nils-herrmann/clean_scirepeval huggingface dataset:
MoXi: real-time ink dispersion in absorbent paper

This paper presents a physically-based method for simulating ink dispersion in absorbent paper for art creation purposes. We devise a novel fluid flow model based on the lattice Boltzmann equation suitable for simulating percolation in disordered media, like paper, in real time. Our model combines the simulations of spontaneous shape evolution and porous media flow under a unified framework. We also couple our physics simulation with simple implicit modeling and image-based methods to render high quality output. We demonstrate the effectiveness of our techniques in a digital paint system and achieve various realistic effects of ink dispersion, including complex flow patterns observed in real artwork, and other special effects.
Insulation Failure Mechanisms of Power Generators

Several statistics have been published dealing with failure causes of high voltage rotating machines i n general and power generators in particular [1 4]. Some of the se statistics only specify the part of the machine which failed without giving any deeper insight in the failure mechanism. Other publications distinguish between the damage which caused the machine to fail and the root cause which effect ed the damage. The survey of 1199 hydrogenerators c ar ied out by the CIGRE study committee SC11, EG11.02 provides an ex mple of such an investigation . It gives det ail d results of 69 incidents. 56% of the failed machines showed an insulation damage, other major types being mecha ni al, thermal and bearing damages (Figure 1a). Root causes which led to these damages are subdivided into 7 differen t groups (Figure 1b).
An upper bound on the length of a Hamiltonian walk of a maximal planar graph

A Hamiltonian walk of a connected graph is a shortest closed walk that passes through every vertex at least once, and the length of a Hamiltonian walk is the total number of edges traversed by the walk. We show that every maximal planar graph with p ( 2 3) vertices has a Hamiltonian cycle or a Hamiltonian walk of length 5 3(p 3)/2.
Hermes: An Optimization of HyperLogLog Counting in real-time data processing

HyperLogLog Counting is widely used in cardinality estimation. It is the foundation of many algorithms in data analysis, commodity recommendation and database optimization. Facing the large scale internet business like electronic commerce, internet companies have an urgent requirement of distributed real-time cardinality estimation with high accuracy and low time cost. In this paper, we propose a distributed real-time cardinality estimation algorithm named Hermes. Hermes adjusts the estimated cardinality dynamically according to the result of HyperLogLog Counting and also optimizes the data distribution strategy of existing distributed cardinality estimation algorithms. Experiments have been carried out and the results show that Hermes has lower estimation error and time cost compared with existing algorithms.
Facial Expression Recognition Using Active Appearance Models

A framework for automatic facial expression recognition combining Active Appearance Model (AAM) and Linear Discriminant Analysis (LDA) is proposed. Seven different expressions of several subjects, representing the neutral face and the facial emotions of happiness, sadness, surprise, anger, fear and disgust were analysed. The proposed solution starts by describing the human face by an AAM model, projecting the appearance results to a Fisherspace using LDA to emphasize the different expression categories. Finaly the performed classification is based on malahanobis distance.
Prediction and explanation in social systems

Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.
Beyond incremental processing : Tracking concept drift

I,c~arriilig in complex, changing erivironrrlents requires rrltlt hods that are able to tolerate noise (less than perIixc t I:edback) a11d &if1 ( concepts that change over time). ‘I‘tic5e two aspecfs of complex environments iriteract with cm.h oltier: w heri some particular learned predictor fails to corrc~cl,ly predict the expected outcome (or when the out(‘011ic’ oc’c urs without havitig been preceded by the learIled prtlclic.Lor), a learner must, be able to dcterrriine whethet 1 t\ts siLuaL.ion is an iIistan<e of noise or an irldication ttlat t ht> c.of~c.t~pL is beginning to drift. We j)rescnt, a learning trlt~lhoci that, is able to learrl complex Boolean characteriz.aliollb while tolerating noise and drift. An analysis of I tit, aIgorit,hrri illust.rates why it teas these desirable bellavions, a.r~ti tmpirical results from an irrij)lerrlcntatiorl (called S’I‘A( ;( ;EI~) are prc:seI~!mi to show its ability to t,rack changirrg coiiccpts over time.
Bringing Transparency Design into Practice

Intelligent systems, which are on their way to becoming mainstream in everyday products, make recommendations and decisions for users based on complex computations. Researchers and policy makers increasingly raise concerns regarding the lack of transparency and comprehensibility of these computations from the user perspective. Our aim is to advance existing UI guidelines for more transparency in complex real-world design scenarios involving multiple stakeholders. To this end, we contribute a stage-based participatory process for designing transparent interfaces incorporating perspectives of users, designers, and providers, which we developed and validated with a commercial intelligent fitness coach. With our work, we hope to provide guidance to practitioners and to pave the way for a pragmatic approach to transparency in intelligent systems.
Mechanism Design for Finding Experts Using Locally Constructed Social Referral Web

In this work, we address the problem of distributed expert finding using chains of social referrals and profile matching with only local information in online social networks. By assuming that users are selfish, rational, and have privately known cost of participating in the referrals, we design a novel truthful efficient mechanism in which an expert-finding query will be relayed by intermediate users. When receiving a referral request, a participant will locally choose among her neighbors some user to relay the request. In our mechanism, several closely coupled methods are carefully designed to improve the performance of distributed search, including, profile matching, social acquaintance prediction, score function for locally choosing relay neighbors, and budget estimation. We conduct extensive experiments on several data sets of online social networks. The extensive study of our mechanism shows that the success rate of our mechanism is about 90 percent in finding closely matched experts using only local search and limited budget, which significantly improves the previously best rate 20 percent. The overall cost of finding an expert by our truthful mechanism is about 20 percent of the untruthful methods, e.g., the method that always selects high-degree neighbors. The median length of social referral chains is 6 using our localized search decision, which surprisingly matches the well-known small-world phenomenon of global social structures.
Part-of-Speech Tagger for Marathi Language using Limited Training Corpora

Part-of-speech tagging in Marathi language is a very complex task as Marathi is highly inflectional in nature & free word order language. In this paper we have demonstrated a rule-based Part-of-Speech tagger for Marathi Language. The hand–constructed rules that are learned from corpus and some manual addition after studying the grammar of Marathi language are added and that are used for developing the tagger. Disambiguation is done by
Control Strategies for Microgrids With Distributed Energy Storage Systems: An Overview

This paper presents an overview of the state of the art control strategies specifically designed to coordinate distributed energy storage (ES) systems in microgrids. Power networks are undergoing a transition from the traditional model of centralised generation towards a smart decentralised network of renewable sources and ES systems, organised into autonomous microgrids. ES systems can provide a range of services, particularly when distributed throughout the power network. The introduction of distributed ES represents a fundamental change for power networks, increasing the network control problem dimensionality and adding long time-scale dynamics associated with the storage systems’ state of charge levels. Managing microgrids with many small distributed ES systems requires new scalable control strategies that are robust to power network and communication network disturbances. This paper reviews the range of services distributed ES systems can provide, and the control challenges they introduce. The focus of this paper is a presentation of the latest decentralised, centralised and distributed multi-agent control strategies designed to coordinate distributed microgrid ES systems. Finally, multi-agent control with agents satisfying Wooldridge’s definition of intelligence is proposed as a promising direction for future research.
Automated Body Structure Extraction from Arbitrary 3D Mesh

Yong Khoo, Sang Chung This paper presents an automated method for 3D character skeleton extraction that can be applied for generic 3D shapes. Our work is motivated by the skeleton-based prior work on automatic rigging focused on skeleton extraction and can automatically aligns the extracted structure to fit the 3D shape of the given 3D mesh. The body mesh can be subsequently skinned based on the extracted skeleton and thus enables rigging process. In the experiment, we apply public dataset to drive the estimated skeleton from different body shapes, as well as the real data obtained from 3D scanning systems. Satisfactory results are obtained compared to the existing approaches.
Zero-Resource Multilingual Model Transfer: Learning What to Share

Modern natural language processing and understanding applications have enjoyed a great boost utilizing neural networks models. However, this is not the case for most languages especially low-resource ones with insufficient annotated training data. Cross-lingual transfer learning methods improve the performance on a lowresource target language by leveraging labeled data from other (source) languages, typically with the help of cross-lingual resources such as parallel corpora. In this work, we propose the first zero-resource multilingual transfer learning model1 that can utilize training data in multiple source languages, while not requiring target language training data nor cross-lingual supervision. Unlike existing methods that only rely on language-invariant features for cross-lingual transfer, our approach utilizes both language-invariant and language-specific features in a coherent way. Our model leverages adversarial networks to learn language-invariant features and mixture-of-experts models to dynamically exploit the relation between the target language and each individual source language. This enables our model to learn effectively what to share between various languages in the multilingual setup. It results in significant performance gains over prior art, as shown in an extensive set of experiments over multiple text classification and sequence tagging tasks including a large-scale real-world industry dataset.
Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts

The objective of this paper is to present the role of Ontology Learning Process in supporting an ontology engineer for creating and maintaining ontologies from textual resources. The knowledge structures that interest us are legal domain-specific ontologies. We will use these ontologies to build legal domain ontology for a Lebanese legal knowledge based system. The domain application of this work is the Lebanese criminal system. Ontologies can be learnt from various sources, such as databases, structured and unstructured documents. Here, the focus is on the acquisition of ontologies from unstructured text, provided as input. In this work, the Ontology Learning Process represents a knowledge extraction phase using Natural Language Processing techniques. The resulted ontology is considered as inexpressive ontology. There is a need to reengineer it in order to build a complete, correct and more expressive domain-specific ontology.
Trading the Bond-CDS Basis-The Role of Credit Risk and Liquidity

We analyze trading opportunities that arise from differences between the bond and the CDS market. By simultaneously entering a position in a CDS contract and the underlying bond, traders can build a default-risk free position that allows them to repeatedly earn the difference between the bond asset swap spread and the CDS, known as the basis. We show that the basis size is closely related to measures of company-specific credit risk and liquidity, and to market conditions. In analyzing the aggregate profits of these basis trading strategies, we document that dissolving a position leads to significant profit variations, but that attractive risk-return characteristics still apply. The aggregate profits depend on the credit risk, liquidity, and market measures even more strongly than the basis itself, and we show which conditions make long and short basis trades more profitable. Finally, we document the impact of the financial crisis on the profits of long and short basis trades, and show that the formerly more profitable long basis trades experienced stronger profit decreases than short basis trades.
LiveEye : Driver Attention Monitoring System

A large majority of road accidents are relative to driver fatigue, distraction and drowsiness which are widely believed to be the largest contributors to fatalities and severe injuries, either as a direct cause of falling asleep at the wheel or as a contributing factor in lowering the attention and reaction time of a driver in critical situations. Thus to prevent road accidents, a countermeasure device has to be used. This paper illuminates and highlights the various measures that have been studied to detect drowsiness such as vehicle based, physiological based, and behavioural based measures. The main objective is to develop a real time non-contact system which will be able to identify driver’s drowsiness beforehand. The system uses an IR sensitive monochrome camera that detects the position and state of the eyes to calculate the drowsiness of a driver. Once the driver is detected as drowsy, the system will generate warning signals to alert the driver. In case the signal is not re-established the system will shut off the engine to prevent any mishap.  Drowsiness, Road Accidents, Eye Detection, Face Detection, Blink Pattern, PERCLOS, MATLAB, Arduino Nano
Cyber Range Infrastructure Limitations and Needs of Tomorrow: A Position Paper

Cyber networks are extremely non-deterministic, complex systems. To address this, we must develop foundational research protocols to enable reproducible cyber experiments that can systematically uncover deep understanding of a cyber system's security posture. One core tenant of this approach is to have a test environment to enable a space to create and test hypotheses about systems and reason about results. To date, this has generally been done through cyber testbeds or cyber ranges. National infrastructure supported by various government agencies have all created multi-million dollar ranges, and support other national infrastructure such as the National Cyber Range (NCR) and the Regional Service Delivery Points (RSDP). The thrust of this paper was based on multi-year studies, the culmination of which uncovered gaps and challenges associated with using various national infrastructures to represent a multitude of complex heterogeneous systems. Ranges, such as the NCR, have experiment life-cycle processes to take a cyber experiment from inception to analysis. However, our position is that processes used are not sufficient to address gaps and challenges. In this position paper, we review current range experiment methodologies and our observations of other considerations that should require inclusion.
Homogenization of the Poisson-Nernst-Planck equations for Ion Transport in Charged Porous Media

Effective Poisson–Nernst–Planck (PNP) equations are derived for ion transport in charged porous media under forced convection (periodic flow in the frame of the mean velocity) by an asymptotic multiscale expansion with drift. The homogenized equations provide a modeling framework for engineering while also addressing fundamental questions about electrodiffusion in charged porous media, relating to electroneutrality, tortuosity, ambipolar diffusion, Einstein’s relation, and hydrodynamic dispersion. The microscopic setting is a two-component periodic composite consisting of a dilute electrolyte continuum (described by standard PNP equations) and a continuous dielectric matrix, which is impermeable to the ions and carries a given surface charge. As a first approximation for forced convection, the electrostatic body force on the fluid and electro-osmotic flows are neglected. Four new features arise in the upscaled equations: (i) the effective ionic diffusivities and mobilities become tensors, related to the microstructure; (ii) the effective permittivity is also a tensor, depending on the electrolyte/matrix permittivity ratio and the ratio of the Debye screening length to the macroscopic length of the porous medium; (iii) the microscopic convection leads to a diffusion-dispersion correction in the effective diffusion tensor; and (iv) the surface charge per volume appears as a continuous “background charge density,” as in classical membrane models. The coefficient tensors in the upscaled PNP equations can be calculated from periodic reference cell problems. For an insulating solid matrix, all gradients are corrected by the same tensor, and the Einstein relation holds at the macroscopic scale, which is not generally the case for a polarizable matrix, unless the permittivity and electric field are suitably defined. In the limit of thin double layers, Poisson’s equation is replaced by macroscopic electroneutrality (balancing ionic and surface charges). The general form of the macroscopic PNP equations may also hold for concentrated solution theories, based on the local-density and mean-field approximations. These results have broad applicability to ion transport in porous electrodes, separators, membranes, ion-exchange resins, soils, porous rocks, and biological tissues.
Coco: Runtime Reasoning about Conflicting Commitments

To interact effectively, agents must enter into commitments. What should an agent do when these commitments conflict? We describe Coco, an approach for reasoning about which specific commitments apply to specific parties in light of general types of commitments, specific circumstances, and dominance relations among specific commitments. Coco adapts answer-set programming to identify a maximal set of nondominated commitments. It provides a modeling language and tool geared to support practical applications.
C-MART: Benchmarking the Cloud

Cloud computing environments provide on-demand resource provisioning, allowing applications to elastically scale. However, application benchmarks currently being used to test cloud management systems are not designed for this purpose. This results in resource underprovisioning and quality-of-service (QoS) violations when systems tested using these benchmarks are deployed in production environments. We present C-MART, a benchmark designed to emulate a modern web application running in a cloud computing environment. It is designed using the cloud computing paradigm of elastic scalability at every application tier and utilizes modern web-based technologies such as HTML5, AJAX, jQuery, and SQLite. C-MART consists of a web application, client emulator, deployment server, and scaling API. The deployment server automatically deploys and configures the test environment in orders of magnitude less time than current benchmarks. The scaling API allows users to define and provision their own customized datacenter. The client emulator generates the web workload for the application by emulating complex and varied client behaviors, including decisions based on page content and prior history. We show that C-MART can detect problems in management systems that previous benchmarks fail to identify, such as an increase from 4.4 to 50 percent error in predicting server CPU utilization and resource underprovisioning in 22 percent of QoS measurements.
A Half-Rate Bang-Bang Phase/Frequency Detector for Continuous-Rate CDR Circuits

A half-rate bang-bang phase and frequency detector (BBPFD) is presented for continuous-rate clock and data recovery (CDR) circuits. The proposed half-rate BBPFD not only preserves the advantages of conventional BBPDs, but also has the infinite unilateral frequency detection range. To verify the proposed circuit, a continuous-rate CDR circuit with the proposed BBPFD has been fabricated in a 0.18um CMOS process. It can recover the NRZ data with the bit rate ranging from 622 Mbps to 3.125 Gbps. The measured bit-error rate is less than 10-12. The core area is 0.33 x 0.27 mm2 and the power consumption is 80 mW from a 1.8 V supply.
Combining Two-view Constraints For Motion Estimation

In this paper we describe two methods for estimating the motion parameters of an image sequence. For a sequence of images, the global motion can be described by independent motion models. On the other hand, in a sequence there exist as many as  pairwise relative motion constraints that can be solve for efficiently. In this paper we show how to linearly solve for consistent global motion models using this highly redundant set of constraints. In the first case, our method involves estimating all available pairwise relative motions and linearly fitting a global motion model to these estimates. In the second instance, we exploit the fact that algebraic (ie. epipolar) constraints between various image pairs are all related to each other by the global motion model. This results in an estimation method that directly computes the motion of the sequence by using all possible algebraic constraints. Unlike using reprojection error, our optimisation method does not solve for the structure of points resulting in a reduction of the dimensionality of the search space. Our algorithms are used for both 3D camera motion estimation and camera calibration. We provide real examples of both applications.
Contrastive Linguistics : Theories and Methods

Narrowly defined, contrastive linguistics can be regarded as a branch of comparative linguistics that is concerned with pairs of languages which are ‘socio-culturally linked’. Two languages can be said to be socio-culturally linked when (i) they are used by a considerable number of bior multilingual speakers, and/or (ii) a substantial amount of ‘linguistic output’ (text, oral discourse) is translated from one language into the other. According to this definition, contrastive linguistics deals with pairs of languages such as Spanish and Basque, but not with Latin and (the Australian language) Dyirbal, as there is no socio-cultural link between these languages.