nils-herrmann/clean_scirepeval huggingface dataset:
Multi-agent planning: An introduction to planning and coordination

Many day-to-day situations involve decision making: for example, a taxi company has some transportation tasks to be carried out, a large firm has to distribute a lot of complicated tasks among its subdivisions or subcontractors, and an air-traffic controller has to assign time slots to planes that are landing or taking off. Intelligent agentscan aid in this decision-making process. Agents are often classified into two categories according to the techniques they employ in their decision making: reactiveagents (cf. (Ferber and Drogoul, 1992)) base their next decision solely on their current sensory input; planning agents, on the other hand, take into account anticipated future developments — for instance as a result of their own actions — to decide on the most favourable course of action. When an agent should plan and when it should be reactive depends on the particular situation it finds itself in. Consider the example where an agent has to plan a route from one place to another. A reactive agent might use a compass to plot its course, whereas a planning agent would consult a map. Clearly, the planning agent will come up with the shortest route in most cases, as it won’t be confounded by uncrossable rivers, one-way streets, and labyrinthine city layouts. On the other hand, there are also situations where a reactive agent can at least be equally effective, for instance if there are no maps to consult, for instance in a domain of (Mars) exploration rovers. Nevertheless, the ability to plan ahead is invaluable in many domains, so in this paper we will focus on plan ing agents. The general structure of a planning problem is easy to explain: (the relevant part of) the world is in a certain state, but managers or directors would like it to be in another state. The (abstract) problem of how one should get from the current state of the world through a sequence of actions to the desired goal state is a planning problem. Ideally, to solve such planning problems, we would like to have a general planning-problem solver. However, such an algorithm solving all planning problems can be proven to be non-existing. 1 We therefore start to concentrate on a simplification of the general planning problem called ‘theclassical planning problem’. Although not all realistic problems can be modeled as a classical planning problem, they can help to solve more
OpenViBE: An Open-Source Software Platform to Design, Test, and Use BrainComputer Interfaces in Real and Virtual Environments

This paper describes the OpenViBE software platform which enables researchers to design, test, and use braincomputer interfaces (BCIs). BCIs are communication systems that enable users to send commands to computers solely by means of brain activity. BCIs are gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). The key features of the platform are (1) high modularity, (2) embedded tools for visualization and feedback based on VR and 3D displays, (3) BCI design made available to non-programmers thanks to visual programming, and (4) various tools offered to the different types of users. The platform features are illustrated in this paper with two entertaining VR applications based on a BCI. In the first one, users can move a virtual ball by imagining hand movements, while in the second one, they can control a virtual spaceship using real or imagined foot movements. Online experiments with these applications together with the evaluation of the platform computational performances showed its suitability for the design of VR applications controlled with a BCI. OpenViBE is a free software distributed under an open-source license.
Local Binary Patterns and Extreme Learning Machine for Hyperspectral Imagery Classification

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.
HSM: a fast packet classification algorithm

Packet classification on multiple header fields is one of the basic techniques used in network devices such as routers and firewalls, and usually the most computation intensive task among others. To determine what action needs to be taken to a packet, a network device responsible for packet classification must identify the packet's property, such as associated packet flow, based on multiple fields of its header. Fast packet classification on multiple fields is known to be difficult mathematically and expensive practically. In this paper, we describe and discuss a fast packet classification algorithm using a multiple stage reduction scheme similar to the previously well-known recursive flow classification (RFC) algorithm. The proposed hierarchical space mapping (HSM) algorithm requires much less memory usage than RFC while keeps average search time on the same order. HSM has been proved to be very effective with commercial products in real networks.
Gradient descent with sparsification: an iterative algorithm for sparse recovery with restricted isometry property

We present an algorithm for finding an <i>s</i>-sparse vector <i>x</i> that minimizes the <i>square-error</i> ∥<i>y</i> -- Φ<i>x</i>∥<sup>2</sup> where Φ satisfies the <i>restricted isometry property</i> (RIP), with <i>isometric constant</i> Δ<sub>2<i>s</i></sub> < 1/3. Our algorithm, called <b>GraDeS</b> (Gradient Descent with Sparsification) iteratively updates <i>x</i> as: 
 where γ > 1 and <i>H<sub>s</sub></i> sets all but <i>s</i> largest magnitude coordinates to zero. <b>GraDeS</b> converges to the correct solution in constant number of iterations. The condition Δ<sub>2<i>s</i></sub> < 1/3 is most general for which a <i>near-linear time</i> algorithm is known. In comparison, the best condition under which a polynomial-time algorithm is known, is Δ<sub>2<i>s</i></sub> < √2 -- 1.
 Our Matlab implementation of <b>GraDeS</b> outperforms previously proposed algorithms like Subspace Pursuit, StOMP, OMP, and Lasso by an order of magnitude. Curiously, our experiments also uncovered cases where L1-regularized regression (Lasso) fails but <b>GraDeS</b> finds the correct solution.
Control strategies for active lower extremity prosthetics and orthotics: a review

Technological advancements have led to the development of numerous wearable robotic devices for the physical assistance and restoration of human locomotion. While many challenges remain with respect to the mechanical design of such devices, it is at least equally challenging and important to develop strategies to control them in concert with the intentions of the user. This work reviews the state-of-the-art techniques for controlling portable active lower limb prosthetic and orthotic (P/O) devices in the context of locomotive activities of daily living (ADL), and considers how these can be interfaced with the user’s sensory-motor control system. This review underscores the practical challenges and opportunities associated with P/O control, which can be used to accelerate future developments in this field. Furthermore, this work provides a classification scheme for the comparison of the various control strategies. As a novel contribution, a general framework for the control of portable gait-assistance devices is proposed. This framework accounts for the physical and informatic interactions between the controller, the user, the environment, and the mechanical device itself. Such a treatment of P/Os – not as independent devices, but as actors within an ecosystem – is suggested to be necessary to structure the next generation of intelligent and multifunctional controllers. Each element of the proposed framework is discussed with respect to the role that it plays in the assistance of locomotion, along with how its states can be sensed as inputs to the controller. The reviewed controllers are shown to fit within different levels of a hierarchical scheme, which loosely resembles the structure and functionality of the nominal human central nervous system (CNS). Active and passive safety mechanisms are considered to be central aspects underlying all of P/O design and control, and are shown to be critical for regulatory approval of such devices for real-world use. The works discussed herein provide evidence that, while we are getting ever closer, significant challenges still exist for the development of controllers for portable powered P/O devices that can seamlessly integrate with the user’s neuromusculoskeletal system and are practical for use in locomotive ADL.
DIVE - a Multi User Virtual Reality System

The Distributed Interactive Virtual Environment (DIVE) is a heterogeneous distributed VR system based on UNIX and Internet networking protocols. Each participating process has a copy of a replicated database and changes are propagated to the other processes with reliable multicast protocols. DIVE provides a dynamic virtual environment whew applications and users can enter and leave the environment on demand. Several user-related abstractions have been introduced to ease the task of application and user interface construction.
Knowledge discovery approach to automated cardiac SPECT diagnosis

The paper describes a computerized process of myocardial perfusion diagnosis from cardiac single proton emission computed tomography (SPECT) images using data mining and knowledge discovery approach. We use a six-step knowledge discovery process. A database consisting of 267 cleaned patient SPECT images (about 3000 2D images), accompanied by clinical information and physician interpretation was created first. Then, a new user-friendly algorithm for computerizing the diagnostic process was designed and implemented. SPECT images were processed to extract a set of features, and then explicit rules were generated, using inductive machine learning and heuristic approaches to mimic cardiologist's diagnosis. The system is able to provide a set of computer diagnoses for cardiac SPECT studies, and can be used as a diagnostic tool by a cardiologist. The achieved results are encouraging because of the high correctness of diagnoses.
Data fusion in intelligent transportation systems: Progress and challenges - A survey

In intelligent transportation systems (ITS), transportation infrastructure is complimented with information and communication technologies with the objectives of attaining improved passenger safety, reduced transportation time and fuel consumption and vehicle wear and tear. With the advent of modern communication and computational devices and inexpensive sensors it is possible to collect and process data from a number of sources. Data fusion (DF) is collection of techniques by which information from multiple sources are combined in order to reach a better inference. DF is an inevitable tool for ITS. This paper provides a survey of how DF is used in different areas of ITS.
The frontal branch of the facial nerve across the zygomatic arch: anatomical relevance of the high-SMAS technique.

The frontal branch has a defined course along the Pitanguy line from tragus to lateral brow, although its depth along this line is controversial. The high-superficial musculoaponeurotic system (SMAS) face-lift technique divides the SMAS above the arch, which conflicts with previous descriptions of the frontal nerve depth. This anatomical study defines the depth and fascial boundaries of the frontal branch of the facial nerve over the zygomatic arch.



Eight fresh cadaver heads were included in the study, with bilateral facial nerves studied (n = 16). The proximal frontal branches were isolated and then sectioned in full-thickness tissue blocks over a 5-cm distance over the zygomatic arch. The tissue blocks were evaluated histologically for the depth and fascial planes surrounding the frontal nerve. A dissection video accompanies this article.



The frontal branch of the facial nerve was identified in each tissue section and its fascial boundaries were easily identified using epidermis and periosteum as reference points. The frontal branch coursed under a separate fascial plane, the parotid-temporal fascia, which was deep to the SMAS as it coursed to the zygomatic arch and remained within this deep fascia over the arch. The frontal branch was intact and protected by the parotid-temporal fascia after a high-SMAS face lift.



The frontal branch of the facial nerve is protected by a deep layer of fascia, termed the parotid-temporal fascia, which is separate from the SMAS as it travels over the zygomatic arch. Division of the SMAS above the arch in a high-SMAS face lift is safe using the technique described in this study.
Statistical Section Segmentation in Free-Text Clinical Records

Automatically segmenting and classifying clinical free text into sections is an important first step to automatic information retrieval, information extraction and data mining tasks, as it helps to ground the significance of the text within. In this work we describe our approach to automatic section segmentation of clinical records such as hospital discharge summaries and radiology reports, along with section classification into pre-defined section categories. We apply machine learning to the problems of section segmentation and section classification, comparing a joint (one-step) and a pipeline (two-step) approach. We demonstrate that our systems perform well when tested on three data sets, two for hospital discharge summaries and one for radiology reports. We then show the usefulness of section information by incorporating it in the task of extracting comorbidities from discharge summaries.
Learning multi-label scene classification

In classic pattern recognition problems, classes are mutually exclusive by de"nition. Classi"cation errors occur when the classes overlap in the feature space. We examine a di5erent situation, occurring when the classes are, by de"nition, not mutually exclusive. Such problems arise in semantic scene and document classi"cation and in medical diagnosis. We present a framework to handle such problems and apply it to the problem of semantic scene classi"cation, where a natural scene may contain multiple objects such that the scene can be described by multiple class labels (e.g., a "eld scene with a mountain in the background). Such a problem poses challenges to the classic pattern recognition paradigm and demands a di5erent treatment. We discuss approaches for training and testing in this scenario and introduce new metrics for evaluating individual examples, class recall and precision, and overall accuracy. Experiments show that our methods are suitable for scene classi"cation; furthermore, our work appears to generalize to other classi"cation problems of the same nature. ? 2004 Pattern Recognition Society.
Policy-driven security management for fog computing: Preliminary framework and a case study

With the increasing user demand for elastic provisioning of resources coupled with ubiquitous and on-demand access to data, cloud computing has been recognized as an emerging technology to meet such dynamic user demands. In addition, with the introduction and rising use of mobile devices, the Internet of Things (IoT) has recently received considerable attention since the IoT has brought physical devices and connected them to the Internet, enabling each device to share data with surrounding devices and virtualized technologies in real-time. Consequently, the exploding data usage requires a new, innovative computing platform that can provide robust real-time data analytics and resource provisioning to clients. As a result, fog computing has recently been introduced to provide computation, storage and networking services between the end-users and traditional cloud computing data centers. This paper proposes a policy-based management of resources in fog computing, expanding the current fog computing platform to support secure collaboration and interoperability between different user-requested resources in fog computing.
Graphical User Interface Concepts for Tactical Augmented Reality

Applied Research Associates and BAE Systems are working together to develop a wearable augmented reality system under the DARPA ULTRA-Vis program † . Our approach to achieve the objectives of ULTRAVis, called iLeader, incorporates a full color 40° field of view (FOV) see-thru holographic waveguide integrated with sensors for full position and head tracking to provide an unobtrusive information system for operational maneuvers. iLeader will enable warfighters to mark-up the 3D battle-space with symbologic identification of graphical control measures, friendly force positions and enemy/target locations. Our augmented reality display provides dynamic real-time painting of symbols on real objects, a pose-sensitive 360 ̊ representation of relevant object positions, and visual feedback for a variety of system activities. The iLeader user interface and situational awareness graphical representations are highly intuitive, nondisruptive, and always tactically relevant. We used best human-factors practices, system engineering expertise, and cognitive task analysis to design effective strategies for presenting real-time situational awareness to the military user without distorting their natural senses and perception. We present requirements identified for presenting information within a see-through display in combat environments, challenges in designing suitable visualization capabilities, and solutions that enable us to bring real-time iconic command and control to the tactical user community.
Model predictive control of the interleaved DC-DC boost converter

This paper extends the recently introduced approach to the modeling and control design in the framework of model predictive control of the dc-dc boost converter to the dc-dc parallel interleaved boost converter. Based on the converter's model a constrained optimal control problem is formulated and solved. This allows the controller to achieve (a) the regulation of the output voltage to a predefined reference value, despite changes in the input voltage and the load, and (b) the load current balancing to the converter's individual legs, by regulating the currents of the circuit's inductors to proper references, set by an outer loop based on an observer. Simulation results are provided to illustrate the merits of the proposed control scheme.
Visualizing evolving networks: minimum spanning trees versus pathfinder networks

Network evolution is an ubiquitous phenomenon in a wide variety of complex systems. There is an increasing interest in statistically modeling the evolution of complex networks such as small-world networks and scale-free networks. In this article, we address a practical issue concerning the visualizations of co-citation networks of scientific publications derived by two widely known link reduction algorithms, namely minimum spanning trees (MSTs) and pathfinder networks (PFNETs). Our primary goal is to identify the strengths and weaknesses of the two methods in fulfilling the need for visualizing evolving networks. Two criteria are derived for assessing visualizations of evolving networks in terms of topological properties and dynamical properties. We examine the animated visualization models of the evolution of botulinum toxin research in terms of its co-citation structure across a 58-year span (1945-2002). The results suggest that although high-degree nodes dominate the structure of MST models, such structures can be inadequate in depicting the essence of how the network evolves because MST removes potentially significant links from high-order shortest paths. In contrast, PFNET models clearly demonstrate their superiority in maintaining the cohesiveness of some of the most pivotal paths, which in turn make the growth animation more predictable and interpretable. We suggest that the design of visualization and modeling tools for network evolution should take the cohesiveness of critical paths into account.
Auto-Context Convolutional Neural Network (Auto-Net) for Brain Extraction in Magnetic Resonance Imaging

Brain extraction or whole brain segmentation is an important first step in many of the neuroimage analysis pipelines. The accuracy and the robustness of brain extraction, therefore, are crucial for the accuracy of the entire brain analysis process. The state-of-the-art brain extraction techniques rely heavily on the accuracy of alignment or registration between brain atlases and query brain anatomy, and/or make assumptions about the image geometry, and therefore have limited success when these assumptions do not hold or image registration fails. With the aim of designing an accurate, learning-based, geometry-independent, and registration-free brain extraction tool, in this paper, we present a technique based on an auto-context convolutional neural network (CNN), in which intrinsic local and global image features are learned through 2-D patches of different window sizes. We consider two different architectures: 1) a voxelwise approach based on three parallel 2-D convolutional pathways for three different directions (axial, coronal, and sagittal) that implicitly learn 3-D image information without the need for computationally expensive 3-D convolutions and 2) a fully convolutional network based on the U-net architecture. Posterior probability maps generated by the networks are used iteratively as context information along with the original image patches to learn the local shape and connectedness of the brain to extract it from non-brain tissue. The brain extraction results we have obtained from our CNNs are superior to the recently reported results in the literature on two publicly available benchmark data sets, namely, LPBA40 and OASIS, in which we obtained the Dice overlap coefficients of 97.73% and 97.62%, respectively. Significant improvement was achieved via our auto-context algorithm. Furthermore, we evaluated the performance of our algorithm in the challenging problem of extracting arbitrarily oriented fetal brains in reconstructed fetal brain magnetic resonance imaging (MRI) data sets. In this application, our voxelwise auto-context CNN performed much better than the other methods (), where the other methods performed poorly due to the non-standard orientation and geometry of the fetal brain in MRI. Through training, our method can provide accurate brain extraction in challenging applications. This, in turn, may reduce the problems associated with image registration in segmentation tasks.