Miracle-dz/UniFAR-FTUs huggingface dataset:
QoS Provisioning and Pricing in Multiservice Networks:Optimal and Adaptive Control over Measurement-based Scheduling

XU, PENG. QoS Provisioning and Pricing in Multiservice Networks: Optimal and Adaptive Control over Measurement-based Scheduling. (Under the direction of Professor Michael Devetsikiotis). In order to ensure efficient performance under inherently and highly variable traffic in multiservice networks, we propose a generalized adaptive and optimal control framework to handle the resource allocation. Even though this framework addresses rigid Quality of Service concerns for the deterministic delay-bound classes by reserving part of the link capacity and employing appropriate admission control and traffic shaping schemes, our research actually emphasizes the adaptive and optimal control of the shared resources for the flexible delay-bound classes. Therefore, the resource allocation is delivered by a subsystem of this generalized framework, the measurement-based optimal resource allocation (MBORA) system. By applying a simple threshold (θ1, θ2) policy, we first validate the advantages of the adaptivity of our proposed framework through extensive simulation results. Then we introduce a generalized profit-oriented formulation inside decision module of MBORA system, that supplies the network provider with criteria in terms of profit, by leveraging the utility charge revenue and delay-incurred cost. The optimal resource allocation will be affected by the various types of pricing models together with the different levels of service guarantee constraints. As a case study, we investigate this generalized profit-oriented formulation under generalized service models. Combining further with a linear pricing model subject to average queue delay constraints, we propose a fast algorithm for online dynamic and optimal resource allocation under this specific scenario. Finally, we propose a delay-sensitive nonlinear pricing model for the generalized profit-oriented formulation, that realizes two-tier delay differentiation. By better understanding the fluid queueing model, we propose a generalized solution strategy for linear, nonlinear or mixed pricing models that is free of the dimensionality problem and amenable to online implementation. QoS Provisioning and Pricing in Multiservice Networks: Optimal and Adaptive Control over Measurement-based Scheduling
SQL Injection Detection with Composite Kernel in Support Vector Machine

Modern web application systems are generally consisted of database systems in order to process and store business information. These systems are highly interesting to hackers as they contain sensitive information and the diversity and amount of attacks severely undermine the effectiveness of classical signature-based detection. In this work we propose a novel approach for learning SQL statements and apply machine learning techniques, such as one class classification, in order to detect malicious behavior between the database and application. The approach incorporates the tree structure of SQL queries as well as input parameter and query value similarity as characteristic to distinguish malicious from benign queries. We develop the learning system integrated in PHP and demonstrate the usefulness of our approach on real-world application.
Feature Extraction Based on Genetic Algorithm and Artificial Neural Network

Feature extraction is an important aspect of pattern recognition.Feature extraction can improve the efficiency and quality of classifying.This paper combines GA and ANN to extract important feature from features,and remove unimportant,and obtain a useful subset.It uses the sonar data of UCI machine learning database to do this experiment.The result shows that our method can extract the subset more completely than others.
Heterocycles [h]-Fused onto 4-Oxoquinoline-3-carboxylic Acid, III. Facile Synthesis and Antitumor Activity of Model Heterocycles [a]-Fused onto Pyrido[2,3-f]quinoxaline-3-carboxylic Acids

Direct interaction between 7-chloro-1-cyclopropyl-6-fluoro-8-nitro-4-oxo-1,4-dihydroquinoline-3-carboxylic acid and each of (S)-proline, (2S,4R)-4-hydroxyproline and (5)-1,2,3,4-tetrahydroisoquinoline-3-carboxylic acid in hot aqueous ethanolic NaHCO 3 yielded the corresponding optically pure N-(4-oxoquinolin-7-yl)-α-amino acids. The latter derivatives underwent reductive lactamization upon treatment with Na 2 S 2 O 4 in aqueous ethanol to afford moderate yields of the respective pyrido[2,3-/]quinoxaline-3-carboxylic acid [fused]- to tetrahydropyrrolo[1,2-a]-, tetrahydrohydroxylpyrrolo[1,2-a]-and tetrahydroisoquin-olino[2,3-a]heterocyclics 10-12, respectively. The antitumor activity against four human tumor cell lines showed that 10-12 displayed high levels of cytotoxicity as compared with Cisplatin. Interestingly, these compounds were more potent against breast carcinoma cell lines (MCF-7 and T-47D) than the lymphoid origin tumor cell lines (Jurkat and BHL-89). In particular, the (S)-proline derivative 10 exhibited preferential cytotoxicity to adherent cells (IC 50 = 0.5 μM), indicative of better potential in blocking the growth of solid tumors rather than the disseminated ones.
Analysis of intracellular reactive oxygen species by micellar electrokinetic capillary chromatography with laser-induced-fluorescence detector

Abstract Reactive Oxygen Species (ROS) are an important part of the normal cell growth cycle and play essential roles in many biological functions. Many techniques have been developed to detect and measure the amount of ROS present in cells. These techniques include spectrophotometry, high performance liquid chromatography (HPLC), capillary electrophoresis with laser-induced-fluorescence detector (CE-LIF) and capillary electrophoresis-on-a-chip with LIF. As ROS has a short half-life outside of the cell, various fluorescent probes, such as dihydrorhodamine 123 (DHR 123), that are membrane permeable have been used in the detection of intracellular ROS. In this paper, micellar electrokinetic capillary chromatography (MEKC) was coupled with LIF detector. Fluorescent probe, 3′-(p-aminophenyl)-fluorescein (APF), was used to detect specific ROS in Chinese Hamster Ovary (CHO) suspension cells as well as attached mouse bone marrow-derived dendritic cells (BMDCs). Separation buffer composition was optimized at a concentration of 25 mM tetraborate buffer with 50 mM sodium dodecyl sulfate at pH 9.3 and lysis of the cells was done successfully with a buffer of 70% ethanol and 0.1mM sodium dodecyl sulfate using a cell amount of 1 × 107. ROS in cells was successfully analyzed by MEKC-LIF method developed, with acceptable RSD for time at 1.54% and area at 2.81%. GRAPHICAL ABSTRACT
The Impact of Computer Self-Efficacy and System Complexity on Acceptance of Information Technologies

The acceptance and use of information technologies by target users remain a key issue in information systems (IS) research and practice. Building on past research and integrating computer self-efficacy (CSE) and perceived system complexity (SC) as external variables to the technology acceptance model (TAM), this study examines the direct and indirect effects of these two factors on system eventual acceptance and use. Overall, both CSE and SC demonstrated significant direct effects on perceived usefulness and perceived ease of use as well as indirect effects on attitude and behavioral intention. With respect to TAM’s variables, perceived ease of use demonstrated a stronger effect on attitude than that of perceived usefulness. Finally, attitude demonstrated a non-significant impact on behavioral intention. Several implications for research and practice can be drawn from the results of this study.
Improved Partial Instrumentation for Dynamic Taint Analysis in the JVM

Dynamic taint tracking is an important field of study with manyJava-based tools and systems created to implement it, includingPhosphor, a general purpose taint tracking tool designed forcommodity JVMs like Oracle and OpenJDK. Phosphor works byinstrumenting core Java libraries and the entire application bytecodewith operations to accurately propagate taint information. Prior workstrived to reduce the performance overhead of Phosphor by doingpartial instrumentation. The analysis that determined which parts ofthe program to instrument was effective but flawed.This paper aims to improve that analysis and further reduce theperformance overhead by instrumenting less of the program. We use thePetablox program analysis tool and custom Datalog rules to perform aninformation flow analysis that better models Phosphor's behavior,including calls across native library boundaries. We find that weobtain a reduction in the amount of a program that needs to beinstrumented by 79.9%.
On Reliable Task Assignment for Spatial Crowdsourcing

The large quantity of mobile devices equipped with various built-in sensors and the easy access to the high-speed wireless networks have made spatial crowdsourcing receive much attention in the research community recently. Generally, the objective of spatial crowdsourcing is to outsource location-based sensing tasks (e.g., traffic monitoring and pollution monitoring) to ordinary mobile workers (e.g., users carrying smartphones) efficiently. In this paper, we study a reliable task assignment problem for spatial crowdsourcing in a large worker market. Specifically, we use worker confidence to represent the reliability of successfully completing the assigned sensing tasks, and we formulate two optimization problems, maximum reliability assignment (MRA) under a recruitment budget and minimum cost assignment (MCA) under a task reliability requirement. We reveal the special structure properties of these problems, based on which we design effective approaches to assign tasks to the most suitable workers. The performances of the proposed algorithms are verified by theoretic analysis and experimental results on both real and synthetic datasets.
Optimization Methods For Large Scale Systems With Applications

Thank you very much for downloading optimization methods for large scale systems with applications. Maybe you have knowledge that, people have look numerous times for their favorite novels like this optimization methods for large scale systems with applications, but end up in harmful downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they juggled with some malicious bugs inside their computer.
Development of an Autonomous Indoor Phenotyping Robot

Abstract. In order to fully understand the interaction between phenotype and genotype x environment to improve crop performance, a large amount of phenotypic data is needed. Studying plants of a given strain under multiple environments can greatly help to reveal their interactions. To collect the labor-intensive data required to perform experiments in this area, an indoor rover has been developed, which can accurately and autonomously move between and inside growth chambers. The system uses mecanum wheels, magnetic tape guidance, a Universal Robots UR 10 robot manipulator, and a Microsoft Kinect v2 3D sensor to position various sensors in this constrained environment. Integration of the motor controllers, robot arm, and a Microsoft Kinect (v2) 3D sensor was achieved in a customized C++ program. Detecting and segmenting plants in a multi-plant environment is a challenging task, which can be aided by integration of depth data into these algorithms. Image-processing functions were implemented to filter the depth image to minimize noise and remove undesired surfaces, reducing the memory requirement and allowing the plant to be reconstructed at a higher resolution in real-time. Three-dimensional meshes representing plants inside the chamber were reconstructed using the Kinect SDK‘s KinectFusion. After transforming user-selected points in camera coordinates to robot-arm coordinates, the robot arm is used in conjunction with the rover to probe desired leaves, simulating the future use of sensors such as a fluorimeter and Raman spectrometer. This paper shows the system architecture and some preliminary results of the system, as tested using a life-sized growth chamber mock-up. A comparison of using raw camera coordinates data and using KinectFusion data is presented. The results suggest that the KinectFusion pose estimation is fairly accurate, only decreasing accuracy by a few millimeters at distances of roughly 0.8 meter.
A Brief survey on Intrusion Detection System for WSN

— Wireless Sensor Networks (WSNs) are the collection of autonomous sensor nodes spread out in various hostile environments statically or dynamically depend upon the application to monitor temperature, sound, pressure, etc. The security is a major component in wireless sensor networks. It degrades the performance of the network. The intrusion detection system is the solution to detect different kinds of attacks occurring on sensor nodes.Wireless sensor networks (WSN) are a group of tiny devices. These tiny devices have few energy, computational power, transmission range and memory. The more modern networks are bi-directional, also enabling control of sensor activity. At the beginning wireless sensor networks was developed for military applications such as battlefield surveillance; nowadays such networks are mostly used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring, and so on. WSN has been used in many applications such habitat monitoring, building monitoring, smart grid and pipeline monitoring. Intrusion detection model takes advantage of cluster-based architecture to reduce energy consumption. This model uses anomaly detection based on support vector machine (SVM) algorithm and a set of signature rules to detect malicious behaviours and provide global lightweight IDS. Though a few restricted survey works on this topic have already been done but there is a great need of performing a detailed study on the vital aspects so that the IDS is analyzed in all the different angles. Three most intrusion detection techniques explored in this paper. Such as Anomaly detection, Misuse detection and Specification based detection. Here in this review paper various attacks on Wireless Sensor Networks and existing Intrusion detection techniques are discussed to detect the compromised node/s.
Multistage Model for Analysis of Opinions

The task of opinion mining has attracted interest during the last years. This is mainly due to the vast availability and value of opinions on-line and the easy access of data through conventional or intelligent crawlers. In order to utilize this information, algorithms make extensive use of word sets with known polarity. We consider the above issues by proposing an approach for a multistage model for analysis of opinions using sentiment orientation with conjunction and iteration at multilevel.
Adjustment and Development of Health User’s Mental Model Completeness Scale in Search Engines

Introduction: Users’ performance and their interaction with information retrieval systems can be observed in development of their mental models. Users, especially users of health, use mental models to facilitate their interactions with these systems and incomplete or incorrect models can cause problems for them . The aim of this study was the adjustment and development of health user’s mental model completeness scale in search engines.Method: This quantitative study uses Delphi method. Among various scales for users’ mental model completeness, Li’s scale was selected and some items were added to this scale based on previous valid literature. Delphi panel members were selected using purposeful sampling method, consisting of 20 and 18 participants in the first and second rounds, respectively. Kendall’s Coefficient of Concordance in SPSS version 16 was used as basis for agreement (95% confidence). Results:The Kendall coefficient of Concordance (W) was calculated to be 0.261(P-value<0.001) for the first and 0.336 (P-value<0.001) for the second round. Therefore, the study was found to be statistically significant with 95% confidence. Since the increase in the coefficient in two consecutive rounds was very little (equal to 0.075), surveying the panel members were stopped based on second Schmidt criterion and Delphi method was stopped after the second round. Finally, the dimensions of Li’s scale (existence and nature, search characteristics and levels of interaction) were confirmed again, but “indexing of pages or websites” was eliminated and “Difference between results of different search engines”, “possibility of access to similar or related webpages”, and “possibility of search for special formats and multimedia” were added to Li’s scale.Conclusion: In this study, the scale for mental model completeness of health users was adjusted and developed; it can help the designers of information retrieval systems in systematic development of these systems and can also help librarians and informatics experts in recognizing the necessary trainings for users in order to improve their information retrieval skills. Also, as a valid and adapted scale for Iranian universities of medical sciences, it can be used for investigating completeness level of health information users’ mental models of search engines.Keywords: Mental Model Completeness Scale, Health users, Search engines
Extending VHDL for state based specifications

Statecharts can complement VHDL, in particular for system level design. We present what would be needed to extend VHDL by state based specification, sharing its syntax and the fundamental notion of time. The resulting integration is very tight, allowing, by comparison to existing approaches, more precise control for synthesis, incorporation of library components, multiple statechart instantiations and smooth paradigm switches. The language is being developed and implemented in the ESPRIT project FORMAT, and has been successfully employed for formal verification against timing diagram specifications.
Architecture GPU pour radar de surveillance spatial et pour radar aéroporté

L'augmentation de la complexite des systemes radar ainsi que le traitement du signal associe impliquent un hausse significative de la charge calculatoire. Le challenge pour les radars bases au sol est la reduction du cout de l'infrastructure de calcul alors que pour les radars aeroportes, l'objectif est d'implementer un traitement lourd, comme le STAP ou le SAR, tout en reduisant la taille du systeme et la consommation 'electrique. Dans un exemple concret de radar de surveillance spatiale, nous analysons toutes les etapes du traitement tournant sur CPU et sur GPU, et nous les comparons au systeme de traitement actuel. Pour l'embarque, nous presentons les avantages d'un traitement STAP sur GPU compare a un processeur generaliste traditionnel. Nous montrons aussi que l'utilisation d'une configuration originale ARM-GPU augmente encore l'efficacite energetique par rapport a une architecture classique x86-GPU.
A secure mobile group buying system based on the concept of spontaneity

Purpose The purpose of this paper is to propose a secure mobile group-buying system for application on spontaneous group-buying websites. Design/methodology/approach The proposed mechanism uses the Schnorr digital signature technique. Findings The proposed scheme is able to satisfy many security requirements. The computation costs of each phase and party were also analyzed, and the communication cost of each phase was calculated. Originality/value The results indicate that the proposed scheme is suitable for real-world application.
Ascertaining Trust of Information Transmitted During a Disaster

During a disaster, especially at the beginning, a huge amount of information is being communicated by the victims to the authority/rescuer. With the presence of ICT applications and smartphone devices, the communications can be carried out easily. However, these ease of communication created one critical issue that is the reliability of the transmitted information. One of the potential solutions is to use trust algorithm to identify the trusted information. The trust engine can help to filter and verify the reliability and validity of the transmitted information. With the ability to identify trusted information, the authority and rescuer would be able to channel and prioritize their rescue efforts to the more critical disaster areas. A prototype, running on Android platform and Web server, was developed to demonstrate the proposed solution
MON: Design and Implementation of Management Overlay Networks for Distributed Systems

While large-scale distributed clusters such as PlanetLab and TeraGrid are being increasingly used to run complex applications, there is a paucity of software that provides simple management functions to developers and deployers of such applications. This paper develops Management Overlay Networks (MONs), that can provide the application deployer the capability (1) to execute short-term (on-demand) commands that query or monitor the status of a distributed group of hosts, and (2) to execute medium-term commands that enable the persistent running of an application in spite of node failure. The MON mechanisms we develop require minimal operator involvement; further, their simplicity, scalability, lightweight-ness and fault-tolerance enable them to run side-by-side with existing distributed applications. As a proof of concept, this paper presents the design of six commands for on-demand monitoring, as well as a Restart mechanism for persistent application execution. We present experimental results from our implementations running on the PlanetLab cluster, with the data being taken in the days leading up to the SOSP deadline. We also present simulation results for larger system sizes. The concept of MONs addresses concerns and opinions raised by both peer-to-peer networking and Grid computing communities in the recent past.
Information Predictions Based on Node Attributes of Social Media

The features of social media,such as node influence are important in information prediction.A new model with attribute,number and views of nodes(ANV) is proposed for event prediction.Experiment is designed to predict the performance of new released movies using back propagation(BP) neural network algorithm with the data of Sina Microblog.Compared to other models,when considering the node attribution,the proposed has higher prediction accuracy.
Spike Separation from EEG/MEG Data Using Morphological Filter and Wavelet Transform

In the analysis of epileptic electroencephalographic (EEG) and magnetoencephalography (MEG) data, spike separation is diagnostically important because localization of epileptic focus often depends on accurate extraction of spiky activity from the raw data. In this paper, we present a method to automatically extract spikes using the wavelet transform combined with morphological filtering based on a circular structuring element. Our experimental results have shown that this method is highly effective in spike separation. Comparisons with the wavelet, bandpass filter, empirical mode decomposition (EMD), and independent component analysis (ICA) methods show that the new method is more effective in estimating both spike amplitudes and locations