nils-herrmann/clean_scirepeval huggingface dataset:
MapReduce advantages over parallel databases include storage-system independence and fine-grain fault tolerance for large jobs.

MA P RE DUCE IS A programming model for processing and generating large data sets.4 Users specify a map function that processes a key/value pair to generate a set of intermediate key/value pairs and a reduce function that merges all intermediate values associated with the same intermediate key. We built a system around this programming model in 2003 to simplify construction of the inverted index for handling searches at Google.com. Since then, more than 10,000 distinct programs have been implemented using MapReduce at Google, including algorithms for large-scale graph processing, text processing, machine learning, and statistical machine translation. The Hadoop open source implementation DOI:10.1145/1629175.1629198
Combining visual and acoustic features for audio classification tasks

In this paper a novel and effective approach for automated audio classification is presented that is based on the fusion of different sets of features, both visual and acoustic. A number of different acoustic and visual features of sounds are evaluated and compared. These features are then fused in an ensemble that produces better classification accuracy than other state-of-the-art approaches. The visual features of sounds are built starting from the audio file and are taken from images constructed from different spectrograms, a gammatonegram, and a rhythm image. These images are divided into subwindows from which a set of texture descriptors are extracted. For each feature descriptor a different Support Vector Machine (SVM) is trained. The SVMs outputs are summed for a final decision. The proposed ensemble is evaluated on three well-known databases of music genre classification (the Latin Music Database, the ISMIR 2004 database, and the GTZAN genre collection), a dataset of Bird vocalization aiming specie recognition, and a dataset of right whale calls aiming whale detection. The MATLAB code for the ensemble of classifiers and for the extraction of the features will be publicly available (https://www.dei.unipd.it/node/2357 +Pattern Recognition and Ensemble Classifiers).
Performance of Drought Indices for Ecological , Agricultural , and Hydrological Applications

In this study, the authors provide a global assessment of the performance of different drought indices for monitoring drought impacts on several hydrological, agricultural, and ecological response variables. For this purpose, they compare the performance of several drought indices [the standardized precipitation index (SPI); four versions of the Palmer drought severity index (PDSI); and the standardized precipitation evapotranspiration index (SPEI)] to predict changes in streamflow, soil moisture, forest growth, and crop yield. The authors found a superior capability of the SPEI and the SPI drought * Corresponding author address: Sergio M. Vicente-Serrano, Instituto Pirenaico de Ecologı́a, Consejo Superior de Investigaciones Cientı́ficas (IPE-CSIC), Campus de Aula Dei, P.O. Box 13034, E-50059 Zaragoza, Spain. E-mail address: svicen@ipe.csic.es Earth Interactions d Volume 16 (2012) d Paper No. 10 d Page 1 DOI: 10.1175/2012EI000434.1  69313 words, 11 Figures, 0 Animations, 3 Tables. http://EarthInteractions.org indices, which are calculated on different time scales than the Palmer indices to capture the drought impacts on the aforementioned hydrological, agricultural, and ecological variables. They detected small differences in the comparative performance of the SPI and the SPEI indices, but the SPEI was the drought index that best captured the responses of the assessed variables to drought in summer, the season in which more drought-related impacts are recorded and in which drought monitoring is critical. Hence, the SPEI shows improved capability to identify drought impacts as compared with the SPI. In conclusion, it seems reasonable to recommend the use of the SPEI if the responses of the variables of interest to drought are not known a priori.
A C-LSTM Neural Network for Text Classification

Neural network models have been demonstrated to be capable of achieving remarkable performance in sentence and document modeling. Convolutional neural network (CNN) and recurrent neural network (RNN) are two mainstream architectures for such modeling tasks, which adopt totally different ways of understanding natural languages. In this work, we combine the strengths of both architectures and propose a novel and unified model called C-LSTM for sentence representation and text classification. C-LSTM utilizes CNN to extract a sequence of higher-level phrase representations, and are fed into a long short-term memory recurrent neural network (LSTM) to obtain the sentence representation. C-LSTM is able to capture both local features of phrases as well as global and temporal sentence semantics. We evaluate the proposed architecture on sentiment classification and question classification tasks. The experimental results show that the C-LSTM outperforms both CNN and LSTM and can achieve excellent performance on these tasks.
Generic Text Summarization Using Relevance Measure and Latent Semantic Analysis

In this paper, we propose two generic text summarization methods that create text summaries by ranking and extracting sentences from the original documents. The first method uses standard IR methods to rank sentence relevances, while the second method uses the latent semantic analysis technique to identify semantically important sentences, for summary creations. Both methods strive to select sentences that are highly ranked and different from each other. This is an attempt to create a summary with a wider coverage of the document's main content and less redundancy. Performance evaluations on the two summarization methods are conducted by comparing their summarization outputs with the manual summaries generated by three independent human evaluators. The evaluations also study the influence of different VSM weighting schemes on the text summarization performances. Finally, the causes of the large disparities in the evaluators' manual summarization results are investigated, and discussions on human text summarization patterns are presented.
Matching Networks for One Shot Learning

Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 87.6% to 93.2% and from 88.0% to 93.8% on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.
Shooting Stars in the Sky: An Online Algorithm for Skyline Queries

Skyline queries ask for a set of interesting points from a potentially large set of data points. If we are traveling, for instance, a restaurant might be interesting if there is no other restaurant which is nearer, cheaper, and has better food. Skyline queries retrieve all such interesting restaurants so that the user can choose the most promising one. In this paper, we present a new online algorithm that computes the Skyline. Unlike most existing algorithms that compute the Skyline in a batch, this algorithm returns the first results immediately, produces more and more results continuously, and allows the user to give preferences during the running time of the algorithm so that the user can control what kind of results are produced next (e.g., rather cheap or rather near restaurants).
On the Danger of Private Blockchains ( When PoW can be Harmful to Applications with Termination Requirements )

Consensus is a fundamental problem of distributed computing. While this problem has been known to be unsolvable since 1985, existing protocols were designed these past three decades to solve consensus under various assumptions. Today, with the recent advent of blockchains, new consensus implementations were proposed to make replicas reach an agreement on the order of transactions updating what is often referred to as a distributed ledger. Very little work has however been devoted to explore its theoretical ramifications. As a result, it is often unclear whether the same systems could be adapted to work in different environments. In this position paper, we explore the use of the Ethereum blockchain protocol in the context of a private chain where the set of participants is controlled. We argue that foundations are needed in order to precisely capture the guarantees of the consensus protocols of novel blockchain systems before one can deploy them safely. To this end, we define the termination of consensus to characterize when blockchain transactions commit and describe the existence of the Blockchain Anomaly in existing proof-of-work private chains.
Low Power Wide Area Networks: A Survey

Low Power Wide Area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of the Internet of Things (IoT), LPWA technologies complement and sometimes supersede the conventional cellular and short range wireless technologies in performance for various emerging smart city and machine-to-machine (M2M) applications. This survey paper presents the design goals and the techniques, which different LPWA technologies exploit to offer wide-area coverage to lowpower devices at the expense of low data rates. We survey several emerging LPWA technologies and the standardization activities carried out by different standards development organizations (e.g., IEEE, IETF, 3GPP, ETSI) as well as the industrial consortia built around individual LPWA technologies (e.g., LORaTM Alliance, WEIGHTLESS-SIG, and DASH7 Alliance). We further note that LPWA technologies adopt similar approaches, thus sharing the same limitations and challenges. This paper expands on these research challenges and identifies potential directions to address them. While the proprietary LPWA technologies are already hitting the market with large nationwide roll-outs, this paper encourages an active engagement of the research community in solving problems that will shape the connectivity of tens of billions of devices in the next decade.
Bioactive Compounds from Macroalgae in the New Millennium: Implications for Neurodegenerative Diseases

Marine environment has proven to be a rich source of structurally diverse and complex compounds exhibiting numerous interesting biological effects. Macroalgae are currently being explored as novel and sustainable sources of bioactive compounds for both pharmaceutical and nutraceutical applications. Given the increasing prevalence of different forms of dementia, researchers have been focusing their attention on the discovery and development of new compounds from macroalgae for potential application in neuroprotection. Neuroprotection involves multiple and complex mechanisms, which are deeply related. Therefore, compounds exerting neuroprotective effects through different pathways could present viable approaches in the management of neurodegenerative diseases, such as Alzheimer's and Parkinson's. In fact, several studies had already provided promising insights into the neuroprotective effects of a series of compounds isolated from different macroalgae species. This review will focus on compounds from macroalgae that exhibit neuroprotective effects and their potential application to treat and/or prevent neurodegenerative diseases.
Structural Attention Neural Networks for improved sentiment analysis

We introduce a tree-structured attention neural network for sentences and small phrases and apply it to the problem of sentiment classification. Our model expands the current recursive models by incorporating structural information around a node of a syntactic tree using both bottomup and top-down information propagation. Also, the model utilizes structural attention to identify the most salient representations during the construction of the syntactic tree. To our knowledge, the proposed models achieve state of the art performance on the Stanford Sentiment Treebank dataset.
Freenet: A Distributed Anonymous Information Storage and Retrieval System

We describe Freenet, an adaptive peer-to-peer network application that permits the publication, replication, and retrieval of data while protecting the anonymity of both authors and readers. Freenet operates as a network of identical nodes that collectively pool their storage space to store data files and cooperate to route requests to the most likely physical location of data. No broadcast search or centralized location index is employed. Files are referred to in a location-independent manner, and are dynamically replicated in locations near requestors and deleted from locations where there is no interest. It is infeasible to discover the true origin or destination of a file passing through the network, and difficult for a node operator to determine or be held responsible for the actual physical contents of her own node.
Alone in the crowd: the structure and spread of loneliness in a large social network.

The discrepancy between an individual's loneliness and the number of connections in a social network is well documented, yet little is known about the placement of loneliness within, or the spread of loneliness through, social networks. The authors use network linkage data from the population-based Framingham Heart Study to trace the topography of loneliness in people's social networks and the path through which loneliness spreads through these networks. Results indicated that loneliness occurs in clusters, extends up to 3 degrees of separation, is disproportionately represented at the periphery of social networks, and spreads through a contagious process. The spread of loneliness was found to be stronger than the spread of perceived social connections, stronger for friends than family members, and stronger for women than for men. The results advance understanding of the broad social forces that drive loneliness and suggest that efforts to reduce loneliness in society may benefit by aggressively targeting the people in the periphery to help repair their social networks and to create a protective barrier against loneliness that can keep the whole network from unraveling.
Applications of Artificial Intelligence to Network Security

Attacks to networks are becoming more complex and sophisticated every day. Beyond the so-called script-kiddies and hacking newbies, there is a myriad of professional attackers seeking to make serious profits infiltrating in corporate networks. Either hostile governments, big corporations or mafias are constantly increasing their resources and skills in cybercrime in order to spy, steal or cause damage more effectively. With the ability and resources of hackers growing, the traditional approaches to Network Security seem to start hitting their limits and it’s being recognized the need for a smarter approach to threat detections. This paper provides an introduction on the need for evolution of Cyber Security techniques and how Artificial Intelligence (AI) could be of application to help solving some of the problems. It provides also, a high-level overview of some state of the art AI Network Security techniques, to finish analysing what is the foreseeable future of the application of AI to Network Security. Applications of Artificial Intelligence (AI) to Network Security 3
Efficient physical defect model applied to PBTI in high-κ stacks

Instabilities in MOS-based devices with various substrates ranging from Si, SiGe, IIIV to 2D channel materials, can be explained by defect levels in the dielectrics and non-radiative multi-phonon (NMP) barriers. However, recent results obtained on single defects have demonstrated that they can show a highly complex behaviour since they can transform between various states. As a consequence, detailed physical models are complicated and computationally expensive. As will be shown here, as long as only lifetime predictions for an ensemble of defects is needed, considerable simplifications are possible. We present and validate an oxide defect model that captures the essence of full physical models while reducing the complexity substantially. We apply this model to investigate the improvement in positive bias temperature instabilities due to a reliability anneal. Furthermore, we corroborate the simulated defect bands with prior defect-centric studies and perform lifetime projections.
Extreme Events : Mechanisms and Prediction

Extreme events, such as rogue waves, earthquakes and stock market crashes, occur spontaneously in many dynamical systems. Because of their usually adverse consequences, quantification, prediction and mitigation of extreme events are highly desirable. Here, we review several aspects of extreme events in phenomena described by high-dimensional, chaotic dynamical systems. We specially focus on two pressing aspects of the problem: (i) Mechanisms underlying the formation of extreme events and (ii) Real-time prediction of extreme events. For each aspect, we explore methods relying on models, data or both. We discuss the strengths and limitations of each approach as well as possible future research directions.
An effective, low-cost measure of semantic relatedness obtained from Wikipedia links

relatedness between terms using the links found within their corresponding Wikipedia articles. Unlike other techniques based on Wikipedia, WLM is able to provide accurate measures efficiently, using only the links between articles rather than their textual content. Before describing the details, we first outline the other systems to which it can be compared. This is followed by a description of the algorithm, and its evaluation against manually-defined ground truth. The paper concludes with a discussion of the strengths and weaknesses of the new approach.
Confidence interval or p-value?: part 4 of a series on evaluation of scientific publications.

An understanding of p-values and confidence intervals is necessary for the evaluation of scientific articles. This article will inform the reader of the meaning and interpretation of these two statistical concepts.



The uses of these two statistical concepts and the differences between them are discussed on the basis of a selective literature search concerning the methods employed in scientific articles.



P-values in scientific studies are used to determine whether a null hypothesis formulated before the performance of the study is to be accepted or rejected. In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. This enables conclusions to be drawn about the statistical plausibility and clinical relevance of the study findings. It is often useful for both statistical measures to be reported in scientific articles, because they provide complementary types of information.
Towards Serverless Execution of Scientific Workflows - HyperFlow Case Study

Scientific workflows consisting of a high number of dependent tasks represent an important class of complex scientific applications. Recently, a new type of serverless infrastructures has emerged, represented by such services as Google Cloud Functions or AWS Lambda. In this paper we take a look at such serverless infrastructures, which are designed mainly for processing background tasks of Web applications. We evaluate their applicability to more computeand dataintensive scientific workflows and discuss possible ways to repurpose serverless architectures for execution of scientific workflows. A prototype workflow executor function has been developed using Google Cloud Functions and coupled with the HyperFlow workflow engine. The function can run workflow tasks on the Google infrastructure, and features such capabilities as data staging to/from Google Cloud Storage and execution of custom application binaries. We have successfully deployed and executed the Montage astronomic workflow, often used as a benchmark, and we report on initial results of performance evaluation. Our findings indicate that the simple mode of operation makes this approach easy to use, although there are costs involved in preparing portable application binaries for execution in a remote environment. While our evaluation uses a preproduction (alpha) version of the Google Cloud Functions platform, we find the presented approach highly promising. We also discuss possible future steps related to execution of scientific workflows in serverless infrastructures, and the implications with regard to resource management for scientific applications in general.
Context-Aware Computing Applications

This paper describes systems that examine and react to an individual's changing context. Such systems can promote and mediate people's interactions with devices, computers, and other people, and they can help navigate unfamiliar places. We believe that a limited amount of information covering a person's proximate environment is most important for this form of computing since the interesting part of the world around us is what we can see, hear, and touch. In this paper we define context-aware computing, and describe four catagories of context-aware applications: proximate selection, automatic contextual reconfiguration, contextual information and commands, and contex-triggered actions. Instances of these application types have been prototyped on the PARCTAB, a wireless, palm-sized computer.
The Fast Bilateral Solver

We present the bilateral solver, a novel algorithm for edgeaware smoothing that combines the flexibility and speed of simple filtering approaches with the accuracy of domain-specific optimization algorithms. Our technique is capable of matching or improving upon state-of-the-art results on several different computer vision tasks (stereo, depth superresolution, colorization, and semantic segmentation) while being 10-1000× faster than baseline techniques with comparable accuracy, and producing lower-error output than techniques with comparable runtimes. The bilateral solver is fast, robust, straightforward to generalize to new domains, and simple to integrate into deep learning pipelines.
Fast Algorithms for Convolutional Neural Networks

Deep convolutional neural networks take GPU-days of computation to train on large data sets. Pedestrian detection for self driving cars requires very low latency. Image recognition for mobile phones is constrained by limited processing resources. The success of convolutional neural networks in these situations is limited by how fast we can compute them. Conventional FFT based convolution is fast for large filters, but state of the art convolutional neural networks use small, 3 3 filters. We introduce a new class of fast algorithms for convolutional neural networks using Winograd's minimal filtering algorithms. The algorithms compute minimal complexity convolution over small tiles, which makes them fast with small filters and small batch sizes. We benchmark a GPU implementation of our algorithm with the VGG network and show state of the art throughput at batch sizes from 1 to 64.