nils-herrmann/clean_scirepeval huggingface dataset:
Experimental Studies of a Neural Oscillator for Biped Locomotion with QRIO

Recently, there has been a growing interest in biologically inspired biped locomotion control with Central Pattern Generator (CPG). However, few experimental attempts on real hardware 3D humanoid robots have yet been made. Our goal in this paper is to present our achievement of 3D biped locomotion using a neural oscillator applied to a humanoid robot, QRIO. We employ reduced number of neural oscillators as the CPG model, along with a task space Cartesian coordinate system and utilizing entrainment property to establish stable walking gait. We verify robustness against lateral perturbation, through numerical simulation of stepping motion in place along the lateral plane. We then implemented it on the QRIO. It could successfully cope with unknown 3mm bump by autonomously adjusting its stepping period. Sagittal motion produced by a neural oscillator is introduced, and then overlapped with the lateral motion generator in realizing 3D biped locomotion on a QRIO humanoid robot.
Believable Agents and Intelligent Story Adaptation for Interactive Storytelling

Interactive Narrative is an approach to interactive entertainment that enables the player to make decisions that directly affect the direction and/or outcome of the narrative experience being delivered by the computer system. Interactive narrative requires two seemingly conflicting requirements: coherent narrative and user agency. We present an interactive narrative system that uses a combination of narrative control and autonomous believable character agents to augment a story world simulation in which the user has a high degree of agency with narrative plot control. A drama manager called the Automated Story Director gives plot-based guidance to believable agents. The believable agents are endowed with the autonomy necessary to carry out directives in the most believable fashion possible. Agents also handle interaction with the user. When the user performs actions that change the world in such a way that the Automated Story Director can no longer drive the intended narrative forward, it is able to adapt the plot to incorporate the user’s changes and still achieve
Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines

Early detection of ventricular fibrillation (VF) and rapid ventricular tachycardia (VT) is crucial for the success of the defibrillation therapy. A wide variety of detection algorithms have been proposed based on temporal, spectral, or complexity parameters extracted from the ECG. However, these algorithms are mostly constructed by considering each parameter individually. In this study, we present a novel life-threatening arrhythmias detection algorithm that combines a number of previously proposed ECG parameters by using support vector machines classifiers. A total of 13 parameters were computed accounting for temporal (morphological), spectral, and complexity features of the ECG signal. A filter-type feature selection (FS) procedure was proposed to analyze the relevance of the computed parameters and how they affect the detection performance. The proposed methodology was evaluated in two different binary detection scenarios: shockable (FV plus VT) versus nonshockable arrhythmias, and VF versus nonVF rhythms, using the information contained in the medical imaging technology database, the Creighton University ventricular tachycardia database, and the ventricular arrhythmia database. sensitivity (SE) and specificity (SP) analysis on the out of sample test data showed values of SE=95%, SP=99%, and SE=92% , SP=97% in the case of shockable and VF scenarios, respectively. Our algorithm was benchmarked against individual detection schemes, significantly improving their performance. Our results demonstrate that the combination of ECG parameters using statistical learning algorithms improves the efficiency for the detection of life-threatening arrhythmias.
High-Efficiency Differential-Drive CMOS Rectifier for UHF RFIDs

A high-efficiency CMOS rectifier circuit for UHF RFIDs was developed. The rectifier has a cross-coupled bridge configuration and is driven by a differential RF input. A differential-drive active gate bias mechanism simultaneously enables both low ON-resistance and small reverse leakage of diode-connected MOS transistors, resulting in large power conversion efficiency (PCE), especially under small RF input power conditions. A test circuit of the proposed differential-drive rectifier was fabricated with 0.18 mu m CMOS technology, and the measured performance was compared with those of other types of rectifiers. Dependence of the PCE on the input RF signal frequency, output loading conditions and transistor sizing was also evaluated. At the single-stage configuration, 67.5% of PCE was achieved under conditions of 953 MHz, - 12.5 dBm RF input and 10 KOmega output load. This is twice as large as that of the state-of-the-art rectifier circuit. The peak PCE increases with a decrease in operation frequency and with an increase in output load resistance. In addition, experimental results show the existence of an optimum transistor size in accordance with the output loading conditions. The multi-stage configuration for larger output DC voltage is also presented.
Synthetic Gecko Foot-Hair Micro / NanoStructures as Dry Adhesives

This paper proposes techniques to fabricate synthetic gecko foot-hairs as dry adhesives for future wallclimbing and surgical robots, and models for understanding the synthetic hair design issues. Two nanomolding fabrication techniques are proposed: the first method uses nanoprobe indented flat wax surface and the second one uses a nano-pore membrane as a template. These templates are molded with silicone rubber, polyimide, etc. type of polymers under vacuum. Next, design parameters such as length, diameter, stiffness, density, and orientation of hairs are determined for non-matting and rough surface adaptability. Preliminary micro/nano-hair prototypes showed adhesion close to the predicted values for natural specimens (around 100 nN each).
Selecting the right planning approach for a product , Supply Chain Management : An International Journal

The purpose of this research paper is to offer a solution to differentiate supply chain planning for products with different demand features and in different life-cycle phases.  A normative framework for selecting a planning approach was developed based on a literature review of supply chain differentiation and supply chain planning. Explorative mini-cases from three companies – Vaisala, Mattel, Inc. and Zara – were investigated to identify the features of their innovative planning solutions. The selection framework was applied to the case company’s new business unit dealing with a product portfolio of highly innovative products as well as commodity items.  The need for planning differentiation is essential for companies with large product portfolios operating in volatile markets. The complexity of market, channel and supply networks makes supply chain planning more intricate. The case company provides an example of using the framework for rough segmentation to differentiate planning.  The paper widens Fisher’s supply chain selection framework to consider the aspects of planning.  Despite substantial resources being used, planning results are often not reliable or consistent enough to ensure cost efficiency and adequate customer service. Therefore there is a need for management to critically consider current planning solutions.  The procedure outlined in this paper is a first illustrative example of the type of processes needed to monitor and select the right planning approach.
Morfessor 2.0: Toolkit for statistical morphological segmentation

Morfessor is a family of probabilistic machine learning methods for finding the morphological segmentation from raw text data. Recent developments include the development of semi-supervised methods for utilizing annotated data. Morfessor 2.0 is a rewrite of the original, widely-used Morfessor 1.0 software, with well documented command-line tools and library interface. It includes new features such as semi-supervised learning, online training, and integrated evaluation code.
Analysis, control and comparison of DC/DC boost converter topologies for fuel cell hybrid electric vehicle applications

Interleaving techniques are widely used to reduce input/output ripples, to increase the efficiency and to increase the power capacity of the boost converters. This paper presents an analysis, design and implementation of a high-power multileg interleaved DC/DC boost converter with a digital signal processor (DSP) based controller. This research focuses on non-isolated DC/DC converter that interfaces the fuel cell to the powertrain of the hybrid electric vehicles. In this paper, two-phase interleaved boost converter (IBC) with digital phase-shift control scheme is proposed in order to reduce the input current ripples, to reduce the output voltage ripples and to reduce the size of passive components with high efficiency for high power applications. The digital control based on DSP is proposed to solve the associated synchronization problem with interleaving converters. In addition, the real time workshop (RTW) is used for automatic real-time code generation. The proposed converter is compared with other topologies, such as conventional boost converter (BC) and multi-device boost converter (MDBC) in order to examine its performance. Moreover, a generalized small-signal model with complete parameters of these DC/DC converters is derived. The PWM DC/DC converter topologies and their control are simulated and investigated by using MATLAB/SIMULINK. Experimentally, a dual-loop average current control implemented in TMS320F2808 DSP is employed to achieve the fast transient response. Furthermore, the simulation and experimental results are provided.
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks

Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of large-scale medical trials and quantitative image analyses. We train and cascade two FCNs for the combined segmentation of the liver and its lesions. As a first step, we train an FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validation results on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for the liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on 38 MRI liver tumor volumes and the public 3DIRCAD dataset.
Deep Hashing Network for Unsupervised Domain Adaptation

In recent years, deep neural networks have emerged as a dominant machine learning tool for a wide variety of application domains. However, training a deep neural network requires a large amount of labeled data, which is an expensive process in terms of time, labor and human expertise. Domain adaptation or transfer learning algorithms address this challenge by leveraging labeled data in a different, but related source domain, to develop a model for the target domain. Further, the explosive growth of digital data has posed a fundamental challenge concerning its storage and retrieval. Due to its storage and retrieval efficiency, recent years have witnessed a wide application of hashing in a variety of computer vision applications. In this paper, we first introduce a new dataset, Office-Home, to evaluate domain adaptation algorithms. The dataset contains images of a variety of everyday objects from multiple domains. We then propose a novel deep learning framework that can exploit labeled source data and unlabeled target data to learn informative hash codes, to accurately classify unseen target data. To the best of our knowledge, this is the first research effort to exploit the feature learning capabilities of deep neural networks to learn representative hash codes to address the domain adaptation problem. Our extensive empirical studies on multiple transfer tasks corroborate the usefulness of the framework in learning efficient hash codes which outperform existing competitive baselines for unsupervised domain adaptation.
Children and adults as intuitive scientists.

The metaphor of children and lay adults as intuitive scientists has gained wide acceptance. Although useful in one sense, pertaining to scientific understanding, in another, pertaining to the process of scientific thinking, the metaphor may be fundamentally misleading. Research is reviewed indicating that processes of scientific thinking differ significantly in children, lay adults, and scientists. Hence, it is the instruments of scientific thinking, not just the products, that undergo "strong restructuring" (Carey, 1986). A framework for conceptualizing development of scientific thinking processes is proposed, centering on progressive differentiation and coordination of theory and evidence. This development is metacognitive, as well as strategic. It requires thinking about theories, rather than merely with them, and thinking about evidence, rather than merely being influenced by it, and, hence, reflects the attainment of control over the interaction of theories and evidence in one's own thinking.
Bagging gradient-boosted trees for high precision, low variance ranking models

Recent studies have shown that boosting provides excellent predictive performance across a wide variety of tasks. In Learning-to-rank, boosted models such as RankBoost and LambdaMART have been shown to be among the best performing learning methods based on evaluations on public data sets. In this paper, we show how the combination of bagging as a variance reduction technique and boosting as a bias reduction technique can result in very high precision and low variance ranking models. We perform thousands of parameter tuning experiments for LambdaMART to achieve a high precision boosting model. Then we show that a bagged ensemble of such LambdaMART boosted models results in higher accuracy ranking models while also reducing variance as much as 50%. We report our results on three public learning-to-rank data sets using four metrics. Bagged LamdbaMART outperforms all previously reported results on ten of the twelve comparisons, and bagged LambdaMART outperforms non-bagged LambdaMART on all twelve comparisons. For example, wrapping bagging around LambdaMART increases NDCG@1 from 0.4137 to 0.4200 on the MQ2007 data set; the best prior results in the literature for this data set is 0.4134 by RankBoost.
Robustness May Be at Odds with Accuracy

We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists even in a fairly simple and natural setting. These findings also corroborate a similar phenomenon observed in practice. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception.
Photometric stereo and weather estimation using internet images

We extend photometric stereo to make it work with internet images, which are typically associated with different viewpoints and significant noise. For popular tourism sites, thousands of images can be obtained from internet search engines. With these images, our method computes the global illumination for each image and the surface orientation at some scene points. The illumination information can then be used to estimate the weather conditions (such as sunny or cloudy) for each image, since there is a strong correlation between weather and scene illumination. We demonstrate our method on several challenging examples.
SODA: Service-Oriented Architecture for Runtime Adaptive Driver Assistance Systems

Unlike state-of-the-art Driver Assistance Systems(DAS) upcoming technologies may not base on a static software and system architecture. This is especially true for DAS for truck and trailer combinations were the components needed are distributed over both parts of the articulated vehicle. As a truck might be connected to different trailers changes are very common. In order to handle these changes at runtime the usage of Service oriented Architecture (SOA) is a promising approach. Using this principle the functionalities are encapsulated into Services which are Re-Orchestrated whenever the system architecture changes. Since the Services in such a system run on small embedded units and are connected through specialized network systems existing SOA frameworks cannot be used here. This paper introduces SODA, a tailored SOA middleware and development process to design runtime adaptive DAS for truck and trailer combinations using Services.
Lipid-based formulations for intestinal lymphatic delivery.

The current state of the art of intestinal lymphatic transport is given by reviewing the more recent publications, which have utilized lipid-based vehicles. The results published often show variable trends depending on, the design of the vehicle, the components used, the physicochemical properties of the drug, the animal model and experimental techniques, these variables often make direct comparisons difficult. Traditionally intestinal lymphatic delivery has been expressed as a percentage of the dose transported in the lymph. Using this parameter results obtained to date, with lipid-based vehicles, are somewhat disappointing maximising at approximately 20-30%, for highly lipophilic compounds including DDT and halofantrine (Hf). Recent data, monitoring Hf, in a fed versus fasted dog study, have shown that a higher degree of lymphatic transport is possible (>50% dose) in the postprandial state, this study should result in stimulating renewed interest in the potential of achieving significant levels of lymphatic targeting. Although some relevant features controlling lymphatic transport have been identified over the years a deeper appreciation of all the mechanisms, which is vital for therapeutic exploitation of lymphatic transport, is still unrealized. This review analyses the success and limitations of a formulation approach using lipid-based vehicles and highlights potential areas for further research.
Network Traffic Anomaly Detection Based on Packet Bytes

Hostile network traffic is often "different" from benign traffic in ways that can be distinguished without knowing the nature of the attack. We describe a two stage anomaly detection system for identifying suspicious traffic. First, we filter traffic to pass only the packets of most interest, e.g. the first few packets of incoming server requests. Second, we model the most common protocols (IP, TCP, telnet, FTP, SMTP, HTTP) at the packet byte level to flag events (byte values) that have not been observed for a long time. This simple system detects 132 of 185 attacks in the 1999 DARPA IDS evaluation data set  with 100 false alarms, after training on one week of attack-free traffic.
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization

We consider the problem of sparse coding, where each sample consists of a sparse linear combination of a set of dictionary atoms, and the task is to learn both the dictionary elements and the mixing coefficients. Alternating minimization is a popular heuristic for sparse coding, where the dictionary and the coefficients are estimated in alternate steps, keeping the other fixed. Typically, the coefficients are estimated via l1 minimization, keeping the dictionary fixed, and the dictionary is estimated through least squares, keeping the coefficients fixed. In this paper, we establish local linear convergence for this variant of alternating minimization and establish that the basin of attraction for the global optimum (corresponding to the true dictionary and the coefficients) is O ( 1/s ) , where s is the sparsity level in each sample and the dictionary satisfies RIP. Combined with the recent results of approximate dictionary estimation, this yields provable guarantees for exact recovery of both the dictionary elements and the coefficients, when the dictionary elements are incoherent.
Poster Abstract: A 6LoWPAN Model for OMNeT++

This paper introduces a 6LoWPAN simulation model for OMNeT++. Providing a 6LoWPAN model is an important step to advance OMNeT++-based Internet of Things simulations. We integrated Contiki’s 6LoWPAN implementation into OMNeT++ in order to avoid problems of non-standard compliant, non-interoperable, or highly abstracted and thus unreliable simulation models. The paper covers the model’s structure as well as its integration and the generic interaction between OMNeT++ / INET and Contiki.
Automatic Accident Detection: Assistance Through Communication Technologies and Vehicles

In this article, e-NOTIFY system is presented, which allows fast detection of traffic accidents, improving the assistance to injured passengers by reducing the response time of emergency services through the efficient communication of relevant information about the accident using a combination of V2V and V2I communications. The proposed system requires installing OBUs in the vehicles, in charge of detecting accidents and notifying them to an external CU, which will estimate the severity of the accident and inform the appropriate emergency services about the incident. This architecture replaces the current mechanisms for notification of accidents based on witnesses, who may provide incomplete or incorrect information after a long time. The development of a low-cost prototype shows that it is feasible to massively incorporate this system in existing vehicles.
Analysis of Soil Behaviour and Prediction of Crop Yield Using Data Mining Approach

Yield prediction is very popular among farmers these days, which particularly contributes to the proper selection of crops for sowing. This makes the problem of predicting the yielding of crops an interesting challenge. Earlier yield prediction was performed by considering the farmer's experience on a particular field and crop. This work presents a system, which uses data mining techniques in order to predict the category of the analyzed soil datasets. The category, thus predicted will indicate the yielding of crops. The problem of predicting the crop yield is formalized as a classification rule, where Naive Bayes and K-Nearest Neighbor methods are used.