nils-herrmann/clean_scirepeval huggingface dataset:
Security for Online Social Networks : Challenges

nline social networks (OSNs) [1, 2] such as Facebook, MySpace, and Twitter enable people to stay in touch with their contacts, reconnect with old acquaintances, and establish new relationships with other people based on shared features such as communities, hobbies, interests, and overlaps in friendship circles. Recent years have seen unprecedented growth in the application of OSNs, with about 300 OSN systems collecting information on more than half a billion registered users . As a result, OSNs store a huge amount of possibly sensitive and private information on users and their interactions. This information is usually private and intended for the eyes of a specific audience only. However, the popularity of OSNs attracts not only faithful users but parties with rather adverse interests as well [3, 4]. The diversification and sophistication of purposes and usage patterns of OSNs inevitably introduce privacy infringement risks to all OSN users as a result of information exchange and sharing on the Internet. It is therefore not surprising that stories about privacy breaches by Facebook and MySpace appear repeatedly in mainstream media [3–5]. Regardless of the purpose of an OSN, one of the main motivations for users to join an OSN, create a profile, and use different applications offered by the OSN is the possibility to easily share information with selected contacts or the public, and facilitate social interactions between the users of OSNs. Disclosing personal information in OSNs is a double-edged sword [6]. On one hand, information exposure is usually a plus, even a must, if people want to participate in social communities. Visibility of users' profiles and public display of connections (friend lists) are necessary for implementing core functionalities of OSNs such as social search and social traver-sal. On the other hand, leakage of personal information, especially one's identity, may invite malicious attacks from the real world and cyberspace, such as stalking, reputation slander, personalized spamming, and phishing [7]. Despite the risks, many of the privacy and access control mechanisms of today's OSNs are purposefully weak to make joining the OSN and sharing information easy. We believe that more effective and flexible security mechanisms are therefore required for the safety of OSN users as well as the continued thriving of OSNs. In this article we present a general framework for assessing the security and privacy of current and next-generation OSNs. Whereas others have considered specific mechanisms for improving OSN users' security and privacy, …
A Survey and Taxonomy of Location Systems for Ubiquitous Computing

Emerging mobile computing applications often need to know where things are physically located. To meet this need, many di erent location systems and technologies have been developed. In this paper we present a the basic techniques used for location-sensing, describe a taxonomy of location system properties, present a survey of research and commercial location systems that de ne the eld, show how the taxonomy can be used to evaluate location-sensing systems, and o er suggestions for future research. It is our hope that this paper is a useful reference for researchers and location-aware application builders alike for understanding and evaluating the many options in this domain.
Fast-and-Light Stochastic ADMM

The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction methods for stochastic gradient, leading to SAG-ADMM and SDCA-ADMM that have fast convergence rates and low iteration complexities. However, their space requirements can still be high. In this paper, we propose an integration of ADMM with the method of stochastic variance reduced gradient (SVRG). Unlike another recent integration attempt called SCAS-ADMM, the proposed algorithm retains the fast convergence benefits of SAG-ADMM and SDCA-ADMM, but is more advantageous in that its storage requirement is very low, even independent of the sample size n. Experimental results demonstrate that it is as fast as SAG-ADMM and SDCA-ADMM, much faster than SCAS-ADMM, and can be used on much bigger data sets.
An Extensive Empirical Study of Feature Selection Metrics for Text Classification

Machine learning for text classification is the cor nerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and m ore accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g . Information Gain) evaluated on a benchmark of 229 text classification problem instances that w ere gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal p ers ectives—accuracy, F-measure, precision, and recall—since each is appropriate in different si tuat ons. The results reveal that a new feature selection me tric we call ‘Bi-Normal Separation’ (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text clas sification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focus es on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspect iv , BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Informati on Gain and Chi-Squared have correlated failures, and so they work poorly together. When c hoosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a membe r of the pair—e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin.
Classification of Model Transformation Approaches

While the current OMG standards such as the Meta Object Facility (MOF) [MOF] and the UML [UML] provide a well-established foundation for defining PIMs and PSMs, no such wellestablished foundation exists for transforming PIMs into PSMs [GLR+02]. In 2002, in its effort to change this situation, the OMG initiated a standardization process by issuing a Request for Proposal (RFP) on Query / Views / Transformations (QVT) [QVT]. This process will eventually lead to an OMG standard for defining model transformations, which will be of interest not only for PIM-to-PSM transformations, but also for defining views on models and synchronization between models. Driven by practical needs and the OMG’s request, a large number of approaches to model transformation have recently been proposed.
Deformable part models are convolutional neural networks

Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition. They are typically viewed as distinct approaches: DPMs are graphical models (Markov random fields), while CNNs are “black-box” non-linear classifiers. In this paper, we show that a DPM can be formulated as a CNN, thus providing a synthesis of the two ideas. Our construction involves unrolling the DPM inference algorithm and mapping each step to an equivalent CNN layer. From this perspective, it is natural to replace the standard image features used in DPMs with a learned feature extractor. We call the resulting model a DeepPyramid DPM and experimentally validate it on PASCAL VOC object detection. We find that DeepPyramid DPMs significantly outperform DPMs based on histograms of oriented gradients features (HOG) and slightly outperforms a comparable version of the recently introduced R-CNN detection system, while running significantly faster.
A Systematic Analysis of XSS Sanitization in Web Application Frameworks

While most research on XSS defense has focused on techniques for securing existing applications and re-architecting browser mechanisms, sanitization remains the industry-standard defense mechanism. By streamlining and automating XSS sanitization, web application frameworks stand in a good position to stop XSS but have received little research attention. In order to drive research on web frameworks, we systematically study the security of the XSS sanitization abstractions frameworks provide. We develop a novel model of the web browserions frameworks provide. We develop a novel model of the web browser and characterize the challenges of XSS sanitization. Based on the model, we systematically evaluate the XSS abstractions in 14 major commercially-used web frameworks. We find that frameworks often do not address critical parts of the XSS conundrum. We perform an empirical analysis of 8 large web applications to extract the requirements of sanitization primitives from the perspective of realworld applications. Our study shows that there is a wide gap between the abstractions provided by frameworks and the requirements of applications.
Improving MapReduce Performance in Heterogeneous Environments

MapReduce is emerging as an important programming model for large-scale data-parallel applications such as web indexing, data mining, and scientific simulation. Hadoop is an open-source implementation of MapReduce enjoying wide adoption and is often used for short jobs where low response time is critical. Hadoop’s performance is closely tied to its task scheduler, which implicitly assumes that cluster nodes are homogeneous and tasks make progress linearly, and uses these assumptions to decide when to speculatively re-execute tasks that appear to be stragglers. In practice, the homogeneity assumptions do not always hold. An especially compelling setting where this occurs is a virtualized data center, such as Amazon’s Elastic Compute Cloud (EC2). We show that Hadoop’s scheduler can cause severe performance degradation in heterogeneous environments. We design a new scheduling algorithm, Longest Approximate Time to End (LATE), that is both simple and highly robust to heterogeneity. LATE can improve Hadoop response times by a factor of 2 in 200-node clusters on EC2.
Group formation in large social networks: membership, growth, and evolution

The processes by which communities come together, attract new members, and develop over time is a central research issue in the social sciences - political movements, professional organizations, and religious denominations all provide fundamental examples of such communities. In the digital domain, on-line groups are becoming increasingly prominent due to the growth of community and social networking sites such as MySpace and LiveJournal. However, the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities has left most basic questions about the evolution of such groups largely unresolved: what are the structural features that influence whether individuals will join communities, which communities will grow rapidly, and how do the overlaps among pairs of communities change over time.Here we address these questions using two large sources of data: friendship links and community membership on LiveJournal, and co-authorship and conference publications in DBLP. Both of these datasets provide explicit user-defined communities, where conferences serve as proxies for communities in DBLP. We study how the evolution of these communities relates to properties such as the structure of the underlying social networks. We find that the propensity of individuals to join communities, and of communities to grow rapidly, depends in subtle ways on the underlying network structure. For example, the tendency of an individual to join a community is influenced not just by the number of friends he or she has within the community, but also crucially by how those friends are connected to one another. We use decision-tree techniques to identify the most significant structural determinants of these properties. We also develop a novel methodology for measuring movement of individuals between communities, and show how such movements are closely aligned with changes in the topics of interest within the communities.
Evaluating Battery Models in Wireless Sensor Networks

Recent measurements highlight the importance of batteryaware evaluation of energy efficiency in wireless sensor networks. However, existing battery models been not investigated in the context of the low duty cycle, short duration loads that are typical of sensor networks. We evaluate three battery models with regard to their applicability in the WSN context. Our evaluation focuses on how the models reflect two key battery discharge behaviors, the rate capacity effect and charge recovery. We find that the models handle the former better than the latter and are more sensitive to a load’s peak current than to its timing.
The Open University ’ s repository of research publications and other research outputs Measuring mathematical resilience : an application of the construct of resilience to the study of mathematics

Motivation, attitudes towards mathematics, and resilience theories are synthesized to develop a theory and instrument useful in studying malleable attributes predictive of persistence in mathematics in spite of adversity.  The MRS was developed and validated using three samples utilizing the techniques of exploratory and confirmatory factor analysis. Evidence presented supports three factors: value, struggle, and growth, and provides a new approach for encouraging greater student participation and persistence in mathematics.
A Deep Dual-Branch Networks for Joint Blind Motion Deblurring and Super-Resolution

Image super-resolution is a fundamental pre-processing technique for the machine vision applications of robotics and other mobile platforms. Inevitably, images captured by the mobile camera tend to emerge severe motion blur and this degradation will deteriorate the performance of current state-of-the-art super-resolution methods. In this paper, we propose a deep dual-branch convolution neural network (CNN) to generate a clear high-resolution image from a single natural image with severe blurs. Compared to off-the-shelf methods, our method, called DB-SRN, can remove the complex non-uniform motion blurs and restore useful texture details simultaneously. By sharing the features from modified residual blocks (ResBlocks), the dual-branch design can promote the performances of both tasks other while retaining network simplicity. Extensive experiments demonstrate that our method produces remarkable deblurred and super-resolved images in terms of quality and quantity with high computational efficiency.
Question-answering by predictive annotation

We present a new technique for question answering called Predictive Annotation. Predictive Annotation identifies potential answers to questions in text, annotates them accordingly and indexes them. This technique, along with a complementary analysis of questions, passage-level ranking and answer selection, produces a system effective at answering natural-language fact-seeking questions posed against large document collections. Experimental results show the effects of different parameter settings and lead to a number of general observations about the question-answering problem.
Direct least squares fitting of ellipses

This work presents a new efficient method for fitting ellipses to scattered data. Previous algorithms either fitted general conics or were computationally expensive. By minimizing the algebraic distance subject to the constraint 4 2 1 the new method incorporates the ellipticity constraint into the normalization factor. The new method combines several advantages: (i) It is ellipse-specific so that even bad data will always return an ellipse; (ii) It can be solved naturally by a generalized eigensystem and (iii) it is extremely robust, efficient and easy to implement. We compare the proposed method to other approaches and show its robustness on several examples in which other non-ellipse-specific approaches would fail or require computationally expensive iterative refinements. Source code for the algorithm is supplied and a demonstration is available on ! " ! $#% '& () "*) & +, ./10 0 32, . 4) "*) "*) % 5* 0
Syntactic Simplification and Text Cohesion

Syntactic simplification is the process of reducing the grammatical complexity of a text, while retaining its information content and meaning. The aim of syntactic simplification is to make text easier to comprehend for human readers, or process by programs. In this thesis, I describe how syntactic simplification can be achieved using shallow robust analysis, a small set of hand-crafted simplification rules and a detailed analysis of the discourse-level aspects of syntactically rewriting text. I offer a treatment of relative clauses, apposition, coordination and subordination. I present novel techniques for relative clause and appositive attachment. I argue that these attachment decisions are not purely syntactic. My approaches rely on a shallow discourse model and on animacy information obtained from a lexical knowledge base. I also show how clause and appositive boundaries can be determined reliably using a decision procedure based on local context, represented by part-of-speech tags and noun chunks. I then formalise the interactions that take place between syntax and discourse during the simplification process. This is important because the usefulness of syntactic simplification in making a text accessible to a wider audience can be undermined if the rewritten text lacks cohesion. I describe how various generation issues like sentence ordering, cue-word selection, referring-expression generation, determiner choice and pronominal use can be resolved so as to preserve conjunctive and anaphoric cohesive-relations during syntactic simplification. In order to perform syntactic simplification, I have had to address various natural language processing problems, including clause and appositive identification and attachment, pronoun resolution and referring-expression generation. I evaluate my approaches to solving each problem individually, and also present a holistic evaluation of my syntactic simplification system.
A High Efficiency two-stage ZVS AC/DC converter with all SiC MOSFET

A High Efficiency two-stage AC/DC converter is presented and discussed in this paper, which can be applied in on-board charger for electric vehicles. The front-end converter is a high efficiency ZVS half bridge power factor correction (PFC) topology with only one device in the current flowing path. Owing to the high voltage stress of the half bridge rectifier, the SiC MOSFET is applied to reduce the conduction and switching losses and achieve high frequency. The downstream stage is a LLC resonant DC/DC converter. The peak efficiency reaches 97.5%.
Zero-bias autoencoders and the benefits of co-adapting features

Regularized training of an autoencoder typically results in hidden unit biases that take on large negative values. We show that negative biases are a natural result of using a hidden layer whose responsibility is to both represent the input data and act as a selection mechanism that ensures sparsity of the representation. We then show that negative biases impede the learning of data distributions whose intrinsic dimensionality is high. We also propose a new activation function that decouples the two roles of the hidden layer and that allows us to learn representations on data with very high intrinsic dimensionality, where standard autoencoders typically fail. Since the decoupled activation function acts like an implicit regularizer, the model can be trained by minimizing the reconstruction error of training data, without requiring any additional regularization.
Authorized Private Keyword Search over Encrypted Data in Cloud Computing

In cloud computing, clients usually outsource their data to the cloud storage servers to reduce the management costs. While those data may contain sensitive personal information, the cloud servers cannot be fully trusted in protecting them. Encryption is a promising way to protect the confidentiality of the outsourced data, but it also introduces much difficulty to performing effective searches over encrypted information. Most existing works do not support efficient searches with complex query conditions, and care needs to be taken when using them because of the potential privacy leakages about the data owners to the data users or the cloud server. In this paper, using on line Personal Health Record (PHR) as a case study, we first show the necessity of search capability authorization that reduces the privacy exposure resulting from the search results, and establish a scalable framework for Authorized Private Keyword Search (APKS) over encrypted cloud data. We then propose two novel solutions for APKS based on a recent cryptographic primitive, Hierarchical Predicate Encryption (HPE). Our solutions enable efficient multi-dimensional keyword searches with range query, allow delegation and revocation of search capabilities. Moreover, we enhance the query privacy which hides users' query keywords against the server. We implement our scheme on a modern workstation, and experimental results demonstrate its suitability for practical usage.
Computer-Mediated Communication Conflict Management in Groups that Work in Two Different Communication Contexts : Face-To-Face and

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An algorithm for fast edit distance computation on GPUs

The problem of finding the edit distance between two sequences (and its closely related problem of longest common subsequence) are important problems with applications in many domains like virus scanners, security kernels, natural language translation and genome sequence alignment. The traditional dynamic-programming based algorithm is hard to parallelize on SIMD processors as the algorithm is memory intensive and has many divergent control paths. In this paper we introduce a new algorithm which modifies the dynamic programming method to reduce its amount of data storage and eliminate control flow divergences. Our algorithm divides the problem into independent `quadrants' and makes efficient use of shared memory and registers available in GPUs to store data between different phases of the algorithm. Further, we eliminate any control flow divergences by embedding condition variables in the program logic to ensure all the threads execute the same instructions even though they work on different data items. We present an implementation of this algorithm on an NVIDIA GeForce GTX 275 GPU and compare against an optimized multi-threaded implementation on an Intel Core i7-920 quad core CPU with hyper-threading support. Our results show that our GPU implementation is up to 8x faster when operating on a large number of sequences.
Analytic moment-based Gaussian process filtering

We propose an analytic moment-based filter for nonlinear stochastic dynamic systems modeled by Gaussian processes. Exact expressions for the expected value and the covariance matrix are provided for both the prediction step and the filter step, where an additional Gaussian assumption is exploited in the latter case. Our filter does not require further approximations. In particular, it avoids finite-sample approximations. We compare the filter to a variety of Gaussian filters, that is, the EKF, the UKF, and the recent GP-UKF proposed by Ko et al. (2007).