nils-herrmann/clean_scirepeval huggingface dataset:
Multi-agent based framework for target tracking using a real time vision system

This paper presents a multi-agent based framework for target tracking. We exploit the agent-oriented software paradigm with its characteristics that provide intelligent autonomous behavior together with a real time computer vision system to achieve high performance real time target tracking. The framework consists of four layers; interface, strategic, management, and operation layers. Interface layer receives from the user the tracking parameters such as the number and type of trackers and targets and type of the tracking environment, and then delivers these parameters to the subsequent layers. Strategic (decision making) layer is provided with a knowledge base of target tracking methodologies that are previously implemented by researchers in diverse target tracking applications and are proven successful. And by inference in the knowledge base using the user input a tracking methodology is chosen. Management layer is responsible for pursuing and controlling the tracking methodology execution. Operation layer represents the phases in the tracking methodology and is responsible for communicating with the real-time computer vision system to execute the algorithms in the phases. The framework is presented with a case study to show its ability to tackle the target tracking problem and its flexibility to solve the problem with different tracking parameters. This paper describes the ability of the agent-based framework to deploy any real-time vision system that fits in solving the target tracking problem. It is a step towards a complete open standard, real-time, agent-based framework for target tracking.
Improved Bounds on Bell Numbers and on Moments of Sums of Random Variables

We provide bounds for moments of sums of sequences of independent random variables. Concentrating on uniformly bounded non-negative random variables, we are able to improve upon previous results due to Johnson, Schechtman and Zinn (1985) and Lata la (1997). Our basic results provide bounds involving Stirling numbers of the second kind and Bell numbers. By deriving novel effective bounds on Bell numbers and the related Bell function, we are able to translate our moment bounds to explicit ones, which are tighter than previous bounds. The study was motivated by a problem in operation research, in which it was required to estimate the Lp-moments of sums of uniformly bounded non-negative random variables (representing the processing times of jobs that were assigned to some machine) in terms of the expectation of their sum.
Awareness and Attitude towards Human Papillomavirus (HPV) Vaccine among Medical Students in a Premier Medical School in India

As preventing cancer with the help of a vaccine is a comparatively new concept, awareness and education about it will have important implication in the implementation of this strategy.



Present explorative questionnaire based survey included 618 MBBS students for final analysis.



Majority of participants (89.6%) were well aware of the preventable nature of cervical cancer. Most of them (89.2%) knew that necessary factor responsible for cervical cancer is infection with high risk HPV. Awareness regarding the availability of vaccine against cervical cancer was 75.6%. Females had a better awareness regarding availability of vaccine, target population for vaccination and about the catch up program. Overall acceptance of HPV vaccine among the population studied was 67.8%. Medical teaching had a definitive impact on the understanding of this important public health issue. Females seemed to be more ready to accept the vaccine and recommend it to others. For our study population the most common source of information was medical school teaching. Majority of participants agreed that the most important obstacle in implementation of HPV vaccination program in our country is inadequate information and 86.2% wanted to be educated by experts in this regard.



HPV vaccine for primary prevention of cervical cancer is a relatively new concept. Health professional will be able to play a pivotal role in popularizing this strategy.
LOSSCALC V 2 : DYNAMIC PREDICTION OF LGD

LossCalcTM version 2.0 is the Moody's KMV model to predict loss given default (LGD) or (1 recovery rate). Lenders and investors use LGD to estimate future credit losses. LossCalc is a robust and validated model of LGD for loans, bonds, and preferred stocks for the US, Canada, the UK, Continental Europe, Asia, and Latin America. It projects LGD for defaults occurring immediately and for defaults that may occur in one year. LossCalc is a statistical model that incorporates information at different levels: collateral, instrument, firm, industry, country, and the macroeconomy to predict LGD. It significantly improves on the use of historical recovery averages to predict LGD, helping institutions to better price and manage credit risk. LossCalc is built on a global dataset of 3,026 recovery observations for loans, bonds, and preferred stock from 1981-2004. This dataset includes over 1,424 defaults of both public and private firms—both rated and unrated instruments—in all industries. LossCalc will help institutions better manage their credit risk and can play a critical role in meeting the Basel II requirements on advanced Internal Ratings Based Approach. This paper describes Moody's KMV LossCalc, its predictive factors, the modeling approach, and its out of-time and out of-sample model validation. AUTHORS Greg M. Gupton Roger M. Stein
Visualizing the affective structure of a text document

This paper introduces an approach for graphically visualizing the affective structure of a text document. A document is first affectively analyzed using a unique textual affect sensing engine, which leverages commonsense knowledge to classify text more reliably and comprehensively than can be achieved with keyword spotting methods alone. Using this engine, sentences are annotated using six basic Ekman emotions. Colors used to represent each of these emotions are sequenced into a color bar, which represents the progression of affect through a text document. Smoothing techniques allow the user to vary the granularity of the affective structure being displayed on the color bar. The bar is hyperlinked in a way such that it can be used to easily navigate the document. A user evaluation demonstrates that the proposed method for visualizing and navigating a document's affective structure facilitates a user's within-document information foraging activity.
Flexible IoT security middleware for end-to-end cloud-fog communication

IoT (Internet of Things) based smart devices such as sensors have been actively used in edge clouds i.e., ‘fogs’ along with public clouds. They provide critical data during scenarios ranging from e.g., disaster response to in-home healthcare. However, for these devices to work effectively, end-to-end security schemes for the device communication protocols have to be flexible and should depend upon the application requirements as well as the resource constraints at the network-edge. In this paper, we present the design and implementation of a flexible IoT security middleware for end-to-end cloud-fog communications involving smart devices and cloud-hosted applications. The novel features of our middleware are in its ability to cope with intermittent network connectivity as well as device constraints in terms of computational power, memory, energy, and network bandwidth. To provide security during intermittent network conditions, we use a ‘Session Resumption’ algorithm in order for our middleware to reuse encrypted sessions from the recent past, if a recently disconnected device wants to resume a prior connection that was interrupted. In addition, we describe an ‘Optimal Scheme Decider’ algorithm that enables our middleware to select the best possible end-to-end security scheme option that matches with a given set of device constraints. Experiment results show how our middleware implementation also provides fast and resource-aware security by leveraging static properties i.e., static pre-shared keys (PSKs) for a variety of IoT-based application requirements that have trade-offs in higher security or faster data transfer rates.
200x Low-dose PET Reconstruction using Deep Learning

Positron emission tomography (PET) is widely used in various clinical applications, including cancer diagnosis, heart disease and neuro disorders. The use of radioactive tracer in PET imaging raises concerns due to the risk of radiation exposure. To minimize this potential risk in PET imaging, efforts have been made to reduce the amount of radiotracer usage. However, lowing dose results in low Signal-toNoise-Ratio (SNR) and loss of information, both of which will heavily affect clinical diagnosis. Besides, the ill-conditioning of low-dose PET image reconstruction makes it a difficult problem for iterative reconstruction algorithms. Previous methods proposed are typically complicated and slow, yet still cannot yield satisfactory results at significantly low dose. Here, we propose a deep learning method to resolve this issue with an encoder-decoder residual deep network with concatenate skip connections. Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose. Different cost functions for training model are explored. Multi-slice input strategy is introduced to provide the network with more structural information and make it more robust to noise. Evaluation on ultra-low-dose clinical data shows that the proposed method can achieve better result than the state-of-the-art methods and reconstruct images with comparable quality using only 0.5% of the original regular dose.
A methodology for generating natural language paraphrases

In this paper we address the topic of generating automatically accurate, meaning preserving and syntactically correct paraphrases of natural language sentences. The design of methods and tools for paraphrasing natural language text is a core task of natural language processing and is quite useful in many applications and procedures. We present a methodology and a tool developed that performs deep analysis of natural language sentences and generate paraphrases of them. The tool performs deep analysis of the natural language sentence and utilizes sets of paraphrasing techniques that can be used to transform structural parts of the dependency tree of a sentence to an equivalent form and also change sentence words with their synonyms and antonyms. In the evaluation study the performance of the method is examined and the accuracy of the techniques is assessed in terms of syntactic correctness and meaning preserving. The results collected are very promising and show the method to be accurate and able to generate quality paraphrases.
Loss, trauma, and human resilience: have we underestimated the human capacity to thrive after extremely aversive events?

Many people are exposed to loss or potentially traumatic events at some point in their lives, and yet they continue to have positive emotional experiences and show only minor and transient disruptions in their ability to function. Unfortunately, because much of psychology's knowledge about how adults cope with loss or trauma has come from individuals who sought treatment or exhibited great distress, loss and trauma theorists have often viewed this type of resilience as either rare or pathological. The author challenges these assumptions by reviewing evidence that resilience represents a distinct trajectory from the process of recovery, that resilience in the face of loss or potential trauma is more common than is often believed, and that there are multiple and sometimes unexpected pathways to resilience.
Photorealistic Monocular Gaze Redirection Using Machine Learning

We propose a general approach to the gaze redirection problem in images that utilizes machine learning. The idea is to learn to re-synthesize images by training on pairs of images with known disparities between gaze directions. We show that such learning-based re-synthesis can achieve convincing gaze redirection based on monocular input, and that the learned systems generalize well to people and imaging conditions unseen during training. We describe and compare three instantiations of our idea. The first system is based on efficient decision forest predictors and redirects the gaze by a fixed angle in real-time (on a single CPU), being particularly suitable for the videoconferencing gaze correction. The second system is based on a deep architecture and allows gaze redirection by a range of angles. The second system achieves higher photorealism, while being several times slower. The third system is based on real-time decision forests at test time, while using the supervision from a “teacher” deep network during training. The third system approaches the quality of a teacher network in our experiments, and thus provides a highly realistic real-time monocular solution to the gaze correction problem. We present in-depth assessment and comparisons of the proposed systems based on quantitative measurements and a user study.
Design and application of 2D illusory vibrotactile feedback for hand-held tablets

In this study, we investigate whether the “out of body” vibrotactile illusion known as funneling could be applied to enrich and thereby improve the interaction performance on a tablet-sized media device. First, a series of pilot tests was taken to determine the appropriate operational conditions and parameters (such as the tablet size, holding position, minimal required vibration amplitude, and the effect of matching visual feedback) for a two-dimensional (2D) illusory tactile rendering method. Two main experiments were then conducted to validate the basic applicability and effectiveness of the rendering method, and to further demonstrate how the illusory tactile feedback could be deployed in an interactive application and actually improve user performance. Our results showed that for a tablet-sized device (e.g., iPad mini and iPad), illusory perception was possible (localization performance of up to 85%) using a rectilinear grid with a resolution of 5 $$\times $$ × 7 (grid size: 2.5 cm) with matching visual feedback. Furthermore, the illusory feedback was found to be a significant factor in improving the user performance in a 2D object search/attention task.
PacketCloud: A Cloudlet-Based Open Platform for In-Network Services

The Internet was designed with the end-to-end principle where the network layer provided merely the best-effort forwarding service. This design makes it challenging to add new services into the Internet infrastructure. However, as the Internet connectivity becomes a commodity, users and applications increasingly demand new in-network services. This paper proposes PacketCloud, a cloudlet-based open platform to host in-network services. Different from standalone, specialized middleboxes, cloudlets can efficiently share a set of commodity servers among different services, and serve the network traffic in an elastic way. PacketCloud can help both Internet Service Providers (ISPs) and emerging application/content providers deploy their services at strategic network locations. We have implemented a proof-of-concept prototype of PacketCloud. PacketCloud introduces a small additional delay, and can scale well to handle high-throughput data traffic. We have evaluated PacketCloud in both a fully functional emulated environment, and the real Internet.
Crowd-Based Ambient Assisted Living to Monitor the Elderly’s Health Outdoors

Ambient assisted living (AAL) technologies can help the elderly maintain their independence while keeping them safer. Sensors monitor their activities to detect situations in which they might need help. Most research in this area has targeted indoor environments, but outdoor activities are just as important; many risky situations might occur outdoors. SafeNeighborhood (SN) is an AAL system that combines data from multiple sources with collective intelligence to tune sensor data. It merges mobile, ambient, and AI technologies with old-fashioned neighborhood ties to create safe outdoor spaces. The initial results indicate SN’s potential use and point toward new opportunities for care of the elderly.
Fitting Square Pegs Through Round Pipes: Unordered Delivery Wire-Compatible with TCP and TLS

Internet applications increasingly employ TCP not as a stream abstraction, but as a substrate for applicationlevel transports, a use that converts TCP’s in-order semantics from a convenience blessing to a performance curse. As Internet evolution makes TCP’s use as a substrate likely to grow, we offer Minion, an architecture for backward-compatible out-of-order delivery atop TCP and TLS. Small OS API extensions allow applications to manage TCP’s send buffer and to receive TCP segments out-of-order. Atop these extensions, Minion builds application-level protocols offering true unordered datagram delivery, within streams preserving strict wirecompatibility with unsecured or TLS-secured TCP connections. Minion’s protocols can run on unmodified TCP stacks, but benefit incrementally when either endpoint is upgraded, for a backward-compatible deployment path. Experiments suggest that Minion can noticeably improve performance of applications such as conferencing, virtual private networking, and web browsing, while incurring minimal CPU or bandwidth costs.
Modeling of particle size segregation : Calibration using the discrete particle method

Over the last 25 years a lot of work has been undertaken on constructing continuum models for segregation of particles of different sizes. We focus on one model that is designed to predict segregation and remixing of two differently sized particle species. This model contains two dimensionless parameters, which in general depend on both the flow and particle properties. One of the weaknesses of the model is that these dependencies are not predicted; these have to be determined by either experiments or simulations. We present steady-state simulations using the discrete particle method (DPM) for bi-disperse systems with different size ratios. The aim is to determine one parameter in the continuum model, i.e., the segregation Péclet number (ratio of the segregation velocity to diffusion) as a function of the particle size ratio. Reasonable agreement is found; but, also measurable discrepancies are reported;
von Bertalanffy 1.0: a COBRA toolbox extension to thermodynamically constrain metabolic models

In flux balance analysis of genome scale stoichiometric models of metabolism, the principal constraints are uptake or secretion rates, the steady state mass conservation assumption and reaction directionality. Here, we introduce an algorithmic pipeline for quantitative assignment of reaction directionality in multi-compartmental genome scale models based on an application of the second law of thermodynamics to each reaction. Given experimental or computationally estimated standard metabolite species Gibbs energy and metabolite concentrations, the algorithms bounds reaction Gibbs energy, which is transformed to in vivo pH, temperature, ionic strength and electrical potential.



This cross-platform MATLAB extension to the COnstraint-Based Reconstruction and Analysis (COBRA) toolbox is computationally efficient, extensively documented and open source.


AVAILABILITY
http://opencobra.sourceforge.net.
Bayesian Computation via Markov chain Monte

A search for Markov chain Monte Carlo (or MCMC) articles on Google Scholar yields over 100,000 hits, and a general web search on Google yields 1.7 million hits. These results stem largely from the ubiquitous use of these algorithms in modern computational statistics, as we shall now describe. MCMC algorithms are used to solve problems in many scientific fields, including physics (where many MCMC algorithms originated) and chemistry and computer science. However, the widespread popularity of MCMC samplers is largely due to their impact on solving statistical computation problems related to Bayesian inference. Specifically, suppose we are given an independent and identically distributed (henceforth iid) sample {x1, . . . , xn} from a parametric sampling density f(x|θ), where x ∈ X ⊂ R and θ ∈ Θ ⊂ R. Suppose we also have some prior density p(θ). Then the Bayesian paradigm prescribes that all aspects of inference should be based on the
" 1 Vehicle Routing with Time Windows using Genetic Algorithms

In vehicle routing problems with time windows (VRPTW), a set of vehicles with limits on capacity and travel time are available to service a set of customers with demands and earliest and latest time for servicing. The objective is to minimize the cost of servicing the set of customers without being tardy or exceeding the capacity or travel time of the vehicles. As finding a feasible solution to the problem is NP-complete, search methods based upon heuristics are most promising for problems of practical size. In this paper we describe GIDEON, a genetic algorithm heuristic for solving the VRPTW. GIDEON consists of a global customer clustering method and a local post-optimization method. The global customer clustering method uses an adaptive search strategy based upon population genetics, to assign vehicles to customers. The best solution obtained from the clustering method is improved by a local post-optimization method. The synergy a between global adaptive clustering method and a local route optimization method produce better results than those obtained by competing heuristic search methods. On a standard set of 56 VRPTW problems obtained from the literature the GIDEON system obtained 41 new best known solutions.
pH-sensitive polymeric nanoparticles to improve oral bioavailability of peptide/protein drugs and poorly water-soluble drugs.

pH-sensitive polymeric nanoparticles are promising for oral drug delivery, especially for peptide/protein drugs and poorly water-soluble medicines. This review describes current status of pH-sensitive polymeric nanoparticles for oral drug delivery and introduces the mechanisms of drug release from them as well as possible reasons for absorption improvement, with emphasis on our contribution to this field. pH-sensitive polymeric nanoparticles are prepared mainly with polyanions, polycations, their mixtures or cross-linked polymers. The mechanisms of drug release are the result of carriers' dissolution, swelling or both of them at specific pH. The possible reasons for improvement of oral bioavailability include the following: improve drug stability, enhance mucoadhesion, prolong resident time in GI tract, ameliorate intestinal permeability and increase saturation solubility and dissolution rate for poorly water-soluble drugs. As for the advantages of pH-sensitive nanoparticles over conventional nanoparticles, we conclude that (1) most carriers used are enteric-coating materials and their safety has been approved. (2) The rapid dissolution or swelling of carriers at specific pH results in quick drug release and high drug concentration gradient, which is helpful for absorption. (3) At the specific pH carriers dissolve or swell, and the bioadhesion of carriers to mucosa becomes high because nanoparticles turn from solid to gel, which can facilitate drug absorption.