OpenRaiser/Intern-Atlas huggingface dataset:
MAC Address De-Anonymisation
Venue: arXiv cs.NI 2018
Year: 2018

This project is an exploration into analysing WiFi probe requests, a management frame described as part of the IEEE 802.11 protocol which publicly broadcasts the senders MAC address. The intention was to collect these probe requests to use as a basis to link people's information to the MAC of their device. This would enable people to be identified in future by the presence of their device near a probe request detector. Due to data protection and privacy issues preventing access to real data of people's names and locations, this project was divided into two parts. Firstly, an identification algorithm was developed and tested on simulated data sets of MAC addresses and names, to prove MAC address identification is possible. And secondly, a distributed system of probe request detectors coupled with a centralised MAC address database was developed to demonstrate that these simulated MAC addresses are obtainable in the real world. The capturing software was initially developed for Unix systems and uses a Django-powered web server to store data from multiple capturing devices. Python was used to model and test the identification algorithm.
Sparse Architectures for Text-Independent Speaker Verification Using Deep Neural Networks
Venue: arXiv cs.SD 2018
Year: 2018

Network pruning is of great importance due to the elimination of the unimportant weights or features activated due to the network over-parametrization. Advantages of sparsity enforcement include preventing the overfitting and speedup. Considering a large number of parameters in deep architectures, network compression becomes of critical importance due to the required huge amount of computational power. In this work, we impose structured sparsity for speaker verification which is the validation of the query speaker compared to the speaker gallery. We will show that the mere sparsity enforcement can improve the verification results due to the possible initial overfitting in the network.
Long-term face tracking in the wild using deep learning
Venue: arXiv cs.CV 2018
Year: 2018

This paper investigates long-term face tracking of a specific person given his/her face image in a single frame as a query in a video stream. Through taking advantage of pre-trained deep learning models on big data, a novel system is developed for accurate video face tracking in the unconstrained environments depicting various people and objects moving in and out of the frame. In the proposed system, we present a detection-verification-tracking method (dubbed as 'DVT') which accomplishes the long-term face tracking task through the collaboration of face detection, face verification, and (short-term) face tracking. An offline trained detector based on cascaded convolutional neural networks localizes all faces appeared in the frames, and an offline trained face verifier based on deep convolutional neural networks and similarity metric learning decides if any face or which face corresponds to the queried person. An online trained tracker follows the face from frame to frame. When validated on a sitcom episode and a TV show, the DVT method outperforms tracking-learning-detection (TLD) and face-TLD in terms of recall and precision. The proposed system is also tested on many other types of videos and shows very promising results.
SRPT for Multiserver Systems
Venue: arXiv cs.PF 2018
Year: 2018

The Shortest Remaining Processing Time (SRPT) scheduling policy and its variants have been extensively studied in both theoretical and practical settings. While beautiful results are known for single-server SRPT, much less is known for multiserver SRPT. In particular, stochastic analysis of the M/G/k under multiserver SRPT is entirely open. Intuition suggests that multiserver SRPT should be optimal or near-optimal for minimizing mean response time. However, the only known analysis of multiserver SRPT is in the worst-case adversarial setting, where SRPT can be far from optimal. In this paper, we give the first stochastic analysis bounding mean response time of the M/G/k under multiserver SRPT. Using our response time bound, we show that multiserver SRPT has asymptotically optimal mean response time in the heavy-traffic limit. The key to our bounds is a strategic combination of stochastic and worst-case techniques. Beyond SRPT, we prove similar response time bounds and optimality results for several other multiserver scheduling policies.
Minimax Lower Bounds for Cost Sensitive Classification
Venue: arXiv cs.LG 2018
Year: 2018

The cost-sensitive classification problem plays a crucial role in mission-critical machine learning applications, and differs with traditional classification by taking the misclassification costs into consideration. Although being studied extensively in the literature, the fundamental limits of this problem are still not well understood. We investigate the hardness of this problem by extending the standard minimax lower bound of balanced binary classification problem (due to \cite{massart2006risk}), and emphasize the impact of cost terms on the hardness.
Adaptive Gains to Super-Twisting Technique for Sliding Mode Design
Venue: arXiv cs.SY 2018
Year: 2018

This paper studies the super-twisting algorithm (STA) for adaptive sliding mode design. The proposed method tunes the two gains of STA on line simultaneously such that a second order sliding mode can take place with small rectifying gains. The perturbation magnitude is obtained exactly by employing a third-order sliding mode observer in opposition to the conventional approximations by using a first order low pass filter. While driving the sliding variable to the sliding mode surface, one gain of the STA automatically converges to an adjacent area of the perturbation magnitude in finite time. The other gain is adjusted by the above gain to guarantee the robustness of the STA. This method requires only one parameter to be adjusted. The adjustment is straightforward because it just keeps increasing until it fulfills the convergence constraints. For large values of the parameter, chattering in the update law of the two gains is avoided by employing a geometry based backward Euler integration method. The usefulness is illustrated by an example of designing an equivalent control based sliding mode control (ECBC-SMC) with the proposed adaptive STA for a perturbed LTI system.
DLBI: Deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy
Venue: arXiv cs.CV 2018
Year: 2018

Super-resolution fluorescence microscopy, with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures. Here we propose a novel deep learning guided Bayesian inference approach, DLBI, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster. The main program is available at https://github.com/lykaust15/DLBI
Network Learning with Local Propagation
Venue: arXiv cs.LG 2018
Year: 2018

This paper presents a locally decoupled network parameter learning with local propagation. Three elements are taken into account: (i) sets of nonlinear transforms that describe the representations at all nodes, (ii) a local objective at each node related to the corresponding local representation goal, and (iii) a local propagation model that relates the nonlinear error vectors at each node with the goal error vectors from the directly connected nodes. The modeling concepts (i), (ii) and (iii) offer several advantages, including (a) a unified learning principle for any network that is represented as a graph, (b) understanding and interpretation of the local and the global learning dynamics, (c) decoupled and parallel parameter learning, (d) a possibility for learning in infinitely long, multi-path and multi-goal networks. Numerical experiments validate the potential of the learning principle. The preliminary results show advantages in comparison to the state-of-the-art methods, w.r.t. the learning time and the network size while having comparable recognition accuracy.
Randomized Strategies for Robust Combinatorial Optimization
Venue: arXiv cs.DS 2018
Year: 2018

In this paper, we study the following robust optimization problem. Given an independence system and candidate objective functions, we choose an independent set, and then an adversary chooses one objective function, knowing our choice. Our goal is to find a randomized strategy (i.e., a probability distribution over the independent sets) that maximizes the expected objective value. To solve the problem, we propose two types of schemes for designing approximation algorithms. One scheme is for the case when objective functions are linear. It first finds an approximately optimal aggregated strategy and then retrieves a desired solution with little loss of the objective value. The approximation ratio depends on a relaxation of an independence system polytope. As applications, we provide approximation algorithms for a knapsack constraint or a matroid intersection by developing appropriate relaxations and retrievals. The other scheme is based on the multiplicative weights update method. A key technique is to introduce a new concept called $(η,γ)$-reductions for objective functions with parameters $η, γ$. We show that our scheme outputs a nearly $α$-approximate solution if there exists an $α$-approximation algorithm for a subproblem defined by $(η,γ)$-reductions. This improves approximation ratio in previous results. Using our result, we provide approximation algorithms when the objective functions are submodular or correspond to the cardinality robustness for the knapsack problem.
Validating WordNet Meronymy Relations using Adimen-SUMO
Venue: arXiv cs.CL 2018
Year: 2018

In this paper, we report on the practical application of a novel approach for validating the knowledge of WordNet using Adimen-SUMO. In particular, this paper focuses on cross-checking the WordNet meronymy relations against the knowledge encoded in Adimen-SUMO. Our validation approach tests a large set of competency questions (CQs), which are derived (semi)-automatically from the knowledge encoded in WordNet, SUMO and their mapping, by applying efficient first-order logic automated theorem provers. Unfortunately, despite of being created manually, these knowledge resources are not free of errors and discrepancies. In consequence, some of the resulting CQs are not plausible according to the knowledge included in Adimen-SUMO. Thus, first we focus on (semi)-automatically improving the alignment between these knowledge resources, and second, we perform a minimal set of corrections in the ontology. Our aim is to minimize the manual effort required for an extensive validation process. We report on the strategies followed, the changes made, the effort needed and its impact when validating the WordNet meronymy relations using improved versions of the mapping and the ontology. Based on the new results, we discuss the implications of the appropriate corrections and the need of future enhancements.
Learning with Non-Convex Truncated Losses by SGD
Venue: arXiv cs.LG 2018
Year: 2018

Learning with a {\it convex loss} function has been a dominating paradigm for many years. It remains an interesting question how non-convex loss functions help improve the generalization of learning with broad applicability. In this paper, we study a family of objective functions formed by truncating traditional loss functions, which is applicable to both shallow learning and deep learning. Truncating loss functions has potential to be less vulnerable and more robust to large noise in observations that could be adversarial. More importantly, it is a generic technique without assuming the knowledge of noise distribution. To justify non-convex learning with truncated losses, we establish excess risk bounds of empirical risk minimization based on truncated losses for heavy-tailed output, and statistical error of an approximate stationary point found by stochastic gradient descent (SGD) method. Our experiments for shallow and deep learning for regression with outliers, corrupted data and heavy-tailed noise further justify the proposed method.
The Governance of Risks in Ridesharing: A Revelatory Case from Singapore
Venue: arXiv cs.CY 2018
Year: 2018

Recently we have witnessed the worldwide adoption of many different types of innovative technologies, such as crowdsourcing, ridesharing, open and big data, aiming at delivering public services more efficiently and effectively. Among them, ridesharing has received substantial attention from decision-makers around the world. Because of the multitude of currently understood or potentially unknown risks associated with ridesharing (unemployment, insurance, information privacy, and environmental risk), governments in different countries apply different strategies to address such risks. Some governments prohibit the adoption of ridesharing altogether, while other governments promote it. In this article, we address the question of how risks involved in ridesharing are governed over time. We present an in-depth single case study on Singapore and examine how the Singaporean government has addressed risks in ridesharing over time. The Singaporean government has a strong ambition to become an innovation hub, and many innovative technologies have been adopted and promoted to that end. At the same time, decision-makers in Singapore are reputed for their proactive style of social governance. The example of Singapore can be regarded as a revelatory case study, helping us further to explore governance practices in other countries. Keywords: risk; ridesharing; transport; governance; innovative technologies; case study; Singapore
Phase retrieval from the norms of affine transformations
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider the generalized phase retrieval from affine measurements. This problem aims to recover signals ${\mathbf x} \in {\mathbb F}^d$ from the affine measurements $y_j=\norm{M_j^*\vx +{\mathbb b}_j}^2,\; j=1,\ldots,m,$ where $M_j \in {\mathbb F}^{d\times r}, {\mathbf b}_j\in {\mathbb F}^{r}, {\mathbb F}\in \{{\mathbb R},{\mathbb C}\}$ and we call it as {\em generalized affine phase retrieval}. We develop a framework for generalized affine phase retrieval with presenting necessary and sufficient conditions for $\{(M_j,{\mathbf b}_j)\}_{j=1}^m$ having generalized affine phase retrieval property. We also establish results on minimal measurement number for generalized affine phase retrieval. Particularly, we show if $\{(M_j,{\mathbf b}_j)\}_{j=1}^m \subset {\mathbb F}^{d\times r}\times {\mathbb F}^{r}$ has generalized affine phase retrieval property, then $m\geq d+\floor{d/r}$ for ${\mathbb F}={\mathbb R}$ ($m\geq 2d+\floor{d/r}$ for ${\mathbb F}={\mathbb C}$ ). We also show that the bound is tight provided $r\mid d$. These results imply that one can reduce the measurement number by raising $r$, i.e. the rank of $M_j$. This highlights a notable difference between generalized affine phase retrieval and generalized phase retrieval. Furthermore, using tools of algebraic geometry, we show that $m\geq 2d$ (resp. $m\geq 4d-1$) generic measurements ${\mathcal A}=\{(M_j,b_j)\}_{j=1}^m$ have the generalized phase retrieval property for ${\mathbb F}={\mathbb R}$ (resp. ${\mathbb F}={\mathbb C}$).
Multi-View Stereo with Asymmetric Checkerboard Propagation and Multi-Hypothesis Joint View Selection
Venue: arXiv cs.CV 2018
Year: 2018

In computer vision domain, how to fast and accurately perform multiview stereo (MVS) is still a challenging problem. In this paper we present a fast yet accurate method for 3D dense reconstruction, called AMHMVS, built on the PatchMatch based stereo algorithm. Different from the regular symmetric propagation scheme, our approach adopts an asymmetric checkerboard propagation strategy, which can adaptively make effective hypotheses expand further according to the confidence of current neighbor hypotheses. In order to aggregate visual information from multiple images better, we propose the multi-hypothesis joint view selection for each pixel, which leverages a cost matrix based on the multiple propagated hypotheses to robustly infer an appropriate aggregation subset parallel. Combined with the above two steps, our approach not only has the capacity of massively parallel computation, but also obtains high accuracy and completeness. Experiments on extensive datasets show that our method achieves more accurate and robust results, and runs faster than the competing methods.
Quantizing Convolutional Neural Networks for Low-Power High-Throughput Inference Engines
Venue: arXiv cs.LG 2018
Year: 2018

Deep learning as a means to inferencing has proliferated thanks to its versatility and ability to approach or exceed human-level accuracy. These computational models have seemingly insatiable appetites for computational resources not only while training, but also when deployed at scales ranging from data centers all the way down to embedded devices. As such, increasing consideration is being made to maximize the computational efficiency given limited hardware and energy resources and, as a result, inferencing with reduced precision has emerged as a viable alternative to the IEEE 754 Standard for Floating-Point Arithmetic. We propose a quantization scheme that allows inferencing to be carried out using arithmetic that is fundamentally more efficient when compared to even half-precision floating-point. Our quantization procedure is significant in that we determine our quantization scheme parameters by calibrating against its reference floating-point model using a single inference batch rather than (re)training and achieve end-to-end post quantization accuracies comparable to the reference model.
Detection of Sensor Attack and Resilient State Estimation for Uniformly Observable Nonlinear Systems having Redundant Sensors
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents a detection algorithm for sensor attacks and a resilient state estimation scheme for a class of uniformly observable nonlinear systems. An adversary is supposed to corrupt a subset of sensors with the possibly unbounded signals, while the system has sensor redundancy. We design an individual high-gain observer for each measurement output so that only the observable portion of the system state is obtained. Then, a nonlinear error correcting problem is solved by collecting all the information from those partial observers and exploiting redundancy. A computationally efficient, on-line monitoring scheme is presented for attack detection. Based on the attack detection scheme, an algorithm for resilient state estimation is provided. The simulation results demonstrate the effectiveness of the proposed algorithm.
Channel Estimation for Visible Light Communications Using Neural Networks
Venue: arXiv cs.NE 2018
Year: 2018

Visible light communications (VLC) is an emerging field in technology and research. Estimating the channel taps is a major requirement for designing reliable communication systems. Due to the nonlinear characteristics of the VLC channel those parameters cannot be derived easily. They can be calculated by means of software simulation. In this work, a novel methodology is proposed for the prediction of channel parameters using neural networks. Measurements conducted in a controlled experimental setup are used to train neural networks for channel tap prediction. Our experiment results indicate that neural networks can be effectively trained to predict channel taps under different environmental conditions.
Empirical Software Engineering: From Discipline to Interdiscipline
Venue: arXiv cs.SE 2018
Year: 2018

Empirical software engineering has received much attention in recent years and coined the shift from a more design-science-driven engineering discipline to an insight-oriented, and theory-centric one. Yet, we still face many challenges, among which some increase the need for interdisciplinary research. This is especially true for the investigation of human-centric aspects of software engineering. Although we can already observe an increased recognition of the need for more interdisciplinary research in (empirical) software engineering, such research configurations come with challenges barely discussed from a scientific point of view. In this position paper, we critically reflect upon the epistemological setting of empirical software engineering and elaborate its configuration as an Interdiscipline. In particular, we (1) elaborate a pragmatic view on empirical research for software engineering reflecting a cyclic process for knowledge creation, (2) motivate a path towards symmetrical interdisciplinary research, and (3) adopt five rules of thumb from other interdisciplinary collaborations in our field before concluding with new emerging challenges. This shall support stopping to treating empirical software engineering as a developing discipline moving towards a paradigmatic stage of normal science, but as a configuration of symmetric interdisciplinary teams and research methods.
AgileNet: Lightweight Dictionary-based Few-shot Learning
Venue: arXiv cs.LG 2018
Year: 2018

The success of deep learning models is heavily tied to the use of massive amount of labeled data and excessively long training time. With the emergence of intelligent edge applications that use these models, the critical challenge is to obtain the same inference capability on a resource-constrained device while providing adaptability to cope with the dynamic changes in the data. We propose AgileNet, a novel lightweight dictionary-based few-shot learning methodology which provides reduced complexity deep neural network for efficient execution at the edge while enabling low-cost updates to capture the dynamics of the new data. Evaluations of state-of-the-art few-shot learning benchmarks demonstrate the superior accuracy of AgileNet compared to prior arts. Additionally, AgileNet is the first few-shot learning approach that prevents model updates by eliminating the knowledge obtained from the primary training. This property is ensured through the dictionaries learned by our novel end-to-end structured decomposition, which also reduces the memory footprint and computation complexity to match the edge device constraints.