nils-herrmann/clean_scirepeval huggingface dataset:
Neurobiology of decision-making: risk and reward.

Neurological patients with bilateral ventromedial (VM) lesions of the prefrontal cortex often deny, or they are not aware that they have a problem. Furthermore, they often pursue actions that bring some reward in the immediate run, despite severe long-term consequences such as the loss of job, home, and family. The somatic marker hypothesis, which provides an account of this defect in decision-making, posits that the impairment is the result of defective activation of somatic markers that normally function as covert or overt signposts for helping with the process of making choices that are advantageous to the organism. Failure to enact somatic states results from dysfunction in a neural system in which the VM cortex is one critical region. However, other neural regions, including the amygdala, and somatosensory cortices (SI, SII, and insula) are also hypothesized to be components of that same neural system. Recent evidence reveals that substance abusers suffer from decision-making deficit akin to that seen with patients with VM lesions. Thus, the strategies used to study decision-making in neurological patients have direct implications for understanding several neuropsychiatric disorders including addiction and pathological gambling.
Crowdfunding Success Factors: The Characteristics of Successfully Funded Projects on Crowdfunding Platforms

Crowdfunding platforms offer promising opportunities for project founders to publish their project ideas and to collect money in order to be able to realize them. Consequently, the question of what influences the successful funding of projects, i.e., reaching the target amount of money, is very important. Building upon media richness theory and the concept of reciprocity, we extend previous research in the field of crowdfunding success factors. We provide a comprehensive view on factors influencing crowdfunding success by both focusing on project-specific as well as founder-specific aspects. Analyzing a sample of projects of the crowdfunding platform kickstarter.com, we find that the project description, related images and videos as well as the question of whether the founder has previously backed other projects influence funding success. Interestingly, the question of whether the founder has previously created other projects has no significant influence. Our results are of high interest for the stakeholders on crowdfunding platforms.
Web evolution and Web Science

This paper examines the evolution of the World Wide Web as a network of networks and discusses the emergence of Web Science as an interdisciplinary area that can provide us with insights on how the Web developed, and how it has affected and is affected by society. Through its different stages of evolution, the Web has gradually changed from a technological network of documents to a network where documents, data, people and organisations are interlinked in various and often unexpected ways. It has developed from a technological artefact separate from people to an integral part of human activity that is having an increasingly significant impact on the world. This paper outlines the lessons from this retrospective examination of the evolution of the Web, presents the main outcomes of Web Science activities and discusses directions along which future developments could be anticipated.
An Innovative Space Rover with Extended Climbing Abilities

Autonomous mobile robots have become a key technology for unmanned planetary missions. To cope with the rough terrain encountered on most of the planets of interest, new locomotion concepts for rovers and micro-rovers have to be developed and investigated. The most advanced locomotion concepts are based on wheels or caterpillars (e.g. Sojourner (NASA) or Nanokhod (ESA)). These rovers have clear advantages regarding power efficiency and complexity if compared with walking robots. However, they still have quite limited climbing abilities. Typically they can only overcome obstacle of their wheel size. In our paper we present an innovative rover concept with 6 motorized wheels. Using a rhombus configuration, the rover has a steering wheel in the front and the rear, and two wheels arranged on a bogie on each side. The front wheel has a spring suspension to guarantee optimal ground contact of all wheels at any time. The steering of the rover is realized by synchronizing the steering of the front and rear wheels and the speed difference of the bogie wheels. This allows for high precision maneuvers and even turning on the spot with minimum slip. The use of parallel articulations for the front wheel and the bogies enables to set a virtual center of rotation at the level of the wheel axis. This insures maximum stability and climbing abilities even for very low friction coefficients between the wheel and the ground. A well functioning prototype has been designed and manufactured in our lab. It shows excellent performance surpassing our expectations. This rover is able to passively overcome unstructured obstacles of up to two times its wheel diameter and can climb stairs with steps of over 20 cm.
ViP-CNN: A Visual Phrase Reasoning Convolutional Neural Network for Visual Relationship Detection

As the intermediate level task connecting image captioning and object detection, visual relationship detection started to catch researchers’ attention because of its descriptive power and clear structure. It localizes the objects and captures their interactions with a subject-predicateobject triplet, e.g. 〈person-ride-horse〉. In this paper, the visual relationship is considered as a phrase with three components. So we formulate the visual relationship detection as three inter-connected recognition problems and propose a Visual Phrase reasoning Convolutional Neural Network (ViP-CNN) to address them simultaneously. In ViP-CNN, we present a Visual Phrase Reasoning Structure (VPRS) to set up the connection among the relationship components and help the model consider the three problems jointly. Corresponding non-maximum suppression method and model training strategy are also proposed. Experimental results show that our ViP-CNN outperforms the stateof-art method both in speed and accuracy. We further pretrain our model on our cleansed Visual Genome Relationship dataset, which is found to perform better than the pretraining on the ImageNet for this task.
Review of high power isolated bi-directional DC-DC converters for PHEV/EV DC charging infrastructure

PHEV/EV DC charging infrastructure attracts more and more attention recently. High power isolated bi-directional DC-DC converters provide galvanic isolation, V2G capability and reduce the cost and footprint of the system. Maintaining high power efficiency in wide vehicle battery pack voltage range is required. Three full bridge based high power bi-directional DC-DC converters are conceptually designed for this application and their advantages and disadvantages are addressed. Experimental test bench is built and efficiency evaluation for bi-directional operation is reported.
Metal nanogrids , nanowires , and nanofi bers for transparent electrodes

VOLUME 36 • OCTOBER 2011 • www.mrs.org/bulletin  Transparent electrodes are ubiquitously used in optoelectronic devices where light must pass through a section, and current or voltage needs to be applied. These devices include thin-fi lm solar cells (organic, inorganic, hybrid), displays (liquid-crystal display, plasma display, organic light-emitting diode [OLED]), touch screens (resistive, capacitive), electronic books, smart windows, fl exible lighting, and passive devices (such as transparent heating, transparent speakers). 1 The properties of transparent electrodes are extremely important for device performance, and the specifi cations vary based on types of devices. For example, while source roughness and work function are insignifi cant for resistive touch screens, they are crucial for OLEDs and organic solar cells. 1 Moreover, the optical haze of particular transparent electrodes is benefi cial for high-effi ciency solar cells but problematic for certain types of displays. The mechanical properties of the electrode are crucial for fl exible devices but not important for devices on rigid substrates. Across all devices, however, the optical transparency ( T ) and electrical sheet resistance ( Rs ) are the two most important parameters. Unfortunately, these two parameters are in constant competition with one another. Since Rs values are often reported at different transparencies, it is hard to compare two transparent electrodes directly, and hence a fi gure of merit ( σ dc / σ op , where σ dc is the dc conductivity, and σ op is the optical conductivity) Metal nanogrids, nanowires, and nanofi bers for transparent electrodes
TEM Horn Antenna Loaded With Absorbing Material for GPR Applications

A novel exponentially tapered TEM horn antenna is presented. It is fed directly by a coax through a compact transition section and possesses a wide bandwidth, good radiation characteristic, and easy integration with microwave circuits. Two specially tapered metal flares have been designed to make a wideband characteristic and reduce the reflection. To overcome aperture reflections at low frequencies and unbalanced aperture field at higher frequencies, an arc surface is added at the end of the flare plates, and an absorbing material is loaded on the outer surface of the flare section. Measured results show that the presented antenna has a wide bandwidth from 0.83 to more than 12.8 GHz under a return loss <;-10 dB, small input reflection, a moderate gain from 0 dBi at 0.83 GHz to 10 dBi at 12 GHz, and a good radiation characteristics in the time domain, which can be suitable for ground penetrating radar (GPR) applications.
Functional Design of a Powered Elbow Orthosis Toward its Clinical Employment

This paper presents the design and preliminary evaluation of a novel version of the robotic elbow exoskeleton NEUROExos, designed for the in-clinic treatment of stroke survivors in acute and subacute phases. The robotic design implements a novel series elastic actuation system, a 4-degree-of-freedom (DoFs) passive mechanism for the anatomical axis alignment, and one active DoF with remote cable-driven actuation. The low-level control system allows two working modalities: a torque control and a joint position control. The high-level control system employs a finite-state machine that allows the setting and execution of these modalities during rehabilitation exercises. Preliminary pilot tests based on passive exercises, with three chronic post-stroke patients, demonstrated the effectiveness of the proposed approach in assessing joint rigidity and its usability within a rehabilitation clinic.
UAVSim: A simulation testbed for unmanned aerial vehicle network cyber security analysis

Increased use of unmanned systems in various tasks enables users to complete important missions without risking human lives. Nonetheless, these systems pose a huge threat if the operational cyber security is not handled properly, especially for the unmanned aerial vehicle systems (UAVS), which can cause catastrophic damages. Therefore, it is important to check the impact of various attack attempts on the UAV system. The most economical and insightful way to do this is to simulate operational scenarios of UAVs in advance. In this paper, we introduce UAVSim, a simulation testbed for Unmanned Aerial Vehicle Networks cyber security analysis. The testbed allows users to easily experiment by adjusting different parameters for the networks, hosts and attacks. In addition, each UAV host works on well-defined mobility framework and radio propagation models, which resembles real-world scenarios. Based on the experiments performed in UAVSim, we evaluate the impact of Jamming attacks against UAV networks and report the results to demonstrate the necessity and usefulness of the testbed.
An analysis of facial expression recognition under partial facial image occlusion

In this paper, an analysis of the effect of partial occlusion on facial expression recognition is investigated. The classification from partially occluded images in one of the six basic facial expressions is performed using a method based on Gabor wavelets texture information extraction, a supervised image decomposition method based on Discriminant Non-negative Matrix Factorization and a shape-based method that exploits the geometrical displacement of certain facial features. We demonstrate how partial occlusion affects the above mentioned methods in the classification of the six basic facial expressions, and indicate the way partial occlusion affects human observers when recognizing facial expressions. An attempt to specify which part of the face (left, right, lower or upper region) contains more discriminant information for each facial expression, is also made and conclusions regarding the pairs of facial expressions misclassifications that each type of occlusion introduces, are drawn.
Learning Dynamic Stream Weights For Coupled-HMM-Based Audio-Visual Speech Recognition

With the increasing use of multimedia data in communication technologies, the idea of employing visual information in automatic speech recognition (ASR) has recently gathered momentum. In conjunction with the acoustical information, the visual data enhances the recognition performance and improves the robustness of ASR systems in noisy and reverberant environments. In audio-visual systems, dynamic weighting of audio and video streams according to their instantaneous confidence is essential for reliably and systematically achieving high performance. In this paper, we present a complete framework that allows blind estimation of dynamic stream weights for audio-visual speech recognition based on coupled hidden Markov models (CHMMs). As a stream weight estimator, we consider using multilayer perceptrons and logistic functions to map multidimensional reliability measure features to audiovisual stream weights. Training the parameters of the stream weight estimator requires numerous input-output tuples of reliability measure features and their corresponding stream weights. We estimate these stream weights based on oracle knowledge using an expectation maximization algorithm. We define 31-dimensional feature vectors that combine model-based and signal-based reliability measures as inputs to the stream weight estimator. During decoding, the trained stream weight estimator is used to blindly estimate stream weights. The entire framework is evaluated using the Grid audio-visual corpus and compared to state-of-the-art stream weight estimation strategies. The proposed framework significantly enhances the performance of the audio-visual ASR system in all examined test conditions.
Parametric modelling of a twin rotor system using genetic algorithms

System identification using parametric linear approaches for modelling a twin rotor multi-input multi-output system (TRMS) in hovering position is presented in this work. The utilisation of a genetic algorithm (GA) optimisation technique for dynamic modelling of a highly non-linear system is studied in comparison to the conventional recursive least squares (RLS) technique. The global search technique of GA is used to identify the parameters of the TRMS based on one-step-ahead prediction. A comparative assessment of the two models in characterising the system is carried out in the time and frequency domains. Experimental results indicate the advantages of GA over RLS in linear parametric modelling. The developed genetic-modelling approach will be used for control design and development in future work.
Sentence-Level and Document-Level Sentiment Mining for Arabic Texts

In this work, we investigate sentiment mining of Arabic text at both the sentence level and the document level. Existing research in Arabic sentiment mining remains very limited. For sentence-level classification, we investigate two approaches. The first is a novel grammatical approach that employs the use of a general structure for the Arabic sentence. The second approach is based on the semantic orientation of words and their corresponding frequencies, to do this we built an interactive learning semantic dictionary which stores the polarities of the roots of different words and identifies new polarities based on these roots. For document-level classification, we use sentences of known classes to classify whole documents, using a novel approach whereby documents are divided dynamically into chunks and classification is based on the semantic contributions of different chunks in the document. This dynamic chunking approach can also be investigated for sentiment mining in other languages. Finally, we propose a hierarchical classification scheme that uses the results of the sentence-level classifier as input to the document-level classifier, an approach which has not been investigated previously for Arabic documents. We also pinpoint the various challenges that are faced by sentiment mining for Arabic texts and propose suggestions for its development. We demonstrate promising results with our sentence-level approach, and our document-level experiments show, with high accuracy, that it is feasible to extract the sentiment of an Arabic document based on the classes of its sentences.
A Framework for Network Function Virtualization by Chang Lan Research Project

The proliferation of network processing appliances (“Middleboxes”) has been accompanied by a growing recognition of the problems they bring, including expensive hardware and complex management. This recognition led the networking industry to launch a concerted effort towards Network Function Virtualization (NFV) with the goal of bringing greater openness and agility to network dataplanes. However, a closer look under the hood reveals a less rosy picture: NFV is currently replacing, on a one-to-one basis, monolithic hardware with monolithic software. Furthermore, while several efforts are exploring a new model for middlebox deployment in which a third-party offers middlebox processing as a service, no solutions address the confidentiality concerns. In order to process an organizations traffic, the cloud sees the traffic unencrypted. This means that the cloud now has access to potentially sensitive packet payloads and headers. In the first part of this thesis, we present E2, a scalable and application-agnostic scheduling framework for packet processing, and compare its performance to current approaches. E2 brings two benefits: (i) it allows developers to rely on external frameworkbased mechanisms for common tasks, freeing them to focus on their core application logic and (ii) it simplifies the operators responsibilities, as it both automates and consolidates common management tasks. In the following chapter, we then present Embark, the first system that enables a cloud provider to support middlebox outsourcing while maintaining the clients confidentiality. Embark supports a wide-range of middleboxes. Our evaluation shows that Embark supports these applications with competitive performance.
Comparison of Texture Features Based on Gabor Filters

Texture features that are based on the local power spectrum obtained by a bank of Gabor filters are compared. The features differ in the type of nonlinear post-processing which is applied to the local power spectrum. The following features are considered: Gabor energy, complex moments, and grating cell operator features. The capability of the corresponding operators to produce distinct feature vector clusters for different textures is compared using two methods: the Fisher criterion and the classification result comparison. Both methods give consistent results. The grating cell operator gives the best discrimination and segmentation results. The texture detection capabilities of the operators and their robustness to nontexture features are also compared. The grating cell operator is the only one that selectively responds only to texture and does not give false response to nontexture features such as object contours.
Enhanced Simulated Annealing for Globally Minimizing Functions of Many-Continuous Variables

A new global optimization algorithm for functions of many continuous variables is presented, derived from the basic Simulated annealing method. Our main contribution lies in dealing with high-dimensionality minimization problems, which are often difficult to solve by all known minimization methods with or without gradient. In this article we take a special interest in the variables discretization issue. We also develop and implement several complementary stopping criteria. The original Metropolis iterative random search, which takes place in a Euclidean space Rn, is replaced by another similar exploration, performed within a succession of Euclidean spaces Rp, with p <<n. This Enhanced Simulated Annealing (ESA) algorithm was validated first on classical highly multimodal functions of 2 to 100 variables. We obtained significant reductions in the number of function evaluations compared to six other global optimization algorithms, selected according to previously published computational results for the same set of test functions. In most cases, ESA was able to closely approximate known global optima. The reduced ESA computational cost helped us to refine further the obtained global results, through the use of some local search. We have used this new minimizing procedure to solve complex circuit design problems, for which the objective function evaluation can be exceedingly costly.
A Neural Network Approach to Missing Marker Reconstruction

Optical motion capture systems have become a widely used technology in various fields, such as augmented reality, robotics, movie production, etc. Such systems use a large number of cameras to triangulate the position of optical markers. These are then used to reconstruct the motion of rigid objects or human articulated bodies, to which the markers are attached. The marker positions are estimated with high accuracy. However, especially when tracking articulated bodies, a fraction of the markers in each timestep are missing from the reconstruction. In this paper, we propose to use a neural network approach to learn how human motion is temporally and spatially correlated, and reconstruct missing markers positions through this model. We experiment with two different models, one LSTM-based and one window-based. Experiments on the CMU Mocap dataset show that we outperform the state of the art by 20% 400%.
A Survey of Artificial Cognitive Systems: Implications for the Autonomous Development of Mental Capabilities in Computational Agents

This survey presents an overview of the autonomous development of mental capabilities in computational agents. It does so based on a characterization of cognitive systems as systems which exhibit adaptive, anticipatory, and purposive goal-directed behavior. We present a broad survey of the various paradigms of cognition, addressing cognitivist (physical symbol systems) approaches, emergent systems approaches, encompassing connectionist, dynamical, and enactive systems, and also efforts to combine the two in hybrid systems. We then review several cognitive architectures drawn from these paradigms. In each of these areas, we highlight the implications and attendant problems of adopting a developmental approach, both from phylogenetic and ontogenetic points of view. We conclude with a summary of the key architectural features that systems capable of autonomous development of mental capabilities should exhibit
GENIA corpus - a semantically annotated corpus for bio-textmining

Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining.



GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.