OpenRaiser/Intern-Atlas huggingface dataset:
Continuous Molecular Communication in one dimensional situation
Venue: arXiv cs.ET 2016
Year: 2016

Molecular Communication as the most potential methods to solve the communication in nano scale, for it's derived from nature, and it becomes more and more prevalent. Though molecular communication happens in three dimensional situation, there are also some situation that are in the one dimensional situation, especially when considering the transmitters and the receivers are in extremely short distance or in long slim pipe. In this paper, we introduce the one dimensional situation, and studied how the continuous information molecules transmitted in this situation, also introduced how to encode and decode the information molecules, and based on the molecular communication model, we studied some metrics of it, such as the distance between transmitter and receiver, the emitting frequency of transmitter. Through the research we know that the distance and frequency are important metrics to the successful communication, which can direct us how to place the nano transmitters and receivers in the future nano network environment.
AccConF: An Access Control Framework for Leveraging In-Network Cached Data in ICNs
Venue: arXiv cs.NI 2016
Year: 2016

The fast-growing Internet traffic is increasingly becoming content-based and driven by mobile users, with users more interested in data rather than its source. This has precipitated the need for an information-centric Internet architecture. Research in information-centric networks (ICNs) have resulted in novel architectures, e.g., CCN/NDN, DONA, and PSIRP/PURSUIT; all agree on named data based addressing and pervasive caching as integral design components. With network-wide content caching, enforcement of content access control policies become non-trivial. Each caching node in the network needs to enforce access control policies with the help of the content provider. This becomes inefficient and prone to unbounded latencies especially during provider outages. In this paper, we propose an efficient access control framework for ICN, which allows legitimate users to access and use the cached content directly, and does not require verification/authentication by an online provider authentication server or the content serving router. This framework would help reduce the impact of system down-time from server outages and reduce delivery latency by leveraging caching while guaranteeing access only to legitimate users. Experimental/simulation results demonstrate the suitability of this scheme for all users, but particularly for mobile users, especially in terms of the security and latency overheads.
Stateless Code Model Checking of Information Flow Security
Venue: arXiv cs.PL 2016
Year: 2016

Observational determinism is a security property that characterizes secure information flow for multithreaded programs. Most of the methods that have been used to verify observational determinism are based on either type systems or conventional model checking techniques. A conventional model checker is stateful and often verifies a system model usually constructed manually. As these methods are based on stateful model checking, they are confronted with the state space explosion problem. In order to verify and test computer programs, stateless code model checking is more appropriate than conventional techniques. It is an effective method for systematic testing of large and complicated concurrent programs, and for exploring the state space of such programs. In this paper, we propose a new method for verifying information flow security in concurrent programs. For the first time, we use stateless code model checking to verify observational determinism.
Communities in Preference Networks: Refined Axioms and Beyond
Venue: arXiv cs.SI 2016
Year: 2016

Borgs et al. [2016] investigated essential requirements for communities in preference networks. They defined six axioms on community functions, i.e., community detection rules. Though having elegant properties, the practicality of this axiom system is compromised by the intractability of checking two critical axioms, so no nontrivial consistent community function was reported inBorgs et al. [2016] By adapting the two axioms in a natural way, we propose two new axioms that are efficiently-checkable. We show that most of the desirable properties of the original axiom system are preserved. More importantly, the new axioms provide a general approach to constructing consistent community functions. We further find a natural consistent community function that is also enumerable and samplable, answering an open problem in the literature.
Powering the Internet of Things with RIOT: Why? How? What is RIOT?
Venue: arXiv cs.CY 2016
Year: 2016

The crucial importance of software platforms was highlighted by recent events both at the political level (e.g. renewed calls for digital data and operating system "sovereignty", following E. Snowden's revelations) and at the business level (e.g. Android generated a new industry worth tens of billions of euros yearly). In the Internet of Things, which is expected to generate business at very large scale, but also to threaten even more individual privacy, such aspects will be exacerbated. The need for an operating system like RIOT stems from this context, and this short article outlines RIOT's main non-technical aspects, as well as its key technical characteristics.
Revealing Utilization at Internet Interconnection Points
Venue: arXiv cs.NI 2016
Year: 2016

Recent Internet interconnection disputes have sparked an in- creased interest in developing methods for gathering and collecting data about utilization at interconnection points. One mechanism, developed by DeepField Networks, allows Internet service providers (ISPs) to gather and aggregate utilization information using network flow statistics, standardized in the Internet Engineering Task Force as IPFIX. This report (1) provides an overview of the method that DeepField Networks is using to measure the utilization of various interconnection links between content providers and ISPs or links over which traffic between content and ISPs flow; and (2) surveys the findings from five months of Internet utilization data provided by seven participating ISPs---Bright House Networks, Comcast, Cox, Mediacom, Midco, Suddenlink, and Time Warner Cable---whose access networks represent about 50% of all U.S. broadband subscribers. The dataset includes about 97% of the paid peering, settlement-free peering, and ISP-paid transit links of each of the participating ISPs. Initial analysis of the data---which comprises more than 1,000 link groups, representing the diverse and substitutable available routes---suggests that many interconnects have significant spare capacity, that this spare capacity exists both across ISPs in each region and in aggregate for any individual ISP, and that the aggregate utilization across interconnects interconnects is roughly 50% during peak periods.
Why 2 times 2 ain't necessarily 4 - at least not in IT security risk assessment
Venue: arXiv cs.CR 2016
Year: 2016

Recently, a novel approach towards semi-quantitative IT security risk assessment has been proposed in the draft IEC 62443-3-2. This approach is analyzed from several different angles, e.g. embedding into the overall standard series, semantic and methodological aspects. As a result, several systematic flaws in the approach are exposed. As a way forward, an alternative approach is proposed which blends together semi-quantitative risk assessment as well as threat and risk analysis.
A Multi-Channel Spectrum Sensing Fusion Mechanism for Cognitive Radio Networks: Design and Application to IEEE 802.22 WRANs
Venue: arXiv cs.NI 2016
Year: 2016

The IEEE 802.22 is a new cognitive radio standard that is aimed at extending wireless outreach to rural areas. Known as wireless regional area networks, and designed based on the not-to-interfere spectrum sharing model, WRANs are channelized and centrally-controlled networks working on the under-utilized UHF/VHF TV bands to establish communication with remote users, so-called customer premises equipment (CPEs). Despite the importance of reliable and interference-free operation in these frequencies, spectrum sensing fusion mechanisms suggested in IEEE 802.22 are rudimentary and fail to satisfy the stringent mandated sensing requirements. Other deep-rooted shortcomings are performance non-uniformity over different signal-to-noise-ratio regimes, unbalanced performance, instability and lack of flexibility. Inspired by these observations, in this paper we propose a distributed spectrum sensing technique for WRANs, named multi-channel learning-based distributed sensing fusion mechanism (MC-LDS). MC-LDS is demonstrated to be self-trained, stable and to compensate for fault reports through its inherent reward-penalty approach. Moreover, MC-LDS exhibits a better uniform performance in all traffic regimes, is fair (reduces the false-alarm/misdetection gap), adjustable (works with several degrees of freedom) and bandwidth efficient (opens transmission opportunities for more CPEs). Simulation results and comparisons unanimously corroborate that MC-LDS outperforms IEEE 802.22 recommended algorithms, i.e., the AND, OR and VOTING rules.
Internal Model Based Active Disturbance Rejection Control
Venue: arXiv cs.SY 2016
Year: 2016

The basic active disturbance rejection control (BADRC) algorithm with only one order higher extended state observer (ESO) proves to be robust to both internal and external disturbances. An advantage of BADRC is that in many applications it can achieve high disturbance attenuation level without requiring a detailed model of the plant or disturbance. However, this can be regarded as a disadvantage when the disturbance characteristic is known since the BADRC algorithm cannot exploit such information. This paper proposes an internal model based ADRC (IADRC) method, which can take advantage of knowing disturbance characteristic to achieve perfect estimation of the disturbance under some mild assumptions. The effectiveness of the proposed method is validated by comprehensive simulations and comparisons with the BADRC algorithm.
Towards using social media to identify individuals at risk for preventable chronic illness
Venue: arXiv cs.CL 2016
Year: 2016

We describe a strategy for the acquisition of training data necessary to build a social-media-driven early detection system for individuals at risk for (preventable) type 2 diabetes mellitus (T2DM). The strategy uses a game-like quiz with data and questions acquired semi-automatically from Twitter. The questions are designed to inspire participant engagement and collect relevant data to train a public-health model applied to individuals. Prior systems designed to use social media such as Twitter to predict obesity (a risk factor for T2DM) operate on entire communities such as states, counties, or cities, based on statistics gathered by government agencies. Because there is considerable variation among individuals within these groups, training data on the individual level would be more effective, but this data is difficult to acquire. The approach proposed here aims to address this issue. Our strategy has two steps. First, we trained a random forest classifier on data gathered from (public) Twitter statuses and state-level statistics with state-of-the-art accuracy. We then converted this classifier into a 20-questions-style quiz and made it available online. In doing so, we achieved high engagement with individuals that took the quiz, while also building a training set of voluntarily supplied individual-level data for future classification.
Mechanism Design for Subadditive Agents via an Ex-Ante Relaxation
Venue: arXiv cs.GT 2016
Year: 2016

We consider the problem of maximizing revenue for a monopolist offering multiple items to multiple heterogeneous buyers. We develop a simple mechanism that obtains a constant factor approximation under the assumption that the buyers' values are additive subject to a feasibility constraint and independent across items. Importantly, different buyers in our setting can have different constraints on the sets of items they desire. Our mechanism is a sequential variant of two-part tariffs. Prior to our work, simple approximation mechanisms for such multi-buyer problems were known only for the special cases of all unit-demand or all additive value buyers. Our work expands upon and unifies long lines of work on unit-demand settings and additive settings. We employ the ex ante relaxation approach developed by Alaei (2011) for reducing a multiple-buyer mechanism design problem with an ex post supply constraint into single-buyer ones with ex ante supply constraints. Solving the single-agent problems requires us to significantly extend techniques developed in the context of additive values by Li and Yao (2013) and their extension to subadditive values by Rubinstein and Weinberg (2015).
Solving MaxSAT by Successive Calls to a SAT Solver
Venue: arXiv cs.AI 2016
Year: 2016

The Maximum Satisfiability (MaxSAT) problem is the problem of finding a truth assignment that maximizes the number of satisfied clauses of a given Boolean formula in Conjunctive Normal Form (CNF). Many exact solvers for MaxSAT have been developed during recent years, and many of them were presented in the well-known SAT conference. Algorithms for MaxSAT generally fall into two categories: (1) branch and bound algorithms and (2) algorithms that use successive calls to a SAT solver (SAT- based), which this paper in on. In practical problems, SAT-based algorithms have been shown to be more efficient. This paper provides an experimental investigation to compare the performance of recent SAT-based and branch and bound algorithms on the benchmarks of the MaxSAT Evaluations.
Near-Isometric Binary Hashing for Large-scale Datasets
Venue: arXiv cs.DS 2016
Year: 2016

We develop a scalable algorithm to learn binary hash codes for indexing large-scale datasets. Near-isometric binary hashing (NIBH) is a data-dependent hashing scheme that quantizes the output of a learned low-dimensional embedding to obtain a binary hash code. In contrast to conventional hashing schemes, which typically rely on an $\ell_2$-norm (i.e., average distortion) minimization, NIBH is based on a $\ell_{\infty}$-norm (i.e., worst-case distortion) minimization that provides several benefits, including superior distance, ranking, and near-neighbor preservation performance. We develop a practical and efficient algorithm for NIBH based on column generation that scales well to large datasets. A range of experimental evaluations demonstrate the superiority of NIBH over ten state-of-the-art binary hashing schemes.
Server-side verification of client behavior in cryptographic protocols
Venue: arXiv cs.CR 2016
Year: 2016

Numerous exploits of client-server protocols and applications involve modifying clients to behave in ways that untampered clients would not, such as crafting malicious packets. In this paper, we demonstrate practical verification of a cryptographic protocol client's messaging behavior as being consistent with the client program it is believed to be running. Moreover, we accomplish this without modifying the client in any way, and without knowing all of the client-side inputs driving its behavior. Our toolchain for verifying a client's messages explores multiple candidate execution paths in the client concurrently, an innovation that we show is both specifically useful for cryptographic protocol clients and more generally useful for client applications of other types, as well. In addition, our toolchain includes a novel approach to symbolically executing the client software in multiple passes that defers expensive functions until their inputs can be inferred and concretized. We demonstrate client verification on OpenSSL to show that, e.g., Heartbleed exploits can be detected without Heartbleed-specific filtering and within seconds of the first malicious packet, and that verification of legitimate clients can keep pace with, e.g., Gmail workloads.
Combinatorial rigidity of Incidence systems and Application to Dictionary learning
Venue: arXiv cs.CG 2016
Year: 2016

Given a hypergraph $H$ with $m$ hyperedges and a set $Q$ of $m$ \emph{pinning subspaces}, i.e.\ globally fixed subspaces in Euclidean space $\mathbb{R}^d$, a \emph{pinned subspace-incidence system} is the pair $(H, Q)$, with the constraint that each pinning subspace in $Q$ is contained in the subspace spanned by the point realizations in $\mathbb{R}^d$ of vertices of the corresponding hyperedge of $H$. This paper provides a combinatorial characterization of pinned subspace-incidence systems that are \emph{minimally rigid}, i.e.\ those systems that are guaranteed to generically yield a locally unique realization. Pinned subspace-incidence systems have applications in the \emph{Dictionary Learning (aka sparse coding)} problem, i.e.\ the problem of obtaining a sparse representation of a given set of data vectors by learning \emph{dictionary vectors} upon which the data vectors can be written as sparse linear combinations. Viewing the dictionary vectors from a geometry perspective as the spanning set of a subspace arrangement, the result gives a tight bound on the number of dictionary vectors for sufficiently randomly chosen data vectors, and gives a way of constructing a dictionary that meets the bound. For less stringent restrictions on data, but a natural modification of the dictionary learning problem, a further dictionary learning algorithm is provided. Although there are recent rigidity based approaches for low rank matrix completion, we are unaware of prior application of combinatorial rigidity techniques in the setting of Dictionary Learning. We also provide a systematic classification of problems related to dictionary learning together with various algorithms, their assumptions and performance.
Exploratory Gradient Boosting for Reinforcement Learning in Complex Domains
Venue: arXiv cs.AI 2016
Year: 2016

High-dimensional observations and complex real-world dynamics present major challenges in reinforcement learning for both function approximation and exploration. We address both of these challenges with two complementary techniques: First, we develop a gradient-boosting style, non-parametric function approximator for learning on $Q$-function residuals. And second, we propose an exploration strategy inspired by the principles of state abstraction and information acquisition under uncertainty. We demonstrate the empirical effectiveness of these techniques, first, as a preliminary check, on two standard tasks (Blackjack and $n$-Chain), and then on two much larger and more realistic tasks with high-dimensional observation spaces. Specifically, we introduce two benchmarks built within the game Minecraft where the observations are pixel arrays of the agent's visual field. A combination of our two algorithmic techniques performs competitively on the standard reinforcement-learning tasks while consistently and substantially outperforming baselines on the two tasks with high-dimensional observation spaces. The new function approximator, exploration strategy, and evaluation benchmarks are each of independent interest in the pursuit of reinforcement-learning methods that scale to real-world domains.
Graph Based Sinogram Denoising for Tomographic Reconstructions
Venue: arXiv cs.CV 2016
Year: 2016

Limited data and low dose constraints are common problems in a variety of tomographic reconstruction paradigms which lead to noisy and incomplete data. Over the past few years sinogram denoising has become an essential pre-processing step for low dose Computed Tomographic (CT) reconstructions. We propose a novel sinogram denoising algorithm inspired by the modern field of signal processing on graphs. Graph based methods often perform better than standard filtering operations since they can exploit the signal structure. This makes the sinogram an ideal candidate for graph based denoising since it generally has a piecewise smooth structure. We test our method with a variety of phantoms and different reconstruction methods. Our numerical study shows that the proposed algorithm improves the performance of analytical filtered back-projection (FBP) and iterative methods ART (Kaczmarz) and SIRT (Cimmino).We observed that graph denoised sinogram always minimizes the error measure and improves the accuracy of the solution as compared to regular reconstructions.
A practical multi-party computation algorithm for a secure distributed online voting system
Venue: arXiv cs.CR 2016
Year: 2016

We present an online voting architecture based on partitioning the election in small clusters of voters and using a new Multi-party Computation algorithm for obtaining voting results from the clusters. This new algorithm has some practical advantages over other previously known algorithms and isn't bound to any specific cryptographic concept; so it can be adapted to future cryptographic exigencies. Compared with other online voting technologies, we see that this new architecture is less vulnerable to hacker attacks and attacks from dishonest authorities, given that no sensitive information is stored in any public server and there is no need for any trustee to safeguard the legality of the election process. Even in case of an attack succeeding, the risks associated with the overall election are far lower than with any other voting system. This architecture can also be combined with any other voting system, inheriting advantages from both systems.
Dynamic Scene Deblurring using a Locally Adaptive Linear Blur Model
Venue: arXiv cs.CV 2016
Year: 2016

State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes, since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise non-uniform blur kernels. We infer bidirectional optical flows to handle motion blurs, and also estimate Gaussian blur maps to remove optical blur from defocus in our new blur model. Therefore, we propose a single energy model that jointly estimates optical flows, defocus blur maps and latent frames. We also provide a framework and efficient solvers to minimize the proposed energy model. By optimizing the energy model, we achieve significant improvements in removing general blurs, estimating optical flows, and extending depth-of-field in blurry frames. Moreover, in this work, to evaluate the performance of non-uniform deblurring methods objectively, we have constructed a new realistic dataset with ground truths. In addition, extensive experimental on publicly available challenging video data demonstrate that the proposed method produces qualitatively superior performance than the state-of-the-art methods which often fail in either deblurring or optical flow estimation.