nils-herrmann/clean_scirepeval huggingface dataset:
Turning the Page : The Impact of Choice Closure on Satisfaction

This paper introduces the concept of choice closure, defined as the psychological process by which consumers come to perceive a decision to be resolved and complete, and focus on the decision outcome. Choice closure inhibits consumers who have already made a choice from reverting to the decision process and engaging in potentially unfavorable comparisons between the chosen and the forgone options. In four studies, we show that physical acts of closure—such as turning one’s back on, covering, and turning the page on the rejected alternatives—facilitate choice closure in the context of difficult decisions involving large choice sets, and that choice closure results in greater satisfaction.
NEOFog: Nonvolatility-Exploiting Optimizations for Fog Computing

Nonvolatile processors have emerged as one of the promising solutions for energy harvesting scenarios, among which Wireless Sensor Networks (WSN) provide some of the most important applications. In a typical distributed sensing system, due to difference in location, energy harvester angles, power sources, etc. different nodes may have different amount of energy ready for use. While prior approaches have examined these challenges, they have not done so in the context of the features offered by nonvolatile computing approaches, which disrupt certain foundational assumptions. We propose a new set of nonvolatility-exploiting optimizations and embody them in the NEOFog system architecture. We discuss shifts in the tradeoffs in data and program distribution for nonvolatile processing-based WSNs, showing how non-volatile processing and non-volatile RF support alter the benefits of computation and communication-centric approaches. We also propose a new algorithm specific to nonvolatile sensing systems for load balancing both computation and communication demands. Collectively, the NV-aware optimizations in NEOFog increase the ability to perform in-fog processing by 4.2X and can increase this to 8X if virtualized nodes are 3X multiplexed.
A New Concept Varistor With Epoxy/Microvaristor Composite

A new concept composite varistor is proposed in this paper. This composite varistor has the chains of microvaristors, which are formed by applying an electric field during the curing process and work as current paths. This composite varistor shows superior nonlinear voltage-current characteristics despite small microvaristor content and has a good response against a steep voltage surge of several tens of nanoseconds in front time. In comparison with ceramic varistors where shapes are usually formed at their sintering temperatures of around 1000°C, the composite varistor has an advantage in flexible shape because its shape is formed by curing the base polymer at a low temperature.
Global and Local Evaluation of Link Prediction Tasks with Neural Embeddings

We focus our attention on the link prediction problem for knowledge graphs, which is treated herein as a binary classification task on neural embeddings of the entities. By comparing, combining and extending different methodologies for link prediction on graph-based data coming from different domains, we formalize a unified methodology for the quality evaluation benchmark of neural embeddings for knowledge graphs. This benchmark is then used to empirically investigate the potential of training neural embeddings globally for the entire graph, as opposed to the usual way of training embeddings locally for a specific relation. This new way of testing the quality of the embeddings evaluates the performance of binary classifiers for scalable link prediction with limited data. Our evaluation pipeline is made open source, and with this we aim to draw more attention of the community towards an important issue of transparency and reproducibility of the neural embeddings evaluations.
Metadating: Exploring the Romance and Future of Personal Data

We introduce Metadating -- a future-focused research and speed-dating event where single participants were invited to "explore the romance of personal data". Participants created "data profiles" about themselves, and used these to "date" other participants. In the rich context of dating, we study how personal data is used conversationally to communicate and illustrate identity. We note the manner in which participants carefully curated their profiles, expressing ambiguity before detail, illustration before accuracy. Our findings proposition a set of data services and features, each concerned with representing and curating data in new ways, beyond a focus on purely rational or analytic relationships with a quantified self. Through this, we build on emerging interest in "lived informatics" and raise questions about the experience and social reality of a "data-driven life".
Learning Scene Flow in 3D Point Clouds

Many applications in robotics and human-computer interaction can benefit from an understanding of 3D motion of points in a dynamic environment, widely noted as scene flow. While most previous methods focus on solving the problem with stereo and RGB-D images, few try to estimate scene flow directly from point clouds. In this work, we propose a novel deep neural network named FlowNet3D that learns scene flow from point clouds in an end-to-end fashion. Our network simultaneously learns deep hierarchical point cloud features, flow embeddings as well as how to smooth the output. We evaluate the network on both challenging synthetic data and real LiDAR scans from KITTI. Trained on synthetic data only, our network is able to generalize well to real scans. Benefited from learning directly in point clouds, our model achieved significantly more accurate scene flow results compared with various baselines on stereo images and RGB-D images.
Realtime ray tracing and interactive global illumination

Interaktive 3D-Computergraphik basiert heutzutage fast ausschliesslich auf dem Rasterisierungsverfahren. Dieses ist heute sehr effizient und günstig realisierbar, stösst aber zunehmend an seine Grenzen hinsichtlich unterstützter Szenenkomplexität und erreichbarer Darstellungsqualität. Eine Alternative zum Rasterisierungsverfahren ist das Ray Tracing Verfahren, welches zwar allgemein für bessere Bildqualität bekannt ist, aber aufgrund hoher Rechenanforderungen bisher als unvereinbar mit interaktiver Performanz galt. In dieser Arbeit geht es um die Entwicklung der Echtzeit Ray Tracing Technologie, mit der das Ray Tracing Verfahren auch für interaktive Anwendungen ermöglicht wird. Die im Rahmen dieser Dissertation entwickelten Techniken ermöglichen nun erstmalig interaktive Ray Tracing Performanz selbst auf Standard PCs. Die dazu entwickelten Methoden beinhalten unter anderem einen extrem schnellen Ray Tracing Kern, ein effizientes Parallelisierungsframework, die Unterstützung dynamischer und extrem komplexer Modelle, sowie eine geeignete Programmierschnittstelle. Darauf aufbauend wurden dann weiterhin Verfahren entwickelt, die es erstmalig ermöglichen, auch die globale Lichtausbreitung interaktiv zu simulieren. Die im Rahmen der Dissertation entwickelten Techniken formen ein komplettes Framework für Echtzeit-Ray Tracing und interaktive Beleuchtungssimulation, welches interaktive Performanz selbst für massiv komplexe Szenen liefert, sowie erstmalig physikalisch basierte Computergraphik unter Echtzeitbedingungen ermöglicht.
Progressive Analytics: A Computation Paradigm for Exploratory Data Analysis

Exploring data requires a fast feedback loop from the analyst to the system, with a latency below about 10 seconds because of human cognitive limitations. When data becomes large or analysis becomes complex, sequential computations can no longer be completed in a few seconds and data exploration is severely hampered. This article describes a novel computation paradigm called Progressive Computation for Data Analysis or more concisely Progressive Analytics, that brings at the programming language level a low-latency guarantee by performing computations in a progressive fashion. Moving this progressive computation at the language level relieves the programmer of exploratory data analysis systems from implementing the whole analytics pipeline in a progressive way from scratch, streamlining the implementation of scalable exploratory data analysis systems. This article describes the new paradigm through a prototype implementation called ProgressiVis, and explains the requirements it implies through examples.
Grammatical error correction in non-native English

Grammatical error correction (GEC) is the task of automatically correcting grammatical errors in written text. Previous research has mainly focussed on individual error types and current commercial proofreading tools only target limited error types. As sentences produced by learners may contain multiple errors of different types, a practical error correction system should be able to detect and correct all errors. In this thesis, we investigate GEC for learners of English as a Second Language (ESL). Specifically, we treat GEC as a translation task from incorrect into correct English, explore new models for developing end-to-end GEC systems for all error types, study system performance for each error type, and examine model generali-sation to different corpora. First, we apply Statistical Machine Translation (SMT) to GEC and prove that it can form the basis of a competitive all-errors GEC system. We implement an SMT-based GEC system which contributes to our winning system submitted to a shared task in 2014. Next, we propose a ranking model to re-rank correction candidates generated by an SMT-based GEC system. This model introduces new linguistic information and we show that it improves correction quality. Finally, we present the first study using Neural Machine Translation (NMT) for GEC. We demonstrate that NMT can be successfully applied to GEC and help capture new errors missed by an SMT-based GEC system. While we focus on GEC for English, our methods presented in this thesis can be easily applied to any language. Acknowledgements First and foremost, I owe a huge debt of gratitude to my supervisor, Ted Briscoe, who has patiently guided me through my PhD and always been very helpful, understanding and supportive. I cannot thank him enough for providing me with opportunities that helped me grow as a researcher and a critical thinker. I am immensely grateful to my examiners, Paula Buttery and Stephen Pulman, for their thorough reading of my thesis, their valuable comments and an enjoyable viva. My appreciation extends to my fellow members of the Natural Language and Information Processing research group, with whom I have always enjoyed discussing our work and other random things. My gratitude goes to Stephen Clark and Ann Copestake for giving me early feedback on my work as well as Christopher Bryant for generously reading my thesis draft. I would especially like to thank Mariano Felice for being not just a great colleague but also a dear friend. A special mention has …
Design of Neuro-Fuzzy System Controller for DC ServomotorBased Satellite Tracking System

A parabolic dish antenna positioning system is a control unit which directs the antenna to desired target automatically. In all its aspects, automation has become widespread and promises to represent the future for satellite tracking antenna systems. The term tracking is used to mean that the control system should utilize suitable algorithms to automate the process of pointing the antenna to a selected satellite thereby establishing the desired line of sight (LOS). This paper aims to present and discuss the results obtained from the development of a DC servomotor-based Neuro-Fuzzy System Controller (NFSC) algorithm which can be applied in the positioning of the parabolic reflector antennas. The advantage of using NFSC method is that it has a high degree of nonlinearity tolerance, learning ability and solves problems that are difficult to address with the conventional techniques such as Proportional Integral Derivative (PID) strategy. The architecture of the proposed antenna control system employs Adaptive Neuro-Fuzzy Inference System (ANFIS) design environment of MATLAB/SIMULINK package. The results obtained indicated that the proposed NFSC was able to achieve desired output DC motor position with reduced rise time and overshoot. Future work is to apply the controller in real time to achieve automatic satellite tracking with parabolic antenna using data derived from the signal strength.
A Spoofing Detection Method for Civilian L1 GPS and the E1-B Galileo Safety of Life Service

The work presented here describes an effective method for signal-authentication and spoofing detection for civilian GNSS receivers using the GPS L1 C/A and the Galileo E1-B Safety of Life service. The paper discusses various spoofing attack profiles and how the proposed method is able to detect these attacks. This method is relatively low cost and can be suitable for numerous mass-market applications.
Engaging learners in cybersecurity careers: lessons from the launch of the national cyber league

Educators and sponsors endorse competitions as a strong, positive influence on career choice. However, empirical studies of cybersecurity competitions are lacking, and evidence from computer science and mathematics competitions has been mixed. Here we report initial results from an ongoing study of the National Cyber League to provide a glimpse of the role of competitions in fostering cybersecurity career engagement. Preliminary results suggest that cyber competitions attract experienced individuals who will remain in the profession for the long-term, but future research is needed to understand how cyber competitions may engage women and those new to the field.
Content-Based, Collaborative Recommendation

The problem of recommending items from some fixed database has been studied extensively, and two main paradigms have emerged. In content-based recommendation one tries to recommend items similar to those a given user has liked in the past, whereas in collaborative recommendation one identifies users whose tastes are similar to those of the given user and recommends items they have liked. Our approach in Fab has been to combine these two methods. Here, we explain how a hybrid system can incorporate the advantages of both methods while inheriting the disadvantages of neither. In addition to what one might call the “generic advantages” inherent in any hybrid system, the particular design of the Fab architecture brings two additional benefits. First, two scaling problems common to all Web services are addressed—an increasing number of users and an increasing number of documents. Second, the system automatically identifies emergent communities of interest in the user population, enabling enhanced group awareness and communications. Here we describe the two approaches for contentbased and collaborative recommendation, explain how a hybrid system can be created, and then describe Fab, an implementation of such a system. For more details on both the implemented architecture and the experimental design the reader is referred to . The content-based approach to recommendation has its roots in the information retrieval (IR) community, and employs many of the same techniques. Text documents are recommended based on a comparison between their content and a user profile.
Modeling and Simulation of a Single Phase Grid Connected Photovoltaic System

This paper gives a complete computer simulation program of a single phase grid connected PV system using Matlab/Simulink and SimPowerSystem tool in order to monitor the performance of each unit of the system during a selected day in the year representing a sunny day and another cloudy day using the hourly data of load demand, solar radiation and temperature at The Kharga Oasis site, as a case study. The system consists of a PV array subsystem as the primary source of energy, the electric grid as an auxiliary source of energy, the battery bank as a stand by source that feeds the electrical load in case of grid failure. This paper also focuses on the operation control of the system. This control is on/off switch control according to modes of operation of the system and there is a control of inverter using PI controller to achieve the maximum power point of the PV array. Finally this paper gives the simulation results of the required system output parameters; PV output power, grid power, load power, battery power, and battery voltage and state of charge.  Renewable energy, Photovoltaic systems, Electric grid, Modeling, Control, Simulation.
Magnetic resonance imaging findings associated with surgically proven rotator interval lesions

To identify shoulder magnetic resonance imaging (MRI) findings associated with surgically proven rotator interval abnormalities. The preoperative MRI examinations of five patients with surgically proven rotator interval (RI) lesions requiring closure were retrospectively evaluated by three musculoskeletal-trained radiologists in consensus. We assessed the structures in the RI, including the coracohumeral ligament, superior glenohumeral ligament, fat tissue, biceps tendon, and capsule for variations in size and signal alteration. In addition, we noted associated findings of rotator cuff and labral pathology. Three of three of the MR arthrogram studies demonstrated extension of gadolinium to the cortex of the undersurface of the coracoid process compared with the control images, seen best on the sagittal oblique images. Four of five of the studies demonstrated subjective thickening of the coracohumeral ligament, and three of five of the studies demonstrated subjective thickening of the superior glenohumeral ligament. Five of five of the studies demonstrated a labral tear. The MRI arthrogram finding of gadolinium extending to the cortex of the undersurface of the coracoid process was noted on the studies of those patients with rotator interval lesions at surgery in this series. Noting this finding—especially in the presence of a labral tear and/or thickening of the coracohumeral ligament or superior glenohumeral ligament—may be helpful in the preoperative diagnosis of rotator interval lesions.
Scalable Formal Concept Analysis algorithm for large datasets using Spark

In the process of knowledge discovery and representation in large datasets using formal concept analysis, complexity plays a major role in identifying all the formal concepts and constructing the concept lattice(digraph of the concepts). For identifying the formal concepts and constructing the digraph from the identified concepts in very large datasets, various distributed algorithms are available in the literature. However, the existing distributed algorithms are not very well suitable for concept generation because it is an iterative process. The existing algorithms are implemented using distributed frameworks like MapReduce and Open MP, these frameworks are not appropriate for iterative applications. Hence, in this paper we proposed efficient distributed algorithms for both formal concept generation and concept lattice digraph construction in large formal contexts using Apache Spark. Various performance metrics are considered for the evaluation of the proposed work, the results of the evaluation proves that the proposed algorithms are efficient for concept generation and lattice graph construction in comparison with the existing algorithms.
Bitcoin Block Withholding Attack: Analysis and Mitigation

We address two problems: first, we study a variant of block withholding (BWH) attack in Bitcoins and second, we propose solutions to prevent all existing types of BWH attacks in Bitcoins. We analyze the strategies of a selfish Bitcoin miner who in connivance with one pool attacks another pool and receives reward from the former mining pool for attacking the latter. We name this attack as “sponsored block withholding attack.” We present detailed quantitative analysis of the monetary incentive that a selfish miner can earn by adopting this strategy under different scenarios. We prove that under certain conditions, the attacker can maximize her revenue by adopting some strategies and by utilizing her computing power wisely. We also show that an attacker may use this strategy for attacking both the pools for earning higher amount of incentives. More importantly, we present a strategy that can effectively counter block withholding attack in any mining pool. First, we propose a generic scheme that uses cryptographic commitment schemes to counter BWH attack. Then, we suggest an alternative implementation of the same scheme using hash function. Our scheme protects a pool from rogue miners as well as rogue pool administrators. The scheme and its variant defend against BWH attack by making it impossible for the miners to distinguish between a partial proof of work and a complete proof of work. The scheme is so designed that the administrator cannot cheat on the entire pool. The scheme can be implemented by making minor changes to existing Bitcoin protocol. We also analyze the security of the scheme.
Quality scores for deep regression systems

Deep methods based on Convolutional Neural Networks serve as accurate facial points and body parts detectors. However, most methods do not provide a confidence score for the quality of the localization process. In real world applications, such a score could be invaluable. We, therefore, study the problem of estimating the success of the localization process during test time. Our method is based on mapping the network activation features to the area under the point-accuracy-curve. Our method greatly outperforms methods that were recently suggested, such as those which are based on the stability of the detection or on the norm of the representation layer.
Simulation and dynamic optimization of taxi services in MATSim

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Institutions: Abstract Model Theory for Specification and Programming

There is a population explosion among the logical systems used in computing science. Examples include first-order logic, equational logic, Horn-clause logic, higher-order logic, infinitary logic, dynamic logic, intuitionistic logic, order-sorted logic, and temporal logic; moreover, there is a tendency for each theorem prover to have its own idiosyncratic logical system. The concept of institution is introduced to formalize the informal notion of “logical system.” The major requirement is that there is a satisfaction relation between models and sentences that is consistent under change of notation. Institutions enable abstracting away from syntactic and semantic detail when working on language structure “in-the-large”; for example, we can define language features for building large logical system. This applies to both specification languages and programming languages. Institutions also have applications to such areas as database theory and the semantics of artificial and natural languages. A first main result of this paper says that any institution such that signatures (which define notation) can be glued together, also allows gluing together theories (which are just collections of sentences over a fixed signature). A second main result considers when theory structuring is preserved by institution morphisms. A third main result gives conditions under which it is sound to use a theorem prover for one institution on theories from another. A fourth main result shows how to extend institutions so that their theories may include, in addition to the original sentences, various kinds of constraint that are useful for defining abstract data types, including both “data” and “hierarchy” constraints. Further results show how to define institutions that allow sentences and constraints from two or more institutions. All our general results apply to such “duplex” and “multiplex” institutions.