nils-herrmann/clean_scirepeval huggingface dataset:
Webzeitgeist: design mining the web

Advances in data mining and knowledge discovery have transformed the way Web sites are designed. However, while visual presentation is an intrinsic part of the Web, traditional data mining techniques ignore render-time page structures and their attributes. This paper introduces design mining for the Web: using knowledge discovery techniques to understand design demographics, automate design curation, and support data-driven design tools. This idea is manifest in Webzeitgeist, a platform for large-scale design mining comprising a repository of over 100,000 Web pages and 100 million design elements. This paper describes the principles driving design mining, the implementation of the Webzeitgeist architecture, and the new class of data-driven design applications it enables.
The large-scale organization of metabolic networks

In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.
STAR: SLA-aware Autonomic Management of Cloud Resources

4 Abstract—Cloud computing has recently emerged as an important service to manage applications efficiently over the Internet. Various 5 cloud providers offer pay per use cloud services that requires Quality of Service (QoS) management to efficiently monitor and measure 6 the delivered services through Internet of Things (IoT) and thus needs to follow Service Level Agreements (SLAs). However, providing 7 dedicated cloud services that ensure user’s dynamic QoS requirements by avoiding SLA violations is a big challenge in cloud 8 computing. As dynamism, heterogeneity and complexity of cloud environment is increasing rapidly, it makes cloud systems insecure 9 and unmanageable. To overcome these problems, cloud systems require self-management of services. Therefore, there is a need to 10 develop a resource management technique that automatically manages QoS requirements of cloud users thus helping the cloud 11 providers in achieving the SLAs and avoiding SLA violations. In this paper, we present SLA-aware autonomic resource management 12 technique called STAR which mainly focuses on reducing SLA violation rate for the efficient delivery of cloud services. The 13 performance of the proposed technique has been evaluated through cloud environment. The experimental results demonstrate that 14 STAR is efficient in reducing SLA violation rate and in optimizing other QoS parameters which effect efficient cloud service delivery.
Speaker Diarization: A Review of Recent Research

Speaker diarization is the task of determining “who spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper, we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.
The way 4WARD to the creation of a future internet

In this paper, we describe the approaches taken in the 4WARD project to address the challenges of the network of the future. Our main hypothesis is that the Future Internet must allow for the fast creation of diverse network designs and paradigms, and must also support their co-existence at run-time. We observe that a pure evolutionary path from the current Internet design will not be able to address, in a satisfactory manner, major issues like the handling of mobile users, information access and delivery, wide area sensor network applications, high management complexity, and malicious traffic that hamper network performance already today. Moreover, the Internetpsilas focus on interconnecting hosts and delivering bits has to be replaced by a more holistic vision of a network of information and content. This is a natural evolution of scope requiring nonetheless a re-design of the architecture. We describe how 4WARD directs research on network virtualisation, novel InNetworkManagement, a generic path concept, and an information centric approach, into a single framework for a diversified, but interoperable, network of the future.
40-Gb/s amplifier and ESD protection circuit in 0.18-/spl mu/m CMOS technology

A triple-resonance LC network increases the bandwidth of cascaded differential pairs by a factor of 2/spl radic/3, yielding a 40-Gb/s CMOS amplifier with a gain of 15 dB and a power dissipation of 190 mW from a 2.2-V supply. An ESD protection circuit employs negative capacitance along with T-coils and pn junctions to operate at 40 Gb/s while tolerating 700-800 V.
Software Test Automation in Practice: Empirical Observations

The objective of this industry study was to shed light on the current situation and improvement needs in software test automation. To this end, 55 industry specialists from 31 organizational units were interviewed. In parallel with the survey, a qualitative study was conducted in 12 selected software development organizations. The results indicated that the software testing processes usually follow systematic methods to a large degree, and have only little immediate or critical requirements for resources. Based on the results, the testing processes have approximately three fourths of the resources they need, and have access to a limited, but usually sufficient group of testing tools. As for the test automation, the situation is not as straightforward: based on our study, the applicability of test automation is still limited and its adaptation to testing contains practical difficulties in usability. In this study, we analyze and discuss these limitations and difficulties.
A Systematic Comparison of Smoothing Techniques for Sentence-Level BLEU

BLEU is the de facto standard machine translation (MT) evaluation metric. However, because BLEU computes a geometric mean of n-gram precisions, it often correlates poorly with human judgment on the sentence-level. Therefore, several smoothing techniques have been proposed. This paper systematically compares 7 smoothing techniques for sentence-level BLEU. Three of them are first proposed in this paper, and they correlate better with human judgments on the sentence-level than other smoothing techniques. Moreover, we also compare the performance of using the 7 smoothing techniques in statistical machine translation tuning.
Are You a Racist or Am I Seeing Things? Annotator Influence on Hate Speech Detection on Twitter

Hate speech in the form of racism and sexism is commonplace on the internet (Waseem and Hovy, 2016). For this reason, there has been both an academic and an industry interest in detection of hate speech. The volume of data to be reviewed for creating data sets encourages a use of crowd sourcing for the annotation efforts. In this paper, we provide an examination of the influence of annotator knowledge of hate speech on classification models by comparing classification results obtained from training on expert and amateur annotations. We provide an evaluation on our own data set and run our models on the data set released by Waseem and Hovy (2016). We find that amateur annotators are more likely than expert annotators to label items as hate speech, and that systems trained on expert annotations outperform systems trained on amateur annotations.
A Statistical Learning Model of Text Classification for Support Vector Machines

This paper develops a theoretical learning model of text classification for Support Vector Machines (SVMs). It connects the statistical properties of text-classification tasks with the generalization performance of a SVM in a quantitative way. Unlike conventional approaches to learning text classifiers, which rely primarily on empirical evidence, this model explains why and when SVMs perform well for text classification. In particular, it addresses the following questions: Why can support vector machines handle the large feature spaces in text classification effectively? How is this related to the statistical properties of text? What are sufficient conditions for applying SVMs to text-classification problems successfully?
Smart Factory Systems

The manufacturing industry has been facing several challenges, including sustainability and performance of production. These challenges are sourced from numerous factors such as an aging workforce, changes in the landscape of global manufacturing and slow adaption of smart manufacturing by implementing IT in manufacturing process. In recent years, German and US governments have established separate initiatives to accelerate the use of the Internet of Things (IoT) and smart analytics technologies in the manufacturing industries and, consequently, to improve the overall performance, quality, and controllability of manufacturing process. The smart factory is the integration of all recent IoT technological advances in computer networks, data integration, and analytics to bring transparency to all manufacturing factories. In this article, we review the most recent logistic decisions for taking smart factories from idea to reality and then describe the possible technologies for smart factories.
Treebank based deep grammar acquisition and Part-Of-Speech Tagging for Sanskrit sentences

Sanskrit since many thousands of years has been the oriental language of India. It is the base for most of the Indian Languages. Ambiguity is inherent in the Natural Language sentences. Here, one word can be used in multiple senses. Morphology process takes word in isolation and fails to disambiguate correct sense of a word. Part-Of-Speech Tagging (POST) takes word sequences in to consideration to resolve the correct sense of a word present in the given sentence. Efficient POST have been developed for processing of English, Japanese, and Chinese languages but it is lacking for Indian languages. In this paper our work present simple rule-based POST for Sanskrit language. It uses rule based approach to tag each word of the sentence. These rules are stored in the database. It parses the given Sanskrit sentence and assigns suitable tag to each word automatically. We have tested this approach for 15 tags and 100 words of the language this rule based tagger gives correct tags for all the inflected words in the given sentence.
DepAudioNet: An Efficient Deep Model for Audio based Depression Classification

This paper presents a novel and effective audio based method on depression classification. It focuses on two important issues, \emph{i.e.} data representation and sample imbalance, which are not well addressed in literature. For the former one, in contrast to traditional shallow hand-crafted features, we propose a deep model, namely DepAudioNet, to encode the depression related characteristics in the vocal channel, combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) to deliver a more comprehensive audio representation. For the latter one, we introduce a random sampling strategy in the model training phase to balance the positive and negative samples, which largely alleviates the bias caused by uneven sample distribution. Evaluations are carried out on the DAIC-WOZ dataset for the Depression Classification Sub-challenge (DCC) at the 2016 Audio-Visual Emotion Challenge (AVEC), and the experimental results achieved clearly demonstrate the effectiveness of the proposed approach.
Intelligent parking assist

In this paper a new parking guiding and information system is described. The system assists the user to find the most suitable parking space based on his/her preferences and learned behavior. The system takes into account parameters such as driver's parking duration, arrival time, destination, type preference, cost preference, driving time, and walking distance as well as time-varying parking rules and pricing. Moreover, a prediction algorithm is proposed to forecast the parking availability for different parking locations for different times of the day based on the real-time parking information, and previous parking availability/occupancy data. A novel server structure is used to implement the system. Intelligent parking assist system reduces the searching time for parking spots in urban environments, and consequently leads to a reduction in air pollutions and traffic congestion. On-street parking meters, off-street parking garages, as well as free parking spaces are considered in our system.
Road direction detection based on vanishing-point tracking

We present a novel approach for vision-based road direction detection for autonomous Unmanned Ground Vehicles (UGVs). The proposed method utilizes only monocular vision information similar to human perception to detect road directions with respect to the vehicle. The algorithm searches for a global feature of the roads due to perspective projection (so-called vanishing point) to distinguish road directions. The proposed approach consists of two stages. The first stage estimates the vanishing-point locations from single frames. The second stage uses a Rao-Blackwellised particle filter to track initial vanishing-point estimations over a sequence of images in order to provide more robust estimation. Simultaneously, the direction of the road ahead of the vehicle is predicted, which is prerequisite information for vehicle steering and path planning. The proposed approach assumes minimum prior knowledge about the environment and can cope with complex situations such as ground cover variations, different illuminations, and cast shadows. Its performance is evaluated on video sequences taken during test run of the DARPA Grand Challenge.
Application-level security for ROS-based applications

While the topic of security in industrial applications has gained some momentum in recent years, there are still severe security vulnerabilities which are actively exploited for attacks. The robot operating system (ROS) is expected to further grow in usage and to be used in many industrial applications. Analysis, however, shows that it lacks several security enhancements in order to make it suitable for industrial use. In its current state, false data and commands can be injected posing a possible safety risk for the resulting product and humans in the production. In addition, data may be eavesdropped and used by outsiders to gain insight into the production process. In this paper we propose a security architecture intended for use on top of ROS on the application level. We use a dedicated authorization server to ensure that only valid nodes are part of the application. Cryptographic methods ensure data confidentiality and integrity. We show in a demonstration with a collaborative robot how our architecture can be used to secure a ROS-based application.
Job Recommendation Systems for Enhancing E-recruitment Process

The Internet caused a substantial impact on the recruitment process through the creation of erecruiting platforms that become a primary recruitment channel in most companies. While companies established job positions on these portals, job-seeker uses them to publish their profiles. E-recruitment platforms accomplished clear advantages for both recruiters and job-seekers by reducing the recruitment time and advertisement cost. However, these platforms suffer from an inappropriateness of traditional information retrieval techniques like the Boolean search methods that caused many applicants missed the opportunity of recruiting. Recommender system technology aims to help users in finding items that match their preferences; it has a successful usage in a wide-range of applications to deal with problems related to information overload efficiently. In order to improve the e-recruiting functionality, many recommender system approaches have been proposed. This paper will analyze e-recruiting process and related issues for building personalized recommender systems of candidates/job
3 The Satisfaction With Life Scale Examining Construct Validity

The views expressed in this paper are those of the author(s) and do not necessarily reflect the policies of Statistics Netherlands Explanation of symbols. data not available * provisional gure ** revised provisional gure (but not dee nite) x publication prohibited (conn dential gure) – nil – (between two gures) inclusive 0 (0.0) less than half of unit concerned empty cell not applicable Due to rounding, some totals may not correspond with the sum of the separate gures.  This report describes the validity testing of a multi-item scale of global life satisfaction, namely the Satisfaction With Life Scale (SWLS). This scale has been proposed as an alternative to single-item life satisfaction measures. As expected, the scale has sufficient construct validity and consists of one underlying dimension. The SWLS is significantly related to an alternative global life satisfaction measure, which indicates convergent validity. However, this correlation is rather low, which raises the question whether the SWLS measures the same as the single-item life satisfaction measure. The correlation of the SWLS with an alternative well-being measure (i.e. mental health index) is lower than the correlation with global life satisfaction. This provides evidence of discriminant validity. Other aspects related to well-being, such as health and illness, also correlate significantly with the SWLS. This shows nomological validity. However, the SWLS also has a number of serious shortcomings. It suffers from data collection mode effects and the data show that a specific group of respondents misinterprets the scale. Furthermore, the SWLS and the single-item global life satisfaction are almost equally related to other aspects that predict well-being. Therefore, the SWLS has no clear added value as an alternative of the single-item life satisfaction. In conclusion, it is recommended that a single-item measure be used instead of the SWLS.
One-Shot Learning for Semantic Segmentation

Low-shot learning methods for image classification support learning from sparse data. We extend these techniques to support dense semantic image segmentation. Specifically, we train a network that, given a small set of annotated images, produces parameters for a Fully Convolutional Network (FCN). We use this FCN to perform dense pixel-level prediction on a test image for the new semantic class. Our architecture shows a 25% relative meanIoU improvement compared to the best baseline methods for one-shot segmentation on unseen classes in the PASCAL VOC 2012 dataset and is at least 3× faster. The code is publicly available at: https://github.com/lzzcd001/OSLSM.
Fast Single Shot Detection and Pose Estimation

For applications in navigation and robotics, estimating the 3D pose of objects is as important as detection. Many approaches to pose estimation rely on detecting or tracking parts or keypoints [11, 21]. In this paper we build on a recent state-of-the-art convolutional network for sliding-window detection [10] to provide detection and rough pose estimation in a single shot, without intermediate stages of detecting parts or initial bounding boxes. While not the first system to treat pose estimation as a categorization problem, this is the first attempt to combine detection and pose estimation at the same level using a deep learning approach. The key to the architecture is a deep convolutional network where scores for the presence of an object category, the offset for its location, and the approximate pose are all estimated on a regular grid of locations in the image. The resulting system is as accurate as recent work on pose estimation (42.4% 8 View mAVP on Pascal 3D+  and significantly faster (46 frames per second (FPS) on a TITAN X GPU). This approach to detection and rough pose estimation is fast and accurate enough to be widely applied as a pre-processing step for tasks including high-accuracy pose estimation, object tracking and localization, and vSLAM.
Sum-product networks for modeling activities with stochastic structure

This paper addresses recognition of human activities with stochastic structure, characterized by variable spacetime arrangements of primitive actions, and conducted by a variable number of actors. We demonstrate that modeling aggregate counts of visual words is surprisingly expressive enough for such a challenging recognition task. An activity is represented by a sum-product network (SPN). SPN is a mixture of bags-of-words (BoWs) with exponentially many mixture components, where subcomponents are reused by larger ones. SPN consists of terminal nodes representing BoWs, and product and sum nodes organized in a number of layers. The products are aimed at encoding particular configurations of primitive actions, and the sums serve to capture their alternative configurations. The connectivity of SPN and parameters of BoW distributions are learned under weak supervision using the EM algorithm. SPN inference amounts to parsing the SPN graph, which yields the most probable explanation (MPE) of the video in terms of activity detection and localization. SPN inference has linear complexity in the number of nodes, under fairly general conditions, enabling fast and scalable recognition. A new Volleyball dataset is compiled and annotated for evaluation. Our classification accuracy and localization precision and recall are superior to those of the state-of-the-art on the benchmark and our Volleyball datasets.