OpenRaiser/Intern-Atlas huggingface dataset:
Reinforcement Learning with Analogical Similarity to Guide Schema Induction and Attention
Venue: arXiv cs.AI 2017
Year: 2017

Research in analogical reasoning suggests that higher-order cognitive functions such as abstract reasoning, far transfer, and creativity are founded on recognizing structural similarities among relational systems. Here we integrate theories of analogy with the computational framework of reinforcement learning (RL). We propose a psychology theory that is a computational synergy between analogy and RL, in which analogical comparison provides the RL learning algorithm with a measure of relational similarity, and RL provides feedback signals that can drive analogical learning. Simulation results support the power of this approach.
An Efficient and Fair Multi-Resource Allocation Mechanism for Heterogeneous Servers
Venue: arXiv cs.DC 2017
Year: 2017

Efficient and fair allocation of multiple types of resources is a crucial objective in a cloud/distributed computing cluster. Users may have diverse resource needs. Furthermore, diversity in server properties/ capabilities may mean that only a subset of servers may be usable by a given user. In platforms with such heterogeneity, we identify important limitations in existing multi-resource fair allocation mechanisms, notably Dominant Resource Fairness (DRF) and its follow-up work. To overcome such limitations, we propose a new server-based approach; each server allocates resources by maximizing a per-server utility function. We propose a specific class of utility functions which, when appropriately parameterized, adjusts the trade-off between efficiency and fairness, and captures a variety of fairness measures (such as our recently proposed Per-Server Dominant Share Fairness). We establish conditions for the proposed mechanism to satisfy certain properties that are generally deemed desirable, e.g., envy-freeness, sharing incentive, bottleneck fairness, and Pareto optimality. To implement our resource allocation mechanism, we develop an iterative algorithm which is shown to be globally convergent. Finally, we show how the proposed mechanism could be implemented in a distributed fashion. We carry out extensive trace-driven simulations to show the enhanced performance of our proposed mechanism over the existing ones.
Learning Deep and Compact Models for Gesture Recognition
Venue: arXiv cs.CV 2017
Year: 2017

We look at the problem of developing a compact and accurate model for gesture recognition from videos in a deep-learning framework. Towards this we propose a joint 3DCNN-LSTM model that is end-to-end trainable and is shown to be better suited to capture the dynamic information in actions. The solution achieves close to state-of-the-art accuracy on the ChaLearn dataset, with only half the model size. We also explore ways to derive a much more compact representation in a knowledge distillation framework followed by model compression. The final model is less than $1~MB$ in size, which is less than one hundredth of our initial model, with a drop of $7\%$ in accuracy, and is suitable for real-time gesture recognition on mobile devices.
Simple cubic graphs with no short traveling salesman tour
Venue: arXiv cs.DM 2017
Year: 2017

Let $tsp(G)$ denote the length of a shortest travelling salesman tour in a graph $G$. We prove that for any $\varepsilon>0$, there exists a simple $2$-connected planar cubic graph $G_1$ such that $tsp(G_1)\ge (1.25-\varepsilon)\cdot|V(G_1)|$, a simple $2$-connected bipartite cubic graph $G_2$ such that $tsp(G_2)\ge (1.2-\varepsilon)\cdot|V(G_2)|$, and a simple $3$-connected cubic graph $G_3$ such that $tsp(G_3)\ge (1.125-\varepsilon)\cdot|V(G_3)|$.
Boosting the Actor with Dual Critic
Venue: arXiv cs.LG 2017
Year: 2017

This paper proposes a new actor-critic-style algorithm called Dual Actor-Critic or Dual-AC. It is derived in a principled way from the Lagrangian dual form of the Bellman optimality equation, which can be viewed as a two-player game between the actor and a critic-like function, which is named as dual critic. Compared to its actor-critic relatives, Dual-AC has the desired property that the actor and dual critic are updated cooperatively to optimize the same objective function, providing a more transparent way for learning the critic that is directly related to the objective function of the actor. We then provide a concrete algorithm that can effectively solve the minimax optimization problem, using techniques of multi-step bootstrapping, path regularization, and stochastic dual ascent algorithm. We demonstrate that the proposed algorithm achieves the state-of-the-art performances across several benchmarks.
A Unified Bayesian Inference Framework for Generalized Linear Models
Venue: arXiv cs.IT 2017
Year: 2017

In this letter, we present a unified Bayesian inference framework for generalized linear models (GLM) which iteratively reduces the GLM problem to a sequence of standard linear model (SLM) problems. This framework provides new perspectives on some established GLM algorithms derived from SLM ones and also suggests novel extensions for some other SLM algorithms. Specific instances elucidated under such framework are the GLM versions of approximate message passing (AMP), vector AMP (VAMP), and sparse Bayesian learning (SBL). It is proved that the resultant GLM version of AMP is equivalent to the well-known generalized approximate message passing (GAMP). Numerical results for 1-bit quantized compressed sensing (CS) demonstrate the effectiveness of this unified framework.
Analytical Inverter Delay Modeling Using Matlab's Curve Fitting Toolbox
Venue: arXiv cs.OH 2017
Year: 2017

This paper presents a new analytical propagation delay model for deep submicron CMOS inverters. The model is inspired by the key observation that the inverter delay is a complicated function of several process parameters as well as load capacitance. These relationships are considered by fitting functions for each parameter derived from the Curve Fitting Toolbox in Matlab. Compared to SPICE simulations based on the BSIM4 transistor model, the analytical delay model shows very good accuracy with an average error less than 2% over a wide range of process parameters and output loads. Hence, the proposed model can be efficiently used for different technology nodes as well as statistical gate delay characterisation.
A Deep Belief Network Based Machine Learning System for Risky Host Detection
Venue: arXiv cs.CR 2017
Year: 2017

To assure cyber security of an enterprise, typically SIEM (Security Information and Event Management) system is in place to normalize security event from different preventive technologies and flag alerts. Analysts in the security operation center (SOC) investigate the alerts to decide if it is truly malicious or not. However, generally the number of alerts is overwhelming with majority of them being false positive and exceeding the SOC's capacity to handle all alerts. There is a great need to reduce the false positive rate as much as possible. While most previous research focused on network intrusion detection, we focus on risk detection and propose an intelligent Deep Belief Network machine learning system. The system leverages alert information, various security logs and analysts' investigation results in a real enterprise environment to flag hosts that have high likelihood of being compromised. Text mining and graph based method are used to generate targets and create features for machine learning. In the experiment, Deep Belief Network is compared with other machine learning algorithms, including multi-layer neural network, random forest, support vector machine and logistic regression. Results on real enterprise data indicate that the deep belief network machine learning system performs better than other algorithms for our problem and is six times more effective than current rule-based system. We also implement the whole system from data collection, label creation, feature engineering to host score generation in a real enterprise production environment.
LaMMos - Latching Mechanism based on Motorized-screw for Reconfigurable Robots and Exoskeleton Suits
Venue: arXiv cs.RO 2017
Year: 2017

Reconfigurable robots refer to a category of robots that their components (individual joints and links) can be assembled in multiple configurations and geometries. Most of existing latching mechanisms are based on physical tools such as hooks, cages or magnets, which limit the payload capacity. Therefore, robots re- quire a latching mechanism which can help to reconfigure itself without sacrificing the payload capability. This paper presents a latching mechanism based on the flexible screw attaching principle. In which, actuators are used to move the robot links and joints while connecting them with a motorized-screw and dis- connecting them by unfastening the screw. The brackets used in our mechanism configuration helps to hold maximum force up to 5000N. The LaMMos - Latching Mechanism based on Motorized- screw has been applied to the DeWaLoP - Developing Water Loss Prevention in-pipe robot. It helps the robot to shrink its body to crawl into the pipe with minimum diameter, by recon- figuring the leg positions. And it helps to recover the legs positions to original status once the robot is inside the pipe. Also, LaMMos add stiffness to the robot legs by dynamically integrate them to the structure. Additionally, we present an application of the LaMMos mechanism to exoskeleton suits, for easing the mo- tors from the joints when carrying heavy weights for long periods of time. This mechanism offers many interesting opportunities for robotics research in terms of functionality, pay- load and size.
Multichannel Robot Speech Recognition Database: MChRSR
Venue: arXiv cs.HC 2017
Year: 2017

In real human robot interaction (HRI) scenarios, speech recognition represents a major challenge due to robot noise, background noise and time-varying acoustic channel. This document describes the procedure used to obtain the Multichannel Robot Speech Recognition Database (MChRSR). It is composed of 12 hours of multichannel evaluation data recorded in a real mobile HRI scenario. This database was recorded with a PR2 robot performing different translational and azimuthal movements. Accordingly, 16 evaluation sets were obtained re-recording the clean set of the Aurora 4 database in different movement conditions.
On approximating the stationary distribution of time-reversible Markov chains
Venue: arXiv cs.DM 2017
Year: 2017

Approximating the stationary probability of a state in a Markov chain through Markov chain Monte Carlo techniques is, in general, inefficient. Standard random walk approaches require $\tilde{O}(τ/π(v))$ operations to approximate the probability $π(v)$ of a state $v$ in a chain with mixing time $τ$, and even the best available techniques still have complexity $\tilde{O}(τ^{1.5}/π(v)^{0.5})$, and since these complexities depend inversely on $π(v)$, they can grow beyond any bound in the size of the chain or in its mixing time. In this paper we show that, for time-reversible Markov chains, there exists a simple randomized approximation algorithm that breaks this "small-$π(v)$ barrier".
Learning Continuous User Representations through Hybrid Filtering with doc2vec
Venue: arXiv cs.IR 2017
Year: 2017

Players in the online ad ecosystem are struggling to acquire the user data required for precise targeting. Audience look-alike modeling has the potential to alleviate this issue, but models' performance strongly depends on quantity and quality of available data. In order to maximize the predictive performance of our look-alike modeling algorithms, we propose two novel hybrid filtering techniques that utilize the recent neural probabilistic language model algorithm doc2vec. We apply these methods to data from a large mobile ad exchange and additional app metadata acquired from the Apple App store and Google Play store. First, we model mobile app users through their app usage histories and app descriptions (user2vec). Second, we introduce context awareness to that model by incorporating additional user and app-related metadata in model training (context2vec). Our findings are threefold: (1) the quality of recommendations provided by user2vec is notably higher than current state-of-the-art techniques. (2) User representations generated through hybrid filtering using doc2vec prove to be highly valuable features in supervised machine learning models for look-alike modeling. This represents the first application of hybrid filtering user models using neural probabilistic language models, specifically doc2vec, in look-alike modeling. (3) Incorporating context metadata in the doc2vec model training process to introduce context awareness has positive effects on performance and is superior to directly including the data as features in the downstream supervised models.
A Systematic Mapping Study on Requirements Engineering in Software Ecosystems
Venue: arXiv cs.SE 2017
Year: 2017

Software ecosystems (SECOs) and open innovation processes have been claimed as a way forward for the software industry. A proper understanding of requirements is as important for these IT-systems as for more traditional ones. This paper presents a mapping study on the issues of requirements engineering and quality aspects in SECOs and analyzes emerging ideas. Our findings indicate that among the various phases or subtasks of requirements engineering, most of the SECO specific research has been accomplished on elicitation, analysis, and modeling. On the other hand, requirements selection, prioritization, verification, and traceability has attracted few published studies. Among the various quality attributes, most of the SECOs research has been performed on security, performance and testability. On the other hand, reliability, safety, maintainability, transparency, usability attracted few published studies. The paper provides a review of the academic literature about SECO-related requirements engineering activities, modeling approaches, and quality attributes, positions the source publications in a taxonomy of issues and identifies gaps where there has been little research.
Restricted Boltzmann Machines for Robust and Fast Latent Truth Discovery
Venue: arXiv cs.LG 2017
Year: 2017

We address the problem of latent truth discovery, LTD for short, where the goal is to discover the underlying true values of entity attributes in the presence of noisy, conflicting or incomplete information. Despite a multitude of algorithms to address the LTD problem that can be found in literature, only little is known about their overall performance with respect to effectiveness (in terms of truth discovery capabilities), efficiency and robustness. A practical LTD approach should satisfy all these characteristics so that it can be applied to heterogeneous datasets of varying quality and degrees of cleanliness. We propose a novel algorithm for LTD that satisfies the above requirements. The proposed model is based on Restricted Boltzmann Machines, thus coined LTD-RBM. In extensive experiments on various heterogeneous and publicly available datasets, LTD-RBM is superior to state-of-the-art LTD techniques in terms of an overall consideration of effectiveness, efficiency and robustness.
Deep Stacked Networks with Residual Polishing for Image Inpainting
Venue: arXiv cs.CV 2017
Year: 2017

Deep neural networks have shown promising results in image inpainting even if the missing area is relatively large. However, most of the existing inpainting networks introduce undesired artifacts and noise to the repaired regions. To solve this problem, we present a novel framework which consists of two stacked convolutional neural networks that inpaint the image and remove the artifacts, respectively. The first network considers the global structure of the damaged image and coarsely fills the blank area. Then the second network modifies the repaired image to cancel the noise introduced by the first network. The proposed framework splits the problem into two distinct partitions that can be optimized separately, therefore it can be applied to any inpainting algorithm by changing the first network. Second stage in our framework which aims at polishing the inpainted images can be treated as a denoising problem where a wide range of algorithms can be employed. Our results demonstrate that the proposed framework achieves significant improvement on both visual and quantitative evaluations.
How will the Internet of Things enable Augmented Personalized Health?
Venue: arXiv cs.CY 2017
Year: 2017

Internet-of-Things (IoT) is profoundly redefining the way we create, consume, and share information. Health aficionados and citizens are increasingly using IoT technologies to track their sleep, food intake, activity, vital body signals, and other physiological observations. This is complemented by IoT systems that continuously collect health-related data from the environment and inside the living quarters. Together, these have created an opportunity for a new generation of healthcare solutions. However, interpreting data to understand an individual's health is challenging. It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual. Interpreting how well a patient is doing also requires looking at his adherence to respective health objectives, application of relevant clinical knowledge and the desired outcomes. We resort to the vision of Augmented Personalized Healthcare (APH) to exploit the extensive variety of relevant data and medical knowledge using Artificial Intelligence (AI) techniques to extend and enhance human health to presents various stages of augmented health management strategies: self-monitoring, self-appraisal, self-management, intervention, and disease progress tracking and prediction. kHealth technology, a specific incarnation of APH, and its application to Asthma and other diseases are used to provide illustrations and discuss alternatives for technology-assisted health management. Several prominent efforts involving IoT and patient-generated health data (PGHD) with respect converting multimodal data into actionable information (big data to smart data) are also identified. Roles of three components in an evidence-based semantic perception approach- Contextualization, Abstraction, and Personalization are discussed.
A total uncertainty measure for D numbers based on belief intervals
Venue: arXiv cs.AI 2017
Year: 2017

As a generalization of Dempster-Shafer theory, the theory of D numbers is a new theoretical framework for uncertainty reasoning. Measuring the uncertainty of knowledge or information represented by D numbers is an unsolved issue in that theory. In this paper, inspired by distance based uncertainty measures for Dempster-Shafer theory, a total uncertainty measure for a D number is proposed based on its belief intervals. The proposed total uncertainty measure can simultaneously capture the discord, and non-specificity, and non-exclusiveness involved in D numbers. And some basic properties of this total uncertainty measure, including range, monotonicity, generalized set consistency, are also presented.
Effective algorithms for homogeneous utility functions
Venue: arXiv cs.DS 2017
Year: 2017

Under the assumption of (positive) homogeneity (PH in the sequel) of the corresponding utility functions, we construct polynomial time algorithms for the weak separability, the collective consumption behavior and some related problems. These problems are known to be at least $NP$-hard if the homogeneity assumption is dropped. Keywords: the utility function, the economic indices theory, the collective axiom of revealed preference, the weak separability property, the class of the differential form of the demand.
Machine Learning for Building Energy and Indoor Environment: A Perspective
Venue: arXiv cs.CY 2017
Year: 2017

Machine learning is a promising technique for many practical applications. In this perspective, we illustrate the development and application for machine learning. It is indicated that the theories and applications of machine learning method in the field of energy conservation and indoor environment are not mature, due to the difficulty of the determination for model structure with better prediction. In order to significantly contribute to the problems, we utilize the ANN model to predict the indoor culturable fungi concentration, which achieves the better accuracy and convenience. The proposal of hybrid method is further expand the application fields of machine learning method. Further, ANN model based on HTS was successfully applied for the optimization of building energy system. We hope that this novel method could capture more attention from investigators via our introduction and perspective, due to its potential development with accuracy and reliability. However, its feasibility in other fields needs to be promoted further.
Stratified Transfer Learning for Cross-domain Activity Recognition
Venue: arXiv cs.CV 2017
Year: 2017

In activity recognition, it is often expensive and time-consuming to acquire sufficient activity labels. To solve this problem, transfer learning leverages the labeled samples from the source domain to annotate the target domain which has few or none labels. Existing approaches typically consider learning a global domain shift while ignoring the intra-affinity between classes, which will hinder the performance of the algorithms. In this paper, we propose a novel and general cross-domain learning framework that can exploit the intra-affinity of classes to perform intra-class knowledge transfer. The proposed framework, referred to as Stratified Transfer Learning (STL), can dramatically improve the classification accuracy for cross-domain activity recognition. Specifically, STL first obtains pseudo labels for the target domain via majority voting technique. Then, it performs intra-class knowledge transfer iteratively to transform both domains into the same subspaces. Finally, the labels of target domain are obtained via the second annotation. To evaluate the performance of STL, we conduct comprehensive experiments on three large public activity recognition datasets~(i.e. OPPORTUNITY, PAMAP2, and UCI DSADS), which demonstrates that STL significantly outperforms other state-of-the-art methods w.r.t. classification accuracy (improvement of 7.68%). Furthermore, we extensively investigate the performance of STL across different degrees of similarities and activity levels between domains. And we also discuss the potential of STL in other pervasive computing applications to provide empirical experience for future research.
Vectorial Boolean functions and linear codes in the context of algebraic attacks
Venue: arXiv cs.IT 2017
Year: 2017

In this paper we study the relationship between vectorial (Boolean) functions and cyclic codes in the context of algebraic attacks. We first derive a direct link between the annihilators of a vectorial function (in univariate form) and certain $2^{n}$-ary cyclic codes (which we prove that they are LCD codes) extending results due to Rønjom and Helleseth. The knowledge of the minimum distance of those codes gives rise to a lower bound on the algebraic immunity of the associated vectorial function. Furthermore, we solve an open question raised by Mesnager and Cohen. We also present some properties of those cyclic codes (whose generator polynomials determined by vectorial functions) as well as their weight enumerator. In addition we generalize the so-called algebraic complement and study its properties.