nils-herrmann/clean_scirepeval huggingface dataset:
Friendship and mobility: user movement in location-based social networks

Even though human movement and mobility patterns have a high degree of freedom and variation, they also exhibit structural patterns due to geographic and social constraints. Using cell phone location data, as well as data from two online location-based social networks, we aim to understand what basic laws govern human motion and dynamics. We find that humans experience a combination of periodic movement that is geographically limited and seemingly random jumps correlated with their social networks. Short-ranged travel is periodic both spatially and temporally and not effected by the social network structure, while long-distance travel is more influenced by social network ties. We show that social relationships can explain about 10% to 30% of all human movement, while periodic behavior explains 50% to 70%. Based on our findings, we develop a model of human mobility that combines periodic short range movements with travel due to the social network structure. We show that our model reliably predicts the locations and dynamics of future human movement and gives an order of magnitude better performance than present models of human mobility.
A Teachable-Agent Arithmetic Game's Effects on Mathematics Understanding, Attitude and Self-efficacy

A teachable-agent arithmetic game is presented and evaluated in terms of student performance, attitude and self-eff icacy. An experimental prepost study design was used, enrolling 153 3 rd and 5 grade students in Sweden. The playing group showed significantly larger gains i math performance and self-efficacy beliefs, but not in general attitude towards math, compared to control groups. The contributions in relation to pr evious work include a novel educational game being evaluated, and an emphasis o n elf-efficacy in the study as a strong predictor of math achievements.
A 3 GHz Low-offset Fully Dynamic Latched Comparator for High-Speed and Low-Power ADCs

In this paper, performances of various types of dynamic latched comparators are compared in terms of their offset voltages, speed and power. The accuracy of comparators, which is defined by its offset, along with power consumption, speed is of keen interest in achieving overall higher performance of ADCs. This can be achieved by the fully dynamic latched comparator which is proposed in this paper. This comparator shows 14.6mV offset which is small when compared to other dynamic comparators and preamplifier based comparators. This comparator not only achieves low-offset but also exhibit high-speed and low power in its operation, which can be used for low power high speed ADC applications. ADC, Fully dynamic, Input-referred offset voltage, Latched Comparator, Pre-amplifier
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks

It is well known that contextual and multi-scale representations are important for accurate visual recognition. In this paper we present the Inside-Outside Net (ION), an object detector that exploits information both inside and outside the region of interest. Contextual information outside the region of interest is integrated using spatial recurrent neural networks. Inside, we use skip pooling to extract information at multiple scales and levels of abstraction. Through extensive experiments we evaluate the design space and provide readers with an overview of what tricks of the trade are important. ION improves state-of-the-art on PASCAL VOC 2012 object detection from 73.9% to 77.9% mAP. On the new and more challenging MS COCO dataset, we improve state-of-the-art from 19.7% to 33.1% mAP. In the 2015 MS COCO Detection Challenge, our ION model won "Best Student Entry" and finished 3rd place overall. As intuition suggests, our detection results provide strong evidence that context and multi-scale representations improve small object detection.
Efficient clustering of high-dimensional data sets with application to reference matching

Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques for clustering when the dataset has either (1) a limited number of clusters, (2) a low feature dimensionality, or (3) a small number of data points. However, there has been much less work on methods of efficiently clustering datasets that are large in all three ways at once—for example, having millions of data points that exist in many thousands of dimensions representing many thousands of clusters. We present a new technique for clustering these large, highdimensional datasets. The key idea involves using a cheap, approximate distance measure to efficiently divide the data into overlapping subsets we call canopies. Then clustering is performed by measuring exact distances only between points that occur in a common canopy. Using canopies, large clustering problems that were formerly impossible become practical. Under reasonable assumptions about the cheap distance metric, this reduction in computational cost comes without any loss in clustering accuracy. Canopies can be applied to many domains and used with a variety of clustering approaches, including Greedy Agglomerative Clustering, K-means and Expectation- We present experimental results on grouping bibliographic citations from the reference sections of research papers. Here the canopy approach reduces computation time over a traditional clustering approach by more than an order of magnitude and decreases error in comparison to a previously used algorithm by 25%.
Chapter 3 Recent Developments in Auxiliary Particle Filtering

State space models (SSMs; sometimes termed hidden Markov models, particularly in the discrete case) are very popular statistical models for time series. Such models describe the trajectory of some system of interest as an unobserved E-valued Markov chain, known as the signal process. Let X1 ∼ ν and Xn|(Xn−1 = xn−1) ∼ f(·|xn−1) denote this process. Indirect observations are available via an observation process, {Yn}n∈N. Conditional upon Xn, Yn is independent of the remainder of the observation and signal processes, with Yn|(Xn = xn) ∼ g(·|xn). For any sequence {zn}n∈N, we write zi:j = (zi, zi+1, ..., zj). In numerous applications, we are interested in estimating, recursively in time, an analytically intractable sequence of posterior distributions {p (x1:n| y1:n)}n∈N, of the form:
Designing Sketches for Similarity Filtering

The amounts of currently produced data emphasize the importance of techniques for efficient data processing. Searching big data collections according to similarity of data well corresponds to human perception. This paper is focused on similarity search using the concept of sketches – a compact bit string representations of data objects compared by Hamming distance, which can be used for filtering big datasets. The object-to-sketch transformation is a form of the dimensionality reduction and thus there are two basic contradictory requirements: (1) The length of the sketches should be small for efficient manipulation, but (2) longer sketches retain more information about the data objects. First, we study various sketching methods for data modeled by metric space and we analyse their quality. Specifically, we study importance of several sketch properties for similarity search and we propose a high quality sketching technique. Further, we focus on the length of sketches by studying mutual influence of sketch properties such as correlation of their bits and the intrinsic dimensionality of a set of sketches. The outcome is an equation that allows us to estimate a suitable length of sketches for an arbitrary given dataset. Finally, we empirically verify proposed approach on two real-life datasets.
A comparison of attitudes toward meat and animals among strict and semi-vegetarians

A number of studies have documented a phenomenon whereby individuals self-identify as vegetarians but then simultaneously acknowledge that they eat red meat, chicken, and/or fish. Despite being a consistent and fairly robust effect, there has been little attempt to explain these semi-vegetarians, why they would define themselves in a category whose membership criteria they violate, and ways they might differ from strict vegetarians. The present research highlights possible reasons for the discrepancy and focuses on several dimensions that may demarcate semi-from strict vegetarians: belief in human-animal similarity and liking of and disgust toward meat. Survey results indicated that semi-vegetarians (n=57) were less likely to dislike meat and to find meat disgusting than were strict vegetarians (n=157), even accounting for diet motives. There were no differences between the groups in their beliefs about human-animal similarity although semi-vegetarians who consumed a wider range of animal products perceived marginally less human-animal similarity than those who consumed only fish. The results suggest that semi-vegetarians are distinct from strict vegetarians primarily in their evaluation of and disgust toward meat, likely as a cause or consequence of their occasional consumption of animal flesh.
Algebraic Graph Theory

Algebraic graph theory comprises both the study of algebraic objects arising in connection with graphs, for example, automorphism groups of graphs along with the use of algebraic tools to establish interesting properties of combinatorial objects. One of the oldest themes in the area is the investigation of the relation between properties of a graph and the spectrum of its adjacency matrix. A central topic and important source of tools is the theory of association schemes. An association scheme is, roughly speaking, a collection of graphs on a common vertex set which fit together in a highly regular fashion. These arise regularly in connection with extremal structures: such structures often have an unexpected degree of regularity and, because of this, often give rise to an association scheme. This in turn leads to a semisimple commutative algebra and the representation theory of this algebra provides useful restrictions on the underlying combinatorial object. Thus in coding theory we look for codes that are as large as possible, since such codes are most effective in transmitting information over noisy channels. The theory of association schemes provides the most effective means for determining just how large is actually possible; this theory rests on Delsarte’s thesis , which showed how to use schemes to translate the problem into a question that be solved by linear programming.
Patus for convenient high-performance stencils: Evaluation in earthquake simulations

Patus is a code generation and auto-tuning framework for stencil computations targeting modern multi and many-core processors. The goals of the framework are productivity and portability for achieving high performance on the target platform. Its stencil specification language allows the programmer to express the computation in a concise way independently of hardware architecture-specific details. Thus, it increases the programmer productivity by removing the need for manual low-level tuning. We illustrate the impact of the stencil code generation in seismic applications, for which both weak and strong scaling are important. We evaluate the performance by focusing on a scalable discretization of the wave equation and testing complex simulation types of the AWP-ODC code to aim at excellent parallel efficiency, preparing for petascale 3-D earthquake calculations.
BOA: the Bayesian optimization algorithm

In this paper, an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed. To estimate the distribution, techniques for modeling multivariate data by Bayesian networks are used. The proposed algorithm identifies, reproduces and mixes building blocks up to a specified order. It is independent of the ordering of the variables in the strings representing the solutions. Moreover, prior information about the problem can be incorporated into the algorithm. However, prior information is not essential. Preliminary experiments show that the BOA outperforms the simple genetic algorithm even on decomposable functions with tight building blocks as a problem size grows.
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting

Dialects are one of the main drivers of language variation, a major challenge for natural language processing tools. In most languages, dialects exist along a continuum, and are commonly discretized by combining the extent of several preselected linguistic variables. However, the selection of these variables is theorydriven and itself insensitive to change. We use Doc2Vec on a corpus of 16.8M anonymous online posts in the German-speaking area to learn continuous document representations of cities. These representations capture continuous regional linguistic distinctions, and can serve as input to downstream NLP tasks sensitive to regional variation. By incorporating geographic information via retrofitting and agglomerative clustering with structure, we recover dialect areas at various levels of granularity. Evaluating these clusters against an existing dialect map, we achieve a match of up to 0.77 V-score (harmonic mean of cluster completeness and homogeneity). Our results show that representation learning with retrofitting offers a robust general method to automatically expose dialectal differences and regional variation at a finer granularity than was previously possible.
Fabrication and measurement of a large, monolithic, PCB-based AESA

This paper discusses a fabrication approach and experimental validation of a very large, planar active electronically scanned array (AESA). The planar AESA architecture employs a monolithic printed circuit board (PCB) with 768 active antenna elements at X-Band. Manufacturing physically large arrays with high element counts is discussed in relation to construction, assembly and yield considerations. Measured active array patterns of the ESA are also presented.
Multi-scale retinex for color image enhancement

The retinex is a human perception-based image processing algorithm which provides color constancy and dynamic range compression. We have previously reported on a single-scale retinex (SSR) and shown that it can either achieve color/lightness rendition or dynamic range compression, but not both simultaneously. We now present a multi-scale retinex (MSR) which overcomes this limitation for most scenes. Both color rendition and dynamic range compression are successfully accomplished except for some \patho-logical" scenes that have very strong spectral characteristics in a single band.
SW-Store: a vertically partitioned DBMS for Semantic Web data management

Efficient management of RDF data is an important prerequisite for realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance of RDF databases and consider a recent suggestion, “property tables”. We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertically partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds. Encouraged by these results, we describe the architecture of SW-Store, a new DBMS we are actively building that implements these techniques to achieve high performance RDF data management.
HellRank: a Hellinger-based centrality measure for bipartite social networks

Measuring centrality in a social network, especially in bipartite mode, poses many challenges, for example, the requirement of full knowledge of the network topology, and the lack of properly detecting top-k behavioral representative users. To overcome the above mentioned challenges, we propose HellRank, an accurate centrality measure for identifying central nodes in bipartite social networks. HellRank is based on the Hellinger distance between two nodes on the same side of a bipartite network. We theoretically analyze the impact of this distance on a bipartite network and find upper and lower bounds for it. The computation of the HellRank centrality measure can be distributed, by letting each node uses local information only on its immediate neighbors. Consequently, one does not need a central entity that has full knowledge of the network topological structure. We experimentally evaluate the performance of the HellRank measure in correlation with other centrality measures on real-world networks. The results show partial ranking similarity between the HellRank and the other conventional metrics according to the Kendall and Spearman rank correlation coefficient.
Virtual peers as partners in storytelling and literacy learning

Literacy learning learning how to read and write, begins long before children enter school. One of the key skills to reading and writing is the ability to represent thoughts symbolically and share them in language with an audience who may not necessarily share the same temporal and spatial context for the story. Children learn and practice these important language skills everyday, telling stories with the peers and adults around them. In particular, storytelling in the context of peer collaboration provides a key environment for children to learn language skills important for literacy. In light of this, we designed Sam, an embodied conversational agent who tells stories collaboratively with children. Sam was designed to look like a peer for preschool children, but to tell stories in a developmentally advanced way: modeling narrative skills important for literacy. Results demonstrated that children who played with the virtual peer told stories that more closely resembled the virtual peer’s linguistically advanced stories: using more quoted speech and temporal and spatial expressions. In addition, children listened to Sam's stories carefully, assisting her and suggesting improvements. The potential benefits of having technology play a social role in young children’s literacy learning is discussed.
LegUp: high-level synthesis for FPGA-based processor/accelerator systems

In this paper, we introduce a new open source high-level synthesis tool called LegUp that allows software techniques to be used for hardware design. LegUp accepts a standard C program as input and automatically compiles the program to a hybrid architecture containing an FPGA-based MIPS soft processor and custom hardware accelerators that communicate through a standard bus interface. Results show that the tool produces hardware solutions of comparable quality to a commercial high-level synthesis tool.
Cartoon-recognition using video & audio descriptors

We present a new approach for classifying mpeg-2 video sequences as `cartoon' or `non-cartoon' by analyzing specific video and audio features of consecutive frames in real-time. This is part of the well-known video-genre-classification problem, where popular TV-broadcast genres like cartoon, commercial, music, news and sports are studied. Such applications have also been discussed in the context of MPEG-7 . In our method the extracted features from the visual descriptors are non-linearly combined using a multilayered perceptron and then considered together with the output of the audio-descriptor to produce a reliable recognition. The results demonstrate a high identification rate based on a large collection of 100 representative video sequences (20 cartoons and 4*20 non-cartoons) gathered from free digital TV-broadcasting.
Using Geomagnetic Field for Indoor Positioning

Geomagnetic field variation in support for indoor positioning and navigation has recently attracted considerable interest because of the advantage that technology based on the geomagnetic field has no infrastructure needs. Test using sensitive magnetometers has confirmed that it is possible to use geomagnetic field information for positioning purposes, however there are still issues to be addressed before it can be used in real applications. For example, are the low cost built-in magnetometers in mobile phones good enough for positioning purposes based on “fingerprinting” approaches? Is the geomagnetic field temporally stable for very long period of time? Tests have been carried out using several devices including smart phones. Because geomagnetic field positioning techniques alone may have difficulties, integration with other positioning methods is necessary. Wi-Fi is one of the obvious technology options. One approach is using Wi-Fi to first estimate approximate position, and then apply geomagnetic field information to refine position estimation. An experiment was carried out to test the proposed approach, and the error of fingerprint-based magnetic field positioning was analysed.
Motion tracking and gait feature estimation for recognising Parkinson's disease using MS Kinect.

Analysis of gait features provides important information during the treatment of neurological disorders, including Parkinson's disease. It is also used to observe the effects of medication and rehabilitation. The methodology presented in this paper enables the detection of selected gait attributes by Microsoft (MS) Kinect image and depth sensors to track movements in three-dimensional space.



The experimental part of the paper is devoted to the study of three sets of individuals: 18 patients with Parkinson's disease, 18 healthy aged-matched individuals, and 15 students. The methodological part of the paper includes the use of digital signal-processing methods for rejecting gross data-acquisition errors, segmenting video frames, and extracting gait features. The proposed algorithm describes methods for estimating the leg length, normalised average stride length (SL), and gait velocity (GV) of the individuals in the given sets using MS Kinect data.



The main objective of this work involves the recognition of selected gait disorders in both the clinical and everyday settings. The results obtained include an evaluation of leg lengths, with a mean difference of 0.004 m in the complete set of 51 individuals studied, and of the gait features of patients with Parkinson's disease (SL: 0.38 m, GV: 0.61 m/s) and an age-matched reference set (SL: 0.54 m, GV: 0.81 m/s). Combining both features allowed for the use of neural networks to classify and evaluate the selectivity, specificity, and accuracy. The achieved accuracy was 97.2 %, which suggests the potential use of MS Kinect image and depth sensors for these applications.



Discussion points include the possibility of using the MS Kinect sensors as inexpensive replacements for complex multi-camera systems and treadmill walking in gait-feature detection for the recognition of selected gait disorders.