OpenRaiser/Intern-Atlas huggingface dataset:
Gradual Tuning: a better way of Fine Tuning the parameters of a Deep Neural Network
Venue: arXiv cs.AI 2017
Year: 2017

In this paper we present an alternative strategy for fine-tuning the parameters of a network. We named the technique Gradual Tuning. Once trained on a first task, the network is fine-tuned on a second task by modifying a progressively larger set of the network's parameters. We test Gradual Tuning on different transfer learning tasks, using networks of different sizes trained with different regularization techniques. The result shows that compared to the usual fine tuning, our approach significantly reduces catastrophic forgetting of the initial task, while still retaining comparable if not better performance on the new task.
A Novel Approach for Security Situational Awareness in the Internet of Things
Venue: arXiv cs.CR 2017
Year: 2017

Internet of Things (IoT) is characterized by various of heterogeneous devices and facing numerous threats. Modeling security of IoT is still a certain challenge. This paper defines a Stochastic Colored Petri Net (SCPN) for IoT-based smart environment and then proposes a Markov Game model for security situational awareness (SSA) in the defined SCPN. All possible attack paths are computed by the SCPN, and antagonistic behavior of both attackers and defenders are taken into consideration dynamically according to Game Theory. Two attack scenarios in smart home environment are taken into consideration to demonstrate the effectiveness of the proposed model. The proposed model can form a macroscopic trend curve of security situation. Analysis of the results shows the capabilities of the proposed model in finding vulnerable devices and potential attack paths, and even mitigating the impact of attacks. To our knowledge, this is the first attempt to establish a dynamic SSA model for a complex IoT-based smart environment.
Using LSTM Encoder-Decoder Algorithm for Detecting Anomalous ADS-B Messages
Venue: arXiv cs.CR 2017
Year: 2017

Although the ADS-B system is going to play a major role in the safe navigation of airplanes and air traffic control (ATC) management, it is also well known for its lack of security mechanisms. Previous research has proposed various methods for improving the security of the ADS-B system and mitigating associated risks. However, these solutions typically require the use of additional participating nodes (or sensors) (e.g., to verify the location of the airplane by analyzing the physical signal) or modification of the current protocol architecture (e.g., adding encryption or authentication mechanisms.) Due to the regulation process regarding avionic systems and the fact that the ADS-B system is already deployed in most airplanes, applying such modifications to the current protocol at this stage is impractical. In this paper we propose an alternative security solution for detecting anomalous ADS-B messages aimed at the detection of spoofed or manipulated ADS- B messages sent by an attacker or compromised airplane. The proposed approach utilizes an LSTM encoder-decoder algorithm for modeling flight routes by analyzing sequences of legitimate ADS-B messages. Using these models, aircraft can autonomously evaluate received ADS-B messages and identify deviations from the legitimate flight path (i.e., anomalies). We examined our approach on six different flight route datasets to which we injected different types of anomalies. Using our approach we were able to detect all of the injected attacks with an average false alarm rate of 4.3% for all of datasets.
Guaranteed Outlier Removal for Point Cloud Registration with Correspondences
Venue: arXiv cs.CV 2017
Year: 2017

An established approach for 3D point cloud registration is to estimate the registration function from 3D keypoint correspondences. Typically, a robust technique is required to conduct the estimation, since there are false correspondences or outliers. Current 3D keypoint techniques are much less accurate than their 2D counterparts, thus they tend to produce extremely high outlier rates. A large number of putative correspondences must thus be extracted to ensure that sufficient good correspondences are available. Both factors (high outlier rates, large data sizes) however cause existing robust techniques to require very high computational cost. In this paper, we present a novel preprocessing method called \emph{guaranteed outlier removal} for point cloud registration. Our method reduces the input to a smaller set, in a way that any rejected correspondence is guaranteed to not exist in the globally optimal solution. The reduction is performed using purely geometric operations which are deterministic and fast. Our method significantly reduces the population of outliers, such that further optimization can be performed quickly. Further, since only true outliers are removed, the globally optimal solution is preserved. On various synthetic and real data experiments, we demonstrate the effectiveness of our preprocessing method. Demo code is available as supplementary material.
Timed Comparisons of Semi-Markov Processes
Venue: arXiv cs.FL 2017
Year: 2017

Semi-Markov processes are Markovian processes in which the firing time of the transitions is modelled by probabilistic distributions over positive reals interpreted as the probability of firing a transition at a certain moment in time. In this paper we consider the trace-based semantics of semi-Markov processes, and investigate the question of how to compare two semi-Markov processes with respect to their time-dependent behaviour. To this end, we introduce the relation of being "faster than" between processes and study its algorithmic complexity. Through a connection to probabilistic automata we obtain hardness results showing in particular that this relation is undecidable. However, we present an additive approximation algorithm for a time-bounded variant of the faster-than problem over semi-Markov processes with slow residence-time functions, and a coNP algorithm for the exact faster-than problem over unambiguous semi-Markov processes.
Tracking for Half an Hour
Venue: arXiv cs.CV 2017
Year: 2017

Long-term tracking requires extreme stability to the multitude of model updates and robustness to the disappearance and loss of the target as such will inevitably happen. For motivation, we have taken 10 randomly selected OTB-sequences, doubled each by attaching a reversed version and repeated each double sequence 20 times. On most of these repetitive videos, the best current tracker performs worse on each loop. This illustrates the difference between optimization for short-term versus long-term tracking. In a long-term tracker a combined global and local search strategy is beneficial, allowing for recovery from failures and disappearance. Most importantly, the proposed tracker also employs cautious updating, guided by self-quality assessment. The proposed tracker is still among the best on the 20-sec OTB-videos while achieving state-of-the-art on the 100-sec UAV20L benchmark. On 10 new half-an-hour videos with city bicycling, sport games etc, the proposed tracker outperforms others by a large margin where the 2010 TLD tracker comes second.
Meteorology-Aware Multi-Goal Path Planning for Large-Scale Inspection Missions with Long-Endurance Solar-Powered Aircraft
Venue: arXiv cs.RO 2017
Year: 2017

Solar-powered aircraft promise significantly increased flight endurance over conventional aircraft. While this makes them promising candidates for large-scale aerial inspection missions, their structural fragility necessitates that adverse weather is avoided using appropriate path planning methods. This paper therefore presents MetPASS, the Meteorology-aware Path Planning and Analysis Software for Solar-powered UAVs. MetPASS is the first path planning framework in the literature that considers all aspects that influence the safety or performance of solar-powered flight: It avoids environmental risks (thunderstorms, rain, wind, wind gusts and humidity) and exploits advantageous regions (high sun radiation or tailwind). It also avoids system risks such as low battery state of charge and returns safe paths through cluttered terrain. MetPASS imports weather data from global meteorological models, propagates the aircraft state through an energetic system model, and then combines both into a cost function. A combination of dynamic programming techniques and an A*-search-algorithm with a custom heuristic is leveraged to plan globally optimal paths in station-keeping, point-to-point or multi-goal aerial inspection missions with coverage guarantees. A full software implementation including a GUI is provided. The planning methods are verified using three missions of ETH Zurich's AtlantikSolar UAV: An 81-hour continuous solar-powered station-keeping flight, a 4000km Atlantic crossing from Newfoundland to Portugal, and two multi-glacier aerial inspection missions above the Arctic Ocean performed near Greenland in summer 2017.
Access control management for e-Healthcare in cloud environment
Venue: arXiv cs.CR 2017
Year: 2017

Semantic web technologies represent much richer forms of relationships among users, resources and actions among different web applications such as clouding computing. However, Semantic web applications pose new requirements for security mechanisms especially in the access control models. This paper addresses existing access control methods and presents a semantic based access control model which considers semantic relations among different entities in cloud computing environment. We have enriched the research for semantic web technology with role-based access control that is able to be applied in the field of medical information system or e-Healthcare system. This work shows how the semantic web technology provides efficient solutions for the management of complex and distributed data in heterogeneous systems, and it can be used in the medical information systems as well.
An Overflow Free Fixed-point Eigenvalue Decomposition Algorithm: Case Study of Dimensionality Reduction in Hyperspectral Images
Venue: arXiv cs.NA 2017
Year: 2017

We consider the problem of enabling robust range estimation of eigenvalue decomposition (EVD) algorithm for a reliable fixed-point design. The simplicity of fixed-point circuitry has always been so tempting to implement EVD algo- rithms in fixed-point arithmetic. Working towards an effective fixed-point design, integer bit-width allocation is a significant step which has a crucial impact on accuracy and hardware efficiency. This paper investigates the shortcomings of the existing range estimation methods while deriving bounds for the variables of the EVD algorithm. In light of the circumstances, we introduce a range estimation approach based on vector and matrix norm properties together with a scaling procedure that maintains all the assets of an analytical method. The method could derive robust and tight bounds for the variables of EVD algorithm. The bounds derived using the proposed approach remain same for any input matrix and are also independent of the number of iterations or size of the problem. Some benchmark hyperspectral data sets have been used to evaluate the efficiency of the proposed technique. It was found that by the proposed range estimation approach, all the variables generated during the computation of Jacobi EVD is bounded within $\pm1$.
A Class of Control Certificates to Ensure Reach-While-Stay for Switched Systems
Venue: arXiv cs.SY 2017
Year: 2017

In this article, we consider the problem of synthesizing switching controllers for temporal properties through the composition of simple primitive reach-while-stay (RWS) properties. Reach-while-stay properties specify that the system states starting from an initial set I, must reach a goal (target) set G in finite time, while remaining inside a safe set S. Our approach synthesizes switched controllers that select between finitely many modes to satisfy the given RWS specification. To do so, we consider control certificates, which are Lyapunov-like functions that represent control strategies to achieve the desired specification. However, for RWS problems, a control Lyapunov-like function is often hard to synthesize in a simple polynomial form. Therefore, we combine control barrier and Lyapunov functions with an additional compatibility condition between them. Using this approach, the controller synthesis problem reduces to one of solving quantified nonlinear constrained problems that are handled using a combination of SMT solvers. The synthesis of controllers is demonstrated through a set of interesting numerical examples drawn from the related work, and compared with the state-of-the-art tool SCOTS. Our evaluation suggests that our approach is computationally feasible, and adds to the growing body of formal approaches to controller synthesis.
Inner Product and Set Disjointness: Beyond Logarithmically Many Parties
Venue: arXiv cs.CC 2017
Year: 2017

A basic goal in complexity theory is to understand the communication complexity of number-on-the-forehead problems $f\colon(\{0,1\}^n)^{k}\to\{0,1\}$ with $k\gg\log n$ parties. We study the problems of inner product and set disjointness and determine their randomized communication complexity for every $k\geq\log n$, showing in both cases that $Θ(1+\lceil\log n\rceil/\log\lceil1+k/\log n\rceil)$ bits are necessary and sufficient. In particular, these problems admit constant-cost protocols if and only if the number of parties is $k\geq n^ε$ for some constant $ε>0.$
Cost-Effective Seed Selection in Online Social Networks
Venue: arXiv cs.DS 2017
Year: 2017

We study the min-cost seed selection problem in online social networks, where the goal is to select a set of seed nodes with the minimum total cost such that the expected number of influenced nodes in the network exceeds a predefined threshold. We propose several algorithms that outperform the previous studies both on the theoretical approximation ratios and on the experimental performance. Under the case where the nodes have heterogeneous costs, our algorithms are the first bi- criteria approximation algorithms with polynomial running time and provable logarithmic performance bounds using a general contagion model. Under the case where the users have uniform costs, our algorithms achieve logarithmic approximation ratio and provable time complexity which is smaller than that of existing algorithms in orders of magnitude. We conduct extensive experiments using real social networks. The experimental results show that, our algorithms significantly outperform the existing algorithms both on the total cost and on the running time, and also scale well to billion-scale networks.
Image2Mesh: A Learning Framework for Single Image 3D Reconstruction
Venue: arXiv cs.CV 2017
Year: 2017

One challenge that remains open in 3D deep learning is how to efficiently represent 3D data to feed deep networks. Recent works have relied on volumetric or point cloud representations, but such approaches suffer from a number of issues such as computational complexity, unordered data, and lack of finer geometry. This paper demonstrates that a mesh representation (i.e. vertices and faces to form polygonal surfaces) is able to capture fine-grained geometry for 3D reconstruction tasks. A mesh however is also unstructured data similar to point clouds. We address this problem by proposing a learning framework to infer the parameters of a compact mesh representation rather than learning from the mesh itself. This compact representation encodes a mesh using free-form deformation and a sparse linear combination of models allowing us to reconstruct 3D meshes from single images. In contrast to prior work, we do not rely on silhouettes and landmarks to perform 3D reconstruction. We evaluate our method on synthetic and real-world datasets with very promising results. Our framework efficiently reconstructs 3D objects in a low-dimensional way while preserving its important geometrical aspects.
Energy-Efficient Time-Domain Vector-by-Matrix Multiplier for Neurocomputing and Beyond
Venue: arXiv cs.AR 2017
Year: 2017

We propose an extremely energy-efficient mixed-signal approach for performing vector-by-matrix multiplication in a time domain. In such implementation, multi-bit values of the input and output vector elements are represented with time-encoded digital signals, while multi-bit matrix weights are realized with current sources, e.g. transistors biased in subthreshold regime. With our approach, multipliers can be chained together to implement large-scale circuits completely in a time domain. Multiplier operation does not rely on energy-taxing static currents, which are typical for peripheral and input/output conversion circuits of the conventional mixed-signal implementations. As a case study, we have designed a multilayer perceptron, based on two layers of 10x10 four-quadrant vector-by-matrix multipliers, in 55-nm process with embedded NOR flash memory technology, which allows for compact implementation of adjustable current sources. Our analysis, based on memory cell measurements, shows that at high computing speed the drain-induced barrier lowering is a major factor limiting multiplier precision to ~6 bit. Post-layout estimates for a conservative 6-bit digital input/output NxN multiplier designed in 55 nm process, including I/O circuitry for converting between digital and time domain representations, show ~7 fJ/Op for N>200, which can be further lowered well below 1 fJ/Op for more optimal and aggressive design.
RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction
Venue: arXiv cs.HC 2017
Year: 2017

RoboJam is a machine-learning system for generating music that assists users of a touchscreen music app by performing responses to their short improvisations. This system uses a recurrent artificial neural network to generate sequences of touchscreen interactions and absolute timings, rather than high-level musical notes. To accomplish this, RoboJam's network uses a mixture density layer to predict appropriate touch interaction locations in space and time. In this paper, we describe the design and implementation of RoboJam's network and how it has been integrated into a touchscreen music app. A preliminary evaluation analyses the system in terms of training, musical generation and user interaction.
Computational Aided Design for Generating a Modular, Lightweight Car Concept
Venue: arXiv cs.CE 2017
Year: 2017

Developing an appropriate design process for a conceptual model is a stepping stone toward designing car bodies. This paper presents a methodology to design a lightweight and modular space frame chassis for a sedan electric car. The dual phase high strength steel with improved mechanical properties is employed to reduce the weight of the car body. Utilizing the finite element analysis yields two models in order to predict the performance of each component. The first model is a beam structure with a rapid response in structural stiffness simulation. This model is used for performing the static tests including modal frequency, bending stiffens and torsional stiffness evaluation. Whereas the second model, i.e., a shell model, is proposed to illustrate every module's mechanical behavior as well as its crashworthiness efficiency. In order to perform the crashworthiness analysis, the explicit nonlinear dynamic solver provided by ABAQUS, a commercial finite element software, is used. The results of finite element beam and shell models are in line with the concept design specifications. Implementation of this procedure leads to generate a lightweight and modular concept for an electric car.
DeepSkeleton: Skeleton Map for 3D Human Pose Regression
Venue: arXiv cs.CV 2017
Year: 2017

Despite recent success on 2D human pose estimation, 3D human pose estimation still remains an open problem. A key challenge is the ill-posed depth ambiguity nature. This paper presents a novel intermediate feature representation named skeleton map for regression. It distills structural context from irrelavant properties of RGB image e.g. illumination and texture. It is simple, clean and can be easily generated via deconvolution network. For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works. We further exploit the power of multiple 3D hypothesis generation to obtain reasonbale 3D pose in consistent with 2D pose detection. The effectiveness of our approach is validated on challenging in-the-wild dataset MPII and indoor dataset Human3.6M.
Enhancing Path-Oriented Test Data Generation Using Adaptive Random Testing Techniques
Venue: arXiv cs.SE 2017
Year: 2017

In this paper, we have developed an approach to generate test data for path coverage based testing. The main challenge of this kind testing lies in its ability to build efficiently such a test suite in order to minimize the number of rejects. We address this problem with a novel divide-and-conquer approach based on adaptive random testing strategy. Our approach takes as input the constraints of an executable path and computes a tight over-approximation of their associated sub-domain by using a dynamic domain partitioning approach. We implemented this approach and got experimental results that show the practical benefits compared to existing approaches. Our method generates less invalid inputs and is capable of obtaining the sub-domain of many complex constraints.
Deconstructing Anomalies in Academic Promotion & Tenure Decisions Using Spectral Graph Theory
Venue: arXiv cs.SI 2017
Year: 2017

Merit based promotion & tenure decision have always been controversial. This paper suggests an agent based model of the decision making processs using spectral graph theory, where the voting agents are the vertices of the graph, and edge weights are determined based on the extent of collaborative research between the agents, as well as their estimated levels of social interactions. The model assumes that agents with lower research productivities tend to interact more often with one another. Using the graph theoretic spectrum, the paper proposes a 2-dimensional influence diagram that maps the voting agents into points on a 2-dimensional grid, where agents that are likely to influence each other more are closely spaced. Next, a dynamic model is addressed, where votes are determined based on very small randomly assigned initial values, and the mutual interaction during the decision making process. The model is able to accurately reproduce a known promotion decision making from a department of a research oriented university which involved a sizeable number of voting agents with low research outputs.
Sparse Photometric 3D Face Reconstruction Guided by Morphable Models
Venue: arXiv cs.CV 2017
Year: 2017

We present a novel 3D face reconstruction technique that leverages sparse photometric stereo (PS) and latest advances on face registration/modeling from a single image. We observe that 3D morphable faces approach provides a reasonable geometry proxy for light position calibration. Specifically, we develop a robust optimization technique that can calibrate per-pixel lighting direction and illumination at a very high precision without assuming uniform surface albedos. Next, we apply semantic segmentation on input images and the geometry proxy to refine hairy vs. bare skin regions using tailored filters. Experiments on synthetic and real data show that by using a very small set of images, our technique is able to reconstruct fine geometric details such as wrinkles, eyebrows, whelks, pores, etc, comparable to and sometimes surpassing movie quality productions.