IMP: Indirect Memory Prefetcher

Machine learning, graph analytics and sparse linear algebra-based applications are dominated by irregular memory accesses resulting from following edges in a graph or non-zero elements in a sparse matrix. These accesses have little temporal or spatial locality, and thus incur long memory stalls and large bandwidth requirements. A traditional streaming or striding prefetcher cannot capture these irregular access patterns. A majority of these irregular accesses come from indirect patterns of the form A[B[i]]. We propose an efficient hardware indirect memory prefetcher (IMP) to capture this access pattern and hide latency. We also propose a partial cacheline accessing mechanism for these prefetches to reduce the network and DRAM bandwidth pressure from the lack of spatial locality. Evaluated on 7 applications, IMP shows 56% speedup on average (up to 2.3×) compared to a baseline 64 core system with streaming prefetchers. This is within 23% of an idealized system. With partial cacheline accessing, we see another 9.4% speedup on average (up to 46.6%).Intel Science and Technology Center for Big Dat

Mal-Netminer: Malware Classification Approach based on Social Network Analysis of System Call Graph

As the security landscape evolves over time, where thousands of species of malicious codes are seen every day, antivirus vendors strive to detect and classify malware families for efficient and effective responses against malware campaigns. To enrich this effort, and by capitalizing on ideas from the social network analysis domain, we build a tool that can help classify malware families using features driven from the graph structure of their system calls. To achieve that, we first construct a system call graph that consists of system calls found in the execution of the individual malware families. To explore distinguishing features of various malware species, we study social network properties as applied to the call graph, including the degree distribution, degree centrality, average distance, clustering coefficient, network density, and component ratio. We utilize features driven from those properties to build a classifier for malware families. Our experimental results show that influence-based graph metrics such as the degree centrality are effective for classifying malware, whereas the general structural metrics of malware are less effective for classifying malware. Our experiments demonstrate that the proposed system performs well in detecting and classifying malware families within each malware class with accuracy greater than 96%.Comment: Mathematical Problems in Engineering, Vol 201

Algorithms for learning from spatial and mobility data

Data from the numerous mobile devices, location-based applications, and collection sensors used currently can provide important insights about human and natural processes. These insights can inform decision making in designing and optimising in frastructure such as transportation or energy. However, extracting patterns related to spatial properties is challenging due to the large quantity of the data produced and the complexity of the processes it describes. We propose scalable, multi-resolution approximation and heuristic algorithms that make use of spatial proximity properties to solve fundamental data mining and optimisation problems with a better running time and accuracy. We observe that abstracting from individual data points and working with units of neighbouring points based on various measures on similarity, improves computational efficiency and diminishes the effects of noise and overfitting. We consider applications in: mobility data compression, transit network planning, and solar power output prediction. Firstly, in order to understand transportation needs, it is essential to have efficient ways to represent large amounts of travel data. In analysing spatial trajectories (for example taxis travelling in a city), one of the main challenges is computing distances between trajectories efficiently; due to their size and complexity this task is computationally expensive. We build data structures and algorithms to sketch trajectory data that make queries such as distance computation, nearest neighbour search and clustering, which are key to finding mobility patterns, more computationally efficient. We use locality sensitive hashing, a technique that associates similar objects to the same hash. Secondly, to build efficient infrastructure it is necessary to satisfy travel demand by placing resources optimally. This is difficult due to external constraints (such as limits on budget) and the complexity of existing road networks that allow for a large number of candidate locations. For this purpose, we present heuristic algorithms for efficient transit network design with a case study on cycling lane placement. The heuristic is based on a new type of clustering by projection, that is both computationally efficient and gives good results in practice. Lastly, we devise a novel method to forecast solar power output based on numerical weather predictions, clear sky predictions and persistence data. The ensemble of a multivariate linear regression model, support vector machines model, and an artificial neural network gives more accurate predictions than any of the individual models. Analysing the performance of the models in a suite of frameworks reveals that building separate models for each self-contained area based on weather patterns gives a better accuracy than a single model that predicts the total. The ensemble can be further improved by giving performance-based weights to the individual models. This suggests that the models identify different patterns in the data, which motivated the choice of an ensemble architecture

IMAGE RETRIEVAL BASED ON COMPLEX DESCRIPTIVE QUERIES

The amount of visual data such as images and videos available over web has increased exponentially over the last few years. In order to efficiently organize and exploit these massive collections, a system, apart from being able to answer simple classification based questions such as whether a specific object is present (or absent) in an image, should also be capable of searching images and videos based on more complex descriptive questions. There is also a considerable amount of structure present in the visual world which, if effectively utilized, can help achieve this goal. To this end, we first present an approach for image ranking and retrieval based on queries consisting of multiple semantic attributes. We further show that there are significant correlations present between these attributes and accounting for them can lead to superior performance. Next, we extend this by proposing an image retrieval framework for descriptive queries composed of object categories, semantic attributes and spatial relationships. The proposed framework also includes a unique multi-view hashing technique, which enables query specification in three different modalities - image, sketch and text. We also demonstrate the effectiveness of leveraging contextual information to reduce the supervision requirements for learning object and scene recognition models. We present an active learning framework to simultaneously learn appearance and contextual models for scene understanding. Within this framework we introduce new kinds of labeling questions that are designed to collect appearance as well as contextual information and which mimic the way in which humans actively learn about their environment. Furthermore we explicitly model the contextual interactions between the regions within an image and select the question which leads to the maximum reduction in the combined entropy of all the regions in the image (image entropy)

PlaceAvoider: Steering First-Person Cameras away from Sensitive Spaces

Abstract—Cameras are now commonplace in our social and computing landscapes and embedded into consumer devices like smartphones and tablets. A new generation of wearable devices (such as Google Glass) will soon make ‘first-person ’ cameras nearly ubiquitous, capturing vast amounts of imagery without deliberate human action. ‘Lifelogging ’ devices and applications will record and share images from people’s daily lives with their social networks. These devices that automatically capture images in the background raise serious privacy concerns, since they are likely to capture deeply private information. Users of these devices need ways to identify and prevent the sharing of sensitive images. As a first step, we introduce PlaceAvoider, a technique for owners of first-person cameras to ‘blacklist ’ sensitive spaces (like bathrooms and bedrooms). PlaceAvoider recognizes images captured in these spaces and flags them for review before the images are made available to applications. PlaceAvoider performs novel image analysis using both fine-grained image features (like specific objects) and coarse-grained, scene-level features (like colors and textures) to classify where a photo was taken. PlaceAvoider combines these features in a probabilistic framework that jointly labels streams of images in order to improve accuracy. We test the technique on five realistic first-person image datasets and show it is robust to blurriness, motion, and occlusion. I