nils-herrmann/clean_scirepeval huggingface dataset:
Internet X.509 Public Key Infrastructure Certificate Policy and Certification Practices Framework

< draft-ietf-pkix-ipki-new-rfc2527-01.txt > Status of this Memo This document is an Internet-Draft and is subject to all provisions of Section 10 of RFC2026. Internet-Drafts are working documents of the Internet Engineering Task Force (IETF), its areas, and its working groups. Note that other groups may also distribute working documents as Internet-Drafts. Internet-Drafts are draft documents valid for a maximum of 6 months and may be updated, replaced, or may become obsolete by other documents at any time. It is inappropriate to use Internet-Drafts as reference material or to cite them other than as work in progress. To view the entire list of current Internet-Drafts, please check the "1id-abstracts.txt" listing contained in the Internet-Drafts Shadow Directories on ftp.is.co.za (Africa), ftp.nordu.net (Northern Europe), ftp.nis.garr.it (Southern Europe), munnari.oz.au (Pacific Rim), ftp.ietf.org (US East Coast), or ftp.isi.edu (US West Coast).  This document presents a framework to assist the writers of certificate policies or certification practice statements for participants within public key infrastructures, such as certification authorities, policy authorities, and communities of interest that wish to rely on certificates. In particular, the framework provides a comprehensive list of topics that potentially (at the writer's discretion) need to be covered in a certificate policy or a certification practice statement. This document is being submitted to the RFC Editor with a request for publication as an Informational RFC that will supercede RFC 2527 [CPF].
Document dissimilarity within and across languages: A benchmarking study

Quantifying the similarity or dissimilarity between documents is an important task in authorship attribution, information retrieval, plagiarism detection, text mining and many other areas of linguistic computing. Numerous similarity indices have been devised and used, but relatively little attention has been paid to calibrating such indices against externally imposed standards, mainly because of the difficulty of establishing agreed reference levels of inter-text similarity. The present paper introduces a multi-register corpus gathered for this purpose in which each text has been located in a similarity space based on ratings by human readers. This provides a resource for testing similarity measures derived from computational text-processing against reference levels derived from human judgement, i.e. external to the texts themselves. We describe the results of a benchmarking study in five different languages in which some widely used measures perform comparatively poorly. In particular, several alternative correlational measures (Pearson’s r, Spearman’s rho, tetrachoric correlation) consistently outperform cosine similarity on our data. A method of using what we call ‘anchor texts’ to extend this method from monolingual inter-text similarity-scoring to inter-text similarity-scoring across languages is also proposed and tested.
Genetic Algorithms for the 0/1 Knapsack Problem

During the last twenty years genetic algorithms [6] and other evolutionary algorithms [11] have been applied to many hard problems with very good results. However, for many constrained problems the results were mixed. It seems, that (in general) there has not been any single accepted strategy to deal with constrained problems: most researchers used some ad-hoc methods for handling problem specific constraints. The reason for this phenomena might be that there is an experimental evidence [10] that incorporation of the problem specific knowledge (i.e., the problem's constraints) into the evolutionary algorithm (i.e., into its chromosomal structures and genetic operators) enhances its performance in a significant way. The constraint-handling techniques for evolutionary algorithms can be grouped into a few categories. One way of dealing with candidates that violate the constraints is to generate potential solutions without considering the constraints and then to penalize them by decreasing the "goodness" of the evaluation function. In other words, a constrained problem is transformed to an unconstrained one by associating a penalty with all constraint violations; these penalties are included in the function evaluation. Of course, there are a variety of possible penalty functions which can be applied. Some penalty functions assign a constant as a penalty measure. Other penalty functions depend on the degree of violation: the larger violation is, the greater penalty is imposed (however, the growth of the function can be logarithmic, linear, quadratic, exponential, etc. with respect to the size of the violation). Each of these categories of penalty functions has its own disadvantages; in  Davis wrote:
ConceptNet at SemEval-2017 Task 2: Extending Word Embeddings with Multilingual Relational Knowledge

This paper describes Luminoso’s participation in SemEval 2017 Task 2, “Multilingual and Cross-lingual Semantic Word Similarity”, with a system based on ConceptNet.  knowledge graph that focuses on general knowledge that relates the meanings of words and phrases. Our submission to SemEval was an update of previous work that builds high-quality, multilingual word embeddings from a combination of ConceptNet and distributional semantics. Our system took first place in both subtasks. It ranked first in 4 out of 5 of the separate languages, and also ranked first in all 10 of the cross-lingual language pairs.
Modulation of visual attention by object affordance

Some objects in our environment are strongly tied to motor actions, a phenomenon called object affordance. A cup, for example, affords us to reach out to it and grasp it by its handle. Studies indicate that merely viewing an affording object triggers motor activations in the brain. The present study investigated whether object affordance would also result in an attention bias, that is, whether observers would rather attend to graspable objects within reach compared to non-graspable but reachable objects or to graspable objects out of reach. To this end, we conducted a combined reaction time and motion tracking study with a table in a virtual three-dimensional space. Two objects were positioned on the table, one near, the other one far from the observer. In each trial, two graspable objects, two non-graspable objects, or a combination of both was presented. Participants were instructed to detect a probe appearing on one of the objects as quickly as possible. Detection times served as indirect measure of attention allocation. The motor association with the graspable object was additionally enhanced by having participants grasp a real object in some of the trials. We hypothesized that visual attention would be preferentially allocated to the near graspable object, which should be reflected in reduced reaction times in this condition. Our results confirm this assumption: probe detection was fastest at the graspable object at the near position compared to the far position or to a non-graspable object. A follow-up experiment revealed that in addition to object affordance per se, immediate graspability of an affording object may also influence this near-space advantage. Our results suggest that visuospatial attention is preferentially allocated to affording objects which are immediately graspable, and thus establish a strong link between an object' s motor affordance and visual attention.
Multi-Label Zero-Shot Learning via Concept Embedding

Zero Shot Learning (ZSL) enables a learning model to classify instances of an unseen class during training. While most research in ZSL focuses on single-label classification, few studies have been done in multi-label ZSL, where an instance is associated with a set of labels simultaneously, due to the difficulty in modeling complex semantics conveyed by a set of labels. In this paper, we propose a novel approach to multi-label ZSL via concept embedding learned from collections of public users’ annotations of multimedia. Thanks to concept embedding, multi-label ZSL can be done by efficiently mapping an instance input features onto the concept embedding space in a similar manner used in single-label ZSL. Moreover, our semantic learning model is capable of embedding an out-of-vocabulary label by inferring its meaning from its co-occurring labels. Thus, our approach allows both seen and unseen labels during the concept embedding learning to be used in the aforementioned instance mapping, which makes multi-label ZSL more flexible and suitable for real applications. Experimental results of multilabel ZSL on images and music tracks suggest that our approach outperforms a state-of-the-art multi-label ZSL model and can deal with a scenario involving out-of-vocabulary labels without re-training the semantics learning model.
Situation Recognition with Graph Neural Networks

We address the problem of recognizing situations in images. Given an image, the task is to predict the most salient verb (action), and fill its semantic roles such as who is performing the action, what is the source and target of the action, etc. Different verbs have different roles (e.g. attacking has weapon), and each role can take on many possible values (nouns). We propose a model based on Graph Neural Networks that allows us to efficiently capture joint dependencies between roles using neural networks defined on a graph. Experiments with different graph connectivities show that our approach that propagates information between roles significantly outperforms existing work, as well as multiple baselines. We obtain roughly 3-5% improvement over previous work in predicting the full situation. We also provide a thorough qualitative analysis of our model and influence of different roles in the verbs.
A robust algorithm for text detection in images

Text detection in images or videos is an important step to achieve multimedia content retrieval. In this paper, an efficient algorithm which can automatically detect, localize and extract horizontally aligned text in images (and digital videos) with complex backgrounds is presented. The proposed approach is based on the application of a color reduction technique, a method for edge detection, and the localization of text regions using projection profile analyses and geometrical properties. The output of the algorithm are text boxes with a simplified background, ready to be fed into an OCR engine for subsequent character recognition. Our proposal is robust with respect to different font sizes, font colors, languages and background complexities. The performance of the approach is demonstrated by presenting promising experimental results for a set of images taken from different types of video sequences.
Type refinement for static analysis of JavaScript

Static analysis of JavaScript has proven useful for a variety of purposes, including optimization, error checking, security auditing, program refactoring, and more. We propose a technique called type refinement that can improve the precision of such static analyses for JavaScript without any discernible performance impact. Refinement is a known technique that uses the conditions in branch guards to refine the analysis information propagated along each branch path. The key insight of this paper is to recognize that JavaScript semantics include many implicit conditional checks on types, and that performing type refinement on these implicit checks provides significant benefit for analysis precision.
 To demonstrate the effectiveness of type refinement, we implement a static analysis tool for reporting potential type-errors in JavaScript programs. We provide an extensive empirical evaluation of type refinement using a benchmark suite containing a variety of JavaScript application domains, ranging from the standard performance benchmark suites (Sunspider and Octane), to open-source JavaScript applications, to machine-generated JavaScript via Emscripten. We show that type refinement can significantly improve analysis precision by up to 86% without affecting the performance of the analysis.
Principles of critical discourse analysis

This paper discusses some principles of critical discourse analysis, such as the explicit sociopolitical stance of discourse analysts, and a focus on dominance relations by elite groups and institutions as they are being enacted, legitimated or otherwise reproduced by text and talk. One of the crucial elements of this analysis of the relations between power and discourse is the patterns of access to (public) discourse for different social groups. Theoretically it is shown that in order to be able to relate power and discourse in an explicit way, we need the cognitive interface of models. knowledge, attitudes and ideologies and other social representations of the social mind, which also relate the individual and the social, and the microand the macro-levels of social structure. Finally, the argument is illustrated with an analysis of parliamentary debates about ethnic affairs.
FastSLAM : An Efficient Solution to the Simultaneous Localization And Mapping Problem with Unknown Data Association

This article provides a comprehensive description of FastSLAM, a new family of algorithms for the simultaneous localization and mapping problem, which specifically address hard data association problems. The algorithm uses a particle filter for sampling robot paths, and extended Kalman filters for representing maps acquired by the vehicle. This article presents two variants of this algorithm, the original algorithm along with a more recent variant that provides improved performance in certain operating regimes. In addition to a mathematical derivation of the new algorithm, we present a proof of convergence and experimental results on its performance on real-world data.
Efficient Algorithms for Mining Outliers from Large Data Sets

In this paper, we propose a novel formulation for distance-based outliers that is based on the distance of a point from its kth nearest neighbor. We rank each point on the basis of its distance to its kth nearest neighbor and declare the top n points in this ranking to be outliers. In addition to developing relatively straightforward solutions to finding such outliers based on the classical nested-loop join and index join algorithms, we develop a highly efficient partition-based algorithm for mining outliers. This algorithm first partitions the input data set into disjoint subsets, and then prunes entire partitions as soon as it is determined that they cannot contain outliers. This results in substantial savings in computation. We present the results of an extensive experimental study on real-life and synthetic data sets. The results from a real-life NBA database highlight and reveal several expected and unexpected aspects of the database. The results from a study on synthetic data sets demonstrate that the partition-based algorithm scales well with respect to both data set size and data set dimensionality.
Unsupervised Discovery of Drug Side-Effects from Heterogeneous Data Sources

Drug side-effects become a worldwide public health concern, which are the fourth leading cause of death in the United States. Pharmaceutical industry has paid tremendous effort to identify drug side-effects during the drug development. However, it is impossible and impractical to identify all of them. Fortunately, drug side-effects can also be reported on heterogeneous platforms (i.e., data sources), such as FDA Adverse Event Reporting System and various online communities. However, existing supervised and semi-supervised approaches are not practical as annotating labels are expensive in the medical field. In this paper, we propose a novel and effective unsupervised model Sifter to automatically discover drug side-effects. Sifter enhances the estimation on drug side-effects by learning from various online platforms and measuring platform-level and user-level quality simultaneously. In this way, Sifter demonstrates better performance compared with existing approaches in terms of correctly identifying drug side-effects. Experimental results on five real-world datasets show that Sifter can significantly improve the performance of identifying side-effects compared with the state-of-the-art approaches.
Map-relative localization in lane-level maps for ADAS and autonomous driving

Future advanced driver assistant systems put high demands on the environmental perception especially in urban environments. Today's on-board sensors and on-board algorithms still do not reach a satisfying level of development from the point of view of robustness and availability. Thus, map data is often used as an additional data input to support the on-board sensor system and algorithms. The usage of map data requires a highly correct pose within the map even in cases of positioning errors by global navigation satellite systems or geometrical errors in the map data. In this paper we propose and compare two approaches for map-relative localization exclusively using a lane-level map. These approaches deliberately avoid the usage of detailed a priori maps containing point-landmarks, grids or road-markings. Additionally, we propose a grid-based on-board fusion of road-marking information and stationary obstacles addressing the problem of missing or incomplete road-markings in urban scenarios.
Faster Teaching via POMDP Planning

Human and automated tutors attempt to choose pedagogical activities that will maximize student learning, informed by their estimates of the student's current knowledge. There has been substantial research on tracking and modeling student learning, but significantly less attention on how to plan teaching actions and how the assumed student model impacts the resulting plans. We frame the problem of optimally selecting teaching actions using a decision-theoretic approach and show how to formulate teaching as a partially observable Markov decision process planning problem. This framework makes it possible to explore how different assumptions about student learning and behavior should affect the selection of teaching actions. We consider how to apply this framework to concept learning problems, and we present approximate methods for finding optimal teaching actions, given the large state and action spaces that arise in teaching. Through simulations and behavioral experiments, we explore the consequences of choosing teacher actions under different assumed student models. In two concept-learning tasks, we show that this technique can accelerate learning relative to baseline performance.
Multilevel Voltage-Source-Converter Topologies for Industrial Medium-Voltage Drives

This paper presents a technology review of voltage-source-converter topologies for industrial medium-voltage drives. In this highly active area, different converter topologies and circuits have found their application in the market. This paper covers the high-power voltage-source inverter and the most used multilevel-inverter topologies, including the neutral-point-clamped, cascaded H-bridge, and flying-capacitor converters. This paper presents the operating principle of each topology and a review of the most relevant modulation methods, focused mainly on those used by industry. In addition, the latest advances and future trends of the technology are discussed. It is concluded that the topology and modulation-method selection are closely related to each particular application, leaving a space on the market for all the different solutions, depending on their unique features and limitations like power or voltage level, dynamic performance, reliability, costs, and other technical specifications.
Using Hidden Semi-Markov Models for Effective Online Failure Prediction

A proactive handling of faults requires that the risk of upcoming failures is continuously assessed. One of the promising approaches is online failure prediction, which means that the current state of the system is evaluated in order to predict the occurrence of failures in the near future. More specifically, we focus on methods that use event-driven sources such as errors. We use hidden semi-Markov models (HSMMs)for this purpose and demonstrate effectiveness based on field data of a commercial telecommunication system. For comparative analysis we selected three well-known failure prediction techniques: a straightforward method that is based on a reliability model, dispersion frame technique by Lin and Siewiorek and the eventset-based method introduced by Vilalta et al. We assess and compare the methods in terms of precision, recall, F-measure, false-positive rate, and computing time. The experiments suggest that our HSMM approach is very effective with respect to online failure prediction.
Augmented digestion of lignocellulose by steam explosion, acid and alkaline pretreatment methods: a review.

Lignocellulosic materials can be explored as one of the sustainable substrates for bioethanol production through microbial intervention as they are abundant, cheap and renewable. But at the same time, their recalcitrant structure makes the conversion process more cumbersome owing to their chemical composition which adversely affects the efficiency of bioethanol production. Therefore, the technical approaches to overcome recalcitrance of biomass feedstock has been developed to remove the barriers with the help of pretreatment methods which make cellulose more accessible to the hydrolytic enzymes, secreted by the microorganisms, for its conversion to glucose. Pretreatment of lignocellulosic biomass in cost effective manner is a major challenge to bioethanol technology research and development. Hence, in this review, we have discussed various aspects of three commonly used pretreatment methods, viz., steam explosion, acid and alkaline, applied on various lignocellulosic biomasses to augment their digestibility alongwith the challenges associated with their processing.
Multi-access Edge Computing: A Survey

Multi-access Edge Computing (MEC) can be defined as a model for enabling business oriented, cloud computing platform within multiple types of the access network (e.g., LTE, 5G, WiFi, FTTH, etc.) at the close proximity of subscribers to serve delay sensitive, context aware applications. To pull out the most of the potential, MEC has to be designed as infrastructure, to support many kind of IoT applications and their eco system, in addition to sufficiently management mechanism. In this context, various research and standardization efforts are ongoing. This paper provides a comprehensive survey of the state-of-the-art research efforts on MEC domain, with focus on the architectural proposals as infrastracture, the issue of the partitioning of processing among the user devices, edge servers, and a cloud, and the issue of the resource management.
Chip-to-rectangular waveguide transition realized in embedded Wafer Level Ball Grid Array (eWLB) package

In this paper, we present for the first time the realization of a 77 GHz chip-to-rectangular waveguide transition realized in an embedded Wafer Level Ball Grid Array (eWLB) package. The chip is contacted with a coplanar waveguide (CPW). For the transformation of the transverse electromagnetic (TEM) mode of the CPW line to the transverse electric (TE) mode of the rectangular waveguide an insert is used in the eWLB package. This insert is based on radio-frequency (RF) printed circuit board (PCB) technology. Micro vias formed in the insert are used to realize the sidewalls of the rectangular waveguide structure in the fan-out area of the eWLB package. The redistribution layers (RDLs) on the top and bottom surface of the package form the top and bottom wall, respectively. We present two possible variants of transforming the TEM mode to the TE mode. The first variant uses a via realized in the rectangular waveguide structure. The second variant uses only the RDLs of the eWLB package for mode conversion. We present simulation and measurement results of both variants. We obtain an insertion loss of 1.5 dB and return loss better than 10 dB. The presented results show that this approach is an attractive candidate for future low loss and highly integrated RF systems.