OpenRaiser/Intern-Atlas huggingface dataset:
Analysis of Service-oriented Modeling Approaches for Viewpoint-specific Model-driven Development of Microservice Architecture
Venue: arXiv cs.SE 2018
Year: 2018

Microservice Architecture (MSA) is a novel service-based architectural style for distributed software systems. Compared to Service-oriented Architecture (SOA), MSA puts a stronger focus on self-containment of services. Each microservice is responsible for realizing exactly one business or technological capability that is distinct from other services' capabilities. Additionally, on the implementation and operation level, microservices are self-contained in that they are developed, tested, deployed and operated independently from each other. Next to these characteristics that distinguish MSA from SOA, both architectural styles rely on services as building blocks of distributed software architecture and hence face similar challenges regarding, e.g., service identification, composition and provisioning. However, in contrast to MSA, SOA may rely on an extensive body of knowledge to tackle these challenges. Thus, due to both architectural styles being service-based, the question arises to what degree MSA might draw on existing findings of SOA research and practice. In this paper we address this question in the field of Model-driven Development (MDD) for design and operation of service-based architectures. Therefore, we present an analysis of existing MDD approaches to SOA, which comprises the identification and semantic clustering of modeling concepts for SOA design and operation. For each concept cluster, the analysis assesses its applicability to MDD of MSA (MSA-MDD) and assigns it to a specific modeling viewpoint. The goal of the presented analysis is to provide a conceptual foundation for an MSA-MDD metamodel.
PANDA: Facilitating Usable AI Development
Venue: arXiv cs.AI 2018
Year: 2018

Recent advances in artificial intelligence (AI) and machine learning have created a general perception that AI could be used to solve complex problems, and in some situations over-hyped as a tool that can be so easily used. Unfortunately, the barrier to realization of mass adoption of AI on various business domains is too high because most domain experts have no background in AI. Developing AI applications involves multiple phases, namely data preparation, application modeling, and product deployment. The effort of AI research has been spent mostly on new AI models (in the model training stage) to improve the performance of benchmark tasks such as image recognition. Many other factors such as usability, efficiency and security of AI have not been well addressed, and therefore form a barrier to democratizing AI. Further, for many real world applications such as healthcare and autonomous driving, learning via huge amounts of possibility exploration is not feasible since humans are involved. In many complex applications such as healthcare, subject matter experts (e.g. Clinicians) are the ones who appreciate the importance of features that affect health, and their knowledge together with existing knowledge bases are critical to the end results. In this paper, we take a new perspective on developing AI solutions, and present a solution for making AI usable. We hope that this resolution will enable all subject matter experts (eg. Clinicians) to exploit AI like data scientists.
Efficient Multidimensional Regularization for Volterra Series Estimation
Venue: arXiv cs.SY 2018
Year: 2018

This paper presents an efficient nonparametric time domain nonlinear system identification method. It is shown how truncated Volterra series models can be efficiently estimated without the need of long, transient-free measurements. The method is a novel extension of the regularization methods that have been developed for impulse response estimates of linear time invariant systems. To avoid the excessive memory needs in case of long measurements or large number of estimated parameters, a practical gradient-based estimation method is also provided, leading to the same numerical results as the proposed Volterra estimation method. Moreover, the transient effects in the simulated output are removed by a special regularization method based on the novel ideas of transient removal for Linear Time-Varying (LTV) systems. Combining the proposed methodologies, the nonparametric Volterra models of the cascaded water tanks benchmark are presented in this paper. The results for different scenarios varying from a simple Finite Impulse Response (FIR) model to a 3rd degree Volterra series with and without transient removal are compared and studied. It is clear that the obtained models capture the system dynamics when tested on a validation dataset, and their performance is comparable with the white-box (physical) models.
Interpenetrating Cooperative Localization in Dynamic Connected Vehicle Networks
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, we proposed the Interpenetrating Cooperative Localization (ICL) method to enhance the localization accuracy in dynamic connected vehicle networks. This mechanism makes the information from one group of connected vehicles interpenetrate to other groups without full communication between all nodes, thus improving the utility of information in a low connected vehicle penetration situation. We tested the approach using the dynamic traffic data collected in the Safety Pilot Model Deployment program in Ann Arbor Michigan, USA, with dynamic changing networks due to the traveling of vehicles and packet drops of the Dedicated Short-Range Communication. Results show enhancement of localization accuracy with errors reduced by up to 70 % even in complex dynamic scenarios.
Automatic generation of CUDA code performing tensor manipulations using C++ expression templates
Venue: arXiv cs.MS 2018
Year: 2018

We present a C++ library, TLoops, which uses a hierarchy of expression templates to represent operations upon tensorial quantities in single lines of C++ code that resemble analytic equations. These expressions may be run as-is, but may also be used to emit equivalent low-level C or CUDA code, which either performs the operations more quickly on the CPU, or allows them to be rapidly ported to run on NVIDIA GPUs. We detail the expression template and C++-class hierarchy that represents the expressions and which makes automatic code-generation possible. We then present benchmarks of the expression-template code, the automatically generated C code, and the automatically generated CUDA code running on several generations of NVIDIA GPU.
Note on the Lower Bounds of Bimachines
Venue: arXiv cs.FL 2018
Year: 2018

This is a brief note on the lower bound of bimachines. Particularly, we report that there is a class of functional transducers with $O(n)$ states that do not admit a bimachine with fewer than $Θ(2^n)$ states.
Intersecting edge distinguishing colorings of hypergraphs
Venue: arXiv cs.DM 2018
Year: 2018

An edge labeling of a graph distinguishes neighbors by sets (multisets, resp.), if for any two adjacent vertices $u$ and $v$ the sets (multisets, resp.) of labels appearing on edges incident to $u$ and $v$ are different. In an analogous way we define total labelings distinguishing neighbors by sets or multisets: for each vertex, we consider labels on incident edges and the label of the vertex itself. In this paper we show that these problems, and also other problems of similar flavor, admit an elegant and natural generalization as a hypergraph coloring problem. An ieds-coloring (iedm-coloring, resp.) of a hypergraph is a vertex coloring, in which the sets (multisets, resp.) of colors, that appear on every pair of intersecting edges are different. We show upper bounds on the size of lists, which guarantee the existence of an ieds- or iedm-coloring, respecting these lists. The proof is essentially a randomized algorithm, whose expected time complexity is polynomial. As corollaries, we derive new results concerning the list variants of graph labeling problems, distinguishing neighbors by sets or multisets. We also show that our method is robust and can be easily extended for different, related problems. We also investigate a close connection between edge labelings of bipartite graphs, distinguishing neighbors by sets, and the so-called property \textbf{B} of hypergraphs. We discuss computational aspects of the problem and present some classes of bipartite graphs, which admit such a labeling using two labels.
Consistency Checking of Functional Requirements
Venue: arXiv cs.LO 2018
Year: 2018

Requirements are informal and semi-formal descriptions of the expected behavior of a system. They are usually expressed in the form of natural language sentences and checked for errors manually, e.g., by peer reviews. Manual checks are error-prone, time-consuming and not scalable. With the increasing complexity of cyber-physical systems and the need of operating in safety- and security-critical environments, it became essential to automatize the consistency check of requirements and build artifacts to help system engineers in the design process.
Linear $(2,p,p)$-AONTs do Exist
Venue: arXiv cs.IT 2018
Year: 2018

A $(t,s,v)$-all-or-nothing transform (AONT) is a bijective mapping defined on $s$-tuples over an alphabet of size $v$, which satisfies that if any $s-t$ of the $s$ outputs are given, then the values of any $t$ inputs are completely undetermined. When $t$ and $v$ are fixed, to determine the maximum integer $s$ such that a $(t,s,v)$-AONT exists is the main research objective. In this paper, we solve three open problems proposed in [IEEE Trans. Inform. Theory 64 (2018), 3136-3143.] and show that there do exist linear $(2,p,p)$-AONTs. Then for the size of the alphabet being a prime power, we give the first infinite class of linear AONTs which is better than the linear AONTs defined by Cauchy matrices. Besides, we also present a recursive construction for general AONTs and a new relationship between AONTs and orthogonal arrays.
Configurations and Diagnosis for Ultra-Dense Heterogeneous Networks: From Empirical Measurements to Technical Solutions
Venue: arXiv cs.NI 2018
Year: 2018

The intense demands for higher data rates and ubiquitous network coverage have raised the stakes on developing new network topology and architecture to meet these ever-increasing demands in a cost-effective manner. The telecommunication industry and international standardization bodies have placed considerable attention to the deployment of ultra-dense heterogeneous small-scale cells over existing cellular systems. Those small-scale cells, although provide higher data rates and better indoor coverage by reducing the distance between base stations (BSs) and end users, have raised severe configuration concerns. As the deployments are becoming irregular and flexible, inappropriate configurations occur frequently and undermine the network reliability and service quality. We envision that the fine-grained characterization of user traffic is a key pillar to diagnosing inappropriate configurations. In this article, we investigate the fine-grained traffic patterns of mobile users by analyzing the network data containing millions of subscribers and covering thousands of cells in a large metropolitan area. We characterize traffic patterns and mobility behaviors of users and geospatial properties of cells, and discuss how the heterogeneity of these characteristics affects network configurations and diagnosis in future ultra-dense small cells. Based on these observations from our measurements, we investigate possible models and corresponding challenges, and propose a heterogeneity-aware scheme that takes into account the disparity of user mobility behaviors and geospatial properties among small cells.
A theory of linear typings as flows on 3-valent graphs
Venue: arXiv cs.LO 2018
Year: 2018

Building on recently established enumerative connections between lambda calculus and the theory of embedded graphs (or "maps"), this paper develops an analogy between typing (of lambda terms) and coloring (of maps). Our starting point is the classical notion of an abelian group-valued "flow" on an abstract graph (Tutte, 1954). Typing a linear lambda term may be naturally seen as constructing a flow (on an embedded 3-valent graph with boundary) valued in a more general algebraic structure consisting of a preordered set equipped with an "implication" operation and unit satisfying composition, identity, and unit laws. Interesting questions and results from the theory of flows (such as the existence of nowhere-zero flows) may then be re-examined from the standpoint of lambda calculus and logic. For example, we give a characterization of when the local flow relations (across vertices) may be categorically lifted to a global flow relation (across the boundary), proving that this holds just in case the underlying map has the orientation of a lambda term. We also develop a basic theory of rewriting of flows that suggests topological meanings for classical completeness results in combinatory logic, and introduce a polarized notion of flow, which draws connections to the theory of proof-nets in linear logic and to bidirectional typing.
Approximating Nash Equilibria for Black-Box Games: A Bayesian Optimization Approach
Venue: arXiv cs.GT 2018
Year: 2018

Game theory has emerged as a powerful framework for modeling a large range of multi-agent scenarios. Many algorithmic solutions require discrete, finite games with payoffs that have a closed-form specification. In contrast, many real-world applications require modeling with continuous action spaces and black-box utility functions where payoff information is available only in the form of empirical (often expensive and/or noisy) observations of strategy profiles. To the best of our knowledge, few tools exist for solving the class of expensive, black-box continuous games. In this paper, we develop a method to find equilibria for such games in a sequential decision-making framework using Bayesian Optimization. The proposed approach is validated on a collection of synthetic game problems with varying degree of noise and action space dimensions. The results indicate that it is capable of improving the maximum regret in noisy and high dimensions to a greater extent than hierarchical or discretized methods.
Recoverable Consensus in Shared Memory
Venue: arXiv cs.DC 2018
Year: 2018

Herlihy's consensus hierarchy ranks the power of various synchronization primitives for solving consensus in a model where asynchronous processes communicate through shared memory and fail by halting. This paper revisits the consensus hierarchy in a model with crash-recovery failures, where the specification of consensus, called \emph{recoverable consensus} in this paper, is weakened by allowing non-terminating executions when a process fails infinitely often. Two variations of this model are considered: independent failures, and simultaneous (i.e., system-wide) failures. Several results are proved in this model: (i) We prove that any primitive at level two of Herlihy's hierarchy remains at level two if simultaneous crash-recovery failures are introduced. This is accomplished by transforming (one instance of) any 2-process conventional consensus algorithm to a 2-process recoverable consensus algorithm. (ii) For any $n > 1$ and $f > 0$, we show how to use $f+1$ instances of any conventional $n$-process consensus algorithm and $Θ(f + n)$ read/write registers to solve $n$-process recoverable consensus when crash-recovery failures are independent, assuming that every execution contains at most $f$ such failures. (iii) Next, we prove for any $f > 0$ that any 2-process recoverable consensus algorithm that uses TAS and read/writer registers requires at least $f+1$ TAS objects, assuming that crash-recovery failures are independent and every execution contains at most $f$ such failures. (iv) Lastly, we generalize and strengthen (iii) by proving that any universal construction of $n$-process recoverable consensus from a type $T$ with consensus number $n$ and read/write registers requires at least $f+1$ base objects of type $T$ in executions with up to $f$ failures.
Human Motion Modeling using DVGANs
Venue: arXiv cs.CV 2018
Year: 2018

We present a novel generative model for human motion modeling using Generative Adversarial Networks (GANs). We formulate the GAN discriminator using dense validation at each time-scale and perturb the discriminator input to make it translation invariant. Our model is capable of motion generation and completion. We show through our evaluations the resiliency to noise, generalization over actions, and generation of long diverse sequences. We evaluate our approach on Human 3.6M and CMU motion capture datasets using inception scores.
Yes and...? Using Improv to Design for Narrative in Lights Out
Venue: arXiv cs.HC 2018
Year: 2018

Mixed reality experiences often require detailed narrative that can be used to craft physical and virtual design components. This work elaborates on a mentoring experience at the Carnegie Mellon's ETC to consider how improv games may be used ideate and iterate on storytelling experiences.
Extracting textual overlays from social media videos using neural networks
Venue: arXiv cs.CV 2018
Year: 2018

Textual overlays are often used in social media videos as people who watch them without the sound would otherwise miss essential information conveyed in the audio stream. This is why extraction of those overlays can serve as an important meta-data source, e.g. for content classification or retrieval tasks. In this work, we present a robust method for extracting textual overlays from videos that builds up on multiple neural network architectures. The proposed solution relies on several processing steps: keyframe extraction, text detection and text recognition. The main component of our system, i.e. the text recognition module, is inspired by a convolutional recurrent neural network architecture and we improve its performance using synthetically generated dataset of over 600,000 images with text prepared by authors specifically for this task. We also develop a filtering method that reduces the amount of overlapping text phrases using Levenshtein distance and further boosts system's performance. The final accuracy of our solution reaches over 80A% and is au pair with state-of-the-art methods.
Fuzzy logic based approaches for gene regulatory network inference
Venue: arXiv cs.CE 2018
Year: 2018

The rapid advancement in high-throughput techniques has fueled the generation of large volume of biological data rapidly with low cost. Some of these techniques are microarray and next generation sequencing which provides genome level insight of living cells. As a result, the size of most of the biological databases, such as NCBI-GEO, NCBI-SRA, is exponentially growing. These biological data are analyzed using computational techniques for knowledge discovery - which is one of the objectives of bioinformatics research. Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. From the last couple of decades, the researchers are interested in developing computational algorithms for GRN inference (GRNI) using high-throughput experimental data. Several computational approaches have been applied for inferring GRN from gene expression data including statistical techniques (correlation coefficient), information theory (mutual information), regression based approaches, probabilistic approaches (Bayesian networks, naive byes), artificial neural networks, and fuzzy logic. The fuzzy logic, along with its hybridization with other intelligent approach, is well studied in GRNI due to its several advantages. In this paper, we present a consolidated review on fuzzy logic and its hybrid approaches for GRNI developed during last two decades.
User-Sensitive Recommendation Ensemble with Clustered Multi-Task Learning
Venue: arXiv cs.IR 2018
Year: 2018

This paper considers recommendation algorithm ensembles in a user-sensitive manner. Recently researchers have proposed various effective recommendation algorithms, which utilized different aspects of the data and different techniques. However, the "user skewed prediction" problem may exist for almost all recommendation algorithms -- algorithms with best average predictive accuracy may cover up that the algorithms may perform poorly for some part of users, which will lead to biased services in real scenarios. In this paper, we propose a user-sensitive ensemble method named "UREC" to address this issue. We first cluster users based on the recommendation predictions, then we use multi-task learning to learn the user-sensitive ensemble function for the users. In addition, to alleviate the negative effects of new user problem to clustering users, we propose an approximate approach based on a spectral relaxation. Experiments on real-world datasets demonstrate the superiority of our methods.
A Cost-Sensitive Deep Belief Network for Imbalanced Classification
Venue: arXiv cs.LG 2018
Year: 2018

Imbalanced data with a skewed class distribution are common in many real-world applications. Deep Belief Network (DBN) is a machine learning technique that is effective in classification tasks. However, conventional DBN does not work well for imbalanced data classification because it assumes equal costs for each class. To deal with this problem, cost-sensitive approaches assign different misclassification costs for different classes without disrupting the true data sample distributions. However, due to lack of prior knowledge, the misclassification costs are usually unknown and hard to choose in practice. Moreover, it has not been well studied as to how cost-sensitive learning could improve DBN performance on imbalanced data problems. This paper proposes an evolutionary cost-sensitive deep belief network (ECS-DBN) for imbalanced classification. ECS-DBN uses adaptive differential evolution to optimize the misclassification costs based on training data, that presents an effective approach to incorporating the evaluation measure (i.e. G-mean) into the objective function. We first optimize the misclassification costs, then apply them to deep belief network. Adaptive differential evolution optimization is implemented as the optimization algorithm that automatically updates its corresponding parameters without the need of prior domain knowledge. The experiments have shown that the proposed approach consistently outperforms the state-of-the-art on both benchmark datasets and real-world dataset for fault diagnosis in tool condition monitoring.
CRAM: Clued Recurrent Attention Model
Venue: arXiv cs.CV 2018
Year: 2018

To overcome the poor scalability of convolutional neural network, recurrent attention model(RAM) selectively choose what and where to look on the image. By directing recurrent attention model how to look the image, RAM can be even more successful in that the given clue narrow down the scope of the possible focus zone. In this perspective, this work proposes clued recurrent attention model (CRAM) which add clue or constraint on the RAM better problem solving. CRAM follows encoder-decoder framework, encoder utilizes recurrent attention model with spatial transformer network and decoder which varies depending on the task. To ensure the performance, CRAM tackles two computer vision task. One is the image classification task, with clue given as the binary image saliency which indicates the approximate location of object. The other is the inpainting task, with clue given as binary mask which indicates the occluded part. In both tasks, CRAM shows better performance than existing methods showing the successful extension of RAM.