nils-herrmann/clean_scirepeval huggingface dataset:
Compound Rank- $k$  Projections for Bilinear Analysis

In many real-world applications, data are represented by matrices or high-order tensors. Despite the promising performance, the existing 2-D discriminant analysis algorithms employ a single projection model to exploit the discriminant information for projection, making the model less flexible. In this paper, we propose a novel compound rank-k projection (CRP) algorithm for bilinear analysis. The CRP deals with matrices directly without transforming them into vectors, and it, therefore, preserves the correlations within the matrix and decreases the computation complexity. Different from the existing 2-D discriminant analysis algorithms, objective function values of CRP increase monotonically. In addition, the CRP utilizes multiple rank-k projection models to enable a larger search space in which the optimal solution can be found. In this way, the discriminant ability is enhanced. We have tested our approach on five data sets, including UUIm, CVL, Pointing'04, USPS, and Coil20. Experimental results show that the performance of our proposed CRP performs better than other algorithms in terms of classification accuracy.
Comparing computational thinking development assessment scores with software complexity metrics

The development of computational thinking skills through computer programming is a major topic in education, as governments around the world are introducing these skills in the school curriculum. In consequence, educators and students are facing this discipline for the first time. Although there are many technologies that assist teachers and learners in the learning of this competence, there is a lack of tools that support them in the assessment tasks. This paper compares the computational thinking score provided by Dr. Scratch, a free/libre/open source software assessment tool for Scratch, with McCabe's Cyclomatic Complexity and Halstead's metrics, two classic software engineering metrics that are globally recognized as a valid measurement for the complexity of a software system. The findings, which prove positive, significant, moderate to strong correlations between them, could be therefore considered as a validation of the complexity assessment process of Dr. Scratch.
Structured light 3D scanning in the presence of global illumination

Global illumination effects such as inter-reflections, diffusion and sub-surface scattering severely degrade the performance of structured light-based 3D scanning. In this paper, we analyze the errors caused by global illumination in structured light-based shape recovery. Based on this analysis, we design structured light patterns that are resilient to individual global illumination effects using simple logical operations and tools from combinatorial mathematics. Scenes exhibiting multiple phenomena are handled by combining results from a small ensemble of such patterns. This combination also allows us to detect any residual errors that are corrected by acquiring a few additional images. Our techniques do not require explicit separation of the direct and global components of scene radiance and hence work even in scenarios where the separation fails or the direct component is too low. Our methods can be readily incorporated into existing scanning systems without significant overhead in terms of capture time or hardware. We show results on a variety of scenes with complex shape and material properties and challenging global illumination effects.
Detection and Classification of Dental Caries in X-ray Images Using Deep Neural Networks

Dental caries, also known as dental cavities, is the most widespread pathology in the world. Up to a very recent period, almost all individuals had the experience of this pathology at least once in their life. Early detection of dental caries can help in a sharp decrease in the dental disease rate. Thanks to the growing accessibility to medical imaging, the clinical applications now have better impact on patient care. Recently, there has been interest in the application of machine learning strategies for classification and analysis of image data. In this paper, we propose a new method to detect and identify dental caries using X-ray images as dataset and deep neural network as technique. This technique is based on stacked sparse auto-encoder and a softmax classifier. Those techniques, sparse auto-encoder and softmax, are used to train a deep neural network. The novelty here is to apply deep neural network to diagnosis of dental caries. This approach was tested on a real dataset and has demonstrated a good performance of detection. Keywords-dental X-ray; classification; Deep Neural Networks; Stacked sparse auto-encoder; Softmax.
Segmenting Tables via Indexing of Value Cells by Table Headers

Correct segmentation of a web table into its component regions is the essential first step to understanding tabular data. Our algorithmic solution to the segmentation problem relies on the property that strings defining row and column header paths uniquely index each data cell in the table. We segment the table using only "logical layout analysis" without resorting to any appearance features or natural language understanding. We start with a CSV table that preserves the 2-dimensional structure and contents of the original source table (e.g., an HTML table) but not font size, font weight, and color. The indexing property of table headers implies a four-quadrant partitioning of the table about a minimum index point. The algorithm finds the index point through an efficient guided search. Experimental results on a 200-table benchmark demonstrate the generality of the algorithm in handling a variety of table styles and forms.
ASSURE: automatic software self-healing using rescue points

Software failures in server applications are a significant problem for preserving system availability. We present ASSURE, a system that introduces rescue points that recover software from unknown faults while maintaining both system integrity and availability, by mimicking system behavior under known error conditions. Rescue points are locations in existing application code for handling a given set of programmer-anticipated failures, which are automatically repurposed and tested for safely enabling fault recovery from a larger class of (unanticipated) faults. When a fault occurs at an arbitrary location in the program, ASSURE restores execution to an appropriate rescue point and induces the program to recover execution by virtualizing the program's existing error-handling facilities. Rescue points are identified using fuzzing, implemented using a fast coordinated checkpoint-restart mechanism that handles multi-process and multi-threaded applications, and, after testing, are injected into production code using binary patching. We have implemented an ASSURE Linux prototype that operates without application source code and without base operating system kernel changes. Our experimental results on a set of real-world server applications and bugs show that ASSURE enabled recovery for all of the bugs tested with fast recovery times, has modest performance overhead, and provides automatic self-healing orders of magnitude faster than current human-driven patch deployment methods.
Has the bug really been fixed?

Software has bugs, and fixing those bugs pervades the software engineering process. It is folklore that bug fixes are often buggy themselves, resulting in bad fixes, either failing to fix a bug or creating new bugs. To confirm this folklore, we explored bug databases of the Ant, AspectJ, and Rhino projects, and found that bad fixes comprise as much as 9% of all bugs. Thus, detecting and correcting bad fixes is important for improving the quality and reliability of software. However, no prior work has systematically considered this bad fix problem, which this paper introduces and formalizes. In particular, the paper formalizes two criteria to determine whether a fix resolves a bug: coverage and disruption. The coverage of a fix measures the extent to which the fix correctly handles all inputs that may trigger a bug, while disruption measures the deviations from the program's intended behavior after the application of a fix. This paper also introduces a novel notion of distance-bounded weakest precondition as the basis for the developed practical techniques to compute the coverage and disruption of a fix.
 To validate our approach, we implemented Fixation, a prototype that automatically detects bad fixes for Java programs. When it detects a bad fix, Fixation returns an input that still triggers the bug or reports a newly introduced bug. Programmers can then use that bug-triggering input to refine or reformulate their fix. We manually extracted fixes drawn from real-world projects and evaluated Fixation against them: Fixation successfully detected the extracted bad fixes.
Ultralow-power electronics for biomedical applications.

The electronics of a general biomedical device consist of energy delivery, analog-to-digital conversion, signal processing, and communication subsystems. Each of these blocks must be designed for minimum energy consumption. Specific design techniques, such as aggressive voltage scaling, dynamic power-performance management, and energy-efficient signaling, must be employed to adhere to the stringent energy constraint. The constraint itself is set by the energy source, so energy harvesting holds tremendous promise toward enabling sophisticated systems without straining user lifestyle. Further, once harvested, efficient delivery of the low-energy levels, as well as robust operation in the aggressive low-power modes, requires careful understanding and treatment of the specific design limitations that dominate this realm. We outline the performance and power constraints of biomedical devices, and present circuit techniques to achieve complete systems operating down to power levels of microwatts. In all cases, approaches that leverage advanced technology trends are emphasized.
Characterizing and modeling internet traffic dynamics of cellular devices

Understanding Internet traffic dynamics in large cellular networks is important for network design, troubleshooting, performance evaluation, and optimization. In this paper, we present the results from our study, which is based upon a week-long aggregated flow level mobile device traffic data collected from a major cellular operator's core network. In this study, we measure and characterize the spatial and temporal dynamics of mobile Internet traffic. We distinguish our study from other related work by conducting the measurement at a larger scale and exploring mobile data traffic patterns along two new dimensions -- device types and applications that generate such traffic patterns. Based on the findings of our measurement analysis, we propose a Zipf-like model to capture the volume distribution of application traffic and a Markov model to capture the volume dynamics of aggregate Internet traffic. We further customize our models for different device types using an unsupervised clustering algorithm to improve prediction accuracy.
Assessing Hypersensitive Narcissism : A Reexamination of Murray ’ s Narcism Scale

A new measure of hypersensitive narcissism was derived by correlating the items of H. A. Murray’s (1938) Narcism Scale with an MMPI-based composite measure of covert narcissism. In three samples of college students (total N 5 403), 10 items formed a reliable measure: the Hypersensitive Narcissism Scale (HSNS). The new HSNS and the MMPI-based composite showed similar patterns of correlations with the Big Five Inventory, and both measures correlated near zero with the Narcissistic Personality Inventory, which assesses overt narcissism. Results support P. Wink’s (1991) distinction between covert and overt narcissistic tendencies in the normal range of individual differences and suggest that it would be beneficial for personality researchers to measure both types of narcissism in future studies.  1997 Academic
Lazy Means Smart: Reducing Repair Bandwidth Costs in Erasure-coded Distributed Storage

Erasure coding schemes provide higher durability at lower storage cost, and thus constitute an attractive alternative to replication in distributed storage systems, in particular for storing rarely accessed "cold" data. These schemes, however, require an order of magnitude higher recovery bandwidth for maintaining a constant level of durability in the face of node failures. In this paper we propose lazy recovery, a technique to reduce recovery bandwidth demands down to the level of replicated storage. The key insight is that a careful adjustment of recovery rate substantially reduces recovery bandwidth, while keeping the impact on read performance and data durability low. We demonstrate the benefits of lazy recovery via extensive simulation using a realistic distributed storage configuration and published component failure parameters. For example, when applied to the commonly used RS(14, 10) code, lazy recovery reduces repair bandwidth by up to 76% even below replication, while increasing the amount of degraded stripes by 0.1 percentage points. Lazy recovery works well with a variety of erasure coding schemes, including the recently introduced bandwidth efficient codes, achieving up to a factor of 2 additional bandwidth savings.
Definitions of bullying: a comparison of terms used, and age and gender differences, in a fourteen-country international comparison.

The study of school bullying has recently assumed an international dimension, but is faced with difficulties in finding terms in different languages to correspond to the English word bullying. To investigate the meanings given to various terms, a set of 25 stick-figure cartoons was devised, covering a range of social situations between peers. These cartoons were shown to samples of 8- and 14-year-old pupils (N = 1,245; n = 604 at 8 years, n = 641 at 14 years) in schools in 14 different countries, who judged whether various native terms cognate to bullying, applied to them. Terms from 10 Indo-European languages and three Asian languages were sampled. Multidimensional scaling showed that 8-year-olds primarily discriminated nonaggressive and aggressive cartoon situations; however, 14-year-olds discriminated fighting from physical bullying, and also discriminated verbal bullying and social exclusion. Gender differences were less appreciable than age differences. Based on the 14-year-old data, profiles of 67 words were then constructed across the five major cartoon clusters. The main types of terms used fell into six groups: bullying (of all kinds), verbal plus physical bullying, solely verbal bullying, social exclusion, solely physical aggression, and mainly physical aggression. The findings are discussed in relation to developmental trends in how children understand bullying, the inferences that can be made from cross-national studies, and the design of such studies.
Virtual hairy brush for painterly rendering

We propose a novel ‘‘e-brush’’ for calligraphy and painting, which meets all the criteria for a good e-brush. We use only four attributes to capture the essential features of the brush, and a suitably powerful modeling metaphor for its behavior. The e-brush s geometry, dynamic motions, and pigment changes are all dealt with in a single model. A single model simplifies the synchronization between the various system modules, thus giving rise to a more stable system, and lower costs. By a careful tradeoff between the complexity of the model and computation efficiency, more elaborate simulation of the e-brush s deformation and its recovery for interactive painterly rendering is made possible. We also propose a novel paper–ink model to complement the brush s model, and a machine intelligence module to empower the user to easily create beautiful calligraphy and painting. Despite the complexity of the modeling behind the scene, the high-level user interface has a simplistic and friendly design. The final results created by our e-brush can rival the real artwork.
Kettlebell training for musculoskeletal and cardiovascular health: a randomized controlled trial.

The aim of this trial was to investigate the effectiveness of a worksite intervention using kettlebell training to improve musculoskeletal and cardiovascular health.



This single-blind randomized controlled trial involved 40 adults from occupations with a high prevalence of reported musculoskeletal pain symptoms (mean age 44 years, body mass index 23 kg/m², 85% women, with pain intensity of the neck/shoulders 3.5 and of the low back 2.8 on a scale of 0-10). A blinded assessor took measures at baseline and follow-up. Participants were randomly assigned to training--consisting of ballistic full-body kettlebell exercise 3 times per week for 8 weeks--or a control group. The main outcome measures were pain intensity of the neck/shoulders and low back, isometric muscle strength, and aerobic fitness.



Compared with the control group, pain intensity of the neck/shoulders decreased 2.1 points [95% confidence interval (95% CI) -3.7- -0.4] and pain intensity of the low back decreased 1.4 points (95% CI -2.7- -0.02) in the training group. Compared with the control group, the training group increased muscle strength of the trunk extensors (P<0.001), but not of the trunk flexors and shoulders. Aerobic fitness remained unchanged.



Worksite intervention using kettlebell training reduces pain in the neck/shoulders and low back and improves muscle strength of the low back among adults from occupations with a high prevalence of reported musculoskeletal pain symptoms. This type of training does not appear to improve aerobic fitness.
Rebalancing Bike Sharing Systems: A Multi-source Data Smart Optimization

Bike sharing systems, aiming at providing the missing links in public transportation systems, are becoming popular in urban cities. A key to success for a bike sharing systems is the effectiveness of rebalancing operations, that is, the efforts of restoring the number of bikes in each station to its target value by routing vehicles through pick-up and drop-off operations. There are two major issues for this bike rebalancing problem: the determination of station inventory target level and the large scale multiple capacitated vehicle routing optimization with outlier stations. The key challenges include demand prediction accuracy for inventory target level determination, and an effective optimizer for vehicle routing with hundreds of stations. To this end, in this paper, we develop a Meteorology Similarity Weighted K-Nearest-Neighbor (MSWK) regressor to predict the station pick-up demand based on large-scale historic trip records. Based on further analysis on the station network constructed by station-station connections and the trip duration, we propose an inter station bike transition (ISBT) model to predict the station drop-off demand. Then, we provide a mixed integer nonlinear programming (MINLP) formulation of multiple capacitated bike routing problem with the objective of minimizing total travel distance. To solve it, we propose an Adaptive Capacity Constrained K-centers Clustering (AdaCCKC) algorithm to separate outlier stations (the demands of these stations are very large and make the optimization infeasible) and group the rest stations into clusters within which one vehicle is scheduled to redistribute bikes between stations. In this way, the large scale multiple vehicle routing problem is reduced to inner cluster one vehicle routing problem with guaranteed feasible solutions. Finally, the extensive experimental results on the NYC Citi Bike system show the advantages of our approach for bike demand prediction and large-scale bike rebalancing optimization.
Mining, indexing, and querying historical spatiotemporal data

In many applications that track and analyze spatiotemporal data, movements obey periodic patterns; the objects follow the same routes (approximately) over regular time intervals. For example, people wake up at the same time and follow more or less the same route to their work everyday. The discovery of hidden periodic patterns in spatiotemporal data, apart from unveiling important information to the data analyst, can facilitate data management substantially. Based on this observation, we propose a framework that analyzes, manages, and queries object movements that follow such patterns. We define the spatiotemporal periodic pattern mining problem and propose an effective and fast mining algorithm for retrieving maximal periodic patterns. We also devise a novel, specialized index structure that can benefit from the discovered patterns to support more efficient execution of spatiotemporal queries. We evaluate our methods experimentally using datasets with object trajectories that exhibit periodicity.
Wideband 120 GHz to 140 GHz MIMO radar: System design and imaging results

This paper presents a wideband FMCW MIMO radar sensor capable of working in the frequency range between 120 GHz and 140 GHz. The sensor is based on a radar chipset fabricated in SiGe technology and uses a MIMO approach to improve the angular resolution. The MIMO operation is implemented by time domain multiplexing of the transmitters. The radar is capable of producing 2D images by using FFT processing and a delay-and-sum beamformer. This paper presents the overall radar system design together with the image reconstruction algorithms as well as first imaging results.
New inspirations in swarm intelligence: a survey

The growing complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.
Multiplicative Normalizing Flows for Variational Bayesian Neural Networks

We reinterpret multiplicative noise in neural networks as auxiliary random variables that augment the approximate posterior in a variational setting for Bayesian neural networks. We show that through this interpretation it is both efficient and straightforward to improve the approximation by employing normalizing flows (Rezende & Mohamed, 2015) while still allowing for local reparametrizations (Kingma et al., 2015) and a tractable lower bound (Ranganath et al., 2015; Maaløe et al., 2016). In experiments we show that with this new approximation we can significantly improve upon classical mean field for Bayesian neural networks on both predictive accuracy as well as predictive uncertainty.
IT Consumerization - A Theory and Practice Review

Consumerization of IT refers to privately-owned IT resources, such as devices or software that are used for business purposes. The effects of consumerization are considered to be a major driver that redefines the relationship between employees (in terms of consumers of enterprise IT) and the IT organization. While there has been extensive debate on these matters in practice, IS research has not developed a clear theoretical understanding of the phenomenon. We present a theory and practice review, where the existing literature on consumerization is reviewed and a clear definition of the concept is developed. This study contributes to a theoretical understanding of IT consumerization in relation to fundamental aspects of IS. Our analysis shows, first, which distinct aspects of IS are affected by consumerization. Secondly, we provide an overview over major advantages and disadvantages for employees and organizations by conducting a systematic analysis of current literature available on the topic.