OpenRaiser/Intern-Atlas huggingface dataset:
Probabilistic Failure Analysis in Model Validation & Verification
Venue: arXiv cs.SE 2016
Year: 2016

Automated fault localization is an important issue in model validation and verification. It helps the end users in analyzing the origin of failure. In this work, we show the early experiments with probabilistic analysis approaches in fault localization. Inspired by the Kullback-Leibler Divergence from Bayesian probabilistic theory, we propose a suspiciousness factor to compute the fault contribution for the transitions in the reachability graph of model checking, using which to rank the potential faulty transitions. To automatically locate design faults in the simulation model of detailed design, we propose to use the statistical model Hidden Markov Model (HMM), which provides statistically identical information to component's real behavior. The core of this method is a fault localization algorithm that gives out the set of suspicious ranked faulty components and a backward algorithm that computes the matching degree between the HMM and the simulation model to evaluate the confidence degree of the localization conclusion.
Software-defined and Virtualized Cellular Networks with M2M Communications
Venue: arXiv cs.NI 2016
Year: 2016

Machine-to-machine (M2M) communications have attracted great attention from both academia and industry. In this paper, with recent advances in wireless network virtualization and software-defined networking (SDN), we propose a novel framework for M2M communications in software-defined cellular networks with wireless network virtualization. In the proposed framework, according to different functions and quality of service (QoS) requirements of machine-type communication devices (MTCDs), a hypervisor enables the virtualization of the physical M2M network, which is abstracted and sliced into multiple virtual M2M networks. Moreover, we formulate a decision-theoretic approach to optimize the random access process of M2M communications. In addition, we develop a feedback and control loop to dynamically adjust the number of resource blocks (RBs) that are used in the random access phase in a virtual M2M network by the SDN controller. Extensive simulation results with different system parameters are presented to show the performance of the proposed scheme.
Convergence rate of stochastic k-means
Venue: arXiv cs.LG 2016
Year: 2016

We analyze online \cite{BottouBengio} and mini-batch \cite{Sculley} $k$-means variants. Both scale up the widely used $k$-means algorithm via stochastic approximation, and have become popular for large-scale clustering and unsupervised feature learning. We show, for the first time, that starting with any initial solution, they converge to a "local optimum" at rate $O(\frac{1}{t})$ (in terms of the $k$-means objective) under general conditions. In addition, we show if the dataset is clusterable, when initialized with a simple and scalable seeding algorithm, mini-batch $k$-means converges to an optimal $k$-means solution at rate $O(\frac{1}{t})$ with high probability. The $k$-means objective is non-convex and non-differentiable: we exploit ideas from recent work on stochastic gradient descent for non-convex problems \cite{ge:sgd_tensor, balsubramani13} by providing a novel characterization of the trajectory of $k$-means algorithm on its solution space, and circumvent the non-differentiability problem via geometric insights about $k$-means update.
Machine Learning Approach for Skill Evaluation in Robotic-Assisted Surgery
Venue: arXiv cs.LG 2016
Year: 2016

Evaluating surgeon skill has predominantly been a subjective task. Development of objective methods for surgical skill assessment are of increased interest. Recently, with technological advances such as robotic-assisted minimally invasive surgery (RMIS), new opportunities for objective and automated assessment frameworks have arisen. In this paper, we applied machine learning methods to automatically evaluate performance of the surgeon in RMIS. Six important movement features were used in the evaluation including completion time, path length, depth perception, speed, smoothness and curvature. Different classification methods applied to discriminate expert and novice surgeons. We test our method on real surgical data for suturing task and compare the classification result with the ground truth data (obtained by manual labeling). The experimental results show that the proposed framework can classify surgical skill level with relatively high accuracy of 85.7%. This study demonstrates the ability of machine learning methods to automatically classify expert and novice surgeons using movement features for different RMIS tasks. Due to the simplicity and generalizability of the introduced classification method, it is easy to implement in existing trainers.
Scalable and Robust Local Community Detection via Adaptive Subgraph Extraction and Diffusions
Venue: arXiv cs.SI 2016
Year: 2016

Local community detection, the problem of identifying a set of relevant nodes nearby a small set of input seed nodes, is an important graph primitive with a wealth of applications and research activity. Recent approaches include using local spectral information, graph diffusions, and random walks to determine a community from input seeds. As networks grow to billions of nodes and exhibit diverse structures, it is important that community detection algorithms are not only efficient, but also robust to different structural features. Toward this goal, we explore pre-processing techniques and modifications to existing local methods aimed at improving the scalability and robustness of algorithms related to community detection. Experiments show that our modifications improve both speed and quality of existing methods for locating ground truth communities, and are more robust across graphs and communities of varying sizes, densities, and diameters. Our subgraph extraction method uses adaptively selected PageRank parameters to improve on the recall and runtime of a walk-based pre-processing technique of Li et al. for extracting subgraphs before searching for a community. We then use this technique to enable the first scalable implementation of the recent Local Fiedler method of Mahoney et al. Our experimental evaluation shows our pre-processed version of Local Fiedler, as well as our novel simplification of the LEMON community detection framework of Li et al., offer significant speedups over their predecessors and obtain cluster quality competitive with the state of the art.
Possibility and Impossibility of Reliable Broadcast in the Bounded Model
Venue: arXiv cs.DC 2016
Year: 2016

The Reliable Broadcast concept allows an honest party to send a message to all other parties and to make sure that all honest parties receive this message. In addition, it allows an honest party that received a message to know that all other honest parties would also receive the same message. This technique is important to ensure distributed consistency when facing failures. In the current paper, we study the ability to use \RR to consistently transmit a sequence of input values in an asynchronous environment with a designated sender. The task can be easily achieved using counters, but cannot be achieved with a bounded memory facing failures. We weaken the problem and ask whether the receivers can at least share a common suffix. We prove that in a standard (lossless) asynchronous system no bounded memory protocol can guarantee a common suffix at all receivers for every input sequence if a single party might crash. We further study the problem facing transient faults and prove that when limiting the problem to transmitting a stream of a single value being sent repeatedly we show a bounded memory self-stabilizing protocol that can ensure a common suffix even in the presence of transient faults and an arbitrary number of crash faults. We further prove that this last problem is not solvable in the presence of a single Byzantine fault. Thus, this problem {\bf separates} Byzantine behavior from crash faults in an asynchronous environment.
Leveraging Decoupling in Enabling Energy Aware D2D Communications
Venue: arXiv cs.NI 2016
Year: 2016

Downlink/Uplink decoupling (DUDe) in LTE net- works has caught the attention of researchers as it provides better uplink SINR and reduced power consumption per device due to decoupled connection of a device with the Macro (in downlink) and a small cell (in uplink). These characteristics of DUDe can be exploited to encourage more D2D communications in the network. This paper first proposes a model to estimate decoupling region within which a device is allowed to perform DUDe. Then, it formulates an equation to calculate the total power saved by devices due to decoupling. Finally, the extra area due to decoupling which can be used to enable D2D pairs is calculated. Simulation results are shown based on different simulation scenarios for different objectives for better understanding the idea proposed.
Search-and-Rescue Rendezvous
Venue: arXiv cs.GT 2016
Year: 2016

We consider a new type of asymmetric rendezvous search problem in which Agent II needs to give Agent I a `gift' which can be in the form of information or material. The gift can either be transfered upon meeting, as in traditional rendezvous, or it can be dropped o? by II at a location he passes, in the hope it will be found by I. The gift might be a water bottle for a traveller lost in the desert; a supply cache for Lieutenant Scott in the Antarctic; or important information (left as a gift). The common aim of the two agents is to minimize the time taken for I to either meet II or find the gift. We find optimal agent paths and droppo? times when the search region is a line, the initial distance between the players is known and one or both of the players can leave gifts. When there are no gifts this is the classical asymmetric rendezvous problem solved by Alpern and Gal in 1995 [10]. We exhibit strategies solving these various problems and use a `rendezvous algorithm' to establish their optimality.
DeMeshNet: Blind Face Inpainting for Deep MeshFace Verification
Venue: arXiv cs.CV 2016
Year: 2016

MeshFace photos have been widely used in many Chinese business organizations to protect ID face photos from being misused. The occlusions incurred by random meshes severely degenerate the performance of face verification systems, which raises the MeshFace verification problem between MeshFace and daily photos. Previous methods cast this problem as a typical low-level vision problem, i.e. blind inpainting. They recover perceptually pleasing clear ID photos from MeshFaces by enforcing pixel level similarity between the recovered ID images and the ground-truth clear ID images and then perform face verification on them. Essentially, face verification is conducted on a compact feature space rather than the image pixel space. Therefore, this paper argues that pixel level similarity and feature level similarity jointly offer the key to improve the verification performance. Based on this insight, we offer a novel feature oriented blind face inpainting framework. Specifically, we implement this by establishing a novel DeMeshNet, which consists of three parts. The first part addresses blind inpainting of the MeshFaces by implicitly exploiting extra supervision from the occlusion position to enforce pixel level similarity. The second part explicitly enforces a feature level similarity in the compact feature space, which can explore informative supervision from the feature space to produce better inpainting results for verification. The last part copes with face alignment within the net via a customized spatial transformer module when extracting deep facial features. All the three parts are implemented within an end-to-end network that facilitates efficient optimization. Extensive experiments on two MeshFace datasets demonstrate the effectiveness of the proposed DeMeshNet as well as the insight of this paper.
An Automated Attendance System based on NFC & X-Bee Technologies with a Remote Database
Venue: arXiv cs.CY 2016
Year: 2016

The main aim of this research was to automate attendance registration, thereby reducing human involvement in the whole process. Typically, the system works by storing vital staff personable information, such as Name, Job specification, etc. into a MySQL database upon engagement. The staff is identified with a unique key associated with an NFC based ID card within the database. So on typical work day, an employee scans his/her ID card on the PN532 reader in close proximity. The exact time and date, together with the unique identifier of the scanned card are stored locally on a storage media, before the data is relayed via the XBee to the remote database. The captured data is then authenticated by comparing with the pre-entered data to give access or authorization to the corporate resources, as well as recorded for attendance purposes. Our experiment shows that the automated attendance system is more effective, efficient and reliable, due to its real time capability, remote monitoring and attendance reports that it provides to the institution.
PCT and Beyond: Towards a Computational Framework for `Intelligent' Communicative Systems
Venue: arXiv cs.AI 2016
Year: 2016

Recent years have witnessed increasing interest in the potential benefits of `intelligent' autonomous machines such as robots. Honda's Asimo humanoid robot, iRobot's Roomba robot vacuum cleaner and Google's driverless cars have fired the imagination of the general public, and social media buzz with speculation about a utopian world of helpful robot assistants or the coming robot apocalypse! However, there is a long way to go before autonomous systems reach the level of capabilities required for even the simplest of tasks involving human-robot interaction - especially if it involves communicative behaviour such as speech and language. Of course the field of Artificial Intelligence (AI) has made great strides in these areas, and has moved on from abstract high-level rule-based paradigms to embodied architectures whose operations are grounded in real physical environments. What is still missing, however, is an overarching theory of intelligent communicative behaviour that informs system-level design decisions in order to provide a more coherent approach to system integration. This chapter introduces the beginnings of such a framework inspired by the principles of Perceptual Control Theory (PCT). In particular, it is observed that PCT has hitherto tended to view perceptual processes as a relatively straightforward series of transformations from sensation to perception, and has overlooked the potential of powerful generative model-based solutions that have emerged in practical fields such as visual or auditory scene analysis. Starting from first principles, a sequence of arguments is presented which not only shows how these ideas might be integrated into PCT, but which also extend PCT towards a remarkably symmetric architecture for a needs-driven communicative agent. It is concluded that, if behaviour is the control of perception, then perception is the simulation of behaviour.
Market Segmentation for Privacy Differentiated "Free" Services
Venue: arXiv cs.GT 2016
Year: 2016

The emerging marketplace for online free services in which service providers earn revenue from using consumer data in direct and indirect ways has lead to significant privacy concerns. This leads to the following question: can the online marketplace sustain multiple service providers (SPs) that offer privacy-differentiated free services? This paper studies the problem of market segmentation for the free online services market by augmenting the classical Hotelling model for market segmentation analysis to include the fact that for the free services market, a consumer values service not in monetized terms but by its quality of service (QoS) and that the differentiator of services is not product price but the privacy risk advertised by a SP. Building upon the Hotelling model, this paper presents a parametrized model for SP profit and consumer valuation of service for both the two- and multi-SP problems to show that: (i) when consumers place a high value on privacy, it leads to a lower use of private data by SPs (i.e., their advertised privacy risk reduces), and thus, SPs compete on the QoS; (ii) SPs that are capable of differentiating on services that do not directly target consumers gain larger market share; and (iii) a higher valuation of privacy by consumers forces SPs with smaller untargeted revenue to offer lower privacy risk to attract more consumers. The work also illustrates the market segmentation problem for more than two SPs and highlights the instability of such markets.
On the Spectral Efficiency and Security Enhancements of NOMA Assisted Multicast-Unicast Streaming
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers the application of non-orthogonal multiple access (NOMA) to a multi-user network with mixed multicasting and unicasting traffic. The proposed design of beamforming and power allocation ensures that the unicasting performance is improved while maintaining the reception reliability of multicasting. Both analytical and simulation results are provided to demonstrate that the use of the NOMA assisted multicast-unicast scheme yields a significant improvement in spectral efficiency compared to orthogonal multiple access (OMA) schemes which realize multicasting and unicasting services separately. Since unicasting messages are broadcasted to all the users, how the use of NOMA can prevent those multicasting receivers intercepting the unicasting messages is also investigated, where it is shown that the secrecy unicasting rate achieved by NOMA is always larger than or equal to that of OMA. This security gain is mainly due to the fact that the multicasting messages can be used as jamming signals to prevent potential eavesdropping when the multicasting and unicasting messages are superimposed together following the NOMA principle.
Self-calibration-based Approach to Critical Motion Sequences of Rolling-shutter Structure from Motion
Venue: arXiv cs.CV 2016
Year: 2016

In this paper we consider critical motion sequences (CMSs) of rolling-shutter (RS) SfM. Employing an RS camera model with linearized pure rotation, we show that the RS distortion can be approximately expressed by two internal parameters of an "imaginary" camera plus one-parameter nonlinear transformation similar to lens distortion. We then reformulate the problem as self-calibration of the imaginary camera, in which its skew and aspect ratio are unknown and varying in the image sequence. In the formulation, we derive a general representation of CMSs. We also show that our method can explain the CMS that was recently reported in the literature, and then present a new remedy to deal with the degeneracy. Our theoretical results agree well with experimental results; it explains degeneracies observed when we employ naive bundle adjustment, and how they are resolved by our method.
Parallel multiple selection by regular sampling
Venue: arXiv cs.DC 2016
Year: 2016

In this paper we present a deterministic parallel algorithm solving the multiple selection problem in congested clique model. In this problem for given set of elements S and a set of ranks $K = \{k_1 , k_2 , ..., k_r \}$ we are asking for the $k_i$-th smallest element of $S$ for $1 \leq i \leq r$. The presented algorithm is deterministic, time optimal , and needs $O(\log^*_{r+1} (n))$ communication rounds, where $n$ is the size of the input set, and $r$ is the size of the rank set. This algorithm may be of theoretical interest, as for $r = 1$ (classic selection problem) it gives an improvement in the asymptotic synchronization cost over previous $O(\log \log p)$ communication rounds solution, where $p$ is size of clique.
Instance-aware Image and Sentence Matching with Selective Multimodal LSTM
Venue: arXiv cs.CV 2016
Year: 2016

Effective image and sentence matching depends on how to well measure their global visual-semantic similarity. Based on the observation that such a global similarity arises from a complex aggregation of multiple local similarities between pairwise instances of image (objects) and sentence (words), we propose a selective multimodal Long Short-Term Memory network (sm-LSTM) for instance-aware image and sentence matching. The sm-LSTM includes a multimodal context-modulated attention scheme at each timestep that can selectively attend to a pair of instances of image and sentence, by predicting pairwise instance-aware saliency maps for image and sentence. For selected pairwise instances, their representations are obtained based on the predicted saliency maps, and then compared to measure their local similarity. By similarly measuring multiple local similarities within a few timesteps, the sm-LSTM sequentially aggregates them with hidden states to obtain a final matching score as the desired global similarity. Extensive experiments show that our model can well match image and sentence with complex content, and achieve the state-of-the-art results on two public benchmark datasets.
Decoupled Signal Detection for the Uplink of Large-Scale MIMO Systems in Heterogeneous Networks
Venue: arXiv cs.IT 2016
Year: 2016

Massive multiple-input multiple-output (MIMO) systems are strong candidates for future fifth generation (5G) heterogeneous cellular networks. For 5G, a network densification with a high number of different classes of users and data service requirements is expected. Such a large number of connected devices needs to be separated in order to allow the detection of the transmitted signals according to different data requirements. In this paper, a decoupled signal detection (DSD) technique which allows the separation of the uplink signals, for each user class, at the base station (BS) is proposed for massive MIMO systems. A mathematical signal model for massive MIMO systems with centralized and distributed antennas in heterogeneous networks is also developed. The performance of the proposed DSD algorithm is evaluated and compared with existing detection schemes in a realistic scenario with distributed antennas. A sum-rate analysis and a computational cost study for DSD are also presented. Simulation results show an excellent performance of the proposed DSD algorithm when combined with linear and successive interference cancellation detection techniques.
Weakly-supervised Learning of Mid-level Features for Pedestrian Attribute Recognition and Localization
Venue: arXiv cs.CV 2016
Year: 2016

State-of-the-art methods treat pedestrian attribute recognition as a multi-label image classification problem. The location information of person attributes is usually eliminated or simply encoded in the rigid splitting of whole body in previous work. In this paper, we formulate the task in a weakly-supervised attribute localization framework. Based on GoogLeNet, firstly, a set of mid-level attribute features are discovered by novelly designed detection layers, where a max-pooling based weakly-supervised object detection technique is used to train these layers with only image-level labels without the need of bounding box annotations of pedestrian attributes. Secondly, attribute labels are predicted by regression of the detection response magnitudes. Finally, the locations and rough shapes of pedestrian attributes can be inferred by performing clustering on a fusion of activation maps of the detection layers, where the fusion weights are estimated as the correlation strengths between each attribute and its relevant mid-level features. Extensive experiments are performed on the two currently largest pedestrian attribute datasets, i.e. the PETA dataset and the RAP dataset. Results show that the proposed method has achieved competitive performance on attribute recognition, compared to other state-of-the-art methods. Moreover, the results of attribute localization are visualized to understand the characteristics of the proposed method.