Miracle-dz/UniFAR-FTUs huggingface dataset:
I Forgot Your Password: Randomness Attacks Against PHP Applications

We provide a number of practical techniques and algorithms for exploiting randomness vulnerabilities in PHP applications. We focus on the predictability of password reset tokens and demonstrate how an attacker can take over user accounts in a web application via predicting or algorithmically derandomizing the PHP core randomness generators. While our techniques are designed for the PHP language, the principles behind our techniques and our algorithms are independent of PHP and can readily apply to any system that utilizes weak randomness generators or low entropy sources. Our results include: algorithms that reduce the entropy of time variables, identifying and exploiting vulnerabilities of the PHP system that enable the recovery or reconstruction of PRNG seeds, an experimental analysis of the Hastad-Shamir framework for breaking truncated linear variables, an optimized online Gaussian solver for large sparse linear systems, and an algorithm for recovering the state of the Mersenne twister generator from any level of truncation. We demonstrate the gravity of our attacks via a number of case studies. Specifically, we show that a number of current widely used web applications can be broken using our techniques including Mediawiki, Joomla, Gallery, osCommerce and others.
Cloud-Assisted Data Fusion and Sensor Selection for Internet of Things

The Internet of Things (IoT) is connecting people and smart devices on a scale that was once unimaginable. One major challenge for the IoT is to handle vast amount of sensing data generated from the smart devices that are resource-limited and subject to missing data due to link or node failures. By exploring cloud computing with the IoT, we present a cloud-based solution that takes into account the link quality and spatio-temporal correlation of data to minimize energy consumption by selecting sensors for sampling and relaying data. We propose a multiphase adaptive sensing algorithm with belief propagation (BP) protocol (ASBP), which can provide high data quality and reduce energy consumption by turning on only a small number of nodes in the network. We formulate the sensor selection problem and solve it using both constraint programming (CP) and greedy search. We then use our message passing algorithm (BP) for performing inference to reconstruct the missing sensing data. ASBP is evaluated based on the data collected from real sensors. The results show that while maintaining a satisfactory level of data quality and prediction accuracy, ASBP can provide load balancing among sensors successfully and preserves 80% more energy compared with the case where all sensor nodes are actively involved.
Fast relocalisation and loop closing in keyframe-based SLAM

In this paper we present for the first time a relocalisation method for keyframe-based SLAM that can deal with severe viewpoint change, at frame-rate, in maps containing thousands of keyframes. As this method relies on local features, it permits the interoperability between cameras, allowing a camera to relocalise in a map built by a different camera. We also perform loop closing (detection + correction), at keyframerate, in loops containing hundreds of keyframes. For both relocalisation and loop closing, we propose a bag of words place recognizer with ORB features, which is able to recognize places spending less than 39 ms, including feature extraction, in databases containing 10K images (without geometrical verification). We evaluate the performance of this recognizer in four different datasets, achieving high recall and no false matches, and getting better results than the state-of-art in place recognition, being one order of magnitude faster.
Conditional Mutual Information Based Boosting for Facial Expression Recognition

This paper proposes a novel approach for facial expression r ecognition by boosting Local Binary Patterns (LBP) based classifiers. L ow-cost LBP features are introduced to effectively describle local fea tures of face images. A novel learning procedure, Conditional Mutual Informatio n based Boosting (CMIB), is proposed. CMIB learns a sequence of weak classifie r that maximize their mutual information about a candidate class, con ditional to the response of any weak classifier already selected; a strong cl assifier is constructed by combining the learned weak classifiers using the Naive-Bayes. Extensive experiments on the Cohn-Kanade database illustr ated that LBP features are effective for expression analysis, and CMIB en ables much faster training than AdaBoost, and yields a classifier of improved c lassification performance.
On the Codebook-Level Duality Between Slepian-Woif Coding and Channel Coding

A codebook-level duality between Slepian-Wolf coding and channel coding is established. Specifically, it is shown that using linear codes over Z M (the ring of integers mod M), each Slepian-Wolf coding problem is equivalent to a channel coding problem for a semi-symmetric additive channel under optimal decoding, belief propagation decoding, and minimum entropy decoding. Various notions of symmetric channels are discussed and their connections with semi-symmetric additive channels are clarified.
Measuring the Discriminative Power of Rating Systems 1

Assessing the discriminative power of rating systems is an important question to banks and to regulators. In this article we analyze the Cumulative Accuracy Profile (CAP) and the Receiver Operating Characteristic (ROC) which are both commonly used in practice. We give a test-theoretic interpretation for the concavity of the CAP and the ROC curve and demonstrate how this observation can be used for more efficiently exploiting the informational contents of accounting ratios. Furthermore, we show that two popular summary statistics of these concepts, namely the Accuracy Ratio and the area below the ROC curve, contain the same information and analyze the statistical properties of these measures. We show in detail how to identify accounting ratios with high discriminative power, how to calculate confidence intervals for the area below the ROC curve, and how to test if two rating models validated on the same data set are different. All concepts are illustrated by applications to real data.
An Approach to Minimizing Downtime Induced by Taking Live Snapshot of Virtual Cluster

Cloud computing has emerged as a new computing paradigm which is adopted by more and more application services based on cluster computing. Live snapshot technology is usually used to ensure the reliability of data and high availability of the running virtual machines. However, existing virtual machine live snapshot technologies are not enough to minimize the downtime induced by taking live snapshot for an entire virtual cluster consisting of a large number of virtual machines. An ordinary approach is taking live snapshot of all virtual machines belonging to the virtual cluster concurrently. But this approach may induce a long downtime of an entire virtual cluster which consists of a large number of virtual machines distributed in a small number of hosts. To address this challenge, this paper surveys the factors which raise the downtime of virtual machines induced by taking live snapshot, and defines five metrics used to determine the concurrency strategy of taking live snapshot of different virtual machines. Based on these metrics, we propose a novel approach named Selection Concurrency Strategy which aims to minimize the downtime of virtual machines, and our experiments show that the downtime of virtual cluster consisting of a large number of virtual machines distributed in a small number of hosts using our approach is 33% shorter than that under ordinary approach.
Support Vector Machines: Training and Applications

The Support Vector Machine (SVM) is a new and very promising classification technique developed by Vapnik and his group at AT\&T Bell Labs. This new learning algorithm can be seen as an alternative training technique for Polynomial, Radial Basis Function and Multi-Layer Perceptron classifiers. An interesting property of this approach is that it is an approximate implementation of the Structural Risk Minimization (SRM) induction principle. The derivation of Support Vector Machines, its relationship with SRM, and its geometrical insight, are discussed in this paper. Training a SVM is equivalent to solve a quadratic programming problem with linear and box constraints in a number of variables equal to the number of data points. When the number of data points exceeds few thousands the problem is very challenging, because the quadratic form is completely dense, so the memory needed to store the problem grows with the square of the number of data points. Therefore, training problems arising in some real applications with large data sets are impossible to load into memory, and cannot be solved using standard non-linear constrained optimization algorithms. We present a decomposition algorithm that can be used to train SVM''s over large data sets. The main idea behind the decomposition is the iterative solution of sub-problems and the evaluation of, and also establish the stopping criteria for the algorithm. We present previous approaches, as well as results and important details of our implementation of the algorithm using a second-order variant of the Reduced Gradient Method as the solver of the sub-problems. As an application of SVM''s, we present preliminary results we obtained applying SVM to the problem of detecting frontal human faces in real images.
Two-level directory based compression

Summary form only given. The basic idea of preprocessing is to transform the text into some intermediate form which can be used as input of any existing general-purpose compressor and compressed more efficiently. Dictionary-based preprocessing is based on the notion of replacing whole words with shorter codes. We present a dictionary-based preprocessing technique and its implementation called TWRT (two-level word replacing transformation). Our preprocessor uses several dictionaries and divides files into various kinds. The first level dictionaries (small dictionaries) are specific for some kind of data (e.g., programming language, references). The second level dictionaries (large dictionaries) are specific for natural languages (e.g., English, Russian, French). On the Calgary corpus, TWRT improves the compression performance of bzip2 by over 7% and PPMonstr by about 6% on average. Even for the top compressor nowadays, PAQ6, the gain is significant - 5%. On multilingual text files, TWRT improves the compression performance of bzip2, PPMonstr, and PAQ6 by about 8%. Moreover, TWRT improves the compression speed with PAQ6 and on larger files with PPMonstr.
Illumination invariance for face verification

The task of face verification is made more difficult when the illumination conditions of image capture are not constrained. The differences in illumination conditions between the stored images of the client and the probe image can be lessened by the application of photometric normalisation. Photometric normalisation is the method of pre-processing an image to a representation that is robust to the illumination conditions of image capture. This thesis presents experiments comparing several photometric normalisation methods. The results demonstrate that the anisotropic smoothing pre-processing algorithm of Gross and Brajovic yields the best results of the photometric normalisations tested. The thesis presents an investigation into the behaviour of the anisotropic smoothing method, showing that performance is sensitive to the selection of its parameter. A method of optimising this parameter is suggested and experimental results show that it offers an improvement in verification rates. The variation of illumination across regions of the face is smaller than across the whole face. A novel component-based approach to face verification is presented to take advantage of this fact. The approach consists of carrying out verification on a number of images containing components of the face and fusing the result. As the component images are more robust to illumination, the choice of photometric normalisation is again investigated in the component-based context. The thesis presents the useful result that the simpler normalisations offer the best results when applied to facial component images. Experiments investigating the various methods of fusing the information from the components are presented, as is the issue of score normalisation. Methods of selecting which components are most useful for verification are also tested. The method of pruning the negative components of the linear discriminant analysis weight vector has been applied to the task of selecting the best subset of face components for verification. The pruned linear discriminant analysis method does not perform as well as the well known sequential floating forward selection method on the well illuminated XM2VTS database, however it achieves better generalisation when applied to the more challenging conditions of the XM2VTS dark set.
Taking Statistical Machine Translation to the Student Translator

Despite the growth of statistical machine translation (SMT) research and development in recent years, it remains somewhat out of reach for the translation community where programming expertise and knowledge of statistics tend not to be commonplace. While the concept of SMT is relatively straightforward, its implementation in functioning systems remains difficult for most, regardless of expertise. More recently, however, developments such as SmartMATE have emerged which aim to assist users in creating their own customized SMT systems and thus reduce the learning curve associated with SMT. In addition to commercial uses, translator training stands to benefit from such increased levels of inclusion and access to state-of-the-art approaches to MT. In this paper we draw on experience in developing and evaluating a new syllabus in SMT for a cohort of post-graduate student translators: we identify several issues encountered in the introduction of student translators to SMT, and report on data derived from repeated measures questionnaires that aim to capture data on students’ self-efficacy in the use of SMT. Overall, results show that participants report significant increases in their levels of confidence and knowledge of MT in general, and of SMT in particular. Additional benefits – such as increased technical competence and confidence – and future refinements are also discussed.
Autonomous collision avoidance based on aircraft performances estimation

The paper describes a resolution manoeuvre definition algorithm for a Sense and Avoid (S&A) system, in which the avoidance manoeuvres are the step variations in the heading angle command of the Flight Path Control System (FPCS). The value of these commands is optimised in order to get the minimum step command value necessary to keep a minimum predefined separation between the ownship and the threat. The computation of the separation distance between the ownship and the threat is based on the estimation of the future trajectory. This estimation is obtained from the response of the linear model of the aircraft modified in order to get the results as close as possible to the nonlinear behaviour of the aircraft. The defined algorithm is tested through simulations in Matlab/Simulink® environment by considering some head-on conflict scenarios with a flying threat approaching from different directions. The threat considered is an aircraft that communicate its state to the system through its Automatic Dependent Surveillance-Broadcast (ADS-B) mode S transponder.
Partitioning and Social Scaling of Political Debates using Signed Bipartite Graphs

Blogosphere plays an increasingly important role as a forum for public debate. In this paper, given a mixed set of blogs debating a set of political issues from opposing camps, we use signed bipartite graphs for modeling debates, and we propose an algorithm for partitioning both the blogs, and the issues (i.e. topics, leaders, etc.) comprising the debate into binary opposing camps. Simultaneously, our algorithm scales both the blogs and the underlying issues on a univariate scale. Using this scale, a researcher can identify moderate and extreme blogs within each camp, and polarizing vs. unifying issues. Through performance evaluations we show that our proposed algorithm provides an eﬀective solution to the problem, and performs much better than existing baseline algorithms adapted to solve this new problem. In our experiments, we used both real data from political blogosphere and US Congress records, as well as synthetic data which were obtained by varying polarization and degree distribution of the vertices of the graph to show the robustness of our algorithm.
XRCE's Participation to ImageCLEFphoto 2007

Our participation to ImageCLEFphoto07, for the first time, was motivated by assessing several transmedia similarity measures that we recently designed and developed. The object of investigation consists here in some “intermediate level” fusion approaches, where we use some principles coming from pseudo-relevance feedback and, more specifically, use transmedia pseudo-relevance feedback for enriching the mono-media representation of an object with features coming from the other media. One issue that arises when adopting such a strategy is to determine how to compute the mono-media similarity between an aggregate of objects coming from a first (pseudo-)feedback step and one single multimodal object. We propose two alternative ways of adressing this issue, that result in what we called the “transmedia document reranking” and “complementary feedback” methods respectively. This year, with a “lightly” annotated corpus of images, it appears that mono-media retrieval performance is more or less equivalent for pure image and pure text content (around 20% MAP). Using our transmedia pseudofeedback-based similarity measures allowed us to dramatically increase the performance by »50% (relative). Trying to model the textual “relevance concept” present in the top ranked documents issued from a first (purely visual) retrieval and combining this model with the textual part of the original query turns out to be the best strategy, being slightly superior to our transmedia document reranking method. Enriching the image annotations by extra tags extracted from an external resource (namely the Flickr database) does not oer a significant advantage in the ImageCLEF07 corpus, even if we observed an improvement using other multimedia corpora and query sets. From a cross-lingual perspective, the use of domain-specific, corpus-adapted probabilistic dictionaries seems to oer better results than the use of a broader, more general standard dictionary. With respect to the monolingual baselines, multilingual runs show a slight degradation of retrieval performance ( »6 to 10% relative).
Variability in Parenting Self-Efficacy Across Prudential Adolescent Behaviors

SYNOPSIS Objective. Extant research examining the predictors and outcomes of parenting self-efficacy has predominantly focused on families with young children. Adolescence is a time of increased autonomy during which parents may be uncertain about their abilities to influence their adolescents’ risk-taking behavior. Design. Parents’ (N = 145 mothers and 53 fathers) confidence in their parenting abilities across prudential adolescent behaviors was investigated, including alcohol consumption, cyber activities, eating behaviors, and problem peer associations. Additionally, we explored how adolescents’ (N = 161, Mage = 14.4 years, 60% female) reports of their engagement in those behaviors were associated with parents’ perceptions of their ability to impact their adolescents’ behavior (i.e., self-efficacy). Results. Mixed-model analysis of variance revealed that mothers and fathers felt most efficacious in reducing their adolescents’ engagement in problematic cyber activities and least efficacious regarding alcohol consumption. Bivariate correlations indicated multiple negative associations between adolescents’ engagement in prudential behaviors and both mothers’ and fathers’ behavior-specific parenting self-efficacy. Conclusions. Results suggest that parents with adolescents have varying levels of confidence in their ability to parent different types of prudential adolescent behaviors. Additionally, for multiple behavior categories, mothers’ and fathers’ behavior-specific parenting self-efficacy was negatively associated with adolescent engagement in corresponding behaviors, such that increased adolescent engagement was related to lower levels of behavior-specific parenting self-efficacy.
Detecting computer network attacks using statistical discriminators and cluster analysis

Attacks represent a serious threat to a network environment, and therefore need to be promptly detected. New attack types, of which detection systems may not even be aware, are the most difficult to detect. Currently, the available methods are mainly based on signature or learning algorithms and generally cannot detect these new attacks. The approach presented here uses a small number of statistical discriminators and cluster analysis to detect attacks, obtaining results which are better than the results found in previous papers. Cluster analysis is an unsupervised technique and, therefore, it is able to detect new attacks. We performed an empirical test using real traces.
Preface Achieving Competitive Advantages in a Digital World: Virtual Organizations and Distributed Process Improvement through E-collaboration Technologies

Can organization processes be improved quickly enough to cope with competition and the drive for change facing virtual organizations today? Will a high degree of virtuality hinder organizations from rapidly adapting to their environment? These questions are examined in this preface chapter, based on a field study of thirty-eight process improvement groups in three organizations: an advertising company based in Brazil, a semi-autonomous branch of the New Zealand Government, and a tertiary education establishment in New Zealand. The findings suggest that organizations where a large amount of communication is carried out through computer networks, and hence where a high degree of virtuality is present, can derive attractive benefits from computer-supported group PI efforts. This is true particularly in incremental process improvement groups.
Unified Color Texture Descriptor Using Gaussian Color Model and Rayleigh Density Function

In Content Based Retrieval Systems, many recent works attempt to combine more than one property of images in a unique framework. We propose a new descriptor based on color-texture features. Our approach is based on the physical properties of light bouncing from a scene. We measure the texture in color images with 3D Gaussians which represents the Gabor-Gaussian probe in the spatial domain. Furthermore, it is claimed that the distribution of Gabor filter outputs can also be provided by a Rayleigh Filter. Thus, we deduce the Rayleigh-Gaussian framework from the Gabor-Gaussian one. Then we compute a descriptor based on statistical measures performed on Rayleigh-Gaussian features.
The Page-Fault Weird Machine: Lessons in Instruction-less Computation

Trust Analysis, i.e. determining that a system will not execute some class of computations, typically assumes that all computation is captured by an instruction trace. We show that powerful computation on ×86 processors is possible without executing any CPU instructions. We demonstrate a Turing-complete execution environment driven solely by the IA32 architecture's interrupt handling and memory translation tables, in which the processor is trapped in a series of page faults and double faults, without ever successfully dispatching any instructions. The "hard-wired" logic of handling these faults is used to perform arithmetic and logic primitives, as well as memory reads and writes. This mechanism can also perform branches and loops if the memory is set up and mapped just right. We discuss the lessons of this execution model for future trustworthy architectures.