nils-herrmann/clean_scirepeval huggingface dataset:
Ontology-based security assessment for software products

This paper proposes an ontology-based approach to analyzing and assessing the security posture for software products. It provides measurements of trust for a software product based on its security requirements and evidence of assurance, which are retrieved from an ontology built for vulnerability management. Our approach differentiates with the previous work in the following aspects: (1) It is a holistic approach emphasizing that the system assurance cannot be determined or explained by its component assurance alone. Instead, the software system as a whole determines its assurance level. (2) Our approach is based on widely accepted standards such as CVSS, CVE, CWE, CPE, and CAPEC. Our ontology integrated these standards seamlessly thus provides a solid foundation for security assessment. (3) Automated tools have been built to support our approach, delivering the environmental scores for software products.
Describing the scene as a whole: Joint object detection, scene classification and semantic segmentation

In this paper we propose an approach to holistic scene understanding that reasons jointly about regions, location, class and spatial extent of objects, presence of a class in the image, as well as the scene type. Learning and inference in our model are efficient as we reason at the segment level, and introduce auxiliary variables that allow us to decompose the inherent high-order potentials into pairwise potentials between a few variables with small number of states (at most the number of classes). Inference is done via a convergent message-passing algorithm, which, unlike graph-cuts inference, has no submodularity restrictions and does not require potential specific moves. We believe this is very important, as it allows us to encode our ideas and prior knowledge about the problem without the need to change the inference engine every time we introduce a new potential. Our approach outperforms the state-of-the-art on the MSRC-21 benchmark, while being much faster. Importantly, our holistic model is able to improve performance in all tasks.
Age , gender and income : do they really moderate online shopping behaviour ?

The objective of this paper is to analyse whether individuals’ socioeconomic characteristics – age, gender and income – influence their online shopping behaviour. The individuals analysed are experienced e-shoppers i.e. individuals who often make purchases on the internet.  The technology acceptance model was broadened to include previous use of the internet and perceived self-efficacy. The perceptions and behaviour of e-shoppers are based on their own experiences. The information obtained has been tested using causal and multi-sample analyses.  The results show that socioeconomic variables moderate neither the influence of previous use of the internet nor the perceptions of e-commerce; in short, they do not condition the behaviour of the experienced e-shopper.  The results obtained help to determine that once individuals attain the status of experienced e-shoppers their behaviour is similar, independently of their socioeconomic characteristics. The internet has become a marketplace suitable for all ages and incomes and both genders, and thus the prejudices linked to the advisability of selling certain products should be revised.  Previous research related to the socioeconomic variables affecting e-commerce has been aimed at forecasting who is likely to make an initial online purchase. In contrast to the majority of existing studies, it is considered that the current development of the online environment should lead to analysis of a new kind of e-shopper (experienced purchaser), whose behaviour differs from that studied at the outset of this research field. The experience acquired with online shopping nullifies the importance of socioeconomic characteristics.
Deep Video Color Propagation

Traditional approaches for color propagation in videos rely on some form of matching between consecutive video frames. Using appearance descriptors, colors are then propagated both spatially and temporally. These methods, however, are computationally expensive and do not take advantage of semantic information of the scene. In this work we propose a deep learning framework for color propagation that combines a local strategy, to propagate colors frame-by-frame ensuring temporal stability, and a global strategy, using semantics for color propagation within a longer range. Our evaluation shows the superiority of our strategy over existing video and image color propagation methods as well as neural photo-realistic style transfer approaches.
Explicit Reasoning over End-to-End Neural Architectures for Visual Question Answering

Many vision and language tasks require commonsense reasoning beyond data-driven image and natural language processing. Here we adopt Visual Question Answering (VQA) as an example task, where a system is expected to answer a question in natural language about an image. Current state-ofthe-art systems attempted to solve the task using deep neural architectures and achieved promising performance. However, the resulting systems are generally opaque and they struggle in understanding questions for which extra knowledge is required. In this paper, we present an explicit reasoning layer on top of a set of penultimate neural network based systems. The reasoning layer enables reasoning and answering questions where additional knowledge is required, and at the same time provides an interpretable interface to the end users. Specifically, the reasoning layer adopts a Probabilistic Soft Logic (PSL) based engine to reason over a basket of inputs: visual relations, the semantic parse of the question, and background ontological knowledge from word2vec and ConceptNet. Experimental analysis of the answers and the key evidential predicates generated on the VQA dataset validate
Time varying undirected graphs

Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using ℓ 1 penalization methods. However, current methods assume that the data are independent and identically distributed. If the distribution, and hence the graph, evolves over time then the data are not longer identically distributed. In this paper we develop a nonparametric method for estimating time varying graphical structure for multivariate Gaussian distributions using an ℓ 1 regularization method, and show that, as long as the covariances change smoothly over time, we can estimate the covariance matrix well (in predictive risk) even when p is large.
Similarity by Composition

We propose a new approach for measuring similarity between two signals, which is applicable to many machine learning tasks, and to many signal types. We say that a signal S1 is “similar” to a signalS2 if it is “easy” to composeS1 from few large contiguous chunks of S2. Obviously, if we use small enough pieces, then any signal can be composed of any other. Therefore, the larger those pieces are, the more similarS1 is to S2. This induces alocal similarity score at every point in the signal, based on the size of its supported surrounding region. These local scores can in turn be accumulated in a principled information-theoretic way into a global similarity scoreof the entireS1 to S2. “Similarity by Composition” can be applied betweenpairs of signals, betweengroups of signals , and alsobetween different portions of the same signal . It can therefore be employed in a wide variety of machine learning problems (clustering, classification, retrieval, segmentation, attention, saliency, labelling, etc.), and can be applied to a wide range of signal types (images, video, audio, biological data, etc.) We show a few such examples.
Adaptive piecewise polynomial estimation via trend filtering

We study trend filtering, a recently proposed tool of Kim et al. (2009) for nonparametric regression. The trend filtering estimate is defined as the minimizer of a penalized least squares criterion, in which the penalty term sums the absolute kth order discrete derivatives over the input points. Perhaps not surprisingly, trend filtering estimates appear to have the structure of kth degree spline functions, with adaptively chosen knot points (we say “appear” here as trend filtering estimates are not really functions over continuous domains, and are only defined over the discrete set of inputs). This brings to mind comparisons to other nonparametric regression tools that also produce adaptive splines; in particular, we compare trend filtering to smoothing splines, which penalize the sum of squared derivatives across input points, and to locally adaptive regression splines (Mammen & van de Geer 1997), which penalize the total variation of the kth derivative. Empirically, we discover that trend filtering estimates adapt to the local level of smoothness much better than smoothing splines, and further, they exhibit a remarkable similarity to locally adaptive regression splines. We also provide theoretical support for these empirical findings; most notably, we prove that (with the right choice of tuning parameter) the trend filtering estimate converges to the true underlying function at the minimax rate for functions whose kth derivative is of bounded variation. This is done via an asymptotic pairing of trend filtering and locally adaptive regression splines, which have already been shown to converge at the minimax rate (Mammen & van de Geer 1997). At the core of this argument is a new result tying together the fitted values of two lasso problems that share the same outcome vector, but have different predictor matrices.
High Order Stochastic Graphlet Embedding for Graph-Based Pattern Recognition

Graph-based methods are known to be successful in many machine learning and pattern classification tasks. These methods consider semistructured data as graphs where nodes correspond to primitives (parts, interest points, and segments) and edges characterize the relationships between these primitives. However, these nonvectorial graph data cannot be straightforwardly plugged into off-the-shelf machine learning algorithms without a preliminary step of--explicit/implicit--graph vectorization and embedding. This embedding process should be resilient to intraclass graph variations while being highly discriminant. In this paper, we propose a novel high-order stochastic graphlet embedding that maps graphs into vector spaces. Our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extracts/samples unlimitedly high-order graphlets. We consider these graphlets, with increasing orders, to model local primitives as well as their increasingly complex interactions. In order to build our graph representation, we measure the distribution of these graphlets into a given graph, using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision. When combined with maximum margin classifiers, these graphlet-based representations have a positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases.
Adding Semantics to Data-Driven Paraphrasing

We add an interpretable semantics to the paraphrase database (PPDB). To date, the relationship between phrase pairs in the database has been weakly defined as approximately equivalent. We show that these pairs represent a variety of relations, including directed entailment (little girl/girl) and exclusion (nobody/someone). We automatically assign semantic entailment relations to entries in PPDB using features derived from past work on discovering inference rules from text and semantic taxonomy induction. We demonstrate that our model assigns these relations with high accuracy. In a downstream RTE task, our labels rival relations from WordNet and improve the coverage of a proof-based RTE system by 17%.
Coupled two-way clustering analysis of gene microarray data.

We present a coupled two-way clustering approach to gene microarray data analysis. The main idea is to identify subsets of the genes and samples, such that when one of these is used to cluster the other, stable and significant partitions emerge. The search for such subsets is a computationally complex task. We present an algorithm, based on iterative clustering, that performs such a search. This analysis is especially suitable for gene microarray data, where the contributions of a variety of biological mechanisms to the gene expression levels are entangled in a large body of experimental data. The method was applied to two gene microarray data sets, on colon cancer and leukemia. By identifying relevant subsets of the data and focusing on them we were able to discover partitions and correlations that were masked and hidden when the full dataset was used in the analysis. Some of these partitions have clear biological interpretation; others can serve to identify possible directions for future research.
Storytelling of Photo Stream with Bidirectional Multi-thread Recurrent Neural Network

Visual storytelling aims to generate human-level narrative language (i.e., a natural paragraph with multiple sentences) from a photo streams. A typical photo story consists of a global timeline with multi-thread local storylines, where each storyline occurs in one different scene. Such complex structure leads to large content gaps at scene transitions between consecutive photos. Most existing image/video captioning methods can only achieve limited performance, because the units in traditional recurrent neural networks (RNN) tend to “forget” the previous state when the visual sequence is inconsistent. In this paper, we propose a novel visual storytelling approach with Bidirectional Multi-thread Recurrent Neural Network (BMRNN). First, based on the mined local storylines, a skip gated recurrent unit (sGRU) with delay control is proposed to maintain longer range visual information. Second, by using sGRU as basic units, the BMRNN is trained to align the local storylines into the global sequential timeline. Third, a new training scheme with a storyline-constrained objective function is proposed by jointly considering both global and local matches. Experiments on three standard storytelling datasets show that the BMRNN model outperforms the state-of-the-art methods.
Improving Neural Machine Translation Models with Monolingual Data

Neural Machine Translation (NMT) has obtained state-of-the art performance for several language pairs, while only using parallel data for training. Targetside monolingual data plays an important role in boosting fluency for phrasebased statistical machine translation, and we investigate the use of monolingual data for NMT. In contrast to previous work, which combines NMT models with separately trained language models, we note that encoder-decoder NMT architectures already have the capacity to learn the same information as a language model, and we explore strategies to train with monolingual data without changing the neural network architecture. By pairing monolingual training data with an automatic backtranslation, we can treat it as additional parallel training data, and we obtain substantial improvements on the WMT 15 task English↔German (+2.8–3.7 BLEU), and for the low-resourced IWSLT 14 task Turkish→English (+2.1–3.4 BLEU), obtaining new state-of-the-art results. We also show that fine-tuning on in-domain monolingual and parallel data gives substantial improvements for the IWSLT 15 task English→German.
Mobile bin picking with an anthropomorphic service robot

Grasping individual objects from an unordered pile in a box has been investigated in static scenarios so far. In this paper, we demonstrate bin picking with an anthropomorphic mobile robot. To this end, we extend global navigation techniques by precise local alignment with a transport box. Objects are detected in range images using a shape primitive-based approach. Our approach learns object models from single scans and employs active perception to cope with severe occlusions. Grasps and arm motions are planned in an efficient local multiresolution height map. All components are integrated and evaluated in a bin picking and part delivery task.
Automatic Patch-Based Exploit Generation is Possible: Techniques and Implications

The automatic patch-based exploit generation problem is: given a program P and a patched version of the program P', automatically generate an exploit for the potentially unknown vulnerability present in P but fixed in P'. In this paper, we propose techniques for automatic patch-based exploit generation, and show that our techniques can automatically generate exploits for 5 Microsoft programs based upon patches provided via Windows Update. Although our techniques may not work in all cases, a fundamental tenant of security is to conservatively estimate the capabilities of attackers. Thus, our results indicate that automatic patch-based exploit generation should be considered practical. One important security implication of our results is that current patch distribution schemes which stagger patch distribution over long time periods, such as Windows Update, may allow attackers who receive the patch first to compromise the significant fraction of vulnerable hosts who have not yet received the patch.
A Comparison of Text Retrieval Models

Many retrieval models have been proposed as the basis of text retrieval systems. The three main classes that have been investigated are the exact-match, vector space and probabilistic models. The retrieval effectiveness of strategies based on these models has been evaluated experimentally, but there has been little in the way of comparison in terms of their formal properties. In this paper we introduce a recent form of the probabilistic model based on inference networks, and show how the vector space and exact-match models can be described in this framework. Differences between these models can be explained as differences in the estimation of probabilities, both in the initial search and during relevance feedback.
Start from Scratch: Towards Automatically Identifying, Modeling, and Naming Visual Attributes

Higher-level semantics such as visual attributes are crucial for fundamental multimedia applications. We present a novel attribute discovery approach that can automatically identify, model and name attributes from an arbitrary set of image and text pairs that can be easily gathered on the Web. Different from conventional attribute discovery methods, our approach does not rely on any pre-defined vocabularies and human labeling. Therefore, we are able to build a large visual knowledge base without any human efforts. The discovery is based on a novel deep architecture, named Independent Component Multimodal Autoencoder (ICMAE), that can continually learn shared higher-level representations across the visual and textual modalities. With the help of the resultant representations encoding strong visual and semantic evidences, we propose to (a) identify attributes and their corresponding high-quality training images, (b) iteratively model them with maximum compactness and comprehensiveness, and (c) name the attribute models with human understandable words. To date, the proposed system has discovered 1,898 attributes over 1.3 million pairs of image and text. Extensive experiments on various real-world multimedia datasets demonstrate the quality and effectiveness of the discovered attributes, facilitating multimedia applications such as image annotation and retrieval as compared to the state-of-the-art approaches.
Impact of Partial Shading on Solar PV Module Containing Series Connected Cells

Open circuit voltage of a silicon solar cell is around 0.6V. A solar module is constructed by connecting a number of cells in series to get a practically usable voltage. Partial shading of a Solar Photovoltaic Module (SPM) is one of the main causes of overheating of shaded cells and reduced energy yield of the module. The present work is a study of harmful effects of partial shading on the performance of a PV module. A PSPICE simulation model that represents 36 cells PV module under partial shaded conditions has been used to test several shading profiles and results are presented.
Data Integration for the Relational Web

The Web contains a vast amount of structured information such as HTML tables, HTML lists and deep-web databases; there is enormous potential in combining and re-purposing this data in creative ways. However, integrating data from this relational web raises several challenges that are not addressed by current data integration systems or mash-up tools. First, the structured data is usually not published cleanly and must be extracted (say, from an HTML list) before it can be used. Second, due to the vastness of the corpus, a user can never know all of the potentially-relevant databases ahead of time (much less write a wrapper or mapping for each one); the source databases must be discovered during the integration process. Third, some of the important information regarding the data is only present in its enclosing web page and needs to be extracted appropriately. This paper describes Octopus, a system that combines search, extraction, data cleaning and integration, and enables users to create new data sets from those found on the Web. The key idea underlying Octopus is to offer the user a set of best-effort operators that automate the most labor-intensive tasks. For example, the Search operator takes a search-style keyword query and returns a set of relevance-ranked and similarity-clustered structured data sources on the Web; the Context operator helps the user specify the semantics of the sources by inferring attribute values that may not appear in the source itself, and the Extend operator helps the user find related sources that can be joined to add new attributes to a table. Octopus executes some of these operators automatically, but always allows the user to provide feedback and correct errors. We describe the algorithms underlying each of these operators and experiments that demonstrate their efficacy.
Resurrecting the ( C ) CAPM : A Cross-Sectional Test When Risk Premia Are Time-Varying

This paper explores the ability of theoretically-based asset pricing models such as the CAPM and the consumption CAPM referred to jointly as the (C)CAPM to explain the cross-section of average stock returns. Unlike many previous empirical tests of the (C)CAPM, we specify the pricing kernel as a conditional linear factor model, as would be expected if risk premia vary over time. Central to our approach is the use of a conditioning variable which proxies for fluctuations in the log consumption-aggregate wealth ratio and is likely to be important for summarizing conditional expectations of excess returns. We demonstrate that such conditional factor models are able to explain a substantial fraction of the cross-sectional variation in portfolio returns. These models perform much better than unconditional (C)CAPM specifications, and about as well as the three-factor Fama-French model on portfolios sorted by size and book-to-market ratios. This specification of the linear conditional consumption CAPM, using aggregate consumption data, is able to account for the difference in returns between low book-to-market and high book-to-market firms and exhibits little evidence of residual size or book-to-market effects.