nils-herrmann/clean_scirepeval huggingface dataset:
Cross-system user modeling and personalization on the Social Web

In order to adapt functionality to their individual users, systems need information about these users. The Social Web provides opportunities to gather user data from outside the system itself. Aggregated user data may be useful to address cold-start problems as well as sparse user profiles, but this depends on the nature of individual user profiles distributed on the Social Web. For example, does it make sense to re-use Flickr profiles to recommend bookmarks in Delicious? In this article, we study distributed form-based and tag-based user profiles, based on a large dataset aggregated from the Social Web. We analyze the completeness, consistency and replication of form-based profiles, which users explicitly create by filling out forms at Social Web systems such as Twitter, Facebook and LinkedIn. We also investigate tag-based profiles, which result from social tagging activities in systems such as Flickr, Delicious and StumbleUpon: to what extent do tag-based profiles overlap between different systems, what are the benefits of aggregating tag-based profiles. Based on these insights, we developed and evaluated the performance of several cross-system user modeling strategies in the context of recommender systems. The evaluation results show that the proposed methods solve the cold-start problem and improve recommendation quality significantly, even beyond the cold-start.
Improving predictive inference under covariate shift by weighting the log-likelihood function

A class of predictive densities is derived by weighting the observed samples in maximizing the log-likelihood function. This approach is e ective in cases such as sample surveys or design of experiments, where the observed covariate follows a di erent distribution than that in the whole population. Under misspeci cation of the parametric model, the optimal choice of the weight function is asymptotically shown to be the ratio of the density function of the covariate in the population to that in the observations. This is the pseudo-maximum likelihood estimation of sample surveys. The optimality is de ned by the expected Kullback–Leibler loss, and the optimal weight is obtained by considering the importance sampling identity. Under correct speci cation of the model, however, the ordinary maximum likelihood estimate (i.e. the uniform weight) is shown to be optimal asymptotically. For moderate sample size, the situation is in between the two extreme cases, and the weight function is selected by minimizing a variant of the information criterion derived as an estimate of the expected loss. The method is also applied to a weighted version of the Bayesian predictive density. Numerical examples as well as Monte-Carlo simulations are shown for polynomial regression. A connection with the robust parametric estimation is discussed.
The Golden Beauty: Brain Response to Classical and Renaissance Sculptures

Is there an objective, biological basis for the experience of beauty in art? Or is aesthetic experience entirely subjective? Using fMRI technique, we addressed this question by presenting viewers, naïve to art criticism, with images of masterpieces of Classical and Renaissance sculpture. Employing proportion as the independent variable, we produced two sets of stimuli: one composed of images of original sculptures; the other of a modified version of the same images. The stimuli were presented in three conditions: observation, aesthetic judgment, and proportion judgment. In the observation condition, the viewers were required to observe the images with the same mind-set as if they were in a museum. In the other two conditions they were required to give an aesthetic or proportion judgment on the same images. Two types of analyses were carried out: one which contrasted brain response to the canonical and the modified sculptures, and one which contrasted beautiful vs. ugly sculptures as judged by each volunteer. The most striking result was that the observation of original sculptures, relative to the modified ones, produced activation of the right insula as well as of some lateral and medial cortical areas (lateral occipital gyrus, precuneus and prefrontal areas). The activation of the insula was particularly strong during the observation condition. Most interestingly, when volunteers were required to give an overt aesthetic judgment, the images judged as beautiful selectively activated the right amygdala, relative to those judged as ugly. We conclude that, in observers naïve to art criticism, the sense of beauty is mediated by two non-mutually exclusive processes: one based on a joint activation of sets of cortical neurons, triggered by parameters intrinsic to the stimuli, and the insula (objective beauty); the other based on the activation of the amygdala, driven by one's own emotional experiences (subjective beauty).
Supporting Cloud Computing in Thin-Client/Server Computing Model

This paper addresses the issue of how to support cloud computing in thin-client/server computing model. We describe the design and implementation of the multiple-application-server thin-client/server computing (MAS TC/S) model that allows users with thin-client devices to roam around a wide area network whilst experiencing transparent working environments. MAS TC/S can be applied to a wide variety of applications in wide area network, such as pervasive software rental, service-oriented infrastructure for an Internet service provider, and office automation for transnational corporations. The MAS TC/S architecture model comprises five major components: the display protocol, the multiple-application-server topology, the application-server discovery protocol, the distributed file system, and the network input/output protocol. We discuss problems and present solutions in the design of each constituent component. A prototype of the MAS TC/S that spans the campuses of two universities in Taiwan has been built – we also report our experiences in constructing this prototype.
Realtime lane tracking of curved local road

A lane detection system is an important component of many intelligent transportation systems. We present a robust realtime lane tracking algorithm for a curved local road. First, we present a comparative study to find a good realtime lane marking classifier. Once lane markings are detected, they are grouped into many lane boundary hypotheses represented by constrained cubic spline curves. We present a robust hypothesis generation algorithm using a particle filtering technique and a RANSAC (random sample concensus) algorithm. We introduce a probabilistic approach to group lane boundary hypotheses into left and right lane boundaries. The proposed grouping approach can be applied to general part-based object tracking problems. It incorporates a likelihood-based object recognition technique into a Markov-style process. An experimental result on local streets shows that the suggested algorithm is very reliable
A lightweight symbolic virtual machine for solver-aided host languages

Solver-aided domain-specific languages (SDSLs) are an emerging class of computer-aided programming systems. They ease the construction of programs by using satisfiability solvers to automate tasks such as verification, debugging, synthesis, and non-deterministic execution. But reducing programming tasks to satisfiability problems involves translating programs to logical constraints, which is an engineering challenge even for domain-specific languages.
 We have previously shown that translation to constraints can be avoided if SDSLs are implemented by (traditional) embedding into a host language that is itself solver-aided. This paper describes how to implement a symbolic virtual machine (SVM) for such a host language. Our symbolic virtual machine is lightweight because it compiles to constraints only a small subset of the host's constructs, while allowing SDSL designers to use the entire language, including constructs for DSL embedding. This lightweight compilation employs a novel symbolic execution technique with two key properties: it produces compact encodings, and it enables concrete evaluation to strip away host constructs that are outside the subset compilable to constraints. Our symbolic virtual machine architecture is at the heart of Rosette, a solver-aided language that is host to several new SDSLs.
Music Emotion Capture: sonifying emotions in EEG data

People’s emotions are not always obviously detectable, due to difficulties expressing emotions, or geographic distance (e.g. if people are communicating online). There are also many occasions where it would be useful for a computer to be able to detect users’ emotions and respond to them appropriately. A person’s brain activity gives vital clues as to emotions they are experiencing at any one time. The aim of this project is to detect, model and sonify people’s emotions. To achieve this, there are two tasks: (1) to detect emotions based on current brain activity as measured by an EEG device; (2) to play appropriate music in real-time, representing the current emotional state of the user. Here we report a pilot study implementing the Music Emotion Capture system. In future work we plan to improve how this project performs emotion detection through EEG, and to generate new music based on emotion-based characteristics of music. Potential applications arise in collaborative/assistive software and brain-computer interfaces for non-verbal communication.
Commercial Visual Analytics Systems-Advances in the Big Data Analytics Field.

Five years after the first state-of-the-art report on Commercial Visual Analytics Systems we present a reevaluation of the Big Data Analytics field. We build on the success of the 2012 survey, which was influential even beyond the boundaries of the InfoVis and Visual Analytics (VA) community. While the field has matured significantly since the original survey, we find that innovation and research-driven development are increasingly sacrificed to satisfy a wide range of user groups. We evaluate new product versions on established evaluation criteria, such as available features, performance, and usability, to extend on and assure comparability with the previous survey. We also investigate previously unavailable products to paint a more complete picture of the commercial VA landscape. Furthermore, we introduce novel measures, like suitability for specific user groups and the ability to handle complex data types, and undertake a new case study to highlight innovative features. We explore the achievements in the commercial sector in addressing VA challenges and propose novel developments that should be on systems' roadmaps in the coming years.
Constructing attack scenarios through correlation of intrusion alerts

Traditional intrusion detection systems (IDSs) focus on low-level attacks or anomalies, and raise alerts independently, though there may be logical connections between them. In situations where there are intensive intrusions, not only will actual alerts be mixed with false alerts, but the amount of alerts will also become unmanageable. As a result, it is difficult for human users or intrusion response systems to understand the alerts and take appropriate actions. This paper presents a practical technique to address this issue. The proposed approach constructs attack scenarios by correlating alerts on the basis of  prerequisites and consequences of intrusions. Intuitively, the prerequisite of an intrusion is the necessary condition for the intrusion to be successful, while the consequence of an intrusion is the possible outcome of the intrusion. Based on the prerequisites and consequences of different types of attacks, the proposed approach correlates alerts by (partially) matching the consequence of some previous alerts and the prerequisite of some later ones. The contribution of this paper includes a formal framework for alert correlation, the implementation of an off-line alert correlator based on the framework, and the evaluation of our method with the 2000 DARPA intrusion detection scenario specific datasets. Our experience and experimental results have demonstrated the potential of the proposed method and its advantage over alternative methods.
Walls Have Ears: Traffic-based Side-channel Attack in Video Streaming

Video streaming takes up an increasing proportion of network traffic nowadays. Dynamic Adaptive Streaming over HTTP (DASH) becomes the de facto standard of video streaming and it is adopted by Youtube, Netflix, etc. Despite of the popularity, network traffic during video streaming shows identifiable pattern which brings threat to user privacy. In this paper, we propose a video identification method using network traffic while streaming. Though there is bitrate adaptation in DASH streaming, we observe that the video bitrate trend remains relatively stable because of the widely used Variable Bit-Rate (VBR) encoding. Accordingly, we design a robust video feature extraction method for eavesdropped video streaming traffic. Meanwhile, we design a VBR based video fingerprinting method for candidate video set which can be built using downloaded video files. Finally, we propose an efficient partial matching method for computing similarities between video fingerprints and streaming traces to derive video identities. We evaluate our attack method in different scenarios for various video content, segment lengths and quality levels. The experimental results show that the identification accuracy can reach up to 90 % using only three-minute continuous network traffic eavesdropping.
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning

Zero-shot learning (ZSL) aims to transfer knowledge from seen classes to unseen ones so that the latter can be recognised without any training samples. This is made possible by learning a projection function between a feature space and a semantic space (e.g. attribute space). Considering the seen and unseen classes as two domains, a big domain gap often exists which challenges ZSL. Inspired by the fact that an unseen class is not exactly ‘unseen’ if it belongs to the same superclass as a seen class, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap. Specifically, we first build a class hierarchy of multiple superclass layers and a single class layer, where the superclasses are automatically generated by data-driven clustering over the semantic representations of all seen and unseen class names. We then exploit the superclasses from the class hierarchy to tackle the domain gap challenge in two aspects: deep feature learning and projection function learning. First, to narrow the domain gap in the feature space, we integrate a recurrent neural network (RNN) defined with the superclasses into a convolutional neural network (CNN), in order to enforce the superclass hierarchy. Second, to A. Li and L. Wang The Key Laboratory of Machine Perception (MOE), School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China E-mail: lax@pku.edu.cn Z. Lu,  School of Information, Renmin University of China, Beijing 100872, China E-mail: zhiwu.lu@gmail.com T. Xiang The School of Electronic Engineering and Computer Science, Queen Mary University of London, Mile End Road, London E1 4NS, United Kingdom E-mail: t.xiang@qmul.ac.uk. further learn a transferrable projection function for ZSL, a novel projection function learning method is proposed by exploiting the superclasses to align the two domains. Importantly, our transferrable feature and projection learning methods can be easily extended to a closely related task – few-shot learning (FSL). Extensive experiments show that the proposed model significantly outperforms the state-ofthe-art alternatives in both ZSL and FSL tasks.
Robust high-dimensional precision matrix estimation

The dependency structure of multivariate data can be analyzed using the covariance matrix Σ. In many fields the precision matrix Σ −1 is even more informative. As the sample covariance estimator is singular in high-dimensions, it cannot be used to obtain a precision matrix estimator. A popular high-dimensional estimator is the graphical lasso, but it lacks robustness. We consider the high-dimensional independent contamination model. Here, even a small percentage of contaminated cells in the data matrix may lead to a high percentage of contaminated rows. Downweighting entire observations, which is done by traditional robust procedures, would then results in a loss of information. In this paper, we formally prove that replacing the sample covariance matrix in the graphical lasso with an elementwise robust covariance matrix leads to an elementwise robust, sparse precision matrix estimator computable in high-dimensions. Examples of such elementwise robust covariance estimators are given. The final precision matrix estimator is positive definite, has a high breakdown point under elementwise contamination and can be computed fast.
JavaScript instrumentation for browser security

It is well recognized that JavaScript can be exploited to launch browser-based security attacks. We propose to battle such attacks using program instrumentation. Untrusted JavaScript code goes through a rewriting process which identifies relevant operations, modifies questionable behaviors, and prompts the user (a web page viewer) for decisions on how to proceed when appropriate. Our solution is parametric with respect to the security policy-the policy is implemented separately from the rewriting, and the same rewriting process is carried out regardless of which policy is in use. Be-sides providing a rigorous account of the correctness of our solution, we also discuss practical issues including policy management and prototype experiments. A useful by-product of our work is an operational semantics of a core subset of JavaScript, where code embedded in (HTML) documents may generate further document pieces (with new code embedded) at runtime, yielding a form of self-modifying code.
Fusion of laser and radar sensor data with a sequential Monte Carlo Bayesian occupancy filter

Occupancy grid mapping is a well-known environment perception approach. A grid map divides the environment into cells and estimates the occupancy probability of each cell based on sensor measurements. An important extension is the Bayesian occupancy filter (BOF), which additionally estimates the dynamic state of grid cells and allows modeling changing environments. In recent years, the BOF attracted more and more attention, especially sequential Monte Carlo implementations (SMC-BOF), requiring less computational costs. An advantage compared to classical object tracking approaches is the object-free representation of arbitrarily shaped obstacles and free-space areas. Unfortunately, publications about BOF based on laser measurements report that grid cells representing big, contiguous, stationary obstacles are often mistaken as moving with the velocity of the ego vehicle (ghost movements). This paper presents a method to fuse laser and radar measurement data with the SMC-BOF. It shows that the doppler information of radar measurements significantly improves the dynamic estimation of the grid map, reduces ghost movements, and in general leads to a faster convergence of the dynamic estimation.
Grace: safe multithreaded programming for C/C++

The shift from single to multiple core architectures means that programmers must write concurrent, multithreaded programs in order to increase application performance. Unfortunately, multithreaded applications are susceptible to numerous errors, including deadlocks, race conditions, atomicity violations, and order violations. These errors are notoriously difficult for programmers to debug.
 This paper presents Grace, a software-only runtime system that eliminates concurrency errors for a class of multithreaded programs: those based on fork-join parallelism. By turning threads into processes, leveraging virtual memory protection, and imposing a sequential commit protocol, Grace provides programmers with the appearance of deterministic, sequential execution, while taking advantage of available processing cores to run code concurrently and efficiently. Experimental results demonstrate Grace's effectiveness: with modest code changes across a suite of computationally-intensive benchmarks (1-16 lines), Grace can achieve high scalability and performance while preventing concurrency errors.
A taxonomy of behaviour change methods: an Intervention Mapping approach

In this paper, we introduce the Intervention Mapping (IM) taxonomy of behaviour change methods and its potential to be developed into a coding taxonomy. That is, although IM and its taxonomy of behaviour change methods are not in fact new, because IM was originally developed as a tool for intervention development, this potential was not immediately apparent. Second, in explaining the IM taxonomy and defining the relevant constructs, we call attention to the existence of parameters for effectiveness of methods, and explicate the related distinction between theory-based methods and practical applications and the probability that poor translation of methods may lead to erroneous conclusions as to method-effectiveness. Third, we recommend a minimal set of intervention characteristics that may be reported when intervention descriptions and evaluations are published. Specifying these characteristics can greatly enhance the quality of our meta-analyses and other literature syntheses. In conclusion, the dynamics of behaviour change are such that any taxonomy of methods of behaviour change needs to acknowledge the importance of, and provide instruments for dealing with, three conditions for effectiveness for behaviour change methods. For a behaviour change method to be effective: (1) it must target a determinant that predicts behaviour; (2) it must be able to change that determinant; (3) it must be translated into a practical application in a way that preserves the parameters for effectiveness and fits with the target population, culture, and context. Thus, taxonomies of methods of behaviour change must distinguish the specific determinants that are targeted, practical, specific applications, and the theory-based methods they embody. In addition, taxonomies should acknowledge that the lists of behaviour change methods will be used by, and should be used by, intervention developers. Ideally, the taxonomy should be readily usable for this goal; but alternatively, it should be clear how the information in the taxonomy can be used in practice. The IM taxonomy satisfies these requirements, and it would be beneficial if other taxonomies would be extended to also meet these needs.
"GrabCut": interactive foreground extraction using iterated graph cuts

The problem of efficient, interactive foreground/background segmentation in still images is of great practical importance in image editing. Classical image segmentation tools use either texture (colour) information, e.g. Magic Wand, or edge (contrast) information, e.g. Intelligent Scissors. Recently, an approach based on optimization by graph-cut has been developed which successfully combines both types of information. In this paper we extend the graph-cut approach in three respects. First, we have developed a more powerful, iterative version of the optimisation. Secondly, the power of the iterative algorithm is used to simplify substantially the user interaction needed for a given quality of result. Thirdly, a robust algorithm for "border matting" has been developed to estimate simultaneously the alpha-matte around an object boundary and the colours of foreground pixels. We show that for moderately difficult examples the proposed method outperforms competitive tools.