nils-herrmann/clean_scirepeval huggingface dataset:
Recurrent Continuous Translation Models

We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.
From learning models of natural image patches to whole image restoration

Learning good image priors is of utmost importance for the study of vision, computer vision and image processing applications. Learning priors and optimizing over whole images can lead to tremendous computational challenges. In contrast, when we work with small image patches, it is possible to learn priors and perform patch restoration very efficiently. This raises three questions - do priors that give high likelihood to the data also lead to good performance in restoration? Can we use such patch based priors to restore a full image? Can we learn better patch priors? In this work we answer these questions. We compare the likelihood of several patch models and show that priors that give high likelihood to data perform better in patch restoration. Motivated by this result, we propose a generic framework which allows for whole image restoration using any patch based prior for which a MAP (or approximate MAP) estimate can be calculated. We show how to derive an appropriate cost function, how to optimize it and how to use it to restore whole images. Finally, we present a generic, surprisingly simple Gaussian Mixture prior, learned from a set of natural images. When used with the proposed framework, this Gaussian Mixture Model outperforms all other generic prior methods for image denoising, deblurring and inpainting.
Exploring motivations for contributing to open source initiatives: The roles of contribution context and personal values

We explore contextual and dispositional correlates of the motivation to contribute to open source initiatives. We examine how the context of the open source project, and the personal values of contributors, are related to the types of motivations for contributing. A web-based survey was administered to 300 contributors in two prominent open source contexts: software and content. As hypothesized, software contributors placed a greater emphasis on reputation-gaining and self-development motivations, compared with content contributors, who placed a greater emphasis on altruistic motives. Furthermore, the hypothesized relationships were found between contributors’ personal values and their motivations for contributing.
A Context-aware Time Model for Web Search

In web search, information about times between user actions has been shown to be a good indicator of users' satisfaction with the search results. Existing work uses the mean values of the observed times, or fits probability distributions to the observed times. This implies a context-independence assumption that the time elapsed between a pair of user actions does not depend on the context, in which the first action takes place. We validate this assumption using logs of a commercial web search engine and discover that it does not always hold. For between 37% to 80% of query-result pairs, depending on the number of observations, the distributions of click dwell times have statistically significant differences in query sessions for which a given result (i) is the first item to be clicked and (ii) is not the first. To account for this context bias effect, we propose a context-aware time model (CATM). The CATM allows us (i) to predict times between user actions in contexts, in which these actions were not observed, and (ii) to compute context-independent estimates of the times by predicting them in predefined contexts. Our experimental results show that the CATM provides better means than existing methods to predict and interpret times between user actions.
Resource Sharing in Continuous Sliding-Window Aggregates

We consider the problem of resource sharing when processing large numbers of continuous queries. We specifically address sliding-window aggregates over data streams, an important class of continuous operators for which sharing has not been addressed. We present a suite of sharing techniques that cover a wide range of possible scenarios: different classes of aggregation functions (algebraic, distributive, holistic), different window types (time-based, tuple-based, suffix, historical), and different input models (single stream, multiple substreams). We provide precise theoretical performance guarantees for our techniques, and show their practical effectiveness through experimental study.
Developing a data quality framework for asset management in engineering organisations

Data Quality (DQ) is seen as critical to effective business decision-making. However, maintaining DQ is often acknowledged as problematic. Asset data is the key enabler in gaining control of assets. The quality asset data provides the foundation for effective Asset Management (AM). Researches have indicated that achieving AM DQ is the key challenge engineering organisations face today. This paper investigates the DQ issues emerging from the unique nature of engineering AM data. It presents an exploratory research of a large scale national-wide DQ survey on how Australian engineering organisations address DQ issues, and proposes an AM specific DQ framework.
Stylistically User-specific Response Generation

Recent neural models for response generation show good results in terms of general responses. In real conversations, however, depending on the speaker/responder, similar utterances should require different responses. In this study, we attempt to consider individual user’s information in adjusting the notable sequence-to-sequence (seq2seq) model for more diverse, userspecific responses. We assume that we need user-specific features to adjust the response and we argue that some selected representative words from the users are suitable for this task. Furthermore, we prove that even for unseen or unknown users, our model can provide more diverse and interesting responses, while maintaining correlation with input utterances. Experimental results with human evaluation show that our model can generate more interesting responses than the popular seq2seqmodel and achieve higher relevance with input utterances than our baseline.
DDoS: threats and mitigation

According to a report from Prolexic, one of the first and largest companies offering DDoS mitigation services, ⁠ attack traffic rose 66% over the course of a year (to Q3 2011). 1 Network-layer attacks accounted for 83%, the rest being application-layer attacks. The average duration was 1.4 days and the average bandwidth consumed was 1.5Gbps. The size of attacks is getting bigger, too. In July 2011, Prolexic announced it had mitigated what it believed to be the largest packet-per-second DDoS attack ever seen in Asia. Consisting of SYN and ICMP floods, the attack deployed 176,000 bots (compared to the 5,000-10,000 bots more normally seen by Prolexic) to generate 25 million packets per second. According to the company, the majority of high-end border rout-ers typically forward 70,000 packets per second. It mitigated the attack by distributing traffic among its Tier 1 carrier partners and scrubbing centres. Ben Petro, senior VP network intelligence & availability at Verisign, traces the rise of DDoS attacks back another year. He says that for years there was little awareness of the problem but that, " 2010 was a dramatic shift – not only in the size, scale and trajectory of DDoS, but also in its proliferation and the number of different types of organisation that were hit. " In the years 2006-2008, he adds, the average attack was somewhere around 40Mbps. " And then, all of a sudden, coming in 2010 we started to see 2Gbps, then 5Gbps, then 8Gbps and 15Gbps attacks, culminating in the largest that we've seen coming in at 84Gbps sustained for a week and a half. That is an incredible amount of traffic when you get down to packets or queries per second – you're over the hundreds of thousands of queries per second per location for Verisign, and we have 165 locations. So it's an enormous volume. " The rising popularity of DDoS attacks may be connected directly to their effectiveness. Paul Sop, CTO at Prolexic, says, " people get creative and they use and invent new ways and reasons to use DDoS. When an idea catches on in the industry there's a tipping point – people say, hey here's a type of attack we can launch and it's very likely we won't go to jail unless we're pretty dumb about it. " He adds: " It is like asymmetric warfare – the attackers have so much advantage in terms …
Deep Sentence-Level Authorship Attribution

We examine the problem of authorship attribution in collaborative documents. We seek to develop new deep learning models tailored to this task. We have curated a novel dataset by parsing Wikipedia’s edit history, which we use to demonstrate the feasiblity of deep models to multi-author attribution at the sentence-level. Though we attempt to formulate models which learn stylometric features based on both grammatical structure and vocabulary, our error analysis suggests that our models mostly learn to recognize vocabulary-based cues, making them non-competitive with baselines tailored to vocabulary-based features. We explore why this may be, and suggest directions for future models to mitigate this shortcoming.
Vision-based and marker-less surgical tool detection and tracking: a review of the literature

In recent years, tremendous progress has been made in surgical practice for example with Minimally Invasive Surgery (MIS). To overcome challenges coming from deported eye-to-hand manipulation, robotic and computer-assisted systems have been developed. Having real-time knowledge of the pose of surgical tools with respect to the surgical camera and underlying anatomy is a key ingredient for such systems. In this paper, we present a review of the literature dealing with vision-based and marker-less surgical tool detection. This paper includes three primary contributions: (1) identification and analysis of data-sets used for developing and testing detection algorithms, (2) in-depth comparison of surgical tool detection methods from the feature extraction process to the model learning strategy and highlight existing shortcomings, and (3) analysis of validation techniques employed to obtain detection performance results and establish comparison between surgical tool detectors. The papers included in the review were selected through PubMed and Google Scholar searches using the keywords: "surgical tool detection", "surgical tool tracking", "surgical instrument detection" and "surgical instrument tracking" limiting results to the year range 2000 2015. Our study shows that despite significant progress over the years, the lack of established surgical tool data-sets, and reference format for performance assessment and method ranking is preventing faster improvement.
Gaining Access with Social Engineering: An Empirical Study of the Threat

Recently, research on information security has expanded from its purely technological orientation into striving to understand and explain the role of human behavior in security breaches. However, an area that has been lacking theory-grounded empirical study is in social engineering attacks. While there exists an extensive body of anecdotal literature, factors that account for attack success remains largely speculative. To better understand this increasing phenomenon, we developed a theoretical framework and conducted an empirical field study to investigate social engineering attacks, and from these results, we make recommendations for practice and further research.
Title What Is the Optimal Way to Deliver a Positive Activity Intervention ? The Case of Writing About One ' s Best Possible Selves Permalink

A 4-week-long experiment examined the effects of a positive activity intervention in which students wrote about their ‘‘best possible selves’’ (BPS) once a week. We manipulated two factors that might affect the success of the happiness-increasing activity—whether the positive activity was administered online versus in-person and whether the participant read a persuasive peer testimonial before completing the activity. Our results indicated that the BPS activity significantly boosted positive affect and flow and marginally increased feelings of relatedness. No differences were found between participants who completed the positive activity online versus in-person. However, students who read a testimonial extolling the virtues of the BPS activity showed larger gains in wellbeing than those who read neutral information or completed a control task. The results lend legitimacy to online self-administered happiness-increasing activities and highlight the importance of participants’ beliefs in the efficacy of such activities for optimum results.
BigDebug: Debugging Primitives for Interactive Big Data Processing in Spark

Developers use cloud computing platforms to process a large quantity of data in parallel when developing big data analytics. Debugging the massive parallel computations that run in today's datacenters is time consuming and error-prone. To address this challenge, we design a set of interactive, real-time debugging primitives for big data processing in Apache Spark, the next generation data-intensive scalable cloud computing platform. This requires rethinking the notion of step-through debugging in a traditional debugger such as gdb, because pausing the entire computation across distributed worker nodes causes significant delay and naively inspecting millions of records using a watchpoint is too time consuming for an end user.
 First, BigDebug's simulated breakpoints and on-demand watchpoints allow users to selectively examine distributed, intermediate data on the cloud with little overhead. Second, a user can also pinpoint a crash-inducing record and selectively resume relevant sub-computations after a quick fix. Third, a user can determine the root causes of errors (or delays) at the level of individual records through a fine-grained data provenance capability. Our evaluation shows that BigDebug scales to terabytes and its record-level tracing incurs less than 25% overhead on average. It determines crash culprits orders of magnitude more accurately and provides up to 100% time saving compared to the baseline replay debugger. The results show that BigDebug supports debugging at interactive speeds with minimal performance impact.
TRACCS : Trajectory-Aware Coordinated Urban CrowdSourcing

We investigate the problem of large-scale mobile crowdtasking, where a large pool of citizen crowd-workers are used to perform a variety of location-specific urban logistics tasks. Current approaches to such mobile crowd-tasking are very decentralized: a crowd-tasking platform usually provides each worker a set of available tasks close to the worker’s current location; each worker then independently chooses which tasks she wants to accept and perform. In contrast, we propose TRACCS, a more coordinated task assignment approach, where the crowd-tasking platform assigns a sequence of tasks to each worker, taking into account their expected location trajectory over a wider time horizon, as opposed to just instantaneous location. We formulate such task assignment as an optimization problem, that seeks to maximize the total payoff from all assigned tasks, subject to a maximum bound on the detour (from the expected path) that a worker will experience to complete her assigned tasks. We develop credible computationally-efficient heuristics to address this optimization problem (whose exact solution requires solving a complex integer linear program), and show, via simulations with realistic topologies and commuting patterns, that a specific heuristic (called Greedy-ILS) increases the fraction of assigned tasks by more than 20%, and reduces the average detour overhead by more than 60%, compared to the current decentralized approach.
Paraphrasing for Automatic Evaluation

This paper studies the impact of paraphrases on the accuracy of automatic evaluation. Given a reference sentence and a machine-generated sentence, we seek to find a paraphrase of the reference sentence that is closer in wording to the machine output than the original reference. We apply our paraphrasing method in the context of machine translation evaluation. Our experiments show that the use of a paraphrased synthetic reference refines the accuracy of automatic evaluation. We also found a strong connection between the quality of automatic paraphrases as judged by humans and their contribution to automatic evaluation.
The Caries Assessment Spectrum and Treatment (CAST) index: rational and development.

Serious difficulties in reporting results were encountered when using ICDAS II and PUFA separately in an epidemiological survey in a child population in Brazil. That necessitated the development of a comprehensive but pragmatic caries assessment index. This publication describes the rationale, development and content of a novel caries assessment index. Strengths and weaknesses of ICDAS II, PUFA and other indices were analysed. The novel caries index developed for use in epidemiological surveys is termed 'Caries Assessment Spectrum and Treatment' (CAST). 'Spectrum' indicates what is considered the main strength of the new index - its usefulness in describing the complete range of stages of carious lesion progression: from no carious lesion, through caries protection (sealant) and caries cure (restoration) to lesions in enamel and dentine, and the advanced stages of carious lesion progression in pulpal and tooth-surrounding tissue. CAST combines elements of the ICDAS II and PUFA indices, and the M- and F-components of the DMF index. A DMF score can easily be calculated from the CAST score, thereby enabling retention of the use of existing DMF scores. The CAST index for use in epidemiological surveys is very promising. It should be validated and its reliability and usefulness be tested in different age groups in different countries and cultures.
The business case for corporate social responsibility : A company-level measurement approach for CSR

r 200 6 eber@un a.de/csm Summary Although theoretical and empirical research often points to a positive relation between CSR and company competitiveness, approaches to measure the company-specific business impacts of CSR are missing in the current literature. However, such an approach could strengthen the overall CSR involvement and support rational decision-making in this area. This paper thus focuses on the question how to measure the business impact of CSR activities from a company perspective. Using a theoretical approach a multi-step measurement model is developed that allows managers to evaluate their company-specific business case for CSR. A case example illustrates the use of the model in practice.
An Improved Technique for Full Spectral Rendering

In this paper we present an improved approach to full spectral rendering. The technique is optimized for quasi-Monte Carlo ray tracing, however the underlying physical theory can be applied to any global illumination scheme. We start with explanation of the necessity of full spectral rendering in any correct global illumination system. Then we present, step by step, a rendering scheme using full spectrum simulation. First, we give details on a random point sampling as a method of representing spectra, then we introduce improved spectral sampling technique, designed to reduce variance of image of wavelength dependent phenomena, and finally we show how to integrate the novel sampling technique with selected ray tracing algorithms.
A renaissance of neural networks in drug discovery.

Neural networks are becoming a very popular method for solving machine learning and artificial intelligence problems. The variety of neural network types and their application to drug discovery requires expert knowledge to choose the most appropriate approach.



In this review, the authors discuss traditional and newly emerging neural network approaches to drug discovery. Their focus is on backpropagation neural networks and their variants, self-organizing maps and associated methods, and a relatively new technique, deep learning. The most important technical issues are discussed including overfitting and its prevention through regularization, ensemble and multitask modeling, model interpretation, and estimation of applicability domain. Different aspects of using neural networks in drug discovery are considered: building structure-activity models with respect to various targets; predicting drug selectivity, toxicity profiles, ADMET and physicochemical properties; characteristics of drug-delivery systems and virtual screening.



Neural networks continue to grow in importance for drug discovery. Recent developments in deep learning suggests further improvements may be gained in the analysis of large chemical data sets. It's anticipated that neural networks will be more widely used in drug discovery in the future, and applied in non-traditional areas such as drug delivery systems, biologically compatible materials, and regenerative medicine.
DAC-MACS: Effective Data Access Control for Multiauthority Cloud Storage Systems

Data access control is an effective way to ensure data security in the cloud. However, due to data outsourcing and untrusted cloud servers, the data access control becomes a challenging issue in cloud storage systems. Existing access control schemes are no longer applicable to cloud storage systems, because they either produce multiple encrypted copies of the same data or require a fully trusted cloud server. Ciphertext-policy attribute-based encryption (CP-ABE) is a promising technique for access control of encrypted data. However, due to the inefficiency of decryption and revocation, existing CP-ABE schemes cannot be directly applied to construct a data access control scheme for multiauthority cloud storage systems, where users may hold attributes from multiple authorities. In this paper, we propose data access control for multiauthority cloud storage (DAC-MACS), an effective and secure data access control scheme with efficient decryption and revocation. Specifically, we construct a new multiauthority CP-ABE scheme with efficient decryption, and also design an efficient attribute revocation method that can achieve both forward security and backward security. We further propose an extensive data access control scheme (EDAC-MACS), which is secure under weaker security assumptions.