Smart Greybox Fuzzing

Coverage-based greybox fuzzing (CGF) is one of the most successful methods for automated vulnerability detection. Given a seed file (as a sequence of bits), CGF randomly flips, deletes or bits to generate new files. CGF iteratively constructs (and fuzzes) a seed corpus by retaining those generated files which enhance coverage. However, random bitflips are unlikely to produce valid files (or valid chunks in files), for applications processing complex file formats. In this work, we introduce smart greybox fuzzing (SGF) which leverages a high-level structural representation of the seed file to generate new files. We define innovative mutation operators that work on the virtual file structure rather than on the bit level which allows SGF to explore completely new input domains while maintaining file validity. We introduce a novel validity-based power schedule that enables SGF to spend more time generating files that are more likely to pass the parsing stage of the program, which can expose vulnerabilities much deeper in the processing logic. Our evaluation demonstrates the effectiveness of SGF. On several libraries that parse structurally complex files, our tool AFLSmart explores substantially more paths (up to 200%) and exposes more vulnerabilities than baseline AFL. Our tool AFLSmart has discovered 42 zero-day vulnerabilities in widely-used, well-tested tools and libraries; so far 17 CVEs were assigned.Comment: Accepted IEEE Transactions on Software Engineering, 202

LCNN: Lookup-based Convolutional Neural Network

Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionary and a small set of linear combinations. The size of the dictionary naturally traces a spectrum of trade-offs between efficiency and accuracy. Our experimental results on ImageNet challenge show that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at inference, but it also enables efficient training. In this paper, we show the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.Comment: CVPR 1

GPU LSM: A Dynamic Dictionary Data Structure for the GPU

We develop a dynamic dictionary data structure for the GPU, supporting fast insertions and deletions, based on the Log Structured Merge tree (LSM). Our implementation on an NVIDIA K40c GPU has an average update (insertion or deletion) rate of 225 M elements/s, 13.5x faster than merging items into a sorted array. The GPU LSM supports the retrieval operations of lookup, count, and range query operations with an average rate of 75 M, 32 M and 23 M queries/s respectively. The trade-off for the dynamic updates is that the sorted array is almost twice as fast on retrievals. We believe that our GPU LSM is the first dynamic general-purpose dictionary data structure for the GPU.Comment: 11 pages, accepted to appear on the Proceedings of IEEE International Parallel and Distributed Processing Symposium (IPDPS'18

Deep Neural Networks Ensemble for Detecting Medication Mentions in Tweets

Objective: After years of research, Twitter posts are now recognized as an important source of patient-generated data, providing unique insights into population health. A fundamental step to incorporating Twitter data in pharmacoepidemiological research is to automatically recognize medication mentions in tweets. Given that lexical searches for medication names may fail due to misspellings or ambiguity with common words, we propose a more advanced method to recognize them. Methods: We present Kusuri, an Ensemble Learning classifier, able to identify tweets mentioning drug products and dietary supplements. Kusuri ("medication" in Japanese) is composed of two modules. First, four different classifiers (lexicon-based, spelling-variant-based, pattern-based and one based on a weakly-trained neural network) are applied in parallel to discover tweets potentially containing medication names. Second, an ensemble of deep neural networks encoding morphological, semantical and long-range dependencies of important words in the tweets discovered is used to make the final decision. Results: On a balanced (50-50) corpus of 15,005 tweets, Kusuri demonstrated performances close to human annotators with 93.7% F1-score, the best score achieved thus far on this corpus. On a corpus made of all tweets posted by 113 Twitter users (98,959 tweets, with only 0.26% mentioning medications), Kusuri obtained 76.3% F1-score. There is not a prior drug extraction system that compares running on such an extremely unbalanced dataset. Conclusion: The system identifies tweets mentioning drug names with performance high enough to ensure its usefulness and ready to be integrated in larger natural language processing systems.Comment: This is a pre-copy-editing, author-produced PDF of an article accepted for publication in JAMIA following peer review. The definitive publisher-authenticated version is "D. Weissenbacher, A. Sarker, A. Klein, K. O'Connor, A. Magge, G. Gonzalez-Hernandez, Deep neural networks ensemble for detecting medication mentions in tweets, Journal of the American Medical Informatics Association, ocz156, 2019

Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices

Recurrent neural networks (RNNs) achieve cutting-edge performance on a variety of problems. However, due to their high computational and memory demands, deploying RNNs on resource constrained mobile devices is a challenging task. To guarantee minimum accuracy loss with higher compression rate and driven by the mobile resource requirement, we introduce a novel model compression approach DirNet based on an optimized fast dictionary learning algorithm, which 1) dynamically mines the dictionary atoms of the projection dictionary matrix within layer to adjust the compression rate 2) adaptively changes the sparsity of sparse codes cross the hierarchical layers. Experimental results on language model and an ASR model trained with a 1000h speech dataset demonstrate that our method significantly outperforms prior approaches. Evaluated on off-the-shelf mobile devices, we are able to reduce the size of original model by eight times with real-time model inference and negligible accuracy loss.Comment: Accepted by IJCAI-ECAI 201

Inner Product Similarity Search using Compositional Codes

This paper addresses the nearest neighbor search problem under inner product similarity and introduces a compact code-based approach. The idea is to approximate a vector using the composition of several elements selected from a source dictionary and to represent this vector by a short code composed of the indices of the selected elements. The inner product between a query vector and a database vector is efficiently estimated from the query vector and the short code of the database vector. We show the superior performance of the proposed group $M$-selection algorithm that selects $M$ elements from $M$ source dictionaries for vector approximation in terms of search accuracy and efficiency for compact codes of the same length via theoretical and empirical analysis. Experimental results on large-scale datasets ($1M$ and $1B$ SIFT features, $1M$ linear models and Netflix) demonstrate the superiority of the proposed approach.Comment: The approach presented in this paper (ECCV14 submission) is closely related to multi-stage vector quantization and residual quantization. Thanks the reviewers (CVPR14 and ECCV14) for pointing out the relationship to the two algorithms. Related paper: http://sites.skoltech.ru/app/data/uploads/sites/2/2013/09/CVPR14.pdf, which also adopts the summation of vectors for vector approximatio

Urban Delay Tolerant Network Simulator (UDTNSim v0.1)

Delay Tolerant Networking (DTN) is an approach to networking which handles network disruptions and high delays that may occur in many kinds of communication networks. The major reasons for high delay include partial connectivity of networks as can be seen in many types of ad hoc wireless networks with frequent network partitions, long propagation time as experienced in inter-planetary and deep space networks, and frequent link disruptions due to the mobility of nodes as observed in terrestrial wireless network environments. Experimenting network architectures, protocols, and mobility models in such real-world scenarios is difficult due to the complexities involved in the network environment. Therefore, in this document, we present the documentation of an Urban Delay Tolerant Network Simulator (UDTNSim) version 0.1, capable of simulating urban road network environments with DTN characteristics including mobility models and routing protocols. The mobility models included in this version of UDTNSim are (i) Stationary Movement, (ii) Simple Random Movement, (iii) Path Type Based Movememt, (iv) Path Memory Based Movement, (v) Path Type with Restricted Movement, and (vi) Path Type with Wait Movement. In addition to mobility models, we also provide three routing and data hand-off protocols: (i) Epidemic Routing, (ii) Superior Only Handoff, and (iii) Superior Peer Handoff. UDTNSim v0.1 is designed using object-oriented programming approach in order to provide flexibility in addition of new features to the DTN environment. UDTNSim v0.1 is distributed as an open source simulator for the use of the research community.Comment: 40 pages and 4 figure

Fast Convolutional Sparse Coding in the Dual Domain

Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as RGB images and videos. Our results show up to 20 times speedup compared to current state-of-the-art CSC solvers

Histopathological Image Classification using Discriminative Feature-oriented Dictionary Learning

In histopathological image analysis, feature extraction for classification is a challenging task due to the diversity of histology features suitable for each problem as well as presence of rich geometrical structures. In this paper, we propose an automatic feature discovery framework via learning class-specific dictionaries and present a low-complexity method for classification and disease grading in histopathology. Essentially, our Discriminative Feature-oriented Dictionary Learning (DFDL) method learns class-specific dictionaries such that under a sparsity constraint, the learned dictionaries allow representing a new image sample parsimoniously via the dictionary corresponding to the class identity of the sample. At the same time, the dictionary is designed to be poorly capable of representing samples from other classes. Experiments on three challenging real-world image databases: 1) histopathological images of intraductal breast lesions, 2) mammalian kidney, lung and spleen images provided by the Animal Diagnostics Lab (ADL) at Pennsylvania State University, and 3) brain tumor images from The Cancer Genome Atlas (TCGA) database, reveal the merits of our proposal over state-of-the-art alternatives. {Moreover, we demonstrate that DFDL exhibits a more graceful decay in classification accuracy against the number of training images which is highly desirable in practice where generous training is often not availableComment: Accepted version to Transaction on Medical Imaging, 13 page

Adaptive Partitioning for Very Large RDF Data

Distributed RDF systems partition data across multiple computer nodes (workers). Some systems perform cheap hash partitioning, which may result in expensive query evaluation, while others apply heuristics aiming at minimizing inter-node communication during query evaluation. This requires an expensive data preprocessing phase, leading to high startup costs for very large RDF knowledge bases. Apriori knowledge of the query workload has also been used to create partitions, which however are static and do not adapt to workload changes; hence, inter-node communication cannot be consistently avoided for queries that are not favored by the initial data partitioning. In this paper, we propose AdHash, a distributed RDF system, which addresses the shortcomings of previous work. First, AdHash applies lightweight partitioning on the initial data, that distributes triples by hashing on their subjects; this renders its startup overhead low. At the same time, the locality-aware query optimizer of AdHash takes full advantage of the partitioning to (i)support the fully parallel processing of join patterns on subjects and (ii) minimize data communication for general queries by applying hash distribution of intermediate results instead of broadcasting, wherever possible. Second, AdHash monitors the data access patterns and dynamically redistributes and replicates the instances of the most frequent ones among workers. As a result, the communication cost for future queries is drastically reduced or even eliminated. To control replication, AdHash implements an eviction policy for the redistributed patterns. Our experiments with synthetic and real data verify that AdHash (i) starts faster than all existing systems, (ii) processes thousands of queries before other systems become online, and (iii) gracefully adapts to the query load, being able to evaluate queries on billion-scale RDF data in sub-seconds.Comment: 25 page