nils-herrmann/clean_scirepeval huggingface dataset:
Language independent web news extraction system based on text detection framework

Web news provides a direct and efficient way to construct large text corpora. The creation of text data requires an understanding of HTML code and the preparation of customized parsing rules to identify text content in a webpage. Typically, parsing rules are written manually and cannot be applied to pages with different layouts. In this study, we present a web news extraction system that is based on a text detection framework. The proposed method scans the input HTML page and creates text statistics as a projection profile. Then, text block identification is applied to determine a set of content candidates. To filter noise, text verification determines whether a given text block can be included with content. We evaluate the proposed approach with the L3S-GN1 corpus and 3506 multilingual news data items randomly sampled from 325 websites (15 geographic regions and 11 distinct languages). We also compare the proposed method to 23 well-known state-of-the-art techniques. The experimental results show that the proposed method outperforms the second best method (NReadability) by 7.30% in the macro F-measure rate and is 16.91 times faster than NReadability. In terms of the perfect rate, the proposed method demonstrates 46.38% accuracy, whereas the Boilerpipe algorithm demonstrates only 21.54% accuracy. The proposed method is very useful for constructing a multilingual corpus because it requires no language-specific processing component.
Cs4fn and Computational Thinking Unplugged

'Computer Science for Fun' (cs4fn) is a public engagement project aiming to both enthuse school students about inter-disciplinary computer science and support computing teachers. It started in 2005, with cs4fn resources now widely used in UK schools as well as internationally. We overview the approach cs4fn has used to inspire students and teachers. In particular we look at how not only subject knowledge but also computational thinking ideas can be taught in an integrated way using cs4fn 'unplugged' activities embedded in stories. We give two illustrative examples, one based on the problem of helping people with locked-in syndrome communicate, the second based around magic tricks.
Thinking inside the Box: five Organizational Strategies Enabled through Information Systems

The relationship between information systems (IS) and organizational strategies has been a much discussed topic with most of the prior studies taking a highly positive view of technology’s role in enabling organizational strategies. Despite this wealth of studies, there is a dearth of empirical investigations on how IS enable specific organizational strategies. Through a qualitative empirical investigation of five case organizations this research derives five organizational strategies that are specifically enabled through IS. The five strategies; (i) generic-heartland, (ii) craft-based selective, (iii) adhoc, IT-driven, (iv) corporative-orchestrated and (v) transformative provide a unique perspective of how IS enable organizational strategy.
Large-scale audio feature extraction and SVM for acoustic scene classification

This work describes a system for acoustic scene classification using large-scale audio feature extraction. It is our contribution to the Scene Classification track of the IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (D-CASE). The system classifies 30 second long recordings of 10 different acoustic scenes. From the highly variable recordings, a large number of spectral, cepstral, energy and voicing-related audio features are extracted. Using a sliding window approach, classification is performed on short windows. SVM are used to classify these short segments, and a majority voting scheme is employed to get a decision for longer recordings. On the official development set of the challenge, an accuracy of 73 % is achieved. SVM are compared with a nearest neighbour classifier and an approach called Latent Perceptual Indexing, whereby SVM achieve the best results. A feature analysis using the t-statistic shows that mainly Mel spectra are the most relevant features.
Programmable Automotive Headlights

The primary goal of an automotive headlight is to improve safety in low light and poor weather conditions. But, despite decades of innovation on light sources, more than half of accidents occur at night even with less traffic on the road. Recent developments in adaptive lighting have addressed some limitations of standard headlights, however, they have limited flexibility switching between high and low beams, turning off beams toward the opposing lane, or rotating the beam as the vehicle turns and are not designed for all driving environments. This paper introduces an ultra-low latency reactive visual system that can sense, react, and adapt quickly to any environment while moving at highway speeds. Our single hardware design can be programmed to perform a variety of tasks. Anti-glare high beams, improved driver visibility during snowstorms, increased contrast of lanes, markings, and sidewalks, and early visual warning of obstacles are demonstrated.
Semantic image segmentation for pedestrian detection

A typical traffic monitoring system for pedestrian detection uses a stationary camera. In Advanced Driving Assistance Systems (ADAS), the camera is mounted in front of the vehicle's window so that the camera and the object move in any arbitrary direction. Semantic image segmentation is widely used for road scene interpretation. In this paper, a method for semantic image segmentation using a convolution neural network is proposed. After a candidate region is segmented we perform pedestrian detection based on shape and size features of the candidate region. The experiments show that the proposed approach can accurately detect pedestrians in real-time (40fps).
A system to detect rooms in architectural floor plan images

In this article, a system to detect rooms in architectural floor plan images is described. We first present a primitive extraction algorithm for line detection. It is based on an original coupling of classical Hough transform with image vectorization in order to perform robust and efficient line detection. We show how the lines that satisfy some graphical arrangements are combined into walls. We also present the way we detect some door hypothesis thanks to the extraction of arcs. Walls and door hypothesis are then used by our room segmentation strategy; it consists in recursively decomposing the image until getting nearly convex regions. The notion of convexity is difficult to quantify, and the selection of separation lines between regions can also be rough. We take advantage of knowledge associated to architectural floor plans in order to obtain mostly rectangular rooms. Qualitative and quantitative evaluations performed on a corpus of real documents show promising results.
Reading order independent grouping proof for RFID tags

Using RFID to verify the simultaneous presence of more than one tag has generated interest since the Yoking Proof mechanism was proposed by A. Juels (2004). Various protocols have been proposed ever since. However, all these protocols require that the tags are ordered in a certain way. This article proposes an order-independent protocol which improves the efficiency and reduced failure rates. Privacy is also protected by not sending the identity in plain text.
Aeromechanics of passive rotation in flapping flight

Flying insects and robots that mimic them flap and rotate (or ‘pitch’) their wings with large angular amplitudes. The reciprocating nature of flapping requires rotation of the wing at the end of each stroke. Insects or flapping-wing robots could achieve this by directly exerting moments about the axis of rotation using auxiliary muscles or actuators. However, completely passive rotational dynamics might be preferred for efficiency purposes, or, in the case of a robot, decreased mechanical complexity and reduced system mass. Herein, the detailed equations of motion are derived for wing rotational dynamics, and a blade-element model is used to supply aerodynamic force and moment estimates. Passive-rotation flapping experiments with insectscale mechanically driven artificial wings are conducted to simultaneously measure aerodynamic forces and three-degree-of-freedom kinematics (flapping, rotation and out-of-plane deviation), allowing a detailed evaluation of the blade-element model and the derived equations of motion. Variations in flapping kinematics, wing-beat frequency, stroke amplitude and torsional compliance are made to test the generality of the model. All experiments showed strong agreement with predicted forces and kinematics, without variation or fitting of model parameters.
2011 Senior Thesis Project Reports Place Recognition for Indoor Blind Navigation Contents

This technical report collects the final reports of the undergraduate Computer Science majors from the Qatar Campus of Carnegie Mellon University who elected to complete a senior research thesis in the academic year 2010–11 as part of their degree. These projects have spanned the students’ entire senior year, during which they have worked closely with their faculty advisors to plan and carry out their projects. This work counts as 18 units of academic credit each semester. In addition to doing the research, the students presented a brief midterm progress report each semester, presented a public poster session in December, presented an oral summary in the year-end campuswide Meeting of the Minds and submitted a written thesis in May.
RN to BSN Transition: A Concept Analysis.

Over 670,000 ADN- and diploma-prepared nurses will need to complete their BSN degrees to meet the Institute of Medicine's recommendation that at least 80% of registered nurses (RNs) be BSN-prepared by year 2020. Understanding motivators, barriers, and the transition experience for RNs to advance their degree will help educators and nurse leaders understand the importance of a partnership to educate and mentor RNs to pursue a BSN degree.
Comparison of SVM and ANN for classification of eye events in EEG

The eye events (eye blink, eyes close and eyes open) are usually considered as biological artifacts in the electroencephalographic (EEG) signal. One can control his or her eye blink by proper training and hence can be used as a control signal in Brain Computer Interface (BCI) applications. Support vector machines (SVM) in recent years proved to be the best classification tool. A comparison of SVM with the Artificial Neural Network (ANN) always provides fruitful results. A one-against-all SVM and a multilayer ANN is trained to detect the eye events. A comparison of both is made in this paper.
Corporate social responsibility as a source of employee satisfaction

Corporate social responsibility has received an increasing amount of attention from practitioners and scholars alike in recent years. However, very little is known about whether or how corporate social responsibility affects employees. Because employees are primary stakeholders who directly contribute to the success of the company, understanding employee reactions to corporate social responsibility may help answer lingering questions about the potential effects of corporate social responsibility on firms as well as illuminate some of the processes responsible for them. To begin our chapter, we provide a brief history of scholarship on corporate social responsibility and highlight some of the major challenges researchers in this area currently face. We then discuss why corporate social responsibility may represent a special opportunity to influence employees’ general impression of their company. Next, we identify four distinct paths through which corporate social responsibility may affect employees’ relationship with their company that correspond to four universal psychological needs: security, self-esteem, belongingness, and a meaningful existence. Finally, we offer an agenda for micro-level research on corporate social responsibility.
Part-of-Speech Tagging for Code-Mixed English-Hindi Twitter and Facebook Chat Messages

The paper reports work on collecting and annotating code-mixed English-Hindi social media text (Twitter and Facebook messages), and experiments on automatic tagging of these corpora, using both a coarse-grained and a fine-grained part-ofspeech tag set. We compare the performance of a combination of language specific taggers to that of applying four machine learning algorithms to the task (Conditional Random Fields, Sequential Minimal Optimization, Naïve Bayes and Random Forests), using a range of different features based on word context and wordinternal information.
Autonomic Fail-over for a Software-Defined Container Computer Network

The ITRI container computer is a modular computer designed to be a building block for constructing cloudscale data centers. Rather than using a traditional enterprise data center network architecture, which is typically based on a combination of Layer 2 switches and Layer 3 routers, the ITRI container computer’s internal interconnection fabric, called Peregrine, is a software-defined network specially architected to meet the scalability, fast fail-over and multi-tenancy requirements of these data centers. Peregrine uses as the underlying physical interconnect a mesh of commodity off-the-shelf Ethernet switches, and adopts a centralized network control architecture that operates these Ethernet switches as a coordinated distributed data plane. Compared with vanilla enterprise networks, Peregrine features a fast fail-over capability not only for network switch/link failures, but also for failures of its own control servers. This paper describes the design and implementation of Peregrine’s fault tolerance mechanisms, and shows their effectiveness using empirical performance measurements taken from a fully working Peregrine prototype under various failure scenarios.
Anonymizing NYC Taxi Data: Does It Matter?

The widespread use of location-based services has led to an increasing availability of trajectory data from urban environments. These data carry rich information that are useful for improving cities through traffic management and city planning. Yet, it also contains information about individuals which can jeopardize their privacy. In this study, we work with the New York City (NYC) taxi trips data set publicly released by the Taxi and Limousine Commission (TLC). This data set contains information about every taxi cab ride that happened in NYC. A bad hashing of the medallion numbers (the ID corresponding to a taxi) allowed the recovery of all the medallion numbers and led to a privacy breach for the drivers, whose income could be easily extracted. In this work, we initiate a study to evaluate whether "perfect" anonymity is possible and if such an identity disclosure can be avoided given the availability of diverse sets of external data sets through which the hidden information can be recovered. This is accomplished through a spatio-temporal join based attack which matches the taxi data with an external medallion data that can be easily gathered by an adversary. Using a simulation of the medallion data, we show that our attack can re-identify over 91% of the taxis that ply in NYC even when using a perfect pseudonymization of medallion numbers. We also explore the effectiveness of trajectory anonymization strategies and demonstrate that our attack can still identify a significant fraction of the taxis in NYC. Given the restrictions in publishing the taxi data by TLC, our results indicate that unless the utility of the data set is significantly compromised, it will not be possible to maintain the privacy of taxi medallion owners and drivers.
A tutorial on cross-layer optimization in wireless networks

This tutorial paper overviews recent developments in optimization-based approaches for resource allocation problems in wireless systems. We begin by overviewing important results in the area of opportunistic (channel-aware) scheduling for cellular (single-hop) networks, where easily implementable myopic policies are shown to optimize system performance. We then describe key lessons learned and the main obstacles in extending the work to general resource allocation problems for multihop wireless networks. Towards this end, we show that a clean-slate optimization-based approach to the multihop resource allocation problem naturally results in a "loosely coupled" cross-layer solution. That is, the algorithms obtained map to different layers [transport, network, and medium access control/physical (MAC/PHY)] of the protocol stack, and are coupled through a limited amount of information being passed back and forth. It turns out that the optimal scheduling component at the MAC layer is very complex, and thus needs simpler (potentially imperfect) distributed solutions. We demonstrate how to use imperfect scheduling in the cross-layer framework and describe recently developed distributed algorithms along these lines. We conclude by describing a set of open research problems
First-order Bayesian logic

Uncertainty is a fundamental and irreducible aspect of our knowledge about the world. Until recently, classical first-order logic has reigned as the de facto standard logical foundation for artificial intelligence. The lack of a built-in, semantically grounded capability for reasoning under uncertainty renders classical first-order logic inadequate for many important classes of problems. General-purpose languages are beginning to emerge for which the fundamental logical basis is probability. Increasingly expressive probabilistic languages demand a theoretical foundation that fully integrates classical first-order logic and probability. In first-order Bayesian logic (FOBL), probability distributions are defined over interpretations of classical first-order axiom systems. Predicates and functions of a classical first-order theory correspond to a random variables in the corresponding first-order Bayesian theory. This is a natural correspondence, given that random variables are formalized in mathematical statistics as measurable functions on a probability space. A formal system called Multi-Entity Bayesian Networks (MEBN) is presented for composing distributions on interpretations by instantiating and combining parameterized fragments of directed graphical models. A construction is given of a MEBN theory that assigns a non-zero probability to any satisfiable sentence in classical first-order logic. By conditioning this distribution on consistent sets of sentences, FOBL can represent a probability distribution over interpretations of any finitely axiomatizable first-order theory, as well as over interpretations of infinite axiom sets when a limiting distribution exists. FOBL is inherently open, having the ability to incorporate new axioms into existing theories, and to modify probabilities in the light of evidence. Bayesian inference provides both a proof theory for combining prior knowledge with observations, and a learning theory for refining a representation as evidence accrues. The results of this paper provide a logical foundation for the rapidly evolving literature on first-order Bayesian knowledge representation, and point the way toward Bayesian languages suitable for generalpurpose knowledge representation and computing. Because FOBL contains classical first-order logic as a deterministic subset, it is a natural candidate as a universal representation for integrating domain ontologies expressed in languages based on classical first-order logic or subsets thereof.
Intrinsic cancer subtypes-next steps into personalized medicine

Recent technological advances have significantly improved our understanding of tumor biology by means of high-throughput mutation and transcriptome analyses. The application of genomics has revealed the mutational landscape and the specific deregulated pathways in different tumor types. At a transcriptional level, multiple gene expression signatures have been developed to identify biologically distinct subgroups of tumors. By supervised analysis, several prognostic signatures have been generated, some of them being commercially available. However, an unsupervised approach is required to discover a priori unknown molecular subtypes, the so-called intrinsic subtypes. Moreover, an integrative analysis of the molecular events associated with tumor biology has been translated into a better tumor classification. This molecular characterization confers new opportunities for therapeutic strategies in the management of cancer patients. However, the applicability of these new molecular classifications is limited because of several issues such as technological validation and cost. Further comparison with well-established clinical and pathological features is expected to accelerate clinical translation. In this review, we will focus on the data reported on molecular classification in the most common tumor types such as breast, colorectal and lung carcinoma, with special emphasis on recent data regarding tumor intrinsic subtypes. Likewise, we will review the potential applicability of these new classifications in the clinical routine.
The effect of focused attention and open monitoring meditation on attention network function in healthy volunteers

Mindfulness meditation techniques are increasingly popular both as a life-style choice and therapeutic adjunct for a range of mental and physical health conditions. However, little is known about the mechanisms through which mindfulness meditation and its constituent practices might produce positive change in cognition and emotion. Our study directly compared the effects of Focused Attention (FA) and Open-Monitoring (OM) meditation on alerting, orienting and executive attention network function in healthy individuals. Participants were randomized to three intervention groups: open-focused meditation, focused attention, and relaxation control. Participants completed an emotional variant of the Attention Network Test (ANT) at baseline and post-intervention. OM and FA practice improved executive attention, with no change observed in the relaxation control group. Improvements in executive attention occurred in the absence of change in subjective/self-report mood and cognitive function. Baseline levels of dispositional/trait mindfulness were positively correlated with executive control in the ANT at baseline. Our results suggest that mindfulness meditation might usefully target deficits in executive attention that characterise mood and anxiety disorders.
Fun and usable: augmented reality instructions in a hospital setting

The differences between Augmented Reality (AR) systems and computer display based systems create a need for a different approach than "traditional" HCI to the design and development of AR systems. This paper presents theoretical and empirical work which uses holistic approach compared to traditional guidelines in human computer interaction. The paper includes a usability study where AR was used for giving instructions to professional users in a hospital. The theoretical stance of Cognitive Systems Engineering is suggested as a possible approach to the design of AR system. The analysis show that the users in the context of medical care are positive towards AR systems as a technology and as a tool for instructions. This indicates that AR technology may become an accepted part of every day work in such a context.