nils-herrmann/clean_scirepeval huggingface dataset:
From novel to familiar: Tuning the brain for metaphors

Metaphors are fundamental to creative thought and expression. Newly coined metaphors regularly infiltrate our collective vocabulary and gradually become familiar, but it is unclear how this shift from novel to conventionalized meaning happens in the brain. We investigated the neural career of metaphors in a functional magnetic resonance imaging study using extensively normed new metaphors and simulated the ordinary, gradual experience of metaphor conventionalization by manipulating participants' exposure to these metaphors. Results showed that the conventionalization of novel metaphors specifically tunes activity within bilateral inferior prefrontal cortex, left posterior middle temporal gyrus, and right postero-lateral occipital cortex. These results support theoretical accounts attributing a role for the right hemisphere in processing novel, low salience figurative meanings, but also show that conventionalization of metaphoric meaning is a bilaterally-mediated process. Metaphor conventionalization entails a decreased neural load within semantic networks rather than a hemispheric or regional shift across brain areas.
A TUTORIAL ON SUBSPACE CLUSTERING

The past few years have witnessed an explosion in the availability of data from multiple sources and modalities. For example, millions of cameras have been installed in buildings, streets, airports and cities around the world. This has generated extraordinary advances on how to acquire, compress, store, transmit and process massive amounts of complex high-dimensional data. Many of these advances have relied on the observation that, even though these data sets are high-dimensional, their intrinsic dimension is often much smaller than the dimension of the ambient space. In computer vision, for example, the number of pixels in an image can be rather large, yet most computer vision models use only a few parameters to describe the appearance, geometry and dynamics of a scene. This has motivated the development of a number of techniques for finding a low-dimensional representation of a high-dimensional data set. Conventional techniques, such as Principal Component Analysis (PCA), assume that the data is drawn from a single low-dimensional subspace of a high-dimensional space. Such approaches have found widespread applications in many fields, e.g., pattern recognition, data compression, image processing, bioinformatics, etc. In practice, however, the data points could be drawn from multiple subspaces and the membership of the data points to the subspaces might be unknown. For instance, a video sequence could contain several moving objects and different subspaces might be needed to describe the motion of different objects in the scene. Therefore, there is a need to simultaneously cluster the data into multiple subspaces and find a low-dimensional subspace fitting each group of points. This problem, known as subspace clustering, has found numerous applications in computer vision (e.g., image segmentation , motion segmentation  and face clustering ), image processing (e.g., image representation and compression ) and systems theory (e.g., hybrid system identification ). A number of approaches to subspace clustering have been proposed in the past two decades. A review of methods from the data mining community can be found in . This article will present methods from the machine learning and computer vision communities, including algebraic methods [7, 8, 9, 10], iterative methods [11, 12, 13, 14, 15], statistical methods [16, 17, 18, 19, 20], and spectral clustering-based methods [7, 21, 22, 23, 24, 25, 26, 27]. We review these methods, discuss their advantages and disadvantages, and evaluate their performance on the motion segmentation and face clustering problems.
Downlink Resource Reuse for Device-to-Device Communications Underlaying Cellular Networks

The full potential of Device-to-device (D2D) communication relies on efficient resource reuse strategies including power control and matching of D2D links and cellular users (CUs). This letter investigates downlink resource reuse between multiple D2D links and multiple CUs. Our goal is to achieve a network utility enhancement for D2D communication while ensuring the QoS of the CUs. Despite the combinatorial nature of the problem and the coupled power constraints, we characterize the optimal D2D-CU matching as well as their power coordination, and propose an efficient algorithm to jointly optimize all D2D links and CUs. The proposed downlink resource reuse strategy shows a superiority over existing D2D schemes.
Amortised MAP Inference for Image Super-resolution

Image Super-resolution (SR) is an underdetermined inverse problem, where a large number of plausible high-resolution images can explain the same downsampled image. Most current single image SR methods use empirical risk minimisation, often with a pixel-wise mean squared error (MSE) loss. However, the outputs from such methods tend to be blurry, over-smoothed and generally appear implausible. A more desirable approach would employ Maximum a Posteriori (MAP) inference, preferring solutions that always have a high probability under the image prior, and thus appear more plausible. Direct MAP estimation for SR is non-trivial, as it requires us to build a model for the image prior from samples. Furthermore, MAP inference is often performed via optimisationbased iterative algorithms which don’t compare well with the efficiency of neuralnetwork-based alternatives. Here we introduce new methods for amortised MAP inference whereby we calculate the MAP estimate directly using a convolutional neural network. We first introduce a novel neural network architecture that performs a projection to the affine subspace of valid SR solutions ensuring that the high resolution output of the network is always consistent with the low resolution input. We show that, using this architecture, the amortised MAP inference problem reduces to minimising the cross-entropy between two distributions, similar to training generative models. We propose three methods to solve this optimisation problem: (1) Generative Adversarial Networks (GAN) (2) denoiser-guided SR which backpropagates gradient-estimates from denoising to train the network, and (3) a baseline method using a maximum-likelihood-trained image prior. Our experiments show that the GAN based approach performs best on real image data, achieving particularly good results in photo-realistic texture SR.
Analysis and Evaluation of Similarity Metrics in Collaborative Filtering Recommender System

KEMI-TORNIO UNIVERSITY OF APPLIED SCIENCES Degree programme: Business Information Technology Writer: Guo, Shuhang Thesis title: Analysis and evaluation of similarity metrics in collaborative filtering recommender system Pages (of which appendix): 62 (1) Date: May 15, 2014 Thesis instructor: Ryabov, Vladimir This research is focused on the field of recommender systems. The general aims of this thesis are to summary the state-of-the-art in recommendation systems, evaluate the efficiency of the traditional similarity metrics with varies of data sets, and propose an ideology to model new similarity metrics. The literatures on recommender systems were studied for summarizing the current development in this filed. The implementation of the recommendation and evaluation was achieved by Apache Mahout which provides an open source platform of recommender engine. By importing data information into the project, a customized recommender engine was built. Since the recommending results of collaborative filtering recommender significantly rely on the choice of similarity metrics and the types of the data, several traditional similarity metrics provided in Apache Mahout were examined by the evaluator offered in the project with five data sets collected by some academy groups. From the evaluation, I found out that the best performance of each similarity metric was achieved by optimizing the adjustable parameters. The features of each similarity metric were obtained and analyzed with practical data sets. In addition, an ideology by combining two traditional metrics was proposed in the thesis and it was proven applicable and efficient by the metrics combination of Pearson correlation and Euclidean distance. The observation and evaluation of traditional similarity metrics with practical data is helpful to understand their features and suitability, from which new models can be created. Besides, the ideology proposed for modeling new similarity metrics can be found useful both theoretically and practically.
A joint model for discovering and linking entities

Entity resolution, the task of automatically determining which mentions refer to the same real-world entity, is a crucial aspect of knowledge base construction and management. However, performing entity resolution at large scales is challenging because (1) the inference algorithms must cope with unavoidable system scalability issues and (2) the search space grows exponentially in the number of mentions. Current conventional wisdom has been that performing coreference at these scales requires decomposing the problem by first solving the simpler task of entity-linking (matching a set of mentions to a known set of KB entities), and then performing entity discovery as a post-processing step (to identify new entities not present in the KB). However, we argue that this traditional approach is harmful to both entity-linking and overall coreference accuracy. Therefore, we embrace the challenge of jointly modeling entity-linking and entity-discovery as a single entity resolution problem. In order to make progress towards scalability we (1) present a model that reasons over compact hierarchical entity representations, and (2) propose a novel distributed inference architecture that does not suffer from the synchronicity bottleneck which is inherent in map-reduce architectures. We demonstrate that more test-time data actually improves the accuracy of coreference, and show that joint coreference is substantially more accurate than traditional entity-linking, reducing error by 75%.
Semantic search

Activities such as Web Services and the Semantic Web are working to create a web of distributed machine understandable data. In this paper we present an application called 'Semantic Search' which is built on these supporting technologies and is designed to improve traditional web searching. We provide an overview of TAP, the application framework upon which the Semantic Search is built. We describe two implemented Semantic Search systems which, based on the denotation of the search query, augment traditional search results with relevant data aggregated from distributed sources. We also discuss some general issues related to searching and the Semantic Web and outline how an understanding of the semantics of the search terms can be used to provide better results.
High quality, scalable and parallel community detection for large real graphs

Community detection has arisen as one of the most relevant topics in the field of graph mining, principally for its applications in domains such as social or biological networks analysis. Different community detection algorithms have been proposed during the last decade, approaching the problem from different perspectives. However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world.
 In this paper, we propose a novel disjoint community detection algorithm called Scalable Community Detection (SCD). By combining different strategies, SCD partitions the graph by maximizing the Weighted Community Clustering (WCC), a recently proposed community detection metric based on triangle analysis. Using real graphs with ground truth overlapped communities, we show that SCD outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and performance. SCD provides the speed of the fastest algorithms and the quality in terms of NMI and F1Score of the most accurate state of the art proposals. We show that SCD is able to run up to two orders of magnitude faster than practical existing solutions by exploiting the parallelism of current multi-core processors, enabling us to process graphs of unprecedented size in short execution times.
Adaptive Model Rules from Data Streams

Decision rules are one of the most expressive languages for machine learning. In this paper we present Adaptive Model Rules (AMRules), the first streaming rule learning algorithm for regression problems. In AMRules the antecedent of a rule is a conjunction of conditions on the attribute values, and the consequent is a linear combination of attribute values. Each rule uses a PageHinkley test to detect changes in the process generating data and react to changes by pruning the rule set. In the experimental section we report the results of AMRules on benchmark regression problems, and compare the performance of our system with other streaming regression algorithms.
Online decision making in crowdsourcing markets: theoretical challenges

Over the past decade, crowdsourcing has emerged as a cheap and efficient method of obtaining solutions to simple tasks that are difficult for computers to solve but possible for humans. The popularity and promise of crowdsourcing markets has led to both empirical and theoretical research on the design of algorithms to optimize various aspects of these markets, such as the pricing and assignment of tasks. Much of the existing theoretical work on crowdsourcing markets has focused on problems that fall into the broad category of online decision making; task requesters or the crowdsourcing platform itself make repeated decisions about prices to set, workers to filter out, problems to assign to specific workers, or other things. Often these decisions are complex, requiring algorithms that learn about the distribution of available tasks or workers over time and take into account the strategic (or sometimes irrational) behavior of workers.
 As human computation grows into its own field, the time is ripe to address these challenges in a principled way. However, it appears very difficult to capture all pertinent aspects of crowdsourcing markets in a single coherent model. In this paper, we reflect on the modeling issues that inhibit theoretical research on online decision making for crowdsourcing, and identify some steps forward. This paper grew out of the authors' own frustration with these issues, and we hope it will encourage the community to attempt to understand, debate, and ultimately address them.
A Filtering Dual-Polarized Antenna Subarray Targeting for Base Stations in Millimeter-Wave 5G Wireless Communications

A <inline-formula> <tex-math notation="LaTeX">$2 \times 2$ </tex-math></inline-formula> dual-polarized antenna subarray with filtering responses is proposed in this paper. This antenna subarray is a multilayered 3-D geometry, including a dual-path <inline-formula> <tex-math notation="LaTeX">$1 \times 4$ </tex-math></inline-formula> feeding network and four cavity-backed slot antennas. The isolation performance between two input ports is greatly improved by a novel method, which only needs to modify several vias in a square resonator. Cavities in the feeding network are properly arranged and coupled using different coupling structures, so that the operation modes in each cavity for different paths can always remain orthogonal, which enables the subarray to exhibit not only filtering functions (in both reflection coefficients and gain responses), but also a low cross-polarization level. A prototype is fabricated with a center frequency of 37 GHz and a bandwidth of 600 MHz for demonstration. Good agreement is achieved between simulation and measurement, for both <inline-formula> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula>-parameter and far-field results. The proposed filtering dual-polarized antenna array is very suitable to be employed as the subarray in millimeter-wave 5G base stations to reduce the complexity and integration loss of such beamforming systems.
Real-time tracking of low-resolution vehicles for wide-area persistent surveillance

Live wide-area persistent surveillance (WAPS) systems must provide effective multi-target tracking on downlinked video streams in real-time. This paper presents the first published aerial tracking system that is documented to process over 100 megapixels per second. The implementation addresses the challenges with the mosaicked, low-resolution, grayscale NITF imagery provided by most currently fielded WAPS platforms and the flexible computation architecture required to provide real-time performance. This paper also provides ground-truth for repeatable evaluation of wide-area persistent surveillance on a 2009 dataset collected by AFRL [1] that is available to the public as well as a quantitative analysis of this real-time implementation. To our knowledge, this is the only publication that (1) provides details of a real-time implementation for detection and tracking in (2) mosaicked, composed imagery from a fielded WAPS sensor, and (3) provides annotation data and quantitative analysis for repeatable WAPS tracking experimentation in the computer vision community.
MOSOCO: a mobile assistive tool to support children with autism practicing social skills in real-life situations

MOSOCO is a mobile assistive application that uses augmented reality and the visual supports of a validated curriculum, the Social Compass, to help children with autism practice social skills in real-life situations. In this paper, we present the results of a seven-week deployment study of MOSOCO in a public school in Southern California with both students with autism and neurotypical students. The results of our study demonstrate that MOSOCO facilitates practicing and learning social skills, increases both quantity and quality of social interactions, reduces social and behavioral missteps, and enables the integration of children with autism in social groups of neurotypical children. The findings from this study reveal emergent practices of the uses of mobile assistive technologies in real-life situations.
What Makes a Good Bug Report?

In software development, bug reports provide crucial information to developers. However, these reports widely differ in their quality. We conducted a survey among developers and users of APACHE, ECLIPSE, and MOZILLA to find out what makes a good bug report.
 The analysis of the 466 responses revealed an information mismatch between what developers need and what users supply. Most developers consider steps to reproduce, stack traces, and test cases as helpful, which are at the same time most difficult to provide for users. Such insight is helpful to design new bug tracking tools that guide users at collecting and providing more helpful information.
 Our CUEZILLA prototype is such a tool and measures the quality of new bug reports; it also recommends which elements should be added to improve the quality. We trained CUEZILLA on a sample of 289 bug reports, rated by developers as part of the survey. In our experiments, CUEZILLA was able to predict the quality of 31--48% of bug reports accurately.
Declarative Data Cleaning: Language, Model, and Algorithms

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Project risk management : lessons learned from software development environment

The challenges and realities in applying effective software risk management processes are difficult, in particular integrating the risk management processes into software development organizations. However, the benefits of implementing effective risk management tools and techniques in software development project are equally great. Current perceptions and emerging trends of various software risk management practices are reviewed and risks specific to software development projects are identified. Implementing effective risk management process will succeed by changing the organizational culture. This paper addresses lessons learned from implementing project risk management practices in software development environment.
ParkNet: drive-by sensing of road-side parking statistics

Urban street-parking availability statistics are challenging to obtain in real-time but would greatly benefit society by reducing traffic congestion. In this paper we present the design, implementation and evaluation of ParkNet, a mobile system comprising vehicles that collect parking space occupancy information while driving by. Each ParkNet vehicle is equipped with a GPS receiver and a passenger-side-facing ultrasonic range-finder to determine parking spot occupancy. The data is aggregated at a central server, which builds a real-time map of parking availability and could provide this information to clients that query the system in search of parking. Creating a spot-accurate map of parking availability challenges GPS location accuracy limits. To address this need, we have devised an environmental fingerprinting approach to achieve improved location accuracy. Based on 500 miles of road-side parking data collected over 2 months, we found that parking spot counts are 95% accurate and occupancy maps can achieve over 90% accuracy. Finally, we quantify the amount of sensors needed to provide adequate coverage in a city. Using extensive GPS traces from over 500 San Francisco taxicabs, we show that if ParkNet were deployed in city taxicabs, the resulting mobile sensors would provide adequate coverage and be more cost-effective by an estimated factor of roughly 10-15 when compared to a sensor network with a dedicated sensor at every parking space, as is currently being tested in San Francisco.
On the need for different security methods on mobile phones

Mobile phones are rapidly becoming small-size general purpose computers, so-called smartphones. However, applications and data stored on mobile phones are less protected from unauthorized access than on most desktop and mobile computers. This paper presents a survey on users' security needs, awareness and concerns in the context of mobile phones. It also evaluates acceptance and perceived protection of existing and novel authentication methods. The responses from 465 participants reveal that users are interested in increased security and data protection. The current protection by using PIN (Personal Identification Number) is perceived as neither adequate nor convenient in all cases. The sensitivity of data stored on the devices varies depending on the data type and the context of use, asking for the need for another level of protection. According to these findings, a two-level security model for mobile phones is proposed. The model provides differential data and service protection by utilizing existing capabilities of a mobile phone for authenticating users.