nils-herrmann/clean_scirepeval huggingface dataset:
Dynamic Movement Primitives – A Framework for Motor Control in Humans and Humanoid Robotics

Given the continuous stream of movements that biological systems exhibit in their daily activities, an account for such versatility and creativity has to assume that movement sequences consist of segments, executed either in sequence or with partial or complete overlap. Therefore, a fundamental question that has pervaded research in motor control both in artificial and biological systems revolves around identifying movement primitives (a.k.a. units of actions, basis behaviors, motor schemas, etc.). What are the fundamental building blocks that are strung together, adapted to, and created for ever new behaviors? This paper summarizes results that led to the hypothesis of Dynamic Movement Primitives (DMP). DMPs are units of action that are formalized as stable nonlinear attractor systems. They are useful for autonomous robotics as they are highly flexible in creating complex rhythmic (e.g., locomotion) and discrete (e.g., a tennis swing) behaviors that can quickly be adapted to the inevitable perturbations of a dynamically changing, stochastic environment. Moreover, DMPs provide a formal framework that also lends itself to investigations in computational neuroscience. A recent finding that allows creating DMPs with the help of well-understood statistical learning methods has elevated DMPs from a more heuristic to a principled modeling approach. Theoretical insights, evaluations on a humanoid robot, and behavioral and brain imaging data will serve to outline the framework of DMPs for a general approach to motor control in robotics and biology.
Why Lurkers Lurk

The goal of this paper is to address the question: ‘why do lurkers lurk?’ Lurkers reportedly makeup the majority of members in online groups, yet little is known about them. Without insight into lurkers, our understanding of online groups is incomplete. Ignoring, dismissing, or misunderstanding lurking distorts knowledge of life online and may lead to inappropriate design of online environments. To investigate lurking, the authors carried out a study of lurking using in-depth, semi-structured interviews with ten members of online groups. 79 reasons for lurking and seven lurkers’ needs are identified from the interview transcripts. The analysis reveals that lurking is a strategic activity involving more than just reading posts. Reasons for lurking are categorized and a gratification model is proposed to explain lurker behavior.
Analysis of users and non-users of smartphone applications

Smartphones facilitate the potential adoption of new mobile applications. The purpose of this research is to study users and non-users of three selected mobile applications, and find out what really drives the intention to use these applications across users and non-users.  The authors measured actual usage of mobile applications in a panel study of 579 Finnish smartphone users, using in-device measurements as an objective way to identify users and non-users. A web-based survey was used in collecting data to test an extended TAM model in explaining intention to use.  Perceived technological barriers negatively affect behavioural control, reflecting people’s assessment of themselves being capable of using the services without trouble. Behavioural control is directly linked to perceived usefulness (except for games) and perceived enjoyment, as hypothesized. Perceived enjoyment and usefulness were generically found to explain intention to use applications for both users and for non-users.  With regards to the impact of social norms, the study finds that further research needs to be done in exploring its impact more thoroughly. The dataset of the research, consisting purely of male-dominated, young smartphone users, make the generalization of results difficult.  There are differences regarding what drives the usage of different kinds of mobile applications. In this study, map applications and mobile Internet, are driven by more utilitarian motivations, whereas games are more hedonic. It is also clear that not everybody are using applications facilitated by smartphones, and therefore the presented approach of studying users and non-users separately provides a new approach to analyze adoption on a practical level.  This research proves that models like TAM should not treat mobile services as a generic concept, but instead to specifically address individual mobile services. The research also demonstrates the unique value of combining objective usage measurements (reflecting actual behaviour) with traditional survey data in more comprehensively modelling service adoption.
Enhancing information retrieval and resource discovery from data using the Semantic Web

Data are everywhere. Often the sheer quantities of data that is stored or archived in repositories or digital libraries make it difficult to navigate data; information in the data is obscured, particularly where we have Big Data. Traditionally, we record metadata on our data items to assist data classification and some information retrieval. The Semantic Web enables us to further unlock and enrich our data, by exploring how different data are related or connected. Using ontologies and Linked Data we can declare, navigate and discover semantic relationships. Relationships exist both locally, within the data, and globally, such that we can enhance our data with information retrieved from a wider context. To illustrate how the application of Semantic Web technologies aids data discovery and information retrieval, I discuss two case studies: (1) Sharing Ancient Wisdoms (SAWS), a Dynamic Library of information on selected ancient wisdom literature; and (2) the DEFRA DTC archive, a repository of data about freshwater quality in the UK.
Sensing and Characterization of the Wrist Using Dart Thrower’s Movement

The dart-throwing movement is engaged to capture wrist movements involved in numerous daily activities. This paper focuses on robust implementation in a wearable platform-based inertial sensors enhance opportunities for exploring day-to-day changes of wrist joint when performing dart-throwing motion. The kinematic characterization of the underlying movement is described in terms of time-series quaternions measured from gyroscope data. The distance metrics between these representations are used to compare quantitatively wrist kinematic performance against clinical observations. A clinical trial was conducted engaging five normal subjects and 10 patients undergoing a series of post-surgical rehabilitation programs. The approach classifies effectively the patients from normal subjects and alleviates the need for range of motion measurements of the wrist joint implying the quaternion trajectory associated with classical dynamic time warping as a useful kinematic description for dart thrower’s movement in assessing and characterizing the wrist performance. Clustering and classification results confirm that this proposed method is well-correlated with clinical assessments based on high positive correlation coefficients. The primary objective of this paper is to enhance the uptake and promote the uses of wearable sensors in longer term monitoring scenarios particularly relevant to non-clinical environments.
Heterogeneous Transfer Learning for Image Classification

Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related auxiliary domains for learning. While most of the existing works in this area are only focused on using the source data with the same representational structure as the target data, in this paper, we push this boundary further by extending a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text documents are arbitrary. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through matrix factorization, and to use the latent semantic features generated by the auxiliary data to build a better image classifier. We empirically verify the effectiveness of our algorithm on the Caltech-256 image dataset.
A family of algorithms for approximate Bayesian inference

One of the major obstacles to using Bayesian methods for pattern recognition has been its computational expense. This thesis presents an approximation technique that can perform Bayesian inference faster and more accurately than previously possible. This method, "Expectation Propagation," unifies and generalizes two previous techniques: assumeddensity filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks. The unification shows how both of these algorithms can be viewed as approximating the true posterior distribution with a simpler distribution, which is close in the sense of KL-divergence.  Propagation exploits the best of both algorithms: the generality of assumed-density filtering and the accuracy of loopy belief propagation. Loopy belief propagation, because it propagates exact belief states, is useful for limited types of belief networks, such as purely discrete networks.  Propagation approximates the belief states with expectations, such as means and variances, giving it much wider scope.  Propagation also extends belief propagation in the opposite direction-propagating richer belief states which incorporate correlations between variables. This framework is demonstrated in a variety of statistical models using synthetic and real-world data. On Gaussian mixture problems, Expectation Propagation is found, for the same amount of computation, to be convincingly better than rival approximation techniques: Monte Carlo, Laplace's method, and variational Bayes. For pattern recognition, Expectation Propagation provides an algorithm for training Bayes Point Machine classifiers that is faster and more accurate than any previously known. The resulting classifiers outperform Support Vector Machines on several standard datasets, in addition to having a comparable training time.  Propagation can also be used to choose an appropriate feature set for classification, via Bayesian model selection.  Associate Professor of Media Arts and Sciences
Mapping Unseen Words to Task-Trained Embedding Spaces

Education University of California, Berkeley (2008-2013) Ph.D. in Computer Science Thesis: Surface Web Semantics for Structured Natural Language Processing Advisor: Dan Klein. Committee members: Dan Klein, Marti Hearst, Line Mikkelsen, Nelson Morgan University of California, Berkeley (2012) Master of Science (M.S.) in Computer Science Thesis: An All-Fragments Grammar for Simple and Accurate Parsing Advisor: Dan Klein Indian Institute of Technology, Kanpur (2004-2008) Bachelor of Technology (B.Tech.) in Computer Science and Engineering GPA: 3.96/4.00 (Institute and Department Rank 2) Cornell University (Summer 2007) CS490 (Independent Research and Reading) GPA: 4.00/4.00 Advisors: Lillian Lee, Claire Cardie
Formalism-Independent Parser Evaluation with CCG and DepBank

A key question facing the parsing community is how to compare parsers which use different grammar formalisms and produce different output. Evaluating a parser on the same resource used to create it can lead to non-comparable accuracy scores and an over-optimistic view of parser performance. In this paper we evaluate a CCG parser on DepBank, and demonstrate the difficulties in converting the parser output into DepBank grammatical relations. In addition we present a method for measuring the effectiveness of the conversion, which provides an upper bound on parsing accuracy. The CCG parser obtains an F-score of 81.9% on labelled dependencies, against an upper bound of 84.8%. We compare the CCG parser against the RASP parser, outperforming RASPby over 5% overall and on the majority of dependency types.
Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm

In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.
Low Power VLSI Design of Modified Booth Multiplier

Low power VLSI circuits became very vital criteria for designing the energy efficient electronic designs for prime performance and compact devices. Multipliers play a very important role for planning energy economical processors that decides the potency of the processor. To scale back the facility consumption of multiplier factor booth coding methodology is being employed to rearrange the input bits. The operation of the booth decoder is to rearrange the given booth equivalent. Booth decoder can increase the range of zeros in variety. Hence the switching activity are going to be reduced that further reduces the power consumption of the design. The input bit constant determines the switching activity part that’s once the input constant is zero corresponding rows or column of the adder ought to be deactivated. When multiplicand contains a lot of number of zeros the higher power reduction will takes place. therefore in booth multiplier factor high power reductions are going to be achieved.
A survey about user requirements for biometric authentication on smartphones

The increasing number of smartphone users stresses the need for an improved protection of users' personal data, such as health information, personal identifiers and financial data. One trend to address this need is the adoption of biometric authentication techniques such as fingerprint and iris recognition. We report on a study that examines user requirements and preferences for biometric authentication on smartphones with the focus on iris recognition. We surveyed users about their perception of different biometric authentication techniques in terms of trust, information security, and data privacy. Furthermore, we assessed tradeoffs that users are willing to accept for additional security. We also examined usability requirements for iris recognition and identified trends between different demographic groups. We discuss the findings and emphasize the need for an improved, data-driven, understanding of the emerging requirements for such biometric mobile systems.
Stabilizing cell phone video using inertial measurement sensors

We present a system that rectifies and stabilizes video sequences on mobile devices with rolling-shutter cameras. The system corrects for rolling-shutter distortions using measurements from accelerometer and gyroscope sensors, and a 3D rotational distortion model. In order to obtain a stabilized video, and at the same time keep most content in view, we propose an adaptive low-pass filter algorithm to obtain the output camera trajectory. The accuracy of the orientation estimates has been evaluated experimentally using ground truth data from a motion capture system. We have conducted a user study, where the output from our system, implemented in iOS, has been compared to that of three other applications, as well as to the uncorrected video. The study shows that users prefer our sensor-based system.
Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition

where fk is the current approximation, and Rk f the current residual (error). Using initial values of Ro f = f , fo = 0, and k = 1 , the M P algorithm is comprised of the following steps, In this paper we describe a recursive algorithm to compute representations of functions with respect to nonorthogonal and possibly overcomplete dictionaries (I) Compute the inner-products {(Rkf, Zn)},. of elementary building blocks e.g. affine (wavelet) frames. We propose a modification to the Matching Pursuit algorithm of Mallat and Zhang (1992) that maintains full backward orthogonality of the residual convergence. We refer to this modified algorithm as Orthogonal Matching Pursuit (OMP). It is shown that all additional computation required for the OMP algorithm may be performed recursively. (11) Find nktl such that (error) at every step and thereby leads to improved I ( R k f , 'nk+I) l 2 asYp I(Rkf, zj)I 1
Deeply Supervised 3D Recurrent FCN for Salient Object Detection in Videos

This paper presents a novel end-to-end 3D fully convolutional network for salient object detection in videos. The proposed network uses 3D filters in the spatiotemporal domain to directly learn both spatial and temporal information to have 3D deep features, and transfers the 3D deep features to pixel-level saliency prediction, outputting saliency voxels. In our network, we combine the refinement at each layer and deep supervision to efficiently and accurately detect salient object boundaries. The refinement module recurrently enhances to learn contextual information into the feature map. Applying deeply-supervised learning to hidden layers, on the other hand, improves details of the intermediate saliency voxel, and thus the saliency voxel is refined progressively to become finer and finer. Intensive experiments using publicly available benchmark datasets confirm that our network outperforms state-of-the-art methods. The proposed saliency model also effectively works for video object segmentation.
Only Aggressive Elephants are Fast Elephants

Yellow elephants are slow. A major reason is that they consume their inputs entirely before responding to an elephant rider’s orders. Some clever riders have trained their yellow elephants to only consume parts of the inputs before responding. However, the teaching time to make an elephant do that is high. So high that the teaching lessons often do not pay off. We take a different approach. We make elephants aggressive; only this will make them very fast. We propose HAIL (Hadoop Aggressive Indexing Library), an enhancement of HDFS and Hadoop MapReduce that dramatically improves runtimes of several classes of MapReduce jobs. HAIL changes the upload pipeline of HDFS in order to create different clustered indexes on each data block replica. An interesting feature of HAIL is that we typically create a win-win situation: we improve both data upload to HDFS and the runtime of the actual Hadoop MapReduce job. In terms of data upload, HAIL improves over HDFS by up to 60% with the default replication factor of three. In terms of query execution, we demonstrate that HAIL runs up to 68x faster than Hadoop. In our experiments, we use six clusters including physical and EC2 clusters of up to 100 nodes. A series of scalability experiments also demonstrates the superiority of HAIL.
Toward a Science of Cyber–Physical System Integration

System integration is the elephant in the china store of large-scale cyber-physical system (CPS) design. It would be hard to find any other technology that is more undervalued scientifically and at the same time has bigger impact on the presence and future of engineered systems. The unique challenges in CPS integration emerge from the heterogeneity of components and interactions. This heterogeneity drives the need for modeling and analyzing cross-domain interactions among physical and computational/networking domains and demands deep understanding of the effects of heterogeneous abstraction layers in the design flow. To address the challenges of CPS integration, significant progress needs to be made toward a new science and technology foundation that is model based, precise, and predictable. This paper presents a theory of composition for heterogeneous systems focusing on stability. Specifically, the paper presents a passivity-based design approach that decouples stability from timing uncertainties caused by networking and computation. In addition, the paper describes cross-domain abstractions that provide effective solution for model-based fully automated software synthesis and high-fidelity performance analysis. The design objectives demonstrated using the techniques presented in the paper are group coordination for networked unmanned air vehicles (UAVs) and high-confidence embedded control software design for a quadrotor UAV. Open problems in the area are also discussed, including the extension of the theory of compositional design to guarantee properties beyond stability, such as safety and performance.
A near-optimal algorithm for computing the entropy of a stream

We describe a simple algorithm for approximating the empirical entropy of a stream of <i>m</i> values in a single pass, using <i>O</i>(ε<sup>-2</sup> log(Δ<sup>-1</sup>) log <i>m</i>) words of space. Our algorithm is based upon a novel extension of a method introduced by Alon, Matias, and Szegedy . We show a space lower bound of Ω(ε<sup>-2</sup> / log(ε<sup>-1</sup>)), meaning that our algorithm is near-optimal in terms of its dependency on ε. This improves over previous work on this problem [8, 13, 17, 5]. We show that generalizing to <i>k</i>th order entropy requires close to linear space for all <i>k</i> ≥ 1, and give additive approximations using our algorithm. Lastly, we show how to compute a multiplicative approximation to the entropy of a random walk on an undirected graph.
Early predictors of job burnout and engagement.

A longitudinal study predicted changes in burnout or engagement a year later by identifying 2 types of early indicators at the initial assessment. Organizational employees (N = 466) completed measures of burnout and 6 areas of worklife at 2 times with a 1-year interval. Those people who showed an inconsistent pattern at Time 1 were more likely to change over the year than were those who did not. Among this group, those who also displayed a workplace incongruity in the area of fairness moved to burnout at Time 2, while those without this incongruity moved toward engagement. The implications of these 2 predictive indicators are discussed in terms of the enhanced ability to customize interventions for targeted groups within the workplace.