nils-herrmann/clean_scirepeval huggingface dataset:
From “ What ? ” to “ Why ? ” : The Social Uses of Personal Photos

To predict the uses of new technology, we present an approach grounded in science and technology studies (STS) that examines the social uses of current technology. As part of ongoing research on next-generation mobile imaging applications, we conducted an empirical study of the social uses of personal photography. We identify three: memory, creating and maintaining relationships, and self-expression. The roles of orality and materiality in these uses help us explain the observed resistances to intangible digital images and to assigning metadata and annotations. We conclude that this approach is useful for understanding the potential uses of technology and for design.
A graph distance metric based on the maximal common subgraph

Error-tolerant graph matching is a powerful concept that has various applications in pattern recognition and machine vision. In the present paper, a new distance measure on graphs is proposed. It is based on the maximal common subgraph of two graphs. The new measure is superior to edit distance based measures in that no particular edit operations together with their costs need to be defined. It is formally shown that the new distance measure is a metric. Potential algorithms for the efficient computation of the new measure are discussed. q 1998 Elsevier Science B.V. All rights reserved.
Retrofitting Sense-Specific Word Vectors Using Parallel Text

Jauhar et al. (2015) recently proposed to learn sense-specific word representations by “retrofitting” standard distributional word representations to an existing ontology. We observe that this approach does not require an ontology, and can be generalized to any graph defining word senses and relations between them. We create such a graph using translations learned from parallel corpora. On a set of lexical semantic tasks, representations learned using parallel text perform roughly as well as those derived from WordNet, and combining the two representation types significantly improves performance.
Rule extraction from trained adaptive neural networks using artificial immune systems

Although artificial neural network (ANN) usually reaches high classification accuracy, the obtained results sometimes may be incomprehensible. This fact is causing a serious problem in data mining applications. The rules that are derived from ANN are needed to be formed to solve this problem and various methods have been improved to extract these rules. Activation function is critical as the behavior and performance of an ANN model largely depends on it. So far there have been limited studies with emphasis on setting a few free parameters in the neuron activation function. ANN’s with such activation function seem to provide better fitting properties than classical architectures with fixed activation function neurons [Xu, S., & Zhang, M. (2005). Data mining – An adaptive neural network model for financial analysis. In Proceedings of the third international conference on information technology and applications]. In this study a new method that uses artificial immune systems (AIS) algorithm has been presented to extract rules from trained adaptive neural network. Two real time problems data were investigated for determining applicability of the proposed method. The data were obtained from University of California at Irvine (UCI) machine learning repository. The datasets were obtained from Breast Cancer disease and ECG data. The proposed method achieved accuracy values 94.59% and 92.31% for ECG and Breast Cancer dataset, respectively. It has been observed that these results are one of the best results comparing with results obtained from related previous studies and reported in UCI web sites.
Leveraging Gloss Knowledge in Neural Word Sense Disambiguation by Hierarchical Co-Attention

The goal of Word Sense Disambiguation (WSD) is to identify the correct meaning of a word in the particular context. Traditional supervised methods only use labeled data (context), while missing rich lexical knowledge such as the gloss which defines the meaning of a word sense. Recent studies have shown that incorporating glosses into neural networks for WSD has made significant improvement. However, the previous models usually build the context representation and gloss representation separately. In this paper, we find that the learning for the context and gloss representation can benefit from each other. Gloss can help to highlight the important words in the context, thus building a better context representation. Context can also help to locate the key words in the gloss of the correct word sense. Therefore, we introduce a co-attention mechanism to generate co-dependent representations for the context and gloss. Furthermore, in order to capture both word-level and sentence-level information, we extend the attention mechanism in a hierarchical fashion. Experimental results show that our model achieves the state-of-the-art results on several standard English all-words WSD test datasets.
Large-Scale Deduplication with Constraints Using Dedupalog

We present a declarative framework for collective deduplication of entity references in the presence of constraints. Constraints occur naturally in many data cleaning domains and can improve the quality of deduplication. An example of a constraint is "each paper has a unique publication venue''; if two paper references are duplicates, then their associated conference references must be duplicates as well. Our framework supports collective deduplication, meaning that we can dedupe both paper references and conference references collectively in the example above. Our framework is based on a simple declarative Datalog-style language with precise semantics. Most previous work on deduplication either ignoreconstraints or use them in an ad-hoc domain-specific manner. We also present efficient algorithms to support the framework. Our algorithms have precise theoretical guarantees for a large subclass of our framework. We show, using a prototype implementation, that our algorithms scale to very large datasets. We provide thoroughexperimental results over real-world data demonstrating the utility of our framework for high-quality and scalable deduplication.
AutoManner: An Automated Interface for Making Public Speakers Aware of Their Mannerisms

Many individuals exhibit unconscious body movements called mannerisms while speaking. These repeated changes often distract the audience when not relevant to the verbal context. We present an intelligent interface that can automatically extract human gestures using Microsoft Kinect to make speakers aware of their mannerisms. We use a sparsity-based algorithm, Shift Invariant Sparse Coding, to automatically extract the patterns of body movements. These patterns are displayed in an interface with subtle question and answer-based feedback scheme that draws attention to the speaker's body language. Our formal evaluation with 27 participants shows that the users became aware of their body language after using the system. In addition, when independent observers annotated the accuracy of the algorithm for every extracted pattern, we find that the patterns extracted by our algorithm is significantly (p<0.001) more accurate than just random selection. This represents a strong evidence that the algorithm is able to extract human-interpretable body movement patterns. An interactive demo of AutoManner is available at http://tinyurl.com/AutoManner.
Comparing the use of tangible and graphical programming languages for informal science education

Much of the work done in the field of tangible interaction has focused on creating tools for learning; however, in many cases, little evidence has been provided that tangible interfaces offer educational benefits compared to more conventional interaction techniques. In this paper, we present a study comparing the use of a tangible and a graphical interface as part of an interactive computer programming and robotics exhibit that we designed for the Boston Museum of Science. In this study, we have collected observations of 260 museum visitors and conducted interviews with 13 family groups. Our results show that visitors found the tangible and the graphical systems equally easy to understand. However, with the tangible interface, visitors were significantly more likely to try the exhibit and significantly more likely to actively participate in groups. In turn, we show that regardless of the condition, involving multiple active participants leads to significantly longer interaction times. Finally, we examine the role of children and adults in each condition and present evidence that children are more actively involved in the tangible condition, an effect that seems to be especially strong for girls.
FABC: Retinal Vessel Segmentation Using AdaBoost

This paper presents a method for automated vessel segmentation in retinal images. For each pixel in the field of view of the image, a 41-D feature vector is constructed, encoding information on the local intensity structure, spatial properties, and geometry at multiple scales. An AdaBoost classifier is trained on 789 914 gold standard examples of vessel and nonvessel pixels, then used for classifying previously unseen images. The algorithm was tested on the public digital retinal images for vessel extraction (DRIVE) set, frequently used in the literature and consisting of 40 manually labeled images with gold standard. Results were compared experimentally with those of eight algorithms as well as the additional manual segmentation provided by DRIVE. Training was conducted confined to the dedicated training set from the DRIVE database, and feature-based AdaBoost classifier (FABC) was tested on the 20 images from the test set. FABC achieved an area under the receiver operating characteristic (ROC) curve of 0.9561, in line with state-of-the-art approaches, but outperforming their accuracy (0.9597 versus 0.9473 for the nearest performer).
3D path planning for mobile robots using annealing neural network

This paper presents a novel potential field based 3D path planning technique for differential drive mobile robots, moving in known environment. The path planners are based on description of the obstacles by simulated annealing neural networks. The generated paths are piecewise linear with changing directions at the corners of the obstacles. The proposed planner can be successfully applied to snake robots, flying robots, and control of Gantry cranes. Several simulation results show the effectiveness of the proposed algorithm.
Integrating Vertical and Horizontal Partitioning Into Automated Physical Database Design

In addition to indexes and materialized views, horizontal and vertical partitioning are important aspects of physical design in a relational database system that significantly impact performance. Horizontal partitioning also provides manageability; database administrators often require indexes and their underlying tables partitioned identically so as to make common operations such as backup/restore easier. While partitioning is important, incorporating partitioning makes the problem of automating physical design much harder since: (a) The choices of partitioning can strongly interact with choices of indexes and materialized views. (b) A large new space of physical design alternatives must be considered. (c) Manageability requirements impose a new constraint on the problem. In this paper, we present novel techniques for designing a scalable solution to this integrated physical design problem that takes both performance and manageability into account. We have implemented our techniques and evaluated it on Microsoft SQL Server. Our experiments highlight: (a) the importance of taking an integrated approach to automated physical design and (b) the scalability of our techniques.
Chopper Stabilization of Analog Multipliers, Variable Gain Amplifiers, and Mixers

We describe a general offset-canceling architecture for analog multiplication using chopper stabilization. Chopping is used to modulate the offset away from the output signal where it can be easily filtered out, providing continuous offset reduction which is insensitive to drift. Both square wave chopping and chopping with orthogonal spreading codes are tested and shown to reduce the offset down to the microvolt level. In addition, we apply the nested chopping technique to an analog multiplier which employs two levels of chopping to reduce the offset even further. We discuss the limits on the performance of the various chopping methods in detail, and present a detailed analysis of the residual offset due to charge injection spikes. An illustrative CMOS prototype in a 0.18 mum process is presented which achieves a worst-case offset of 1.5 muV. This is the lowest measured offset reported in the DC analog multiplier literature by a margin of two orders of magnitude. The prototype multiplier is also tested with AC inputs as a squarer, variable gain amplifier, and direct-conversion mixer, demonstrating that chopper stabilization is effective for both DC and AC multiplication. The AC measurements show that chopping removes not only offset, but also 1/f noise and second-order harmonic distortion.
Double Actuator Unit with Planetary Gear Train for a Safe Manipulator

Control of a robot manipulator in contact with the environment is usually conducted by the direct feedback control system using a force-torque sensor or the indirect impedance control scheme. Although these methods have been successfully applied to many applications, simultaneous control of force and position cannot be achieved. Furthermore, collision safety has been of primary concern in recent years with emergence of service robots in direct contact with humans. To cope with such problems, redundant actuation has been used to enhance the performance of a position/force controller. In this paper, the novel design of a double actuator unit (DAU) composed of double actuators and a planetary gear train is proposed to provide the capability of simultaneous control of position and force as well as the improved collision safety. Since one actuator controls position and the other actuator modulates stiffness, DAU can control the position and stiffness simultaneously at the same joint. The torque exerted on the joint can be estimated without an expensive torque/force sensor. DAU is capable of detecting dynamic collision by monitoring the speed of the stiffness modulator. Upon detection of dynamic collision, DAU immediately reduces its joint stiffness according to the collision magnitude, thus providing the optimum collision safety. It is shown from various experiments that DAU can provide good performance of position tracking, force estimation and collision safety.
Activity recognition and monitoring using multiple sensors on different body positions

The design of an activity recognition and monitoring system based on the eWatch, multi-sensor platform worn on different body positions, is presented in this paper. The system identifies the user's activity in realtime using multiple sensors and records the classification results during a day. We compare multiple time domain feature sets and sampling rates, and analyze the tradeoff between recognition accuracy and computational complexity. The classification accuracy on different body positions used for wearing electronic devices was evaluated
The small world of the American corporate elite , 1982 – 2001

This paper examines the degree of stability in the structure of the corporate elite network in the US during the 1980s and 1990s. Several studies have documented that board-toboard ties serve as a mechanism for the diffusion of corporate practices, strategies, and structures; thus, the overall structure of the network can shape the nature and rate of aggregate corporate change. But upheavals in the nature of corporate governance and nearly complete turnover in the firms and directors at the core of the network since 1980 prompt a reassessment of the network’s topography.We find that the aggregate connectivity of the network is remarkably stable and appears to be an intrinsic property of the interlock network, resilient to major changes in corporate governance.After a brief review of elite studies in the US, we take advantage of the recent advances in the theoretical and methodological tools for analyzing network structures to examine the network properties of the directors and companies in 1982, 1990, and 1999. We use concepts from smallworld analysis to explain our finding that the structure of the corporate elite is resilient to macro and micro changes affecting corporate governance.
Beyond Self-Protection: Self-Affirmation Benefits Hedonic and Eudaimonic Well-Being.

Limited work has examined how self-affirmation might lead to positive outcomes beyond the maintenance of a favorable self-image. To address this gap in the literature, we conducted two studies in two cultures to establish the benefits of self-affirmation for psychological well-being. In Study 1, South Korean participants who affirmed their values for 2 weeks showed increased eudaimonic well-being (need satisfaction, meaning, and flow) relative to control participants. In Study 2, U.S. participants performed a self-affirmation activity for 4 weeks. Extending Study 1, after 2 weeks, self-affirmation led both to increased eudaimonic well-being and hedonic well-being (affect balance). By 4 weeks, however, these effects were non-linear, and the increases in affect balance were only present for vulnerable participants-those initially low in eudaimonic well-being. In sum, the benefits of self-affirmation appear to extend beyond self-protection to include two types of well-being.
Work Groups, Structural Diversity, and Knowledge Sharing in a Global Organization

E work groups engage in external knowledge sharing—the exchange of information, know-how, and feedback with customers, organizational experts, and others outside of the group. This paper argues that the value of external knowledge sharing increases when work groups are more structurally diverse. A structurally diverse work group is one in which the members, by virtue of their different organizational affiliations, roles, or positions, can expose the group to unique sources of knowledge. It is hypothesized that if members of structurally diverse work groups engage in external knowledge sharing, their performance will improve because of this active exchange of knowledge through unique external sources. A field study of 182 work groups in a Fortune 500 telecommunications firm operationalizes structural diversity as member differences in geographic locations, functional assignments, reporting managers, and business units, as indicated by corporate database records. External knowledge sharing was measured with group member surveys and performance was assessed using senior executive ratings. Ordered logit analyses showed that external knowledge sharing was more strongly associated with performance when work groups were more structurally diverse. Implications for theory and practice around the integration of work groups and social networks are addressed.
Very High Frequency PWM Buck Converters Using Monolithic GaN Half-Bridge Power Stages With Integrated Gate Drivers

Integration is a key step in utilizing advances in GaN technologies and enabling efficient switched-mode power conversion at very high frequencies (VHF). This paper addresses design and implementation of monolithic GaN half-bridge power stages with integrated gate drivers optimized for pulsewidth-modulated (PWM) dc-dc converters operating at 100 MHz switching frequency. Three gate-driver circuit topologies are considered for integration with half-bridge power stages in a 0.15-μm depletion-mode GaN-on-SiC process: an active pull-up driver, a bootstrapped driver, and a novel modified active pull-up driver. An analytical loss model is developed and used to optimize the monolithic GaN chips, which are then used to construct 20 V, 5 W, 100 MHz synchronous buck converter prototypes. With the bootstrapped and the modified pull-up gate-driver circuits, power stage efficiencies above 91% and total efficiencies close to 88% are demonstrated. The modified active pull-up driver, which offers 80% reduction in the driver area, is found to be the best-performing approach in the depletion-mode GaN process. These results demonstrate feasibility of high-efficiency VHF PWM dc-dc converters based on high levels of integration in GaN processes.
Parallel CRC Realization

This paper presents a theoretical result in the context of realizing high speed hardware for parallel CRC checksums. Starting from the serial implementation widely reported in literature, we have identified a recursive formula from which our parallel implementation is derived. In comparison with previous works, the new scheme is faster and more compact and is independent of the technology used in its realization. In our solution, the number of bits processed in parallel can be different from the degree of the polynomial generator. Lastly, we have also developed high level parametric codes that are capable of generating the circuits autonomously, when only the polyonomial is given.
The Robotic Autonomy Mobile Robotics Course: Robot Design, Curriculum Design and Educational Assessment

Robotic Autonomy is a seven-week, hands-on introduction to robotics designed for high school students. The course presents a broad survey of robotics, beginning with mechanism and electronics and ending with robot behavior, navigation and remote teleoperation. During the summer of 2002, Robotic Autonomy was taught to twenty eight students at Carnegie Mellon West in cooperation with NASA/Ames (Moffett Field, CA). The educational robot and course curriculum were the result of a ground-up design effort chartered to develop an effective and low-cost robot for secondary level education and home use. Cooperation between Carnegie Mellon’s Robotics Institute, Gogoco, LLC. and Acroname Inc. yielded notable innovations including a fast-build robot construction kit, indoor/outdoor terrainability, CMOS vision-centered sensing, back-EMF motor speed control and a Java-based robot programming interface. In conjunction with robot and curriculum design, the authors at the Robotics Institute and the University of Pittsburgh’s Learning Research and Development Center planned a methodology for evaluating the educational efficacy of Robotic Autonomy, implementing both formative and summative evaluations of progress as well as an indepth, one week ethnography to identify micro-genetic mechanisms of learning that would inform the broader evaluation. This article describes the robot and curriculum design processes and then the educational analysis methodology and statistically significant results, demonstrating the positive impact of Robotic Autonomy on student learning well beyond the boundaries of specific technical concepts in robotics.
Image super-resolution: The techniques, applications, and future

Super-resolution (SR) technique reconstructs a higher-resolution image or sequence from the observed LR images. As SR has been developed for more than three decades, both multi-frame and single-frame SR have significant applications in our daily life. This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years. Regularized SR methods are most commonly employed in the last decade. Technical details are discussed in this article, including reconstruction models, parameter selection methods, optimization algorithms and acceleration strategies. Moreover, an exhaustive summary of the current applications using SR techniques has been presented. Lastly, the article discusses the current obstacles for future research. & 2016 Elsevier B.V. All rights reserved.