nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Identification of Manipulator : Comparison between CAD and Actual Parameters

It is essential to know the dynamic parameters of the robot for its precise control and simulation. Philosophy of identification is based on finding the model using its input-output data. The identification equation of the manipulator is derived from Newton-Euler equations, using manipulator kinematic, i.e., geometric parameters and joint values as input and joint torque data as output. In this paper, the dynamic parameters are identified for the CAD model provided by the robot manufacturer in simulation. And experimentally for the installed seven degrees of freedom (DOF) robot KUKA-iiwaR800. The variation between the joint torques predicted from the estimated base parameters obtained using CAD model and actual robot are presented. The factors responsible for the variation are also highlighted.
RBFOpt: an open-source library for black-box optimization with costly function evaluations

We consider the problem of optimizing an unknown function given as an oracle over a mixed-integer box-constrained set. We assume that the oracle is expensive to evaluate, so that estimating partial derivatives by finite differences is impractical. In the literature, this is typically called a black-box optimization problem with costly evaluation. This paper describes the solutionmethodology implemented in the open-source library RBFOpt, available on COIN-OR. The algorithm is based on the Radial Basis Function method originally proposed by Gutmann ( 2001. https:// doi.org/10.1023/A:1011255519438), which builds and iteratively refines a surrogate model of the unknown objective function. The twomain methodological contributions of this paper are an approach to exploit a noisy but less expensive oracle to accelerate convergence to the optimum of the exact oracle, and the introduction of an automatic model selection phase during the optimization process. Numerical experiments show thatRBFOpt is highly competitive on a test set of continuous andmixed-integer nonlinear unconstrained problems taken from the literature: it outperforms the open-source solvers included in our comparison by a large amount, and performs slightly better than a commercial solver. Our empirical evaluation provides insight on which parameterizations of the algorithm are the most effective in practice. The software reviewed as part of this submission was given the Digital Object Identifier (DOI) https://doi. org/10.5281/zenodo.597767.
Quadruped Robot Running With a Bounding Gait

Scout II, an autonomous four-legged robot with only one actuator per compliant leg is described. We demonstrate the need to model the actuators and the power source of the robot system carefully in order to obtain experimentally valid models for simulation and analysis. We describe a new, simple running controller that requires minimal task level feedback, yet achieves reliable and fast running up to 1.2 m/s. These results contribute to the increasing evidence that apparently complex dynamically dexterous tasks may be controlled via simple control laws. In addition, the simple mechanical design of our robot may be used as a template for the control of higher degree of freedom quadrupeds. An energetics analysis reveals a highly efficient system with a specific resistance of 0.32 when based on mechanical power dissipation and of 1.0 when based on total electrical power dissipation.
A circuit representation technique for automated circuit design

We present a method of automatically generating circuit designs using evolutionary search and a set of circuit-constructing primitives arranged in a linear sequence. This representation has the desirable property that virtually all sets of circuit-constructing primitives result in valid circuit graphs. While this representation excludes certain circuit topologies, it is capable of generating a rich set of them including many of the useful topologies seen in hand-designed circuits. Our system allows circuit size (number of devices), circuit topology, and device values to be evolved. Using a parallel genetic algorithm and circuit simulation software, we present experimental results as applied to three analog filter and two amplifier design tasks. In all tasks, our system is able to generate circuits that achieve the target specifications. Although the evolved circuits exist as software models, detailed examinations of each suggest that they are electrically well behaved and thus suitable for physical implementation. The modest computational requirements suggest that the ability to evolve complex analog circuit representations in software is becoming more approachable on a single engineering workstation.
Characterizing hypervisor vulnerabilities in cloud computing servers

The rise of the Cloud Computing paradigm has led to security concerns, taking into account that resources are shared and mediated by a Hypervisor which may be targeted by rogue guest VMs and remote attackers. In order to better define the threats to which a cloud server's Hypervisor is exposed, we conducted a thorough analysis of the codebase of two popular open-source Hypervisors, Xen and KVM, followed by an extensive study of the vulnerability reports associated with them. Based on our findings, we propose a characterization of Hypervisor Vulnerabilities comprised of three dimensions: the trigger source (i.e. where the attacker is located), the attack vector (i.e. the Hypervisor functionality that enables the security breach), and the attack target (i.e. the runtime domain that is compromised). This can be used to understand potential paths different attacks can take, and which vulnerabilities enable them. Moreover, most common paths can be discovered to learn where the defenses should be focused, or conversely, least common paths can be used to find yet-unexplored ways attackers may use to get into the system.
Do Language Models Understand Anything? On the Ability of LSTMs to Understand Negative Polarity Items

In this paper, we attempt to link the inner workings of a neural language model to linguistic theory, focusing on a complex phenomenon well discussed in formal linguistics: (negative) polarity items. We briefly discuss the leading hypotheses about the licensing contexts that allow negative polarity items and evaluate to what extent a neural language model has the ability to correctly process a subset of such constructions. We show that the model finds a relation between the licensing context and the negative polarity item and appears to be aware of the scope of this context, which we extract from a parse tree of the sentence. With this research, we hope to pave the way for other studies linking formal linguistics to deep learning.
Low-Loss Integrated-Waveguide Passive Circuits Using Liquid-Crystal Polymer System-on-Package (SOP) Technology for Millimeter-Wave Applications

In this paper, we show a low-loss integrated waveguide (IWG), microstrip line-to-IWG transition, IWG bandpass filter (BPF), and system-on-package (SOP) using a liquid-crystal polymer (LCP) substrate, which can be used toward SOP technology for millimeter-wave applications. The proposed IWG can be used as a low-loss millimeter-wave transmission line on this substrate. The measured insertion loss of the IWG is -0.12 dB/mm and the measured insertion loss of two microstrip line-to-IWG transitions is -0.14 dB at 60 GHz. The evaluated IWG filter is demonstrated as the pre-select filter for RF front-end modules at the millimeter-wave band. The fabricated three-pole BPF at a center frequency of 61.1 GHz has specifications: a 3-dB bandwidth of approximately 13.4% (~8.4 GHz), an insertion loss of -1.8 dB at the center frequency of 61.1 GHz, and a rejection of >15 dB over the passband. The proposed IWG can also be used as a low-loss millimeter-wave feed-through transition and interconnection between the monolithic microwave integrated circuit and the module instead of the vertical via structure. In terms of a SOP on LCP for millimeter-wave applications, the top face of the IWG does not have any electromagnetic effects, and a package lid can be attached to provide a hermetic sealing. These low-loss IWG circuits on LCP can easily be used in many millimeter-wave packaging applications
Anti-inflammatory effects of caper (Capparis spinosa L.) fruit aqueous extract and the isolation of main phytochemicals.

Caper (Capparis spinosa L.) fruits have been used as food as well as folk medicine in the treatment of inflammatory disorders, such as rheumatism. The present study was carried out to study the anti-inflammatory activities of C. spinosa L. fruit (CSF) aqueous extract and to isolate main phytochemicals from its bioactive fractions. The CSF aqueous extract were separated into three fractions (CSF1-CSF3) by macroporous adsorption resins. The fractions CSF2 and CSF3 effectively inhibited the carrageenan-induced paw edema in mice. Systematic fractionation and isolation from CSF2+3 led to the identification of 13 compounds (1-13). Their chemical structures were elucidated by spectroscopic analyses including nuclear magnetic resonance (NMR) and mass spectrometry (MS) and literature comparisons. Major compounds found in the bioactive fraction CSF2+3 are flavonoids, indoles, and phenolic acids. To our knowledge, 8 of these 13 compounds (1-4, 6-7, 10, and 13) were identified from caper fruits for the first time. The anti-inflammatory effects of these purified compounds are currently under investigation.
On the Module of Internet Banking System

Because of the speed, flexibility, and efficiency that it offers, the Internet has become the means for conducting growing numbers of transactions between suppliers and large international corporations. In this way, the Internet has opened new markets to the world and has accelerated the diffusion of knowledge. The meaning of Internet markets or online business has been widely used in these days. The success of the business depends on its flexibility, availability and security. Since that the web-based systems should have a special way to design the system and implement it. Nowadays, the Internet Banking System widely used and the banks looking to provide the best quality system with highly available, fast response, secure and safe to use. The Unified Modelling Language (UML) is the uniquely language which is used to analyse and design any system. In this paper, the UML diagrams has been proposed to illustrate the design phase for any banking system. The authors, presented two types of architecture which is used for the Internet Banking System.
Real Time Face Detection and Facial Expression Recognition: Development and Applications to Human Computer Interaction.

Computer animated agents and robots bring a social dimension to human computer interaction and force us to think in new ways about how computers could be used in daily life. Face to face communication is a real-time process operating at a a time scale in the order of 40 milliseconds. The level of uncertainty at this time scale is considerable, making it necessary for humans and machines to rely on sensory rich perceptual primitives rather than slow symbolic inference processes. In this paper we present progress on one such perceptual primitive. The system automatically detects frontal faces in the video stream and codes them with respect to 7 dimensions in real time: neutral, anger, disgust, fear, joy, sadness, surprise. The face finder employs a cascade of feature detectors trained with boosting techniques [15, 2]. The expression recognizer receives image patches located by the face detector. A Gabor representation of the patch is formed and then processed by a bank of SVM classifiers. A novel combination of Adaboost and SVM's enhances performance. The system was tested on the Cohn-Kanade dataset of posed facial expressions [6]. The generalization performance to new subjects for a 7- way forced choice correct. Most interestingly the outputs of the classifier change smoothly as a function of time, providing a potentially valuable representation to code facial expression dynamics in a fully automatic and unobtrusive manner. The system has been deployed on a wide variety of platforms including Sony's Aibo pet robot, ATR's RoboVie, and CU animator, and is currently being evaluated for applications including automatic reading tutors, assessment of human-robot interaction.
A Region-Growing Segmentation Algorithm for GPUs

This letter proposes a parallel version for graphics processing units (GPU) of a region-growing image segmentation algorithm widely used by the geographic object-based image analysis (GEOBIA) community. Initially, all image pixels are considered as seeds or primitive segments. Fine-grained parallel threads assigned to individual pixels merge adjacent segments iteratively always ensuring to minimize the overall heterogeneity increase. Besides spectral features the merging criterion considers morphological features that can be efficiently computed in the underlying GPU architecture. Two alternatives using different merging criteria are proposed and tested. An experimental analysis upon five different test images has shown that the parallel algorithm may run up to 19 times faster than its sequential counterpart.
Multithreaded Sliding Window Approach to Improve Exact Pattern Matching Algorithms

In this paper an efficient pattern matching approach, based on a multithreading sliding window technique, is proposed to improve the efficiency of the common sequential exact pattern matching algorithms including: (i) Brute Force, (ii) Knuth-Morris-Pratt and (iii) Boyer-Moore. The idea is to divide the text under-search into blocks, each block is allocated one or two threads running concurrently. Reported experimental results indicated that the proposed approach improves the performance of the well-known pattern matching algorithms, in terms of search time, especially when the searched patterns are located at the middle or at the end of the text. pattern matching; multithreading; sliding window; Brute Force; Knuth-Morris-Pratt; Boyer-Moore
Multiview projectors/cameras system for 3D reconstruction of dynamic scenes

Active vision systems are usually limited to either partial or static scene reconstructions. In this paper, we propose to acquire the entire 3D shape of a dynamic scene. This is performed using a multiple projectors and cameras system, that allows to recover the entire shape of the object within a single scan at each frame. Like previous approaches, a static and simple pattern is used to avoid interferences of multiple patterns projected on the same object. In this paper, we extend the technique to capture a dense entire shape of a moving object with accuracy and high video frame rate. To achieve this, we mainly propose two additional steps; one is checking the consistency between the multiple cameras and projectors, and the other is an algorithm for light sectioning based on a plane parameter optimization. In addition, we also propose efficient noise reduction and mesh generation algorithm which are necessary for practical applications. In the experiments, we show that we can successfully reconstruct dense entire shapes of moving objects. Results are illustrated on real data from a system composed of six projectors and six cameras that was actually built.
SD-Layer: Stain Deconvolutional Layer for CNNs in Medical Microscopic Imaging

Convolutional Neural Networks (CNNs) are typically trained in the RGB color space. However, in medical imaging, we believe that pixel stain quantities offer a fundamental view of the interaction between tissues and stain chemicals. Since the optical density (OD) colorspace allows to compute pixel stain quantities from pixel RGB intensities using the Beer-Lambert’s law, we propose a stain deconvolutional layer, hereby named as SD-Layer, affixed at the front of CNN that performs two functions: (1) it transforms the input RGB microscopic images to Optical Density (OD) space and (2) this layer deconvolves OD image with the stain basis learned through backpropagation and provides tissue-specific stain absorption quantities as input to the following CNN layers. With the introduction of only nine additional learnable parameters in the proposed SD-Layer, we obtain a considerably improved performance on two standard CNN architectures: AlexNet and T-CNN. Using the T-CNN architecture prefixed with the proposed SD-Layer, we obtain 5-fold crossvalidation accuracy of 93.2% in the problem of differentiating malignant immature White Blood Cells (WBCs) from normal immature WBCs for cancer detection.
A Blind Navigation System Using RFID for Indoor Environments

A location and tracking system becomes very important to our future world of pervasive computing, where information is all around us. Location is one of the most needed information for emerging and future applications. Since the public use of GPS satellite is allowed, several state-of-the-art devices become part of our life, e.g. a car navigator and a mobile phone with a built-in GPS receiver. However, location information for indoor environments is still very limited as mentioned in paper. Several techniques are proposed to get location information in buildings such as using a radio signal triangulation. Using Radio Frequency Identification (RFID) tags is a new way of giving location information to users. Due to its passive communication circuit, RFID tags can be embedded almost anywhere without an energy source. The tags stores location information and give it to any reader that is within a proximity range which can be up to 7-10 centimeters. In this paper RFID-based system for navigation in a building for blind people or visually impaired
How is pneumonia diagnosed in clinical stroke research? A systematic review and meta-analysis.

Diagnosis of pneumonia complicating stroke is challenging, and there are currently no consensus diagnostic criteria. As a first step in developing such consensus-based diagnostic criteria, we undertook a systematic review to identify the existing diagnostic approaches to pneumonia in recent clinical stroke research to establish the variation in diagnosis and terminology.



Studies of ischemic stroke, intracerebral hemorrhage, or both, which reported occurrence of pneumonia from January 2009 to March 2014, were considered and independently screened for inclusion by 2 reviewers after multiple searches using electronic databases. The primary analysis was to identify existing diagnostic approaches for pneumonia. Secondary analyses explored potential reasons for any heterogeneity where standard criteria for pneumonia had been applied.



Sixty-four studies (56% ischemic stroke, 6% intracerebral hemorrhage, 38% both) of 639 953 patients were included. Six studies (9%) reported no information on the diagnostic approach, whereas 12 (19%) used unspecified clinician-reported diagnosis or initiation of antibiotics. The majority used objective diagnostic criteria: 20 studies (31%) used respiratory or other published standard criteria; 26 studies (41%) used previously unpublished ad hoc criteria. The overall occurrence of pneumonia was 14.3% (95% confidence interval 13.2%-15.4%; I(2)=98.9%). Occurrence was highest in studies applying standard criteria (19.1%; 95% confidence interval 15.1%-23.4%; I(2)=98.5%). The substantial heterogeneity observed was not explained by stratifying for other potential confounders.



We found considerable variation in terminology and the diagnostic approach to pneumonia. Our review supports the need for consensus development of operational diagnostic criteria for pneumonia complicating stroke.
Structured Receptive Fields in CNNs

Learning powerful feature representations with CNNs is hard when training data are limited. Pre-training is one way to overcome this, but it requires large datasets sufficiently similar to the target domain. Another option is to design priors into the model, which can range from tuned hyperparameters to fully engineered representations like Scattering Networks. We combine these ideas into structured receptive field networks, a model which has a fixed filter basis and yet retains the flexibility of CNNs. This flexibility is achieved by expressing receptive fields in CNNs as a weighted sum over a fixed basis which is similar in spirit to Scattering Networks. The key difference is that we learn arbitrary effective filter sets from the basis rather than modeling the filters. This approach explicitly connects classical multiscale image analysis with general CNNs. With structured receptive field networks, we improve considerably over unstructured CNNs for small and medium dataset scenarios as well as over Scattering for large datasets. We validate our findings on ILSVRC2012, Cifar-10, Cifar-100 and MNIST. As a realistic small dataset example, we show state-of-the-art classification results on popular 3D MRI brain-disease datasets where pre-training is difficult due to a lack of large public datasets in a similar domain.
A Distributed Security Mechanism for Resource-Constrained IoT Devices

Internet of Things (IoT) devices have developed to comprise embedded systems and sensors with the ability to connect, collect, and transmit data over the Internet. Although solutions to secure IoT systems exist, Class-0 IoT devices with insufficient resources to support such solutions are considered a resourceconstrained in terms of secure communication. This paper provides a distributed security mechanism that targets Class-0 IoT devices. The research goal is to secure the entire data path in two segments, deviceto-gateway and gateway-to-server data communications. The main concern in the provided solution is that lighter security operations with minimal resource requirements are performed in the IoT device, while heavier tasks are performed in the gateway side. The proposed mechanism utilizes a symmetric encryption for data objects combined with the native wireless security to offer a layered security technique between the device and the gateway. In the offered solution, the IoT gateways provide additional protection by securing data using Transport Layer Security (TLS). Real-time experimental evaluations have demonstrated the applicability of the proposed mechanism pertaining to the security assurance and the consumed resources of the target Class-0 IoT devices.
The Networked Sensory Landscape: Capturing and Experiencing Ecological Change Across Scales

What role will ubiquitous sensing play in our understanding and experience of ecology in the future? What opportunities are created by weaving a continuously sampling, geographically dense web of sensors into the natural environment, from the ground up? In this article, we explore these questions holistically, and present our work on an environmental sensor network designed to support a diverse array of applications, interpretations, and artistic expressions, from primary ecological research to musical composition. Over the past four years, we have been incorporating our ubiquitous sensing framework into the design and implementation of a large-scale wetland restoration, creating a broad canvas for creative exploration at the landscape scale. The projects we present here span the development and wide deployment of custom sensor node hardware, novel web services for providing real-time sensor data to end user applications, public-facing user interfaces for open-ended exploration of the data, as well as more radical UI modalities, through unmanned aerial vehicles, virtual and augmented reality, and wearable devices for sensory augmentation. From this work, we distill the Networked Sensory Landscape, a vision for the intersection of ubiquitous computing and environmental restoration. Sensor network technologies and novel approaches to interaction promise to reshape presence, opening up sensorial connections to ecological processes across spatial and temporal scales.