nils-herrmann/clean_scirepeval huggingface dataset:
Differentially Private Online Learning

In this paper, we consider the problem of preserving privacy in the context of online learning. Online learning involves learning from data in real-time, due to which the learned model as well as its predictions are continuously changing. This makes preserving privacy of each data point significantly more challenging as its effect on the learned model can be easily tracked by observing changes in the subsequent predictions. Furthermore, with more and more online systems (e.g. search engines like Bing, Google etc.) trying to learn their customers’ behavior by leveraging their access to sensitive customer data (through cookies etc.), the problem of privacy preserving online learning has become critical. We study the problem in the framework of online convex programming ( popular online learning setting with several theoretical and practical implications—while using differential privacy as the formal measure of privacy. For this problem, we provide a generic framework that can be used to convert any given OCP algorithm into a private OCP algorithm with provable privacy as well as regret guarantees (utility), provided that the given OCP algorithm satisfies the following two criteria: 1) linearly decreasing sensitivity, i.e., the effect of the new data points on the learned model decreases linearly, 2) sub-linear regret. We then illustrate our approach by converting two popular OCP algorithms into corresponding differentially private algorithms while guaranteeing Õ( √ T ) regret for strongly convex functions. Next, we consider the practically important class of online linear regression problems, for which we generalize the approach by Dwork et al. (2010a) to provide a differentially private algorithm with just poly-log regret. Finally, we show that our online learning framework can be used to provide differentially private algorithms for the offline learning problem as well. For the offline learning problem, our approach guarantees better error bounds and is more practical than the existing state-of-the-art methods (Chaudhuri et al., 2011; Rubinstein et al., 2009).
ARMin - Exoskeleton for Arm Therapy in Stroke Patients

Task-oriented repetitive movement can improve movement performance in patients with neurological lesions. The application of robotics can serve to assist, enhance, evaluate and document rehabilitation of movements. ARMin is a robot for arm therapy applicable to the arm training in clinics. It has an exoskeleton structure and is equipped with position and force sensors. Our latest version ARMin II has six degrees of freedom. The mechanical structure, the actuators, and the sensors of the robot are optimized for patient-cooperative control strategies based on impedance and admittance architectures. The device can work in three therapy modes: passive mobilization, game therapy, and task-oriented training. This paper presents the technical components of the new version ARMin II, the therapy modes, the control strategy for a new example of a game therapy, and clinical results of a pilot study with 11 chronic stroke patients and of single case studies conducted with three chronic stroke patients.
Valuing American Options by Simulation : A Simple Least-Squares Approach

This article presents a simple yet powerful new approach for approximating the value of American options by simulation. The key to this approach is the use of least squares to estimate the conditional expected payoff to the optionholder from continuation. This makes this approach readily applicable in path-dependent and multifactor situations where traditional finite difference techniques cannot be used. We illustrate this technique with several realistic examples including valuing an option when the underlying asset follows a jump-diffusion process and valuing an American swaption in a 20-factor string model of the term structure.
Entity Comparison in RDF Graphs

In many applications, there is an increasing need for the new types of RDF data analysis that are not covered by standard reasoning tasks such as SPARQL query answering. One such important analysis task is entity comparison, i.e., determining what are similarities and differences between two given entities in an RDF graph. For instance, in an RDF graph about drugs, we may want to compare Metamizole and Ibuprofen and automatically find out that they are similar in that they are both analgesics but, in contrast to Metamizole, Ibuprofen also has a considerable anti-inflammatory effect. Entity comparison is a widely used functionality available in many information systems, such as universities or product comparison websites. However, comparison is typically domain-specific and depends on a fixed set of aspects to compare. In this paper, we propose a formal framework for domain-independent entity comparison over RDF graphs. We model similarities and differences between entities as SPARQL queries satisfying certain additional properties, and propose algorithms for computing them.
Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments

Recommendersystems leverage product and community information to target products to consumers. Researchershave developed collaborati ve recommenders, content-basedrecommenders,and a few hybrid systems. We proposea unifiedprobabilisticframework for merging collaborati ve andcontent-basedrecommendations. We extend Hofmann’s (1999) aspect model to incorporatethree-way co-occurrence dataamongusers,items,anditem content.The relative influence of collaborationdata versus contentdatais not imposedasanexogenousparameter , but ratheremergesnaturally from the givendatasources.However, globalprobabilistic modelscoupledwith standardEM learningalgorithmstendto drasticallyoverfit in thesparsedatasituationstypical of recommendationapplications. We show that secondarycontent information can often be usedto overcomesparsity. Experimentson datafrom the ResearchIndex library of ComputerSciencepublications show that appropriatemixture modelsincorporatingsecondarydataproducesignificantlybetter quality recommendersthan -nearestneighbors ( -NN). Global probabilisticmodelsalsoallow moregeneralinferencesthanlocal methodslike -NN.
An application of analytic hierarchy process ( AHP ) to investment portfolio selection in the banking sector of the Nigerian capital market

The importance of investment to the individual, a nation and the world economy cannot be over emphasized. Investment involves the sacrifice of immediate consumption to achieve greater consumption in the future. The Nigerian banking sector has made tremendous success in the recent past. All the banks that approached the capital market through public offers and right issues to raise their capital base recorded huge success. Investors in bank stocks have enjoyed high returns on investment, despite the slow growth in the nation’s economy. However, the recent financial crisis that started in America, which has caused economy meltdown in many nations of the world and sudden fall in share prices, has brought about a higher risk than envisaged on investors, particularly those investing in bank stocks. This study underlines the importance of different criteria, factors and alternatives that are essential to successful investment decisions by applying the Analytic Hierarchy Process (AHP) in the selection of investment in bank stocks in the circumstances of financial crisis. The study provides recommendation on strategic investment decision options.
Gaps as characters in sequence-based phylogenetic analyses.

In the analysis of sequence-based data matrices, the use of different methods of treating gaps has been demonstrated to influence the resulting phylogenetic hypotheses (e.g., Eernisse and Kluge, 1993; Vogler and DeSalle, 1994; Simons and May den, 1997). Despite this influence, a well-justified, uniformly applied method of treating gaps is lacking in sequence-based phylogenetic studies. Treatment of gaps varies widely from secondarily mapping gaps onto the tree inferred from base characters to treating all gaps as separate characters or character states (Gonzalez, 1996). This diversity of approaches demonstrates the need for a comprehensive discussion of indel (insertion or deletion) coding and a robust method with which to incorporate gap characters into tree searches. We use the term "indel coding" instead of "gap coding" because the term "gap coding" has already been applied to coding quantitative characters (Mickevich and Johnson, 1976; Archie, 1985). Although "indel coding" undesirably refers to processes that are not observed (insertions and deletions) instead of patterns that are observed (gaps), the term is unambiguous and does not co-opt established terminology. The purpose of this paper is to discuss the implications of each of the methods of treating gaps in phylogenetic analyses, to allow workers to make informed choices among them. We suggest that gaps should be coded as characters in phylogenetic analyses, and we propose two indel-coding methods. We discuss four main points: (1) the logical independence of alignment and tree search; (2) why gaps are properly coded as characters; (3) how gaps should be coded as characters; and (4) problems with a priori weighting of gap characters during tree search.
Computational Collective Intelligence

Although many powerful applications are able to locate vast amounts of digital information, effective tools for selecting, structuring, personalizing, and making sense of the digital resources available to us are lacking. As a result, the opportunities to connect and empower knowledge workers are severely limited. In recognizing these constraints, predictions of the ‘Next Knowledge Management (KM) Generation’ focus on nurturing personal and social settings and on utilizing existing and creating new knowledge. Levy even envisages a decentralizing KM revolution that gives more power and autonomy to individuals and self-organized groups. But, such promising scenarios have not materialized yet. It might be time to follow Pollard’s suggestion of going back to the original premise and promise of KM and start again but this time from the bottom up by developing processes, programs, and tools to improve knowledge workers’ effec‐ tiveness and sense-making. As part of an ongoing design science research (DSR) project, this paper contributes to prior publications by synthesizing renowned computer-based methods of collective intelligence to provide a visual metaperspective of a novel personal knowledge management (PKM) concept and prototype application. In focusing on time, space, and causality, the bottom-up approach taken, pictures the relevant personal and organizational knowledge spaces as a substitute for the intangible KM territory and provides a guiding map for knowledge workers and KM education.
Quantifying ruggedness of continuous landscapes using entropy

A major unsolved problem in the field of optimisation and computational intelligence is how to determine which algorithms are best suited to solving which problems. This research aims to analytically characterise individual problems as a first step towards attempting to link problem types with the algorithms best suited to solving them. In particular, an information theoretic technique for analysing the ruggedness of a fitness landscape with respect to neutrality was adapted to work in continuous landscapes and to output a single measure of ruggedness. Experiments run on test functions with increasing ruggedness show that the proposed measure of ruggedness produced relative values consistent with a visual inspection of the problem landscapes. Combined with other measures of complexity, the proposed ruggedness measure could be used to more broadly characterise the complexity of fitness landscapes in continuous domains.
SPICE Models to Analyze Radiated and Conducted Susceptibilities of Shielded Coaxial Cables

This paper presents simulation program with integrated circuit emphasis (SPICE) models for the analyses of the radiated and conducted susceptibilities of shielded coaxial cables. Derived from the transmission-line equations of shielded cables, these models can be directly used for the time-domain and frequency-domain analyses, and connected to nonlinear and time-varying loads with the models already available in SPICE. In the proposed models, the usual discretization of shielded cables is not needed, and both the transfer impedance and admittance have been taken into account. Results obtained with these new models are in a good agreement with those obtained by using other approaches.
New Concept of Telemetry X-Band Circularly Polarized Antenna Payload for CubeSat

This letter presents a new concept of compact circularly polarized X-band [8–8.4 GHz] antenna for the 3U-CubeSat platforms. Despite the integration constraints on the top face of the CubeSat, the design aims at an isoflux radiation pattern. This antenna associates a driving patch antenna and 12 parasitic crossed dipoles, both to minimize the axial ratio in the opening angle θ = ±65° and to shape the radiation pattern. The patch excitation is carried out by a compact sequential-phase feed microstrip circuit. This antenna is manufactured and measured.
Convolutional Neural Networks for Steady Flow Approximation

In aerodynamics related design, analysis and optimization problems, flow fields are simulated using computational fluid dynamics (CFD) solvers. However, CFD simulation is usually a computationally expensive, memory demanding and time consuming iterative process. These drawbacks of CFD limit opportunities for design space exploration and forbid interactive design. We propose a general and flexible approximation model for real-time prediction of non-uniform steady laminar flow in a 2D or 3D domain based on convolutional neural networks (CNNs). We explored alternatives for the geometry representation and the network architecture of CNNs. We show that convolutional neural networks can estimate the velocity field two orders of magnitude faster than a GPU-accelerated CFD solver and four orders of magnitude faster than a CPU-based CFD solver at a cost of a low error rate. This approach can provide immediate feedback for real-time design iterations at the early stage of design. Compared with existing approximation models in the aerodynamics domain, CNNs enable an efficient estimation for the entire velocity field. Furthermore, designers and engineers can directly apply the CNN approximation model in their design space exploration algorithms without training extra lower-dimensional surrogate models.
Eye localization from thermal infrared images

By using the knowledge of facial structure and temperature distribution, this paper proposes an automatic eye localization method from long wave infrared thermal images both with eyeglasses and without eyeglasses. First, with the help of support vector machine classifier, three gray-projection features are defined to determine whether a subject is with eyeglasses. For subjects with eyeglasses, the locations of valleys in the projection curve are used to perform eye localization. For subjects without eyeglasses, a facial structure consisting of 15 sub-regions is proposed to extract Haar-like features. Eight classifiers are learned from the features selected by Adaboost algorithm for left and right eye, respectively. A vote strategy is employed to find the most likely eyes. To evaluate the effectiveness of our approach, experiments are performed on NVIE and Equinox databases. The eyeglass detection results on NVIE database and Equinox database are 99.36% and 95%, respectively, which demonstrate the effectiveness and robustness of our eyeglass detection method. Eye localization results of withindatabase experiments and cross-database experiments on these two databases are very comparable with the previous results in this field, verifying the effectiveness and the generalization ability of our approach. & 2013 Elsevier Ltd. All rights reserved.
Ensemble-style Self-training on Citation Classification

Classification of citations into categories such as use, refutation, comparison etc. may have several relevant applications for digital libraries such as paper browsing aids, reading recommendations, qualified citation indexing, or fine-grained impact factor calculation. Most citation classification approaches described so far heavily rely on rule systems and patterns tailored to specific science domains. We focus on a less manual approach by learning domaininsensitive features from textual, physical, and syntactic aspects. Our experiments show the effectiveness of this feature set with various machine learning algorithms on datasets of different sizes. Furthermore, we build an ensemble-style selftraining classification model and get better classification performance using only few training data, which largely reduces the manual annotation work in this task.
MATLAB functions to analyze directional (azimuthal) data - I: Single-sample inference

Data that represent azimuthal directions cannot be analyzed statistically in the same manner as ordinary variables that measure, for example, size or quality. Many statistical techniques have been developed for dealing with directional data, but available software for the unique calculations is scarce. This paper describes a MATLAB script, located on the IAMG server, that calculates descriptive statistics and performs inference on azimuthal parameters. The calculations include tests for specific distributions, and tests on the preferred direction and concentration of vectors about this direction. The inference methods use large-sample approximations, plus resampling methods (bootstrap) for smaller sample sizes.
E-government implementation: A bird's eye view of issues relating to costs, opportunities, benefits and risks

After more than a decade of comprehensive research work in the area of electronic government (e-government), no attempt has yet been made to undertake a systematic literature review on the costs, opportunities, benefits and risks that influence the implementation of e-government. This is particularly significant given the various related challenges that governments have faced over the years when implementing e-government initiatives. Hence, the aim of this paper is to undertake a comprehensive analysis of relevant literature addressing these issues using a systematic review of 132 studies identified from the Scopus online database and Google Scholar together with a manual review of relevant papers from journals dedicated to electronic government research such as Electronic Government, an International Journal (EGIJ), International Journal of Electronic Government Research (IJEGR) and Transforming Government: People, Process, and Policy (TGPPP). The overall review indicated that although a large number of papers discuss costs, opportunities, benefits and risks, treatment of these issues have tended to be superficial.Moreover, there is a lack of empirical studies which can statistically evaluate the performance of these constructs in relation to the various egovernment systems. Therefore, this research would help governments to better analyse the impact of costs, opportunities, benefits and risks on the success of e-government systems and its pre-adoption from an implementation perspective.
Using Dynamic Programming for Solving Variational Problems in Vision

Variational approaches have been proposed for solving many inverse problems in early vision, such as in the computation of optical flow, shape from shading, and energy-minimizing active contour models. In general however, variational approaches do not guarantee global optimality of the solution, require estimates of higher order derivatives of the discrete data, and do not allow direct and natural enforcement of constraints. In this paper we discuss dynamic programming as a novel approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, it is numerically stable, and it allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the efficacy of the proposed approach, application of dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a " time-delayed " discrete dynamic programming algorithm. A parallel procedure is discussed that can result in savings in computational costs.
Improving government services with social media feedback

Social media is an invaluable source of feedback not just about consumer products and services but also about the effectiveness of government services. Our aim is to help analysts identify how government services can be improved based on citizen-contributed feedback found in publicly available social media. We present ongoing research for a social media monitoring interactive prototype with federated search and text analysis functionality. The prototype, developed to fit the workflow of social media monitors in the government sector, collects, analyses, and provides overviews of social media content. It facilitates relevance judgements on specific social media posts to decide whether or not to engage online. Our user log analysis validates the original design requirements and indicates ongoing utility to our federated search approach.
Analysis of Ethernet AVB for automotive networks using Network Calculus

Future functions of vehicles to make driving safer, more comfortable and more exciting rely on suitable in-vehicle networks. Legacy Ethernet extended by AVB to meet recent and future requirements is discussed as a proper candidate. Investigation of its forwarding, synchronization and resource reservation mechanisms are an important prerequisite for the object: The progressive vehicle integration unleashing its whole potential as an in-vehicle network. This paper focuses on AVB's forwarding policy by using Network Calculus as a theory for calculating worst case delays.
Matroids and the greedy algorithm

(0) Many discrete programming algorithms are being proposed. One thing most of them have in common is that they do not work very well. Solving problems that are a priori finite, astronomically finite, is preferably a matter of finding algorithms that are s o m e h o w better than finite. These considerations prompt looking for good algorithms and trying to understand how and why at least a few combinatorial problems have them. (1) Let H be a finite (for convenience) set of real-valued vectors , x = [xj] , ] ~ E. Often all the members of H will be integer-valued; often they will all be {0, 1}-valued. The index-set, E, is any finite set of elements. Let c = [cj], ] ~ E, be any real vector on E, called the objective or weighting of E. The problem of finding a member o f H which maximizes (or minimizes) cx = iN cjx/, j ~ E, we call a loco problem or loco programming. "Loco" stands for "linear-objective combinatorial". (2) In order for a loco problem to be a completely defined problem, the way H is given must of course be specified. There are various ways to describe implicitly very large sets H so tha t it is relatively easy to determine whether or not any particular vector is a member of H. One well-known:way is in linear programming, where H is the set of extreme points of the solution-set of a given finite system, L, of linear equations and <_ type linear inequalities in the variables xj (briefly, a