nils-herrmann/clean_scirepeval huggingface dataset:
DeepID3: Face Recognition with Very Deep Neural Networks
Venue: ArXiv
Year: 2015

The state of the art of face recognition has been significantly advanced by the emergence of deep learning. Very deep neural networks recently achieved great success on general object recognition because of their superb learning capacity. This motivates us to investigate their effectiveness on face recognition. This paper proposes two very deep neural network architectures, referred to as DeepID3, for face recognition. These two architectures are rebuilt from stacked convolution and inception layers proposed in VGG net and GoogLeNet to make them suitable to face recognition. Joint face identification verification supervisory signals are added to both intermediate and final feature extraction layers during training. An ensemble of the proposed two architectures achieves 99.53% LFW face verification accuracy and 96.0% LFW rank 1 face identification accuracy, respectively. A further discussion of LFW face verification result is given in the end.
Beyond Speech: Generalizing D Vectors for Biometric Verification
Venue: AAAI
Year: 2019

Deep learning based automatic feature extraction methods have radically transformed speaker identification and facial recognition. Current approaches are typically specialized for individual domains, such as Deep Vectors (D Vectors) for speaker identification. We provide two distinct contributions: a generalized framework for biometric verification inspired by D Vectors and novel models that outperform current stateof the art approaches. Our approach supports substitution of various feature extraction models and improves the robustness of verification tests across domains. We demonstrate the framework and models for two different behavioral biometric verification problems: keystroke and mobile gait. We present a comprehensive empirical analysis comparing our framework to the state of the art in both domains. Our models perform verification with higher accuracy using orders of magnitude less data than state of the art approaches in both domains. We believe that the combination of high accuracy and practical data requirements will enable application of behavioral biometric models outside of the laboratory in support of much needed improvements to cyber security.
Electric vehicle battery capacity allocation and recycling with downstream competition
Venue: Eur. J. Oper. Res.
Year: 2020

We study the optimal channel choice and battery capacity allocation strategies of an electric vehicle (EV) manufacturer in the presence of battery recycling. The EV manufacturer also produces EV batteries. As an upstream manufacturer, this firm has the option to supply batteries to its competitor, a downstream EV manufacturer which is unable to produce batteries. We characterise the equilibrium decisions of the two EV manufacturers under different channel structures. One finding is that the optimal channel choice of each EV manufacturer depends on the procurement cost from an external battery supplier. If the procurement cost is moderate, the downstream manufacturer prefers to order from the upstream manufacturer and the upstream manufacturer will supply batteries to satisfy the order. In addition, we investigate the effects of key parameters on the equilibrium capacity allocation decisions and manufacturers' profits using numerical experiments. We also show that to maximise social welfare, the upstream EV manufacturer should not supply batteries to its competitor if the procurement cost from the external supplier is low, which is contrary to the case of profit maximisation.
Digital transformation: A multidisciplinary reflection and research agenda
Year: 2021

Digital transformation and resultant business model innovation have fundamentally altered consumers' expectations and behaviors, putting immense pressure on traditional firms, and disrupting numerous markets. Drawing on extant literature, we identify three stages of digital transformation: digitization, digitalization, and digital transformation. We identify and delineate growth strategies for digital firms as well as the assets and capabilities required in order to successfully transform digitally. We posit that digital transformation requires specific organizational structures and bears consequences for the metrics used to calibrate performance. Finally, we provide a research agenda to stimulate and guide future research on digital transformation.
Distributionally robust design for bicycle sharing closed loop supply chain network under risk averse criterion
Year: 2020

The requirement of environmental improvement has led to the innovative emergence of shared bicycles. The production and recycling of shared bicycles are a closed logistics network, which can be considered a typical closed loop supply chain (CLSC) problem. In practice, the CLSC network is influenced by social, economic and environmental factors, which impose high degrees of uncertainty and usually trigger various unanticipated risks, so controlling uncertain parameters becomes a key issue in supply chain decisions. The purpose of this research is to construct a new distributionally robust optimization model for a multi product, multi echelon CLSC network, in which the distributions of uncertain transportation cost, demand and the returned product are only partially known in advance. In the proposed model, robust mean CVaR optimization formulation is employed as the objective function for a trade off between the expected cost and the risk in the CLSC network. Further, to overcome the obstacle of model solvability resulting from imprecise probability distributions, two kinds of ambiguity sets are used to transform the robust counterpart into its computationally tractable forms. Finally, a case study on a Chinese bicycle sharing company is addressed to validate the proposed robust optimization model. A comparison study is conducted on the performance between our robust optimization method and the traditional optimization method. In addition, a sensitivity analysis is performed with respect to the risk aversion parameter and the confidence level.
Surface energy of coal particles under quasi static compression and dynamic impact based on fractal theory
Year: 2020

The crushing energy of coal plays an important role in mining, gas extraction, occurrence of outburst accidents, and coal pulverisation engineering. To calculate the crushing energy of coal, a fractal model considering the crack area was established. The fractal fragmentation distribution and the fractal crack distribution, with their fractal dimensions (Df and Ds) calculated from the particle size distribution, were used to calculate the new specific surface area (SSA) of coal after crushing. Two different experimental methods, quasi static compression on a single particle (strain rate: 0.0005 0.001 s 1) and dynamic impact on particles (strain rate: 300 s 1) were used to crush coal particles to particle size of 2 3 mm. The experimental results show that Df for coal particles under quasi static compression (QSC) and dynamic impact (DI) are 1.44 1.85 and 1.9 2.74, respectively. Df increases significantly with the crushing ratio, and there is a limit value, which is defined as Ds. Ds measured by QSC and DI were 1.87 and 2.75, respectively. The SSA of a single particle obtained by the proposed fractal model increases with the decrease in the particle size, which can be highly consistent with the BET SSA by fitting. The surface energy of coal particles under QSC and DI calculated by the traditional theory, which does not take into account the crack area, is 240.10 J/m2 and 180.92 J/m2, respectively, independent of the crushing ratio. The surface energy calculated by the fractal model is 3.55 J/m2, taking into account the frictional heat.
On Literary Image Transmission from the Perspective of functional Equivalence: Taking the English Versions of Bian Cheng by Shen Congwen for Example
Year: 2019

In recent years, the study of literary image transmission has become one of the hot topics in the field of translation. This article will try to analyze the image transmission in four English versions of Bian Cheng from the perspective of functional equivalence and find out that stylistic equivalence and textual equivalence played an important role in image transmission.
An empirical model of factors affecting farmers' participation in natural resources conservational programs in Iran
Year: 2009

The threats weaved by human to natural resources have impressed all of worlds' assemblies making decisions about environmental schemes. Appearance of challenges in natural resources management presented by governmental organizations signals a new and more comprehensive approach to natural resources management. This management is the responsibility of the system including all of stakeholders, in particular local farmers. In fact, farmers' participation in natural resources conservational programs (NRCPs) can be a context for making important and active decisions relating to environmental conservation. This study has attempted to response to the question toward which factors affecting farmers' participation in NRCPs. The data collected for this study were derived through a stratified random sampling procedure from 308 farmers in Qazvin province, Iran. The multiple regression analysis revealed that factors such as age, family size, distance from Governmental Natural Resources Centers, environmental attitude, and need to extension programs relating to natural resources conservation influence directly on farmers' participation, while factors such as conservational knowledge and behavior influence indirectly on farmers' participation in NRCPs. The paper concludes that factors affecting farmers' participation present information that can be suitable for success of environmental decision making.
The Sociolinguistics of Globalization
Year: 2009

Human language has changed in the age of globalization: no longer tied to stable and resident communities, it moves across the globe, and it changes in the process. The world has become a complex 'web' of villages, towns, neighbourhoods and settlements connected by material and symbolic ties in often unpredictable ways. This phenomenon requires us to revise our understanding of linguistic communication. In The Sociolinguistics of Globalization Jan Blommaert constructs a theory of changing language in a changing society, reconsidering locality, repertoires, competence, history and sociolinguistic inequality. There is great interest in the issue of globalization and this book will appeal to scholars and students in linguistics, sociolinguistics, applied linguistics and anthropology Richly illustrated with examples from around the globe Presents a profound revision of sociolinguistic work in the area of linguistic communication
A danca na educacao fisica: um estudo bibliografico sobre a danca zumba na qualidade da saude
Year: 2020

A danca Zumba e um ritmo que se espalhou pelo mundo e tem tomando conta das academias por todo Brasil. Ela conquista nao apenas por ser uma danca divertida e contagiante, mas tambem porque pode ser usada em varios ritmos musicais, ou seja, ela se adequa a comunidade local, e conquista criancas, jovens, adultos e idosos. Este trabalho tem por objetivo avaliar mediante uma revisao bibliografica a importancia da danca zumba na qualidade de vida das pessoas. Deste modo foi pesquisado em artigos cientificos nacionais ja publicados em revistas e/ou livros publicados entre os anos de 2010 e 2019 em biblioteca digitais. sendo assim analisados 17 artigos para compor a base teorica deste trabalho. Nota se atraves da analise realizada que a danca zumba pode ajudar na qualidade da saude das pessoas e que seus beneficios sao inumeros e nao apenas nos aspectos fisicos, pois e uma ginastica aerobica. Todavia, pode se notar tambem nos periodicos estudados que ainda existem poucos estudos sobre a danca zumba e espera se assim, que o presente artigo possa futuramente contribuir para maiores pesquisa sobre o tema estudo.
Domain wall encoding of integer variables for quantum annealing and QAOA
Year: 2019

In this paper I propose a new method of encoding integer variables into Ising model qubits for quantum optimization. The new method is based on the physics of domain walls in one dimensional Ising spin chains. I find that these encodings and the encoding of arbitrary two variable interactions is possible with only two body Ising terms. Following on from similar results for the `one hot' method of encoding integer variables [Hadfield et. al. Algorithms 12.2 (2019) 34] I also demonstrate that it is possible to construct two body mixer terms which do not leave the logical subspace, an important consideration for optimising using the quantum alternating operator ansatz (QAOA) I additionally discuss how, since the couplings in the domain wall encoding only need to be ferromagnetic and therefore could in principle be much stronger than anti ferromagnetic couplers, that application specific quantum annealers for integer problems based on this construction may be beneficial. Finally, I compare embedding for synthetic scheduling and colouring problems with the domain wall and one hot encodings on two graphs which are relevant for quantum annealing, the chimera graph and the Pegasus graph. For every case I examine I find a similar or better performance from the domain wall encoding as compared to one hot, but this advantage is highly dependent on the structure of the problem. For encoding some problems, I find an advantage similar to the one found by embedding in a Pegasus graph compared to embedding in a chimera graph.
Bridging art and science: using game techniques to create an application for the Entropy project
Year: 2016

This project looks at the Entropy project bringing together art and science for a public performance, and how an application could be made to fit the project's needs. An overview of Cinder the code library used in the application is given, as well as background research into games engine features that can be utilized in the creation of higher performance visualization. The development cycle of the application is given, along with the structure, features and issues faced during development, before testing performance features implemented to find which are most effective. Finally, the project looks at possible improvements for future development.
Two decades on the MACBETH approach: a bibliometric analysis
Venue: Ann. Oper. Res.
Year: 2021

MACBETH stands for measuring attractiveness by a categorical based evaluation technique It is a multiple criteria decision analysis technique created by Carlos Bana e Costa and Jean Claude Vansnick in the early 1990s. Due to both the increasing application of the technique and progress achieved to date, this study aims to conduct a bibliometric literature review on MACBETH. The database for the review contains 192 scientific articles published in scholarly publications indexed in the Scopus database from 1994 to 2016. Although other literature reviews of the technique exist, this study has a different emphasis and uses two distinct citation analysis methods bibliographic coupling and co citation allowing us to generate new insights regarding some of the most active and influential articles, journals and authors, as well as to reveal rankings, clusters of contributions and collaboration networks. The results obtained are important to enhance our understanding of the technique's anatomy (i.e. its intellectual structure) and can support the identification of new lines of application and development.
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Venue: KDD 2013
Year: 2013

It is our great pleasure to welcome you to the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) The annual ACM SIGKDD conference is the premier international forum for data mining, knowledge discovery and big data. It brings together researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD 2013 features plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, demonstrations, and the KDD Cup competition. Today, you hear a lot about big data, data science and data intensive computing. The core of this work is extracting knowledge and useful information from data, which for science leads to beautiful insights, and for applications leads to actions, alerts and decisions. The KDD community has always been at the center of this activity and it is clear from this conference that it will continue to drive this broader field of big data. This year there were 726 submissions to the KDD Research Track, and 125 papers were accepted. There were 136 submissions to the KDD Industry and Government Track, and 34 papers were accepted. KDD also has a history of inviting talks that are of broad interest to the KDD community. This year we chose to have 4 plenary talks. A program committee also selected 8 talks to present at the Industry Practice Exposition. A strength of the KDD conference is the number of workshops and tutorials that are co located with it. This year there were 10 full day workshops, 5 half day workshops, and 6 tutorials. We thank all sponsors, who are a very important part of the conference, and the members of the Organizing Committee and our other colleagues who volunteered their time during the past year to make this conference a success. Special thanks goes to the Research Track Co Chairs and the Industry and Government Track Co Chairs. Also special thanks are due to the Local Arrangements Chair, the Treasurer, the Proceedings Co Chairs, and the KDD Cup Committee. We are grateful to the several program committees that provided the advice necessary to put together a quality program the Research Track Program Committee, the Research Track Senior Program Committee, the Industry and Government Track Program Committee, the Industry Practice Expo Program Committee, the Workshop Program Committee, the Tutorial Program Committee, and the Demo Program Committee. We know that you will find this year's exhibits and demonstrations exciting and remind you that some of the most interesting discussions can be found there. Please join us for KDD 2013 to gain new knowledge and to exchange exciting new research results, leading practices, and high impact applications in big data, knowledge discovery and data mining. We hope that you will find this program interesting and thought provoking and that the conference will provide you with a valuable opportunity to share ideas with other researchers and practitioners from institutions around the world.
BOARD INVITED REVIEW: Immunocontraception as a possible tool to reduce feral pig populations: recent and future perspectives.
Venue: Journal of animal science
Year: 2019

The feral pig populations of many countries continue to increase. Scientific studies on population size are scarce, while the numbers of reported observations on presence of and damage caused by feral pigs are increasing. Feral pigs can carry and spread several diseases (including zoonotic) but African Swine Fever (ASF) is of most concern. It is a highly transmissible viral disease associated with an extremely high mortality rate. Since 2009 ASF has appeared in several European countries, with cases being identified first among local feral pigs and consequently in domestic pig production units, indicating a clear linkage with the movement of the feral pig population and the spread of the disease across national boundaries. Control of feral pig populations is currently under discussion. Because massive culling raises questions of animal welfare and ethics, fertility control could represent an important and effective means to control feral pig populations. Contraceptive vaccines have been used with some degree of success in many wild species because they are able to provide a long term effect without any consequent health problems. However, extensive and efficacious use of vaccines to control feral pig populations is not simple. The aim of this article was to review the progress in immunocontraception use in feral pigs, providing an account of the current status and future perspectives.
Learning to Recommend Accurate and Diverse Items
Venue: WWW
Year: 2017

In this study, we investigate diversified recommendation problem by supervised learning, seeking significant improvement in diversity while maintaining accuracy. In particular, we regard each user as a training instance, and heuristically choose a subset of accurate and diverse items as ground truth for each user. We then represent each user or item as a vector resulted from the factorization of the user item rating matrix. In our paper, we try to discover a factorization for matching the following supervised learning task. In doing this, we define two coupled optimization problems, parameterized matrix factorization and structural learning, to formulate our task. And we propose a diversified collaborative filtering algorithm (DCF) to solve the coupled problems. We also introduce a new pairwise accuracy metric and a normalized topic coverage diversity metric to measure the performance of accuracy and diversity respectively. Extensive experiments on benchmark datasets show the performance gains of DCF in comparison with the state of the art algorithms.
Isolating Patterns in Open Reaction Diffusion Systems
Venue: Bulletin of mathematical biology
Year: 2021

Realistic examples of reaction diffusion phenomena governing spatial and spatiotemporal pattern formation are rarely isolated systems, either chemically or thermodynamically. However, even formulations of 'open' reaction diffusion systems often neglect the role of domain boundaries. Most idealizations of closed reaction diffusion systems employ no flux boundary conditions, and often patterns will form up to, or along, these boundaries. Motivated by boundaries of patterning fields related to the emergence of spatial form in embryonic development, we propose a set of mixed boundary conditions for a two species reaction diffusion system which forms inhomogeneous solutions away from the boundary of the domain for a variety of different reaction kinetics, with a prescribed uniform state near the boundary. We show that these boundary conditions can be derived from a larger heterogeneous field, indicating that these conditions can arise naturally if cell signalling or other properties of the medium vary in space. We explain the basic mechanisms behind this pattern localization and demonstrate that it can capture a large range of localized patterning in one, two, and three dimensions and that this framework can be applied to systems involving more than two species. Furthermore, the boundary conditions proposed lead to more symmetrical patterns on the interior of the domain and plausibly capture more realistic boundaries in developmental systems. Finally, we show that these isolated patterns are more robust to fluctuations in initial conditions and that they allow intriguing possibilities of pattern selection via geometry, distinct from known selection mechanisms.
Stresses Induced on Existing Pipeline Due to Laying of New Pipeline
Year: 2020

In recent times, increasing demand for energy has led to rapid development of subsea and offshore assets for oil and gas transportation. Cross country pipelines are laid to connect and transport hydrocarbons from different source stations to its consumers across the countries. Case study of a new subsea pipeline proposed to be laid on seabed and routed through cross existing pipelines is discussed in this paper. Numerical modeling using Finite Element Analysis (FEA) is carried out to analyze present stress level on existing pipelines, i.e. stress due to backfill and increment in vertical stresses in the soil just above the existing pipeline due to proposed new pipeline. Different pipeline loading conditions along with methodology and assumptions of static analysis are presented. Maximum vertical stress in the soil at the top of existing pipelines due to laying of proposed pipeline is computed. These induced stresses are also compared with the initial stresses on the existing pipeline.