OpenRaiser/Intern-Atlas huggingface dataset:
Investigating Storage as a Service Cloud Platform: pCloud as a Case Study
Venue: arXiv cs.CR 2017
Year: 2017

Due to the flexibility, affordability and portability of cloud storage, individuals and companies envisage the cloud storage as one of the preferred storage media nowadays. This attracts the eyes of cyber criminals, since much valuable informa- tion such as user credentials, and private customer records are stored in the cloud. There are many ways for criminals to compromise cloud services; ranging from non-technical attack methods, such as social engineering, to deploying advanced malwares. Therefore, it is vital for cyber forensics examiners to be equipped and informed about best methods for investigation of different cloud platforms. In this chapter, using pCloud (an extensively used online cloud storage service) as a case study, and we elaborate on different kinds of artefacts retrievable during a forensics examination. We carried out our experiments on four different virtual machines running four popular operating systems: a 64 bit Windows 8, Ubuntu 14.04.1 LTS, Android 4.4.2, and iOS 8.1. Moreover, we examined cloud remnants of two different web browsers: Internet Explorer and Google Chrome on Windows. We believe that our study would promote awareness among digital forensic examiners on how to conduct cloud storage forensics examination.
Transmit Antenna Selection for Physical-Layer Network Coding Based on Euclidean Distance
Venue: arXiv cs.IT 2017
Year: 2017

Physical-layer network coding (PNC) is now well-known as a potential candidate for delay-sensitive and spectrally efficient communication applications, especially in two-way relay channels (TWRCs). In this paper, we present the error performance analysis of a multiple-input single-output (MISO) fixed network coding (FNC) system with two different transmit antenna selection (TAS) schemes. For the first scheme, where the antenna selection is performed based on the strongest channel, we derive a tight closed-form upper bound on the average symbol error rate (SER) with $M$-ary modulation and show that the system achieves a diversity order of 1 for $M > 2$. Next, we propose a Euclidean distance (ED) based antenna selection scheme which outperforms the first scheme in terms of error performance and is shown to achieve a diversity order lower bounded by the minimum of the number of antennas at the two users.
Transitivity of Commutativity for Linear Time-Varying Analog Systems
Venue: arXiv cs.SY 2017
Year: 2017

In this contribution, the transitivity property of commutative first-order linear time-varying systems is investigated with and without initial conditions. It is proven that transitivity property of first-order systems holds with and without initial conditions. On the base of impulse response function, transitivity of commutation property is formulated for any triplet of commutative linear time-varying relaxed systems. Transitivity proves are given for some special combinations of first and second-order linear time-varying systems which are initially relaxed.
Method for Aspect-Based Sentiment Annotation Using Rhetorical Analysis
Venue: arXiv cs.CL 2017
Year: 2017

This paper fills a gap in aspect-based sentiment analysis and aims to present a new method for preparing and analysing texts concerning opinion and generating user-friendly descriptive reports in natural language. We present a comprehensive set of techniques derived from Rhetorical Structure Theory and sentiment analysis to extract aspects from textual opinions and then build an abstractive summary of a set of opinions. Moreover, we propose aspect-aspect graphs to evaluate the importance of aspects and to filter out unimportant ones from the summary. Additionally, the paper presents a prototype solution of data flow with interesting and valuable results. The proposed method's results proved the high accuracy of aspect detection when applied to the gold standard dataset.
State-Secrecy Codes for Networked Linear Systems
Venue: arXiv cs.SY 2017
Year: 2017

In this paper, we study the problem of remote state estimation, in the presence of a passive eavesdropper. An authorized user estimates the state of an unstable linear plant, based on the packets received from a sensor, while the packets may also be intercepted by the eavesdropper. Our goal is to design a coding scheme at the sensor, which encodes the state information, in order to impair the eavesdropper's estimation performance, while enabling the user to successfully decode the sent messages. We introduce a novel class of codes, termed State-Secrecy Codes, which use acknowledgment signals from the user and apply linear time-varying transformations to the current and previously received states. By exploiting the properties of the system's process noise, the channel physical model and the dynamics, these codes manage to be fast, efficient and, thus, suitable for real-time dynamical systems. We prove that under minimal conditions, State-Secrecy Codes achieve perfect secrecy, namely the eavesdropper's estimation error grows unbounded almost surely, while the user's estimation performance is optimal. These conditions only require that at least once, the user receives the corresponding packet while the eavesdropper fails to intercept it. Even one occurrence of this event renders the eavesdropper's error unbounded with asymptotically optimal rate of increase. State-Secrecy Codes are provided and studied for two cases, i) when direct state measurements are available, and ii) when we only have output measurements. The theoretical results are illustrated in simulations.
Ignoring Distractors in the Absence of Labels: Optimal Linear Projection to Remove False Positives During Anomaly Detection
Venue: arXiv cs.LG 2017
Year: 2017

In the anomaly detection setting, the native feature embedding can be a crucial source of bias. We present a technique, Feature Omission using Context in Unsupervised Settings (FOCUS) to learn a feature mapping that is invariant to changes exemplified in training sets while retaining as much descriptive power as possible. While this method could apply to many unsupervised settings, we focus on applications in anomaly detection, where little task-labeled data is available. Our algorithm requires only non-anomalous sets of data, and does not require that the contexts in the training sets match the context of the test set. By maximizing within-set variance and minimizing between-set variance, we are able to identify and remove distracting features while retaining fidelity to the descriptiveness needed at test time. In the linear case, our formulation reduces to a generalized eigenvalue problem that can be solved quickly and applied to test sets outside the context of the training sets. This technique allows us to align technical definitions of anomaly detection with human definitions through appropriate mappings of the feature space. We demonstrate that this method is able to remove uninformative parts of the feature space for the anomaly detection setting.
When Waiting is not an Option : Learning Options with a Deliberation Cost
Venue: arXiv cs.AI 2017
Year: 2017

Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance. While the problem of "how" to learn options is increasingly well understood, the question of "what" good options should be has remained elusive. We formulate our answer to what "good" options should be in the bounded rationality framework (Simon, 1957) through the notion of deliberation cost. We then derive practical gradient-based learning algorithms to implement this objective. Our results in the Arcade Learning Environment (ALE) show increased performance and interpretability.
Two-weight codes and second order recurrences
Venue: arXiv cs.IT 2017
Year: 2017

Cyclic codes of dimension $2$ over a finite field are shown to have at most two nonzero weights. This extends a construction of Rao et al (2010) and disproves a conjecture of Schmidt-White (2002). We compute their weight distribution, and give a condition on the roots of their check polynomials for them to be MDS.
Learning to Segment Instances in Videos with Spatial Propagation Network
Venue: arXiv cs.CV 2017
Year: 2017

We propose a deep learning-based framework for instance-level object segmentation. Our method mainly consists of three steps. First, We train a generic model based on ResNet-101 for foreground/background segmentations. Second, based on this generic model, we fine-tune it to learn instance-level models and segment individual objects by using augmented object annotations in first frames of test videos. To distinguish different instances in the same video, we compute a pixel-level score map for each object from these instance-level models. Each score map indicates the objectness likelihood and is only computed within the foreground mask obtained in the first step. To further refine this per frame score map, we learn a spatial propagation network. This network aims to learn how to propagate a coarse segmentation mask spatially based on the pairwise similarities in each frame. In addition, we apply a filter on the refined score map that aims to recognize the best connected region using spatial and temporal consistencies in the video. Finally, we decide the instance-level object segmentation in each video by comparing score maps of different instances.
Modeling Library Dependencies and Updates in Large Software Repository Universes
Venue: arXiv cs.SE 2017
Year: 2017

Popular (re)use of third-party open-source software (OSS) is evidence of the impact of hosting repositories like maven on software development today. Updating libraries is crucial, with recent studies highlighting the associated vulnerabilities with aging OSS libraries. The decision to migrate to a newer library can range from trivial (security threat) to complex (assessment of work required to accommodate the changes). By leveraging the `wisdom of the software repository crowd' we propose a simple and efficient approach to recommending `consented' library updates. Our Software Universe Graph (SUG) models library dependency and update information mined from super repositories to provide different metrics and visualizations that aid in the update decision. To evaluate, we first constructed a SUG from 188,951 nodes of 6,374 maven unique artifacts. Then, we demonstrate how our metrics and visualizations are applied through real-world examples. As an extension, we show how the SUG can compare dependencies between different super repositories. From a sample of 100 GitHub applications, our method found that on average 79% similar overlapping dependencies combinations exist between the maven and github super repository universes.
Warmstarting of Model-based Algorithm Configuration
Venue: arXiv cs.AI 2017
Year: 2017

The performance of many hard combinatorial problem solvers depends strongly on their parameter settings, and since manual parameter tuning is both tedious and suboptimal the AI community has recently developed several algorithm configuration (AC) methods to automatically address this problem. While all existing AC methods start the configuration process of an algorithm A from scratch for each new type of benchmark instances, here we propose to exploit information about A's performance on previous benchmarks in order to warmstart its configuration on new types of benchmarks. We introduce two complementary ways in which we can exploit this information to warmstart AC methods based on a predictive model. Experiments for optimizing a very flexible modern SAT solver on twelve different instance sets show that our methods often yield substantial speedups over existing AC methods (up to 165-fold) and can also find substantially better configurations given the same compute budget.
A Randomly Expandable Method for Data Layout of RAID Storage Systems
Venue: arXiv cs.DC 2017
Year: 2017

With the increase of huge amounts of data in volume, velocity, and variety, the need for capacity of Redundant Arrays of Inexpensive Disks (RAID) storage systems is dramatically growing. However, the probability of disk failures in RAID storage systems is sharply high with the increase of program/erase cycles, read cycles, and retention time. Furthermore, they are faced with more challenges in fault tolerance, storage efficiency, computational complexity, and expandability. This article presents a novel data layout scheme for RAID storage System using Random Binary Extensive Code (RBEC), which are designed to ensure random expandability, high reliability, and availability of data in RAID storage systems. RBEC is a family of systematic code, in which the generator matrix consists of two submatrices with entries over GF(2), an identity matrix on the top, and another submatrix on the bottom. Compared with the existed approaches, the attractive advantages of our schemes include 1) they are completely implemented based on only simple eXclusive OR (XOR) operations and have systematic code property, 2) they can provide arbitrary fault tolerance, 3) their storage efficiency is quasi-optimal, and 4) data and parity disks of RAID storage systems can be randomly expanded according to practical requirements. Thus, our scheme is particularly suitable for RAID storage systems that need higher reliability, availability, and expandability.
Shannon entropy for imprecise and under-defined or over-defined information
Venue: arXiv cs.IT 2017
Year: 2017

Shannon entropy was defined for probability distributions and then its using was expanded to measure the uncertainty of knowledge for systems with complete information. In this article, it is proposed to extend the using of Shannon entropy to under-defined or over-defined information systems. To be able to use Shannon entropy, the information is normalized by an affine transformation. The construction of affine transformation is done in two stages: one for homothety and another for translation. Moreover, the case of information with a certain degree of imprecision was included in this approach. Besides, the article shows the using of Shannon entropy for some particular cases such as: neutrosophic information both in the trivalent and bivalent case, bifuzzy information, intuitionistic fuzzy information, imprecise fuzzy information, and fuzzy partitions.
Exploring Food Detection using CNNs
Venue: arXiv cs.CV 2017
Year: 2017

One of the most common critical factors directly related to the cause of a chronic disease is unhealthy diet consumption. In this sense, building an automatic system for food analysis could allow a better understanding of the nutritional information with respect to the food eaten and thus it could help in taking corrective actions in order to consume a better diet. The Computer Vision community has focused its efforts on several areas involved in the visual food analysis such as: food detection, food recognition, food localization, portion estimation, among others. For food detection, the best results evidenced in the state of the art were obtained using Convolutional Neural Network. However, the results of all these different approaches were gotten on different datasets and therefore are not directly comparable. This article proposes an overview of the last advances on food detection and an optimal model based on GoogLeNet Convolutional Neural Network method, principal component analysis, and a support vector machine that outperforms the state of the art on two public food/non-food datasets.
TraceTracker: Hardware/Software Co-Evaluation for Large-Scale I/O Workload Reconstruction
Venue: arXiv cs.DC 2017
Year: 2017

Block traces are widely used for system studies, model verifications, and design analyses in both industry and academia. While such traces include detailed block access patterns, existing trace-driven research unfortunately often fails to find true-north due to a lack of runtime contexts such as user idle periods and system delays, which are fundamentally linked to the characteristics of target storage hardware. In this work, we propose TraceTracker, a novel hardware/software co-evaluation method that allows users to reuse a broad range of the existing block traces by keeping most their execution contexts and user scenarios while adjusting them with new system information. Specifically, our TraceTracker's software evaluation model can infer CPU burst times and user idle periods from old storage traces, whereas its hardware evaluation method remasters the storage traces by interoperating the inferred time information, and updates all inter-arrival times by making them aware of the target storage system. We apply the proposed co-evaluation model to 577 traces, which were collected by servers from different institutions and locations a decade ago, and revive the traces on a high-performance flash-based storage array. The evaluation results reveal that the accuracy of the execution contexts reconstructed by TraceTracker is on average 99% and 96% with regard to the frequency of idle operations and the total idle periods, respectively.
Enhancing Predicate Pairing with Abstraction for Relational Verification
Venue: arXiv cs.LO 2017
Year: 2017

Relational verification is a technique that aims at proving properties that relate two different program fragments, or two different program runs. It has been shown that constrained Horn clauses (CHCs) can effectively be used for relational verification by applying a CHC transformation, called predicate pairing, which allows the CHC solver to infer relations among arguments of different predicates. In this paper we study how the effects of the predicate pairing transformation can be enhanced by using various abstract domains based on linear arithmetic (i.e., the domain of convex polyhedra and some of its subdomains) during the transformation. After presenting an algorithm for predicate pairing with abstraction, we report on the experiments we have performed on over a hundred relational verification problems by using various abstract domains. The experiments have been performed by using the VeriMAP transformation and verification system, together with the Parma Polyhedra Library (PPL) and the Z3 solver for CHCs.
A New Semantic Theory of Natural Language
Venue: arXiv cs.CL 2017
Year: 2017

Formal Semantics and Distributional Semantics are two important semantic frameworks in Natural Language Processing (NLP). Cognitive Semantics belongs to the movement of Cognitive Linguistics, which is based on contemporary cognitive science. Each framework could deal with some meaning phenomena, but none of them fulfills all requirements proposed by applications. A unified semantic theory characterizing all important language phenomena has both theoretical and practical significance; however, although many attempts have been made in recent years, no existing theory has achieved this goal yet. This article introduces a new semantic theory that has the potential to characterize most of the important meaning phenomena of natural language and to fulfill most of the necessary requirements for philosophical analysis and for NLP applications. The theory is based on a unified representation of information, and constructs a kind of mathematical model called cognitive model to interpret natural language expressions in a compositional manner. It accepts the empirical assumption of Cognitive Semantics, and overcomes most shortcomings of Formal Semantics and of Distributional Semantics. The theory, however, is not a simple combination of existing theories, but an extensive generalization of classic logic and Formal Semantics. It inherits nearly all advantages of Formal Semantics, and also provides descriptive contents for objects and events as fine-gram as possible, descriptive contents which represent the results of human cognition.
A linear time algorithm to cover and hit a set of line segments optimally by two axis-parallel squares
Venue: arXiv cs.CG 2017
Year: 2017

This paper discusses the problem of covering and hitting a set of line segments $\cal L$ in ${\mathbb R}^2$ by a pair of axis-parallel squares such that the side length of the larger of the two squares is minimized. We also discuss the restricted version of covering, where each line segment in $\cal L$ is to be covered completely by at least one square. The proposed algorithm for the covering problem reports the optimum result by executing only two passes of reading the input data sequentially. The algorithm proposed for the hitting and restricted covering problems produces optimum result in $O(n)$ time. All the proposed algorithms are in-place, and they use only $O(1)$ extra space. The solution of these problems also give a $\sqrt{2}$ approximation for covering and hitting those line segments $\cal L$ by two congruent disks of minimum radius with same computational complexity.
Optimal broadcasting in networks with faulty nodes
Venue: arXiv cs.DS 2017
Year: 2017

Large computer networks are an essential part of modern technology, and quite often information needs to be broadcast to all the computers in the network. If all computers work perfectly all the time, this is simple. Suppose, however, that some of the computers fail occasionally. What is the fastest way to ensure that with high probability all working computers get the information? In this paper, we analyze three algorithms to do so. All algorithms terminate in logarithmic time, assuming computers fail with probability $1-p$ independently of each other. We prove that the third algorithm, which runs in time $(1+o(1))(\frac{\log N}{\log(1+p)})$, is asymptotically optimal.
Towards Baselines for Shoulder Surfing on Mobile Authentication
Venue: arXiv cs.CR 2017
Year: 2017

Given the nature of mobile devices and unlock procedures, unlock authentication is a prime target for credential leaking via shoulder surfing, a form of an observation attack. While the research community has investigated solutions to minimize or prevent the threat of shoulder surfing, our understanding of how the attack performs on current systems is less well studied. In this paper, we describe a large online experiment (n=1173) that works towards establishing a baseline of shoulder surfing vulnerability for current unlock authentication systems. Using controlled video recordings of a victim entering in a set of 4- and 6-length PINs and Android unlock patterns on different phones from different angles, we asked participants to act as attackers, trying to determine the authentication input based on the observation. We find that 6-digit PINs are the most elusive attacking surface where a single observation leads to just 10.8% successful attacks, improving to 26.5\% with multiple observations. As a comparison, 6-length Android patterns, with one observation, suffered 64.2% attack rate and 79.9% with multiple observations. Removing feedback lines for patterns improves security from 35.3\% and 52.1\% for single and multiple observations, respectively. This evidence, as well as other results related to hand position, phone size, and observation angle, suggests the best and worst case scenarios related to shoulder surfing vulnerability which can both help inform users to improve their security choices, as well as establish baselines for researchers.
Geometric clustering in normed planes
Venue: arXiv cs.CG 2017
Year: 2017

Given two sets of points $A$ and $B$ in a normed plane, we prove that there are two linearly separable sets $A'$ and $B'$ such that $\mathrm{diam}(A')\leq \mathrm{diam}(A)$, $\mathrm{diam}(B')\leq \mathrm{diam}(B)$, and $A'\cup B'=A\cup B.$ This extends a result for the Euclidean distance to symmetric convex distance functions. As a consequence, some Euclidean $k$-clustering algorithms are adapted to normed planes, for instance, those that minimize the maximum, the sum, or the sum of squares of the $k$ cluster diameters. The 2-clustering problem when two different bounds are imposed to the diameters is also solved. The Hershberger-Suri's data structure for managing ball hulls can be useful in this context.