OpenRaiser/Intern-Atlas huggingface dataset:
On the set of uniquely decodable codes with a given sequence of code word lengths
Venue: arXiv cs.IT 2016
Year: 2016

For every natural number $n\geq 2$ and every finite sequence $L$ of natural numbers, we consider the set $UD_n(L)$ of all uniquely decodable codes over an $n$-letter alphabet with the sequence $L$ as the sequence of code word lengths, as well as its subsets $PR_n(L)$ and $FD_n(L)$ consisting of, respectively, the prefix codes and the codes with finite delay. We derive the estimation for the quotient $|UD_n(L)|/|PR_n(L)|$, which allows to characterize those sequences $L$ for which the equality $PR_n(L)=UD_n(L)$ holds. We also characterize those sequences $L$ for which the equality $FD_n(L)=UD_n(L)$ holds.
USBee: Air-Gap Covert-Channel via Electromagnetic Emission from USB
Venue: arXiv cs.CR 2016
Year: 2016

In recent years researchers have demonstrated how attackers could use USB connectors implanted with RF transmitters to exfiltrate data from secure, and even air-gapped, computers (e.g., COTTONMOUTH in the leaked NSA ANT catalog). Such methods require a hardware modification of the USB plug or device, in which a dedicated RF transmitter is embedded. In this paper we present USBee, a software that can utilize an unmodified USB device connected to a computer as a RF transmitter. We demonstrate how a software can intentionally generate controlled electromagnetic emissions from the data bus of a USB connector. We also show that the emitted RF signals can be controlled and modulated with arbitrary binary data. We implement a prototype of USBee, and discuss its design and implementation details including signal generation and modulation. We evaluate the transmitter by building a receiver and demodulator using GNU Radio. Our evaluation shows that USBee can be used for transmitting binary data to a nearby receiver at a bandwidth of 20 to 80 BPS (bytes per second).
Improving the Correlation Lower Bound for Simultaneous Orthogonal Matching Pursuit
Venue: arXiv cs.IT 2016
Year: 2016

The simultaneous orthogonal matching pursuit (SOMP) algorithm aims to find the joint support of a set of sparse signals acquired under a multiple measurement vector model. Critically, the analysis of SOMP depends on the maximal inner product of any atom of a suitable dictionary and the current signal residual, which is formed by the subtraction of previously selected atoms. This inner product, or correlation, is a key metric to determine the best atom to pick at each iteration. This paper provides, for each iteration of SOMP, a novel lower bound of the aforementioned metric for the atoms belonging to the correct and common joint support of the multiple signals. Although the bound is obtained for the noiseless case, its main purpose is to intervene in noisy analyses of SOMP. Finally, it is shown for specific signal patterns that the proposed bound outperforms state-of-the-art results for SOMP, and orthogonal matching pursuit (OMP) as a special case.
On the Efficiency and Fairness of Multiplayer HTTP-based Adaptive Video Streaming
Venue: arXiv cs.MM 2016
Year: 2016

User-perceived quality-of-experience (QoE) is critical in internet video delivery systems. Extensive prior work has studied the design of client-side bitrate adaptation algorithms to maximize single-player QoE. However, multiplayer QoE fairness becomes critical as the growth of video traffic makes it more likely that multiple players share a bottleneck in the network. Despite several recent proposals, there is still a series of open questions. In this paper, we bring the problem space to light from a control theory perspective by formalizing the multiplayer QoE fairness problem and addressing two key questions in the broader problem space. First, we derive the sufficient conditions of convergence to steady state QoE fairness under TCP-based bandwidth sharing scheme. Based on the insight from this analysis that in-network active bandwidth allocation is needed, we propose a non-linear MPC-based, router-assisted bandwidth allocation algorithm that regards each player as closed-loop systems. We use trace-driven simulation to show the improvement over existing approaches. We identify several research directions enabled by the control theoretic modeling and envision that control theory can play an important role on guiding real system design in adaptive video streaming.
Error Invariants for Concurrent Traces
Venue: arXiv cs.SE 2016
Year: 2016

Error invariants are assertions that over-approximate the reachable program states at a given position in an error trace while only capturing states that will still lead to failure if execution of the trace is continued from that position. Such assertions reflect the effect of statements that are involved in the root cause of an error and its propagation, enabling slicing of statements that do not contribute to the error. Previous work on error invariants focused on sequential programs. We generalize error invariants to concurrent traces by augmenting them with additional information about hazards such as write-after-write events, which are often involved in race conditions and atomicity violations. By providing the option to include varying levels of details in error invariants-such as hazards and branching information-our approach allows the programmer to systematically analyze individual aspects of an error trace.We have implemented a hazard-sensitive slicing tool for concurrent traces based on error invariants and evaluated it on benchmarks covering a broad range of real-world concurrency bugs. Hazard-sensitive slicing significantly reduced the length of the considered traces and still maintained the root causes of the concurrency bugs.
The Frobenius problem for the shuffle operation
Venue: arXiv cs.FL 2016
Year: 2016

We characterize the finite sets S of words such that that the iterated shuffle of S is co-finite and we give some bounds on the length of a longest word not in the iterated shuffle of S.
Hanani-Tutte for Radial Planarity II
Venue: arXiv cs.CG 2016
Year: 2016

A drawing of a graph $G$ is radial if the vertices of $G$ are placed on concentric circles $C_1, \ldots, C_k$ with common center $c$, and edges are drawn radially: every edge intersects every circle centered at $c$ at most once. $G$ is radial planar if it has a radial embedding, that is, a crossing-free radial drawing. If the vertices of $G$ are ordered or partitioned into ordered levels (as they are for leveled graphs), we require that the assignment of vertices to circles corresponds to the given ordering or leveling. A pair of edges $e$ and $f$ in a graph is independent if $e$ and $f$ do not share a vertex. We show that a graph $G$ is radial planar if $G$ has a radial drawing in which every two independent edges cross an even number of times; the radial embedding has the same leveling as the radial drawing. In other words, we establish the strong Hanani-Tutte theorem for radial planarity. This characterization yields a very simple algorithm for radial planarity testing.
Engagement Detection in Meetings
Venue: arXiv cs.CV 2016
Year: 2016

Group meetings are frequent business events aimed to develop and conduct project work, such as Big Room design and construction project meetings. To be effective in these meetings, participants need to have an engaged mental state. The mental state of participants however, is hidden from other participants, and thereby difficult to evaluate. Mental state is understood as an inner process of thinking and feeling, that is formed of a conglomerate of mental representations and propositional attitudes. There is a need to create transparency of these hidden states to understand, evaluate and influence them. Facilitators need to evaluate the meeting situation and adjust for higher engagement and productivity. This paper presents a framework that defines a spectrum of engagement states and an array of classifiers aimed to detect the engagement state of participants in real time. The Engagement Framework integrates multi-modal information from 2D and 3D imaging and sound. Engagement is detected and evaluated at participants and aggregated at group level. We use empirical data collected at the lab of Konica Minolta, Inc. to test initial applications of this framework. The paper presents examples of the tested engagement classifiers, which are based on research in psychology, communication, and human computer interaction. Their accuracy is illustrated in dyadic interaction for engagement detection. In closing we discuss the potential extension to complex group collaboration settings and future feedback implementations.
Making On-Demand Routing Efficient with Route-Request Aggregation
Venue: arXiv cs.NI 2016
Year: 2016

In theory, on-demand routing is very attractive for mobile ad hoc networks (MANET), because it induces signaling only for those destinations for which there is data traffic. However, in practice, the signaling overhead of existing on-demand routing protocols becomes excessive as the rate of topology changes increases due to mobility or other causes. We introduce the first on-demand routing approach that eliminates the main limitation of on-demand routing by aggregating route requests (RREQ) for the same destinations. The approach can be applied to any existing on-demand routing protocol, and we introduce the Ad-hoc Demand-Aggregated Routing with Adaptation (ADARA) as an example of how RREQ aggregation can be used. ADARA is compared to AODV and OLSR using discrete-event simulations, and the results show that aggregating RREQs can make on-demand routing more efficient than existing proactive or on-demand routing protocols.
Collective Intelligence for Smarter API Recommendations in Python
Venue: arXiv cs.SE 2016
Year: 2016

Software developers use Application Programming Interfaces (APIs) of libraries and frameworks extensively while writing programs. In this context, the recommendations provided in code completion pop-ups help developers choose the desired methods. The candidate lists recommended by these tools, however, tend to be large, ordered alphabetically and sometimes even incomplete. A fair amount of work has been done recently to improve the relevance of these code completion results, especially for statically typed languages like Java. However, these proposed techniques rely on the static type of the object and are therefore inapplicable for a dynamically typed language like Python. In this paper, we present PyReco, an intelligent code completion system for Python which uses the mined API usages from open source repositories to order the results based on relevance rather than the conventional alphabetic order. To recommend suggestions that are relevant for a working context, a nearest neighbor classifier is used to identify the best matching usage among all the extracted usage patterns. To evaluate the effectiveness of our system, the code completion queries are automatically extracted from projects and tested quantitatively using a ten-fold cross validation technique. The evaluation shows that our approach outperforms the alphabetically ordered API recommendation systems in recommending APIs for standard, as well as, third-party libraries.
Security checklist for IaaS cloud deployments
Venue: arXiv cs.CR 2016
Year: 2016

In this article, we provide a cloud-security checklist for IaaS cloud deployments. The elements of the checklist are established by surveying the related literature on cloud-threat models and various security recommendations. We define the elements of the list on a level of abstraction that helps keep the size of the list manageable while preserving the list's practical applicability.
Radiation Search Operations using Scene Understanding with Autonomous UAV and UGV
Venue: arXiv cs.RO 2016
Year: 2016

Autonomously searching for hazardous radiation sources requires the ability of the aerial and ground systems to understand the scene they are scouting. In this paper, we present systems, algorithms, and experiments to perform radiation search using unmanned aerial vehicles (UAV) and unmanned ground vehicles (UGV) by employing semantic scene segmentation. The aerial data is used to identify radiological points of interest, generate an orthophoto along with a digital elevation model (DEM) of the scene, and perform semantic segmentation to assign a category (e.g. road, grass) to each pixel in the orthophoto. We perform semantic segmentation by training a model on a dataset of images we collected and annotated, using the model to perform inference on images of the test area unseen to the model, and then refining the results with the DEM to better reason about category predictions at each pixel. We then use all of these outputs to plan a path for a UGV carrying a LiDAR to map the environment and avoid obstacles not present during the flight, and a radiation detector to collect more precise radiation measurements from the ground. Results of the analysis for each scenario tested favorably. We also note that our approach is general and has the potential to work for a variety of different sensing tasks.
Image segmentation based on histogram of depth and an application in driver distraction detection
Venue: arXiv cs.CV 2016
Year: 2016

This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the driver's position inside the car, we came up with a simple solution to track the driver motion. With the analysis of the difference between initial and current frame, a change of cluster position or depth value in the interested region, which cross the preset threshold, is considered as a distracted activity. The experiment results demonstrated the success of the algorithm in detecting typical distracted driving activities such as using phone for calling or texting, adjusting internal devices and drinking in real time.
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
Venue: arXiv cs.CV 2016
Year: 2016

Event recognition in still images is an intriguing problem and has potential for real applications. This paper addresses the problem of event recognition by proposing a convolutional neural network that exploits knowledge of objects and scenes for event classification (OS2E-CNN). Intuitively, it stands to reason that there exists a correlation among the concepts of objects, scenes, and events. We empirically demonstrate that the recognition of objects and scenes substantially contributes to the recognition of events. Meanwhile, we propose an iterative selection method to identify a subset of object and scene classes, which help to more efficiently and effectively transfer their deep representations to event recognition. Specifically, we develop three types of transferring techniques: (1) initialization-based transferring, (2) knowledge-based transferring, and (3) data-based transferring. These newly designed transferring techniques exploit multi-task learning frameworks to incorporate extra knowledge from other networks and additional datasets into the training procedure of event CNNs. These multi-task learning frameworks turn out to be effective in reducing the effect of over-fitting and improving the generalization ability of the learned CNNs. With OS2E-CNN, we design a multi-ratio and multi-scale cropping strategy, and propose an end-to-end event recognition pipeline. We perform experiments on three event recognition benchmarks: the ChaLearn Cultural Event Recognition dataset, the Web Image Dataset for Event Recognition (WIDER), and the UIUC Sports Event dataset. The experimental results show that our proposed algorithm successfully adapts object and scene representations towards the event dataset and that it achieves the current state-of-the-art performance on these challenging datasets.
A Non-iterative Method for (Re)Construction of Phase from STFT Magnitude
Venue: arXiv cs.SD 2016
Year: 2016

A non-iterative method for the construction of the Short-Time Fourier Transform (STFT) phase from the magnitude is presented. The method is based on the direct relationship between the partial derivatives of the phase and the logarithm of the magnitude of the un-sampled STFT with respect to the Gaussian window. Although the theory holds in the continuous setting only, the experiments show that the algorithm performs well even in the discretized setting (Discrete Gabor transform) with low redundancy using the sampled Gaussian window, the truncated Gaussian window and even other compactly supported windows like the Hann window. Due to the non-iterative nature, the algorithm is very fast and it is suitable for long audio signals. Moreover, solutions of iterative phase reconstruction algorithms can be improved considerably by initializing them with the phase estimate provided by the present algorithm. We present an extensive comparison with the state-of-the-art algorithms in a reproducible manner.
Autonomous driving challenge: To Infer the property of a dynamic object based on its motion pattern using recurrent neural network
Venue: arXiv cs.CV 2016
Year: 2016

In autonomous driving applications a critical challenge is to identify action to take to avoid an obstacle on collision course. For example, when a heavy object is suddenly encountered it is critical to stop the vehicle or change the lane even if it causes other traffic disruptions. However,there are situations when it is preferable to collide with the object rather than take an action that would result in a much more serious accident than collision with the object. For example, a heavy object which falls from a truck should be avoided whereas a bouncing ball or a soft target such as a foam box need not be.We present a novel method to discriminate between the motion characteristics of these types of objects based on their physical properties such as bounciness, elasticity, etc.In this preliminary work, we use recurrent neural net-work with LSTM cells to train a classifier to classify objects based on their motion trajectories. We test the algorithm on synthetic data, and, as a proof of concept, demonstrate its effectiveness on a limited set of real-world data.
Multi-Path Low Delay Network Codes
Venue: arXiv cs.NI 2016
Year: 2016

The capability of mobile devices to use multiple interfaces to support a single session is becoming more prevalent. Prime examples include the desire to implement WiFi offloading and the introduction of 5G. Furthermore, an increasing fraction of Internet traffic is becoming delay sensitive. These two trends drive the need to investigate methods that enable communication over multiple parallel heterogeneous networks, while also ensuring that delay constraints are met. This paper approaches these challenges using a multi-path streaming code that uses forward error correction to reduce the in-order delivery delay of packets in networks with poor link quality and transient connectivity. A simple analysis is developed that provides a good approximation of the in-order delivery delay. Furthermore, numerical results help show that the delay penalty of communicating over multiple paths is insignificant when considering the potential throughput gains obtained through the fusion of multiple networks.
Chemical Propagation Pattern for Molecular Communications
Venue: arXiv cs.ET 2016
Year: 2016

In a diffusion-based molecular communication system, molecules are employed to convey information. When propagation and reception processes are considered in a framework of first passage processes, we need to focus on absorbing receivers. For this kind of molecular communication system, the characteristics of the channel is also affected by the shape of the transmitter. In the literature, most studies focus on systems with a point transmitter due to circular symmetry. In this letter, we address propagation and reception pattern for chemical signals emitted from a spherical transmitter. We also investigate the directivity gain achieved by the reflecting spherical transmitter. We quantify the power gain by measuring the received power at different angles on a circular region. Moreover, we define three metrics, i.e., the half-power pattern-width, the directivity gain, and the peak time of the signal, for analyzing the received signal pattern.
Device-to-device Cooperation in Massive MIMO Systems with Cascaded Precoding
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates user cooperation in massive multiple-input multiple-output (MIMO) systems with cascaded precoding. The high-dimensional physical channel in massive MIMO systems can be converted into a low-dimensional effective channel through the inner precoder to reduce the overhead of channel estimation and feedback. The inner precoder depends on the spatial covariance matrix of the channels, and thus the same precoder can be used for different users as long as they have the same spatial covariance matrix. Spatial covariance matrix is determined by the surrounding environment of user terminals. Therefore, the users that are close to each other will share the same spatial covariance matrix. In this situation, it is possible to achieve user cooperation by sharing receiver information through some dedicated link, such as device-to-device communications. To reduce the amount of information that needs to be shared, we propose a decoding codebook based scheme, which can achieve user cooperation without the need of channel state information. Moreover, we also investigate the amount of bandwidth required to achieve efficient user cooperation. Simulation results show that user cooperation can improve the capacity compared to the non-cooperation scheme.
Safety Barrier Certificates for Heterogeneous Multi-Robot Systems
Venue: arXiv cs.RO 2016
Year: 2016

This paper presents a formal framework for collision avoidance in multi-robot systems, wherein an existing controller is modified in a minimally invasive fashion to ensure safety. We build this framework through the use of control barrier functions (CBFs) which guarantee forward invariance of a safe set; these yield safety barrier certificates in the context of heterogeneous robot dynamics subject to acceleration bounds. Moreover, safety barrier certificates are extended to a distributed control framework, wherein neighboring agent dynamics are unknown, through local parameter identification. The end result is an optimization-based controller that formally guarantees collision free behavior in heterogeneous multi-agent systems by minimally modifying the desired controller via safety barrier constraints. This formal result is verified in simulation on a multi-robot system consisting of both cumbersome and agile robots, is demonstrated experimentally on a system with a Magellan Pro robot and three Khepera III robots.