nils-herrmann/clean_scirepeval huggingface dataset:
Towards Ontology-based E-mail Fraud Detection

The European FF POIROT project (IST-2001-38248) aims at developing applications for tackling financial fraud, using formal ontological repositories as well as multilingual terminological resources. In this article, we want to focus on the development cycle towards an application recognizing several types of e-mail fraud, such as phishing, Nigerian advance fee fraud and lottery scam. The development cycle covers four tracks of development - language engineering, terminology engineering, knowledge engineering and system engineering. These development tracks are preceded by a problem determination phase and followed by a deployment phase. Each development track is supported by a methodology. All methodologies and phases in the development cycle will be discussed in detail
Improving Semantic Parsing via Answer Type Inference

In this work, we show the possibility of inferring the answer type before solving a factoid question and leveraging the type information to improve semantic parsing. By replacing the topic entity in a question with its type, we are able to generate an abstract form of the question, whose answer corresponds to the answer type of the original question. A bidirectional LSTM model is built to train over the abstract form of questions and infer their answer types. It is also observed that if we convert a question into a statement form, our LSTM model achieves better accuracy. Using the predicted type information to rerank the logical forms returned by AgendaIL, one of the leading semantic parsers, we are able to improve the F1-score from 49.7% to 52.6% on the WEBQUESTIONS data.
Fast collapsed gibbs sampling for latent dirichlet allocation

In this paper we introduce a novel collapsed Gibbs sampling method for the widely used latent Dirichlet allocation (LDA) model. Our new method results in significant speedups on real world text corpora. Conventional Gibbs sampling schemes for LDA require O(K) operations per sample where K is the number of topics in the model. Our proposed method draws equivalent samples but requires on average significantly less then K operations per sample. On real-word corpora FastLDA can be as much as 8 times faster than the standard collapsed Gibbs sampler for LDA. No approximations are necessary, and we show that our fast sampling scheme produces exactly the same results as the standard (but slower) sampling scheme. Experiments on four real world data sets demonstrate speedups for a wide range of collection sizes. For the PubMed collection of over 8 million documents with a required computation time of 6 CPU months for LDA, our speedup of 5.7 can save 5 CPU months of computation.
A Genre-Aware Attention Model to Improve the Likability Prediction of Books

Likability prediction of books has many uses. Readers, writers, as well as the publishing industry, can all benefit from automatic book likability prediction systems. In order to make reliable decisions, these systems need to assimilate information from different aspects of a book in a sensible way. We propose a novel multimodal neural architecture that incorporates genre supervision to assign weights to individual feature types. Our proposed method is capable of dynamically tailoring weights given to feature types based on the characteristics of each book. Our architecture achieves competitive results and even outperforms state-of-the-art for this task.
Assessment of Eco-friendly Gases for Electrical Insulation to Replace the Most Potent Industrial Greenhouse Gas SF6.

Gases for electrical insulation are essential for the operation of electric power equipment. This Review gives a brief history of gaseous insulation that involved the emergence of the most potent industrial greenhouse gas known today, namely sulfur hexafluoride. SF6 paved the way to space-saving equipment for the transmission and distribution of electrical energy. Its ever-rising usage in the electrical grid also played a decisive role in the continuous increase of atmospheric SF6 abundance over the last decades. This Review broadly covers the environmental concerns related to SF6 emissions and assesses the latest generation of eco-friendly replacement gases. They offer great potential for reducing greenhouse gas emissions from electrical equipment but at the same time involve technical trade-offs. The rumors of one or the other being superior seem premature, in particular because of the lack of dielectric, environmental, and chemical information for these relatively novel compounds and their dissociation products during operation.
Reservoir computing approaches to recurrent neural network training

Echo State Networks and Liquid State Machines introduced a new paradigm in artificial recurrent neural network (RNN) training, where an RNN (the reservoir) is generated randomly and only a readout is trained. The paradigm, becoming known as reservoir computing, greatly facilitated the practical application of RNNs and outperformed classical fully trained RNNs in many tasks. It has lately become a vivid research field with numerous extensions of the basic idea, including reservoir adaptation, thus broadening the initial paradigm to using different methods for training the reservoir and the readout. This review systematically surveys both: current ways of generating/adapting the reservoirs and training different types of readouts. It offers a natural conceptual classification of the techniques, which transcends boundaries of the current “brand-names” of reservoir methods, and thus aims to help unifying the field and providing the reader with a detailed “map” of
Comparing Predictive Accuracy

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Brain evolution by brain pathway duplication

Understanding the mechanisms of evolution of brain pathways for complex behaviours is still in its infancy. Making further advances requires a deeper understanding of brain homologies, novelties and analogies. It also requires an understanding of how adaptive genetic modifications lead to restructuring of the brain. Recent advances in genomic and molecular biology techniques applied to brain research have provided exciting insights into how complex behaviours are shaped by selection of novel brain pathways and functions of the nervous system. Here, we review and further develop some insights to a new hypothesis on one mechanism that may contribute to nervous system evolution, in particular by brain pathway duplication. Like gene duplication, we propose that whole brain pathways can duplicate and the duplicated pathway diverge to take on new functions. We suggest that one mechanism of brain pathway duplication could be through gene duplication, although other mechanisms are possible. We focus on brain pathways for vocal learning and spoken language in song-learning birds and humans as example systems. This view presents a new framework for future research in our understanding of brain evolution and novel behavioural traits.
A hierarchical approach to computer Hex

Hex is a beautiful game with simple rules and a strategic complexity comparable to that of Chess and Go. The massive game-tree search techniques developed mostly for Chess and successfully used for Checkers and a number of other games, become less useful for games with large branching factors like Hex and Go. In this paper, we describe deduction rules, which are used to calculate values of complex Hex positions recursively starting from the simplest ones. We explain how this approach is implemented in HEXY—the strongest Hex-playing computer program, the Gold medallist of the 5th Computer Olympiad in London, August 2000.
Robust Principal Component Analysis with Complex Noise

The research on robust principal component analysis (RPCA) has been attracting much attention recently. The original RPCA model assumes sparse noise, and use the L1-norm to characterize the error term. In practice, however, the noise is much more complex and it is not appropriate to simply use a certainLp-norm for noise modeling. We propose a generative RPCA model under the Bayesian framework by modeling data noise as a mixture of Gaussians (MoG). The MoG is a universal approximator to continuous distributions and thus our model is able to fit a wide range of noises such as Laplacian, Gaussian, sparse noises and any combinations of them. A variational Bayes algorithm is presented to infer the posterior of the proposed model. All involved parameters can be recursively updated in closed form. The advantage of our method is demonstrated by extensive experiments on synthetic data, face modeling and background subtraction.
Deep Learning for Ontology Reasoning

In this work, we present a novel approach to ontology reasoning that is based on deep learning rather than logic-based formal reasoning. To this end, we introduce a new model for statistical relational learning that is built upon deep recursive neural networks, and give experimental evidence that it can easily compete with, or even outperform, existing logic-based reasoners on the task of ontology reasoning. More precisely, we compared our implemented system with one of the best logic-based ontology reasoners at present, RDFox, on a number of large standard benchmark datasets, and found that our system attained high reasoning quality, while being up to two orders of magnitude faster.
Mobile Edge Computing: A Survey on Architecture and Computation Offloading

Technological evolution of mobile user equipment (UEs), such as smartphones or laptops, goes hand-in-hand with evolution of new mobile applications. However, running computationally demanding applications at the UEs is constrained by limited battery capacity and energy consumption of the UEs. A suitable solution extending the battery life-time of the UEs is to offload the applications demanding huge processing to a conventional centralized cloud. Nevertheless, this option introduces significant execution delay consisting of delivery of the offloaded applications to the cloud and back plus time of the computation at the cloud. Such a delay is inconvenient and makes the offloading unsuitable for real-time applications. To cope with the delay problem, a new emerging concept, known as mobile edge computing (MEC), has been introduced. The MEC brings computation and storage resources to the edge of mobile network enabling it to run the highly demanding applications at the UE while meeting strict delay requirements. The MEC computing resources can be exploited also by operators and third parties for specific purposes. In this paper, we first describe major use cases and reference scenarios where the MEC is applicable. After that we survey existing concepts integrating MEC functionalities to the mobile networks and discuss current advancement in standardization of the MEC. The core of this survey is, then, focused on user-oriented use case in the MEC, i.e., computation offloading. In this regard, we divide the research on computation offloading to three key areas: 1) decision on computation offloading; 2) allocation of computing resource within the MEC; and 3) mobility management. Finally, we highlight lessons learned in area of the MEC and we discuss open research challenges yet to be addressed in order to fully enjoy potentials offered by the MEC.
Representation Learning for Answer Selection with LSTM-Based Importance Weighting

We present an approach to non-factoid answer selection with a separate component based on BiLSTM to determine the importance of segments in the input. In contrast to other recently proposed attention-based models within the same area, we determine the importance while assuming the independence of questions and candidate answers. Experimental results show the effectiveness of our approach, which outperforms several state-of-the-art attention-based models on the recent non-factoid answer selection datasets InsuranceQA v1 and v2. We show that it is possible to perform effective importance weighting for answer selection without relying on the relatedness of questions and answers. The source code of our experiments is publicly
BigDataBench: A big data benchmark suite from internet services

As architecture, systems, and data management communities pay greater attention to innovative big data systems and architecture, the pressure of benchmarking and evaluating these systems rises. However, the complexity, diversity, frequently changed workloads, and rapid evolution of big data systems raise great challenges in big data benchmarking. Considering the broad use of big data systems, for the sake of fairness, big data benchmarks must include diversity of data and workloads, which is the prerequisite for evaluating big data systems and architecture. Most of the state-of-the-art big data benchmarking efforts target evaluating specific types of applications or system software stacks, and hence they are not qualified for serving the purposes mentioned above. This paper presents our joint research efforts on this issue with several industrial partners. Our big data benchmark suite-BigDataBench not only covers broad application scenarios, but also includes diverse and representative data sets. Currently, we choose 19 big data benchmarks from dimensions of application scenarios, operations/ algorithms, data types, data sources, software stacks, and application types, and they are comprehensive for fairly measuring and evaluating big data systems and architecture. BigDataBench is publicly available from the project home page http://prof.ict.ac.cn/BigDataBench. Also, we comprehensively characterize 19 big data workloads included in BigDataBench with varying data inputs. On a typical state-of-practice processor, Intel Xeon E5645, we have the following observations: First, in comparison with the traditional benchmarks: including PARSEC, HPCC, and SPECCPU, big data applications have very low operation intensity, which measures the ratio of the total number of instructions divided by the total byte number of memory accesses; Second, the volume of data input has non-negligible impact on micro-architecture characteristics, which may impose challenges for simulation-based big data architecture research; Last but not least, corroborating the observations in CloudSuite and DCBench (which use smaller data inputs), we find that the numbers of L1 instruction cache (L1I) misses per 1000 instructions (in short, MPKI) of the big data applications are higher than in the traditional benchmarks; also, we find that L3 caches are effective for the big data applications, corroborating the observation in DCBench.
A Context Based Text Summarization System

Text summarization is the process of creating a shorter version of one or more text documents. Automatic text summarization has become an important way of finding relevant information in large text libraries or in the Internet. Extractive text summarization techniques select entire sentences from documents according to some criteria to form a summary. Sentence scoring is the technique most used for extractive text summarization, today. Depending on the context, however, some techniques may yield better results than some others. This paper advocates the thesis that the quality of the summary obtained with combinations of sentence scoring methods depend on text subject. Such hypothesis is evaluated using three different contexts: news, blogs and articles. The results obtained show the validity of the hypothesis formulated and point at which techniques are more effective in each of those contexts studied.
A Unified Syntax-aware Framework for Semantic Role Labeling

Brussels, Belgium, October 31 November 4, 2018.  Si Department of Computer Science and Engineering, Shanghai Jiao Tong University Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, Shanghai, China School of Cyber Security, Shanghai Jiao Tong University, China Alibaba Group, Hangzhou, China {charlee,heshexia,caijiaxun,zhangzs}@sjtu.edu.cn, zhaohai@cs.sjtu.edu.cn, lgshen@sjtu.edu.cn,
Efficiency Analysis of Provisioning Microservices

Microservice architecture has started a new trend for application development/deployment in cloud due to its flexibility, scalability, manageability and performance. Various microservice platforms have emerged to facilitate the whole software engineering cycle for cloud applications from design, development, test, deployment to maintenance. In this paper, we propose a performance analytical model and validate it by experiments to study the provisioning performance of microservice platforms. We design and develop a microservice platform on Amazon EC2 cloud using Docker technology family to identify important elements contributing to the performance of microservice platforms. We leverage the results and insights from experiments to build a tractable analytical performance model that can be used to perform what-if analysis and capacity planning in a systematic manner for large scale microservices with minimum amount of time and cost.
Structured Sequence Modeling with Graph Convolutional Recurrent Networks

This paper introduces Graph Convolutional Recurrent Network (GCRN), a deep learning model able to predict structured sequences of data. Precisely, GCRN is a generalization of classical recurrent neural networks (RNN) to data structured by an arbitrary graph. Such structured sequences can represent series of frames in videos, spatio-temporal measurements on a network of sensors, or random walks on a vocabulary graph for natural language modeling. The proposed model combines convolutional neural networks (CNN) on graphs to identify spatial structures and RNN to find dynamic patterns. We study two possible architectures of GCRN, and apply the models to two practical problems: predicting moving MNIST data, and modeling natural language with the Penn Treebank dataset. Experiments show that exploiting simultaneously graph spatial and dynamic information about data can improve both precision and learning speed.
Interactive coin offerings

Ethereum has emerged as a dynamic platform for exchanging cryptocurrency tokens. While token crowdsales cannot simultaneously guarantee buyers both certainty of valuation and certainty of participation, we show that if each token buyer specifies a desired purchase quantity at each valuation then everyone can successfully participate. Our implementation introduces smart contract techniques which recruit outside participants in order to circumvent computational complexity barriers. 1 A crowdsale dilemma This year has witnessed the remarkable rise of token crowdsales. Token incentives enable new community structures by employing novel combinations of currency rewards, software use rights, protocol governance, and traditional equity. Excluding Bitcoin, the total market cap of the token market surged over 60 billion USD in June 20171. Most tokens originate on the Ethereum network, and, at times, the network has struggled to keep up with purchase demands. On several occasions, single crowdsales have consumed the network’s entire bandwidth for consecutive hours. Token distributions can take many forms. Bitcoin, for example, continues to distribute tokens through a competitive, computational process known as mining. In this exposition, we shall concern ourselves exclusively https://coinmarketcap.com/charts/
A bayesian market maker

Ensuring sufficient liquidity is one of the key challenges for designers of prediction markets. Variants of the logarithmic market scoring rule (LMSR) have emerged as the standard. LMSR market makers are loss-making in general and need to be subsidized. Proposed variants, including liquidity sensitive market makers, suffer from an inability to react rapidly to jumps in population beliefs. In this paper we propose a Bayesian Market Maker for binary outcome (or continuous 0-1) markets that learns from the informational content of trades. By sacrificing the guarantee of bounded loss, the Bayesian Market Maker can simultaneously offer: (1) significantly lower expected loss at the same level of liquidity, and, (2) rapid convergence when there is a jump in the underlying true value of the security. We present extensive evaluations of the algorithm in experiments with intelligent trading agents and in human subject experiments. Our investigation also elucidates some general properties of market makers in prediction markets. In particular, there is an inherent tradeoff between adaptability to market shocks and convergence during market equilibrium.
Aiming at Theory Building : Adopting and Adapting the Case Study Design

Although the advantages of case study design are widely recognised, its original positivist underlying assumptions may mislead interpretive researchers aiming at theory building. The paper discusses the limitations of the case study design for theory building and explains how grounded theory systemic process adds to the case study design. The author reflects upon his experience in conducting research on the articulation of both traditional social networks and new virtual networks in six rural communities in Peru, using both case study design and grounded theory in a combined fashion in order to discover an emergent theory.
Design and Implementation of a New Multilevel Inverter Topology

Multilevel inverters have been widely accepted for high-power high-voltage applications. Their performance is highly superior to that of conventional two-level inverters due to reduced harmonic distortion, lower electromagnetic interference, and higher dc link voltages. However, it has some disadvantages such as increased number of components, complex pulsewidth modulation control method, and voltage-balancing problem. In this paper, a new topology with a reversing-voltage component is proposed to improve the multilevel performance by compensating the disadvantages mentioned. This topology requires fewer components compared to existing inverters (particularly in higher levels) and requires fewer carrier signals and gate drives. Therefore, the overall cost and complexity are greatly reduced particularly for higher output voltage levels. Finally, a prototype of the seven-level proposed topology is built and tested to show the performance of the inverter by experimental results.
Defense-GAN: Protecting Classifiers Against Adversarial Attacks Using Generative Models

In recent years, deep neural network approaches have been widely adopted for machine learning tasks, including classification. However, they were shown to be vulnerable to adversarial perturbations: carefully crafted small perturbations can cause misclassification of legitimate images. We propose Defense-GAN, a new framework leveraging the expressive capability of generative models to defend deep neural networks against such attacks. Defense-GAN is trained to model the distribution of unperturbed images and, at inference time, finds a close output to a given image. This output will not contain the adversarial changes and is fed to the classifier. Our proposed method can be used with any classification model and does not modify the classifier structure or training procedure. It can also be used as a defense against any attack as it does not assume knowledge of the process for generating the adversarial examples. We empirically show that Defense-GAN is consistently effective against different attack methods and improves on existing defense strategies.