nils-herrmann/clean_scirepeval huggingface dataset:
Combining reconstructive and discriminative subspace methods for robust classification and regression by subsampling

Linear subspace methods that provide sufficient reconstruction of the data, such as PCA, offer an efficient way of dealing with missing pixels, outliers, and occlusions that often appear in the visual data. Discriminative methods, such as LDA, which, on the other hand, are better suited for classification tasks, are highly sensitive to corrupted data. We present a theoretical framework for achieving the best of both types of methods: an approach that combines the discrimination power of discriminative methods with the reconstruction property of reconstructive methods which enables one to work on subsets of pixels in images to efficiently detect and reject the outliers. The proposed approach is therefore capable of robust classification with a high-breakdown point. We also show that subspace methods, such as CCA, which are used for solving regression tasks, can be treated in a similar manner. The theoretical results are demonstrated on several computer vision tasks showing that the proposed approach significantly outperforms the standard discriminative methods in the case of missing pixels and images containing occlusions and outliers.
Image collection summarization via dictionary learning for sparse representation

In this paper, a novel framework is developed to achieve effective summarization of large-scale image collection by treating the problem of automatic image summarization as the problem of dictionary learning for sparse representation, e.g., the summarization task can be treated as a dictionary learning task (i.e., the given image set can be reconstructed sparsely with this dictionary). For image set of a specific category or a mixture of multiple categories, we have built a sparsity model to reconstruct all its images by using a subset of most representative images (i.e., image summary); and we adopted the simulated annealing algorithm to learn such sparse dictionary by minimizing an explicit optimization function. By investigating their reconstruction ability under sparsity constrain and diversity constrain, we have quantitatively measure the performance of various summarization algorithms. Our experimental results have shown that our dictionary learning for sparse representation algorithm can obtain more accurate summary as compared with other baseline algorithms.
Software-Defined Networking: State of the Art and Research Challenges

Plug-and-play information technology (IT) infrastructure has been expanding very rapidly in recent years. With the advent of cloud computing, many ecosystem and business paradigms are encountering potential changes and may be able to eliminate their IT infrastructure maintenance processes. Real-time performance and high availability requirements have induced telecom networks to adopt the new concepts of the cloud model: software-defined networking (SDN) and network function virtualization (NFV). NFV introduces and deploys new network functions in an open and standardized IT environment, while SDN aims to transform the way networks function. SDN and NFV are complementary technologies; they do not depend on each other. However, both concepts can be merged and have the potential to mitigate the challenges of legacy networks. In this paper, our aim is to describe the benefits of using SDN in a multitude of environments such as in data centers, data center networks, and Network as Service offerings. We also present the various challenges facing SDN, from scalability to reliability and security concerns, and discuss existing solutions to these challenges. Software-Defined Networking, OpenFlow, Datacenters, Network as a Service, Network Function Virtualization.
Automatic graph drawing and readability of diagrams

-Diagrams are widely used in several areas of computer wience, and their effectiveness is thoroughly recognized. One of the main qualities requested for them is readability; this is especially, but not exclusively, true in the area of information systems, where diagrams are used to model data and functions of the application. Up to now, diagrams have been produced manually or with the aid of a graphic editor; in both caws placement of symbols and routing of connections are under responsibility of the designer. The goal of the work is to investigate how readability of diagrams can be achieved by means of automatic tools. Existing results in the literature are compared, and a comprehensive algorithmic approach to the problem is proposed. The algorithm presented draws graphs on a grid and is suitable for both undirected graphs and mixed graphs that contain as subgraphs hierarchic structures. Finally, several applications of a graphic tool that embodies the aforementioned facility are shown.
Discriminatively Trained Dense Surface Normal Estimation

In this work we propose the method for a rather unexplored problem of computer vision discriminatively trained dense surface normal estimation from a single image. Our method combines contextual and segment-based cues and builds a regressor in a boosting framework by transforming the problem into the regression of coefficients of a local coding. We apply our method to two challenging data sets containing images of man-made environments, the indoor NYU2 data set and the outdoor KITTI data set. Our surface normal predictor achieves results better than initially expected, significantly outperforming state-of-the-art.
Background Recovery by Fixed-Rank Robust Principal Component Analysis

recovery is a very important theme in computer vision applications. Recent research shows that robust principal component analysis (RPCA) is a promising approach for solving problems such as noise removal, video background modeling, and removal of shadows and specularity. RPCA utilizes the fact that the background is common in multiple views of a scene, and attempts to decompose the data matrix constructed from input images into a low-rank matrix and a sparse matrix. This is possible if the sparse matrix is sufficiently sparse, which may not be true in computer vision applications. Moreover, algorithmic parameters need to be fine tuned to yield accurate results. This paper proposes a fixed-rank RPCA algorithm for solving background recovering problems whose low-rank matrices have known ranks. Comprehensive tests show that, by fixing the rank of the low-rank matrix to a known value, the fixed-rank algorithm produces more reliable and accurate results than existing low-rank RPCA algorithm.
In a World That Counts: Clustering and Detecting Fake Social Engagement at Scale

How can web services that depend on user generated content discern fake social engagement activities by spammers from legitimate ones? In this paper, we focus on the social site of YouTube and the problem of identifying bad actors posting inorganic contents and inflating the count of social engagement metrics. We propose an effective method, Leas (Local Expansion at Scale), and show how the fake engagement activities on YouTube can be tracked over time by analyzing the temporal graph based on the engagement behavior pattern between users and YouTube videos. With the domain knowledge of spammer seeds, we formulate and tackle the problem in a semi-supervised manner — with the objective of searching for individuals that have similar pattern of behavior as the known seeds — based on a graph diffusion process via local spectral subspace. We offer a fast, scalable MapReduce deployment adapted from the localized spectral clustering algorithm. We demonstrate the effectiveness of our deployment at Google by achieving a manual review accuracy of 98% on YouTube Comments graph in practice. Comparing with the state-of-the-art algorithm CopyCatch, Leas achieves 10 times faster running time on average. Leas is now actively in use at Google, searching for daily deceptive practices on YouTube’s engagement graph spanning over a
How Unique and Traceable Are Usernames?

Usernames are ubiquitously used for identification and authentication purposes on web services and the Internet at large, ranging from the local-part of email addresses to identifiers in social networks. Usernames are generally alphanumerical strings chosen by the users and, by design, are unique within the scope of a single organization or web service. In this paper we investigate the feasibility of using usernames to trace or link multiple profiles across services that belong to the same individual. The intuition is that the probability that two usernames refer to the same physical person strongly depends on the “entropy” of the username string itself. Our experiments, based on usernames gathered from real web services, show that a significant portion of the users’ profiles can be linked using their usernames. In collecting the data needed for our study, we also show that users tend to choose a small number of related usernames and use them across many services. To the best of our knowledge, this is the first time that usernames are considered as a source of information when profiling users on the Internet.
Dynamic Texture Segmentation

We address the problem of segmenting a sequence of images of natural scenes into disjoint regions that are characterized by constant spatio-temporal statistics. We model the spatio-temporal dynamics in each region by Gauss-Markov models, and infer the model parameters as well as the boundary of the regions in a variational optimization framework. Numerical results demonstrate that – in contrast to purely texture-based segmentation schemes – our method is effective in segmenting regions that differ in their dynamics even when spatial statistics are identical.
Two Birds with One Stone: Two-Factor Authentication with Security Beyond Conventional Bound

As the most prevailing two-factor authentication mechanism, smart-card-based password authentication has been a subject of intensive research in the past two decades, and hundreds of this type of schemes have wave upon wave been proposed. In most of these studies, there is no comprehensive and systematical metric available for schemes to be assessed objectively, and the authors present new schemes with assertions of the superior aspects over previous ones, while overlooking dimensions on which their schemes fare poorly. Unsurprisingly, most of them are far from satisfactory—either are found short of important security goals or lack of critical properties, especially being stuck with the security-usability tension. To overcome this issue, in this work we first explicitly define a security model that can accurately capture the practical capabilities of an adversary and then suggest a broad set of twelve properties framed as a systematic methodology for comparative evaluation, allowing schemes to be rated across a common spectrum. As our main contribution, a new scheme is advanced to resolve the various issues arising from user corruption and server compromise, and it is formally proved secure under the harshest adversary model so far. In particular, by integrating “honeywords”, traditionally the purview of system security, with a “fuzzy-verifier”, our scheme hits “two birds”: it not only eliminates the long-standing security-usability conflict that is considered intractable in the literature, but also achieves security guarantees beyond the conventional optimal security bound.
Analysis on Traffic Accident Injury Level Using Classification

Traffic Accidents are occurring due to development of automobile industry and the accidents are unavoidable even the traffic rules are very strictly maintained. Data mining algorithm is applied to model the traffic accident injury level by using traffic accident dataset. It helped by obtaining the characteristics of drivers behavior, road condition and weather condition, Accident severity that are connected with different injury severities and death. This paper presents some models to predict the severity of injury using some data mining algorithms. The study focused on collecting the real data from previous research and obtains the injury severity level of traffic accident data.
Detecting Silicone Mask-Based Presentation Attack via Deep Dictionary Learning

In movies, film stars portray another identity or obfuscate their identity with the help of silicone/latex masks. Such realistic masks are now easily available and are used for entertainment purposes. However, their usage in criminal activities to deceive law enforcement and automatic face recognition systems is also plausible. Therefore, it is important to guard biometrics systems against such realistic presentation attacks. This paper introduces the first-of-its-kind silicone mask attack database which contains 130 real and attacked videos to facilitate research in developing presentation attack detection algorithms for this challenging scenario. Along with silicone mask, there are several other presentation attack instruments that are explored in literature. The next contribution of this research is a novel multilevel deep dictionary learning-based presentation attack detection algorithm that can discern different kinds of attacks. An efficient greedy layer by layer training approach is formulated to learn the deep dictionaries followed by SVM to classify an input sample as genuine or attacked. Experimental are performed on the proposed SMAD database, some samples with real world silicone mask attacks, and four existing presentation attack databases, namely, replay-attack, CASIA-FASD, 3DMAD, and UVAD. The results show that the proposed algorithm yields better performance compared with state-of-the-art algorithms, in both intra-database and cross-database experiments.
Anticipating Accidents in Dashcam Videos

We propose a Dynamic-Spatial-Attention (DSA) Recurrent Neural Network (RNN) for anticipating accidents in dashcam videos . Our DSA-RNN learns to (1) distribute soft-attention to candidate objects dynamically to gather subtle cues and (2) model the temporal dependencies of all cues to robustly anticipate an accident. Anticipating accidents is much less addressed than anticipating events such as changing a lane, making a turn, etc., since accidents are rare to be observed and can happen in many different ways mostly in a sudden. To overcome these challenges, we (1) utilize state-of-the-art object detector  to detect candidate objects, and (2) incorporate full-frame and object-based appearance and motion features in our model. We also harvest a diverse dataset of 678 dashcam accident videos on the web . The dataset is unique, since various accidents (e.g., a motorbike hits a car, a car hits another car, etc.) occur in all videos. We manually mark the time-location of accidents and use them as supervision to train and evaluate our method. We show that our method anticipates accidents about 2 seconds before they occur with 80% recall and 56.14% precision. Most importantly, it achieves the highest mean average precision (74.35%) outperforming other baselines without attention or RNN. 2 Fu-Hsiang Chan, Yu-Ting Chen, Yu Xiang, Min Sun
Finite-Sample Analysis of Fixed-k Nearest Neighbor Density Functional Estimators

We provide finite-sample analysis of a general framework for using k-nearest neighbor statistics to estimate functionals of a nonparametric continuous probability density, including entropies and divergences. Rather than plugging a consistent density estimate (which requires k → ∞ as the sample size n → ∞) into the functional of interest, the estimators we consider fix k and perform a bias correction. This is more efficient computationally, and, as we show in certain cases, statistically, leading to faster convergence rates. Our framework unifies several previous estimators, for most of which ours are the first finite sample guarantees.
Threatening communication: a critical re-analysis and a revised meta-analytic test of fear appeal theory

Despite decades of research, consensus regarding the dynamics of fear appeals remains elusive. A meta-analysis was conducted that was designed to resolve this controversy. Publications that were included in previous meta-analyses were re-analysed, and a number of additional publications were located. The inclusion criteria were full factorial orthogonal manipulations of threat and efficacy, and measurement of behaviour as an outcome. Fixed and random effects models were used to compute mean effect size estimates. Meta-analysis of the six studies that satisfied the inclusion criteria clearly showed a significant interaction between threat and efficacy, such that threat only had an effect under high efficacy (d = 0.31), and efficacy only had an effect under high threat (d = 0.71). Inconsistency in results regarding the effectiveness of threatening communication can likely be attributed to flawed methodology. Proper tests of fear appeal theory yielded the theoretically hypothesised interaction effect. Threatening communication should exclusively be used when pilot studies indicate that an intervention successfully enhances efficacy.
Automatic Variational Inference in Stan

Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it di cult for non-experts to use. We propose an automatic variational inference algorithm, automatic di erentiation variational inference ( ); we implement it in Stan (code available), a probabilistic programming system. In the user provides a Bayesian model and a dataset, nothing else. We make no conjugacy assumptions and support a broad class of models. The algorithm automatically determines an appropriate variational family and optimizes the variational objective. We compare to sampling across hierarchical generalized linear models, nonconjugate matrix factorization, and a mixture model. We train the mixture model on a quarter million images. With we can use variational inference on any model we write in Stan.
Time series shapelets: a new primitive for data mining

Classification of time series has been attracting great interest over the past decade. Recent empirical evidence has strongly suggested that the simple nearest neighbor algorithm is very difficult to beat for most time series problems. While this may be considered good news, given the simplicity of implementing the nearest neighbor algorithm, there are some negative consequences of this. First, the nearest neighbor algorithm requires storing and searching the entire dataset, resulting in a time and space complexity that limits its applicability, especially on resource-limited sensors. Second, beyond mere classification accuracy, we often wish to gain some insight into the data.
 In this work we introduce a new time series primitive, time series shapelets, which addresses these limitations. Informally, shapelets are time series subsequences which are in some sense maximally representative of a class. As we shall show with extensive empirical evaluations in diverse domains, algorithms based on the time series shapelet primitives can be interpretable, more accurate and significantly faster than state-of-the-art classifiers.
Global-local and trail-making tasks by monolingual and bilingual children: beyond inhibition.

In 3 experiments, a total of 151 monolingual and bilingual 6-year-old children performed similarly on measures of language and cognitive ability; however, bilinguals solved the global-local and trail-making tasks more rapidly than monolinguals. This bilingual advantage was found not only for the traditionally demanding conditions (incongruent global-local trials and Trails B) but also for the conditions not usually considered to be cognitively demanding (congruent global-local trials and Trails A). All the children performed similarly when congruent trials were presented in a single block or when perceptually simple stimuli were used, ruling out speed differences between the groups. The results demonstrate a bilingual advantage in processing complex stimuli in tasks that require executive processing components for conflict resolution, including switching and updating, even when no inhibition appears to be involved. They also suggest that simple conditions of the trail-making and global-local tasks involve some level of effortful processing for young children. Finally, the bilingual advantage in the trail-making task suggests that the interpretation of standardized measures of executive control needs to be reconsidered for children with specific experiences, such as bilingualism.
Twitter: who gets caught? observed trends in social micro-blogging spam

Spam in Online Social Networks (OSNs) is a systemic problem that imposes a threat to these services in terms of undermining their value to advertisers and potential investors, as well as negatively affecting users' engagement. In this work, we present a unique analysis of spam accounts in OSNs viewed through the lens of their behavioral characteristics (i.e., profile properties and social interactions). Our analysis includes over 100 million tweets collected over the course of one month, generated by approximately 30 million distinct user accounts, of which over 7% are suspended or removed due to abusive behaviors and other violations. We show that there exist two behaviorally distinct categories of twitter spammers and that they employ different spamming strategies. The users in these two categories demonstrate different individual properties as well as social interaction patterns. As the Twitter spammers continuously keep creating newer accounts upon being caught, a behavioral understanding of their spamming behavior will be vital in the design of future social media defense mechanisms.
Optimal DNN primitive selection with partitioned boolean quadratic programming

Deep Neural Networks (DNNs) require very large amounts of computation, and many different algorithms have been proposed to implement their most expensive layers, each of which has a large number of variants with different trade-offs of parallelism, locality, memory footprint, and execution time. In addition, specific algorithms operate much more efficiently on specialized data layouts. 
 We state the problem of optimal primitive selection in the presence of data layout transformations, and show that it is NP-hard by demonstrating an embedding in the Partitioned Boolean Quadratic Assignment problem (PBQP). We propose an analytic solution via a PBQP solver, and evaluate our approach experimentally by optimizing several popular DNNs using a library of more than 70 DNN primitives, on an embedded platform and a general purpose platform. We show experimentally that significant gains are possible versus the state of the art vendor libraries by using a principled analytic solution to the problem of primitive selection in the presence of data layout transformations.
Did the Model Understand the Question?

We analyze state-of-the-art deep learning models for three tasks: question answering on (1) images, (2) tables, and (3) passages of text. Using the notion of attribution (word importance), we find that these deep networks often ignore important question terms. Leveraging such behavior, we perturb questions to craft a variety of adversarial examples. Our strongest attacks drop the accuracy of a visual question answering model from 61.1% to 19%, and that of a tabular question answering model from 33.5% to 3.3%. Additionally, we show how attributions can strengthen attacks proposed by Jia and Liang (2017) on paragraph comprehension models. Our results demonstrate that attributions can augment standard measures of accuracy and empower investigation of model performance. When a model is accurate but for the wrong reasons, attributions can surface erroneous logic in the model that indicates inadequacies in the test data.