nils-herrmann/clean_scirepeval huggingface dataset:
Neural network based adaptive output feedback formation control for multi agent systems
Year: 2012

This paper investigates the problem of output feedback formation tracking control for second order multi agent systems under an undirected connected graph and in the presence of dynamic uncertainties and bounded external disturbances. Two state tracking error measures (i.e. absolute and relative state tracking errors) are considered for each individual agent in the formation, and linear reduced order observers are constructed based on the lumped state tracking errors which include absolute and relative state tracking errors. Chebyshev neural networks are used to approximate unknown nonlinear function in the agent dynamics on line, and the implementation of the basis functions of Chebyshev neural networks depends only on the desired signals. The smooth projection algorithm is applied to guarantee that the estimated parameters remain in some known bounded sets. Numerical simulations are presented to illustrate the performance of the proposed controller.
Impact of Concept Mapping and Modular Learning Techniques on Pupils' Achievement for the Selected Topics in Mathematics
Year: 2018

This research emphasizes on an alternate instructional tools called "Concept Map and Modular learning techniques" The goal of this study was to determine the effectiveness of using concept maps and modular learning techniques in improving the mathematics achievement of higher secondary students and compare it with a traditional approach for a mathematics unit. Both the control and the experimental groups were required to take a pre test before instruction and a post test at the end of instruction. The test consisting of 30 questions was used to assess learning gains on a Mathematics Unit about Trigonometry and Differential Calculus. Moreover, results showed that the levels of concept mapping and modular learning techniques ability were associated with the students' learning gains. The study suggests that, when carefully integrated into the normal classroom procedure and when other contributing factors such as student motivation and preparedness, reading ability levels, time and classroom environment are considered, concept mapping and modular learning techniques has a potential to be an effective instructional strategy. To enforce the title of the study, the researcher had determined to verify the results from a different angle using the normative survey method to justify the impact of attitude towards mathematics homework. Based on the findings and discussions, XI standard students are having positive mathematics homework attitude towards concept mapping and modular learning techniques for the topics trigonometry and differential calculus.
Color Prediction Model for Flexographic Prints Based on Spectral Reflectance
Year: 2019

In order to promote the greening of flexographic printing and reduce the waste of consumables such as plates and printing materials. It is necessary to accurately predict the spectral reflectance of flexographic film type printing and establish a spectral reflectance color prediction model for flexographic film prints. The prediction model of flexographic film prints is based on the theory of multiple internal reflections of photons in a plastic film substrate and horizontal transmission inside the plastic substrate, assume that the film and the ink have the same refractive index and the flexographic print is placed on a standard white background. Finally, we tested our model on a flexographic printing machine. Good agreement was found between the predicted and measured reflectance for the flexographic printed patches. The model could be used in prepress color prediction and virtual color proofing, significantly reduce printing materials and energy consumption.
Robust Real time RGB D Visual Odometry in Dynamic Environments via Rigid Motion Model
Venue: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2019

In the paper, we propose a robust real time visual odometry in dynamic environments via rigid motion model updated by scene flow. The proposed algorithm consists of spatial motion segmentation and temporal motion tracking. The spatial segmentation first generates several motion hypotheses by using a grid based scene flow and clusters the extracted motion hypotheses, separating objects that move independently of one another. Further, we use a dual mode motion model to consistently distinguish between the static and dynamic parts in the temporal motion tracking stage. Finally, the proposed algorithm estimates the pose of a camera by taking advantage of the region classified as static parts. In order to evaluate the performance of visual odometry under the existence of dynamic rigid objects, we use self collected dataset containing RGB D images and motion capture data for ground truth. We compare our algorithm with state of the art visual odometry algorithms. The validation results suggest that the proposed algorithm can estimate the pose of a camera robustly and accurately in dynamic environments.
Detect Rumors on Twitter by Promoting Information Campaigns with Generative Adversarial Learning
Venue: WWW
Year: 2019

Rumors can cause devastating consequences to individual and/or society. Analysis shows that widespread of rumors typically results from deliberately promoted information campaigns which aim to shape collective opinions on the concerned news events. In this paper, we attempt to fight such chaos with itself to make automatic rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN) We propose a GAN style approach, where a generator is designed to produce uncertain or conflicting voices, complicating the original conversational threads in order to pressurize the discriminator to learn stronger rumor indicative representations from the augmented, more challenging examples. Different from traditional data driven approach to rumor detection, our method can capture low frequency but stronger non trivial patterns via such adversarial training. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection method achieves much better results than state of the art methods.
Virtual U: Defeating Face Liveness Detection by Building Virtual Models from Your Public Photos
Venue: USENIX Security Symposium
Year: 2016

In this paper, we introduce a novel approach to bypass modern face authentication systems. More specifically, by leveraging a handful of pictures of the target user taken from social media, we show how to create realistic, textured, 3D facial models that undermine the security of widely used face authentication solutions. Our framework makes use of virtual reality (VR) systems, incorporating along the way the ability to perform animations (e.g. raising an eyebrow or smiling) of the facial model, in order to trick liveness detectors into believing that the 3D model is a real human face. The synthetic face of the user is displayed on the screen of the VR device, and as the device rotates and translates in the real world, the 3D face moves accordingly. To an observing face authentication system, the depth and motion cues of the display match what would be expected for a human face. We argue that such VR based spoofing attacks constitute a fundamentally new class of attacks that point to a serious weaknesses in camera based authentication systems: Unless they incorporate other sources of verifiable data, systems relying on color image data and camera motion are prone to attacks via virtual realism. To demonstrate the practical nature of this threat, we conduct thorough experiments using an end to end implementation of our approach and show how it undermines the security of several face authentication solutions that include both motion based and liveness detectors.
Sustainability in art conservation: a novel bio based organogel for the cleaning of water sensitive works of art
Year: 2017

Organo and hydrogels have been proposed in the restoration field to treat different types of surfaces. The possibility to retain solvents and to have a controlled and superficial action allowed to use these materials for the removal of very thin layers applied on ancient historical objects, when the under paint layers are particularly delicate and water sensitive. In the last years, an increased attention has been devoted to the proposal of more healthy products to guarantee the safeguard of the operators. Few attention has been devoted to the development of green methods which foresee the use of renewable and biodegradable materials. The aim of this paper is to test a green organo gel for the cleaning of water sensitive surfaces like varnished egg tempera paintings. The gel has been tested experimented on mock ups varnished with natural and synthetic materials and has been validated on a small portion of a Cimabue painting for the removal of two varnishes applied on two different test areas of the painting.
The Role of Environmental Taxation: Economics and the Law
Year: 2012

This contribution to the Handbook of Research on Environmental Taxation discusses the law and economics of environmental taxation. The chapter provides an overview of taxation and other incentive based regulations from an environmental law and economics perspective by addressing their strengths and weaknesses. Issues such as flexibility and cost effectiveness, and also their susceptibility to lobbying at the legislative level and to regulate capture, are examined. The chapter shows that it is not surprising that there differing approaches since none of the policy instruments (taxes, contracts, liability rules, regulation or market based instruments) may alone be optimal to provide adequate incentives for pollution control. A combination may be necessary, ideally making optimal use of the advantages of each particular policy instrument. To some extent, this can be observed in practice as well. The chapter also discusses the necessity to search for optimal instrument mixes and shows how those mixes of instruments are also applied in practice, especially in the European Union. However, using a behavioural approach, the chapter also shows some of the limits of instrument mixes.
The BCD of response time analysis in experimental economics
Venue: Experimental economics
Year: 2018

For decisions in the wild, time is of the essence. Available decision time is often cut short through natural or artificial constraints, or is impinged upon by the opportunity cost of time. Experimental economists have only recently begun to conduct experiments with time constraints and to analyze response time (RT) data, in contrast to experimental psychologists. RT analysis has proven valuable for the identification of individual and strategic decision processes including identification of social preferences in the latter case, model comparison/selection, and the investigation of heuristics that combine speed and performance by exploiting environmental regularities. Here we focus on the benefits, challenges, and desiderata of RT analysis in strategic decision making. We argue that unlocking the potential of RT analysis requires the adoption of process based models instead of outcome based models, and discuss how RT in the wild can be captured by time constrained experiments in the lab. We conclude that RT analysis holds considerable potential for experimental economics, deserves greater attention as a methodological tool, and promises important insights on strategic decision making in naturally occurring environments.
STOCK MARKET CAPITALIZATION AND INTEREST RATE IN NIGERIA: A TIME SERIES ANALYSIS
Year: 2007

Obviously for the Nigerian capital market to enhance economic growth and development and compare favourably with those of developed market economies, investors will need to be abreast with the happenings and great benefits of the stock market. In this case a lot depends on considerable control of the interest rate, the degree of freedom permitted the exchange in dealing with pricing of securities, strict maintenance of professional standards and other purposeful innovations.  (2) 2007: pp. 36 47
Experimental demonstration of 60 Gb/s optical OFDM transmissions at 1550 nm over 100 m OM1 MMF IMDD system with central launching
Year: 2017

Record high 60 Gb/s optical orthogonal frequency division multiplexing (OFDM) transmissions over intensity modulation and direct detection (IMDD) based 100 m optical mode (OM1) multi mode fiber (MMF) links are experimentally demonstrated, utilizing 10 GHz electro absorption modulated laser intensity modulators at a single 1550 nm wavelength. Adaptive bit loading and a simple central launching scheme of the proposed scheme show an effective way for combating the channel fading and simplifying the system structure. It shows good potential in short reach data center interconnections.
The Internationalization Processes of SMEs
Year: 2018

This chapter examines the internationalization processes of SMEs. More specifically, it analyzes the concept of internationalization and its evolutionary path according to the main theoretical perspectives. In looking at the main aspects of the internationalization processes of SMEs, the chapter draws on the motivations that drive SMEs in internationalize their activities, the barriers that they face on the international scenario, and the impact of the internationalization process on their performance levels. The chapter continues the scholarly conversation on the pages of the previous parts by investigating the key approaches developed by the main literature to describe the internationalization process of SMEs, such as stage models, network approaches, resource based methods, the international entrepreneurship model, and the parachuting internationalization metaphor. Recognizing the fact that some small firms conduct international activities from an early stage in their development and follow faster internationalization patterns than those suggested by traditional perspectives, the chapter also explores the phenomenon of international new ventures, born globals, and global start ups.
Research on the Impact of Equity Incentive and CSR on Corporate Performance based on Data Mining Method
Year: 2017

The agency behavior under the modern enterprise system due to the asymmetric information between the owner and the manager in the two sides cannot be effectively supervision and management, employee stock ownership equity incentive system to gradually solve the problem. In this paper, the authors analyse the impact of equity incentive and CSR on corporate performance based on data mining method. On the basis of reference to previous literature, by taking Chinese manufacturing listed companies as the analysis object, we use crosssectional data to test the influence of equity incentive to corporate performance. The empirical analysis show that there exists a positive incentive effect in Chinese listed company's employee stock ownership plan, which shows that employee stock ownership plans can really mobilize staff working enthusiasm and ability.
Active Transportation on a Complete Street: Perceived and Audited Walkability Correlates
Venue: International journal of environmental research and public health
Year: 2017

Few studies of walkability include both perceived and audited walkability measures. We examined perceived walkability (Neighborhood Environment Walkability Scale Abbreviated, NEWS A) and audited walkability (Irvine Minnesota Inventory, IMI) measures for residents living within 2 km of a "complete street" one renovated with light rail, bike lanes, and sidewalks. For perceived walkability, we found some differences but substantial similarity between our final scales and those in a prior published confirmatory factor analysis. Perceived walkability, in interaction with distance, was related to complete street active transportation. Residents were likely to have active transportation on the street when they lived nearby and perceived good aesthetics, crime safety, and traffic safety. Audited walkability, analyzed with decision trees, showed three general clusters of walkability areas, with 12 specific subtypes. A subset of walkability items (n 11) including sidewalks, zebra striped crosswalks, decorative sidewalks, pedestrian signals, and blank walls combined to cluster street segments. The 12 subtypes yielded 81% correct classification of residents' active transportation. Both perceived and audited walkability were important predictors of active transportation. For audited walkability, we recommend more exploration of decision tree approaches, given their predictive utility and ease of translation into walkability interventions.
Resistive switching memory devices based on all inorganic perovskite CsPbBr3 quantum dot
Venue: Symposium on Novel Photoelectronic Detection Technology and Application
Year: 2019

All inorganic perovskite quantum dots (QDs) have widely used in a lot of micro nano photoelectric devices. However, resistive random access memory (RRAM) devices based on All inorganic perovskite QDs are relatively scarce. In this work, a RRAM, which exhibits the write once read many times (WORM) memory effect, based on CsPbBr3 QDs was successfully fabricated by solution processed method at room temperature. The CsPbBr3 QDs based memory shows great reproducibility, good data retention ability, irreversible electrical transition from the high resistance state (HRS) or OFF state to the low resistance state (LRS) or ON state and the resistance ratio (ON/OFF) can reach almost 107. To study the CsPbBr3 QDs based WORM memory provides an opportunity to develop the next generation high performance and stable WORM devices.
Multi Task Joint Learning Model for Segmenting and Classifying Tongue Images Using a Deep Neural Network
Venue: IEEE Journal of Biomedical and Health Informatics
Year: 2020

Automatic tongue image segmentation and tongue image classification are two crucial tongue characterization tasks in traditional Chinese medicine (TCM) Due to the complexity of tongue segmentation and fine grained traits of tongue image classification, both tasks are challenging. Fortunately, from the perspective of computer vision, these two tasks are highly interrelated, making them compatible with the idea of Multi Task Joint learning (MTL) By sharing the underlying parameters and adding two different task loss functions, an MTL method for segmenting and classifying tongue images is proposed in this paper. Moreover, two state of the art deep neural network variants (UNET and Discriminative Filter Learning (DFL) are fused into the MTL to perform these two tasks. To the best of our knowledge, our method is the first attempt to manage both tasks simultaneously with MTL. We conducted extensive experiments with the proposed method. The experimental results show that our joint method outperforms the existing tongue characterization methods. Besides, visualizations and ablation studies are provided to aid in understanding our approach, which suggest that our method is highly consistent with human perception.
Cavitation vortex dynamics of unsteady sheet/cloud cavitating flows with shock wave using different vortex identification methods
Venue: Journal of Hydrodynamics
Year: 2019

Cavitation is a complex multiphase flow phenomenon with an abrupt transient phase change between the liquid and the vapor, including multiscale vortical motions. The transient cavitation dynamics is closely associated with the evolution of the cavitation vortex structures. The present paper investigates the cavitation vortex dynamics using different vortex identification methods, including the vorticity method, the Q criterion method, the Omega method (O) the l2 method and the Rortex method. The Q criterion is an eigenvalue based criterion, and in the Omega method, the parameter is normalized, is independent of the threshold value and in most conditions O=0.52. The Rortex method is based on an eigenvector based criterion. Numerical simulations are conducted using the implemented compressible cavitation solver in the open source software OpenFOAM for the sheet/cloud cavitating flows around a NACA66 (mod) hydrofoil fixed at a=6deg, s=1.25 and Re=7.96x105. The flow is characterized by the alternate interactions of the re entrant flow and the collapse induced shock wave. Results include the vapor structures and the vortex dynamics in the unsteady sheet/cloud cavitating flows, with emphasis on the vortex structures in the cavitation region, the cavity interface, the cavity closure, the cavity wakes, and the foil wakes with the shedding cavity. The comparisons of the various methods, including that the vorticity method, the Q criterion method, the Omega method, the l2 method and the Rortex method, show the performances of different methods in identifying the cavitation vortex structures. Generally, during the attached cavity growth stage, the Q criteria can well predict the vortex structures in the cavitation region and at the foil trailing edge in the pure liquid region, while with the Omega method and the Rortex method, the vortex structures outside the attached cavity and on the foil pressure side can also be predicted. The l2 method can well predict the vortex structures in the cavity closure region. During the re entrant jet development stage, the vortex structures in the re entrant jet region is weak. During the cavity cloud shedding stage, the vortex dynamics at the foil leading edge covered by newly grown cavity sheet is different from that during the attached cavity sheet growth stage. During the shock wave formation and propagation stage, strong vortex structures with both the size and the strength are observed owing to the cavity cloud shedding and collapse behavior. The influence of the small parameter e in the Omega method on the cavitation vortex identification is discussed.
High entropy fluorite oxides
Year: 2018

Eleven fluorite oxides with five principal cations (in addition to a four principal cation (Hf0.25Zr0.25Ce0.25Y0.25)O2 d as a start point and baseline) were fabricated via high energy ball milling, spark plasma sintering, and annealing in air. Eight of the compositions, namely (Hf0.25Zr0.25Ce0.25Y0.25)O2 d, (Hf0.25Zr0.25Ce0.25)(Y0.125Yb0.125)O2 d, (Hf0.2Zr0.2Ce0.2)(Y0.2Yb0.2)O2 d, (Hf0.25Zr0.25Ce0.25)(Y0.125Ca0.125)O2 d, (Hf0.25Zr0.25Ce0.25)(Y0.125Gd0.125)O2 d, (Hf0.2Zr0.2Ce0.2)(Y0.2Gd0.2)O2 d, (Hf0.25Zr0.25Ce0.25)(Yb0.125Gd0.125)O2 d, and (Hf0.2Zr0.2Ce0.2)(Yb0.2Gd0.2)O2 d, possess single phase solid solutions of the fluorite crystal structure with high configurational entropies (on the cation sublattices) akin to those high entropy alloys and ceramics reported in prior studies. Most high entropy fluorite oxides (HEFOs) except for the two containing both Yb and Gd, can be sintered to high relative densities. These single phase HEFOs exhibit lower electrical conductivities and comparable hardness (even with higher contents of softer components such as Y2O3 and Yb2O3) in comparison with 8 mol. Y2O3 stabilized ZrO2 (8YSZ) Notably, these single phase HEFOs possess lower thermal conductivities than that of 8YSZ, presumably due to high phonon scattering by multiple cations and strained lattices.