Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and characterization of a video-on-demand service workload

Video-on-Demand (VoD) and video sharing services account for a large percentage of the total downstream Internet traffic. In order to provide a better understanding of the load on these services, we analyze and model a workload trace from a VoD service provided by a major Swedish TV broadcaster. The trace contains over half a million requests generated by more than 20000 unique users. Among other things, we study the request arrival rate, the inter-arrival time, the spikes in the workload, the video popularity distribution, the streaming bit-rate distribution and the video duration distribution. Our results show that the user and the session arrival rates for the TV4 workload does not follow a Poisson process. The arrival rate distribution is modeled using a lognormal distribution while the inter-arrival time distribution is modeled using a stretched exponential distribution. We observe the "impatient user" behavior where users abandon streaming sessions after minutes or even seconds of starting them. Both very popular videos and non-popular videos are particularly affected by impatient users. We investigate if this behavior is an invariant for VoD workloads.
Real-time object detection using an evolutionary boosting strategy

This paper presents a brief introduction to the nowadays most used object detection scheme. From this scheme, we highlight the two critical points of this scheme in terms of training time, and present a variant of this scheme that solves one of these points. Our proposal is to replace the WeakLearner in the Adaboost algorithm by a genetic algorithm. In addition, this approach allows us to work with high dimensional feature spaces which can not be used in the traditional scheme. In this paper we also use the dissociated dipoles, a generalized version of the Haarlike features used on the detection scheme. This type of features is an example of high dimensional feature space, moreover, when we extend it to color spaces.
2M: un espace de conception pour i'interaction bi-manuelle

In the physical world, most human activities involve the use of two hands. As part of ubiquitous computing that aims at combining the physical and the digital worlds, understanding how to design two-handed interaction is therefore important. This article presents a new design space for two-handed interaction, called 2M. The 2M design space takes its foundations in two complementary research domains: multimodal interaction and two-handed interaction. On the one hand we enrich the multimodal design space to take into account the roles of the two hands. On the other hand, we extend the two-handed interaction design space to cover the temporal as well as fusion aspects of multimodal interaction. This article presents the resulting 2M design space and demonstrates its descriptive power. We finally study the generative capacity of 2M for designing new forms of two-handed interaction in the context of the NavGraphe project that focuses on the interactive manipulation of very large biological structures (e.g., RNA structures).
Macrohedging for Financial Institutions: Beyond Duration

This article proposes two extensions of current practice in applying duration gaps for macrohedging the equity position of a financial institution against interest rate risk. The first adjusts for the relative convexities of the asset and liability portfolios, which we call the convexity gap. When the second-order (convexity) condition is not satisfied, satisfying the first-order (duration) condition is not sufficient to achieve a perfect hedge against interest rate risk. The second extension shows how to modify duration gaps recognizing that banks and other financial institutions hold assets that are not default-free. Numerical examples demonstrate that ignoring this adjustment introduces considerable hedging error.
A Novel Scalable Texture Video Coding Scheme with GPCA

This paper proposes a novel SNR scalable coding method with the support of generalized principle component analysis (GPCA). This method encodes the low-pass and high-pass pictures generated by the MCTF decomposition with a hybrid linear model instead of traditional block-based DCT transform. GPCA is a powerful tool to identify the hybrid linear model in the textures, which segment the texture into heterogeneous regions, and then encode each region with PCA method. By keeping various proportions of PCA coefficients, and altering the quantization step sizes for different layers, a better scalable coding result can be achieved.
Solvation free energies of methane and alkali halide ion pairs: An expanded ensemble molecular dynamics simulation study

Solvation free energy for methane, dissolved both in pure water, water/methane mixture (14 mol % methane) and in aqueous NaCl solution, is calculated using the expanded ensemble molecular dynamics method. Dependencies due to system size and potential model are investigated. Results, using a simple one-site methane model, together with large enough system size, are found in very good agreement with experimental data, while calculations using a flexible five-site methane model give too high free energies. Also, the solvation energy for 20 different ion pairs of alkali halides is calculated in a systematic study. Very good overall agreement is found for the solvation energies of all the ion pairs. Calculations of solvation free energies provide a sensitive test of the used potential models.
Multimodal biometric recognition system using SURF algorithm

Personal identification systems based on biometrics are employed in many applications to enrich the security of person identification systems. A number of biometric traits are used in various applications. Among the different biometric traits, the palm vein images are used for person recognition because of its uniqueness. To improve the recognition rate of the person authentication system, we proposed the multimodal biometric recognition system, in which the palm print and palm vein images are fused together. Initially the captured palmprint and palm vein images are enhanced by Adaptive Histogram Equalization. Then the features are extracted from the palm print and palm vein images using Speeded Up Robust Feature algorithm. These extracted features are stored as feature vectors in the database. These feature vectors are combined using simple sum rule. Finally, the Sum of Absolute Difference distance measure is used to find the similarity between the test and database feature vectors.
Anti-windup synthesis for guaranteed L2 performance

We consider the problem of anti-windup compensation based on coprime factorizations, for asymptotically stable plants with an input saturation nonlinearity. Firstly, we state a stability criterion for the nonlinear system which guarantees finitegain L2 stability. Then we propose an H∞-based synthesis method which is guaranteed a priori to stabilize the system with a guaranteed level of L2 performance.
A Data Selective Segment Proportionate Affine Projection Algorithm for Echo Cancellation

The segment proportionate normalized least mean-square algorithm (SPNLMS) has been proposed with the objective of improving the adaptation convergence rate when modeling high-order sparse impulse response systems as compared to their conventional counterparts. When the excitation signal is colored, especially the speech, the convergence performance of SPNLMS algorithm demonstrates slow convergence speed. The segment proportionate affine projection (SPAP) algorithm is an useful adaptive filter to improve the convergence speed of PNLMS-type filter by updating the weight vector based on several previous input vectors, Unfortunately, the SPAP algorithm obtains a faster convergence of the SPNLMS comes at the expense of an increase in the computational complexity linked to the amount of reuses employed. For this reason, the idea of proportionate adaptation combined with the framework of set-membership filtering in an attempt to reduce computational complexity of algorithm is presented. The proposed algorithm allows the reduction of the frequency of updates of the filter coefficients, where the filter coefficients are updated such that the output estimation error is upper bounded by a pre-determined threshold. Simulations show that its overall complexity is lower compared to fully updated SPAP and its performance is close to that of the SPAP algorithm.
Time for a Radical Reappraisal of Tourist Decision Making? Toward a New Conceptual Model

General models of tourist decision making have been developed to theorize tourist decision processes. These models have been based on the premise that tourists are rational decision makers and utility maximizers. Further, these models have been operationalized through input–output models to measure preferences and behavioral intentions. The extent that they remain viable to explain and predict tourist behavior as tourism markets mature however is uncertain. This review article critiques these approaches and proposes a new general model based on dual system theory to account for different types of choice strategies, the constructive nature of preferences, and to recognize the individual and contextual factors that influence choice processes. The article argues that a general tourist choice model should integrate the psychological processes that determine choice strategies, or heuristics, and consider choice context. These include individual differences, task-related factors, and principles determining system engagement. Future research and practical implications are outlined.
The Stock Exchanges of the United States and Europe: Automation, Globalization, and Consolidation

The stock market has become an important part of our lives. The New York Stock Exchange ("NYSE") estimates that in 1998, 84 million Americans, or 44% of the adult population, owned shares of stock directly or through mutual funds or pension plans. ' And adults are not the only people who own shares. The New York Times reports that 200,000 American teenagers may be speculating in the stock market and that brokerage-firm officials are giving investment seminars to third graders, thus assuring the existence of the next generation of investors.2 Stock market fever has also spread to Europe, where many people who had previously put their savings in bank accounts or real estate are now buying shares of stock.3 During the past decade, the equity markets of the west-
Model Uncertainty and Forecasting, a Practitioner Point of View

Should we run one regression forecast? We confront the Bayesian Model Averaging (BMA) with two major automatic forecasting procedures: the dynamic factor model (DFA) and the ”general-to-specific” (GETS) algorithm, to gauge whether introducing Bayesian model uncertainty significantly improves forecasting. We consider those methods, including the ”Median BMA” strategy to forecast quarterly US GDP growth on an original monthly basis. All models consistently select the same coincident and leading series, giving a robustness check of the best US GDP predictors. We also assess relative model performance in and out-of-sample using forecast accuracy tests and directional change metrics. Results suggest that the simple BMA approach, the GETS strategy or the DFA model tend to be equivalent.
In Their Own Words: Stories of Emotional Labor from the Library Workforce

This research investigates emotional labor events in libraries. Twenty-three librarians kept work diaries for 5 days recording details of interactions with customers and fellow employees in which the emotions the librarians were feeling were in contrast with the emotional expressions they believe their organization desires. Participants reported 83 events, which were analyzed by type of event, interaction partner, feelings and behaviors that were displayed, outcomes that resulted from the event, and the reflection participants provided on how they and their organization could have handled the situation differently. The findings show that emotional labor exists primarily when interacting with customers, colleagues, and superiors. Librarians performed significantly more surface acting than deep acting in regulating their emotional expressions and reported feeling a wide range of emotions during the events. The findings lead to some suggestions for library managers to consider in order to lessen the negative effects of emotional labor in the workplace.
A rule generation model using S-PSO for Misuse Intrusion Detection

Facing the increasingly important problem of computer security, Intrusion Detection System (IDS) has become an essential mechanism to protect computer and network system from malicious behaviors. In pursing high accuracy of detection rate, research in IDS is focusing on rule generation. Developing rules manually through human analysis on attack signatures often results in meaningful but costly work as it is difficult to define threshold. In this paper, we present a rule generation model for Misuse Intrusion Detection using a combination of statistical approach and particle swarm optimization (PSO) to achieve the rapid feature selection and rule optimization. Experimental results prove the effectiveness and robustness of the model we proposed, rules generated from which show both a high classification rate and a low false positive rate.
Survey on Security Issues and Strategies of Cloud Computing

– Clod computing has brought new changes and opportunities in IT Industry. The cloud Computing is a new computing model which comes from grid computing, Distributed computing, Parellel computing and visualization technology. Cloud computing security has become a hot topic in industry and academic research. The focus of this paper is, a detailed view of the cloud computing security and its strategy. Privacy of data and services used in cloud computing are the key security issues. Not only one security issue can not resolve the cloud computing security problems, so many new technologies must be introduced for protecting the data in cloud computing system.
An integrated model of intrusion detection based on neural network and expert system

Intrusion detection technology is an effective approach to dealing with the problems of network security. In this paper, it presents an intrusion detection model based on neural network and expert system. The key idea is to aim at taking advantage of classification abilities of neural network for unknown attacks and the expert-based system for the known attacks. We employ data from the third international knowledge discovery and data mining tools competition (KDDcup'99) to train and test the feasibility of our proposed neural network component. According to the results of our experiment, our model achieves 96.6 percent detection rate for DOS and probing intrusions, and less than 0.04 percent false alarm rate. Expert system can detect R2L and U2R intrusions more accurately than neural network. Therefore, hybrid model improves the performance to detect intrusions
Yersinia enterocolitica YopT and Clostridium difficile Toxin B Induce Expression of GILZ in Epithelial Cells

Glucocorticoid induced-leucine zipper (GILZ) has been shown to be induced in cells by different stimuli such as glucocorticoids, IL-10 or deprivation of IL-2. GILZ has anti-inflammatory properties and may be involved in signalling modulating apoptosis. Herein we demonstrate that wildtype Yersinia enterocolitica which carry the pYV plasmid upregulated GILZ mRNA levels and protein expression in epithelial cells. Infection of HeLa cells with different Yersinia mutant strains revealed that the protease activity of YopT, which cleaves the membrane-bound form of Rho GTPases was sufficient to induce GILZ expression. Similarly, Clostridium difficile toxin B, another bacterial inhibitor of Rho GTPases induced GILZ expression. YopT and toxin B both increased transcriptional activity of the GILZ promoter in HeLa cells. GILZ expression could not be linked to the inactivation of an individual Rho GTPase by these toxins. However, forced expression of RhoA and RhoB decreased basal GILZ promoter activity. Furthermore, MAPK activation proved necessary for profound GILZ induction by toxin B. Promoter studies and gel shift analyses defined binding of upstream stimulatory factor (USF) 1 and 2 to a canonical c-Myc binding site (E-box) in the GILZ promoter as a crucial step of its trans-activation. In addition we could show that USF-1 and USF-2 are essential for basal as well as toxin B induced GILZ expression. These findings define a novel way of GILZ promoter trans-activation mediated by bacterial toxins and differentiate it from those mediated by dexamethasone or deprivation of IL-2.
Multi-objective optimization for initial virtual machine placement in cloud data center

Virtual machine (VM) placement in the cloud infrastructure is an important problem that remains to be effectively addressed. Fine-grained virtual machine resource allocation and reallocation are possible in order to meet the performance targets of applications running on virtual machines. On the other hand, these capabilities create demands on system management, especially for cloud data center. In this paper, a management framework for virtual machine placement in an IaaS environment was firstly presented, then the initial VM placement problem was defined as a multi-objective optimization problem and finally multi-objective optimization for initial virtual machine placement based on Ant Colony Optimization (ACO) was proposed to determine VM placement strategy. The proposed algorithm is a distributed optimization method, which is beneficial to parallel computing. It has the positive feedback mechanism, and through the pheromone is constantly updated, it can get the optimal solution by the efficient convergence. Experimental results show that compared to heuristic method and genetic algorithm, the proposed algorithm can achieve the optimal balance in multiple conflict objectives, which effectively reduces the resource wastage and power consumption, and minimize violation of SLA.
Testing for Linear Cointegration Against Smooth-Transition Cointegration

This paper studies a smooth-transition (ST) type cointegration. The proposed ST cointegration allows for regime switching structure in a cointegrated system, and nests the linear cointegration developed by Engle and Granger (1987) and the threshold cointe- gration studied by Balke and Fomby (1997). Based on a class of vector ST cointegrating regression models, we develop F-type tests to examine linear cointegration against ST cointegration. The null asymptotic distributions of the tests with choosing various sta- tionary transition variables are derived. Finite-sample distributions of those tests are studied by Monto Carlo simulation. The small-sample performance of the tests are also included and it is shown that our F-type tests have a better power when the system contains a ST cointegration than that when the system is linearly cointegrated. Two empirical examples for the purchasing power parity (PPP) data are illustrated by apply- ing the testing procedures in this paper. It is found that, for each of them, there is no linear cointegration in the system, but there exits a ST cointegration in the system.
Promoter Methylation Status Modulate the Expression of Tumor Suppressor (RbL2/p130) Gene in Breast Cancer

Background Aberrant expression of tumor suppressor genes may correspond to the abnormal cell development and tumorigenesis. Rbl2/p130, a member of retinoblastoma family of proteins, has growth suppressive properties. Numerous studies reported de-regulation of Rbl2/p130 in various types of cancer as a consequence of a number of genetic alterations. However, role of epigenetic mechanisms like DNA methylation in Rbl2/p130 expression remains elusive. Methods In the current study, 76 breast cancer tumors along with normal tissues (n = 76), blood (n = 76) of respective individuals and control blood (n = 50) were analyzed. Rbl2/p130 expression was analyzed by quantitative real time PCR (syber green method). Promoter methylation status was studied through methylation specific PCR of bisulfite converted genomic DNA. Data was analyzed using various statistical tests. Results We report significantly reduced Rbl2/p130 expression (P = 0.001) in tumors tissues as compared to control samples. Similarly, Rbl2/p130 expression varies with age and disease stages (P = 0.022), which suggest its involvement in tumor progression. Aberrant promoter methylation (Δmeth) was found in almost all the diseased samples and that was significantly different (P<0.001) with control samples. Similarly, methylation status varies significantly with tumor progression stages (P = 0.022). Hyper-methylation was observed at -1, +3, +15 and +75 of Rbl2/p130 promoter flanking around the TSS. Statistical analysis revealed that Rbl2/p130 expression negatively correlates to its promoter methylation (r = -0.412) in tumor tissues. Our results reflect an epigenetic regulation of Rbl2/p130 expression in breast cancer. This highlights the importance of Rbl2/p130 promoter methylation in breast cancer pathogenesis.
Implications of declining government debt for financial markets and monetary operations in Australia

Like a number of countries, Australia has undergone a substantial fiscal consolidation in recent years and a consequent reduction in government debt. On a cash basis, the Commonwealth government moved into surplus in the 1997/98 fiscal year, and has since run an average surplus of around 1% of GDP. Official projections over the next three years are for surpluses of similar magnitude to be maintained, resulting in substantial further reductions in the government’s net debt.
The DSO Feature Based Point Cloud Simplification

This study proposes an effective low-error point cloud simplification method to retain the physical features of the models. The value of Discrete Shape Operator (DSO) is adopted to extract the features points of the models, and those are postponed to simplify. The value of DSO is defined as the discrete sum over the directional curvature and torsion. The proposed method improves the Quadric Error Metric of vertex pair contraction, it not only effectively simplifies the point cloud model and keeps the features of object model, but also decreases the preprocessing time cost associated with feature analysis. This study also proposes a method to obtain unique simplified model for each model and the time cost involved in calculating DSO is about 17.29% of the execution time. The unique simplified model obtained by this study can significantly reduce the computation cost about 72.72% than mesh simplification which reconstruct original points first.
A type and effect system for deadlock avoidance in low-level languages

The possibility to run into a deadlock is an annoying and commonly occurring hazard associated with the concurrent execution of programs. In this paper we present a polymorphic type and effect system that can be used to dynamically avoid deadlocks, guided by information about the order of lock and unlock operations which is computed statically. In contrast to most other type-based approaches to deadlock freedom, our system does not insist that programs adhere to a strict lock acquisition order or use locking primitives in a block-structured way. Lifting these restrictions is primarily motivated by our desire to target low-level languages, such as C with pthreads, but it also allows our system to be directly applicable in optimizing compilers for high-level languages, such as Java. To show the effectiveness of our approach, we have also developed a tool that uses static analysis to instrument concurrent programs written in C/pthreads and then links these programs with a run-time system that avoids possible deadlocks. Although our tool is still in an early development stage, in the sense that currently its analysis only handles a limited class of programs, our benchmark results are very promising: they show that it is not only possible to avoid all deadlocks with a small run-time overhead, but also often achieve better throughput in highly concurrent programs by naturally reducing lock contention.