nils-herrmann/clean_scirepeval huggingface dataset:
Closed Loop Product Lifecycle Management Based on a Multi agent System for Decision Making in Collaborative Design
Venue: KSEM

In the collaborative design environment, there is an increasing demand for information exchange and sharing to reduce lead time and to improve product quality and value. Software and communication technologies can be a relevant approach in this context, using for instance PLM (Product Lifecycle Management) systems. Each product lifecycle development phase generates knowledge, and managing this knowledge can be placed in a closed loop. In this paper, we present a research in progress that exposes a collaborative architecture based on a multi agent system which aims to support the knowledge management process in the closed loop. This is a new strategic approach to manage the product lifecycle information efficiently in a distributed environment. The purpose of this paper is to illustrate the use of DOCK (Design based on Organization, Competence and Knowledge) methodology for the design of our multi agent system and to demonstrate how to handle intelligent knowledge via a use case study.
Influence of regional hydrogeological systems at a local scale: Analyzing the coupled effects of hydrochemistry and biological activity in a Fe and CO2 rich spring.
Venue: The Science of the total environment

A multidisciplinary approach was used in this study to determine the origin of a Fe and CO2 rich spring, an extreme freshwater system and to evaluate the coupled effects of hydrochemistry and biological activity at a local scale. An electrical resistivity tomography survey was conducted to delineate the geological context in which water emerges from a Plio Quaternary supracrustal fault zone, and a bulk resistivity decrease was detected when CO2 rich groundwater occurred. Nine water samples, together with eight biofilm samples, and three sediment samples were taken along the spring canal for their analysis. Major ions, nutrients, and metals were analysed in water samples. Sediment analyses determined the main solid phases precipitated (mainly as CaCO3 and Fe(OH)3(a) Biofilm analyses permitted to obtain biovolume per cell measures, total biovolume values, diatom density, chlorophyll a concentrations, and the Margalef Index values. Inverse modeling and batch reaction models were used to determine the physicochemical processes affecting the spring water, obtaining the total amount of CaCO3/L formed; the Fe and Mn compounds, which mainly precipitated as Fe(OH)3(a) and Mn(OH)2; as well as the total CO2 released to the atmosphere. Analyzing these results together with the patterns of variation of hydrochemical and biological parameters, different interactions were observed: a) the effects of Fe inhibition in travertine formation, even though when the highest CO2 release was occurring; b) the fate and effects of chemicals limiting and/or inhibiting algal growth (mainly Fe, As and phosphate) c) the lack of coincidence between algal growth and tufa limestone precipitation; d) the relationship between some divalent metals (Mn and Co) and biotic activity.
Digital Narrative: Negotiating a Path between Experimental Writing and Popular Culture

Is there room, between the aggressively avant gardistic, conceptual, often not to be repeated experiments of electronic literature, and the stereotyped or utilitarian narratives of computer games and social media, for new forms of narrative that take advantage of the affordances of digital technology without sacrificing what makes narrative so important for the life of the mind, namely its ability to capture actual or imaginary life experience In this presentation I will discuss three digital or semi digital texts that could fill the gap between the 'North Pole' of novelty for novelty's sake and the Tropics of popular culture: the reworking of the tale of 'Little Red Riding Hood' in the independent game The Path, the linear, multimodal story Inanimate Alice by Kate Pullinger, and the augmented book Night Film by Marisha Pessl.
Deep Learning and Data Labeling for Medical Applications
Venue: Lecture Notes in Computer Science

This study addresses the recognition problem of the HEp 2 cell using indirect immunofluorescent (IIF) image analysis, which can facilitate the diagnosis of many autoimmune diseases by finding antibodies in the patient serum. Recently, a lot of automatic HEp 2 cell classification strategies including both shallow and deep methods have been developed, wherein the deep Convolutional Neural Networks (CNNs) have been proven to achieve impressive performance. However, the deep CNNs in general requires a fixed size of image as the input. In order to conquer the limitation of the fixed size problem, a spatial pyramid pooling (SPP) strategy has been proposed in general object recognition and detection. The SPP net usually exploit max pooling strategies for aggregating all activated status of a specific neuron in a predefined spatial region by only taking the maximum activation, which achieved superior performance compared with mean pooling strategy in the traditional state of the art coding methods such as sparse coding, linear locality constrained coding and so on. However, the max pooling strategy in SPP net only retains the strongest activated pattern, and would completely ignore the frequency: an important signature for identifying different types of images, of the activated patterns. Therefore, this study explores a generalized spatial pooling strategy, called K support spatial pooling, in deep CNNs by integrating not only the maximum activated magnitude but also the response magnitude of the relatively activated patterns of a specific neuron together. This proposed K support spatial pooling strategy in deep CNNs combines the popularly applied mean and max pooling methods, and then avoid awfully emphasizing of the maximum activation but preferring a group of activations in a supported region. The deep CNNs with the proposed K support spatial pooling is applied for HEp 2 cell classification, and achieve promising performance compared with the state of the art approaches.
LTE in the sky: trading off propagation benefits with interference costs for aerial nodes
Venue: IEEE Communications Magazine

The popularity of unmanned aerial vehicles has exploded over the last few years, urgently demanding solutions to transfer large amounts of data from the UAV to the ground. Conversely, a control channel to the UAV is desired, in order to safely operate these vehicles remotely. This article analyzes the use of LTE for realizing this downlink data and uplink control. By means of measurements and simulations, we study the impact of interference and path loss when transmitting data to and from the UAV. Two scenarios are considered in which UAVs act as either base stations transmitting in downlink or UEs transmitting in uplink, and their impact on the respective downlink and uplink performance of an LTE ground network is analyzed. Both measurements and simulations are used to quantify such impact for a range of scenarios with varying altitude, distance from the base station, or UAV density. The measurement sets show that signal to interference ratio decreases up to 7 dB for UAVs at 150 m compared to ground users. Simulation results show that a UAV density of 10/km2 gives an average degradation of the signal to interference ratio of more than 6 dB. It is concluded that interference is going to be a major limiting factor when LTE enabled UAVs are introduced, and that strong technical solutions will have to be found.
Electrochemical sensor based on porous silicon/silver nanocomposite for the determination of hydrogen peroxide

In this work, a porous silicon/silver nanocomposite was prepared and used as a modifier in carbon paste electrode, as an electrochemical sensor for the determination of hydrogen peroxide (H 2 O 2 By using a simple and fast galvanic replacement reaction between Si atoms in the porous silicon and silver cation in aqueous HF solution, silver nanoparticles were decorated on the porous silicon without using any reducing agent. This novel nanocomposite was characterized by means of X ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy and cyclic voltammetry. The modified carbon paste electrode was used to study the reduction of H 2 O 2 in 0.1 mol L 1 phosphate buffer solution (pH 7.0) Moreover, chronoamperometery was used for determination of H 2 O 2 at the applied potential of 0.45 V vs. Ag/AgCl) The sensor has a linear response range of 1.65 mmol L 1 0.5 mmol L 1 with a detection limit of 0.45 mmol L 1 H 2 O 2 The electrochemical sensor showed fast and selective responses to H 2 O 2 concentration. The applicability of the sensor was checked using real samples with satisfactory results.
Carbon coated porous silicon composites as high performance Li ion battery anode materials: can the production process be cheaper and greener?

As the most promising next generation lithium ion battery anode materials, porous silicon based materials are attracting great attention nowadays, mainly because of silicon's exceptionally high lithium storage capacity. However, how to realize the large scale manufacture of these materials at low cost still remains a big challenge. In this work, we report the direct preparation of porous Si materials from metallurgical grade Si in an autoclave, which is the most environmentally friendly route to produce alkoxysilane monomers in the organic silicon industry. In this reaction, Cu based catalysts catalyze the reaction of Si particles with alcohols to create a porous structure within Si, followed by carbon deposition via the chemical vapor deposition method. The micro morphology and structure of the porous Si materials can be well tuned by adjusting the synthesis conditions. When used as the anode materials for lithium ion batteries, the charge capacity of the obtained porous Si/C materials was 1240 mA h g( 1) at a current density of 50 mA g( 1) after 50 cycles, much higher than that of the commercial graphite. This work provides an economic and scalable approach to the preparation of porous Si/C anode materials from commercial Si powders for lithium ion batteries.
A fast registration algorithm of rock point cloud based on spherical projection and feature extraction
Venue: Frontiers of Computer Science

Point cloud registration is an essential step in the process of 3D reconstruction. In this paper, a fast registration algorithm of rock mass point cloud is proposed based on the improved iterative closest point (ICP) algorithm. In our proposed algorithm, the point cloud data of single station scanner is transformed into digital images by spherical polar coordinates, then image features are extracted and edge points are removed, the features used in this algorithm is scale invariant feature transform (SIFT) By analyzing the corresponding relationship between digital images and 3D points, the 3D feature points are extracted, from which we can search for the two way correspondence as candidates. After the false matches are eliminated by the exhaustive search method based on random sampling, the transformation is computed via the Levenberg Marquardt Iterative Closest Point (LM ICP) algorithm. Experiments on real data of rock mass show that the proposed algorithm has the similar accuracy and better registration efficiency compared with the ICP algorithm and other algorithms.
Self assembled IR780 loaded transferrin nanoparticles as an imaging, targeting and PDT/PTT agent for cancer therapy
Venue: Scientific reports

Combination of photothermal and photodynamic therapy (PTT/PDT) offer unique advantages over PDT alone. However, to achieve synergetic PDT/PTT effect, one generally needs two lasers with different wavelengths. Near infrared dye IR 780 could be used as photosensitizer both for PTT and PDT, but its lipophilicity limits its practical use and in vivo efficiency. Herein, a simple multifunctional IR780 loaded nanoplatform based on transferrin was developed for targeted imaging and phototherapy of cancer compatible with a single NIR laser irradiation. The self assembled transferrin IR780 nanoparticles (Tf IR780 NPs) exhibited narrow size distribution, good photo stability, and encouraging photothermal performance with enhanced generation of ROS under laser irradiation. Following intravenous injection, Tf IR780 NPs had a high tumor to background ratio in CT26 tumor bearing mice. Treatment with Tf IR780 NPs resulted in significant tumor suppression. Overall, the Tf IR780 NPs show notable targeting and theranostic potential in cancer therapy.
Highly Effective Dual Function Near Infrared (NIR) Photosensitizer for Fluorescence Imaging and Photodynamic Therapy (PDT) of Cancer.
Venue: Journal of medicinal chemistry

We report herein the synthesis and biological efficacy of near infrared (NIR) bacteriochlorin analogues: 3 (1' butyloxy)ethyl 3 deacetyl bacteriopurpurin 18 N butylimide methyl ester (3) and the corresponding carboxylic acid 10. In in vitro assays, compared to its methyl ester analogue 3, the corresponding carboxylic acid derivative 10 showed higher photosensitizing efficacy. However, due to drastically different pharmacokinetics in vivo, the PS 3 (HPLC purity >99% showed higher tumor uptake and long term tumor cure than 10 (HPLC purity >96.5% in BALB/c mice bearing Colon 26 tumors. Isomerically pure R and S isomers of 3 (3a and 3b, purity by HPLC 99% under similar treatment parameters showed identical efficacy in vitro and in vivo. In addition, photosensitizer (PS) 3 showed limited skin phototoxicity and provides an additional advantage over the clinically approved chemically complex hematoporphyrin derivative as well as other porphyrin based PDT agents, which makes 3 a promising dual function agent for fluorescence guided surgery with an option of phototherapy of cancer.
The bionic hand movement using myo sensor and neural networks
Venue: 2016 International Conference on Knowledge Creation and Intelligent Computing (KCIC)

One part of human body in which very useful in this life is a hand. This life will be disrupted if one or both of these hands do not work, due to disability or amputation. One of the contributions that help this condition is the bionic hand. This study have developed a bionic hand that consist of six servo motors to drive the fingers movement. Myo sensor is used in this bionic hand, it is located in the forearm. It has been used to record the muscle signals. Feature extraction of the muscle signal was formulated by using root mean square (rms) Kind of the bionic hand movement was found by identifying the movement signal pattern using back propagation through the artificial neural network. The output data obtained from back propagation was processed by microcontroller and transmitted to the servo motors for getting a movement of the bionic hand. Based on experimental data, five kinds of movement were tested in this system, average of recognition for each movement: handful accuracy movement 65% pointing fingers 75% wave in 100% wave out 75% and spread 100% Testing for the whole movements (five kinds) were performed with the average error 17%
MODELLING OF MAGNETORHEOLOGICAL DAMPER FOR INTELLIGENT BIONIC LEG AND SIMULATION OF KNEE JOINT MOVEMENT CONTROL

To compensate for missing functions of lower extremity amputees, this paper proposes a model of an intelligent bionic leg semi actively controlled by a magnetorheological damper (MRD) A mechanical structure of the leg is designed, and with the help of Bouc Wen model, a MRD forward dynamics model is constructed for simulation. On the basis of data obtained from simulations, the MRD inverse dynamics model is constructed by BP neural network. An integrated control platform is constructed for the intelligent bionic leg, on which the simulation for knee joint control is conducted. Results of the research show that the MRD forward dynamics model can precisely express relationships among damping force, velocity, and displacement, while the inverse forward dynamics model can accurately predict MRD control currents, thus obtaining precise trajectory tracking effects of knee joint movement. (Received, processed and accepted by the Chinese Representative Office.
Structure Design and Movement Simulation Research of Bionic Artificial Leg

Bionic artificial leg is used to simulate the disabled health leg and provided ideal following gait for intelligent prosthesis test, so it raises a high demand about humanoid character and intelligent control. Referring to the parameters of human leg and the movement mechanism, the bionic artificial leg was designed and analyzed in this paper. In order to improve the humanoid character and stability of bionic artificial leg, the knee joint was designed as a four bar linkage which was driven by two pneumatic artificial muscles and the lengths of bars were optimized by the genetic algorithm. Then, this paper analyzed the bionic artificial leg movement and established the system model. Finally, the stability and humanoid character of system is verified by the MATLAB movement simulation.
Hyper resolution mapping of regional storm surge and tide flooding: comparison of static and dynamic models
Venue: Natural Hazards

Storm tide (combination of storm surge and the astronomical tide) flooding is a natural hazard with significant global social and economic consequences. For this reason, government agencies and stakeholders need storm tide flood maps to determine population and infrastructure at risk to present and future levels of inundation. Computer models of varying complexity are able to produce regional scale storm tide flood maps and current model types are either static or dynamic in their implementation. Static models of storm tide utilize storm tide heights to inundate locations hydrologically connected to the coast, whilst dynamic models simulate physical processes that cause flooding. Static models have been used in regional scale storm tide flood impact assessments, but model limitations and coarse spatial resolutions contribute to uncertain impact estimates. Dynamic models are better at estimating flooding and impact but are computationally expensive. In this study we have developed a dynamic reduced complexity model of storm tide flooding that is computationally efficient and is applied at hyper resolutions <100 m cell size) over regional scales. We test the performance of this dynamic reduced complexity model and a separate static model at three test sites where storm tide observational data are available. Additionally, we perform a flood impact assessment at each site using the dynamic reduced complexity and static model outputs. Our results show that static models can overestimate observed flood areas up to 204 and estimate more than twice the number of people, infrastructure, and agricultural land affected by flooding. Overall we find that that a reduced complexity dynamic model of storm tide provides more conservative estimates of coastal flooding and impact.
Real time sign language recognition using the leap motion controller
Venue: 2016 International Conference on Inventive Computation Technologies (ICICT)

Hearing and speech impaired people use Sign Language to convey their message to normal people. Sign Language has evolved as one of the major areas of research and study in computer vision. Researchers in sign language recognition used different input devices such as data gloves, web camera, depth camera, color camera, Microsoft's Kinect sensor, etc. to capture hand signs. In this paper we display the importance of American Sign Language and proposed technique for classification and their efficient results. American Sign Language uses only one hand to display the gestures and thus makes it easy for interpretation and understanding. The signs are captured using new digital sensor called "Leap Motion Controller" LMC is 3D non contact motion sensor which can tracks and detects hands, fingers, bones and finger like objects. Proposed system used Multi Layer Perceptron (MLP) neural network with Back Propagation (BP) algorithm to build a classification model which takes feature set as input. Multi Layer Perceptron (MLP) neural network used to recognize different signs. We have considered 26 different alphabets of American Sign Language. Multi Layer Perceptron (MLP) is executed on a dataset of total 520 samples (consisting of 20 samples of each alphabet) Recognition rate of proposed system is 96.15%
Learning Possibilistic Logic Theories from Default Rules
Venue: IJCAI

We introduce a setting for learning possibilistic logic theories from defaults of the form "if alpha then typically beta" We first analyse this problem from the point of view of machine learning theory, determining the VC dimension of possibilistic stratifications as well as the complexity of the associated learning problems, after which we present a heuristic learning algorithm that can easily scale to thousands of defaults. An important property of our approach is that it is inherently able to handle noisy and conflicting sets of defaults. Among others, this allows us to learn possibilistic logic theories from crowdsourced data and to approximate propositional Markov logic networks using heuristic MAP solvers. We present experimental results that demonstrate the effectiveness of this approach.
[The Identification of Lettuce Varieties by Using Unsupervised Possibilistic Fuzzy Learning Vector Quantization and Near Infrared Spectroscopy]
Venue: Guang pu xue yu guang pu fen xi Guang pu

To solve the noisy sensitivity problem of fuzzy learning vector quantization (FLVQ) unsupervised possibilistic fuzzy learning vector quantization (UPFLVQ) was proposed based on unsupervised possibilistic fuzzy clustering (UPFC) UPFLVQ aimed to use fuzzy membership values and typicality values of UPFC to update the learning rate of learning vector quantization network and cluster centers. UPFLVQ is an unsupervised machine learning algorithm and it can be applied to classify without learning samples. UPFLVQ was used in the identification of lettuce varieties by near infrared spectroscopy (NIS) Short wave and long wave near infrared spectra of three types of lettuces were collected by FieldSpec@3 portable spectrometer in the wave length range of 350 2 500 nm. When the near infrared spectra were compressed by principal component analysis (PCA) the first three principal components explained 97.50% of the total variance in near infrared spectra. After fuzzy c means (FCM) clustering was performed for its cluster centers as the initial cluster centers of UPFLVQ, UPFLVQ could classify lettuce varieties with the terminal fuzzy membership values and typicality values. The experimental results showed that UPFLVQ together with NIS provided an effective method of identification of lettuce varieties with advantages such as fast testing, high accuracy rate and non destructive characteristics. UPFLVQ is a clustering algorithm by combining UPFC and FLVQ, and it need not prepare any learning samples for the identification of lettuce varieties by NIS. UPFLVQ is suitable for linear separable data clustering and it provides a novel method for fast and nondestructive identification of lettuce varieties.