nils-herrmann/clean_scirepeval huggingface dataset:
Introducing Docear's research paper recommender system

In this demo paper we present Docear's research paper recommender system. Docear is an academic literature suite to search, organize, and create research articles. The users' data (papers, references, annotations, etc.) is managed in mind maps and these mind maps are utilized for the recommendations. Using content-based filtering methods, Docear's recommender achieves click-through rates around 6%, in some scenarios even over 10%.
A Fast Algorithm for Sparse Reconstruction Based on Shrinkage, Subspace Optimization, and Continuation

for recovering sparse solutions to an undetermined system of linear equations Ax = b. The algorithm is divided into two stages that are performed repeatedly. In the first stage a first-order iterative method called “shrinkage” yields an estimate of the subset of components of x likely to be nonzero in an optimal solution. Restricting the decision variables x to this subset and fixing their signs at their current values reduces the l1-norm ‖x‖1 to a linear function of x. The resulting subspace problem, which involves the minimization of a smaller and smooth quadratic function, is solved in the second phase. Our code FPC AS embeds this basic two-stage algorithm in a continuation (homotopy) approach by assigning a decreasing sequence of values to μ. This code exhibits state-of-the-art performance both in terms of its speed and its ability to recover sparse signals. It can even recover signals that are not as sparse as required by current compressive sensing theory.
Unsupervised Real-to-Virtual Domain Unification for End-to-End Highway Driving

In the spectrum of vision-based autonomous driving, vanilla end-to-end models are not interpretable and suboptimal in performance, while mediated perception models require additional intermediate representations such as segmentation masks or detection bounding boxes, whose annotation can be prohibitively expensive as we move to a larger scale. Raw images and existing intermediate representations are also loaded with nuisance details that are irrelevant to the prediction of vehicle commands, e.g. the style of the car in front or the view beyond the road boundaries. More critically, all prior works fail to deal with the notorious domain shift if we were to merge data collected from different sources, which greatly hinders the model generalization ability. In this work, we address the above limitations by taking advantage of virtual data collected from driving simulators, and present DU-drive, an unsupervised real to virtual domain unification framework for end-to-end driving. It transforms real driving data to its canonical representation in the virtual domain, from which vehicle control commands are predicted. Our framework has several advantages: 1) it maps driving data collected from different source distributions into a unified domain, 2) it takes advantage of annotated virtual data which is free to obtain, 3) it learns an interpretable, canonical representation of driving image that is specialized for vehicle command prediction. Extensive experiments on two public highway driving datasets clearly demonstrate the performance superiority and interpretive capability of DU-drive.
Personal servers as digital keys

Personal servers are an attractive concept: people carry around a device that takes care of computing, storage and communication on their behalf in a pervasive computing environment. So far personal servers have mainly been considered for accessing personal information. In this paper, we consider personal servers in the context of a digital key system. Digital keys are an interesting alternative to physical keys for mail or good delivery companies whose employees access tens of private buildings every day. We present a digital key system tailored for the current incarnation of personal servers, i.e., a Bluetooth-enabled mobile phone. We describe how to use Bluetooth for this application, we present a simple authentication protocol and we provide a detailed analysis of response time and energy consumption on the mobile phone.
Unsupervised RGBD Video Object Segmentation Using GANs

Video object segmentation is a fundamental step in many advanced vision applications. Most existing algorithms are based on handcrafted features such as HOG, super-pixel segmentation or texturebased techniques, while recently deep features have been found to be more efficient. Existing algorithms observe performance degradation in the presence of challenges such as illumination variations, shadows, and color camouflage. To handle these challenges we propose a fusion based moving object segmentation algorithm which exploits color as well as depth information using GAN to achieve more accuracy. Our goal is to segment moving objects in the presence of challenging background scenes, in real environments. To address this problem, GAN is trained in an unsupervised manner on color and depth information independently with challenging video sequences. During testing, the trained GAN generates backgrounds similar to that in the test sample. The generated background samples are then compared with the test sample to segment moving objects. The final result is computed by fusion of object boundaries in both modalities, RGB and the depth. The comparison of our proposed algorithm with five state-of-the-art methods on publicly available dataset has shown the strength of our algorithm for moving object segmentation in videos in the presence of challenging real scenarios.
Dependency Parsing by Belief Propagation

We formulate dependency parsing as a graphical model with the novel ingredient of global constraints. We show how to apply loopy belief propagation (BP), a simple and effective tool for approximate learning and inference. As a parsing algorithm, BP is both asymptotically and empirically efficient. Even with second-order features or latent variables, which would make exact parsing considerably slower or NP-hard, BP needs only O(n) time with a small constant factor. Furthermore, such features significantly improve parse accuracy over exact first-order methods. Incorporating additional features would increase the runtime additively rather than multiplicatively.
Secret-Sharing Schemes: A Survey

A secret-sharing scheme is a method by which a dealer distributes shares to parties such that only authorized subsets of parties can reconstruct the secret. Secret-sharing schemes are important tools in cryptography and they are used as a building box in many secure protocols, e.g., general protocol for multiparty computation, Byzantine agreement, threshold cryptography, access control, attribute-based encryption, and generalized oblivious transfer. In this survey, we will describe the most important constructions of secret-sharing schemes, explaining the connections between secret-sharing schemes and monotone formulae and monotone span programs. The main problem with known secret-sharing schemes is the large share size: it is exponential in the number of parties. We conjecture that this is unavoidable. We will discuss the known lower bounds on the share size. These lower bounds are fairly weak and there is a big gap between the lower and upper bounds. For linear secret-sharing schemes, which is a class of schemes based on linear algebra that contains most known schemes, super-polynomial lower bounds on the share size are known. We will describe the proofs of these lower bounds. We will also present two results connecting secret-sharing schemes for a Hamiltonian access structure to the NP vs. coNP problem and to a major open problem in cryptography – constructing oblivious-transfer protocols from one-way functions.
Risk evaluation approaches in failure mode and effects analysis: A literature review

0957-4174/$ see front matter 2012  A http://dx.doi.org/10.1016/j.eswa.2012.08.010 ⇑ Corresponding author.  College of De University, 1239 Siping Road, Shanghai 200092, PR 6598 3432. E-mail addresses: huchenliu@foxmaill.com (H.-C (L. Liu), liunan@cqjtu.edu.cn (N. Liu). Failure mode and effects analysis (FMEA) is a risk assessment tool that mitigates potential failures in systems, processes, designs or services and has been used in a wide range of industries. The conventional risk priority number (RPN) method has been criticized to have many deficiencies and various risk priority models have been proposed in the literature to enhance the performance of FMEA. However, there has been no literature review on this topic. In this study, we reviewed 75 FMEA papers published between 1992 and 2012 in the international journals and categorized them according to the approaches used to overcome the limitations of the conventional RPN method. The intention of this review is to address the following three questions: (i) Which shortcomings attract the most attention? (ii) Which approaches are the most popular? (iii) Is there any inadequacy of the approaches? The answers to these questions will give an indication of current trends in research and the best direction for future research in order to further address the known deficiencies associated with the traditional FMEA.
The spread of obesity in a large social network over 32 years.

The prevalence of obesity has increased substantially over the past 30 years. We performed a quantitative analysis of the nature and extent of the person-to-person spread of obesity as a possible factor contributing to the obesity epidemic.



We evaluated a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. The body-mass index was available for all subjects. We used longitudinal statistical models to examine whether weight gain in one person was associated with weight gain in his or her friends, siblings, spouse, and neighbors.



Discernible clusters of obese persons (body-mass index [the weight in kilograms divided by the square of the height in meters], > or =30) were present in the network at all time points, and the clusters extended to three degrees of separation. These clusters did not appear to be solely attributable to the selective formation of social ties among obese persons. A person's chances of becoming obese increased by 57% (95% confidence interval [CI], 6 to 123) if he or she had a friend who became obese in a given interval. Among pairs of adult siblings, if one sibling became obese, the chance that the other would become obese increased by 40% (95% CI, 21 to 60). If one spouse became obese, the likelihood that the other spouse would become obese increased by 37% (95% CI, 7 to 73). These effects were not seen among neighbors in the immediate geographic location. Persons of the same sex had relatively greater influence on each other than those of the opposite sex. The spread of smoking cessation did not account for the spread of obesity in the network.



Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.
A Survey of Visualization Systems for Network Security

Security Visualization is a very young term. It expresses the idea that common visualization techniques have been designed for use cases that are not supportive of security-related data, demanding novel techniques fine tuned for the purpose of thorough analysis. Significant amount of work has been published in this area, but little work has been done to study this emerging visualization discipline. We offer a comprehensive review of network security visualization and provide a taxonomy in the form of five use-case classes encompassing nearly all recent works in this area. We outline the incorporated visualization techniques and data sources and provide an informative table to display our findings. From the analysis of these systems, we examine issues and concerns regarding network security visualization and provide guidelines and directions for future researchers and visual system developers.
Schism: a Workload-Driven Approach to Database Replication and Partitioning

We present Schism, a novel workload-aware approach for database partitioning and replication designed to improve scalability of sharednothing distributed databases. Because distributed transactions are expensive in OLTP settings (a fact we demonstrate through a series of experiments), our partitioner attempts to minimize the number of distributed transactions, while producing balanced partitions. Schism consists of two phases: i) a workload-driven, graph-based replication/partitioning phase and ii) an explanation and validation phase. The first phase creates a graph with a node per tuple (or group of tuples) and edges between nodes accessed by the same transaction, and then uses a graph partitioner to split the graph into k balanced partitions that minimize the number of cross-partition transactions. The second phase exploits machine learning techniques to find a predicate-based explanation of the partitioning strategy (i.e., a set of range predicates that represent the same replication/partitioning scheme produced by the partitioner). The strengths of Schism are: i) independence from the schema layout, ii) effectiveness on n-to-n relations, typical in social network databases, iii) a unified and fine-grained approach to replication and partitioning. We implemented and tested a prototype of Schism on a wide spectrum of test cases, ranging from classical OLTP workloads (e.g., TPC-C and TPC-E), to more complex scenarios derived from social network websites (e.g., Epinions.com), whose schema contains multiple n-to-n relationships, which are known to be hard to partition. Schism consistently outperforms simple partitioning schemes, and in some cases proves superior to the best known manual partitioning, reducing the cost of distributed transactions up to 30%.
Medical diagnostic expert system based on PDP model

The applicability of PDP (parallel distributed processing) models to knowledge processing is clarified. The authors evaluate the diagnostic capabilities of a prototype medical diagnostic expert system based on a multilayer network. After having been trained on only 300 patients, the prototype system shows diagnostic capabilities almost equivalent to those of a symbolic expert system. Symbolic knowledge is extracted from what the multilayer network has learned. The extracted knowledge is compared with doctors' knowledge. Moreover, a method to extract rules from the network and usage of the rules in a confirmation process are proposed.<<ETX>>
User Profile Preserving Social Network Embedding

This paper addresses social network embedding, which aims to embed social network nodes, including user profile information, into a latent lowdimensional space. Most of the existing works on network embedding only consider network structure, but ignore user-generated content that could be potentially helpful in learning a better joint network representation. Different from rich node content in citation networks, user profile information in social networks is useful but noisy, sparse, and incomplete. To properly utilize this information, we propose a new algorithm called User Profile Preserving Social Network Embedding (UPPSNE), which incorporates user profile with network structure to jointly learn a vector representation of a social network. The theme of UPPSNE is to embed user profile information via a nonlinear mapping into a consistent subspace, where network structure is seamlessly encoded to jointly learn informative node representations. Extensive experiments on four real-world social networks show that compared to state-of-the-art baselines, our method learns better social network representations and achieves substantial performance gains in node classification and clustering tasks.
Control of elastic soft robots based on real-time finite element method

In this paper, we present a new method for the control of soft robots with elastic behavior, piloted by several actuators. The central contribution of this work is the use of the Finite Element Method (FEM), computed in real-time, in the control algorithm. The FEM based simulation computes the nonlinear deformations of the robots at interactive rates. The model is completed by Lagrange multipliers at the actuation zones and at the end-effector position. A reduced compliance matrix is built in order to deal with the necessary inversion of the model. Then, an iterative algorithm uses this compliance matrix to find the contribution of the actuators (force and/or position) that will deform the structure so that the terminal end of the robot follows a given position. Additional constraints, like rigid or deformable obstacles, or the internal characteristics of the actuators are integrated in the control algorithm. We illustrate our method using simulated examples of both serial and parallel structures and we validate it on a real 3D soft robot made of silicone.
Training selection for tuning entity matching

Entity matching is a crucial and difficult task for data integration. An effective solution strategy typically has to combine several techniques and to find suitable settings for critical configuration parameters such as similarity thresholds. Supervised (trainingbased) approaches promise to reduce the manual work for determining (learning) effective strategies for entity matching. However, they critically depend on training data selection which is a difficult problem that has so far mostly been addressed manually by human experts. In this paper we propose a trainingbased framework called STEM for entity matching and present different generic methods for automatically selecting training data to combine and configure several matching techniques. We evaluate the proposed methods for different match tasks and smalland medium-sized training sets.
Some Advances in Transformation-Based Part of Speech Tagging

Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In (Brill 1992), a trainable rule-based tagger was described that obtained performance comparable to that of stochastic taggers, but captured relevant linguistic information in a small number of simple non-stochastic rules. In this paper, we describe a number of extensions to this rule-based tagger. First, we describe a method for expressing lexical relations in tagging that stochastic taggers are currently unable to express. Next, we show a rule-based approach to tagging unknown words. Finally, we show how the tagger can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.
Unsteady aerodynamics and flow control for flapping wing flyers

The creation of micro air vehicles (MAVs) of the same general sizes and weight as natural fliers has spawned renewed interest in flapping wing flight. With a wingspan of approximately 15 cm and a flight speed of a few meters per second, MAVs experience the same low Reynolds number (10–10) flight conditions as their biological counterparts. In this flow regime, rigid fixed wings drop dramatically in aerodynamic performance while flexible flapping wings gain efficacy and are the preferred propulsion method for small natural fliers. Researchers have long realized that steady-state aerodynamics does not properly capture the physical phenomena or forces present in flapping flight at this scale. Hence, unsteady flow mechanisms must dominate this regime. Furthermore, due to the low flight speeds, any disturbance such as gusts or wind will dramatically change the aerodynamic conditions around the MAV. In response, a suitable feedback control system and actuation technology must be developed so that the wing can maintain its aerodynamic efficiency in this extremely dynamic situation; one where the unsteady separated flow field and wing structure are tightly coupled and interact nonlinearly. For instance, birds and bats control their flexible wings with muscle tissue to successfully deal with rapid changes in the flow environment. Drawing from their example, perhaps MAVs can use lightweight actuators in conjunction with adaptive feedback control to shape the wing and achieve active flow control. This article first reviews the scaling laws and unsteady flow regime constraining both biological and man-made fliers. Then a summary of vortex dominated unsteady aerodynamics follows. Next, aeroelastic coupling and its effect on lift and thrust are discussed. Afterwards, flow control strategies found in nature and devised by man to deal with separated flows are examined. Recent work is also presented in using microelectromechanical systems (MEMS) actuators and angular speed variation to achieve active flow control for MAVs. Finally, an explanation for aerodynamic gains seen in flexible versus rigid membrane wings, derived from an unsteady three-dimensional computational fluid dynamics model with an integrated distributed control algorithm, is presented.
A clickstream-based collaborative filtering personalization model: towards a better performance

In recent years, clickstream-based Web personalization models for collaborative filtering recommendation have received much attention mainly due to their scalability [10,16,19]. The common personalization models are the Markov model, (sequential) association rule, and clustering. These models have shown strengths and weaknesses in their performance: for instance, the Markov model has higher precision and lower recall than (sequential) association rule and clustering, and vice versa [22]. In order to address the trade-off relationship of precision and recall, some study has combined two or more different models [22] or applied multi-order models [24,27]. The performance increases by these models, however, are at best marginal and still there is room for improving the performance because of their first order (one model type) application in making recommendation. We propose a new hybrid model for improving the performance, especially recall. The proposed hybrid model applies four prediction models - the Markov model, sequential association rule, association rule, and a default model [1,17] - in tandem in their precision order. We evaluated our model with Web usage data, and the result is promising.
Cheap, Fast and Good Enough: Automatic Speech Recognition with Non-Expert Transcription

Deploying an automatic speech recognition system with reasonable performance requires expensive and time-consuming in-domain transcription. Previous work demonstrated that non-professional annotation through Amazon’s Mechanical Turk can match professional quality. We use Mechanical Turk to transcribe conversational speech for as little as one thirtieth the cost of professional transcription. The higher disagreement of nonprofessional transcribers does not have a significant effect on system performance. While previous work demonstrated that redundant transcription can improve data quality, we found that resources are better spent collecting more data. Finally, we suggest a concrete method for quality control without needing professional transcription.