nils-herrmann/clean_scirepeval huggingface dataset:
BlueGene/L Failure Analysis and Prediction Models

The growing computational and storage needs of several scientific applications mandate the deployment of extreme-scale parallel machines, such as IBM's BlueGene/L which can accommodate as many as 128 K processors. One of the challenges when designing and deploying these systems in a production setting is the need to take failure occurrences, whether it be in the hardware or in the software, into account. Earlier work has shown that conventional runtime fault-tolerant techniques such as periodic checkpointing are not effective to the emerging systems. Instead, the ability to predict failure occurrences can help develop more effective checkpointing strategies. Failure prediction has long been regarded as a challenging research problem, mainly due to the lack of realistic failure data from actual production systems. In this study, we have collected RAS event logs from BlueGene/L over a period of more than 100 days. We have investigated the characteristics of fatal failure events, as well as the correlation between fatal events and non-fatal events. Based on the observations, we have developed three simple yet effective failure prediction methods, which can predict around 80% of the memory and network failures, and 47% of the application I/O failures
A miniaturized wilkinson power divider for ultra wide-band operation

This paper presents a new miniaturized multi-section Wilkinson power divider based on coupled-line for ultra-wideband operation. The proposed divider is composed of three-sections. An even- and odd-mode analysis is used to calculate the characteristics impedances at the designed frequency, and the performance is optimized for ultra-wideband operation from 0.9 to 4.1 GHz. The realized power divider has return loss lower than -10 dB and insertion loss better than -3.8 dB for the frequency band 0.9-4.1 GHz. Moreover, it occupies more than 60% smaller circuit area compared to conventional ring structure Wilkinson power divider. The measurement results show the good agreement with simulated ones.
How to Select Good Neighboring Images in Depth-Map Merging Based 3D Modeling

Depth-map merging based 3D modeling is an effective approach for reconstructing large-scale scenes from multiple images. In addition to generate high quality depth maps at each image, how to select suitable neighboring images for each image is also an important step in the reconstruction pipeline, unfortunately to which little attention has been paid in the literature untill now. This paper is intended to tackle this issue for large scale scene reconstruction where many unordered images are captured and used with substantial varying scale and view-angle changes. We formulate the neighboring image selection as a combinatorial optimization problem and use the quantum-inspired evolutionary algorithm to seek its optimal solution. Experimental results on the ground truth data set show that our approach can significantly improve the quality of the depth-maps as well as final 3D reconstruction results with high computational efficiency.
A Survey on Software Estimation Techniques in Traditional and Agile Development Models

Software projects mostly exceeds budget, delivered late and does not meet with the customer’s satisfaction for years. In the past, many traditional development models like waterfall, spiral, iterative, and prototyping methods are used to build the software systems. In recent years, agile models are widely used in developing the software products. The major reasons are – simplicity, incorporating the requirement changes at any time, light-weight approach and delivering the working product early and in short duration. Whatever the development model used, it still remains a challenge for software engineer’s to accurately estimate the size, effort and the time required for developing the software system. This survey focuses on the existing estimation models used in traditional as well in agile software development.
AC-grid generation using dual-input hybrid inverter circuit

Whilst a normal inverter converts the DC voltage into AC voltage, an off grid inverter occupies input from either solar panel or wind turbine. This paper is about the design and simulation of a bi-input inverter using PV and Wind power system. The overall design will result in a regulated AC output load for AC grid connected system. This paper also discusses on two circuits which is charge controller and inverter. It is found that regulated output AC voltage level of 110 V and current 13 A have been successfully simulated using MULTISIM Power Pro Edition 12.0 which is applicable for national 50-Hz transmission grid line.
Measurements and Modelling of Base Station Power Consumption under Real Traffic Loads †

Base stations represent the main contributor to the energy consumption of a mobile cellular network. Since traffic load in mobile networks significantly varies during a working or weekend day, it is important to quantify the influence of these variations on the base station power consumption. Therefore, this paper investigates changes in the instantaneous power consumption of GSM (Global System for Mobile Communications) and UMTS (Universal Mobile Telecommunications System) base stations according to their respective traffic load. The real data in terms of the power consumption and traffic load have been obtained from continuous measurements performed on a fully operated base station site. Measurements show the existence of a direct relationship between base station traffic load and power consumption. According to this relationship, we develop a linear power consumption model for base stations of both technologies. This paper also gives an overview of the most important concepts which are being proposed to make cellular networks more energy-efficient.
' s personal copy Core , animal reminder , and contamination disgust : Three kinds of disgust with distinct personality , behavioral , physiological , and clinical correlates

We examined the relationships between sensitivity to three kinds of disgust (core, animalreminder, and contamination) and personality traits, behavioral avoidance, physiological responding, and anxiety disorder symptoms. Study 1 revealed that these disgusts are particularly associated with neuroticism and behavioral inhibition. Moreover, the three disgusts showed a theoretically consistent pattern of relations on four disgust-relevant behavioral avoidance tasks in Study 2. Similar results were found in Study 3 such that core disgust was significantly related to increased physiological responding during exposure to vomit, while animal-reminder disgust was specifically related to physiological responding during exposure to blood. Lastly, Study 4 revealed that each of the three disgusts showed a different pattern of relations with fear of contamination, fear of animals, and fear of blood– injury relevant stimuli. These findings provide support for the convergent and divergent validity of core, animal-reminder, and contamination disgust. These findings also highlight the possibility that the three kinds of disgust may manifest as a function of different psychological mechanisms (i.e., oral incorporation, mortality defense, disease avoidance) that may give rise to different clinical conditions. However, empirical examination of the mechanisms that underlie the three disgusts will require further refinement of the psychometric properties of the disgust scale.
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification

In this work, we present a fully automated lung computed tomography (CT) cancer diagnosis system, DeepLung. DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant). Considering the 3D nature of lung CT data and the compactness of dual path networks (DPN), two deep 3D DPN are designed for nodule detection and classification respectively. Specifically, a 3D Faster Regions with Convolutional Neural Net (R-CNN) is designed for nodule detection with 3D dual path blocks and a U-net-like encoder-decoder structure to effectively learn nodule features. For nodule classification, gradient boosting machine (GBM) with 3D dual path network features is proposed. The nodule classification subnetwork was validated on a public dataset from LIDC-IDRI, on which it achieved better performance than state-of-the-art approaches and surpassed the performance of experienced doctors based on image modality. Within the DeepLung system, candidate nodules are detected first by the nodule detection subnetwork, and nodule diagnosis is conducted by the classification subnetwork. Extensive experimental results demonstrate that DeepLung has performance comparable to experienced doctors both for the nodule-level and patient-level diagnosis on the LIDC-IDRI dataset.
Ontology learning with complex data type for Web service clustering

Clustering Web services into functionally similar clusters is a very efficient approach to service discovery. A principal issue for clustering is computing the semantic similarity between services. Current approaches use similarity-distance measurement methods such as keyword, information-retrieval or ontology based methods. These approaches have problems that include discovering semantic characteristics, loss of semantic information and a shortage of high-quality ontologies. Further, current clustering approaches are considered only have simple data types in services' input and output. However, services that published on the web have input/ output parameter of complex data type. In this research, we propose clustering approach that considers the simple type as well as complex data type in measuring the service similarity. We use hybrid term similarity method which we proposed in our previous work to measure the similarity. We capture the semantic pattern exist in complex data types and simple data types to improve the ontology learning method. Experimental results show our clustering approach which uses complex data types in measuring similarity works efficiently.
Visual Fatigue Prediction for Stereoscopic Image

In this letter, we propose a visual fatigue prediction metric which can replace subjective evaluation for stereoscopic images. It detects stereoscopic impairments caused by inappropriate shooting parameters or camera misalignment which induces excessive horizontal and vertical disparities. Pearson's correlation was measured between the proposed metrics and the subjective results by using k-fold cross-validation, acquiring ranges of 78-87% with sparse feature and 74-85% with dense feature.
High-level visual features for underwater place recognition

This paper reports on a method to perform robust visual relocalization between temporally separated sets of underwater images gathered by a robot. The place recognition and relocalization problem is more challenging in the underwater environment mainly due to three factors: 1) changes in illumination; 2) long-term changes in the visual appearance of features because of phenomena like biofouling on man-made structures and growth or movement in natural features; and 3) low density of visually salient features for image matching. To address these challenges, a patch-based feature matching approach is proposed, which uses image segmentation and local intensity contrast to locate salient patches and HOG description to make correspondences between patches. Compared to traditional point-based features that are sensitive to dramatic appearance changes underwater, patch-based features are able to encode higher level information such as shape or structure which tends to persist across years in underwater environments. The algorithm is evaluated on real data, from multiple years, collected by a Hovering Autonomous Underwater Vehicle for ship hull inspection. Results in relocalization performance across missions from different years are compared to other traditional methods.
Knowledge manipulation activities: results of a Delphi study

Knowledge-based organizations are hosts for multitudes of knowledge management (KM) episodes. Each episode is triggered by a knowledge need and culminates with the satisfaction of that need (or its abandonment). Within an episode, one or more of the organization’ processors (human and/or computer-based) manipulate knowledge resources in various ways in an effort to meet the need. This paper identifies and characterizes a generic set of elemental knowledge manipulation activities that can be arranged in a variety of patterns within KM episodes. It also indicates possible knowledge flows that can occur among the activities. This descriptive framework was developed using conceptual synthesis and a Delphi methodology involving an international panel of researchers and practitioners in the KM field. The framework can serve as a common language for discourse about knowledge manipulation. For researchers, it suggests issues that deserve investigation and concepts that must be considered in explorations of KM episodes. For practitioners, the framework provides a perspective on activities that need to be considered in the design, measurement, control, coordination, and support of an organization’ KM episodes.
Symmetric Tensor Completion from Multilinear Entries and Learning Product Mixtures over the Hypercube

We give an algorithm for completing an order-m symmetric low-rank tensor from its multilinear entries in time roughly proportional to the number of tensor entries. We apply our tensor completion algorithm to the problem of learning mixtures of product distributions over the hypercube, obtaining new algorithmic results. If the centers of the product distribution are linearly independent, then we recover distributions with as many as Ω(n) centers in polynomial time and sample complexity. In the general case, we recover distributions with as many as Ω̃(n) centers in quasi-polynomial time, answering an open problem of Feldman et al. ( for the special case of distributions with incoherent bias vectors. Our main algorithmic tool is the iterated application of a low-rank matrix completion algorithm for matrices with adversarially missing entries. UC Berkeley, tschramm@cs.berkeley.edu. Supported by an NSF Graduate Research Fellowship (NSF award no 1106400). UC Berkeley, bsweitz@cs.berkeley.edu. Supported by an NSF Graduate Research Fellowship (NSF award no DGE 1106400).
Multidimensional Constellations for Uplink SCMA Systems - A Comparative Study

Sparse code multiple access (SCMA) is a class of non-orthogonal multiple access (NOMA) that is proposed to support uplink machine-type communication services. In an SCMA system, designing multidimensional constellation plays an important role in the performance of the system. Since the behavior of multidimensional constellations highly depends on the type of the channel, it is crucial to employ a constellation that is suitable for a certain application. In this paper, we first highlight and review the key performance indicators (KPIs) of multidimensional constellations that should be considered in their design process for various channel scenarios. We then provide a survey on the known multidimensional constellations in the context of SCMA systems with their design criteria. The performance of some of those constellations are evaluated for uncoded, high-rate, and low-rate LTE turbo-coded SCMA systems under different channel conditions through extensive simulations. All turbo-coded comparisons are performed for bit-interleaved coded modulation, with a concatenated detection and decoding scheme. Simulation results confirm that multidimensional constellations that satisfy KPIs of a certain channel scenario outperform others. Moreover, the bit error rate performance of uncoded systems, and the performance of the coded systems are coupled to their bit-labeling. The performance of the systems also remarkably depends on the behavior of the multi-user detector at different signal-to-noise ratio regions.
Cross Lingual Syntax Projection for Resource-Poor Languages Vamshi Ambati

Over the past few decades, supervised learning in structured spaces has been quite successful in syntactic analysis problems in natural language processing. These learning techniques exploit large amounts of annotated data to learn models that can perform linguistic analysis on unseen data. Acquiring such supervised linguistic annotations for a language is important for natural language processing and it usually involves significant human efforts. The quantities of the annotated data are far from being sufficient for the majority of the languages. Languages like English have been well supported in the linguistics community, and therefore there is a wealth of language analysis tools for them. We also have large amounts of annotated data available. This makes English a resource-rich language and attractive for computational linguists to work on. There are only a few more languages in the world that enjoy the status of a resource-rich language. Many other languages either do not have analysis tools or do not have annotated data from which state-of-the-art tools can be induced. This makes these languages resourcepoor both in terms of data and tools. Even after 50 years of notable contributions made in the area of computational linguistics, we are still far from being able to deal with many other languages. The advent of the World Wide Web and the advances in the digital media world have helped the language community immensely. We now see a lot of data on the internet and a lot of parallel data for various languages pairs. There are also known techniques for harvesting parallel texts from the World Wide Web (Resnik and Smith 2003). A pair of texts is parallel when a document in one language, often the source language, has an identified mapping with another document in the second language, called the target language, and one is an equivalent translation of the other. The availability of parallel data has opened various research ideas for the creation of multilingual applications, in particular for the resource-poor languages. One approach is to project syntactic annotations and structures from the resource-rich source language to the target language which is resource-poor. This is often called "projection of annotation" or simply "syntax projection". The goal of syntax projection is to induce multilingual text analysis tools automatically for a target language. This problem can be complicated because of the differences in syntactic structures between the source and target languages. Usually, the projection is not merely a one-to-one mapping. Rather, syntactic relations can also be one-to-many, many-to-one, many-to-many or even unmapped. Annotated data that we get from direct projection using parallel corpora contains errors. Thus, training accurate stochastic text analyzers from noisy data becomes a challenging task. Many efforts have been developed and put into practice in the last 10 years to solve the challenges faced by syntax projection (Yarowsky, Ngai, and Wicentowski 2001;
Learning semantic attributes via a common latent space

Semantic attributes represent an adequate knowledge that can be easily transferred to other domains where lack of information and training samples exist. However, in the classical object recognition case, where training data is abundant, attribute-based recognition usually results in poor performance compared to methods that used image features directly. We introduce a generic framework that boosts the performance of semantic attributes considerably in traditional classification and knowledge transfer tasks, such as zero-shot learning. It incorporates the discriminative power of the visual features and the semantic meaning of the attributes by learning a common latent space that joins both spaces. We also specifically account for the presence of attribute correlations in the source dataset to generalize more efficiently across domains. Our evaluation of the proposed approach on standard public datasets shows that it is not only simple and computationally efficient but also performs remarkably better than the common direct attribute model.
Decontamination of 16S rRNA gene amplicon sequence datasets based on bacterial load assessment by qPCR

Identification of unexpected taxa in 16S rRNA surveys of low-density microbiota, diluted mock communities and cultures demonstrated that a variable fraction of sequence reads originated from exogenous DNA. The sources of these contaminants are reagents used in DNA extraction, PCR, and next-generation sequencing library preparation, and human (skin, oral and respiratory) microbiota from the investigators. For in silico removal of reagent contaminants, a pipeline was used which combines the relative abundance of operational taxonomic units (OTUs) in V3–4 16S rRNA gene amplicon datasets with bacterial DNA quantification based on qPCR targeting of the V3 segment of the 16S rRNA gene. Serially diluted cultures of Escherichia coli and Staphylococcus aureus were used for 16S rDNA profiling, and DNA from each of these species was used as a qPCR standard. OTUs assigned to Escherichia or Staphylococcus were virtually unaffected by the decontamination procedure, whereas OTUs from Pseudomonas, which is a major reagent contaminant, were completely or nearly completely removed. The decontamination procedure also attenuated the trend of increase in OTU richness in serially diluted cultures. Removal of contaminant sequences derived from reagents based on use of qPCR data may improve taxonomic representation in samples with low DNA concentration. Using the described pipeline, OTUs derived from cross-contamination of negative extraction controls were not recognized as contaminants and not removed from the sample dataset.
Data Integration: A Theoretical Perspective

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.
Perspectives on usability guidelines for smartphone applications: An empirical investigation and systematic literature review

Several usability guidelines have been proposed to improve the usability of smartphone apps. These guidelines can be classified into three disjoint sets: platform specific guidelines, genre specific guidelines, and generic guidelines. However, smartphone applications are usually developed for multiple platforms and targeted for a variety of users. Hence the usefulness of existing guidelines is severally limited.  This study aims to develop a comprehensive list of usability guidelines suitable for multiple platforms and genres of smartphone applications.
DRCD: a Chinese Machine Reading Comprehension Dataset

重視硫酸的種類以及它們在醫學上的價值...... 在 1746年,John Roebuck則運用這個原則,開 創鉛室法,以更低成本有效地大量生產硫 酸。...... ... Others, such as Ibn Sīnā, are more concerned about the type of sulfuric acid and their medical value. In 1746, John Roebuck applied this method to create lead chamber process to efficiently produce large quantities of sulfuric acid at a lower cost.  重視硫酸的種類以及它們在醫學上的 價值地為哪位醫師? Who value the type of sulfuric acid and their medical value?  伊本·西那/ Ibn Sīnā Question: 鉛室法於西元幾年開創? / When did lead chamber process being invented?