OpenRaiser/Intern-Atlas huggingface dataset:
Self-Taught Support Vector Machine
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, a new approach for classification of target task using limited labeled target data as well as enormous unlabeled source data is proposed which is called self-taught learning. The target and source data can be drawn from different distributions. In the previous approaches, covariate shift assumption is considered where the marginal distributions p(x) change over domains and the conditional distributions p(y|x) remain the same. In our approach, we propose a new objective function which simultaneously learns a common space T(.) where the conditional distributions over domains p(T(x)|y) remain the same and learns robust SVM classifiers for target task using both source and target data in the new representation. Hence, in the proposed objective function, the hidden label of the source data is also incorporated. We applied the proposed approach on Caltech-256, MSRC+LMO datasets and compared the performance of our algorithm to the available competing methods. Our method has a superior performance to the successful existing algorithms.
An Improved Naive Bayes Classifier-based Noise Detection Technique for Classifying User Phone Call Behavior
Venue: arXiv cs.LG 2017
Year: 2017

The presence of noisy instances in mobile phone data is a fundamental issue for classifying user phone call behavior (i.e., accept, reject, missed and outgoing), with many potential negative consequences. The classification accuracy may decrease and the complexity of the classifiers may increase due to the number of redundant training samples. To detect such noisy instances from a training dataset, researchers use naive Bayes classifier (NBC) as it identifies misclassified instances by taking into account independence assumption and conditional probabilities of the attributes. However, some of these misclassified instances might indicate usages behavioral patterns of individual mobile phone users. Existing naive Bayes classifier based noise detection techniques have not considered this issue and, thus, are lacking in classification accuracy. In this paper, we propose an improved noise detection technique based on naive Bayes classifier for effectively classifying users' phone call behaviors. In order to improve the classification accuracy, we effectively identify noisy instances from the training dataset by analyzing the behavioral patterns of individuals. We dynamically determine a noise threshold according to individual's unique behavioral patterns by using both the naive Bayes classifier and Laplace estimator. We use this noise threshold to identify noisy instances. To measure the effectiveness of our technique in classifying user phone call behavior, we employ the most popular classification algorithm (e.g., decision tree). Experimental results on the real phone call log dataset show that our proposed technique more accurately identifies the noisy instances from the training datasets that leads to better classification accuracy.
VOIDD: automatic vessel of intervention dynamic detection in PCI procedures
Venue: arXiv cs.CV 2017
Year: 2017

In this article, we present the work towards improving the overall workflow of the Percutaneous Coronary Interventions (PCI) procedures by capacitating the imaging instruments to precisely monitor the steps of the procedure. In the long term, such capabilities can be used to optimize the image acquisition to reduce the amount of dose or contrast media employed during the procedure. We present the automatic VOIDD algorithm to detect the vessel of intervention which is going to be treated during the procedure by combining information from the vessel image with contrast agent injection and images acquired during guidewire tip navigation. Due to the robust guidewire tip segmentation method, this algorithm is also able to automatically detect the sequence corresponding to guidewire navigation. We present an evaluation methodology which characterizes the correctness of the guide wire tip detection and correct identification of the vessel navigated during the procedure. On a dataset of 2213 images from 8 sequences of 4 patients, VOIDD identifies vessel-of-intervention with accuracy in the range of 88% or above and absence of tip with accuracy in range of 98% or above depending on the test case.
Balancing expression dags for more efficient lazy adaptive evaluation
Venue: arXiv cs.CG 2017
Year: 2017

Arithmetic expression dags are widely applied in robust geometric computing. In this paper we restructure expression dags by balancing consecutive additions or multiplications. We predict an asymptotic improvement in running time and experimentally confirm the theoretical results. Finally, we discuss some pitfalls of the approach resulting from changes in evaluation order.
4-connected planar graphs are in $B_3$-EPG
Venue: arXiv cs.CG 2017
Year: 2017

We show that every 4-connected planar graph has a $B_3$-EPG representation, i.e., every vertex is represented by a curve on the grid with at most three bends, and two vertices are adjacent if and only if the corresponding curves share an edge of the grid. Our construction is based on a modification of the representation by touching thickened $L$-shapes proposed by Gonçalves et al.
An empirical study of touch-based authentication methods on smartwatches
Venue: arXiv cs.HC 2017
Year: 2017

The emergence of smartwatches poses new challenges to information security. Although there are mature touch-based authentication methods for smartphones, the effectiveness of using these methods on smartwatches is still unclear. We conducted a user study (n=16) to evaluate how authentication methods (PIN and Pattern), UIs (Square and Circular), and display sizes (38mm and 42mm) affect authentication accuracy, speed, and security. Circular UIs are tailored to smartwatches with fewer UI elements. Results show that 1) PIN is more accurate and secure than Pattern; 2) Pattern is much faster than PIN; 3) Square UIs are more secure but less accurate than Circular UIs; 4) display size does not affect accuracy or speed, but security; 5) Square PIN is the most secure method of all. The study also reveals a security concern that participants' favorite method is not the best in any of the measures. We finally discuss implications for future touch-based smartwatch authentication design.
Reduction of Look Up Tables for Computation of Reciprocal of Square Roots
Venue: arXiv cs.NA 2017
Year: 2017

Among many existing algorithms, convergence methods are the most popular means of computing square root and the reciprocal of square root of numbers. An initial approximation is required in these methods. Look up tables (LUT) are employed to produce the initial approximation. In this paper a number of methods are suggested to reduce the size of the look up tables. The precision of the initial approximation plays an important role in the quality of the final result. There are constraints for the use of a LUT in terms of its size and its access time. Therefore, the optimization of the LUTs must be done in a way to minimize hardware while offering acceptable convergence speed and exactitude.
RAWSim-O: A Simulation Framework for Robotic Mobile Fulfillment Systems
Venue: arXiv cs.MA 2017
Year: 2017

This paper deals with a new type of warehousing system, Robotic Mobile Fulfillment Systems (RMFS). In such systems, robots are sent to carry storage units, so-called "pods", from the inventory and bring them to human operators working at stations. At the stations, the items are picked according to customers' orders. There exist new decision problems in such systems, for example, the reallocation of pods after their visits at work stations or the selection of pods to fulfill orders. In order to analyze decision strategies for these decision problems and relations between them, we develop a simulation framework called "RAWSim-O" in this paper. Moreover, we show a real-world application of our simulation framework by integrating simple robot prototypes based on vacuum cleaning robots.
HyperENTM: Evolving Scalable Neural Turing Machines through HyperNEAT
Venue: arXiv cs.AI 2017
Year: 2017

Recent developments within memory-augmented neural networks have solved sequential problems requiring long-term memory, which are intractable for traditional neural networks. However, current approaches still struggle to scale to large memory sizes and sequence lengths. In this paper we show how access to memory can be encoded geometrically through a HyperNEAT-based Neural Turing Machine (HyperENTM). We demonstrate that using the indirect HyperNEAT encoding allows for training on small memory vectors in a bit-vector copy task and then applying the knowledge gained from such training to speed up training on larger size memory vectors. Additionally, we demonstrate that in some instances, networks trained to copy bit-vectors of size 9 can be scaled to sizes of 1,000 without further training. While the task in this paper is simple, these results could open up the problems amendable to networks with external memories to problems with larger memory vectors and theoretically unbounded memory sizes.
Deep Learning for Case-Based Reasoning through Prototypes: A Neural Network that Explains Its Predictions
Venue: arXiv cs.AI 2017
Year: 2017

Deep neural networks are widely used for classification. These deep models often suffer from a lack of interpretability -- they are particularly difficult to understand because of their non-linear nature. As a result, neural networks are often treated as "black box" models, and in the past, have been trained purely to optimize the accuracy of predictions. In this work, we create a novel network architecture for deep learning that naturally explains its own reasoning for each prediction. This architecture contains an autoencoder and a special prototype layer, where each unit of that layer stores a weight vector that resembles an encoded training input. The encoder of the autoencoder allows us to do comparisons within the latent space, while the decoder allows us to visualize the learned prototypes. The training objective has four terms: an accuracy term, a term that encourages every prototype to be similar to at least one encoded input, a term that encourages every encoded input to be close to at least one prototype, and a term that encourages faithful reconstruction by the autoencoder. The distances computed in the prototype layer are used as part of the classification process. Since the prototypes are learned during training, the learned network naturally comes with explanations for each prediction, and the explanations are loyal to what the network actually computes.
Deep Regression Bayesian Network and Its Applications
Venue: arXiv cs.LG 2017
Year: 2017

Deep directed generative models have attracted much attention recently due to their generative modeling nature and powerful data representation ability. In this paper, we review different structures of deep directed generative models and the learning and inference algorithms associated with the structures. We focus on a specific structure that consists of layers of Bayesian Networks due to the property of capturing inherent and rich dependencies among latent variables. The major difficulty of learning and inference with deep directed models with many latent variables is the intractable inference due to the dependencies among the latent variables and the exponential number of latent variable configurations. Current solutions use variational methods often through an auxiliary network to approximate the posterior probability inference. In contrast, inference can also be performed directly without using any auxiliary network to maximally preserve the dependencies among the latent variables. Specifically, by exploiting the sparse representation with the latent space, max-max instead of max-sum operation can be used to overcome the exponential number of latent configurations. Furthermore, the max-max operation and augmented coordinate ascent are applied to both supervised and unsupervised learning as well as to various inference. Quantitative evaluations on benchmark datasets of different models are given for both data representation and feature learning tasks.
A Robust Dynamic Edge Network Architecture for the Internet-of-Things
Venue: arXiv cs.NI 2017
Year: 2017

A massive number of devices are expected to fulfill the missions of sensing, processing and control in cyber-physical Internet-of-Things (IoT) systems with new applications and connectivity requirements. In this context, scarce spectrum resources must accommodate a high traffic volume with stringent requirements of low latency, high reliability and energy efficiency. Conventional centralized network architectures may not be able to fulfill these requirements due to congestion in backhaul links. This article presents a novel design of a robust dynamic edge network architecture (RDNA) for IoT which leverages the latest advances of mobile devices (e.g., their capability to act as access points, storing and computing capabilities) to dynamically harvest unused resources and mitigate network congestion. However, traffic dynamics may compromise the availability of terminal access points and channels and, thus, network connectivity. The proposed design embraces solutions at physical, access, networking, application, and business layers to improve network robustness. The high density of mobile devices provides alternatives for close connectivity which reduces interference and latency, and thus, increases reliability and energy efficiency. Moreover, the computing capabilities of mobile devices project smartness onto the edge which is desirable for autonomous and intelligent decision making. A case study is included to illustrate the performance of RDNA. Potential applications of this architecture in the context of IoT are outlined. Finally, some challenges for future research are presented.
On the Placement and Delivery Schemes for Decentralized Coded Caching System
Venue: arXiv cs.IT 2017
Year: 2017

Network based on distributed caching of content is a new architecture to alleviate the ongoing explosive demands for rate of multi-media traffic. In caching networks, coded caching is a recently proposed technique that achieves significant performance gains compared to uncoded caching schemes. In this paper, we derive a lower bound on the average rate with a memory constraint for a family of caching allocation placement and a family of XOR cooperative delivery. The lower bound inspires us how placement and delivery affect the rate memory tradeoff. Based on the clues, we design a new placement and two new delivery algorithms. On one hand, the new placement scheme can allocate the cache more flexibly compared to grouping scheme. On the other hand, the new delivery can exploit more cooperative opportunities compared to the known schemes. The simulations validate our idea.
Knowledge is at the Edge! How to Search in Distributed Machine Learning Models
Venue: arXiv cs.DC 2017
Year: 2017

With the advent of the Internet of Things and Industry 4.0 an enormous amount of data is produced at the edge of the network. Due to a lack of computing power, this data is currently send to the cloud where centralized machine learning models are trained to derive higher level knowledge. With the recent development of specialized machine learning hardware for mobile devices, a new era of distributed learning is about to begin that raises a new research question: How can we search in distributed machine learning models? Machine learning at the edge of the network has many benefits, such as low-latency inference and increased privacy. Such distributed machine learning models can also learn personalized for a human user, a specific context, or application scenario. As training data stays on the devices, control over possibly sensitive data is preserved as it is not shared with a third party. This new form of distributed learning leads to the partitioning of knowledge between many devices which makes access difficult. In this paper we tackle the problem of finding specific knowledge by forwarding a search request (query) to a device that can answer it best. To that end, we use a entropy based quality metric that takes the context of a query and the learning quality of a device into account. We show that our forwarding strategy can achieve over 95% accuracy in a urban mobility scenario where we use data from 30 000 people commuting in the city of Trento, Italy.
Robots as-a-Service in Cloud Computing: Search and Rescue in Large-scale Disasters Case Study
Venue: arXiv cs.DC 2017
Year: 2017

Internet of Things (IoT) is expected to enable a myriad of applications by interconnecting objects - such as sensors and robots - over the Internet. IoT applications range from healthcare to autonomous vehicles and include disaster management. Enabling these applications in cloud environments requires the design of appropriate IoT Infrastructure-as-a-Service (IoT IaaS) to ease the provisioning of the IoT objects as cloud services. This paper discusses a case study on search and rescue IoT applications in large-scale disaster scenarios. It proposes an IoT IaaS architecture that virtualizes robots (IaaS for robots) and provides them to the upstream applications as-a-Service. Node- and Network-level robots virtualization are supported. The proposed architecture meets a set of identified requirements, such as the need for a unified description model for heterogeneous robots, publication/discovery mechanism, and federation with other IaaS for robots when needed. A validating proof of concept is built and experiments are made to evaluate its performance. Lessons learned and prospective research directions are discussed.
Fundamental Limitations in Performance and Interpretability of Common Planar Rigid-Body Contact Models
Venue: arXiv cs.RO 2017
Year: 2017

The ability to reason about and predict the outcome of contacts is paramount to the successful execution of many robot tasks. Analytical rigid-body contact models are used extensively in planning and control due to their computational efficiency and simplicity, yet despite their prevalence, little if any empirical comparison of these models has been made and it is unclear how well they approximate contact outcomes. In this paper, we first formulate a system identification approach for six commonly used contact models in the literature, and use the proposed method to find parameters for an experimental data-set of impacts. Next, we compare the models empirically, and establish a task specific upper bound on the performance of the models and the rigid-body contact model paradigm. We highlight the limitations of these models, salient failure modes, and the care that should be taken in parameter selection, which are ultimately difficult to give a physical interpretation.
Potential Conditional Mutual Information: Estimators, Properties and Applications
Venue: arXiv cs.IT 2017
Year: 2017

The conditional mutual information I(X;Y|Z) measures the average information that X and Y contain about each other given Z. This is an important primitive in many learning problems including conditional independence testing, graphical model inference, causal strength estimation and time-series problems. In several applications, it is desirable to have a functional purely of the conditional distribution p_{Y|X,Z} rather than of the joint distribution p_{X,Y,Z}. We define the potential conditional mutual information as the conditional mutual information calculated with a modified joint distribution p_{Y|X,Z} q_{X,Z}, where q_{X,Z} is a potential distribution, fixed airport. We develop K nearest neighbor based estimators for this functional, employing importance sampling, and a coupling trick, and prove the finite k consistency of such an estimator. We demonstrate that the estimator has excellent practical performance and show an application in dynamical system inference.
Learning Phrase Embeddings from Paraphrases with GRUs
Venue: arXiv cs.CL 2017
Year: 2017

Learning phrase representations has been widely explored in many Natural Language Processing (NLP) tasks (e.g., Sentiment Analysis, Machine Translation) and has shown promising improvements. Previous studies either learn non-compositional phrase representations with general word embedding learning techniques or learn compositional phrase representations based on syntactic structures, which either require huge amounts of human annotations or cannot be easily generalized to all phrases. In this work, we propose to take advantage of large-scaled paraphrase database and present a pair-wise gated recurrent units (pairwise-GRU) framework to generate compositional phrase representations. Our framework can be re-used to generate representations for any phrases. Experimental results show that our framework achieves state-of-the-art results on several phrase similarity tasks.
MinDelay: Low-latency Forwarding and Caching Algorithms for Information-Centric Networks
Venue: arXiv cs.NI 2017
Year: 2017

We present a new unified framework for minimizing congestion-dependent network cost in information-centric networks by jointly optimizing forwarding and caching strategies. As caching variables are integer-constrained, the resulting optimization problem is NP-hard. To make progress, we focus on a relaxed version of the optimization problem, where caching variables are allowed to be real-valued. We develop necessary optimality conditions for the relaxed problem, and leverage this result to design MinDelay, an adaptive and distributed joint forwarding and caching algorithm, based on the conditional gradient algorithm. The MinDelay algorithm elegantly yields feasible routing variables and integer caching variables at each iteration, and can be implemented in a distributed manner with low complexity and overhead. Over a wide range of network topologies, simulation results show that MinDelay typically has significantly better delay performance in the low to moderate request rate regions. Furthermore, the MinDelay and VIP algorithms complement each other in delivering superior delay performance across the entire range of request arrival rates.
BrainSegNet : A Segmentation Network for Human Brain Fiber Tractography Data into Anatomically Meaningful Clusters
Venue: arXiv cs.CV 2017
Year: 2017

The segregation of brain fiber tractography data into distinct and anatomically meaningful clusters can help to comprehend the complex brain structure and early investigation and management of various neural disorders. We propose a novel stacked bidirectional long short-term memory(LSTM) based segmentation network, (BrainSegNet) for human brain fiber tractography data classification. We perform a two-level hierarchical classification a) White vs Grey matter (Macro) and b) White matter clusters (Micro). BrainSegNet is trained over three brain tractography data having over 250,000 fibers each. Our experimental evaluation shows that our model achieves state-of-the-art results. We have performed inter as well as intra class testing over three patient's brain tractography data and achieved a high classification accuracy for both macro and micro levels both under intra as well as inter brain testing scenario.