Abstract Finding a combination between privacy and accountability in the online world is a challenge. Too little accountability supports problematic behaviour. Too little privacy undermines individual freedom and has a chilling effect. This paper describes the identity infrastructure of a new open source community platform called PubHubs. It combines local group conversations, via its own adaptation of Matrix, with proportional authentication of users, in local identity spaces. PubHubs thus achieves a combination of privacy and accountability. The technical core of the paper describes the cryptographic protocols for managing digital identities, via both personal attributes and local pseudonyms. Around this core, the roles of digital identities within the PubHubs platform are explained in functional terms, giving participants for instance more certainty about others in a conversation, or giving moderators new tools.

Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.

Software performance testing is an essential quality assurance mechanism that can identify optimization opportunities. Automating this process requires strong tool support, especially in the case of Continuous Integration (CI) where tests need to run completely automatically and it is desirable to provide developers with actionable feedback. A lack of existing tools means that performance testing is normally left out of the scope of CI. In this paper, we propose a toolchain - PerfCI - to pave the way for developers to easily set up and carry out automated performance testing under CI. Our toolchain is based on allowing users to (1) specify performance testing tasks, (2) analyze unit tests on a variety of python projects ranging from scripts to full-blown flask-based web services, by extending a performance analysis framework (VyPR) and (3) evaluate performance data to get feedback on the code. We demonstrate the feasibility of our toolchain by using it on a web service running at the Compact Muon Solenoid (CMS) experiment at the world's largest particle physics laboratory --- CERN.

Runtime verification (RV) is a field of study which suffers from a lack of dedicated benchmarks. Many published evaluations of RV tools rely on workloads which are not representative of real-world programs. In this paper, we present a methodology to automatically discover relevant open-source projects for evaluating RV tools. This is done by analyzing unit tests on a large number of projects hosted on GitHub. Our evaluation shows that analyzing a large number of open-source projects-instead of a handful of manually selected workloads-provides better insight into the behavior of three state-of-the-art RV tools (JavaMOP, MarQ, and Muffin) based on two metrics (memory utilization and runtime overhead). By monitoring test executions of a large number of projects, we show that none of the evaluated RV tools wins for both metrics.

Android has become one of the most popular operating systems for mobile devices. As the number of applications for the Android ecosystem grows, so is their complexity, increasing the need for runtime verification on the Android platform. Unfortunately, despite the presence of several runtime verification frameworks for Java bytecode, DEX bytecode used in Android does not benefit from such a wide support. While a few runtime verification tools support applications developed for Android, such tools offer only limited bytecode coverage and may not be able to detect property violations in certain classes. In this paper, we show that ADRENALIN-RV, our new runtime verification tool for Android, overcomes this limitation. In contrast to other frameworks, ADRENALIN-RV weaves monitoring code at load time and is able to instrument all loaded classes. In addition to the default classes inside the application package (APK), ADRENALIN-RV covers both the Android class library and libraries dynamically loaded from the storage, network, or generated dynamically, which related tools cannot verify. Evaluation results demonstrate the increased code coverage of ADRENALIN-RV with respect to other runtime validation tools for Android. Thanks to ADRENALIN-RV, we were able to detect violations that cannot be detected by other tools.

Researchers often rely on benchmarks to demonstrate feasibility or efficiency of their contributions. However, finding the right benchmark suite can be a daunting task - existing benchmark suites may be outdated, known to be flawed, or simply irrelevant for the proposed approach. Creating a proper benchmark suite is challenging, extremely time consuming, and also - unless it becomes widely popular - a thankless endeavor. In this paper, we introduce a novel approach to help researchers find relevant workloads for their experimental evaluation needs. Our approach relies on the huge number of open-source projects available in public repositories, and on unit testing having become best practice in software development. Using a repository crawler employing pluggable static and dynamic analyses for filtering and workload characterization, we allow users to automatically find projects with relevant workloads. Preliminary results presented here show that unit tests can provide a viable source of workloads, and that the combination of static and dynamic analyses improves the ability to identify relevant workloads that can serve as the basis for custom benchmark suites.

Detecting pedestrians at a distance from large-format wide-area imagery is a challenging problem because of low ground sampling distance (GSD) and low frame rate of the imagery. In such a scenario, the approaches based on appearance cues alone mostly fail because pedestrians are only a few pixels in size. Frame-differencing and optical flow based approaches also give poor detection results due to noise, camera jitter and parallax in aerial videos. To overcome these challenges, we propose a novel approach to extract Multi-scale Intrinsic Motion Structure features from pedestrian's motion patterns for pedestrian detection. The MIMS feature encodes the intrinsic motion properties of an object, which are location, velocity and trajectory-shape invariant. The extracted MIMS representation is robust to noisy flow estimates. In this paper, we give a comparative evaluation of the proposed method and demonstrate that MIMS outperforms the state of the art approaches in identifying pedestrians from low resolution airborne videos.

We present a unified framework for detecting and classifying people interactions in unconstrained user generated images. Unlike previous approaches that directly map people/face locations in 2D image space into features for classification, we first estimate camera viewpoint and people positions in 3D space and then extract spatial configuration features from explicit 3D people positions. This approach has several advantages. First, it can accurately estimate relative distances and orientations between people in 3D. Second, it encodes spatial arrangements of people into a richer set of shape descriptors than afforded in 2D. Our 3D shape descriptors are invariant to camera pose variations often seen in web images and videos. The proposed approach also estimates camera pose and uses it to capture the intent of the photo. To achieve accurate 3D people layout estimation, we develop an algorithm that robustly fuses semantic constraints about human interpositions into a linear camera model. This enables our model to handle large variations in people size, heights (e.g. age) and poses. An accurate 3D layout also allows us to construct features informed by Proxemics that improves our semantic classification. To characterize the human interaction space, we introduce visual proxemes, a set of prototypical patterns that represent commonly occurring social interactions in events. We train a discriminative classifier that classifies 3D arrangements of people into visual proxemes and quantitatively evaluate the performance on a large, challenging dataset.

In this paper, we propose a novel approach to extract primary object segments in videos in the `object proposal' domain. The extracted primary object regions are then used to build object models for optimized video segmentation. The proposed approach has several contributions: First, a novel layered Directed Acyclic Graph (DAG) based framework is presented for detection and segmentation of the primary object in video. We exploit the fact that, in general, objects are spatially cohesive and characterized by locally smooth motion trajectories, to extract the primary object from the set of all available proposals based on motion, appearance and predicted-shape similarity across frames. Second, the DAG is initialized with an enhanced object proposal set where motion based proposal predictions (from adjacent frames) are used to expand the set of object proposals for a particular frame. Last, the paper presents a motion scoring function for selection of object proposals that emphasizes high optical flow gradients at proposal boundaries to discriminate between moving objects and the background. The proposed approach is evaluated using several challenging benchmark videos and it outperforms both unsupervised and supervised state-of-the-art methods.

We propose to use action, scene and object concepts as semantic attributes for classification of video events in InTheWild content, such as YouTube videos. We model events using a variety of complementary semantic attribute features developed in a semantic concept space. Our contribution is to systematically demonstrate the advantages of this concept-based event representation (CBER) in applications of video event classification and understanding. Specifically, CBER has better generalization capability, which enables to recognize events with a few training examples. In addition, CBER makes it possible to recognize a novel event without training examples (i.e., zero-shot learning). We further show our proposed enhanced event model can further improve the zero-shot learning. Furthermore, CBER provides a straightforward way for event recounting/understanding. We use the TRECVID Multimedia Event Detection (MED11) open source event definitions and datasets as our test bed and show results on over 1400 hours of videos.

Low-level appearance as well as spatio-temporal features, appropriately quantized and aggregated into Bag-of-Words (BoW) descriptors, have been shown to be effective in many detection and recognition tasks. However, their effcacy for complex event recognition in unconstrained videos have not been systematically evaluated. In this paper, we use the NIST TRECVID Multimedia Event Detection (MED11 [1]) open source dataset, containing annotated data for 15 high-level events, as the standardized test bed for evaluating the low-level features. This dataset contains a large number of user-generated video clips. We consider 7 different low-level features, both static and dynamic, using BoW descriptors within an SVM approach for event detection. We present performance results on the 15 MED11 events for each of the features as well as their combinations using a number of early and late fusion strategies and discuss their strengths and limitations.

We address the problem of interactive search for a target of interest in surveillance imagery. Our solution consists of iteratively learning a distance metric for retrieval, based on user feedback. The approach employs (retrieval) rank based constraints and convex optimization to efficiently learn the distance metric. The algorithm uses both user labeled and unlabeled examples in the learning process. The method is fast enough for a new metric to be learned interactively for each target query. In order to reduce the burden on the user, a model-independent active learning method is used to select key examples, for response solicitation. This leads to a significant reduction in the number of user-interactions required for retrieving the target of interest. The proposed method is evaluated on challenging pedestrian and vehicle data sets, and compares favorably to the state of the art in target re-acquisition algorithms.

Background subtraction algorithms define the background as parts of a scene that are at rest. Traditionally, these algorithms assume a stationary camera, and identify moving objects by detecting areas in a video that change over time. In this paper, we extend the concept of `subtracting' areas at rest to apply to video captured from a freely moving camera. We do not assume that the background is well-approximated by a plane or that the camera center remains stationary during motion. The method operates entirely using 2D image measurements without requiring an explicit 3D reconstruction of the scene. A sparse model of background is built by robustly estimating a compact trajectory basis from trajectories of salient features across the video, and the background is `subtracted' by removing trajectories that lie within the space spanned by the basis. Foreground and background appearance models are then built, and an optimal pixel-wise foreground/background labeling is obtained by efficiently maximizing a posterior function.

Recent advances in computer vision and artificial intelligence algorithms have allowed automatic extraction of metadata from video. This metadata can be represented by using the RDF/OWL ontology which can encode scene objects and their relationships in an unambiguous and well-formed manner. The encoded data can be queried using SPARQL. However, SPARQL has a steep learning curve and cannot be directly utilized by a general user for video content search. In this paper, we propose a method to bridge this gap by automatically translating user provided natural language query into an ontology-based SPARQL query for semantic video search. The proposed method consists of three major steps. First, semantically labeled training corpus of natural language query sentences is used for learning the Semantic Stochastic Context Free Grammar (SSCFG). Second, given a user provided natural language query sentence, we use the Earley-Stolcke parsing algorithm to determine the maximum likelihood semantic parsing of the query sentence. This parsing infers the semantic meaning for each word in the query sentence from which the SPARQL query is constructed. Third, the SPARQL query is executed to retrieve relevant video segments from the RDF-OWL video content database. The method is evaluated by running natural language queries on surveillance videos from maritime and land-based domains, though the framework itself is general and extensible to search videos from other domains.

This paper presents an unsupervised data driven scheme to automatically estimate the relative topology of overlapping cameras in a large visual sensor network. The proposed method learns the camera topology by employing the statistics of co-occurring observations (of moving targets) in each sensor. Since target observation data is typically very noisy in realistic scenarios, an efficient two step method is used for robust estimation of the planar homography between camera views. In the first step, modes in the co-occurrence data are learned using meanshift. In the second step, a RANSAC based procedure is used to estimate the homography from weighted co-occurrence modes. Note that the first step not only lessens the effects of noise but also reduces the search space for efficient calculation. Unlike most existing algorithms for overlapping camera calibration, the proposed method uses an update mechanism to adapt online to the changes in network topology. The method does not assume prior knowledge about the scene, target, or network properties. It is also robust to noise, traffic intensity, and the amount of overlap between the fields of view. Experiments and quantitative evaluation using both synthetic and real data are presented to support the above claims.

We propose a new framework in wavelet video coding to improve the compression rate by exploiting the spatiotemporal regularity of the data. A sequence of images creates a spatiotemporal volume. This volume is said to be regular along the directions in which the pixels vary the least, hence the entropy is the lowest. The wavelet decomposition of regularized data results in a fewer number of significant coefficients, thus yielding a higher compression rate. The directions of regularity of an image sequence depend on both its motion content and spatial structure. We propose the representation of these directions by a 3-D vector field, which we refer to as the spatiotemporal regularity flow (SPREF). SPREF uses splines to approximate the directions of regularity. The compactness of the spline representation results in a low storage overhead for SPREF, which is a desired property in compression applications. Once SPREF directions are known, they can be converted into actual paths along which the data is regular. Directional decomposition of the data along these paths can be further improved by using a special class of wavelet basis called the 3-D orthonormal bandelet basis. SPREF -based video compression not only removes the temporal redundancy, but it also compensates for the spatial redundancy. Our experiments on several standard video sequences demonstrate that the proposed method results in higher compression rates as compared to the standard wavelet based compression.

Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for training such a classifier. Moreover, once trained, the boosted classifier cannot adjust to the particular scenario in which it is employed. In this paper, we propose a co-training based approach to continuously label incoming data and use it for online update of the boosted classifier that was initially trained from a small labeled example set. The main contribution of our approach is that it is an online procedure in which separate views (features) of the data are used for co-training, while the combined view (all features) is used to make classification decisions in a single boosted framework. The features used for classification are derived from principal component analysis of the appearance templates of the training examples. In order to speed up the classification, background modeling is used to prune away stationary regions in an image. Our experiments indicate that starting from a classifier trained on a small training set, significant performance gains can be made through online updation from the unlabeled data.

When viewed from a system of multiple cameras with non-overlapping fields of view, the appearance of an object in one camera view is usually very different from its appearance in another camera view due to the differences in illumination, pose and camera parameters. In order to handle the change in observed colors of an object as it moves from one camera to another, we show that all brightness transfer functions from a given camera to another camera lie in a low dimensional subspace and demonstrate that this subspace can be used to compute appearance similarity. In the proposed approach, the system learns the subspace of inter-camera brightness transfer functions in a training phase during which object correspondences are assumed to be known. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework using both location and appearance cues. We evaluate the proposed method under several real world scenarios obtaining encouraging results.

This paper proposes a new technique in wavelet video compression that exploits the spatiotemporal regularity of the video. A sequence of frames is said to be regular along the directions in which the pixels vary the least. The directions of regularity of a sequence depend on both its motion content and its spatial structure. We model these directions by a 3D vector field, referred as the structural flow. This flow determines the paths of regularity along which the entropy of the data is smaller. We use these paths to construct a special class of wavelet basis, i.e., the 3D orthonormal bandelet basis for the directional decomposition of the sequence. Our experiments on several standard video sequences demonstrate the significant improvement in compression compared to the standard wavelet video coding.

Conventional tracking approaches assume proximity in space, time and appearance of objects in successive observations. However, observations of objects are often widely separated in time and space when viewed from multiple non-overlapping cameras. To address this problem, we present a novel approach for establishing object correspondence across non-overlapping cameras. Our multicamera tracking algorithm exploits the redundance in paths that people and cars tend to follow, e.g. roads, walk-ways or corridors, by using motion trends and appearance of objects, to establish correspondence. Our system does not require any inter-camera calibration, instead the system learns the camera topology and path probabilities of objects using Parzen windows, during a training phase. Once the training is complete, correspondences are assigned using the maximum a posteriori (MAP) estimation framework. The learned parameters are updated with changing trajectory patterns. Experiments with real world videos are reported, which validate the proposed approach.

In this paper, we present a wide area surveillance system that detects, tracks and classifies moving objects across multiple cameras. At the single camera level, tracking is performed using a voting based approach that utilizes color and shape cues to establish correspondence. The system uses the single camera tracking results along with the relationship between camera field of view (FOV) boundaries to establish correspondence between views of the same object in multiple cameras. To this end, a novel approach is described to find the relationships between the FOV lines of cameras. The proposed approach combines tracking in cameras with overlapping and/or non-overlapping FOVs in a unified framework, without requiring explicit calibration. The proposed algorithm has been implemented in a real time system. The system uses a client-server architecture and runs at 10 Hz with three cameras.

In this paper, we present a method to remove commercials from talk and game show videos and to segment these videos into host and guest shots. In our approach, we mainly rely on information contained in shot transitions, rather than analyzing the scene content of individual frames. We utilize the inherent differences in scene structure of commercials and talk shows to differentiate between them. Similarly, we make use of the well-defined structure of talk shows, which can be exploited to classify shots as host or guest shots. The entire show is first segmented into camera shots based on color histogram. Then, we construct a data-structure (shot connectivity graph) which links similar shots over time. Analysis of the shot connectivity graph helps us to automatically separate commercials from program segments. This is done by first detecting stories, and then assigning a weight to each story based on its likelihood of being a commercial. Further analysis on stories is done to distinguish shots of the hosts from shots of the guests. We have tested our approach on several full-length shows (including commercials) and have achieved video segmentation with high accuracy. The whole scheme is fast and works even on low quality video (160/spl times/120 pixel images at 5 Hz).