Miracle-dz/UniFAR-FTUs huggingface dataset:
Resolving multi objectives resource allocation problem based on inputs and Outputs using data envelopment analysis method

Abstract: The problem of allocating resources among the various projects or business units to meetthe expected objectives falls under multi objectives resource allocation problem (MORAP). Resourcesto accomplish the goals are in limited supply. For this reason, the MORAP will be formulated as acomplex multi objectives optimization model. Data envelopment analysis (DEA) is applied to obtaina set of Pareto solutions efficiently. We propose a process in which instead of maximizing profit andminimizing cost, total efficiency of each allocation that is obtained by DEA is considered. Based onthe efficiency scores we obtain new data and use them to form production possibility set to find theset of Pareto solutions. Numerical examples are presented to illustrate the application procedure of theproposed approach. Key words: Multi objective resource allocation problem, multi objectives optimization model,data envelopment analysis, efficiency INTRODUCTION Multiple-objective optimization problem has received increased interest from researchers with variousbackgrounds since early 1960. In a multi objective optimization problem, multiple-objective functions need tobe optimized simultaneously. In the case of multiple-objectives, there is not necessarily a solution thatoptimizes all objectives simultaneously, because of incommensurability and conflict among objectives. Asolution may be optimal in one objective but worst in another. Therefore, there usually exist a set of solutionsfor the multiple-objective case which cannot simply be compared with each other. According to the findingsby M. Gen and R.Cheng (2000) for such solutions, called non-dominated solutions or Pareto optimal solutions,no improvement is possible in any objective function without sacrificing at least one of the other objectivefunctions. In the past few years, there has been a boom in applying genetic algorithms to solving the multiobjective optimization problem known as evolutionary multi objective optimization or genetic multi objectiveoptimization. The basic feature of genetic algorithms is the multiple directional and global searches, in whicha population of potential solutions is maintained from generation to generation. The population-to-populationapproach, proposed by M. Gen and R. Cheng (1997), is beneficial in the exploration of Pareto solutions.Resource allocation problem (RAP) is the process of allocating resources among the various projects orbusiness units for maximization of profit or minimization of cost. The process of the RAP seeks to find anoptimal allocation of a limited amount of resource to a number of tasks for optimizing their objectives subjectto the given resource constraint. Resource may be a person, asset, material, or capital which can be used toaccomplish a goal. A goal maybe objective or target, usually driven by specific future financial needs. Thebest or optimal solution may mean maximizing profits, minimizing costs, or achieving the best possible quality.Osman
Type2 Fuzzy Soft Computing Technique for Image Enhancement

The mainpurpose of Image enhancement is to process an image so that outcome is more appropriate than original image for definite application. The fuzzy logic isone of the soft computing techniques to enhance the images by eliminating uncertainty.In this paper efficient type2 fuzzy logic technique is used to get betterquality image. This method consists of two steps. In the First step fisher criterion function is useful to generate type1 fuzzy membership value. In the second step based on type1 membership value fuzzy rules are derived to enhance the image. The type2 fuzzy method is compared with type1 fuzzy. The table values and graphs provethat the proposed method gives better results compared with fuzzy type1 method.
The compactness locus of a geometric functor and the formal construction of the Adams isomorphism

We introduce the compactness locus of a geometric functor between rigidly‐compactly generated tensor‐triangulated categories, and describe it for several examples arising in equivariant homotopy theory and algebraic geometry. It is a subset of the tensor‐triangular spectrum of the target category which, crudely speaking, measures the failure of the functor to satisfy Grothendieck–Neeman duality (or equivalently, to admit a left adjoint). We prove that any geometric functor — even one which does not admit a left adjoint — gives rise to a Wirthmüller isomorphism once one passes to a colocalization of the target category determined by the compactness locus. When applied to the inflation functor in equivariant stable homotopy theory, this produces the Adams isomorphism.
Point-Based Modeling, Animation and Rendering of Dynamic Objects (Puntgebaseerde modellering, animatie en visualisatie van dynamische objecten)

rithms, often based on physical laws, are proposed to alter the shape and appearance of virtual 3D objects. The visual richness of the resulting computer generated images has to match with the increasing demand for more and more realism. In this dissertation we assess a recent trend to resort to point primitives for the representation of such dynamic 3D objects and propose techniques to optimally exploit the characteristics of these point primitives in a wide range of computer graphics applications. It is shown how the absence of explicit connectivity information between the point samples significantly facilitates the handling of dynamically changing objects. Based on customized data structures and resampling operators, efficient algorithms are proposed for point-based modeling, animation and rendering of dynamic objects. In the first part of this thesis, geometric modeling algorithms are proposed to interactively alter the shape and appearance of point-sampled 3D surfaces. Building on efficient point data structures, we show how boolean operations such as the union and intersection of complex surfaces can be computed at interactive rates. A 3D painting application is proposed entirely centered around the use of point primitives. It allows painting arbitrary levels of detail on point-sampled surfaces using virtual 3D brushes. Local resampling operators are given to maintain an adequate sampling density during the whole modeling process. The second part of this work uses point primitives for physics-based modeling of dynamic objects. Next to point sampling the object's surface, a point-sampled volume representation is used to efficiently solve the equations governing the dynamic behavior. By decoupling the volume and surface representation, a trade-off can be made between visual complexity and physical accuracy. We show how the use of point primitives leads to efficient algorithms for the simulation of elastic solids and viscous fluids and propose dynamic point sampling operators to handle brittle and ductile fracturing. The last part of this dissertation discusses ray tracing algorithms for point set surfaces. Spatial and temporal coherence is exploited to efficiently visualize dynamically changing point surfaces. Sharp features, such as the ones obtained by boolean operations and fracture animation, are faithfully reproduced using the concept of surface clipping relations. The discussed rendering algorithm is illustrated on various examples generated with the point-based animation framework introduced earlier in this dissertation. All presented techniques contribute to the increasing requirement to efficiently handle dynamically changing objects in computer graphics applications.
Polynomial Time Approximation Schemes

Let Π be an NP-hard optimization problem, and let A be an approximation algorithm for Π. For an instance I of Π, let A(I) denote the objective value when running A on I, and let OPT (I) denote the optimal objective value. The approximation ratio of A for the instance I is RA(I) = A(I)/OPT (I), thus, when Π is minimization (maximization) problem RA(I) ≥ 1 (RA(I) ≤ 1). A polynomial time approximation scheme is an algorithm which takes as input an additional parameter, e, which determines the desired approximation ratio. This ratio can be arbitrarily close to 1, when e approaches 0. The time complexity of the scheme is polynomial in the input size but may be exponential in 1/e. This gives a clear trade-off between running time and quality of approximation. Formally,
Resonance-like dynamics in radial cyclic injection flows of immiscible fluids in homogeneous porous media

Interfacial instabilities of immiscible radial displacements in homogeneous porous media are analysed in the case of sinusoidal injection flows. The analysis is carried out through numerical simulations based on the immersed interface and level set methods. Investigations of the effects of the period of the sinusoidal injection flows revealed a novel resonance effect where, for a critical period, the number of fingers as well as their structures are considerably changed. The resonance in the flow development is clearly identified through the abrupt changes in the Fourier spectrum of the interface as well as quantitative characteristics of the flow in the form of the minimum and maximum radii of the interface. For the range of parameters examined in this study that correspond to instabilities dominated by viscous forces, the resonance period was found to correlate with a characteristic time of the flow and the fluids mobility ratio. This new physical phenomenon offers new perspectives for using the flow instability to determine important physical properties such as the viscosity and the surface tension of fluids.
Application of ROC analysis in machine learning

Receiver Operating Characteristics(ROC) analysis is a technique for organizing classifiers and visualizing their performance.Comparing with general accuracy,ROC could describe the classify capability adequately and be adopted in all conditions.This method has many characteristic such as high reliability,object and accurate describe and especially it cannot be influenced by the data environment.In this paper,we systematically discuss the research achievement and introduce the advantages and shortcomings about ROC analysis.In the end we look forward to its development.
Real-Time Implementation Issues in Passive Automatic Focusing for Digital Still Cameras

Due to consumers' demand for faster picture shot time in the rapidly expanding digital still camera market, it is of importance to address the real-time implementation issues in the development of passive automatic focusing for digital still cameras. This article discusses such real-time implementation issues that are often overlooked when designing passive contrast sensing automatic focusing on digital still camera processors. Specifically, algorithmic design tradeoffs between automatic focusing speed, accuracy, and power consumption, are addressed. A sample implementation and its performance results on an actual digital still camera hardware platform powered by the Texas Instruments TMS320DM270 processor are presented to further convey these real-time implementation issues.
The Detection of Influential Subsets in Linear Regression by Using an Influence Matrix

This paper presents a new method to identify influential subsets in linear regression problems. The procedure uses the eigenstructure of an influence matrix which is defined as the matrix of uncentered covariance of the effect on the whole data set of deleting each observation, normalized to include the univariate Cook's statistics in the diagonal. It is shown that points in an influential subset will appear with large weight in at least one of the eigenvector linked to the largest eigenvalues in this influence matrix. The method is illustrated with several well-known examples in the literature, and in all of them it succeeds in identifying the relevant influential subsets.
Applying NLP Metrics to Students’ Self-Explanations

Intelligent Tutoring Systems (ITSs) are becoming an increasingly common method for students to engage with and learn course material. ITSs are designed to provide students with one-on-one learning that is tailored to their own pace and needs. These systems can adapt to each users’ individual knowledge and ability level to provide the most pedagogically effective learning environment. Tutoring systems have been designed that cover a variety of topics, including both well-defined and ill-defined domains. ITSs have seen great success within well-defined domains, where the topic itself provides only a limited set of responses. For example, in the domain of algebra, there is a limited set of possible actions that can be performed to solve for an unknown variable. Knowing this complete set of actions allows the tutoring system to predict all possible responses from the user. In contrast, ill-defined domains are more abstract and open ended. Reading comprehension is an ill-defined, open ended domain that can incorporate text from any subject, and involve numerous processes and problems for the learner. The number of associations that learners can make with a given text (e.g., based on personal memories, previous courses, ideas within different parts of the same text, etc.) is virtually infinite. These associations make it almost impossible to predict how a user will respond to a text. In addition to working with more abstract concepts, ITSs within ill-defined domains often have the added challenge of interpreting natural language user input. Incorporating natural language allows learners to use their own words and ideas as they interact with the content; however, this also increases the ambiguity of the interaction and decreases the system’s ability to build a precise model of the learner. Building an accurate learner model is essential for the system to adapt the interaction in a pedagogically appropriate manner.
Event tree,fault tree,decision-making tree and Bayesian network

Based on an introduction to the Bayesian network and the analysis of three examples,an event tree of a dam failure,a fault tree for unexpected ignition of a missile engine,and a decision-making tree for sale of cars,the methods of the event tree,the fault tree and the decision-making tree were compared with the Bayesian network.A general law was developed to translate the event tree,the fault tree and the decision-making tree into the Bayesian network.Firstly,in the Bayesian network the nodes denoted the events.Secondly,the nodes were connected with directed arcs according to the causality and influence of the events.Finally,the conditional probability table for the nodes was determined in terms of the given data or experts' experience.The results show that the Bayesian network has advantages in dealing with complex multi-state models and bi-directional reasoning.
Generic Validation Criteria and Methodologies for SDN Applications

Programmable control plane in software-defined networking (SDN), plays an essential role in the SDN architecture. The network function provided by the specialized hardware in a legacy network can be created in the form of software-based “SDN application” running on the controllers to manipulate entire network configurations. Therefore, the risk of having software bugs and errors in the SDN applications may threaten the normal operations of SDN networks. This paper presents systematic validation criteria and test cases based on the proposed novel methodologies for SDN application testing. The test framework can perform testbed build-up, generate desired packet sequences, and analyze results automatically. According to the results of a generic test suite, several issues are unveiled in the application under test (AUT). Some AUTs, which need to check all the incoming packets from OpenFlow switches, fail to meet the test criteria of burst packet-in and flow self-recycling. For most of the applications based on the Ryu controller, the evaluation results reveal that some are unable to recycle flow entries after they are unloaded. It is recommended that all flows populated by SDN applications must have timeout value specified to prevent unnecessary entries kept in the flow table.
Large-Scale Matrix Completion Using Orthogonal Rank-One Matrix Pursuit, Divide-Factor-Combine, and Apache Spark

As the size and scope of valuable datasets has exploded across many industries and fields of research in recent years, an increasingly diverse audience has sought out effective tools for their large-scale data analytics needs. Over this period, machine learning researchers have also been very prolific in designing improved algorithms which are capable of finding the hidden structure within these datasets. As consumers of popular Big Data frameworks have sought to apply and benefit from these improved learning algorithms, the problems encountered with the frameworks have motivated a new generation of Big Data tools to address the shortcomings of the previous generation. One important example of this is the improved performance in the newer tools with the large class of machine learning algorithms which are highly iterative in nature. In this thesis project, I set about to implement a low-rank matrix completion algorithm (as an example of a highly iterative algorithm) within a popular Big Data framework, and to evaluate its performance processing the Netflix Prize dataset. I begin by describing several approaches which I attempted, but which did not perform adequately. These include an implementation of the Singular Value Thresholding (SVT) algorithm within the Apache Mahout framework, which runs on top of the Apache Hadoop MapReduce engine. I then describe an approach which uses the Divide-Factor-Combine (DFC) algorithmic framework to parallelize the state-of-the-art low-rank completion algorithm Orthogoal Rank-One Matrix Pursuit (OR1MP) within the Apache Spark engine. I describe the results of a series of tests running this implementation with the Netflix dataset on clusters of various sizes, with various degrees of parallelism. For these experiments, I utilized the Amazon Elastic Compute Cloud (EC2) web service.
High-Throughput Computation of Shannon Mutual Information on Chip

—Exploration problems are fundamental to robotics, arising in various domains, ranging from search and rescue to space exploration. Many effective exploration algorithms rely on the computation of mutual information between the current map and potential future measurements in order to make planning decisions. Unfortunately, computing mutual information metrics is computationally challenging. In fact, a large fraction of the current literature focuses on approximation techniques to devise computationally-efﬁcient algorithms. In this paper, we propose a novel computing hardware architecture to efﬁciently compute Shannon mutual information. The proposed architecture consists of multiple mutual information computation cores, each evaluating the mutual information between a single sensor beam and the occupancy grid map. The key challenge is to ensure that each core is supplied with data when requested, so that all cores are maximally utilized. Our key contribution consists of a novel memory architecture and data delivery method that ensures effective utilization of all mutual information computation cores. This architecture was optimized for 16 mutual information computation cores, and was implemented on an FPGA. We show that it computes the mutual information metric for an entire map of 20 m × 20 m at 0 . 1 m resolution in near real time, at 2 frames per second, which is approximately two orders of magnitude faster, while consuming an order of magnitude less power, when compared to an equivalent implementation on a Xeon CPU.
Foundations of processing instruction

Abstract This paper reviews six issues that are key to understanding processing instruction (PI): (1) the distinction between processing and noticing; (2) the nature of input processing; (3) the nature of language (i.e., what gets acquired); (4) the distinction between knowledge and skill; (5) the explicit/implicit “debate”; and (6) the distinction between method and intervention. The goal of the paper is to clarify misconceptions about the nature of PI and what it purports to effect, thus distinguishing it from other pedagogical interventions such as text enhancement, recasts, and dictoglosses. In so doing, the paper offers six summary statements that, together, form the foundations of PI.
Autonomous Mobile Robot Obstacle Avoidance System Based on Optical Flow

Since real-time obstacle avoidance is a key part of mobile robot autonomous navigation, an autonomous mobile robot obstacle avoidance system based on optical flow is proposed. This system's input is the image sequence grabbed by the camera on the robot in motion. The optical flow information is extracted by Pyramid Lucas-Kanade method to calculate focus of expansion and time to contact.The possible position of the obstacle is estimated with improved balance strategy. Experimental results show that this system can detect and avoid obstacles with the width larger than 10 centimeters but not cover the camera view correctly, which validates the method's feasibility.
On the validity and errors of the pseudo-first-order kinetics in ligand–receptor binding

The simple bimolecular ligand–receptor binding interaction is often linearized by assuming pseudo-first-order kinetics when one species is present in excess. Here, a phase-plane analysis allows the derivation of a new condition for the validity of pseudo-first-order kinetics that is independent of the initial receptor concentration. The validity of the derived condition is analyzed from two viewpoints. In the first, time courses of the exact and approximate solutions to the ligand–receptor rate equations are compared when all rate constants are known. The second viewpoint assess the validity through the error induced when the approximate equation is used to estimate kinetic constants from data. Although these two interpretations of validity are often assumed to coincide, we show that they are distinct, and that large errors are possible in estimated kinetic constants, even when the linearized and exact rate equations provide nearly identical solutions.
A New Control Architecture for Internet Based Tele-Robot System Using Server Push Technique

A new control architecture for Internet based tele-robot system was pro-posed. With the introduction of Server Push technique, real time on-line data acquisition and fast control signal response, which enable this new architecture suitable for online real time tele-operation, can be achieved by using hyper-text transfer protocol. A 6DOF PUMA robot tele-operation system has been developed using this architecture and the experimental results illustrate that the performance of the tele-manipulation system had been greatly improved compared with that of the traditional control system using CGI and HTTP Server. The architecture is also open-structured and can be applied in other general purposed tele-operation systems.
On the Theorem of Ошкин

In this paper the normality of a family of holomorphic functions is studied and the following result is obtained:Theorem 1 Suppose is a family of holomorphic function in a domain D.n,k(≥2) are positive integers, b≠ 0 is a finite complex number, a1(z) ,a2(z) , … ,ak(z) are holomorphic functions in D . If for every f(z) in 3,(1) the order of zeros of f(z) ≥ k ;(2)fn(z){f(k)U) +a1(2)f(k-1))(2) + … +ak(z)/(z)}≠b. Then is normal in D .