Miracle-dz/UniFAR-FTUs huggingface dataset:
An intuitionistic fuzzy multi-objective vendor selection problem

A vendor selection problem is a multiobjective decision making problem involving the optimization of cost, quality and delivery performance on the basis of evaluation of criteria like cost, quality, service etc. The problem of vendor selection involves its selection and allocation of order. The selections of appropriate vendors enable firms to improve performance related to customer's needs, requirements and satisfaction. In real world most of the input information related to criteria is not known precisely due to several conflicting factors. Imprecision is best handled by fuzzy set theory. A more advanced form of fuzzy set theory with an additional degree of freedom is intuitionistic fuzzy sets. We choose triangular intuitionistic fuzzy number to represent the criteria because of its ability to handle uncertainty in data and its simple arithmetic operations to solve any linear programming problem. This paper develops an intuitionistic fuzzy multiobjective linear model (IFMOLM) to vendor selection problem with the coefficient of the objectives as triangular intuitionistic fuzzy numbers and rest of the data in the constraints as crisp. The multiple objectives are minimization of net price, maximization of quality and maximization of on time delivery for vendors. Specifically the paper incorporates the LPP is formulated in intuitionistic fuzzy form and a ranking function converts the IFMOLM to equivalent crisp linear form (Dubey and Mehra(2011)). A numerical example illustrates the application of the methodology.
Internet Public Opinion Hot Spot Mining Based on Voting Mechanism and MapReduce Platform

In the era of big data, the value of Internet public opinion hot spot extraction is particularly prominent. In order to further develop the application value of Internet public opinion hot extraction, this paper constructs the Internet public opinion hot spot extraction framework, and puts forward an Internet public opinion hot spot extraction method VM-Rep based on voting mechanism and MapReduce framework. A heuristic algorithm based on voting mechanism is proposed for the hot spot of Internet public opinion, and MapReduce is used to improve the ability and efficiency of processing massive data. Experimental results show: VM-Rep’s coverage is significantly better than Top-k, K-means and Agglo, and the redundancy of VM-Rep is the least; VM-Rep takes the least time in the four methods, embodies the advantages of VM-Rep method for massive data.
Portable Wireless Device for Automated Agitation Detection

The objective of this chapter is to provide an overview of existing portable medical devices. The chapter then focuses on portable automated agitation detection. The design and prototyping of a device capable of portable wireless agitation detection is detailed. In addition, the agitation detection algorithm, which uses Support Vector Machines (SVM) based on the measurement of skin conductivity, skin temperature, and inter-beat interval, is described. Experimental results pertaining to the performance of the device and the agitation detection are provided. The chapter concludes with challenges to the development of medical portable devices in general and to agitation detection specifically. Some potential future research directions are highlighted. DOI: 10.4018/978-1-61350-123-8.ch012
Subjective Assessment for Standard Television Sequences and Videotoms - H.264/AVC Video Coding Standard

This paper presents comparison of videotoms and standard television sequences in terms of image distortions and perceived subjective quality affected by H.264/AVC compression with changed bit rate. Results from initial tests, performed as laboratory exercise can be a reference to show scale of diversity in both level of degradation and Mean Opinion Score (MOS) evaluation. Results and comments included in this paper give overview on the codec influence on videotoms and can suggest approach for further tests and experiments. Keywords—digital television, IPTV, Quality of Experience, Quality of Service, videotoms.
Social Cognition Inference And Attribution

Thank you for downloading social cognition inference and attribution. As you may know, people have search hundreds times for their favorite readings like this social cognition inference and attribution, but end up in infectious downloads. Rather than reading a good book with a cup of tea in the afternoon, instead they are facing with some infectious bugs inside their desktop computer. social cognition inference and attribution is available in our digital library an online access to it is set as public so you can download it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the social cognition inference and attribution is universally compatible with any devices to read.
Compound Adder Design Using Carry-Look-ahead / Carry Select Adders

To reduce fan-out load at the final multiplexor stage and to relieve the speed requirement of compound adders, a parallel global carry generation is used for a hybrid carry-look ahead/ carryselect adder design. To develop the speed and to minimize silicon area, a new logical decomposition is derived. The new architecture has been explained with a 64-bit adder design using dynamic CMOS circuit implementations. The 64-bit adder has the delay of 525ps in 0. 225µm bulk CMOS technology
Achieving Data Confidentiality And Access Control Through Ciphertext Property Based on Symmetric Encryption with Undeniable Assignment

In the cloud, for achieving access control and keeping data confidential, the data owners could adopt attribute-based encryption to encrypt the stored data. Users with limited computing power are however more likely to delegate the mask of the decryption task to the cloud servers to reduce the computing cost. As a result, attribute-based encryption with delegation emerges. Still, there are caveats and questions remaining in the previous relevant works. For instance, during the delegation, the cloud servers could tamper or replace the delegated ciphertext and respond a forged computing result with malicious intent. They may also cheat the eligible users by responding them that they are ineligible for the purpose of cost saving. Furthermore, during the encryption, the access policies may not be flexible enough as well. Since policy for general circuits enables to achieve the strongest form of access control, a construction for realizing circuit ciphertext-policy attribute-based hybrid encryption with verifiable delegation has been considered in our work. In such a system, combined with verifiable computation and encrypt-then-mac mechanism, the data confidentiality, the fine-grained access control and the correctness of the delegated computing results are well guaranteed at the same time. Besides, our scheme achieves security against chosen-plaintext attacks under the k-multilinear Decisional Diffie-Hellman assumption. Moreover, an extensive simulation campaign confirms the feasibility and efficiency of the proposed solution. Index Terms—Ciphertext-policy attribute-based encryption, Circuits, Verifiable delegation, Multilinear map, Hybrid encryption. F
Three-dimensional SPIHT Coding of Hyperspectral Images with Random Access and Resolution Scalability

With the increase of remote sensing images, fast access to some features of the image is becoming critical. This access could be some part of the spectrum, some area of the image, high spatial resolution. An adaptation of 3D-SPIHT image compression algorithm is presented to allow random access to some part of the image, whether spatial or spectral. Resolution scalability is also available, enabling the decoding of different resolution images from the compressed bitstream of the hyperspectral data. Final spatial and spectral resolutions are chosen independently. From the same compressed bitstream, various resolutions and quality images can be extracted while reading a minimum amount of bits from the coded data. All this is done while reducing the memory necessary during the compression.
The Development Trend of the Natural Language Processing

Based on needs for the development of intelligent internet and Artificial Intelligence at the background of cognitive science,this paper examines current main problems in Natural Language Processing(NLP)through analyzing the development of NLP,one of core technologies of cognitive science.This paper suggests that the change of thought of"from the whole to the part"will be the key of whether breakthrough can be obtained in NLP of next phase.Dynamic semantic analysis needs the introduction of pragmatic techniques,therefore,after the stages of syntactics and semantics,NLP’s transformation to pragmatic stage is an inevitable trend.
Joint learning and pruning of decision forests

Decision forests, such as Random forest (Breiman, 2001) and Extremely randomized trees (Geurts et al., 2006), are state-of-the-art supervised learning methods. Unfortunately, the size of the trees increases (at worst linearly) with the size of the training set, leading to substantial memory footprints when storing the forests. Several post-processing methods have been proposed to tackle this problem (Meinshausen et al., 2009; Joly et al., 2012; De Vleeschouwer et al., 2015; Ren et al., 2015). Although very e↵ective, they require building the whole forest first and then solving a highdimensional optimization problem to (post-)prune it. In this research, we would like to investigate whether a similar compression e↵ect could be achieved directly during tree growing, without ever requiring the development of the whole forest. For that purpose, we propose a joint learning and pruning (JLP) method which iteratively and greedily deepens the trees by optimizing globally the sequence of nodes to develop, while still choosing locally the splitting variables and cut points at all tree nodes based on the standard score criterion. The algorithm is presented in Section 2 and some results and comparisons with more naive and post-pruning methods are reported in Section 3.
Open Systems Dependability

• Demands for the dependability of huge and complex software systems – which include black box software such as legacy codes and off the shelf components – which are connected to networks that may cause security and integrity problems D d f i ith i t l d • eman s or cop ng w env ronmen a an requirement changes in operation – functions, user interfaces, performance, etc – networks and services on networks • Necessity of Continuous Operations C i t f / t lif l • onsc ousness o per ormance cos over ecyc e • Increased accountability to service/system providers
Research on an Optimized RBF Neural Network Model Applied to CPI Forecast

This paper,aiming at the nonlinear characteristics of CPI and the parameters of being difficult to be objectively determined in RBF neural network,puts forward a kind of optimization of RBF neural network method which combines orthogonal least squares(OLS),K-means clustering and gradient descent algorithm,computing activation function with the linear combinations of Gauss,reflected sigmoidal and inverse multiquadrics radial basis functions,then builds a model for CPI to fit and forecast by using the optimization algorithm of RBF neural network.Experimental results show that the model is of a good convergence and generalization ability,the model has a certain universal applicability in the prediction performance which is obviously superior to the single method forecast and the hybrid network on Gauss kernel function.
Efficient Scalar Multiplication over Elliptic Curve

Elliptic Curve Scalar multiplication is the method of adding a point on the curve to itself every time[1]. In recent years, research in Scalar Multiplication over Elliptic curves (EC) over a finite fields attracted many researchers, working in field of cryptography, to find out how elliptic curves cryptography (ECC) can be implemented and how to reduce its complexity [4]. The efficient techniques used in Elliptic curve cryptography are Elliptic curve scalar multiplication using pointhalving algorithm [2], then double-base (DB) chain algorithm, and after that step multi-base representation (SMBR), but these techniques have their drawbacks. So, it become imperative to find out a new approach which can be efficiently used for implementation of ECC and further reducing its complexity. The paper proposes a new algorithm Treble algorithm for affine coordinates. We continued doing work using the binary concept or double and add operation with the help of treble approach to make it more efficient which relates to the use of all input values in producing any type of output, including how much time and energy are required The results show that our contribution can significantly enhance EC scalar multiplication. Index Term—Elliptic curve cryptography, double and add operation, affine coordinates.
Doing Psychological Research

Thank you very much for reading doing psychological research. As you may know, people have search hundreds times for their favorite readings like this doing psychological research, but end up in harmful downloads. Rather than enjoying a good book with a cup of tea in the afternoon, instead they juggled with some malicious bugs inside their computer. doing psychological research is available in our book collection an online access to it is set as public so you can download it instantly. Our digital library spans in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the doing psychological research is universally compatible with any devices to read.
A ROI-based Infrared Ship Target Detection Approach

A kind of ROI-based IR ship detection approach was proposed.Based on attention mechanism and top-down model,it carves out the hot region firstly,which always refers to the engine or chimney of a ship as the guidance of ROI abstraction;secondly it proposes the waterline and maximum edge gradient detection,which can seek out the correct ROI.Finally,the segment and characteristic detection is accomplished in the chosen ROI.Experiments demonstrate that the approach proposed can work exactly and efficiently,and is adaptive to IR ship target detection.
Constrained Versions of DEDICOM for Use in Unsupervised Part-Of-Speech Tagging

This reports describes extensions of DEDICOM (DEcomposition into DIrectional COMponents) data models [3] that incorporate bound and linear constraints. The main purpose of these extensions is to investigate the use of improved data models for unsupervised part-of-speech tagging, as described by Chew et al. [2]. In that work, a single domain, two-way DEDICOM model was computed on a matrix of bigram fre- quencies of tokens in a corpus and used to identify parts-of-speech as an unsupervised approach to that problem. An open problem identi ed in that work was the com- putation of a DEDICOM model that more closely resembled the matrices used in a Hidden Markov Model (HMM), speci cally through post-processing of the DEDICOM factor matrices. The work reported here consists of the description of several models that aim to provide a direct solution to that problem and a way to t those models. The approach taken here is to incorporate the model requirements as bound and lin- ear constrains into the DEDICOM model directly and solve the data tting problem as a constrained optimization problem. This is in contrast to the typical approaches in the literature, where the DEDICOM model is t using unconstrained optimization approaches, andmore » model requirements are satis ed as a post-processing step. « less
Design Principles for Interactive Learning Environments with Embedded Formative Assessments

The new technology-enhanced conception of assessment stands in contrast to the traditional view of assessments as tests of a learner’s ability to recall facts. Because technology mediates learning in new ways, it engenders new forms of knowledge as well as possibilities for documentation and analysis. Many have noted that games, simulations, and cases are engaging for learners and thus warrant further development for their expanded use. Research on learning and assessment suggests design principles that developers should follow when investing such effort in order to capitalize on how the interactive aspects of these tools can serve as assessments about what, how, and when learning is occurring. The ETIPS application described here serves as an example of how such principles can be used to guide priorities and decisions for developers as well as applied as criteria for determining the quality of such digital tools as learning environments and assessment tools.
Importing Binned Background Traffic Flows into OPNET

1 Kristof Sleurs is a Research Assistant of the Fund for Scientific Research Flanders (FWO-Vlaanderen), Belgium. Abstract Developers of network applications or network hardware need simulations to test their products before introducing them into the market. Today, several traffic models are available that can generate packet streams with a behavior close to real-life network traffic. Most of these are discrete models, and thus generate a sequence of packet or byte counts for subsequent time intervals.
Real-Like Virtual Fitting for Single Image

An image based real-like virtual fitting system focusing on head accessories such as glasses and hat is proposed in this paper. The key techniques are 3D registration and virtual-real synthesis. Firstly, a facial landmark detection and pose estimation based algorithm for capturing registration data is presented. Then the approach using color blending and depth buffering to solve occlusion and model material problem is discussed. The registration algorithm is evaluated on the AFLW(Annotated Facial Landmarks in the Wild) dataset and testified to be precise enough for virtual fitting. Finally, a series of virtual fitting results are showed. The experimental results, which are obtained on the condition of pose variation and part occlusion, indicate that the proposed virtual fitting technique is fast, accurate and real-like.
The Application of Combined Linguistic Features in Semantic Relation Extraction

Semantic relation extraction is one of the important fields in information extraction research.The present feature vector based approach for semantic relation extraction can hardly be improved simply by mining new features.This paper presents a novel method through combining the diverse basic lexical,syntactic and semantic features to form new combined features.The experiments show that these combined features positively improve the precision and recall of the SVM based relation extraction.The F-score of relation extraction for the 7 major types and 23 subtypes in ACE 2004 corpora achieves 66.6% and 59.50% respectively.
Task classification method oriented to cloud computing

To improve the resource utilization,the task resource requirement features of processor,network,disk and so on were efficiently estimated through analyzing the way of the task request,measuring the performance of application program in task or simulating to run the task.Afterwards,according to the features of resource requirement,the tasks could be classified into processor bound task,communicate bound task,disk bound task and others.And then the classified tasks were integrated with specific virtual machines to make all kinds of resources to be used efficiently.The research shows that the method can classify the task efficiently.And compared to unclassified method,it can reduce the times of virtual machines migration or integration.
Data structures

The term structure is used in many different fields to denote objects that are constructed from their components in a regular and characteristic way. Loosely, a data structure is a structure whose components are data objects. As the term is used more precisely in computer science, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data. If any one of these three characteristics is missing or not stated precisely, the structure being examined does not qualify as a data structure.
Amorphous Photonic Crystals with Only Short-Range Order

Distinct from conventional photonic crystals with both shortand long-range order, amorphous photonic crystals that possess only short-range order show interesting optical responses owing to their unique structural features. Amorphous photonic crystals exhibit unique light scattering and transport, which lead to a variety of interesting phenomena such as isotropic photonic bandgaps or pseudogaps, noniridescent structural colors, and light localization. Recent experimental and theoretical advances in the study of amorphous photonic crystals are summarized, focusing on their unique optical properties, artifi cial fabrication, bionspiration, and potential applications.
Authenticity Challenges of Wearable Technologies

In this chapter the security challenges raised by wearable technologies concerning the authenticity of information and subjects are discussed. Following a conceptualization of the capabilities of wearable technology, an authenticity analysis framework for wearable devices is presented. This framework includes graphic classification classes of authenticity risks in wearable devices that are expected to improve the awareness of users on the risks of using those devices, so that they can moderate their behaviors and take into account the inclusion of controls aimed to protect authenticity. Building on the results of the application of the framework to a list of wearable devices, a solution is presented to mitigate the risk for authenticity based on digital signatures.
Energy Efficient Load Balancing of Virtual Machines in Cloud Environments

Today Cloud computing is used in a wide range of domains. By using cloud computing a user can utilize services and pool of resources through internet. The cloud computing platform guarantees subscribers that it will live up to the service level agreement (SLA) in providing resources as service and as per needs. However, it is essential that the provider be able to effectively manage the resources. One of the important roles of the cloud computing platform is to balance the load amongst different servers in order to avoid overloading in any host and improve resource utilization. The concept of Genetic algorithm is specifically useful in load balancing for best virtual machines distribution across servers. In this paper, we focus on load balancing and also on efficient use of resources to reduce the energy consumption without degrading cloud performance.
Selecting Appropriate Individual and Group-Based Assessments

Purpose – To provide classroom teachers with an overview of a range of assessments that can be administered either individually or to a group. Design/methodology/approach – The chapter is organized from early literacy skill assessments (both individual and group based) to comprehension and standardized tests. Findings – Provides detailed information on skills required for each element of reading, design of assessment, intended purpose, and process of administration. Research limitations/implications – This is not an exhaustive list, the authors strove to highlight the most reliable and practical assessments from a large body of possible choices. Practical implications – This is a valuable source for classroom teachers who are provided with a wide-range of assessment choices covering the breadth of reading skills with extensive details on each. Originality/value of paper – Teachers need a range of assessments to choose from to make decisions at the individual, class and school level.