OpenRaiser/Intern-Atlas huggingface dataset:
Deep Restricted Boltzmann Networks
Venue: arXiv cs.LG 2016
Year: 2016

Building a good generative model for image has long been an important topic in computer vision and machine learning. Restricted Boltzmann machine (RBM) is one of such models that is simple but powerful. However, its restricted form also has placed heavy constraints on the models representation power and scalability. Many extensions have been invented based on RBM in order to produce deeper architectures with greater power. The most famous ones among them are deep belief network, which stacks multiple layer-wise pretrained RBMs to form a hybrid model, and deep Boltzmann machine, which allows connections between hidden units to form a multi-layer structure. In this paper, we present a new method to compose RBMs to form a multi-layer network style architecture and a training method that trains all layers jointly. We call the resulted structure deep restricted Boltzmann network. We further explore the combination of convolutional RBM with the normal fully connected RBM, which is made trivial under our composition framework. Experiments show that our model can generate descent images and outperform the normal RBM significantly in terms of image quality and feature quality, without losing much efficiency for training.
Highly-Secure Physically Unclonable Cryptographic Primitives Using Nonlinear Conductance and Analog State Tuning in Memristive Crossbar Arrays
Venue: arXiv cs.ET 2016
Year: 2016

The rapidly expanding hardware-intrinsic security primitives are aimed at addressing significant security challenges of a massively interconnected world in the age of information technology. The main idea of such primitives is to employ instance-specific process-induced variations in electronic hardware as a source of cryptographic data. Among the emergent technologies, memristive devices provide unique opportunities for security applications due to the underlying stochasticity in their operation. Herein, we report a prototype of a robust, dense, and reconfigurable physical unclonable function primitives based on the three-dimensional passive metal-oxide memristive crossbar circuits, by making positive use of process-induced variations in the devices' nonlinear I-Vs and their analog tuning. We first characterize security metrics for a basic building block of the security primitives based on a two layer stack with monolithically integrated 10x10 250-nm half-pitch memristive crossbar circuits. The experimental results show that the average uniformity and diffusivity, measured on a random sample of 6,000 64-bit responses, out of ~697,000 total, is close to ideal 50% with 5% standard deviation for both metrics. The uniqueness, which was evaluated on a smaller sample by readjusting conductances of crosspoint devices within the same crossbar, is also close to the ideal 50% +/- 1%, while the smallest bit error rate, i.e. reciprocal of reliability, measured over 30-day window under +/-20% power supply variations, was ~ 1.5% +/- 1%. We then utilize multiple instances of the basic block to demonstrate physically unclonable functional primitive with 10-bit hidden challenge generation that encodes more than 10^19 challenge response pairs and has comparable uniformity, diffusiveness, and bit error rate.
Deep Joint Face Hallucination and Recognition
Venue: arXiv cs.CV 2016
Year: 2016

Deep models have achieved impressive performance for face hallucination tasks. However, we observe that directly feeding the hallucinated facial images into recog- nition models can even degrade the recognition performance despite the much better visualization quality. In this paper, we address this problem by jointly learning a deep model for two tasks, i.e. face hallucination and recognition. In particular, we design an end-to-end deep convolution network with hallucination sub-network cascaded by recognition sub-network. The recognition sub- network are responsible for producing discriminative feature representations using the hallucinated images as inputs generated by hallucination sub-network. During training, we feed LR facial images into the network and optimize the parameters by minimizing two loss items, i.e. 1) face hallucination loss measured by the pixel wise difference between the ground truth HR images and network-generated images; and 2) verification loss which is measured by the classification error and intra-class distance. We extensively evaluate our method on LFW and YTF datasets. The experimental results show that our method can achieve recognition accuracy 97.95% on 4x down-sampled LFW testing set, outperforming the accuracy 96.35% of conventional face recognition model. And on the more challenging YTF dataset, we achieve recognition accuracy 90.65%, a margin over the recognition accuracy 89.45% obtained by conventional face recognition model on the 4x down-sampled version.
Optimal Codes from Fibonacci Polynomials and Secret Sharing Schemes
Venue: arXiv cs.IT 2016
Year: 2016

In this work, we study cyclic codes that have generators as Fibonacci polynomials over finite fields. We show that these cyclic codes in most cases produce families of maximum distance separable and optimal codes with interesting properties. We explore these relations and present some examples. Also, we present applications of these codes to secret sharing schemes.
Question Retrieval for Community-based Question Answering via Heterogeneous Network Integration Learning
Venue: arXiv cs.IR 2016
Year: 2016

Community based question answering platforms have attracted substantial users to share knowledge and learn from each other. As the rapid enlargement of CQA platforms, quantities of overlapped questions emerge, which makes users confounded to select a proper reference. It is urgent for us to take effective automated algorithms to reuse historical questions with corresponding answers. In this paper we focus on the problem with question retrieval, which aims to match historical questions that are relevant or semantically equivalent to resolve one s query directly. The challenges in this task are the lexical gaps between questions for the word ambiguity and word mismatch problem. Furthermore, limited words in queried sentences cause sparsity of word features. To alleviate these challenges, we propose a novel framework named HNIL which encodes not only the question contents but also the askers social interactions to enhance the question embedding performance. More specifically, we apply random walk based learning method with recurrent neural network to match the similarities between askers question and historical questions proposed by other users. Extensive experiments on a large scale dataset from a real world CQA site show that employing the heterogeneous social network information outperforms the other state of the art solutions in this task.
Compressive Data Aggregation on Mobile Wireless Sensor Networks for Sensing in Bike Races
Venue: arXiv cs.NI 2016
Year: 2016

-This paper presents an efficient approach to data collection in mobile wireless sensor networks, with the specific application of sensing in bike races. Recent sensor technology permits to track GPS position of each bike. Because of the inherent correlation between bike positions in a bike race, a simple GPS log is inefficient. The idea presented in this work is to aggregate GPS data at sensors using compressive sensing techniques. We enforce, in addition to signal sparsity, a spatial prior on biker motion because of the group behaviour (peloton) in bike races. The spatial prior is modeled by a graphical model and the data aggregation problem is solved, with both the sparsity and the spatial prior, by belief propagation. We validate our approach on a bike race simulator using trajectories of motorbikes in a real bike race, the "crit{é}rium du dauphin{é} 20XX" .
On Stochastic Sensor Network Scheduling for Multiple Processes
Venue: arXiv cs.SY 2016
Year: 2016

We consider the problem of multiple sensor scheduling for remote state estimation of multiple process over a shared link. In this problem, a set of sensors monitor mutually independent dynamical systems in parallel but only one sensor can access the shared channel at each time to transmit the data packet to the estimator. We propose a stochastic event-based sensor scheduling in which each sensor makes transmission decisions based on both channel accessibility and distributed event-triggering conditions. The corresponding minimum mean squared error (MMSE) estimator is explicitly given. Considering information patterns accessed by sensor schedulers, time-based ones can be treated as a special case of the proposed one. By ultilizing realtime information, the proposed schedule outperforms the time-based ones in terms of the estimation quality. Resorting to solving an Markov decision process (MDP) problem with average cost criterion, we can find optimal parameters for the proposed schedule. As for practical use, a greedy algorithm is devised for parameter design, which has rather low computational complexity. We also provide a method to quantify the performance gap between the schedule optimized via MDP and any other schedules.
Detecting communities is hard, and counting them is even harder
Venue: arXiv cs.CC 2016
Year: 2016

We consider the algorithmic problem of community detection in networks. Given an undirected friendship graph $G=\left(V,E\right)$, a subset $S\subseteq V$ is an $\left(α,β\right)$-community if: * Every member of the community is friends with an $α$-fraction of the community; * Every non-member is friends with at most a $β$-fraction of the community. Arora et al [AGSS12] gave a quasi-polynomial time algorithm for enumerating all the $\left(α,β\right)$-communities for any constants $α>β$. Here, we prove that, assuming the Exponential Time Hypothesis (ETH), quasi-polynomial time is in fact necessary - and even for a much weaker approximation desideratum. Namely, distinguishing between: * $G$ contains an $\left(1,o\left(1\right)\right)$-community; and * $G$ does not contain an $\left(β+o\left(1\right),β\right)$-community for any $β\in\left[0,1\right]$. We also prove that counting the number of $\left(1,o\left(1\right)\right)$-communities requires quasi-polynomial time assuming the weaker #ETH.
Decision Support Systems in Fisheries and Aquaculture: A systematic review
Venue: arXiv cs.AI 2016
Year: 2016

Decision support systems help decision makers make better decisions in the face of complex decision problems (e.g. investment or policy decisions). Fisheries and Aquaculture is a domain where decision makers face such decisions since they involve factors from many different scientific fields. No systematic overview of literature describing decision support systems and their application in fisheries and aquaculture has been conducted. This paper summarizes scientific literature that describes decision support systems applied to the domain of Fisheries and Aquaculture. We use an established systematic mapping survey method to conduct our literature mapping. Our research questions are: What decision support systems for fisheries and aquaculture exists? What are the most investigated fishery and aquaculture decision support systems topics and how have these changed over time? Do any current DSS for fisheries provide real- time analytics? Do DSSes in Fisheries and Aquaculture build their models using machine learning done on captured and grounded data? The paper then detail how we employ the systematic mapping method in answering these questions. This results in 27 papers being identified as relevant and gives an exposition on the primary methods concluded in the study for designing a decision support system. We provide an analysis of the research done in the studies collected. We discovered that most literature does not consider multiple aspects for multiple stakeholders in their work. In addition we observed that little or no work has been done with real-time analysis in these decision support systems.
A Second Order Derivatives based Approach for Steganography
Venue: arXiv cs.MM 2016
Year: 2016

Steganography schemes are designed with the objective of minimizing a defined distortion function. In most existing state of the art approaches, this distortion function is based on image feature preservation. Since smooth regions or clean edges define image core, even a small modification in these areas largely modifies image features and is thus easily detectable. On the contrary, textures, noisy or chaotic regions are so difficult to model that the features having been modified inside these areas are similar to the initial ones. These regions are characterized by disturbed level curves. This work presents a new distortion function for steganography that is based on second order derivatives, which are mathematical tools that usually evaluate level curves. Two methods are explained to compute these partial derivatives and have been completely implemented. The first experiments show that these approaches are promising.
FPGA Implementation of $\mathbb{F}_2$-Linear Pseudorandom Number Generators Based on Zynq MPSoC: a Chaotic Iterations Post Processing Case Study
Venue: arXiv cs.CR 2016
Year: 2016

Pseudorandom number generation (PRNG) is a key element in hardware security platforms like field-programmable gate array FPGA circuits. In this article, 18 PRNGs belonging in 4 families (xorshift, LFSR, TGFSR, and LCG) are physically implemented in a FPGA and compared in terms of area, throughput, and statistical tests. Two flows of conception are used for Register Transfer Level (RTL) and High-level Synthesis (HLS). Additionally, the relations between linear complexity, seeds, and arithmetic operations on the one hand, and the resources deployed in FPGA on the other hand, are deeply investigated. In order to do that, a SoC based on Zynq EPP with ARM Cortex-$A9$ MPSoC is developed to accelerate the implementation and the tests of various PRNGs on FPGA hardware. A case study is finally proposed using chaotic iterations as a post processing for FPGA. The latter has improved the statistical profile of a combination of PRNGs that, without it, failed in the so-called TestU01 statistical battery of tests.
On the Evaluation of the Privacy Breach in Disassociated Set-Valued Datasets
Venue: arXiv cs.CR 2016
Year: 2016

Data anonymization is gaining much attention these days as it provides the fundamental requirements to safely outsource datasets containing identifying information. While some techniques add noise to protect privacy others use generalization to hide the link between sensitive and non-sensitive information or separate the dataset into clusters to gain more utility. In the latter, often referred to as bucketization, data values are kept intact, only the link is hidden to maximize the utility. In this paper, we showcase the limits of disassociation, a bucketization technique that divides a set-valued dataset into $k^m$-anonymous clusters. We demonstrate that a privacy breach might occur if the disassociated dataset is subject to a cover problem. We finally evaluate the privacy breach using the quantitative privacy breach detection algorithm on real disassociated datasets.
The Graph of the Pedigree Polytope is Asymptotically Almost Complete (Extended Abstract)
Venue: arXiv cs.DM 2016
Year: 2016

Graphs (1-skeletons) of Traveling-Salesman-related polytopes have attracted a lot of attention. Pedigree polytopes are extensions of the classical Symmetric Traveling Salesman Problem polytopes (Arthanari 2000) whose graphs contain the TSP polytope graphs as spanning subgraphs (Arthanari 2013). Unlike TSP polytopes, Pedigree polytopes are not "symmetric", e.g., their graphs are not vertex transitive, not even regular. We show that in the graph of the pedigree polytope, the quotient minimum degree over number of vertices tends to 1 as the number of cities tends to infinity.
How Close to the Edge? Delay/utilization tradeoffs in MEC
Venue: arXiv cs.NI 2016
Year: 2016

Virtually all of the rapidly increasing data traffic consumed by mobile users requires some kind of processing, normally performed at cloud servers. A recent thrust, {\em mobile edge computing}, moves such processing to servers {\em within} the cellular mobile network. The large temporal and spatial variations to which mobile data usage is subject could make the reduced latency that edge clouds offer come at an unacceptable cost in redundant and underutilized infrastructure. We present some first empirical results on this question, based on large scale sampled crowd-sourced traces from several major cities spanning multiple operators and identifying the applications in use. We find opportunities to obtain both high server utilization and low application latency, but the best approaches will depend on the individual network operator's deployment strategy and geographic specifics of the cities we study.
User Point Processes in Cellular Networks
Venue: arXiv cs.IT 2016
Year: 2016

The point process of concurrent users is critical for the analysis of cellular networks, in particular for the uplink and for full-duplex communication. We analyze the properties of two popular models. For the first one, we provide an accurate characterization of the pair correlation functions from the user and the base station point of view, which are applied to approximate the user process by Poisson and Ginibre point processes. For the second model, which includes the first model asymptotically, we study the cell vacancy probability, the mean area of vacant and occupied cells, the user-base station distance, and the pair correlation function in lightly and heavily loaded regimes.
Whom to befriend to influence people
Venue: arXiv cs.DS 2016
Year: 2016

Alice wants to join a new social network, and influence its members to adopt a new product or idea. Each person $v$ in the network has a certain threshold $t(v)$ for {\em activation}, i.e adoption of the product or idea. If $v$ has at least $t(v)$ activated neighbors, then $v$ will also become activated. If Alice wants to activate the entire social network, whom should she befriend? More generally, we study the problem of finding the minimum number of links that a set of external influencers should form to people in the network, in order to activate the entire social network. This {\em Minimum Links} Problem has applications in viral marketing and the study of epidemics. Its solution can be quite different from the related and widely studied Target Set Selection problem. We prove that the Minimum Links problem cannot be approximated to within a ratio of $O(2^{\log^{1-ε} n})$, for any fixed $ε>0$, unless $NP\subseteq DTIME(n^{polylog(n)})$, where $n$ is the number of nodes in the network. On the positive side, we give linear time algorithms to solve the problem for trees, cycles, and cliques, for any given set of external influencers, and give precise bounds on the number of links needed. For general graphs, we design a polynomial time algorithm to compute size-efficient link sets that can activate the entire graph.
What Can Be Predicted from Six Seconds of Driver Glances?
Venue: arXiv cs.CV 2016
Year: 2016

We consider a large dataset of real-world, on-road driving from a 100-car naturalistic study to explore the predictive power of driver glances and, specifically, to answer the following question: what can be predicted about the state of the driver and the state of the driving environment from a 6-second sequence of macro-glances? The context-based nature of such glances allows for application of supervised learning to the problem of vision-based gaze estimation, making it robust, accurate, and reliable in messy, real-world conditions. So, it's valuable to ask whether such macro-glances can be used to infer behavioral, environmental, and demographic variables? We analyze 27 binary classification problems based on these variables. The takeaway is that glance can be used as part of a multi-sensor real-time system to predict radio-tuning, fatigue state, failure to signal, talking, and several environment variables.
Integrating High Level and Low Level Planning
Venue: arXiv cs.RO 2016
Year: 2016

We present a possible method for integrating high level and low level planning. To do so, we introduce the global plan random \emph{trajectory} $\boldsymbolη_0 \colon [1,T] \to \mathbb R^2$, measured by goals $G_i$ and governed by the distribution $p(\boldsymbolη_0 \mid \{ G_i\}_{i=1}^m)$. This distribution is combined with the low level robot-crowd planner $p(\mathbf{f}^{R},\mathbf{f}^{1},\ldots,\mathbf{f}^{n}\mid\mathbf{z}_{1:t})$ (from~\cite{trautmanicra2013, trautmaniros}) in the distribution $p(\boldsymbolη_0,\mathbf{f}^{(R)},\mathbf{f}\mid\mathbf{z}_{1:t})$. We explore this \emph{integrated planning} formulation in three case studies, and in the process find that this formulation 1) generalizes the ROS navigation stack in a practically useful way 2) arbitrates between high and low level decision making in a statistically sound manner when unanticipated local disturbances arise and 3) enables the integration of an onboard operator providing real time input at either the global (e.g., waypoint designation) or local (e.g., joystick) level. Importantly, the integrated planning formulation $p(\boldsymbolη_0,\mathbf{f}^{(R)},\mathbf{f}\mid\mathbf{z}_{1:t})$ highlights failure modes of the ROS navigation stack (and thus for standard hierarchical planning architectures); these failure modes are resolved by using $p(\boldsymbolη_0,\mathbf{f}^{(R)},\mathbf{f}\mid\mathbf{z}_{1:t})$. Finally, we conclude with a discussion of how results from formal methods can guide our factorization of $p(\boldsymbolη_0,\mathbf{f}^{(R)},\mathbf{f}\mid\mathbf{z}_{1:t})$.
High-Throughput Data Detection for Massive MU-MIMO-OFDM using Coordinate Descent
Venue: arXiv cs.IT 2016
Year: 2016

Data detection in massive multi-user (MU) multiple-input multiple-output (MIMO) wireless systems is among the most critical tasks due to the excessively high implementation complexity. In this paper, we propose a novel, equalization-based soft-output data-detection algorithm and corresponding reference FPGA designs for wideband massive MU-MIMO systems that use orthogonal frequency-division multiplexing (OFDM). Our data-detection algorithm performs approximate minimum mean-square error (MMSE) or box-constrained equalization using coordinate descent. We deploy a variety of algorithm-level optimizations that enable near-optimal error-rate performance at low implementation complexity, even for systems with hundreds of base-station (BS) antennas and thousands of subcarriers. We design a parallel VLSI architecture that uses pipeline interleaving and can be parametrized at design time to support various antenna configurations. We develop reference FPGA designs for massive MU-MIMO-OFDM systems and provide an extensive comparison to existing designs in terms of implementation complexity, throughput, and error-rate performance. For a 128 BS antenna, 8 user massive MU-MIMO-OFDM system, our FPGA design outperforms the next-best implementation by more than 2.6x in terms of throughput per FPGA look-up tables.