OpenRaiser/Intern-Atlas huggingface dataset:
New Threats to SMS-Assisted Mobile Internet Services from 4G LTE: Lessons Learnt from Distributed Mobile-Initiated Attacks towards Facebook and Other Services
Venue: arXiv cs.CR 2015
Year: 2015

Mobile Internet is becoming the norm. With more personalized mobile devices in hand, many services choose to offer alternative, usually more convenient, approaches to authenticating and delivering the content between mobile users and service providers. One main option is to use SMS (i.e., short messaging service). Such carrier-grade text service has been widely used to assist versatile mobile services, including social networking, banking, to name a few. Though the text service can be spoofed via certain Internet text service providers which cooperated with carriers, such attacks haven well studied and defended by industry due to the efforts of research community. However, as cellular network technology advances to the latest IP-based 4G LTE, we find that these mobile services are somehow exposed to new threats raised by this change, particularly on 4G LTE Text service (via brand-new distributed Mobile-Initiated Spoofed SMS attack which is not available in legacy 2G/3G systems). The reason is that messaging service over LTE shifts from the circuit-switched (CS) design to the packet-switched (PS) paradigm as 4G LTE supports PS only. Due to this change, 4G LTE Text Service becomes open to access. However, its shields to messaging integrity and user authentication are not in place. As a consequence, such weaknesses can be exploited to launch attacks (e.g., hijack Facebook accounts) against a targeted individual, a large scale of mobile users and even service providers, from mobile devices. Current defenses for Internet-Initiated Spoofed SMS attacks cannot defend the unprecedented attack. Our study shows that 53 of 64 mobile services over 27 industries are vulnerable to at least one threat. We validate these proof-of-concept attacks in one major US carrier which supports more than 100 million users. We finally propose quick fixes and discuss security insights and lessons we have learnt.
Scheduling under Linear Constraints
Venue: arXiv cs.DS 2015
Year: 2015

We introduce a parallel machine scheduling problem in which the processing times of jobs are not given in advance but are determined by a system of linear constraints. The objective is to minimize the makespan, i.e., the maximum job completion time among all feasible choices. This novel problem is motivated by various real-world application scenarios. We discuss the computational complexity and algorithms for various settings of this problem. In particular, we show that if there is only one machine with an arbitrary number of linear constraints, or there is an arbitrary number of machines with no more than two linear constraints, or both the number of machines and the number of linear constraints are fixed constants, then the problem is polynomial-time solvable via solving a series of linear programming problems. If both the number of machines and the number of constraints are inputs of the problem instance, then the problem is NP-Hard. We further propose several approximation algorithms for the latter case.
Location-Based Beamforming for Rician Wiretap Channels
Venue: arXiv cs.IT 2015
Year: 2015

We propose a location-based beamforming scheme for wiretap channels, where a source communicates with a legitimate receiver in the presence of an eavesdropper. We assume that the source and the eavesdropper are equipped with multiple antennas, while the legitimate receiver is equipped with a single antenna. We also assume that all channels are in a Rician fading environment, the channel state information from the legitimate receiver is perfectly known at the source, and that the only information on the eavesdropper available at the source is her location. We first describe how the beamforming vector that minimizes the secrecy outage probability of the system is obtained, illustrating its dependence on the eavesdropper's location. We then derive an easy-to-compute expression for the secrecy outage probability when our proposed location-based beamforming is adopted. Finally, we investigate the impact location uncertainty has on the secrecy outage probability, showing how our proposed solution can still allow for secrecy even when the source has limited information on the eavesdropper's location.
Spiking Deep Networks with LIF Neurons
Venue: arXiv cs.LG 2015
Year: 2015

We train spiking deep networks using leaky integrate-and-fire (LIF) neurons, and achieve state-of-the-art results for spiking networks on the CIFAR-10 and MNIST datasets. This demonstrates that biologically-plausible spiking LIF neurons can be integrated into deep networks can perform as well as other spiking models (e.g. integrate-and-fire). We achieved this result by softening the LIF response function, such that its derivative remains bounded, and by training the network with noise to provide robustness against the variability introduced by spikes. Our method is general and could be applied to other neuron types, including those used on modern neuromorphic hardware. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this difficult task. It also provides new methods for training deep networks to run on neuromorphic hardware, with the aim of fast, power-efficient image classification for robotics applications.
A Fast Hyperplane-Based Minimum-Volume Enclosing Simplex Algorithm for Blind Hyperspectral Unmixing
Venue: arXiv cs.CG 2015
Year: 2015

Hyperspectral unmixing (HU) is a crucial signal processing procedure to identify the underlying materials (or endmembers) and their corresponding proportions (or abundances) from an observed hyperspectral scene. A well-known blind HU criterion, advocated by Craig in early 1990's, considers the vertices of the minimum-volume enclosing simplex of the data cloud as good endmember estimates, and it has been empirically and theoretically found effective even in the scenario of no pure pixels. However, such kind of algorithms may suffer from heavy simplex volume computations in numerical optimization, etc. In this work, without involving any simplex volume computations, by exploiting a convex geometry fact that a simplest simplex of N vertices can be defined by N associated hyperplanes, we propose a fast blind HU algorithm, for which each of the N hyperplanes associated with the Craig's simplex of N vertices is constructed from N-1 affinely independent data pixels, together with an endmember identifiability analysis for its performance support. Without resorting to numerical optimization, the devised algorithm searches for the N(N-1) active data pixels via simple linear algebraic computations, accounting for its computational efficiency. Monte Carlo simulations and real data experiments are provided to demonstrate its superior efficacy over some benchmark Craig-criterion-based algorithms in both computational efficiency and estimation accuracy.
The Virtual Experiences Lab - a platform for global collaborative engineering and beyond
Venue: arXiv cs.OH 2015
Year: 2015

We are developing the Virtual Experiences (Vx)Lab, a research and research training infrastructure and capability platform for global collaboration. VxLab comprises labs with visualisation capabilities, including underpinning networking to global points of presence, videoconferencing and high-performance computation, simulation and rendering, and sensors and actuators such as robotic instruments locally and in connected remote labs. VxLab has been used for industry projects in industrial automation, experimental research in cloud deployment, workshops and remote capability demonstrations, teaching advanced-level courses in games development, and student software engineering projects. Our goal is for resources to become a "catalyst" for IT-driven research results both within the university and with external industry partners. Use cases include: multi-disciplinary collaboration, prototyping and troubleshooting requiring multiple viewpoints and architectures, dashboards and decision support for global remote planning and operations.
SentiWords: Deriving a High Precision and High Coverage Lexicon for Sentiment Analysis
Venue: arXiv cs.CL 2015
Year: 2015

Deriving prior polarity lexica for sentiment analysis - where positive or negative scores are associated with words out of context - is a challenging task. Usually, a trade-off between precision and coverage is hard to find, and it depends on the methodology used to build the lexicon. Manually annotated lexica provide a high precision but lack in coverage, whereas automatic derivation from pre-existing knowledge guarantees high coverage at the cost of a lower precision. Since the automatic derivation of prior polarities is less time consuming than manual annotation, there has been a great bloom of these approaches, in particular based on the SentiWordNet resource. In this paper, we compare the most frequently used techniques based on SentiWordNet with newer ones and blend them in a learning framework (a so called 'ensemble method'). By taking advantage of manually built prior polarity lexica, our ensemble method is better able to predict the prior value of unseen words and to outperform all the other SentiWordNet approaches. Using this technique we have built SentiWords, a prior polarity lexicon of approximately 155,000 words, that has both a high precision and a high coverage. We finally show that in sentiment analysis tasks, using our lexicon allows us to outperform both the single metrics derived from SentiWordNet and popular manually annotated sentiment lexica.
Completeness-via-canonicity for coalgebraic logics
Venue: arXiv cs.LO 2015
Year: 2015

This thesis aims to provide a suite of techniques to generate completeness results for coalgebraic logics with axioms of arbitrary rank. We have chosen to investigate the possibility to generalize what is arguably one of the most successful methods to prove completeness results in `classical' modal logic, namely completeness-via-canonicity. This technique is particularly well-suited to a coalgebraic generalization because of its clean and abstract algebraic formalism.
Continuous Time Gathering of Agents with Limited Visibility and Bearing-Only Sensing
Venue: arXiv cs.MA 2015
Year: 2015

A group of mobile agents, identical, anonymous, and oblivious (memoryless), having the capability to sense only the relative direction (bearing) to neighborhing agents within a finite visibility range, are shown to gather to a meeting point in finite time by applying a very simple rule of motion. The agents' rule of motion is : set your velocity vector to be the sum of the two unit vectors in R^2 pointing to your "extremal" neighbours determining the smallest visibility disc sector in which all your visible neighbors reside, provided it spans an angle smaller than pi, otherwise, since you are "surrounded" by visible neighbors, simply stay put (set your velocity to 0). Of course, the initial constellation of agents must have a visibility graph that is connected, and provided this we prove that the agents gather to a common meeting point in finite time, while the distances between agents that initially see each other monotically decreases. We will also prove a geometrical result, a tight lower bound on the sum of cosines of the interior angles of a convex polygon, that we will use to prove the gathering of our dynamical system.
Estimating Target Signatures with Diverse Density
Venue: arXiv cs.CV 2015
Year: 2015

Hyperspectral target detection algorithms rely on knowing the desired target signature in advance. However, obtaining an effective target signature can be difficult; signatures obtained from laboratory measurements or hand-spectrometers in the field may not transfer to airborne imagery effectively. One approach to dealing with this difficulty is to learn an effective target signature from training data. An approach for learning target signatures from training data is presented. The proposed approach addresses uncertainty and imprecision in groundtruth in the training data using a multiple instance learning, diverse density (DD) based objective function. After learning the target signature given data with uncertain and imprecise groundtruth, target detection can be applied on test data. Results are shown on simulated and real data.
Computational Network Design from Functional Specifications
Venue: arXiv cs.CG 2015
Year: 2015

Connectivity and layout of underlying networks largely determine the behavior of many environments. For example, transportation networks determine the flow of traffic in cities, or maps determine the difficulty and flow in games. Designing such networks from scratch is challenging as even local network changes can have large global effects. We investigate how to computationally create networks starting from {\em only} high-level functional specifications. Such specifications can be in the form of network density, travel time versus network length, traffic type, destination locations, etc. We propose an integer programming-based approach that guarantees that the resultant networks are valid by fulfilling all specified hard constraints, and score favorably in terms of the objective function. We evaluate our algorithm in three different design settings (i.e., street layout, floorplanning, and game level design) and demonstrate, for the first time, that diverse networks can emerge purely from high-level functional specifications.
FendOff encryption software to secure personal information on computers and mobile devices
Venue: arXiv cs.CR 2015
Year: 2015

The paper describes several original cryptographic cipher modules (VSEM) that are based on using one time pseudorandom pad and pseudorandom transpositions. The VSEM includes 4 modules of encryption that can be applied in combinations. We studied ability of these modules to secure the private data against attacks and their speed of encryption. The VSEM encryption was implemented in Fendoff applications for mobile devices on iOS and Android platforms as well as in computer application running Window or Mac OS. We describe these applications designed to encrypt/decrypt various personal data such as passwords, credit card or bank information as well as to secure content of any text or image files.
Fast Neuromimetic Object Recognition using FPGA Outperforms GPU Implementations
Venue: arXiv cs.CV 2015
Year: 2015

Recognition of objects in still images has traditionally been regarded as a difficult computational problem. Although modern automated methods for visual object recognition have achieved steadily increasing recognition accuracy, even the most advanced computational vision approaches are unable to obtain performance equal to that of humans. This has led to the creation of many biologically-inspired models of visual object recognition, among them the HMAX model. HMAX is traditionally known to achieve high accuracy in visual object recognition tasks at the expense of significant computational complexity. Increasing complexity, in turn, increases computation time, reducing the number of images that can be processed per unit time. In this paper we describe how the computationally intensive, biologically inspired HMAX model for visual object recognition can be modified for implementation on a commercial Field Programmable Gate Array, specifically the Xilinx Virtex 6 ML605 evaluation board with XC6VLX240T FPGA. We show that with minor modifications to the traditional HMAX model we can perform recognition on images of size 128x128 pixels at a rate of 190 images per second with a less than 1% loss in recognition accuracy in both binary and multi-class visual object recognition tasks.
Regional Active Contours based on Variational level sets and Machine Learning for Image Segmentation
Venue: arXiv cs.CV 2015
Year: 2015

Image segmentation is the problem of partitioning an image into different subsets, where each subset may have a different characterization in terms of color, intensity, texture, and/or other features. Segmentation is a fundamental component of image processing, and plays a significant role in computer vision, object recognition, and object tracking. Active Contour Models (ACMs) constitute a powerful energy-based minimization framework for image segmentation, which relies on the concept of contour evolution. Starting from an initial guess, the contour is evolved with the aim of approximating better and better the actual object boundary. Handling complex images in an efficient, effective, and robust way is a real challenge, especially in the presence of intensity inhomogeneity, overlap between the foreground/background intensity distributions, objects characterized by many different intensities, and/or additive noise. In this thesis, to deal with these challenges, we propose a number of image segmentation models relying on variational level set methods and specific kinds of neural networks, to handle complex images in both supervised and unsupervised ways. Experimental results demonstrate the high accuracy of the segmentation results, obtained by the proposed models on various benchmark synthetic and real images compared with state-of-the-art active contour models.
A Novel Play-out Algorithm for HTTP Adaptive Streaming
Venue: arXiv cs.NI 2015
Year: 2015

In the paper, we proposed a novel algorithm dedicated to adaptive video streaming based on HTTP. The algorithm employs a hybrid play-out strategy which combines two popular approaches: an estimation of network bandwidth and a control of a player buffer. The proposed algorithm was implemented in two versions which differ in the method of handling fluctuations of network throughput. The proposed hybrid algorithm was evaluated against solutions which base their play-out strategy purely on bandwidth or buffer level assessment. The comparison was performed in an environment which emulated two systems: a Wi-Fi network with a single immobile node and HSPA (High Speed Packet Access) network with a mobile node. The evaluation shows that the hybrid approach in most cases achieves better results compared to its competitors, being able to stream the video more smoothly without unnecessary bit-rate switches. However, in certain network conditions, this score is traded for a worse throughput utilisation compared to other play-out strategies.
Pointer Race Freedom
Venue: arXiv cs.PL 2015
Year: 2015

We propose a novel notion of pointer race for concurrent programs manipulating a shared heap. A pointer race is an access to a memory address which was freed, and it is out of the accessor's control whether or not the cell has been re-allocated. We establish two results. (1) Under the assumption of pointer race freedom, it is sound to verify a program running under explicit memory management as if it was running with garbage collection. (2) Even the requirement of pointer race freedom itself can be verified under the garbage-collected semantics. We then prove analogues of the theorems for a stronger notion of pointer race needed to cope with performance-critical code purposely using racy comparisons and even racy dereferences of pointers. As a practical contribution, we apply our results to optimize a thread-modular analysis under explicit memory management. Our experiments confirm a speed-up of up to two orders of magnitude.
Optimized Mission Planning for Planetary Exploration Rovers
Venue: arXiv cs.RO 2015
Year: 2015

The exploration of planetary surfaces is predominately unmanned, calling for a landing vehicle and an autonomous and/or teleoperated rover. Artificial intelligence and machine learning techniques can be leveraged for better mission planning. This paper describes the coordinated use of both global navigation and metaheuristic optimization algorithms to plan the safe, efficient missions. The aim is to determine the least-cost combination of a safe landing zone (LZ) and global path plan, where avoiding terrain hazards for the lander and rover minimizes cost. Computer vision methods were used to identify surface craters, mounds, and rocks as obstacles. Multiple search methods were investigated for the rover global path plan. Several combinatorial optimization algorithms were implemented to select the shortest distance path as the preferred mission plan. Simulations were run for a sample Google Lunar X Prize mission. The result of this study is an optimization scheme that path plans with the A* search method, and uses simulated annealing to select ideal LZ-path- goal combination for the mission. Simulation results show the methods are effective in minimizing the risk of hazards and increasing efficiency. This paper is specific to a lunar mission, but the resulting architecture may be applied to a large variety of planetary missions and rovers.
Exploiting Redundant Computation in Communication-Avoiding Algorithms for Algorithm-Based Fault Tolerance
Venue: arXiv cs.DC 2015
Year: 2015

Communication-avoiding algorithms allow redundant computations to minimize the number of inter-process communications. In this paper, we propose to exploit this redundancy for fault-tolerance purpose. We illustrate this idea with QR factorization of tall and skinny matrices, and we evaluate the number of failures our algorithm can tolerate under different semantics.
Using Raspberry Pi for scientific video observation of pedestrians during a music festival
Venue: arXiv cs.OH 2015
Year: 2015

The document serves as a reference for researchers trying to capture a large portion of a mass event on video for several hours, while using a very limited budget.
Five Constructions of Permutation Polynomials over $\gf(q^2)$
Venue: arXiv cs.IT 2015
Year: 2015

Four recursive constructions of permutation polynomials over $\gf(q^2)$ with those over $\gf(q)$ are developed and applied to a few famous classes of permutation polynomials. They produce infinitely many new permutation polynomials over $\gf(q^{2^\ell})$ for any positive integer $\ell$ with any given permutation polynomial over $\gf(q)$. A generic construction of permutation polynomials over $\gf(2^{2m})$ with o-polynomials over $\gf(2^m)$ is also presented, and a number of new classes of permutation polynomials over $\gf(2^{2m})$ are obtained.
Z Specification for the W3C Editor's Draft Core SHACL Semantics
Venue: arXiv cs.DB 2015
Year: 2015

This article provides a formalization of the W3C Draft Core SHACL Semantics specification using Z notation. This formalization exercise has identified a number of quality issues in the draft. It has also established that the recursive definitions in the draft are well-founded. Further formal validation of the draft will require the use of an executable specification technology.