nils-herrmann/clean_scirepeval huggingface dataset:
CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms

Cloud computing is a recent advancement wherein IT infrastructure and applications are provided as “services” to endusers under a usage-based payment model. They can leverage virtualized services even on the fly based on requirements (workload patterns and QoS) varying with time. The application services hosted under Cloud computing model have complex provisioning, composition, configuration, and deployment requirements. Evaluating the performance of Cloud provisioning policies, application workload models, and resources performance models in a repeatable manner under varying system and user configurations and requirements is difficult to achieve. To overcome this challenge, we propose CloudSim: an extensible simulation toolkit that enables modeling and simulation of Cloud computing systems and application provisioning environments. The CloudSim toolkit supports both system and behaviour modeling of Cloud system components such as data centers, virtual machines (VMs) and resource provisioning policies. It implements generic application provisioning techniques that can be extended with ease and limited efforts. Currently, it supports modeling and simulation of Cloud computing environments consisting of both single and inter-networked clouds (federation of clouds). Moreover, it exposes custom interfaces for implementing policies and provisioning techniques for allocation of VMs under inter-networked Cloud computing scenarios. Several researchers from organisations such as HP Labs in USA are using CloudSim in their investigation on Cloud resource provisioning and energy-efficient management of data center resources. The usefulness of CloudSim is demonstrated by a case study involving dynamic provisioning of application services in hybrid federated clouds environment. The result of this case study proves that the federated Cloud computing model significantly improves the application QoS requirements under fluctuating resource and service demand patterns.
An ontology mindset for system engineering

This paper presents an approach to support model-based system engineering (MBSE) which combines a standard metamodel such as UML, SysML, or NAF, with dedicated project ontologies that ease the understanding and creation of models. The approach is formalized using category theory to determine the conditions for use of project ontologies while ensuring the resulting models comply with the selected metamodel. The paper exposes the advantages of the proposed approach over a direct metamodel-based approach or an ontology-based approach using a domain specific language (DSL).
Beware the Middleman: Empirical Analysis of Bitcoin-Exchange Risk

Bitcoin has enjoyed wider adoption than any previous crypto-currency; yet its success has also attracted the attention of fraudsters who have taken advantage of operational insecurity and transaction irreversibility. We study the risk investors face from Bitcoin exchanges, which convert between Bitcoins and hard currency. We examine the track record of 40 Bitcoin exchanges established over the past three years, and find that 18 have since closed, with customer account balances often wiped out. Fraudsters are sometimes to blame, but not always. Using a proportional hazards model, we find that an exchange’s transaction volume indicates whether or not it is likely to close. Less popular exchanges are more likely to be shut than popular ones. We also present a logistic regression showing that popular exchanges are more likely to suffer a security breach.
Digital color halftoning with generalized error diffusion and multichannel green-noise masks

In this paper, we introduce two novel techniques for digital color halftoning with green-noise--stochastic dither patterns generated by homogeneously distributing minority pixel clusters. The first technique employs error diffusion with output-dependent feedback where, unlike monochrome image halftoning, an interference term is added such that the overlapping of pixels of different colors can be regulated for increased color control. The second technique uses a green-noise mask, a dither array designed to create green-noise halftone patterns, which has been constructed to also regulate the overlapping of different colored pixels. As is the case with monochrome image halftoning, both techniques are tunable, allowing for large clusters in printers with high dot-gain characteristics, and small clusters in printers with low dot-gain characteristics.
Advanced modeling and trajectory optimization framework for reusable launch vehicles

Launch vehicle dynamics modeling, simulation, and trajectory optimization within a single modeling tool is a challenging task due to the highly interconnected disciplines involved such as propulsion, aerodynamics, structures, mechanisms, and GNC, amongst others. In particular, changing environmental conditions and perturbations have to be considered throughout the ascent of expendable launch vehicles (ELV) as well as in the more complex scenario of the ascent and descent of reusable launch vehicles (RLV). Both the multidisciplinary design approach and the vehicle's mission definition can have considerable consequences for the overall modeling and optimization strategy. Therefore, a standardized modeling tool able to meet design requirements for a broad range of mission scenarios from vertical takeoff vertical landing (VTVL) to winged horizontal takeoff horizontal landing (HTHL) configurations is needed. Dedicated developments of multidisciplinary frameworks for launch vehicle modeling and preliminary design optimization have been presented in the relevant literature and also developed in the industry. It is common that the modeling of launch vehicles is performed by several independent, discipline-specific tools. With such an approach, only a limited amount of interactions of the involved disciplines with the overall system dynamics can be accounted for. Therefore, we propose a new multidisciplinary modeling framework considering all relevant effects on the system dynamics of launch vehicles using the object-oriented, equation-based, multi-physical, and acausal modeling language MODELICA. By capitalizing MODELICA's modeling capabilities, the framework enables the object-oriented and mathematically efficient modeling of subsystems and components related to most of the key disciplines of a launcher system. Another objective of this paper is to present a subset of the modeling framework for expendable and reusable launch vehicles regarding Functional Mock-up Units (FMU) and to demonstrate the advantages and capabilities of such a modeling approach within a combined trajectory optimization of the ascent and descent phases of launch vehicles. The modeling framework is shown for a standardized three degrees of freedom (3-DOF) model, covering the kinematics and dynamics formulation, environmental effects, aerodynamics, and propulsion models for system dynamics and subsequent trajectory simulations. The 3-DOF launch vehicle model is integrated as an FMU into the Trajectory Optimization Package trajOpt of DLR-SR's multi-objective optimization tool MOPS. The benefits of our modeling framework are discussed in terms of future rigid and flexible multibody modeling capabilities as well as GNC design and trade-off studies.
Row-wise parallel predicate evaluation

Table scans have become more interesting recently due to greater use of ad-hoc queries and greater availability of multicore, vector-enabled hardware. Table scan performance is limited by value representation, table layout, and processing techniques. In this paper we propose a new layout and processing technique for efficient one-pass predicate evaluation. Starting with a set of rows with a fixed number of bits per column, we append columns to form a set of banks and then pad each bank to a supported machine word length, typically 16, 32, or 64 bits. We then evaluate partial predicates on the columns of each bank, using a novel evaluation strategy that evaluates column level equality, range tests, IN-list predicates, and conjuncts of these predicates, simultaneously on multiple columns within a bank, and on multiple rows within a machine register. This approach outperforms pure column stores, which must evaluate the partial predicates one column at a time. We evaluate and compare the performance and representation overhead of this new approach and several proposed alternatives.
3-Axis Magnetic Sensor Array System for Tracking Magnet's Position and Orientation

In medical diagnoses and treatments, e.g., the endoscopy, the dosage transition monitoring, it is often desirable to wirelessly track an object that moves through the human GI tract. In this paper, we present a magnetic localization and orientation system for such applications. This system uses a small magnet enclosed in the object to serve as excitation source. It does not require the connection wire and power supply for excitation signal. When the magnet moves, it establishes a static magnetic field around, whose intensity is related to the magnet's position and orientation. With the magnetic sensors, the magnetic intensities in some pre-determined spatial points can be detected, and the magnet's position and orientation parameters can be calculated based on an appropriate algorithm. Here, we propose a real-time tracking system built by Honeywell 3-axis magnetic sensors, HMC1053, as well as the computer sampling circuit. The results show that satisfactory tracking accuracy (average localization error is 3.3 mm) can be achieved using a sensor array with enough number of the 3-axis magnetic sensors
A Sequential Neural Encoder With Latent Structured Description for Modeling Sentences

In this paper, we propose a sequential neural encoder with latent structured description SNELSD for modeling sentences. This model introduces latent chunk-level representations into conventional sequential neural encoders, i.e., recurrent neural networks with long short-term memory LSTM units, to consider the compositionality of languages in semantic modeling. An SNELSD model has a hierarchical structure that includes a detection layer and a description layer. The detection layer predicts the boundaries of latent word chunks in an input sentence and derives a chunk-level vector for each word. The description layer utilizes modified LSTM units to process these chunk-level vectors in a recurrent manner and produces sequential encoding outputs. These output vectors are further concatenated with word vectors or the outputs of a chain LSTM encoder to obtain the final sentence representation. All the model parameters are learned in an end-to-end manner without a dependency on additional text chunking or syntax parsing. A natural language inference task and a sentiment analysis task are adopted to evaluate the performance of our proposed model. The experimental results demonstrate the effectiveness of the proposed SNELSD model on exploring task-dependent chunking patterns during the semantic modeling of sentences. Furthermore, the proposed method achieves better performance than conventional chain LSTMs and tree-structured LSTMs on both tasks.
Optimising Results from Minimal Access Cranial Suspension Lifting (MACS-Lift)

Between November 1999 and February 2005, 450 minimal access cranial suspension (MACS) lifts were performed. Starting with the idea of suspension for sagging soft tissues using permanent purse-string sutures, a new comprehensive approach to facial rejuvenation was developed in which the vertical vector appeared to be essential. The neck is corrected by extended submental liposuction and strong vertical traction on the lateral part of the platysma by means of a first vertical purse-string suture. The volume of the jowls and the cheeks is repositioned in a cranial direction with a second, slightly oblique purse-string suture. The descent of the midface is corrected by suspending the malar fat pad in a nearly vertical direction. In 23 cases (5.1%), the result in the neck was unsatisfactory, and additional work had to be done secondarily, or in later cases, primarily. The problem that appeared was unsatisfactory correction of platysmal bands (resolved with an additional anterior cervicoplasty) or vertical skin folds that appeared in the infralobular region (corrected with an additional posterior cervicoplasty). This article describes two ancillary procedures that, although not frequently necessary, can optimise the result of MACS lifting.
Inspired by distraction: mind wandering facilitates creative incubation.

Although anecdotes that creative thoughts often arise when one is engaged in an unrelated train of thought date back thousands of years, empirical research has not yet investigated this potentially critical source of inspiration. We used an incubation paradigm to assess whether performance on validated creativity problems (the Unusual Uses Task, or UUT) can be facilitated by engaging in either a demanding task or an undemanding task that maximizes mind wandering. Compared with engaging in a demanding task, rest, or no break, engaging in an undemanding task during an incubation period led to substantial improvements in performance on previously encountered problems. Critically, the context that improved performance after the incubation period was associated with higher levels of mind wandering but not with a greater number of explicitly directed thoughts about the UUT. These data suggest that engaging in simple external tasks that allow the mind to wander may facilitate creative problem solving.
A survey of effective heuristics and their application to a variety of knapsack problems

We present a family of knapsack problems (KPs) while highlighting their particular applications. Though most of the problems are derived from the classical KP, the differences arise in the addition or modification of the constraints or in the way the objective function is defined. Appropriate techniques that were found to be successful in solving these problems are briefly reviewed. Hybrid methods that combine the strengths of different methods such as exact and heuristics are also briefly discussed. Some research avenues that we believe to be useful and challenging are also pointed out.
Robust Median Reversion Strategy for Online Portfolio Selection

Online portfolio selection has attracted increasing attention from data mining and machine learning communities in recent years. An important theory in financial markets is mean reversion, which plays a critical role in some state-of-the-art portfolio selection strategies. Although existing mean reversion strategies have been shown to achieve good empirical performance on certain datasets, they seldom carefully deal with noise and outliers in the data, leading to suboptimal portfolios, and consequently yielding poor performance in practice. In this paper, we propose to exploit the reversion phenomenon by using robust <inline-formula><tex-math notation="LaTeX"> $L_1$</tex-math><alternatives><inline-graphic xlink:type="simple" xlink:href="huang-ieq1-2563433.gif"/></alternatives> </inline-formula>-median estimators, and design a novel online portfolio selection strategy named “Robust Median Reversion” (RMR), which constructs optimal portfolios based on the improved reversion estimator. We examine the performance of the proposed algorithms on various real markets with extensive experiments. Empirical results show that RMR can overcome the drawbacks of existing mean reversion algorithms and achieve significantly better results. Finally, RMR runs in linear time, and thus is suitable for large-scale real-time algorithmic trading applications.
Supervised clustering of streaming data for email batch detection

We address the problem of detecting batches of emails that have been created according to the same template. This problem is motivated by the desire to filter spam more effectively by exploiting collective information about entire batches of jointly generated messages. The application matches the problem setting of supervised clustering, because examples of correct clusterings can be collected. Known decoding procedures for supervised clustering are cubic in the number of instances. When decisions cannot be reconsidered once they have been made --- owing to the streaming nature of the data --- then the decoding problem can be solved in linear time. We devise a sequential decoding procedure and derive the corresponding optimization problem of supervised clustering. We study the impact of collective attributes of email batches on the effectiveness of recognizing spam emails.
Toward Modeling and Simulation of Critical National Infrastructure Interdependencies

Modern society is dependent upon a complex network of interdependent critical infrastructures. Continued prosperity and national security of the U.S. depend on our ability to understand and analyze this complex network, and to timely and effectively respond to potential disruptions. In this paper, we propose an innovative modeling and analysis framework for interdependent critical infrastructures based on system dynamics, IDEF, and nonlinear optimization algorithms. Collaborative efforts among Sandia, government agencies including DHS, commercial/private industries, and academics have resulted in realistic models, including critical infrastructure models, e.g. power, petroleum, natural gas, water, and communication, and economic sector models, e.g. residence, industry, commercial sector, and transportation. The proposed framework and models are demonstrated for hypothetical disruption of a critical infrastructure and optimal response.
QoS Support in Wireless Sensor Networks: A Survey

In this paper, we assess the state of the art of Quality of Services (QoS) support in wireless sensor networks (WSNs). Unlike traditional end-to-end multimedia applications, many non-end-to-end mission-critical applications envisioned for WSNs have brought forward new QoS requirements on the network. Further, unique characteristics of WSNs, such as extremely resource-constrained sensors, large-scale random deployment, and novel data-centric communication protocols, pose unprecedented challenges in the area of QoS support in WSNs. Thus, we first review the techniques for QoS support in traditional networks, analyze new QoS requirements in WSNs from a wide variety of applications classified by data delivery models, and propose some non-end-to-end collective QoS parameters. Next, the challenges of QoS support in this new paradigm are presented. Finally, we comment on current research efforts and identify many exciting open issues in order to stimulate more research interest in this largely unexplored area.  Wireless networks, wireless sensor networks, QoS, collective QoS
Applications of Lucid Dreaming in Sports

The following article has above all a practical orientation. The various possibilities for the application of lucid dreaming in sports training are presented and briefly illustrated. These theses are based on findings from experiments with experienced lucid dreamers who were instructed to carry out various routine and sport-related actions while lucid-dreaming, with the object of observing the effects on both dreaming and waking states (Tholey, 1981a). Additionally, I report here on numerous spontaneous accounts of athletes who have mastered lucid dreaming, as well as my own years of experience as a lucid dreamer and as an active competitor in different types of sports.
Spike-timing dynamics of neuronal groups.

A neuronal network inspired by the anatomy of the cerebral cortex was simulated to study the self-organization of spiking neurons into neuronal groups. The network consisted of 100 000 reentrantly interconnected neurons exhibiting known types of cortical firing patterns, receptor kinetics, short-term plasticity and long-term spike-timing-dependent plasticity (STDP), as well as a distribution of axonal conduction delays. The dynamics of the network allowed us to study the fine temporal structure of emerging firing patterns with millisecond resolution. We found that the interplay between STDP and conduction delays gave rise to the spontaneous formation of neuronal groups--sets of strongly connected neurons capable of firing time-locked, although not necessarily synchronous, spikes. Despite the noise present in the model, such groups repeatedly generated patterns of activity with millisecond spike-timing precision. Exploration of the model allowed us to characterize various group properties, including spatial distribution, size, growth, rate of birth, lifespan, and persistence in the presence of synaptic turnover. Localized coherent input resulted in shifts of receptive and projective fields in the model similar to those observed in vivo.
The elements of statistical learning: data mining, inference, and prediction, 2nd Edition

by E.T. Jaynes CAMBRIDGE,  2003 727pp. $65.00 HARDBACK ISBN 0-521-59271-2 The Fundamentals of Risk Measurement by Chris Marrison BOSTON, McGRAW-HILL, 2002 415pp. $44.95 HARDBACK ISBN 0-07-138627-0 The Elements of Statistical Learning: Data Mining, Inference and Prediction by Trevor Hastie, Robert Tibshirani and Jerome Friedman NEW YORK, SPRINGER, 2001 533pp. $82.95 HARDBACK ISBN 0-387-95284-5
Using Spin Images for Efficient Object Recognition in Cluttered 3D Scenes

We present a 3-D shape-based object recognition system for simultaneous recognition of multiple objects in scenes containing clutter and occlusion. Recognition is based on matching surfaces by matching points using the spin-image representation. The spin-image is a data level shape descriptor that is used to match surfaces represented as surface meshes. We present a compression scheme for spin-images that results in efficient multiple object recognition which we verify with results showing the simultaneous recognition of multiple objects from a library of 20 models. Furthermore, we demonstrate the robust performance of recognition in the presence of clutter and occlusion through analysis of recognition trials on 100 scenes. This research was performed at Carnegie Mellon University and was supported by the US Department of Energy under contract DE-AC21-92MC29104.
Pricing Strategies for Information Technology Services: A Value-Based Approach

While commoditization is creating opportunities for customers of information technology services, it is creating new challenges for the service providers. Pricing strategies are one of the most important challenges and decisions for today's IT service providers. Pricing strategies for IT services have traditionally focused on covering costs, achieving desired margins and meeting the competition. These pricing schemes range from simple approaches, easily copied by competitors, to complex models with high management costs. In order to be successful in today's competitive business world, the service providers need to define their pricing strategies by considering the customer's perceived value from the service they receive rather than using traditional cost-based pricing strategies. This paper surveys literature on IT services pricing and presents a value-based approach to effectively price IT services.
Temporal localization of audio events for conflict monitoring in social media

With the explosion in the availability of user-generated videos documenting any conflicts and human rights abuses around the world, analysts and researchers increasingly find themselves overwhelmed with massive amounts of video data to acquire and analyze useful information. In this paper, we develop a temporal localization framework for intense audio events in videos which addresses the problem. The proposed method utilizes Localized Self-Paced Reranking (LSPaR) to refine the localization results. LSPaR utilizes samples from easy to noisier ones so that it can overcome the noisiness of the initial retrieval results from user-generated videos. We show our framework's efficacy on localizing intense audio event like gunshot, and further experiments also indicate that our methods can be generalized to localizing other audio events in noisy videos.