OpenRaiser/Intern-Atlas huggingface dataset:
Leveraging Implicit Spatial Information in Global Features for Image Retrieval
Venue: arXiv cs.CV 2018
Year: 2018

Most image retrieval methods use global features that aggregate local distinctive patterns into a single representation. However, the aggregation process destroys the relative spatial information by considering orderless sets of local descriptors. We propose to integrate relative spatial information into the aggregation process by taking into account co-occurrences of local patterns in a tensor framework. The resulting signature called Improved Spatial Tensor Aggregation (ISTA) is able to reach state of the art performances on well known datasets such as Holidays, Oxford5k and Paris6k.
Golden interpolation
Venue: arXiv cs.GR 2018
Year: 2018

For the classic aesthetic interpolation problem, we propose an entirely new thought: apply the golden section. For how to apply the golden section to interpolation methods, we present three examples: the golden step interpolation, the golden piecewise linear interpolation and the golden curve interpolation, which respectively deal with the applications of golden section in the interpolation of degree 0, 1, and 2 in the plane. In each example, we present our basic ideas, the specific methods, comparative examples and applications, and relevant criteria. And it is worth mentioning that for aesthetics, we propose two novel concepts: the golden cuspidal hill and the golden domed hill. This paper aims to provide the reference for the combination of golden section and interpolation, and stimulate more and better related researches.
Segmentation of Overlapped Steatosis in Whole-Slide Liver Histopathology Microscopy Images
Venue: arXiv cs.CV 2018
Year: 2018

An accurate steatosis quantification with pathology tissue samples is of high clinical importance. However, such pathology measurement is manually made in most clinical practices, subject to severe reader variability due to large sampling bias and poor reproducibility. Although some computerized automated methods are developed to quantify the steatosis regions, they present limited analysis capacity for high resolution whole-slide microscopy images and accurate overlapped steatosis division. In this paper, we propose a method that extracts an individual whole tissue piece at high resolution with minimum background area by estimating tissue bounding box and rotation angle. This is followed by the segmentation and segregation of steatosis regions with high curvature point detection and an ellipse fitting quality assessment method. We validate our method with isolated and overlapped steatosis regions in liver tissue images of 11 patients. The experimental results suggest that our method is promising for enhanced support of steatosis quantization during the pathology review for liver disease treatment.
Analysis of Cellular Feature Differences of Astrocytomas with Distinct Mutational Profiles Using Digitized Histopathology Images
Venue: arXiv cs.CV 2018
Year: 2018

Cellular phenotypic features derived from histopathology images are the basis of pathologic diagnosis and are thought to be related to underlying molecular profiles. Due to overwhelming cell numbers and population heterogeneity, it remains challenging to quantitatively compute and compare features of cells with distinct molecular signatures. In this study, we propose a self-reliant and efficient analysis framework that supports quantitative analysis of cellular phenotypic difference across distinct molecular groups. To demonstrate efficacy, we quantitatively analyze astrocytomas that are molecularly characterized as either Isocitrate Dehydrogenase (IDH) mutant (MUT) or wildtype (WT) using imaging data from The Cancer Genome Atlas database. Representative cell instances that are phenotypically different between these two groups are retrieved after segmentation, feature computation, data pruning, dimensionality reduction, and unsupervised clustering. Our analysis is generic and can be applied to a wide set of cell-based biomedical research.
WPSE: Fortifying Web Protocols via Browser-Side Security Monitoring
Venue: arXiv cs.CR 2018
Year: 2018

We present WPSE, a browser-side security monitor for web protocols designed to ensure compliance with the intended protocol flow, as well as confidentiality and integrity properties of messages. We formally prove that WPSE is expressive enough to protect web applications from a wide range of protocol implementation bugs and web attacks. We discuss concrete examples of attacks which can be prevented by WPSE on OAuth 2.0 and SAML 2.0, including a novel attack on the Google implementation of SAML 2.0 which we discovered by formalizing the protocol specification in WPSE. Moreover, we use WPSE to carry out an extensive experimental evaluation of OAuth 2.0 in the wild. Out of 90 tested websites, we identify security flaws in 55 websites (61.1%), including new critical vulnerabilities introduced by tracking libraries such as Facebook Pixel, all of which fixable by WPSE. Finally, we show that WPSE works flawlessly on 83 websites (92.2%), with the 7 compatibility issues being caused by custom implementations deviating from the OAuth 2.0 specification, one of which introducing a critical vulnerability.
Raise Your Game for Split Manufacturing: Restoring the True Functionality Through BEOL
Venue: arXiv cs.CR 2018
Year: 2018

Split manufacturing (SM) seeks to protect against piracy of intellectual property (IP) in chip designs. Here we propose a scheme to manipulate both placement and routing in an intertwined manner, thereby increasing the resilience of SM layouts. Key stages of our scheme are to (partially) randomize a design, place and route the erroneous netlist, and restore the original design by re-routing the BEOL. Based on state-of-the-art proximity attacks, we demonstrate that our scheme notably excels over the prior art (i.e., 0% correct connection rates). Our scheme induces controllable PPA overheads and lowers commercial cost (the latter by splitting at higher layers).
Dilated Temporal Fully-Convolutional Network for Semantic Segmentation of Motion Capture Data
Venue: arXiv cs.CV 2018
Year: 2018

Semantic segmentation of motion capture sequences plays a key part in many data-driven motion synthesis frameworks. It is a preprocessing step in which long recordings of motion capture sequences are partitioned into smaller segments. Afterwards, additional methods like statistical modeling can be applied to each group of structurally-similar segments to learn an abstract motion manifold. The segmentation task however often remains a manual task, which increases the effort and cost of generating large-scale motion databases. We therefore propose an automatic framework for semantic segmentation of motion capture data using a dilated temporal fully-convolutional network. Our model outperforms a state-of-the-art model in action segmentation, as well as three networks for sequence modeling. We further show our model is robust against high noisy training labels.
Framework for Opinion Mining Approach to Augment Education System Performance
Venue: arXiv cs.IR 2018
Year: 2018

The extensive expansion growth of social networking sites allows the people to share their views and experiences freely with their peers on internet. Due to this, huge amount of data is generated on everyday basis which can be used for the opinion mining to extract the views of people in a particular field. Opinion mining finds its applications in many areas such as Tourism, Politics, education and entertainment, etc. It has not been extensively implemented in area of education system. This paper discusses the malpractices in the present examination system. In the present scenario, Opinion mining is vastly used for decision making. The authors of this paper have designed a framework by applying Naïve Bayes approach to the education dataset. The various phases of Naïve Bayes approach include three steps: conversion of data into frequency table, making classes of dataset and apply the Naïve Bayes algorithm equation to calculate the probabilities of classes. Finally the highest probability class is the outcome of this prediction. These predictions are used to make improvements in the education system and help to provide better education.
Encoding Sets as Real Numbers (Extended version)
Venue: arXiv cs.LO 2018
Year: 2018

We study a variant of the Ackermann encoding $\mathbb{N}(x) := \sum_{y\in x}2^{\mathbb{N}(y)}$ of the hereditarily finite sets by the natural numbers, applicable to the larger collection $\mathsf{HF}^{1/2}$ of the hereditarily finite hypersets. The proposed variation is obtained by simply placing a `minus' sign before each exponent in the definition of $\mathbb{N}$, resulting in the expression $\mathbb{R}(x) := \sum_{y\in x}2^{-\mathbb{R}(y)}$. By a careful analysis, we prove that the encoding $\mathbb{R}_{A}$ is well-defined over the whole collection $\mathsf{HF}^{1/2}$, as it allows one to univocally assign a real-valued code to each hereditarily finite hyperset. We also address some preliminary cases of the injectivity problem for $\mathbb{R}_{A}$.
SAQL: A Stream-based Query System for Real-Time Abnormal System Behavior Detection
Venue: arXiv cs.CR 2018
Year: 2018

Recently, advanced cyber attacks, which consist of a sequence of steps that involve many vulnerabilities and hosts, compromise the security of many well-protected businesses. This has led to the solutions that ubiquitously monitor system activities in each host (big data) as a series of events, and search for anomalies (abnormal behaviors) for triaging risky events. Since fighting against these attacks is a time-critical mission to prevent further damage, these solutions face challenges in incorporating expert knowledge to perform timely anomaly detection over the large-scale provenance data. To address these challenges, we propose a novel stream-based query system that takes as input, a real-time event feed aggregated from multiple hosts in an enterprise, and provides an anomaly query engine that queries the event feed to identify abnormal behaviors based on the specified anomalies. To facilitate the task of expressing anomalies based on expert knowledge, our system provides a domain-specific query language, SAQL, which allows analysts to express models for (1) rule-based anomalies, (2) time-series anomalies, (3) invariant-based anomalies, and (4) outlier-based anomalies. We deployed our system in NEC Labs America comprising 150 hosts and evaluated it using 1.1TB of real system monitoring data (containing 3.3 billion events). Our evaluations on a broad set of attack behaviors and micro-benchmarks show that our system has a low detection latency (<2s) and a high system throughput (110,000 events/s; supporting ~4000 hosts), and is more efficient in memory utilization than the existing stream-based complex event processing systems.
Outage of Periodic Downlink Wireless Networks with Hard Deadlines
Venue: arXiv cs.IT 2018
Year: 2018

We consider a downlink periodic wireless communications system where multiple access points (APs) cooperatively transmit packets to a number of devices, e.g. actuators in an industrial control system. Each period consists of two phases: an uplink training phase and a downlink data transmission phase. Each actuator must successfully receive its unique packet within a single transmission phase, else an outage is declared. Such an outage can be caused by two events: a transmission error due to transmission at a rate that the channel cannot actually support or time overflow, where the downlink data phase is too short given the channel conditions to successfully communicate all the packets. We determine closed-form expressions for the probability of time overflow when there are just two field devices, as well as the probability of transmission error for an arbitrary number of devices. Also, we provide upper and lower bounds on the time overflow probability for an arbitrary number of devices. We propose a novel variable-rate transmission method that eliminates time overflow. Detailed system-level simulations are used to identify system design guidelines, such as the optimal amount of uplink training time, as well as for benchmarking the proposed system design versus non-cooperative cellular, cooperative fixed-rate, and cooperative relaying.
Finding models through graph saturation
Venue: arXiv cs.LO 2018
Year: 2018

We give a procedure that can be used to automatically satisfy invariants of a certain shape. These invariants may be written with the operations intersection, composition and converse over binary relations, and equality over these operations. We call these invariants \tr{}s that we interpret over graphs. For questions stated through sets of these sentences, this paper gives a semi-decision procedure we call graph saturation. It decides entailment over these \tr{}s, inspired on graph rewriting. We prove correctness of the procedure. Moreover, we show the corresponding decision problem to be undecidable. This confirms a conjecture previously stated by the author.
Performance Analysis of Unsupervised LTE Device-to-Device (D2D) Communication
Venue: arXiv cs.NI 2018
Year: 2018

Cellular network technology based device-to-device communication attracts increasing attention for use cases such as the control of autonomous vehicles on the ground and in the air. LTE provides device-to-device communication options, however, the configuration options are manifold (leading to 150+ possible combinations) and therefore the ideal combination of parameters is hard to find. Depending on the use case, either throughput, reliability or latency constraints may be the primary concern of the service provider. In this work we analyze the impact of different configuration settings of unsupervised LTE device-to-device (sidelink) communication on the system performance. Using a simulative approach we vary the length of the PSCCH period and the number of PSCCH subframes and determine the impact of different combinations of those parameters on the resulting latency, reliability and the interarrival times of the received packets. Furthermore we examine the system limitations by a scalability analysis. In this context, we propose a modified HARQ process to mitigate scalability constraints. Our results show that the proposed reduced HARQ retransmission probability can increase the system performance regarding latency and interarrival times as well as the packet transmission reliability for higher channel utilization.
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
Venue: arXiv cs.AI 2018
Year: 2018

Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness. Embedding models attain state-of-the-art accuracy in knowledge base completion, but their predictions are notoriously hard to interpret. In this paper, we adapt "pedagogical approaches" (from the literature on neural networks) so as to interpret embedding models by extracting weighted Horn rules from them. We show how pedagogical approaches have to be adapted to take upon the large-scale relational aspects of knowledge bases and show experimentally their strengths and weaknesses.
The Emotional Voices Database: Towards Controlling the Emotion Dimension in Voice Generation Systems
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we present a database of emotional speech intended to be open-sourced and used for synthesis and generation purpose. It contains data for male and female actors in English and a male actor in French. The database covers 5 emotion classes so it could be suitable to build synthesis and voice transformation systems with the potential to control the emotional dimension in a continuous way. We show the data's efficiency by building a simple MLP system converting neutral to angry speech style and evaluate it via a CMOS perception test. Even though the system is a very simple one, the test show the efficiency of the data which is promising for future work.
Cryptanalysis of a Chaotic Key based Image Encryption Scheme
Venue: arXiv cs.CR 2018
Year: 2018

Security of multimedia data is a major concern due to its widespread transmission over various communication channels. Hence design and study of good image encryption schemes has become a major research topic. During the last few decades, there has been a increasing in chaos-based cryptography. This paper proposes an attack on a recently proposed chaos based image encryption scheme. The cryptosystem under study proceed by first shuffling the original image to disturb the arrangement of pixels by applying a chaotic map several times. Second, a keystream is generated using Chen's chaotic system to mix it with the shuffled pixels to finally obtain the cipher image. A chosen ciphertext attack can be done to recover the system without any knowledge of the key. It simply demands two pairs of plaintext-ciphertext to completely break the cryptosystem.
Using NLP on news headlines to predict index trends
Venue: arXiv cs.CL 2018
Year: 2018

This paper attempts to provide a state of the art in trend prediction using news headlines. We present the research done on predicting DJIA trends using Natural Language Processing. We will explain the different algorithms we have used as well as the various embedding techniques attempted. We rely on statistical and deep learning models in order to extract information from the corpuses.
A Machine-learning framework for automatic reference-free quality assessment in MRI
Venue: arXiv cs.CV 2018
Year: 2018

Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and therefore time- and cost-intensive. Any imaging artifacts originating from scanner hardware, signal processing or induced by the patient may reduce the image quality and complicate the diagnosis or any image post-processing. Therefore, the assessment or the ensurance of sufficient image quality in an automated manner is of high interest. Usually no reference image is available or difficult to define. Therefore, classical reference-based approaches are not applicable. Model observers mimicking the human observers (HO) can assist in this task. Thus, we propose a new machine-learning-based reference-free MR image quality assessment framework which is trained on HO-derived labels to assess MR image quality immediately after each acquisition. We include the concept of active learning and present an efficient blinded reading platform to reduce the effort in the HO labeling procedure. Derived image features and the applied classifiers (support-vector-machine, deep neural network) are investigated for a cohort of 250 patients. The MR image quality assessment framework can achieve a high test accuracy of 93.7$\%$ for estimating quality classes on a 5-point Likert-scale. The proposed MR image quality assessment framework is able to provide an accurate and efficient quality estimation which can be used as a prospective quality assurance including automatic acquisition adaptation or guided MR scanner operation, and/or as a retrospective quality assessment including support of diagnostic decisions or quality control in cohort studies.
Sounderfeit: Cloning a Physical Model using a Conditional Adversarial Autoencoder
Venue: arXiv cs.SD 2018
Year: 2018

An adversarial autoencoder conditioned on known parameters of a physical modeling bowed string synthesizer is evaluated for use in parameter estimation and resynthesis tasks. Latent dimensions are provided to capture variance not explained by the conditional parameters. Results are compared with and without the adversarial training, and a system capable of "copying" a given parameter-signal bidirectional relationship is examined. A real-time synthesis system built on a generative, conditioned and regularized neural network is presented, allowing to construct engaging sound synthesizers based purely on recorded data.
Person Re-Identification in Identity Regression Space
Venue: arXiv cs.CV 2018
Year: 2018

Most existing person re-identification (re-id) methods are unsuitable for real-world deployment due to two reasons: Unscalability to large population size, and Inadaptability over time. In this work, we present a unified solution to address both problems. Specifically, we propose to construct an Identity Regression Space (IRS) based on embedding different training person identities (classes) and formulate re-id as a regression problem solved by identity regression in the IRS. The IRS approach is characterised by a closed-form solution with high learning efficiency and an inherent incremental learning capability with human-in-the-loop. Extensive experiments on four benchmarking datasets(VIPeR, CUHK01, CUHK03 and Market-1501) show that the IRS model not only outperforms state-of-the-art re-id methods, but also is more scalable to large re-id population size by rapidly updating model and actively selecting informative samples with reduced human labelling effort.
The Hamming and Golay Number-Theoretic Transforms
Venue: arXiv cs.IT 2018
Year: 2018

New number-theoretic transforms are derived from known linear block codes over finite fields. In particular, two new such transforms are built from perfect codes, namely the \textit {Hamming number-theoretic transform} and the \textit {Golay number-theoretic transform}. A few properties of these new transforms are presented.
REST-ler: Automatic Intelligent REST API Fuzzing
Venue: arXiv cs.SE 2018
Year: 2018

Cloud services have recently exploded with the advent of powerful cloud-computing platforms such as Amazon Web Services and Microsoft Azure. Today, most cloud services are accessed through REST APIs, and Swagger is arguably the most popular interface-description language for REST APIs. A Swagger specification describes how to access a cloud service through its REST API (e.g., what requests the service can handle and what responses may be expected). This paper introduces REST-ler, the first automatic intelligent REST API security-testing tool. REST-ler analyzes a Swagger specification and generates tests that exercise the corresponding cloud service through its REST API. Each test is defined as a sequence of requests and responses. REST-ler generates tests intelligently by (1) inferring dependencies among request types declared in the Swagger specification (e.g., inferring that "a request B should not be executed before a request A" because B takes as an input argument a resource-id x returned by A) and by (2) analyzing dynamic feedback from responses observed during prior test executions in order to generate new tests (e.g., learning that "a request C after a request sequence A;B is refused by the service" and therefore avoiding this combination in the future). We show that these two techniques are necessary to thoroughly exercise a service under test while pruning the large search space of possible request sequences. We also discuss the application of REST-ler to test GitLab, a large popular open-source self-hosted Git service, and the new bugs that were found.
Causal Inference for Early Detection of Pathogenic Social Media Accounts
Venue: arXiv cs.SI 2018
Year: 2018

Pathogenic social media accounts such as terrorist supporters exploit communities of supporters for conducting attacks on social media. Early detection of PSM accounts is crucial as they are likely to be key users in making a harmful message "viral". This paper overviews my recent doctoral work on utilizing causal inference to identify PSM accounts within a short time frame around their activity. The proposed scheme (1) assigns time-decay causality scores to users, (2) applies a community detection-based algorithm to group of users sharing similar causality scores and finally (3) deploys a classification algorithm to classify accounts. Unlike existing techniques that require network structure, cascade path, or content, our scheme relies solely on action log of users.