nils-herrmann/clean_scirepeval huggingface dataset:
Language-independent sandboxing of just-in-time compilation and self-modifying code

When dealing with dynamic, untrusted content, such as on the Web, software behavior must be sandboxed, typically through use of a language like JavaScript. However, even for such specially-designed languages, it is difficult to ensure the safety of highly-optimized, dynamic language runtimes which, for efficiency, rely on advanced techniques such as Just-In-Time (JIT) compilation, large libraries of native-code support routines, and intricate mechanisms for multi-threading and garbage collection. Each new runtime provides a new potential attack surface and this security risk raises a barrier to the adoption of new languages for creating untrusted content.
 Removing this limitation, this paper introduces general mechanisms for safely and efficiently sandboxing software, such as dynamic language runtimes, that make use of advanced, low-level techniques like runtime code modification. Our language-independent sandboxing builds on Software-based Fault Isolation (SFI), a traditionally static technique. We provide a more flexible form of SFI by adding new constraints and mechanisms that allow safety to be guaranteed despite runtime code modifications.
 We have added our extensions to both the x86-32 and x86-64 variants of a production-quality, SFI-based sandboxing platform; on those two architectures SFI mechanisms face different challenges. We have also ported two representative language platforms to our extended sandbox: the Mono common language runtime and the V8 JavaScript engine. In detailed evaluations, we find that sandboxing slowdown varies between different benchmarks, languages, and hardware platforms. Overheads are generally moderate and they are close to zero for some important benchmark/platform combinations.
Exploiting plug-and-play synthetic biology for drug discovery and production in microorganisms

One of the most promising applications of synthetic biology is the biosynthesis of new drugs from secondary metabolites. Here, we survey a wide range of strategies that control the activity of biosynthetic modules in the cell in space and time, and illustrate how these strategies can be used to design efficient cellular synthetic production systems. Re-engineered versions of secondary metabolite biosynthetic pathways identified from any genomic sequence can then be inserted into these systems in a plug-and-play fashion.
Addressing Complexities of Machine Learning in Big Data: Principles, Trends and Challenges from Systematical Perspectives

The concept of ‘big data’ has been widely discussed, and its value has been illuminated throughout a variety of domains. To quickly mine potential values and alleviate the ever-increasing volume of information, machine learning is playing an increasingly important role and faces more challenges than ever. Because few studies exist regarding how to modify machine learning techniques to accommodate big data environments, we provide a comprehensive overview of the history of the evolution of big data, the foundations of machine learning, and the bottlenecks and trends of machine learning in the big data era. More specifically, based on learning principals, we discuss regularization to enhance generalization. The challenges of quality in big data are reduced to the curse of dimensionality, class imbalances, concept drift and label noise, and the underlying reasons and mainstream methodologies to address these challenges are introduced. Learning model development has been driven by domain specifics, dataset complexities, and the presence or absence of human involvement. In this paper, we propose a robust learning paradigm by aggregating the aforementioned factors. Over the next few decades, we believe that these perspectives will lead to novel ideas and encourage more studies aimed at incorporating knowledge and establishing data-driven learning systems that involve both data quality considerations and human interactions.
Deep Spatial-Temporal Joint Feature Representation for Video Object Detection

With the development of deep neural networks, many object detection frameworks have shown great success in the fields of smart surveillance, self-driving cars, and facial recognition. However, the data sources are usually videos, and the object detection frameworks are mostly established on still images and only use the spatial information, which means that the feature consistency cannot be ensured because the training procedure loses temporal information. To address these problems, we propose a single, fully-convolutional neural network-based object detection framework that involves temporal information by using Siamese networks. In the training procedure, first, the prediction network combines the multiscale feature map to handle objects of various sizes. Second, we introduce a correlation loss by using the Siamese network, which provides neighboring frame features. This correlation loss represents object co-occurrences across time to aid the consistent feature generation. Since the correlation loss should use the information of the track ID and detection label, our video object detection network has been evaluated on the large-scale ImageNet VID dataset where it achieves a 69.5% mean average precision (mAP).
The Vector Space Model in Information Retrieval-Term Weighting Problem

Many traditional information retrieval (IR) tasks, such as text search, text clustering or text categorization, have natural language documents as their first-class objects, in the sense that the algorithms that are meant to solve these tasks require explicit internal representations of the documents they need to deal with. In IR documents are usually given as extensional vectorial representation, in which the dimensions (features) of the vector representing a document are the terms occurring in the document. The approach to term representation that the IR community has almost universally adopted is known as the bag-of-words approach: a document dj is represented as a vector of term weights −→ dj = 〈ω1j , ..., ωrj〉, where r is the cardinality of the dictionary and 0 ≤ ωkj ≤ 1 represents the contribution of term tk to the specification of the semantics of dj . This article analyses and compares many different bag-of-words approaches.
Abnormal use of facial information in high-functioning autism.

Altered visual exploration of faces likely contributes to social cognition deficits seen in autism. To investigate the relationship between face gaze and social cognition in autism, we measured both face gaze and how facial regions were actually used during emotion judgments from faces. Compared to IQ-matched healthy controls, nine high-functioning adults with autism failed to make use of information from the eye region of faces, instead relying primarily on information from the mouth. Face gaze accounted for the increased reliance on the mouth, and partially accounted for the deficit in using information from the eyes. These findings provide a novel quantitative assessment of how people with autism utilize information in faces when making social judgments.
Neither more nor less: Optimizing thread-level parallelism for GPGPUs

General-purpose graphics processing units (GPGPUs) are at their best in accelerating computation by exploiting abundant thread-level parallelism (TLP) offered by many classes of HPC applications. To facilitate such high TLP, emerging programming models like CUDA and OpenCL allow programmers to create work abstractions in terms of smaller work units, called cooperative thread arrays (CTAs). CTAs are groups of threads and can be executed in any order, thereby providing ample opportunities for TLP. The state-of-the-art GPGPU schedulers allocate maximum possible CTAs per-core (limited by available on-chip resources) to enhance performance by exploiting TLP. However, we demonstrate in this paper that executing the maximum possible number of CTAs on a core is not always the optimal choice from the performance perspective. High number of concurrently executing threads might cause more memory requests to be issued, and create contention in the caches, network and memory, leading to long stalls at the cores. To reduce resource contention, we propose a dynamic CTA scheduling mechanism, called DYNCTA, which modulates the TLP by allocating optimal number of CTAs, based on application characteristics. To minimize resource contention, DYNCTA allocates fewer CTAs for applications suffering from high contention in the memory sub-system, compared to applications demonstrating high throughput. Simulation results on a 30-core GPGPU platform with 31 applications show that the proposed CTA scheduler provides 28% average improvement in performance compared to the existing CTA scheduler.
Dynamic Bernoulli Embeddings for Language Evolution

Word embeddings are a powerful approach for unsupervised analysis of language. Recently, Rudolph et al. (2016) developed exponential family embeddings, which cast word embeddings in a probabilistic framework. Here, we develop dynamic embeddings, building on exponential family embeddings to capture how the meanings of words change over time. We use dynamic embeddings to analyze three large collections of historical texts: the U.S. Senate speeches from 1858 to 2009, the history of computer science ACM abstracts from 1951 to 2014, and machine learning papers on the Arxiv from 2007 to 2015. We find dynamic embeddings provide better fits than classical embeddings and capture interesting patterns about how language changes.
Pathwise coordinate optimization

We consider “one-at-a-time” coordinate-wise descent algorithms for a class of convex optimization problems. An algorithm of this kind has been proposed for the L1-penalized regression (lasso) in the literature, but it seems to have been largely ignored. Indeed, it seems that coordinate-wise algorithms are not often used in convex optimization. We show that this algorithm is very competitive with the well-known LARS (or homotopy) procedure in large lasso problems, and that it can be applied to related methods such as the garotte and elastic net. It turns out that coordinate-wise descent does not work in the “fused lasso,” however, so we derive a generalized algorithm that yields the solution in much less time that a standard convex optimizer. Finally, we generalize the procedure to the two-dimensional fused lasso, and demonstrate its performance on some image smoothing problems.
SPARK: A New VANET-Based Smart Parking Scheme for Large Parking Lots

Searching for a vacant parking space in a congested area or a large parking lot and preventing auto theft are major concerns to our daily lives. In this paper, we propose a new smart parking scheme for large parking lots through vehicular communication. The proposed scheme can provide the drivers with real-time parking navigation service, intelligent antitheft protection, and friendly parking information dissemination. Performance analysis via extensive simulations demonstrates its efficiency and practicality.  Vehicular communications; smart parking; navigation; anti-theft; information dissemination; security & privacy
An Enhanced Neural Network Technique for Software Risk Analysis

An enhanced technique for risk categorization is presented. This technique, PCA-ANN, provides an improved capability to discriminate high-risk software. The approach draws on the combined strengths of pattern recognition, multivariate statistics and neural networks. Principal component analysis is utilized to provide a means of normalizing and orthogonalizing the input data, thus eliminating the ill effects of multicollinearity. A neural network is used for risk determination/classification. A significant feature of this approach is a procedure, herein termed cross-normalization. This procedure provides the technique with capability to discriminate data sets that include disproportionately large numbers of high-risk software modules.
A New Hierarchical Structure for Speech Recognition by units smaller than words , using Wavelet Packet and SVM

This study proposes using units smaller than words, such as phonemes and syllables, as base units for speech recognition. The system presented here was developed with a hierarchical recognition logic based on the production characteristics of phonemes in Brazilian Portuguese. Decisions are made by Support Vector Machine neural networks grouped to form Specialist Machines. The descriptors used was the Wavelet Packet Transform (WPT) and Mel-Frequency Cepstral Coefficient (MFCC). The method proposed yielded good mean recognition rate results: 98.16% for vowel recognition and 98.41% for consonant recognition. Final total word recognition was 96.82%.
The Fakability of Bar-On ’ s Emotional Quotient Inventory Short Form : Catch Me if You Can

This study investigated the fakability of the Emotional Quotient Inventory Short Form (EQ-i:S), a mixed-model emotional intelligence test developed by Bar-On (2002). A sample of 229 undergraduate students from a southeastern university completed a battery of selection and assessment measures in both an honest and faking good condition. When responded to honestly, the EQ-i:S is predicted by The Big Five with a multiple correlation of .79. Therefore, the EQ-i:S can be viewed as an aggregation of The Big Five constructs. When faking, respondents were able to improve scores on the EQ-i:S, each of its subtests, and each of The Big Five measures. Respondents improved scores on the EQ-i:S by .83 SD. Faking on the EQ-i:S was primarily predicted by cognitive ability and agreeableness. The relative ease with which respondents can substantially raise their scores limits the value of the EQ-i:S as an applicant screening tool. The substantial extent to which the EQ-i:S is predicted by The Big Five casts doubt on the construct of emotional intelligence as operationalized in the EQ-i:S.
Life-iNet: A Structured Network-Based Knowledge Exploration and Analytics System for Life Sciences

Search engines running on scientific literature have been widely used by life scientists to find publications related to their research. However, existing search engines in the life-science domain, such as PubMed, have limitations when applied to exploring and analyzing factual knowledge (e.g., disease-gene associations) in massive text corpora. These limitations are mainly due to the problems that factual information exists as an unstructured form in text, and also keyword and MeSH term-based queries cannot effectively imply semantic relations between entities. This demo paper presents the Life-iNet system to address the limitations in existing search engines on facilitating life sciences research. Life-iNet automatically constructs structured networks of factual knowledge from large amounts of background documents, to support efficient exploration of structured factual knowledge in the unstructured literature. It also provides functionalities for finding distinctive entities for given entity types, and generating hypothetical facts to assist literaturebased knowledge discovery (e.g., drug target prediction).
Caesar: A content router for high-speed forwarding on content names

Today, high-end routers forward hundreds of millions of packets per second by means of longest prefix match on forwarding tables with less than a million IP prefixes. Information-Centric Networking, a novel form of networking where content is requested by its name, poses a new challenge in the design of high-end routers: process at least the same amount of packets, assuming a forwarding table that contains hundreds of millions of content prefixes. In this work we design and preliminarily evaluate Caesar, the first content router that supports name-based forwarding at high speed. Caesar efficiently uses available processing and memory units in a high-end router to support forwarding tables containing a billion content prefixes with unlimited characters.
Chinese Word Segmentation as Character Tagging

In this paper we report results of a supervised machine-learning approach to Chinese word segmentation. A maximum entropy tagger is trained on manually annotated data to automatically assign to Chinese characters, or hanzi, tags that indicate the position of a hanzi within a word. The tagged output is then converted into segmented text for evaluation. Preliminary results show that this approach is competitive against other supervised machine-learning segmenters reported in previous studies, achieving precision and recall rates of 95.01% and 94.94% respectively, trained on a 237K-word training set.
An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market

This paper evaluates the performance of a number of modelling approaches for future mortgage default status. Boosted regression trees, random forests, penalised linear and semi-parametric logistic regression models are applied to four portfolios of over 300,000 Irish owner-occupier mortgages. The main findings are that the selected approaches have varying degrees of predictive power and that boosted regression trees significantly outperform logistic regression. This suggests that boosted regression trees can be a useful addition to the current toolkit for mortgage credit risk assessment by banks and regulators.
Minimax Regret Bounds for Reinforcement Learning

We consider the problem of efficient exploration in finite horizon MDPs. We show that an optimistic modification to modelbased value iteration, can achieve a regret bound Õ( √ HSAT+HSA+H √ T ) where H is the time horizon, S the number of states, A the number of actions and T the time elapsed. This result improves over the best previous known bound Õ(HS √ AT ) achieved by the UCRL2 algorithm Jaksch et al. (2010a). The key significance of our new results is that when T ≥ HSA and SA ≥ H , it leads to a regret of Õ( √ HSAT ) that matches the established lower bounds of Ω( √ HSAT ) up to a logarithmic factor. Our analysis contain two key insights. We use careful application of concentration inequalities to the optimal value function as a whole, rather than to the transitions probabilities (to improve scaling in S), and we use "exploration bonuses" based on Bernstein’s inequality, together with using a recursive -Bellman-typeLaw of Total Variance (to improve scaling in H).
Moving towards a new vision: implementation of a public health policy intervention

Public health systems in Canada have undergone significant policy renewal over the last decade in response to threats to the public's health, such as severe acute respiratory syndrome. There is limited research on how public health policies have been implemented or what has influenced their implementation. This paper explores policy implementation in two exemplar public health programs -chronic disease prevention and sexually-transmitted infection prevention - in Ontario, Canada. It examines public health service providers', managers' and senior managements' perspectives on the process of implementation of the Ontario Public Health Standards 2008 and factors influencing implementation.



Public health staff from six health units representing rural, remote, large and small urban settings were included. We conducted 21 focus groups and 18 interviews between 2010 (manager and staff focus groups) and 2011 (senior management interviews) involving 133 participants. Research assistants coded transcripts and researchers reviewed these; the research team discussed and resolved discrepancies. To facilitate a breadth of perspectives, several team members helped interpret the findings. An integrated knowledge translation approach was used, reflected by the inclusion of academics as well as decision-makers on the team and as co-authors.



Front line service providers often were unaware of the new policies but managers and senior management incorporated them in operational and program planning. Some participants were involved in policy development or provided feedback prior to their launch. Implementation was influenced by many factors that aligned with Greenhalgh and colleagues' empirically-based Diffusion of Innovations in Service Organizations Framework. Factors and related components that were most clearly linked to the OPHS policy implementation were: attributes of the innovation itself; adoption by individuals; diffusion and dissemination; the outer context - interorganizational networks and collaboration; the inner setting - implementation processes and routinization; and, linkage at the design and implementation stage.



Multiple factors influenced public health policy implementation. Results provide empirical support for components of Greenhalgh et al's framework and suggest two additional components - the role of external organizational collaborations and partnerships as well as planning processes in influencing implementation. These are important to consider by government and public health organizations when promoting new or revised public health policies as they evolve over time. A successful policy implementation process in Ontario has helped to move public health towards the new vision.
Collaborative color calibration for multi-camera systems

This paper proposes a collaborative color calibration meth od for multi-camera systems. The multi-camera color calibr t on problem is formulated as an overdetermined linear system, i n which a dynamic range shaping is incorporated to ensure the high contrasts for captured images. The cameras are calibrated w ith the parameters obtained by solving the linear system. Fo r non-planar multi-camera systems, we design a novel omnidirectional co lor checker and present a method for establishing global cor respondences to facilitate automatic color calibration without m anual adjustment. According to experimental results on bot h synthetic and real-system datasets, the proposed method shows high perfo rmance in achieving inter-camera color consistency and hig h dynamic range. Thanks to the generality of the linear system formula tion and the flexibility of the designed color checker, the pr oposed method is applicable to various multi-camera systems.
Endurability , Engagement and Expectations : Measuring Children ’ s Fun

This paper describes a toolkit for measuring fun with children aged between 5 and 10. The relationship of fun to usability is discussed and three dimensions of fun; Endurability, Engagement, and Expectations are described. A set of tools that can be used in empirical studies to measure these dimensions of fun is described in detail. These tools include a Smileyometer, a Fun-Sorter and an AgainAgain table. Three user trials, in which the tools have been used, are described and suggestions are made for the use of each of the tools with children. A discussion of the efficacy of the tools presents the findings that there is a sufficient subset that can be used to give a reliable measure of fun. Dependent on the nature of the event being evaluated and on the number of activities being compared; the subset will be differently constructed. The paper concludes with observations about the difficulties of measuring fun with children, and some areas for further work are presented.
Effects of Online Reputation Service in Electronic Markets: A Trust-Based Empirical Study

Web-mediated consumer-to-consumer auctions are often regarded as risky due to the opportunistic behaviors in transaction. Online virtual community using feedback-based reputation service is an emerging trust building technology to reduce risk in online auction market. The purpose of this paper is to study the effect of feedback profiles on buyer’s purchase behavior. Based on previous researches, a trust-based model is developed to study the relationships among online reputation, trust, risk perception and intention. The model considers both similarities and differences between online and traditional market, it is expected to be a more effective basis for investigation of online consumer behavior.