Miracle-dz/UniFAR-FTUs huggingface dataset:
Porting LibRIPC to iWARP

Cloud computing has become a major economical factor in the recent development of computer systems. Companies tend to draw computational power for their data processing from the cloud, instead of hosting their own servers, in order to save costs. The providers of cloud systems run huge and cost efficient data centers, profiting from economies of scale. In order to further reduce the costs, these data centers currently move to more power efficient systems. Many applications that are now used in the cloud were not created with a cloud environment in mind, but have been "moved" to the cloud. These applications usually use TCP/IP for their intercommunication mechanisms, which is the defacto standard for current applications in the Internet. Unfortunately, these TCP/IP based implementations rely on the Berkeley socket API, which does not match the demands of power efficient systems. Sockets introduce much CPU involvement, taking away precious computational time from the real applications. Several specialized network architectures, such as InfiniBand, overcome this issue. They make use of offloading techniques, such as RDMA, in which the network hardware takes over the responsibility of protocol processing and I/O. As a drawback, the equipment for these fabrics is usually expensive, due to their high specialization. Furthermore, the corresponding interfaces for these architectures are extensive, making it expensive to adapt existing applications. LibRIPC is a network library introduced to overcome the efforts that are accompanied by the usage of these architecture’s interfaces. It provides a neat, message based interface, which abstracts from any specifics of the underlying network architecture. This provides ease of integration and portability to other network fabrics, and yet does not sacrifice performance. Currently, there exist a prototype implementation of LibRIPC for InfiniBand. In this thesis, we present our port of LibRIPC to iWARP, which enables the library for its use over Ethernet. Ethernet is one of the most cost efficient network fabrics and provides capabilities for high performance networking since the upcoming of standards that specify data rates of 10 Gbit/s and above. iWARP is a protocol stack that is based upon TCP/IP, thus compatible for use in Internet based WANs. Yet it is completely independent from the socket API and its correspond-
Techniques for Virtual Machine Placement in Clouds

Traditional data centers offer many services hosted on dedicated physical servers, which are often under-utilized in terms of resources used. Virtualization allows these data centers to consolidate their services onto a lesser number of physical servers than originally required. There may be hundreds of such services running in a single data center. In our work, we have implemented different heuristics for server consolidation which work with such large number of services being hosted in the data center. We have also presented a study of approaches adopted in literature which implicitly assume small number of services leading to potentially unscalable solutions. The final goal of our project is to come up with a scalable system that takes the resource requirements of virtual machines and the number of physical machines available as input and gives a mapping of virtual machines to physical machines which considers any virtualization related scenarios that arise in data centers.
Computer vision algorithms for autonomous mobile robot map building and path planning

Describes an algorithm for autonomous mobile robot map building and path planning in an unknown environment. One potential application is that of the AMR transporting an individual in response to a verbal command. i.e. an intelligent wheelchair. The sensor system used by the robot is stereo vision. We use a view-based, probabilistic approach, whereby the AMR learns its environment as a series of visually different locations. The algorithm is particularly robust, and notably the robustness does not vary with the size of the learnt environment. This enables large environments to be learnt reliably. This was previously an unsolved problem. The algorithm enables the AMR to learn an unknown environment without the need for internal distance measurements (odometry), using feature observations only. The algorithm allows an AMR to learn a new environment with a single passing and once the environment has been mapped, its topology can be inferred from the stored map. Hence all possible paths through that environment are navigable, even paths previously untraversed. Furthermore, using a limited amount of real-world distance information, derived from stereo cameras, the robot is able to construct a robust internal representation of its position with respect to visible features, and thus perform accurate intra-region localisation.
Maximization of power dissipation in large CMOS circuits considering spurious transitions

With the high demand for reliability and performance, accurate estimation of maximum power dissipation in CMOS circuits is essential to determine the IR drop on supply lines and to optimize the power and ground routing. Unfortunately, the problem of determining the input patterns to induce maximum current and, hence, the maximum power, is NP complete. Even for circuits with small number of primary inputs (PIs), it is CPU time intensive to conduct exhaustive search in the input vector space. In this paper we present an automatic test generation (ATG)-based technique to efficiently generate tight lower bounds of the maximum power for large CMOS circuits under nonzero gate delays. Power dissipation due to spurious transitions has been considered by incorporating static timing analysis into the estimation process. Experiments were performed on ISCAS and MCNC benchmarks. Results show that the ATG-based technique is superior to the traditional simulation-based technique in both speed and performance. On average, for sequential circuits having over 10000 gates (ISCAS-89 benchmarks), the ATG-based approach executes 261 times faster, and generates a lower bound which is 1.8 times better compared to simulation based approaches.
Tail Call Optimization in the Java HotSpot™ VM

Many programming language implementations compile to Java bytecode, which is executed by a virtual machine (e.g the Java HotSpot TM VM). Among these languages are functional languages, which require an optimization that guarantees that certain kinds of method calls do not cause the execution stack to grow unlimitedly. This optimization is called tail call optimization and is currently not supported by the HotSpot TM VM. Implementations of functional languages have to resort to alternative techniques to guarantee that the stack space does not increase unboundedly. These techniques complicate the implementation and also incur a performance penalty. This thesis presents techniques for supporting tail call optimization in the Java HotSpot TM Virtual Machine. Our optimization is implemented in the interpreter, the client compiler and the server compiler. Tail call optimization normally removes stack frames to guarantee that the stack space stays bounded. However, some stack frames are required for the Java access security mechanism to work and hence cannot be removed. The virtual machine features a mechanism called deoptimization that allows one to rewrite stack frames. We describe an approach that uses the deoptimization infrastructure to compress stack frames when tail call optimization was disabled because of the security mechanism. This approach allows a series of tail calls to execute in bounded stack space in the presence of a stack-based security mechanism.
Short-Term and Medium-Term Load Forecasting for Jordan's Power System

Several electric power companies are now forecasting electric loads based on conventional methods. However, since the relationship between loads and factors influencing these loads is nonlinear, it is difficult to identify its nonlinearity by using conventional methods. Most of papers deal with 24-h-ahead load forecasting or next day peak load forecasting. These methods forecast the demand power by using forecasted temperature as forecast information. But, when the temperature curves change rapidly on the forecast day, loads change greatly and forecast error would be going to increase. Typically, load forecasting can be long-term, medium-term, short-term or very short-term. This paper concentrates on short-term load forecasting and partially on medium-term load forecasting applying regression models.
Robotic Grasping of Novel Objects

We consider the problem of grasping novel objects, speci cally ones that are being seen for the first time through vision. We present a learning algorithm that neither requires, nor tries to build, a 3-d model of the object. Instead it predicts, directly as a function of the images, a point at which to grasp the object. Our algorithm is trained via supervised learning, using synthetic images for the training set. We demonstrate on a robotic manipulation platform that this approach successfully grasps a wide variety of objects, such as wine glasses, duct tape, markers, a translucent box, jugs, knife-cutters, cellphones, keys, screwdrivers, staplers, toothbrushes, a thick coil of wire, a strangely shaped power horn, and others, none of which were seen in the training set.
Enabling communication rationale via annotations: a document-based cooperation

This paper describes a cooperative tool used by a mechanical engineering team carrying out an asynchronous and distributed work. The tool supports communication phases as well as document and product creation phases by means of annotations. Memorizing all the annotations and creating documents from these annotations enable capturing the "communication rationale" of the team. Annotation is here defined as a continuum fulfilling several purposes from communication to indexation.
Nature Inspired Load Balancing Algorithms in a Cloud Computing Environment

Cloud computing is a general term for anything that involves delivering hosted services over the Internet, One of the primary issues in cloud computing is implementation of a novel load balancing approach. The demanding thirst for optimal performance of the system is creating research interest in this area. Many Load Balancing algorithms that aim to enhance the overall system performance have been proposed. In this paper, we survey a special group of Load balancing algorithms that have taken inspiration from nature. We provide an overview of the current trends in the field by discussing and comparing these algorithms.
Available Bit Rate Traffic Engineering in MPLS Networks with Flow-Based Multipath Routing ∗∗

SUMMARY In this paper, we propose a novel traffic engineering architecture for IP networks with MPLS backbones. In this architecture, two link-disjoint label switched paths, namely the primary and secondary paths, are established among every pair of IP routers located at the edges of an MPLS backbone network. As the main building block of this architecture, we propose that primary paths are given higher priority against the secondary paths in the MPLS data plane to cope with the so-called knock-on effect. Inspired by the ABR flow control mechanism in ATM networks, we propose to split traffic between a source-destination pair between the primary and secondary paths using explicit rate feedback from the network. Taking into consideration the performance deteriorating impact of packet reordering in packet-based load balancing schemes, we propose a traffic splitting mechanism that operates on a per-flow basis (i.e., flow-based multipath routing). We show via an extensive simulation study that using flowbased multipath traffic engineering with explicit rate feedback not only provides consistently better throughput than that of a single path but is also
Object-based Semantics For Natural LanguageEngineering In Higher Education

We present an object-based semantics for informative question answering in the University domain. Semantic rules specify how the internal representation is built from the parse tree for a sentence. A collection of rules is given for generating internal representations for the natural language requests that arise in the University domain. The action of the semantic interpreter is also illustrated by showing how it applies the rules to a particular sentence to produce an internal representation.
International Journal of Engineering

In traditional scheduling literature, it is generally assumed that the location of facilities are predetermined and fixed in advance. However, these decisions are interrelated and may impact each other significantly. Therefore finding a schedule and facility location has become an important problem as an extension of the well-known scheduling problems. In this research we consider joint decisions on planning of machines’ layout and scheduling of jobs on each machine in a Flexible Job Shop environment. The aim is to minimize maximum completion time. The problem is formulated as a mathematical programming model and is solved using an enhanced particle swarm optimization (PSO). Furthermore, parameters of algorithm is optimized by Taguchi statistical tool. A lower bound is also devised for evaluating the obtained results
ClusTheDroid: Clustering Android Malware

The volume of new Android malware is growing at an exponential pace. This cries for automated tools that can aid the malware analyst in dissecting the behaviours of new malicious applications. In this paper, we present ClusTheDroid, a system for clustering Android malware so as to identify malicious applications that exhibit similar behaviours. For this, ClusTheDroid extracts feature sets from profiles of reconstructed malicious Android behaviours. These feature sets serve as input to a clustering algorithm that allows for volumes larger than demanded by the anti-malware industry today. We implemented ClusTheDroid and evaluated it on real-world malware. Our results show that ClusTheDroid is competitive with existing solutions targeting similar problems on Android malware, but lacks accuracy to similar solutions that focus on PC malware. We furthermore evaluate the use of the cluster-validity measure C-index, which suggest for a more flexible approach to identifying near-optimal clusters than that of previous literature.
Pseudo-Online Classification Mental Tasks Using Kullback-Leibler Symmetric Divergence

This paper presents a brain-computer interface (BCI) architecture for robotic devices. Two datasets are used to perform a simulation of real-time classification, which is a pseudo-online technique, to measure the performance of the proposed BCI architecture. Dataset V comprises three mental tasks, namely, left or right hand movement imagery and the thought of generating words beginning with a given random letter and dataset IIIa comprises four motor mental tasks, namely right hand, left hand, foot, and tongue movement imagery. The partial auto-correlation function is used to estimate the size of the time windows. Power spectral density is used for feature extraction and the Kullback-Leibler symmetric divergence is adopted to select the electroencephalogram channel and frequency. A Bayesian classifier is used to recognize the mental tasks and a reclassification model is proposed to improve the response of the classifier. The proposed BCI can identify 3 and 4 mental tasks with accuracies of up to 94.9% and 72%, respectively.
Bump Hunting With SAS ® : A Macro Approach To Employing PRIM

Many learning-based data mining methods can be used to classify particular subgroups of interest. One such method, the patient rule induction method (PRIM), is used to identify subgroups with extreme values of the target variable (i.e., outcome or dependent variable). This paper examines the PRIM algorithm and its uses, provides an example with data, and discusses SAS code that can be used to generate the PRIM algorithm.
Frame error rates for convolutional codes on fading channels and the concept of effective E/sub b//N/sub 0/

We consider the estimation of Viterbi decoder performance for convolutional codes with interleaving over fading channels subject to practical power control algorithms. Given a received vector of (time-varying) E/sub b//N/sub 0/ values for a frame, we compute the error probability and effective E/sub b//N/sub 0/ for that frame, where the latter is defined as the E/sub b//N/sub 0/ on an AWGN channel that results in the same frame error rate. We validate our analysis using the R=1/3 convolutional code and interleaver used on the IS-95 CDMA reverse channel.
Bibliometric Methods in Management and Organization

We aim to develop a meaningful single-source reference for management and organization scholars interested in using bibliometric methods for mapping research specialties. Such methods introduce a measure of objectivity into the evaluation of scientific literature and hold the potential to increase rigor and mitigate researcher bias in reviews of scientific literature by aggregating the opinions of multiple scholars working in the field. We introduce the bibliometric methods of citation analysis, co-citation analysis, bibliographical coupling, co-author analysis, and co-word analysis and present a workflow for conducting bibliometric studies with guidelines for researchers. We envision that bibliometric methods will complement meta-analysis and qualitative structured literature reviews as a method for reviewing and evaluating scientific literature. To demonstrate bibliometric methods, we performed a citation and co-citation analysis to map the intellectual structure of the Organizational Research Methods journal.
Overlapping decomposition and decentralized LQG control for interconnected power systems

In this paper the overlapping decomposition for stochastic systems is presented by using the stochastic inclusion principle. A special attention is paid to the decompositions satisfying restriction, aggregation, and both restriction and aggregation conditions of the inclusion, respectively. Taking a 19th order model for load-frequency control of a two-area power system with overlapping structures as an example, it is shown that the overlapping subsystems of the system are decoupled with balance by the special decompositions, and that the decentralized LQG suboptimal control for the original system is implemented by the proper contraction of the dynamic controllers formulated for the decoupled subsystems.
Predicting road hazards caused by rain, freezing rain and wet surfaces and the role of weather radar

Freezing rain in the winter of 1995/96 has drawn attention to the severity of black‐ice problems on British roads, additional to the normal increased hazards presented to drivers by any form of precipitation. Disruption to traffic was considerable on several days. Weather radar provides improved nowcasting, for both winter and summer conditions, where available real time directly to highway engineers, but this is not yet generally the case in England and Wales. New developments from the Meteorological Office offer improved possibilities at reasonable costs. Copyright © 1997 Royal Meteorological Society.
An Application of Robert Gagné's Nine Events of Instruction to the Teaching of Website Localization

Website localization is an important part of international technical communication. However, at present, few technical communication programs offer courses in localization. This article provides an overview of a course devised to familiarize students with ideas and approaches related to website localization. The course was based upon Robert Gagné's nine events of instruction—an approach that allowed students to move from the learning of abstract ideas to the application of knowledge to the website localization process.
ADAPTIVE FUZZY CONTROL FOR TRANSFER CHANNELS IN PARTICLE ACCELERATORS

Long-term objective of this work is to develop a fuzzy technology based control framework to be applied in particle accelerators. Main motivation for this is the promise of fuzzy systems to exploit the tolerance for imprecision, un-certainty, and partial truth to achieve tractability, robustness, and low solution cost. Intended areas of application are manifold: we think on automatic operation, optimization of the operating conditions and yields; applied to various stages in the processing of circular and linear accelerators. As a first step towards this goal a fuzzy control system for a transfer channel in a particle accelerator has been developed. For it we built up the machinery, i.e. algorithms, data structures, integration in the existing control system and did a first proof-of-concept. Special emphasis is given on handling high dimensional data streams and the immanent challenges as sparsity and equidistance of the data.
An improved simulation model for power MOSFET

A new model is proposed to make a more precise simulation of a power MOSFET using PSPICE. The application results for all types of HEXFET devices have a good agreement with the corresponding curves in the IR databook. A method of parameter extraction and some simulation results are presented for a demonstration HEXFET device.
Model-driven contour extraction for physically deformed objects -application to analysis of stomach X-ray images

Proposes a model-driven method for extracting the contour of a physically deformed object using a flexible model which simulates the deformation. This method has been developed to extract a stomach contour from a double contrast X-ray image (abbreviated as the DC image). First, the method automatically constructs a stomach model based on the stomach shape extracted from a different type of X-ray image of the same stomach. The model consists of springs to represent the elasticity of a stomach. By using the model to simulate the stomach deformation between the two images, the stomach shape in the DC image is predicted. The predicted shape is registered in the DC image and is made closer to the exact contour by a two-staged method. Stomach contours were accurately obtained using 13 actual DC images. <<ETX>>
A Voice Controlled Service Robot Using Support Vector{® º æ √ ƒ … ¿

This paper proposes a SVM(Support Vector Machine) training algorithm to control a service robot with voice command. The service robot with a stereo vision system and dual manipulators of four degrees of freedom implements a User-Dependent Voice Control System. The training of SVM algorithm that is one of the statistical learning theories leads to a QP(quadratic programming) problem. In this paper, we present an efficient SVM speech recognition scheme especially based on less learning data comparing with conventional approaches. SVM discriminator decides rejection or acceptance of user's extracted voice features by the MFCC(Mel Frequency Cepstrum Coefficient). Among several SVM kernels, the exponential RBF function gives the best classification and the accurate user recognition. The numerical simulation and the experiment verified the usefulness of the proposed algorithm. �{