OpenRaiser/Intern-Atlas huggingface dataset:
Run Time Prediction for Big Data Iterative ML Algorithms: a KMeans case study
Venue: arXiv cs.DC 2017
Year: 2017

Data science and machine learning algorithms running on big data infrastructure are increasingly important in activities ranging from business intelligence and analytics to cybersecurity, smart city management, and many fields of science and engineering. As these algorithms are further integrated into daily operations, understanding how long they take to run on a big data infrastructure is paramount to controlling costs and delivery times. In this paper we discuss the issues involved in understanding the run time of iterative machine learning algorithms and provide a case study of such an algorithm - including a statistical characterization and model of the run time of an implementation of K-Means for the Spark big data engine using the Edward probabilistic programming language.
Different Applications of Mobile Robots in Education
Venue: arXiv cs.RO 2017
Year: 2017

This paper presents different possibilities of using mobile robots in education. Through the application of mobile mechatronic robotic system "Robotino" this paper shows the possibilities of developing interactive lectures and exercises in order to raise the quality of education and to provide new competencies for students. Application of robot as a real system supports strengthening specific areas of knowledge and skills that the students develop through design, creation, assembly and operating with the robot. This way of learning contains a very important element and that is "Playful learning" or learning through play. Along with technical competences, combining this method with teamwork improves also social skills and motivation for learning. This paper will present application of the robot in education on examples of modelling and designing of mechatronic systems, simulating and parameters monitoring of mechatronic systems, and collecting, processing and application of data from sensors in mechatronic systems.
Finger Based Techniques for Nonvisual Touchscreen Text Entry
Venue: arXiv cs.HC 2017
Year: 2017

This research proposes Finger Based Technique (FBT) for non-visual touch screen device interaction designed for blind users. Based on the proposed technique, the blind user can access virtual keys based on finger holding positions. Three different models have been proposed. They are Single Digit Finger-Digit Input (FDI), Double Digit FDI for digital text entry, and Finger-Text Input (FTI) for normal text entry. All the proposed models were implemented with voice feedback while enabling touch as the input gesture. The models were evaluated with 7 blind participants with Samsung Galaxy S2 apparatus. The results show that Single Digit FDI is substantially faster and more accurate than Double Digit FDI and iPhone voice-over. FTI also looks promising for text entry. Our study also reveals 11 accessible regions to place widgets for quick access by blind users in flat touch screen based smartphones. Identification of these accessible regions will promote dynamic interactions for blind users and serve as a usability design framework for touch screen applications.
Coexistence of Terrestrial and Aerial Users in Cellular Networks
Venue: arXiv cs.NI 2017
Year: 2017

Enabling the integration of aerial mobile users into existing cellular networks would make possible a number of promising applications. However, current cellular networks have not been designed to serve aerial users, and hence an exploration of design parameters is required in order to allow network providers to modify their current infrastructure. As a first step in this direction, this paper provides an in-depth analysis of the coverage probability of the downlink of a cellular network that serves both aerial and ground users. We present an exact mathematical characterization of the coverage probability, which includes the effect of base stations (BSs) height, antenna pattern and drone altitude for various type of urban environments. Interestingly, our results show that the favorable propagation conditions that aerial users enjoys due to its altitude is also their strongest limiting factor, as it leaves them vulnerable to interference. This negative effect can be substantially reduced by optimizing the flying altitude, the base station height and antenna down-tilt. Moreover, lowering the base station height and increasing down-tilt angle are in general beneficial for both terrestrial and aerial users, pointing out a possible path to enable their coexistence.
Handwritten digit string recognition by combination of residual network and RNN-CTC
Venue: arXiv cs.CV 2017
Year: 2017

Recurrent neural network (RNN) and connectionist temporal classification (CTC) have showed successes in many sequence labeling tasks with the strong ability of dealing with the problems where the alignment between the inputs and the target labels is unknown. Residual network is a new structure of convolutional neural network and works well in various computer vision tasks. In this paper, we take advantage of the architectures mentioned above to create a new network for handwritten digit string recognition. First we design a residual network to extract features from input images, then we employ a RNN to model the contextual information within feature sequences and predict recognition results. At the top of this network, a standard CTC is applied to calculate the loss and yield the final results. These three parts compose an end-to-end trainable network. The proposed new architecture achieves the highest performances on ORAND-CAR-A and ORAND-CAR-B with recognition rates 89.75% and 91.14%, respectively. In addition, the experiments on a generated captcha dataset which has much longer string length show the potential of the proposed network to handle long strings.
Threefold Analysis of Distributed Systems: IMDS, Petri Net and Distributed Automata DA3
Venue: arXiv cs.SE 2017
Year: 2017

Integrated Model of Distributed Systems is used for specification and verification of distributed systems. In the formalism, a system is modeled as a set of servers' states and agents' messages. The operation of a system is modeled as actions converting global system configuration (a set of states and messages) to a new configuration. The formalism is used in Dedan verification environment, in which specification and verification of distributed systems is performed. Equivalent Petri nets are used for structural analysis. For the graphical specification and simulation of distributed systems, Distributed Autonomous and Asynchronous Automata (DA3) are invented. Such simulation does not require calculation of global configuration space of a system. Two forms of DA3 are shown: Server-DA3 (SDA3) for the server view and Agent-DA3 (ADA3) for the agent view.
Deep Learning Paradigm with Transformed Monolingual Word Embeddings for Multilingual Sentiment Analysis
Venue: arXiv cs.CL 2017
Year: 2017

The surge of social media use brings huge demand of multilingual sentiment analysis (MSA) for unveiling cultural difference. So far, traditional methods resorted to machine translation---translating texts in other languages to English, and then adopt the methods once worked in English. However, this paradigm is conditioned by the quality of machine translation. In this paper, we propose a new deep learning paradigm to assimilate the differences between languages for MSA. We first pre-train monolingual word embeddings separately, then map word embeddings in different spaces into a shared embedding space, and then finally train a parameter-sharing deep neural network for MSA. The experimental results show that our paradigm is effective. Especially, our CNN model outperforms a state-of-the-art baseline by around 2.1% in terms of classification accuracy.
Function space analysis of deep learning representation layers
Venue: arXiv cs.AI 2017
Year: 2017

In this paper we propose a function space approach to Representation Learning and the analysis of the representation layers in deep learning architectures. We show how to compute a weak-type Besov smoothness index that quantifies the geometry of the clustering in the feature space. This approach was already applied successfully to improve the performance of machine learning algorithms such as the Random Forest and tree-based Gradient Boosting. Our experiments demonstrate that in well-known and well-performing trained networks, the Besov smoothness of the training set, measured in the corresponding hidden layer feature map representation, increases from layer to layer. We also contribute to the understanding of generalization by showing how the Besov smoothness of the representations, decreases as we add more mis-labeling to the training data. We hope this approach will contribute to the de-mystification of some aspects of deep learning.
One-bit compressed sensing with partial Gaussian circulant matrices
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we consider memoryless one-bit compressed sensing with randomly subsampled Gaussian circulant matrices. We show that in a small sparsity regime and for small enough accuracy $δ$, $m\sim δ^{-4} s\log(N/sδ)$ measurements suffice to reconstruct the direction of any $s$-sparse vector up to accuracy $δ$ via an efficient program. We derive this result by proving that partial Gaussian circulant matrices satisfy an $\ell_1/\ell_2$ RIP-property. Under a slightly worse dependence on $δ$, we establish stability with respect to approximate sparsity, as well as full vector recovery results.
Protecting sensitive research data and meeting researchers needs: Duke University's Protected Network
Venue: arXiv cs.CR 2017
Year: 2017

Research use of sensitive information -- personally identifiable information (PII), protected health information (PHI), commercial or proprietary data, and the like -- is increasing as researchers' skill with "big data" matures. Duke University's Protected Network is an environment with technical controls in place that provide research groups with essential pieces of security measures needed for studies using sensitive information. The environment uses virtualization and authorization groups extensively to isolate data, provide elasticity of resources, and flexibly meet a range of computational requirements within tightly controlled network boundaries. Since its beginning in 2011, the environment has supported about 200 research projects and groups and has served as a foundation for specialized and protected IT infrastructures in the social sciences, population studies, and medical research. This article lays out key features of the development of the Protected Network and outlines the IT infrastructure design and organizational features that Duke has used in establishing this resource for researchers. It consists of four sections: 1. Context, 2. Infrastructure, 3. Authentication and identity management, and 4. The infrastructure as a "platform."
Standard detectors aren't (currently) fooled by physical adversarial stop signs
Venue: arXiv cs.CV 2017
Year: 2017

An adversarial example is an example that has been adjusted to produce the wrong label when presented to a system at test time. If adversarial examples existed that could fool a detector, they could be used to (for example) wreak havoc on roads populated with smart vehicles. Recently, we described our difficulties creating physical adversarial stop signs that fool a detector. More recently, Evtimov et al. produced a physical adversarial stop sign that fools a proxy model of a detector. In this paper, we show that these physical adversarial stop signs do not fool two standard detectors (YOLO and Faster RCNN) in standard configuration. Evtimov et al.'s construction relies on a crop of the image to the stop sign; this crop is then resized and presented to a classifier. We argue that the cropping and resizing procedure largely eliminates the effects of rescaling and of view angle. Whether an adversarial attack is robust under rescaling and change of view direction remains moot. We argue that attacking a classifier is very different from attacking a detector, and that the structure of detectors - which must search for their own bounding box, and which cannot estimate that box very accurately - likely makes it difficult to make adversarial patterns. Finally, an adversarial pattern on a physical object that could fool a detector would have to be adversarial in the face of a wide family of parametric distortions (scale; view angle; box shift inside the detector; illumination; and so on). Such a pattern would be of great theoretical and practical interest. There is currently no evidence that such patterns exist.
Proofs as Relational Invariants of Synthesized Execution Grammars
Venue: arXiv cs.PL 2017
Year: 2017

The automatic verification of programs that maintain unbounded low-level data structures is a critical and open problem. Analyzers and verifiers developed in previous work can synthesize invariants that only describe data structures of heavily restricted forms, or require an analyst to provide predicates over program data and structure that are used in a synthesized proof of correctness. In this work, we introduce a novel automatic safety verifier of programs that maintain low-level data structures, named LTTP. LTTP synthesizes proofs of program safety represented as a grammar of a given program's control paths, annotated with invariants that relate program state at distinct points within its path of execution. LTTP synthesizes such proofs completely automatically, using a novel inductive-synthesis algorithm. We have implemented LTTP as a verifier for JVM bytecode and applied it to verify the safety of a collection of verification benchmarks. Our results demonstrate that LTTP can be applied to automatically verify the safety of programs that are beyond the scope of previously-developed verifiers.
On the Service Capacity Region of Accessing Erasure Coded Content
Venue: arXiv cs.IT 2017
Year: 2017

Cloud storage systems generally add redundancy in storing content files such that $K$ files are replicated or erasure coded and stored on $N > K$ nodes. In addition to providing reliability against failures, the redundant copies can be used to serve a larger volume of content access requests. A request for one of the files can be either be sent to a systematic node, or one of the repair groups. In this paper, we seek to maximize the service capacity region, that is, the set of request arrival rates for the $K$ files that can be supported by a coded storage system. We explore two aspects of this problem: 1) for a given erasure code, how to optimally split incoming requests between systematic nodes and repair groups, and 2) choosing an underlying erasure code that maximizes the achievable service capacity region. In particular, we consider MDS and Simplex codes. Our analysis demonstrates that erasure coding makes the system more robust to skews in file popularity than simply replicating a file at multiple servers, and that coding and replication together can make the capacity region larger than either alone.
Real-Time Action Detection in Video Surveillance using Sub-Action Descriptor with Multi-CNN
Venue: arXiv cs.CV 2017
Year: 2017

When we say a person is texting, can you tell the person is walking or sitting? Emphatically, no. In order to solve this incomplete representation problem, this paper presents a sub-action descriptor for detailed action detection. The sub-action descriptor consists of three levels: the posture, the locomotion, and the gesture level. The three levels give three sub-action categories for one action to address the representation problem. The proposed action detection model simultaneously localizes and recognizes the actions of multiple individuals in video surveillance using appearance-based temporal features with multi-CNN. The proposed approach achieved a mean average precision (mAP) of 76.6% at the frame-based and 83.5% at the video-based measurement on the new large-scale ICVL video surveillance dataset that the authors introduce and make available to the community with this paper. Extensive experiments on the benchmark KTH dataset demonstrate that the proposed approach achieved better performance, which in turn boosts the action recognition performance over the state-of-the-art. The action detection model can run at around 25 fps on the ICVL and more than 80 fps on the KTH dataset, which is suitable for real-time surveillance applications.
On Preemption and Overdetermination in Formal Theories of Causality
Venue: arXiv cs.AI 2017
Year: 2017

One of the key challenges when looking for the causes of a complex event is to determine the causal status of factors that are neither individually necessary nor individually sufficient to produce that event. In order to reason about how such factors should be taken into account, we need a vocabulary to distinguish different cases. In philosophy, the concept of overdetermination and the concept of preemption serve an important purpose in this regard, although their exact meaning tends to remain elusive. In this paper, I provide theory-neutral definitions of these concepts using structural equations in the Halpern-Pearl tradition. While my definitions do not presuppose any particular causal theory, they take such a theory as a variable parameter. This enables us to specify formal constraints on theories of causality, in terms of a pre-theoretic understanding of what preemption and overdetermination actually mean. I demonstrate the usefulness of this by presenting and arguing for what I call the principle of presumption. Roughly speaking, this principle states that a possible cause can only be regarded as having been preempted if there is independent evidence to support such an inference. I conclude by showing that the principle of presumption is violated by the two main theories of causality formulated in the Halpern-Pearl tradition. The paper concludes by defining the class of empirical causal theories, characterised in terms of a fixed-point of counterfactual reasoning about difference-making. It is argued that theories of actual causality ought to be empirical.
Counterfactual Causality from First Principles?
Venue: arXiv cs.LO 2017
Year: 2017

In this position paper we discuss three main shortcomings of existing approaches to counterfactual causality from the computer science perspective, and sketch lines of work to try and overcome these issues: (1) causality definitions should be driven by a set of precisely specified requirements rather than specific examples; (2) causality frameworks should support system dynamics; (3) causality analysis should have a well-understood behavior in presence of abstraction.
An Analysis of Dropout for Matrix Factorization
Venue: arXiv cs.LG 2017
Year: 2017

Dropout is a simple yet effective algorithm for regularizing neural networks by randomly dropping out units through Bernoulli multiplicative noise, and for some restricted problem classes, such as linear or logistic regression, several theoretical studies have demonstrated the equivalence between dropout and a fully deterministic optimization problem with data-dependent Tikhonov regularization. This work presents a theoretical analysis of dropout for matrix factorization, where Bernoulli random variables are used to drop a factor, thereby attempting to control the size of the factorization. While recent work has demonstrated the empirical effectiveness of dropout for matrix factorization, a theoretical understanding of the regularization properties of dropout in this context remains elusive. This work demonstrates the equivalence between dropout and a fully deterministic model for matrix factorization in which the factors are regularized by the sum of the product of the norms of the columns. While the resulting regularizer is closely related to a variational form of the nuclear norm, suggesting that dropout may limit the size of the factorization, we show that it is possible to trivially lower the objective value by doubling the size of the factorization. We show that this problem is caused by the use of a fixed dropout rate, which motivates the use of a rate that increases with the size of the factorization. Synthetic experiments validate our theoretical findings.
Traffic Sign Timely Visual Recognizability Evaluation Based on 3D Measurable Point Clouds
Venue: arXiv cs.CV 2017
Year: 2017

The timely provision of traffic sign information to drivers is essential for the drivers to respond, to ensure safe driving, and to avoid traffic accidents in a timely manner. We proposed a timely visual recognizability quantitative evaluation method for traffic signs in large-scale transportation environments. To achieve this goal, we first address the concept of a visibility field to reflect the visible distribution of three-dimensional (3D) space and construct a traffic sign Visibility Evaluation Model (VEM) to measure the traffic sign visibility for a given viewpoint. Then, based on the VEM, we proposed the concept of the Visual Recognizability Field (VRF) to reflect the visual recognizability distribution in 3D space and established a Visual Recognizability Evaluation Model (VREM) to measure a traffic sign visual recognizability for a given viewpoint. Next, we proposed a Traffic Sign Timely Visual Recognizability Evaluation Model (TSTVREM) by combining VREM, the actual maximum continuous visual recognizable distance, and traffic big data to measure a traffic sign visual recognizability in different lanes. Finally, we presented an automatic algorithm to implement the TSTVREM model through traffic sign and road marking detection and classification, traffic sign environment point cloud segmentation, viewpoints calculation, and TSTVREM model realization. The performance of our method for traffic sign timely visual recognizability evaluation is tested on three road point clouds acquired by a mobile laser scanning system (RIEGL VMX-450) according to Road Traffic Signs and Markings (GB 5768-1999 in China), showing that our method is feasible and efficient.
PID-EVOA: Tuning PID Controller Parameter Optimzation based on the Habits of the Egyptian Vulture
Venue: arXiv cs.SY 2017
Year: 2017

Proportional - Integral - Derivative (PID) Controller is a primary component in industrial control systems nowadays. Its gain parameters have a powerful effect on its transient response criteria such as integral squared error (ISE), settling time, rise time and overshooting. Tuning gain parameters of PID to reach the optimal transient response of control systems is a hard problem. The standard method is Ziegler-Nichols (ZN) method that initially computes the values of the parameters which produce reasonable response but can be enhanced. In this paper Egyptian Vulture Optimization Algorithm (EVOA) is used to tune the parameters of PID controller by minimizing the ISE function more than that using ZN. The results show that using EVOA enhanced the performance of the controller response better than ZN method when implemented to liquid level control system. The ISE was reduced from 32 to 8.9 and the settling time from 189 to 81 second.
Fast and Strong Convergence of Online Learning Algorithms
Venue: arXiv cs.LG 2017
Year: 2017

In this paper, we study the online learning algorithm without explicit regularization terms. This algorithm is essentially a stochastic gradient descent scheme in a reproducing kernel Hilbert space (RKHS). The polynomially decaying step size in each iteration can play a role of regularization to ensure the generalization ability of online learning algorithm. We develop a novel capacity dependent analysis on the performance of the last iterate of online learning algorithm. The contribution of this paper is two-fold. First, our nice analysis can lead to the convergence rate in the standard mean square distance which is the best so far. Second, we establish, for the first time, the strong convergence of the last iterate with polynomially decaying step sizes in the RKHS norm. We demonstrate that the theoretical analysis established in this paper fully exploits the fine structure of the underlying RKHS, and thus can lead to sharp error estimates of online learning algorithm.