Miracle-dz/UniFAR-FTUs huggingface dataset:
A Consolidation Strategy Supporting Resources Oversubscription in Cloud Computing

Consolidation is an approach that optimizes the utilization of the computing resources. Many datacenter virtualization solutions employ it to reduce the number of physical machines (PMs), and thus save money on hardware, cooling, and electricity. Resources oversubscription, as an effective resource management policy is used to free idle resource in cloud. More virtual machines (VMs) can host in the same PM. However, the limited underlying resource (such as last level cache or I/O bandwidth) provided by PM cannot support much more VMs. The performance of VMs may not be guaranteed due to the underlying resources starvation. Meanwhile, the oversubscription strategy is failure for some VMs when they are migrated to a new PM according to consolidation strategy. In this paper, we present a performance-aware consolidation strategy targeting the oversubscribed cloud. It constructs a performance alarm and the corresponding threshold to control the density of consolidation and maintain the performance isolation among the co-hosting VMs. It also finds the appropriate combination of VMs hosting in the same PM to better support oversubscription. We performed our evaluation on a virtual datacenter simulated by Xen. Our evaluation results show that performance alarm effectively stop the performance isolation destroyed. Furthermore, the policy of VM placement reduces the number of used PMs and also protects maximum oversubscription for each VM.
MLPnP - A Real-Time Maximum Likelihood Solution to the Perspective-n-Point Problem

Abstract. In this paper, a statistically optimal solution to the Perspective-n-Point (PnP) problem is presented. Many solutions to the PnP problem are geometrically optimal, but do not consider the uncertainties of the observations. In addition, it would be desirable to have an internal estimation of the accuracy of the estimated rotation and translation parameters of the camera pose. Thus, we propose a novel maximum likelihood solution to the PnP problem, that incorporates image observation uncertainties and remains real-time capable at the same time. Further, the presented method is general, as is works with 3D direction vectors instead of 2D image points and is thus able to cope with arbitrary central camera models. This is achieved by projecting (and thus reducing) the covariance matrices of the observations to the corresponding vector tangent space.
Injection/withdrawal scheduling for natural gas storage facilities

Control decisions for gas storage facilities are made in the face of extreme uncertainty over future natural gas prices on world markets. We examine the problem faced by owners of storage contracts of how to manage the injection/withdrawal schedule of gas, given past price behavior and a predictive model of future prices. Real options theory provides a framework for making such decisions. We describe the theory behind our model and a software application that seeks to optimize the expected value of the storage facility, given capacity and deliverability constraints, via Monte-Carlo simulation. Our approach also allows us to determine an upper bound on the expected valuation of the remaining storage facility contract and the gas stored therein.
Research on the application of image fusion and second-generation bandelet transform in image denoising

Effectively geometric representation of images is a significant image denoising tool. In order to obtain a clearer image, we used second-generation bandelet bases to establish the geometric stream represents of images, and combined with the image fusion technology to improve the quality of images. Though the adaptive threshold and global threshold of numerous experiments, and finding a optimal wavelet function to use image fusion operation. This is the innovation of the paper. Therefore, using bandelet and image fusion improve the quality of images. The result of the paper is better than the traditional denoising method, wavelet denoising and bandelet denoising.
College for All

This study addresses missing links in “college for all” debates by investigating gaps between actual and desirable math achievement trajectories for students’ college readiness. Linking multiple national data sets across P–16 education levels, the study estimates college readiness benchmarks separately for two-year and four-year college entrance and completion. The goals of the study are to compare performance standards, benchmarks, and norms for college readiness and to assess college readiness gaps among all students as well as gaps among racial and social subgroups. The results suggest that entrance into and completion of two-year versus four-year colleges require substantially different levels of math achievement in earlier education periods and that meeting national versus state proficiency standards leads to differences in postsecondary education outcomes and can mean the difference between bachelor’s and associate’s degree attainment. Persistent racial and social gaps in college readiness threaten the goal of getting all students academically ready for at least two-year college completion.
Assessing the Fidelity of COTS 802.11 Sniffers

Recent measurement studies have analyzed WLAN performance by means of wireless sniffers that passively capture transmitted frames. Also, for relatively large (enterprise) WLAN scenarios, previous work has investigated multi-sniffer deployments with devices placed far apart in order to capture all traffic in the network (even frames transmitted simultaneously by different nodes at non-interfering locations). However, for both these single- and multi-sniffer scenarios, little attention has been given to the fidelity of an individual device, i.e., the ability of a given sniffer to capture all frames that could have been captured by a more faithful device. We assess this fidelity (a term we make precise in this paper) by running controlled experiments inside an anechoic chamber and analyzing the similarities and differences between the trace file from the device under study and those of additional "shadow" devices placed in its close proximity. Our results show that fidelity varies significantly across sniffers, both quantitatively and qualitatively, and that performance may also depend on the nature of the experiment under study and on slight changes of the sniffer position.
Provably correct inline monitoring for multithreaded Java-like programs

Inline reference monitoring is a powerful technique to enforce security policies on untrusted programs. The security-by-contract paradigm proposed by the EU FP6 S 3MS project uses policies, monitoring, and monitor inlining to secure third-party applications running on mobile devices. The focus of this paper is on multi-threaded Java bytecode. An important consideration is that inlining should interfere with the client program only when mandated by the security policy. In a multi-threaded setting, however, this requirement turns out to be problematic. Generally, inliners use locks to control access to shared resources such as an embedded monitor state. This will interfere with application program non-determinism due to Java's relaxed memory consistency model, and rule out the transparency property, that all policy-adherent behaviour of an application program is preserved under inlining. In its place we propose a notion of strong conservativity, to formalise the property that the inliner can terminate the client program only when the policy is about to be violated. An example inlining algorithm is given and proved to be strongly conservative. Finally, benchmarks are given for four example applications studied in the S 3MS project.
The I4U system in NIST 2008 speaker recognition evaluation

This paper describes the performance of the I4U speaker recognition system in the NIST 2008 Speaker Recognition Evaluation. The system consists of seven subsystems, each with different cepstral features and classifiers. We describe the I4U Primary system and report on its core test results as they were submitted, which were among the best-performing submissions. The I4U effort was led by the Institute for Infocomm Research, Singapore (IIR), with contributions from the University of Science and Technology of China (USTC), the University of New South Wales, Australia (UNSW), Nanyang Technological University, Singapore (NTU) and Carnegie Mellon University, USA (CMU).
Theory and practice in quantitative genetics.

With the rapid advances in molecular biology, the near completion of the human genome, the development of appropriate statistical genetic methods and the availability of the necessary computing power, the identification of quantitative trait loci has now become a realistic prospect for quantitative geneticists. We briefly describe the theoretical biometrical foundations underlying quantitative genetics. These theoretical underpinnings are translated into mathematical equations that allow the assessment of the contribution of observed (using DNA samples) and unobserved (using known genetic relationships) genetic variation to population variance in quantitative traits. Several statistical models for quantitative genetic analyses are described, such as models for the classical twin design, multivariate and longitudinal genetic analyses, extended twin analyses, and linkage and association analyses. For each, we show how the theoretical biometrical model can be translated into algebraic equations that may be used to generate scripts for statistical genetic software packages, such as Mx, Lisrel, SOLAR, or MERLIN. For using the former program a web-library (available from http://www.psy.vu.nl/mxbib) has been developed of freely available scripts that can be used to conduct all genetic analyses described in this paper.
An R Package to Fit Psychometric Functions for Multiple Groups by

quickpsy is a package to parametrically fit psychometric functions. In comparison with previous R packages, quickpsy was built to easily fit and plot data for multiple groups. Here, we describe the standard parametric model used to fit psychometric functions and the standard estimation of its parameters using maximum likelihood. We also provide examples of usage of quickpsy, including how allowing the lapse rate to vary can sometimes eliminate the bias in parameter estimation, but not in general. Finally, we describe some implementation details, such as how to avoid the problems associated to round-off errors in the maximisation of the likelihood or the use of closures and non-standard evaluation functions.
Chain-Consistent Tight Bounds of True Index Numbers of Productivity:

It has been seldom recognized, after an early clarification made by Samuelson (1947, p. 151) and successive systematic developments of Afriat, that chainconsistent relative price levels can be reconstructed (up to a constant) using several observation points directly and simultaneously. The derived ratios of price levels are mutually consistent by construction and thereby satisfy the circularity or transitivity test as well as other desirable requirements. By contrast, to the best of our knowledge, all the other existing index number methods fall into the realm of the so-called “impossibility theorem”, failing in particular the circularity test intrinsically. This paper builds on the recent reappraisal of this approach by Afriat and Milana (2008) and compares it with akin non-parametric techniques that are based on revealed preferences. An application to productivity measurement using EU KLEMS data highlights the usefulness of the method.
Optimization in Dynamic Environments Utilizing a Novel Method Based on Particle Swarm Optimization

In numerous real world optimization problems, objective function or constraints of the problem can be changed during time. If these undefined situations are occurred in optimization process, this problem is called dynamic. There are several challenges in dynamic environments optimization, so that algorithms designed for optimization in these environments would utilize several mechanisms in order to conquer the challenges. In this paper, a novel algorithm for optimization in dynamic environments is proposed based on particle swarm optimization in which a novel mechanism have been used for improving the performance. In this mechanism, it is tried to increase the ability of local search around optimum with focusing on best found peak in each environments. The results of the proposed approach are evaluated on moving peak benchmarks and are compared with results of several state of the art algorithms. Experimental results show the superiority of the proposed method.
Scheduling in Interference Environment

Broadcast is a fundamental operation in wireless networks, and naïve flooding is not practical, because it cannot deal with interference. Scheduling is a good way of avoiding interference, but previous studies on broadcast scheduling algorithms all assume highly theoretical models such as the unit disk graph model. In this work, we reinvestigate this problem by using the 2-Disk and the signal-to-interference-plus-noise-ratio (SINR) models. We first design a constant approximation algorithm for the 2-Disk model and then extend it to the SINR model. This result, to the best of our knowledge, is the first result on broadcast scheduling algorithms in the SINR model.
The relevance of tuning the parameters of metaheuristics . A case study : The vehicle routing problem with stochastic demand

Metaheuristics are a class of promising algorithms for combinatorial optimization. A basic implementation of a metaheuristic typically requires rather little development effort. With a significantly larger investment in the design, implementation, and fine-tuning, metaheuristics can often produce state-of-the-art results. We say that, according to the specific context of applications, either a metaheuristic can be used out-of-the-box, or a custom implementation can be developed. This flexibility is one of the major strengths of metaheuristics but it also hides some possible catches. In particular, it should be noticed that results obtained with out-of-the-box implementations cannot be always generalized to custom ones, and vice versa. As a case study, this paper focuses on the vehicle routing problem with stochastic demand and on five among the most successful metaheuristics—namely, tabu search, simulated annealing, genetic algorithm, iterated local search and ant colony optimization. We show that the relative performance of these algorithms strongly varies whether one considers out-of-the-box implementations or custom ones, in which the parameters are accurately fine-tuned.
Saccharomyces genome database provides new regulation data

The Saccharomyces Genome Database (SGD; http://www.yeastgenome.org) is the community resource for genomic, gene and protein information about the budding yeast Saccharomyces cerevisiae, containing a variety of functional information about each yeast gene and gene product. We have recently added regulatory information to SGD and present it on a new tabbed section of the Locus Summary entitled ‘Regulation’. We are compiling transcriptional regulator–target gene relationships, which are curated from the literature at SGD or imported, with permission, from the YEASTRACT database. For nearly every S. cerevisiae gene, the Regulation page displays a table of annotations showing the regulators of that gene, and a graphical visualization of its regulatory network. For genes whose products act as transcription factors, the Regulation page also shows a table of their target genes, accompanied by a Gene Ontology enrichment analysis of the biological processes in which those genes participate. We additionally synthesize information from the literature for each transcription factor in a free-text Regulation Summary, and provide other information relevant to its regulatory function, such as DNA binding site motifs and protein domains. All of the regulation data are available for querying, analysis and download via YeastMine, the InterMine-based data warehouse system in use at SGD.
Replication in FicusDistributed File Systems

Ficus is a replicated general ling environment for Unix intended to scale to very large (nationwide) networks. The system employs an optimistic \one copy availability" model in which connicting updates to the le system's directory information are automatically reconciled, while connicting le updates are reliably detected and reported. The system architecture is based on a stackable layers methodology which permits a high degree of mod-ularity and extensibility of le system services. This paper presents the motivations for replica-tion and summarizes the case for optimistic concur-rency control for large scale distributed le systems. It presents a brief description of the Ficus le system and concludes with a number of outstanding issues which must be addressed. 1 Overview At UCLA, we have been working for some time on the design and development of an easily installed, replicated ling system that can be added to existing systems. Two fundamental characteristics of the work are its approach to modularity through a stackable le system architecture; and an optimistic view of update, in which any le or directory may be updated, so long as some copy is available. Con-icts are addressed when reconnection occurs. The system, which we call Ficus, is now operational. It has been constructed in a manner that can be added to an operating system that provides a faithful implementation of the vnode interface 10], even using conventional Unix le systems such as UFS for le storage. Various optimizations are possible through extensions to the basic structure. In the next section, we summarize the motivations for our replication work, providing a view of the goals of the research. The approach to modu-larity and optimistic operation are then described. Next, the actual Ficus project is summarized, and some observations about the utility of these approaches are ooered. Finally, a number of other issues are raised. In the general purpose and distributed ling environment , replication serves at least two important purposes: improving performance and increasing availability. Performance improvement occurs when access to data from a local storage medium is faster, cheaper, or less subject to congestion than from a remote store. In our view, a replication solution which does not take this consideration into
TurboTiling: Leveraging Prefetching to Boost Performance of Tiled Codes

Loop tiling or blocking improves temporal locality by dividing the problem domain into tiles and then repeatedly accessing the data within a tile. While this reduces reuse, it also leads to an often ignored side-effect: breaking the streaming data access pattern. As a result, tiled codes are unable to exploit the sophisticated hardware prefetchers in present-day processors to extract extra performance. In this work, we propose a tiling algorithm to leverage prefetching to boost the performance of tiled codes. To achieve this, we propose to tile for the last-level cache as opposed to tiling for higher levels of cache as generally recommended. This approach not only exposes streaming access patterns in the tiled code that are amenable for prefetching, but also allows for a reduction in the off-chip traffic to memory (and therefore, better scaling with the number of cores). As a result, although we tile for the last level cache, we effectively access the data in the higher levels of cache because the data is prefetched in time for computation. To achieve this, we propose an algorithm to select a tile size that aims to maximize data reuse and minimize conflict misses in the shared last-level cache in modern multi-core processors. We find that the combined effect of tiling for the last-level cache and effective hardware prefetching gives significant improvement over existing tiling algorithms that target higher level L1/L2 caches and do not leverage the hardware prefetchers. When run on an Intel 8-core machine using different problem sizes, it achieves an average improvement of 27% and 48% for smaller and larger problem sizes, respectively, over the best tile sizes selected by state-of-the-art algorithms.
Content-based Image Retrieval by Indexing Random Subwindows with Randomized Trees

We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted from a sample of images. We also present the possibility of updating the model as new images come in, and the capability of comparing new images using a model previously constructed from a different set of images. The approach is quantitatively evaluated on various types of images with state-of-the-art results despite its conceptual simplicity and computational efficiency.
Adaptive Block State Update Method for Separating Background

— In this paper, we proposed the robust mobile object detection method for light effect in the night street image block based updating reference background model using block state analysis. Experiment image is acquired sequence color video from steady camera. When suddenly appeared artificial illumination, reference background model update this information such as street light, sign light. Generally natural illumination is change by temporal, but artificial illumination is suddenly appearance. So in this paper for exactly detect artificial illumination have 2 state process. First process is compare difference between current image and reference background by block based, it can know changed blocks. Second process is difference between current image’s edge map and reference background image’s edge map, it possible to estimate illumination at any block. This information is possible to exactly detect object, artificial illumination and it was generating reference background more clearly. Block is classified by block-state analysis. Block-state has a 4 state (i.e. transient, stationary, background, artificial illumination). Fig. 1 is show characteristic of block-state respectively [1]. Experimental results show that the presented approach works well in the presence of illumination variance.
Remembering exploration based single-query probabilistic path planning

In this paper we introduce a novel planning algorithm which we call the remembering exploration based single-query probabilistic planning algorithm, RESPP. It uses a remembering exploration algorithm as extension method of the tree data structure of RESPP. RESPP discriminates between explored nodes and unexplored nodes in the tree data structure, and uses only the unexplored nodes for the extension of the tree data structure. Less number of used nodes indicates the low computation load of the planning algorithm as shown in the experimental analysis. We also introduce its variant algorithm, bidirectional RESPP. For comparing the performance of our algorithms with other planning algorithms, we had experiments in 2D and 3D configuration space and got better performance from our suggested algorithms.
C to Java migration experiences

With the growing popularity of the Java programming language for both client and server side applications in network-centric computing, there is a rising need for programming libraries that can be easily integrated into Java programs. In a previous paper, we surveyed current strategies for integrating C source code into Java programs, pointed out their weaknesses and presented goals for an improved migration approach. In this paper, we present the Ephedra approach to software migration and report on the results of three case studies transliterating C source code to Java using the Ephedra environment.