nils-herrmann/clean_scirepeval huggingface dataset:
PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs

Large-scale graph-structured computation is central to tasks ranging from targeted advertising to natural language processing and has led to the development of several graph-parallel abstractions including Pregel and GraphLab. However, the natural graphs commonly found in the real-world have highly skewed power-law degree distributions, which challenge the assumptions made by these abstractions, limiting performance and scalability. In this paper, we characterize the challenges of computation on natural graphs in the context of existing graphparallel abstractions. We then introduce the PowerGraph abstraction which exploits the internal structure of graph programs to address these challenges. Leveraging the PowerGraph abstraction we introduce a new approach to distributed graph placement and representation that exploits the structure of power-law graphs. We provide a detailed analysis and experimental evaluation comparing PowerGraph to two popular graph-parallel systems. Finally, we describe three different implementation strategies for PowerGraph and discuss their relative merits with empirical evaluations on large-scale real-world problems demonstrating order of magnitude gains.
One Parser, Many Languages

We train a language-universal dependency parser on a multilingual collection of treebanks. The parsing model uses multilingual word embeddings alongside learned and specified typological information, enabling generalization based on linguistic universals and based on typological similarities. We evaluate our parser’s performance on languages in the training set as well as on the unsupervised scenario where the target language has no trees in the training data, and find that multilingual training outperforms standard supervised training on a single language, and that generalization to unseen languages is competitive with existing model-transfer approaches.
Information Retrieval in Folksonomies: Search and Ranking

Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. At the moment, however, the information retrieval support is limited. We present a formal model and a new search algorithm for folksonomies, calledFolkRank, that exploits the structure of the folksonomy. The proposed algorithm is also applied to find communities within the folksonomy and is used to structure search results. All findings are demonstrated on a large scale dataset.
The International Journal on Advances in Systems and Measurements is published by IARIA

ing is permitted with credit to the source. Libraries are permitted to photocopy or print, providing the reference is mentioned and that the resulting material is made available at no cost. Reference should mention: International Journal on Advances in Systems and Measurements, issn 1942-261x vol. 9, no. 3 & 4, year 2016, http://www.iariajournals.org/systems_and_measurements/ The copyright for each included paper belongs to the authors. Republishing of same material, by authors or persons or organizations, is not allowed. Reprint rights can be granted by IARIA or by the authors, and must include proper reference. Reference to an article in the journal is as follows: <Author list>, “<Article title>” International Journal on Advances in Systems and Measurements, issn 1942-261x vol. 9, no. 3 & 4, year 2016, http://www.iariajournals.org/systems_and_measurements/ IARIA journals are made available for free, proving the appropriate references are made when their
Data mining application to detect financial fraud in Indonesia's public companies

Association of Certified Fraud Examiners explains that there are 3 types of occupational fraud: financial statement fraud, asset misappropriation and corruption. Among these three, financial statement fraud caused the biggest losses, which amounted to $ 1,000,000 in 2014. Financial statement has important role as an indicator of the success of a company, also for depicting the overall condition of the company, deciding company's stock price, and determining whether the company could be granted a loan or not. Given its important role, many cases of fraud occur. Audit activities are conducted to minimize losses, but the number of available auditors is limited, and the time required for traditional audit is quite long. Therefore, an effective model of financial fraud detection is needed to help auditors in analyzing financial statements. Data mining algorithms, support vector machine (SVM) and artificial neural network (ANN), were applied in this study. The results of this study give insight to the auditor that significant indicators in detecting financial fraud are profitability and efficiency. Feature selection improves SVM algorithm accuracy to 88.37%. ANN produces the highest accuracy, 90.97%, for data without feature selection.
Component-Based Software Engineering

Component based software development approach is based on the idea to develop software systems by selecting appropriate off-the shelf components and then to assemble them with a well-defined software architecture. Software community faces a major challenge that is raised by fast growing demand for rapid and cost-effective development and maintenance of large scale and complex software systems. To overcome the challenge, the new trend is to adopt component based software engineering (CBSE).The key difference between CBSE and traditional software engineering is that CBSE views a software system as a set of off-the-shelf components integrated within appropriate software architecture. CBSE promotes large-scale reuse, as it focuses on building software on building software systems by assembling
Structured Attention Networks

Attention networks have proven to be an effective approach for embedding categorical inference within a deep neural network. However, for many tasks we may want to model richer structural dependencies without abandoning end-to-end training. In this work, we experiment with incorporating richer structural distributions, encoded using graphical models, within deep networks. We show that these structured attention networks are simple extensions of the basic attention procedure, and that they allow for extending attention beyond the standard softselection approach, such as attending to partial segmentations or to subtrees. We experiment with two different classes of structured attention networks: a linearchain conditional random field and a graph-based parsing model, and describe how these models can be practically implemented as neural network layers. Experiments show that this approach is effective for incorporating structural biases, and structured attention networks outperform baseline attention models on a variety of synthetic and real tasks: tree transduction, neural machine translation, question answering, and natural language inference. We further find that models trained in this way learn interesting unsupervised hidden representations that generalize simple attention.
Dependency Link Embeddings: Continuous Representations of Syntactic Substructures

We present a simple method to learn continuous representations of dependency substructures (links), with the motivation of directly working with higher-order, structured embeddings and their hidden relationships, and also to avoid the millions of sparse, template-based word-cluster features in dependency parsing. These link embeddings allow a significantly smaller and simpler set of unary features for dependency parsing, while maintaining improvements similar to state-of-the-art, n-ary word-cluster features, and also stacking over them. Moreover, these link vectors (made publicly available) are directly portable as offthe-shelf, dense, syntactic features in various NLP tasks. As one example, we incorporate them into constituent parse reranking, where their small feature set again matches the performance of standard non-local, manuallydefined features, and also stacks over them.
Powering the Internet of Things

Various industry forecasts project that, by 2020, there will be around 50 billion devices connected to the Internet of Things (IoT), helping to engineer new solutions to societal-scale problems such as healthcare, energy conservation, transportation, etc. Most of these devices will be wireless due to the expense, inconvenience, or in some cases, the sheer infeasibility of wiring them. Further, many of them will have stringent size constraints. With no cord for power and limited space for a battery, powering these devices (to achieve several months to possibly years of unattended operation) becomes a daunting challenge. This paper highlights some promising directions for addressing this challenge, focusing on three main building blocks: (a) the design of ultra-low power hardware platforms that integrate computing, sensing, storage, and wireless connectivity in a tiny form factor, (b) the development of intelligent system-level power management techniques, and (c) the use of environmental energy harvesting to make IoT devices self-powered, thus decreasing -- in some cases, even eliminating -- their dependence on batteries. We discuss these building blocks in detail and illustrate case-studies of systems that use them judiciously, including the QUBE wireless embedded platform, which exploits the characteristics of emerging non-volatile memory technologies to seamlessly and efficiently enable long-running computations in systems that experience frequent power loss (i.e., intermittently powered systems).
Tracking the Evolution of Communities in Dynamic Social Networks

Real-world social networks from a variety of domains can naturally be modelled as dynamic graphs. However, approaches to detecting communities have largely focused on identifying communities in static graphs. Recently, researchers have begun to consider the problem of tracking the evolution of groups of users in dynamic scenarios. Here we describe a model for tracking the progress of communities over time in a dynamic network, where each community is characterised by a series of significant evolutionary events. This model is used to motivate a community-matching strategy for efficiently identifying and tracking dynamic communities. Evaluations on synthetic graphs containing embedded events demonstrate that this strategy can successfully track communities over time in volatile networks. In addition, we describe experiments exploring the dynamic communities detected in a real mobile operator network containing millions of users.
Network hubs in the human brain

Virtually all domains of cognitive function require the integration of distributed neural activity. Network analysis of human brain connectivity has consistently identified sets of regions that are critically important for enabling efficient neuronal signaling and communication. The central embedding of these candidate 'brain hubs' in anatomical networks supports their diverse functional roles across a broad range of cognitive tasks and widespread dynamic coupling within and across functional networks. The high level of centrality of brain hubs also renders them points of vulnerability that are susceptible to disconnection and dysfunction in brain disorders. Combining data from numerous empirical and computational studies, network approaches strongly suggest that brain hubs play important roles in information integration underpinning numerous aspects of complex cognitive function.
Unsupervised POS Induction with Word Embeddings

Unsupervised word embeddings have been shown to be valuable as features in supervised learning problems; however, their role in unsupervised problems has been less thoroughly explored. In this paper, we show that embeddings can likewise add value to the problem of unsupervised POS induction. In two representative models of POS induction, we replace multinomial distributions over the vocabulary with multivariate Gaussian distributions over word embeddings and observe consistent improvements in eight languages. We also analyze the effect of various choices while inducing word embeddings on “downstream” POS induction results.
LATENT EMBEDDING OPTIMIZATION

Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this lowdimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.
A novel method of text representation on hybrid neural networks

Text representation is one of the fundamental problems in text analysis tasks. The key of text representation is to extract and express the semantic and syntax feature of texts. The order-sensitive sequence models based on neural networks have achieved great progress in text representation. Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks, as an extension of Recurrent Neural Networks (RNN), not only can deal with variable-length texts, capture the long-term dependencies in texts, but also model the forward and backward sequence contexts. Moreover, typical neural networks, Convolutional Neural Networks (CNN), can extract more semantic and structural information from texts, because of their convolution and pooling operations. The paper proposes a hybrid model, which combines the BiLSTM with 2-dimensial convolution and 1-dimensial pooling operations. In other words, the model firstly captures the abstract representation vector of texts by the BiLSTM, and then extracts text semantic features by 2-dimensial convolutional and 1-dimensial pooling operations. Experiments on text classification tasks show that our method obtains preferable performances compared with the state-of-the-art models when applied on the MR1 sentence polarity dataset.
Right parietal cortex plays a critical role in change blindness.

There is increasing evidence from functional magnetic resonance imaging (fMRI) that visual awareness is not only associated with activity in ventral visual cortex but also with activity in the parietal cortex. However, due to the correlational nature of neuroimaging, it remains unclear whether this parietal activity plays a causal role in awareness. In the experiment presented here we disrupted activity in right or left parietal cortex by applying repetitive transcranial magnetic stimulation (rTMS) over these areas while subjects attempted to detect changes between two images separated by a brief interval (i.e. 1-shot change detection task). We found that rTMS applied over right parietal cortex but not left parietal cortex resulted in longer latencies to detect changes and a greater rate of change blindness compared with no TMS. These results suggest that the right parietal cortex plays a critical role in conscious change detection.
Fast Image Processing with Fully-Convolutional Networks

We present an approach to accelerating a wide variety of image processing operators. Our approach uses a fully-convolutional network that is trained on input-output pairs that demonstrate the operator’s action. After training, the original operator need not be run at all. The trained network operates at full resolution and runs in constant time. We investigate the effect of network architecture on approximation accuracy, runtime, and memory footprint, and identify a specific architecture that balances these considerations. We evaluate the presented approach on ten advanced image processing operators, including multiple variational models, multiscale tone and detail manipulation, photographic style transfer, nonlocal dehazing, and nonphoto- realistic stylization. All operators are approximated by the same model. Experiments demonstrate that the presented approach is significantly more accurate than prior approximation schemes. It increases approximation accuracy as measured by PSNR across the evaluated operators by 8.5 dB on the MIT-Adobe dataset (from 27.5 to 36 dB) and reduces DSSIM by a multiplicative factor of 3 com- pared to the most accurate prior approximation scheme, while being the fastest. We show that our models general- ize across datasets and across resolutions, and investigate a number of extensions of the presented approach.
An Arabic Twitter Corpus for Subjectivity and Sentiment Analysis

We present a newly collected data set of 8,868 gold-standard annotated Arabic twitter feeds. The corpus is manually labelled for subjectivity and sentiment analysis (SSA) (κ = 0.816). In addition, the corpus is annotated with a variety of linguistically motivated feature-sets that have previously shown positive impact on classification performance. The paper highlights issues posed by twitter as a genre, such as a mixture of language varieties and topic-shifts. Our next step is to extend the current corpus, using online semi-supervised learning. A first sub-corpus will be released via the ELRA repository as part of this submission.
A SWOT Analysis of the Field of Virtual Reality Rehabilitation and Therapy

The use of virtual-reality technology in the areas of rehabilitation and therapy continues to grow, with encouraging results being reported for applications that address human physical, cognitive, and psychological functioning. This article presents a SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis for the field of VR rehabilitation and therapy. The SWOT analysis is a commonly employed framework in the business world for analyzing the factors that influence a company's competitive position in the marketplace with an eye to the future. However, the SWOT framework can also be usefully applied outside of the pure business domain. A quick check on the Internet will turn up SWOT analyses for urban-renewal projects, career planning, website design, youth sports programs, and evaluation of academic research centers, and it becomes obvious that it can be usefully applied to assess and guide any organized human endeavor designed to accomplish a mission. It is hoped that this structured examination of the factors relevant to the current and future status of VR rehabilitation will provide a good overview of the key issues and concerns that are relevant for understanding and advancing this vital application area.
Perceptual coding of digital audio

During the last decade, CD-quality digital audio has essentially replaced analog audio. Emerging digital audio applications for network, wireless, and multimedia computing systems face a series of constraints such as reduced channel bandwidth, limited storage capacity, and low cost. These new applications have created a demand for high-quality digital audio delivery at low bit rates. In response to this need, considerable research has been devoted to the development of algorithms for perceptually transparent coding of high-fidelity (CD-quality) digital audio. As a result, many algorithms have been proposed, and several have now become international and/or commercial product standards. This paper reviews algorithms for perceptually transparent coding of CD-quality digital audio, including both research and standardization activities. This paper is organized as follows. First, psychoacoustic principles are described, with the MPEG psychoacoustic signal analysis model 1 discussed in some detail. Next, filter bank design issues and algorithms are addressed, with a particular emphasis placed on the modified discrete cosine transform, a perfect reconstruction cosine-modulated filter bank that has become of central importance in perceptual audio coding. Then, we review methodologies that achieve perceptually transparent coding of FM- and CD-quality audio signals, including algorithms that manipulate transform components, subband signal decompositions, sinusoidal signal components, and linear prediction parameters, as well as hybrid algorithms that make use of more than one signal model. These discussions concentrate on architectures and applications of those techniques that utilize psychoacoustic models to exploit efficiently masking characteristics of the human receiver. Several algorithms that have become international and/or commercial standards receive in-depth treatment, including the ISO/IEC MPEG family (-1, -2, -4), the Lucent Technologies PAC/EPAC/MPAC, the Dolby AC-2/AC-3, and the Sony ATRAC/SDDS algorithms. Then, we describe subjective evaluation methodologies in some detail, including the ITU-R BS.1116 recommendation on subjective measurements of small impairments. This paper concludes with a discussion of future research directions.
Dynamic Taint Analysis for Automatic Detection, Analysis, and SignatureGeneration of Exploits on Commodity Software

Software vulnerabilities have had a devastating effect on the Internet. Worms such as CodeRed and Slammer can compromise hundreds of thousands of hosts within hours or even minutes, and cause millions of dollars of damage [32, 51]. To successfully combat these fast automatic Internet attacks, we need fast automatic attack detection and filtering mechanisms. In this paper we propose dynamic taint analysis for automatic detection and analysis of overwrite attacks, which include most types of exploits. This approach does not need source code or special compilation for the monitored program, and hence works on commodity software. To demonstrate this idea, we have implemented TaintCheck, a mechanism that can perform dynamic taint analysis by performing binary rewriting at run time. We show that TaintCheck reliably detects most types of exploits. We found that TaintCheck produced no false positives for any of the many different programs that we tested. Further, we show how we can use a two-tiered approach to build a hybrid exploit detector that enjoys the same accuracy as TaintCheck but have extremely low performance overhead. Finally, we propose a new type of automatic signature generation—semanticanalysis based signature generation. We show that by backtracing the chain of tainted data structure rooted at the detection point, TaintCheck can automatically identify which original flow and which part of the original flow have caused the attack and identify important invariants of the payload that can be used as signatures. Semantic-analysis based signature generation can be more accurate, resilient against polymorphic worms, and robust to attacks exploiting polymorphism than the pattern-extraction based signature generation methods.
Inside Waymo's self-driving car: My favorite transistors

Waymo's self-driving cars contain a broad set of technologies that enable our cars to sense the vehicle surroundings, perceive and understand what is happening in the vehicle vicinity, and determine the safe and efficient actions that the vehicle should take. Many of these technologies are rooted in advanced semiconductor technologies, e.g. faster transistors that enable more compute or low noise designs that enable the faintest sensor signals to be perceived. This paper summarizes a few areas where semiconductor technologies have proven to be fundamentally enabling to self-driving capabilities. The paper also lays out some of the challenges facing advanced semiconductors in the automotive context, as well as some of the opportunities for future innovation.