sequences of method invocations, i.e., interaction protocols involving more than two modules. Our approach combines parameterized slicing and non-deterministic symbolic execution. Slicing produces an executable portion of the bytecode of the system under analysis relevant to the given architectural property, and symbolic execution is applied to the slice to check all paths and all interleavings for any violations of the given architectural constraints. We have implemented our approach in a prototype, where IBM WALA library is used for slicing, Javassist is used to aid in the mocking of the unused code, and Symbolic PathFinder is used for symbolic execution. Two case studies on veriﬁcation of Model-View-Controller systems have demonstrated the usefulness of our approach. In particular, property guided automatic slicing, in some cases, signiﬁcantly reduces the size of the input to the symbolic execution, resulting in a reduction of veriﬁcation time.
Reverse Engineering Mobile Apps for Model Generation Using a Hybrid Approach

The popularity of mobile devices is ever increasing which led to rapid increase in the development of mobile applications. Model-Based testing can improve the quality of mobile application but the models are not always available or are of inadequate quality. Reverse engineering approaches are used to automatically generate model from the GUI of mobile applications for model-based testing. This paper proposes a hybrid approach for reverse engineering mobile applications which exploit the capabilities of both static and dynamic approaches while trying to maximize the quality of the generated models. The insight of this approach is to use static analysis on app’s source to identify supported events. The generated events can be used to dynamically explore an app at run-time to generate a state model of the app’s GUI. The preliminary results from our approach indicated that the technique can generate high quality models from android apps.
Negation in American Sign Language: The view from the Interface

This paper provides an analysis for a phenomenon known as negative incorporation in American Sign Language. Negative incorporation is defined as a twisting outward or downward movement of the hand(s) at the end of a sign. We provided an analysis accounting for both the syntactic and phonological behaviour of negative incorporation in ASL which makes coherent the link between syntax and phonology mediated by a distinct morphological component argued for in the Distributed morphology framework.
Video Compression Using EZW and FSBM

In this paper video compression is done using EZW as intra compression and seven different algorithms of the block matching algorithms used for motion estimation in video compression. It implements and compares 7 different types of block matching algorithms that range from the very basic Exhaustive Search to fast adaptive algorithms like Adaptive Rood Pattern Search. The algorithms that are evaluated in this paper are widely accepted by the video compressing community and have been used in implementing various standards, ranging from MPEG1 / H.261 to MPEG4 / H.263. The paper also presents a very brief introduction to the entire flow of video compression.
Game Consoles - Are they secure?

The new game consoles and handheld machines available on the market today are designed with Internet and multiplayer connectivity in mind. They are also designed to be used for several years to come, and to be connected to the Internet 24 hours a day. This combination of computational power and connectivity could make the game consoles lucrative targets for viruses, Trojans, botnets, spam and other malware. Users of home computers are slowly learning that they need to protect their computers through the use of firewalls, antivirus and the like. But most of us probably do not consider a game console to be a computer, and therefore does not consider protecting it the same way we protect a normal computer. The goal of this thesis is to find out if new vulnerabilities are introduced into the home when connecting a gaming console to the Internet. We also take a look at children’s use of computer and video games, and what the parents know about it. We try to combine this information into an overview of how secure it is to use a game console and play computer and video games online.
Use of ICT based resources and services in special libraries in Kerala

Presents results of an investigation to study the contemporary use of Information and Communication Technology (ICT) based resources and services in special libraries in Kerala. The study was performed via a questionnaire survey of the library users and confined only to special libraries with ICT based resources and services. The study also investigated further areas, including: library professionals' help in the use of ICT based resources and services; any change in speed in academic/research work by using ICT; users' satisfaction with the application of ICT; reasons for their dissatisfaction; users' suggestions for training; and their opinion about the user education on ICT. The data collected were analysed and inferences made based on standard statistical methods. The results of the study revealed that the ICT based resource used by the largest percentage of users was the e-mail. The next most widely used resource was WWW, which was being used by nearly 60 per cent of the library users. A good number of the users were not satisfied with the application of ICT in their libraries and indicated 'inadequate ICT infrastructure' as their reason for dissatisfaction. Users proposed a variety of measures of formal orientation and training in ICT based resources and services to become more effective users.
Extracting Structue Data From UnStructured Data Through HiveQL

RDBMS can store structured data up to some GB of data. Processing of large data is very difficult to handle and also time consumption process. To overcome these problems made of using Hadoop. Apache Hadoop is a framework for big data management and analysis. The Hadoop core provides storing of structured, unstructured and semi structured data with the Hadoop Distributed File System(HDFS) and a simple MapReduce programming model to process and analyze data in comparable, the data stored in this distributed system. Apache Hive is a data warehouse built on top of Hadoop that allows you to query and manage large sets in scattered storage space using a SQL-like lingo call HiveQL, Hive translate queries into a series of MapReduce jobs. In existing system unstructured data stored in HDFS can't be retrieve into structured format through HiveQL. In this project It is converting twitter data into a structured format by using HiveQL with SerDe. HDFS can stores twitter data by using data streaming process.
Hands-on Tabletop Units for Addressing Persistent Conceptual Difficulties in Continuity and Frictional Loss in Fluid Mechanics

The difficulty in covering chemical engineering concepts using traditional lectures and whiteboard teaching approaches means today’s students’ learning demands are unfulfilled, so alternate methods are needed. Desktop learning modules (DLMs) are designed to show industrial fluid flow and heat transfer concepts in a standard classroom so students can immediately gain an intuitive understanding of processes and combine mathematical models with physical reality. In a previous engineering class on open channel flow concepts, a large average effect size, d=0.98, between the experimental and control group shows a statistically significant gain. In this paper, we build on this approach by the design of two simple classroom DLMs to demonstrate continuity and pressure drop. Our approach consists of a take home quiz, worksheet, pretest and posttest assessments, and an end of semester survey. Pretest and Posttest results show there were no significant differences between the DLM and lecture groups; however, both improved their performance on posttests. Survey assessment results show both DLM and lecture students strongly favor a mix of DLM with lectures rather than having one predominant approach.
Learning with multiple representations : algorithms and applications

Recently, lots of visual representations have been developed for computer vision applications. As different types of visual representations may reflect different kinds of information about the original data, their differentiation ability may vary greatly. As the existing machine learning algorithms are mostly based on the single data representation, it becomes more and more important to develop machine learning algorithms for tackling data with multiple representations. Therefore, in this thesis we study the problem of learning with multiple representations. We develop several novel algorithms to tackle data with multiple representations under three different learning scenarios, and we apply the proposed algorithms to a few computer vision applications. Specifically, we first study the learning with multiple kernels under fully supervised setting. Based on a hard margin perspective for the dual form of the traditional l1-norm Multiple Kernel Learning (MKL), we introduce a new “kernel slack variable” and propose a Soft Margin framework for Multiple Kernel Learning (SMMKL). By incorporating the hinge loss for kernel slack variables, a new box constraint for the kernel coefficients is introduced for Multiple Kernel Learning. The square hinge loss and the square loss soft margin MKLs naturally incorporate the family of elastic-net MKL and l2MKL, respectively. We demonstrate the effectiveness of our proposed algorithms on benchmark data sets as well as several computer vision data sets. Second, we study the learning with multiple kernels for weakly labeled data. Based on “input-output kernels”, we propose a unified Input-output Kernel Learning (IOKL) framework for handling weakly labeled data with multiple representations. Under this framework, the general data ambiguity problems such as SSL, MIL and clustering with multiple representations are solved in a unified framework. We formulate the learning problem as a group sparse MKL problem to incorporate the intrinsic group structure among the input-output kernels. A group sparse soft margin regularization is
A Verification Method of Security Model Based on UML and Model Checking

As the development of security operating system,the formal analysis and verification of the security models has been one of the hottest topics now.A new method to verify the correctness of security models is proposed in this paper based on the study of predecessors' work,which made use of the Unified Modeling Language(UML) and model checking.This approach first used the UML to specify the security model in the form of finite state machine diagrams and the class diagrams,and then translated these UML diagrams to the input language of model checkers.And it used the model checker to verify the model's correctness or the violation of security properties for the last step.The authors demonstrate the violation of confidentiality of the DBLP and SLCF model by the new approach.
Efficient Stockwell Transform with Applications to Image Processing

Multiresolution analysis (MRA) has fairly recently become important, and even essential, to image processing and signal analysis, and is thus having a growing impact on image and signal related areas. As one of the most famous family members of the MRA, the wavelet transform (WT) has demonstrated itself in numerous successful applications in various fields, and become one of the most powerful tools in the fields of image processing and signal analysis. Due to the fact that only the scale information is supplied in WT, the applications using the wavelet transform may be limited when the absolutely-referenced frequency and phase information are required. The Stockwell transform (ST) is a recently proposed multiresolution transform that supplies the absolutely-referenced frequency and phase information. However, the ST redundantly doubles the dimension of the original data set. Because of this redundancy, use of the ST is computationally expensive and even infeasible on some large size data sets. Thus, I propose the use of the discrete orthonormal Stockwell transform (DOST), a non-redundant version of ST. This thesis will continue to implement the theoretical research on the DOST and elaborate on some of our successful applications using the DOST. We uncover the fast calculation mechanism of the DOST using an equivalent matrix form that we discovered. We also highlight applications of the DOST in image compression and image restoration, and analyze the global and local translation properties. The local nature of the DOST suggests that it could be used in many other local applications.
The high performance packet capture based on the PF_RING socket in Linux

Many network monitoring tools are based on packet capture,but the performance of these popular tools is not satisfactory.Herein,a new method based on PF_RING Socket was used to improve the performance of the packet capture under the heavy computer traffics.The ring buffer in the kernel can be accessed and configured freely by the applications of the user space.The experimental result shows that,combined with the dev_polling,the PF_RING socket can improve the performance of the packet capture significantly,even in the 100M or the Gbit network.
The method for improving the range resolution in a passive location system

In the passive location system based on the FM broadcast,signals are uncooperative and non-stationary.With the generation theory of the signals taken into account,it is pointed out that the range resolution of the modulated signals declines because of the intermission of modulating signals.The method for confirming intermission based on the derivative of the signals is presented.The method for setting the intermission to zeros is analyzed to improve the range resolution.The results of the procession of the field data validate the proposed method to improve the range resolution with low computation and easy real-time procession.
A study of fuzzy logic ensemble system performance on face recognition problem

Some problems are difficult to solve by using a single intelligent information technology (IIT). The ensemble of the various data mining (DM) techniques is a set of models which are able to solve the problem by itself, but the combination of which allows increasing the efficiency of the system as a whole. Using the IIT ensembles can improve the reliability and efficiency of the final decision, since it emphasizes on the diversity of its components. The new method of the intellectual informational technology ensemble design is considered in this paper. It is based on the fuzzy logic and is designed to solve the classification and regression problems. The ensemble consists of several data mining algorithms: artificial neural network, support vector machine and decision trees. These algorithms and their ensemble have been tested by solving the face recognition problems. Principal components analysis (PCA) is used for feature selection.
Software and hardware infrastructure for data stream processing

In this paper state-of-the-art hardware and software technologies for stream data processing are reviewed. IBM InfoSphere Streams and Apache Spark are among of the most popular software products that alleviates burden of distributed program development for data analysis tasks. Capabilities of these systems are considered in application to the time series analysis. IBM InfoSphere Streams turns to be more suitable for online processing, whereas Apache Spark time series library focuses on a bulk processing of big collections of the time series.
Interactive Exploration of Volumetric Data Sets with a Combined Visual and Haptic Interface

The analysis of complex volumetric data sets is a critical component of many scientific and engineering applications. The difficulty of understanding the increasing amount of data generated by these applications motivates the need for effective and intuitive visualization approaches that allow users to extract relevant information from the data in a relatively short amount of time. Even though a great variety of visualization techniques are available today, the effectiveness of a particular approach is often limited by the ability of the user to interact with the presented information. Incorporating three-dimensional interaction metaphors into the visualization environment has great potential to assist the user during the data exploration process. This dissertation develops and evaluates interactive visualization methods that combine three-dimensional graphical and haptic displays for the exploration of volumetric data sets. A number of techniques are suggested that are suitable for augmenting the traditional visualization interface with visual and haptic interaction methods for guiding the user during exploration. The advantage of the presented approach is that the rendering algorithms do not require a preprocessing stage to extract intermediate geometric representations from the data. Thus, the user has direct control over the visual and haptic display via a set of visualization parameters. In addition to demonstrating the effectiveness of the presented methods over previous approaches, the dissertation describes a controlled experiment that examines the contribution of haptic guidance to vector field exploration tasks. The results of the evaluation suggest that properly combined visual and haptic feedback can significantly improve the speed and accuracy of volumetric data exploration. The presented methods are further illustrated with examples of representative visualization applications.
Modifying SBA-15 with Binary Elements of Chromium and Molybdenum for Improved Catalytic Performance in the Oxidative Dehydrogenation of Isobutane to Isobutene

In the oxidative dehydrogenation of isobutane to isobutene, selectivity and stability were improved by introducing chromium and molybdenum into SBA-15. The direct synthesis method (DM) was used to introduce these binary elements into SBA-15. Use of the DM resulted in a higher specific surface area of the catalyst and a greater dispersion of chromium and molybdenum species compared with a corresponding binary catalyst prepared using the incipient wetness impregnation method (IM). Selectivity to isobutene was improved, along with a decrease in the selectivities to CO and CO2 with the introduction of greater amounts of molybdenum, which suggests that molybdenum must suppress the tendency of isobutene to over-oxidate to either CO or CO2. The molybdenum species must be in close proximity to the chromium species, which results in the formation of an active Cr-O-Mo site.
Software Behavior Model Based on System Objects

On the basis of traditional FSA(finite state automaton),system objects can be resolved from the parameters of system call,and a Software Behavior model based on system object(SBO) is presented.This model defines the software state as all states of system objects,which are owned by the software,and then each state in the model has been assigned semantic information.Therefore,SBO can solve a problem of irrelevant semantics between different traces using the semantic information,and it can detect data semantic attacks,which directly or indirectly modifies system call parameters.Finally,a software anomaly intrusion detection prototype system based on SBO(SBOIDS) is implemented.The experimental and analysis results show that SBO can effectively detect data semantic attack and control the flow-based and mimicry attacks.
Security And Game Theory Algorithms Deployed Systems Lessons Learned

Thank you very much for reading security and game theory algorithms deployed systems lessons learned. As you may know, people have search numerous times for their favorite books like this security and game theory algorithms deployed systems lessons learned, but end up in infectious downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they are facing with some infectious virus inside their desktop computer.
Deep learning classifier based on NPCA and orthogonal feature selection

In this paper the idea of deep learning classifier is developed. The effectiveness of discriminative classifier, as e.g. multilayer perceptron, support vector machine can be improved by adding the data preprocessing blocks: orthogonal feature selection (Gram-Schmidt method) and nonlinear principal component analysis. We present the case study of various structures of deep learning systems (scenarios).
Extracting Indoor Map Data From Public Escape Plans On Mobile Devices

Due to the non-applicability of GPS devices and the higher need for accuracy, collecting indoor spatial data presents different challenges than mapping outdoor environments. Considering usability or technical requirements, existing approaches for data capture indoors are poorly suited for non-professional collaborators of volunteered geographic information (VGI) communities, such as OpenStreetMap (OSM). This work investigates an alternative to measuring what has been measured already. The idea is to extract map data from public escape plans by means of computer vision. An approach to automatically interpreting photos of escape plans on mobile devices is developed and evaluated.
RELIABLE MINIMUM ENERGY COST ROUTING ALGORITHM TO FIND THE ENERGY-EFFICIENT AND RELIABLE ROUTES CONSIDERING RESIDUAL ENERGY IN WIRELESS AD-HOC NETRWORKS

Abstract In this paper we proposed Reliable Minimum Energy Cost Routing (RMECR) for wireless Ad-hoc networks (WANET). RMECR will select the route which is consuming less energy, route which have high reliability, high quality and also important factor is RMECR considers the remaining battery energy of the nodes there by increases the lifetime of the network to the greater extent. Simulation studies shows that RMECR is able to find the reliable and energy efficient route like RMER[12] but major factor which RMECR considers is the remaining battery energy of the nodes thereby increasing the network lifetime.
Research on Reliability Estimation Method for Zero-Failure Test Data

In theory, the larger sample capacity is, the more accurate to estimate the reliability. But many weapons are expansive and dangerous, so we cannot analysis the reliability on large numbers data. Under the zero-failure test data circumstances, in this paper, the estimation method of the index distribution system based on success or failure test data of components was studied.