OpenRaiser/Intern-Atlas huggingface dataset:
Markov Chain Neural Networks
Venue: arXiv cs.LG 2018
Year: 2018

In this work we present a modified neural network model which is capable to simulate Markov Chains. We show how to express and train such a network, how to ensure given statistical properties reflected in the training data and we demonstrate several applications where the network produces non-deterministic outcomes. One example is a random walker model, e.g. useful for simulation of Brownian motions or a natural Tic-Tac-Toe network which ensures non-deterministic game behavior.
Formal Process Virtual Machine for Smart Contracts Verification
Venue: arXiv cs.PL 2018
Year: 2018

This paper reports on the development and verification of a novel formal symbolic process virtual machine (FSPVM) for verifying the reliability and security of Ethereum smart contracts, denoted as FSPVM-E, completely in Coq proof assistant. It adopts execution-verification isomorphism (EVI), an extension of Curry-Howard isomorphism (CHI), as its fundamental theoretical framework. The current version of FSPVM-E is constructed on a general, extensible, and reusable formal memory (GERM) framework, an extensible and universal formal intermediate programming language Lolisa, which is a large subset of the Solidity programming language using generalized algebraic datatypes, and the corresponding formally verified interpreter of Lolisa, denoted as FEther. It supports the ERC20 standard and can automatically simultaneously symbolically execute the smart contract programs of Ethereum and verify their reliability and security properties using Hoare logic in Coq. In addition, this work, contributes to solving the problems of automation, inconsistency and reusability in higher-order logic theorem proving.
Differential Geometric Approach to Trajectory Planning: Cooperative Transport by a Team of Autonomous Marine Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

In this paper we addressed the cooperative transport problem for a team of autonomous surface vehicles (ASVs) towing a single buoyant load. We consider the dynamics of the constrained system and decompose the cooperative transport problem into a collection of subproblems. Each subproblem consists of an ASV and load pair where each ASV is attached to the load at the same point. Since the system states evolve on a smooth manifold, we use the tools from differential geometry to model the holonomic constraint arising from the cooperative transport problem and the non-holonomic constraints arising from the ASV dynamics. We then synthesize distributed feedback control strategies using the proposed mathematical modeling framework to enable the team transport the load on a desired trajectory. We experimentally validate the proposed strategy using a team of micro ASVs.
Exploring Users' Perception of Collaborative Explanation Styles
Venue: arXiv cs.IR 2018
Year: 2018

Collaborative filtering systems heavily depend on user feedback expressed in product ratings to select and rank items to recommend. In this study we explore how users value different collaborative explanation styles following the user-based or item-based paradigm. Furthermore, we explore how the characteristics of these rating summarizations, like the total number of ratings and the mean rating value, influence the decisions of online users. Results, based on a choice-based conjoint experimental design, show that the mean indicator has a higher impact compared to the total number of ratings. Finally, we discuss how these empirical results can serve as an input to developing algorithms that foster items with a, consequently, higher probability of choice based on their rating summarizations or their explainability due to these ratings when ranking recommendations.
Fine-Grained Facial Expression Analysis Using Dimensional Emotion Model
Venue: arXiv cs.CV 2018
Year: 2018

Automated facial expression analysis has a variety of applications in human-computer interaction. Traditional methods mainly analyze prototypical facial expressions of no more than eight discrete emotions as a classification task. However, in practice, spontaneous facial expressions in naturalistic environment can represent not only a wide range of emotions, but also different intensities within an emotion family. In such situation, these methods are not reliable or adequate. In this paper, we propose to train deep convolutional neural networks (CNNs) to analyze facial expressions explainable in a dimensional emotion model. The proposed method accommodates not only a set of basic emotion expressions, but also a full range of other emotions and subtle emotion intensities that we both feel in ourselves and perceive in others in our daily life. Specifically, we first mapped facial expressions into dimensional measures so that we transformed facial expression analysis from a classification problem to a regression one. We then tested our CNN-based methods for facial expression regression and these methods demonstrated promising performance. Moreover, we improved our method by a bilinear pooling which encodes second-order statistics of features. We showed such bilinear-CNN models significantly outperformed their respective baselines.
Architecture for Analysis of Streaming Data
Venue: arXiv cs.SE 2018
Year: 2018

While several attempts have been made to construct a scalable and flexible architecture for analysis of streaming data, no general model to tackle this task exists. Thus, our goal is to build a scalable and maintainable architecture for performing analytics on streaming data. To reach this goal, we introduce a 7-layered architecture consisting of microservices and publish-subscribe software. Our study shows that this architecture yields a good balance between scalability and maintainability due to high cohesion and low coupling of the solution, as well as asynchronous communication between the layers. This architecture can help practitioners to improve their analytic solutions. It is also of interest to academics, as it is a building block for a general architecture for processing streaming data.
Large-Scale Unsupervised Deep Representation Learning for Brain Structure
Venue: arXiv cs.LG 2018
Year: 2018

Machine Learning (ML) is increasingly being used for computer aided diagnosis of brain related disorders based on structural magnetic resonance imaging (MRI) data. Most of such work employs biologically and medically meaningful hand-crafted features calculated from different regions of the brain. The construction of such highly specialized features requires a considerable amount of time, manual oversight and careful quality control to ensure the absence of errors in the computational process. Recent advances in Deep Representation Learning have shown great promise in extracting highly non-linear and information-rich features from data. In this paper, we present a novel large-scale deep unsupervised approach to learn generic feature representations of structural brain MRI scans, which requires no specialized domain knowledge or manual intervention. Our method produces low-dimensional representations of brain structure, which can be used to reconstruct brain images with very low error and exhibit performance comparable to FreeSurfer features on various classification tasks.
Data Driven Reference Architecture for Smart City Ecosystems
Venue: arXiv cs.SE 2018
Year: 2018

With the convergence of information and telecommunication technologies, the vision of the Smart City is fast becoming a reality. City governments in a growing number of countries are capitalizing on these advances to enhance the lives of their citizens and to increase efficiency and sustainability. In this paper, we elaborate on smartCityRA, a reference architecture for Smart City projects, which serves as the design language for creating smart cities blueprints. Such a blueprint caters for diverse stakeholders, devices, platforms, and technologies. We report on our experience in carrying out a proof-of-concept use case with a major telecommunication provider in the UAE. In doing so, we refined our multiple-view model of the initial smartCityRA reference architecture. We show that Data in smart city applications drive the entire development lifecycle and should be considered early in the development cycle. In addition, Data affects all the other views in the smartCityRA and hence the Data View needs to be at the heart of the entire smartCityRA. Realizing the Data view using a component like a Data Hub helped in creating a central integration location for disparate data from different sources, thus reliving developers from dealing with several entities individually. Finally, we show that any smart city reference architecture, like smartCityRA, should be at the right level of abstraction to enable the flexibility of adoption and adaptation by different stakeholders and components.
Does Journaling Encourage Healthier Choices? Analyzing Healthy Eating Behaviors of Food Journalers
Venue: arXiv cs.SI 2018
Year: 2018

Past research has shown the benefits of food journaling in promoting mindful eating and healthier food choices. However, the links between journaling and healthy eating have not been thoroughly examined. Beyond caloric restriction, do journalers consistently and sufficiently consume healthful diets? How different are their eating habits compared to those of average consumers who tend to be less conscious about health? In this study, we analyze the healthy eating behaviors of active food journalers using data from MyFitnessPal. Surprisingly, our findings show that food journalers do not eat as healthily as they should despite their proclivity to health eating and their food choices resemble those of the general populace. Furthermore, we find that the journaling duration is only a marginal determinant of healthy eating outcomes and sociodemographic factors, such as gender and regions of residence, are much more predictive of healthy food choices.
VINE: An Open Source Interactive Data Visualization Tool for Neuroevolution
Venue: arXiv cs.NE 2018
Year: 2018

Recent advances in deep neuroevolution have demonstrated that evolutionary algorithms, such as evolution strategies (ES) and genetic algorithms (GA), can scale to train deep neural networks to solve difficult reinforcement learning (RL) problems. However, it remains a challenge to analyze and interpret the underlying process of neuroevolution in such high dimensions. To begin to address this challenge, this paper presents an interactive data visualization tool called VINE (Visual Inspector for NeuroEvolution) aimed at helping neuroevolution researchers and end-users better understand and explore this family of algorithms. VINE works seamlessly with a breadth of neuroevolution algorithms, including ES and GA, and addresses the difficulty of observing the underlying dynamics of the learning process through an interactive visualization of the evolving agent's behavior characterizations over generations. As neuroevolution scales to neural networks with millions or more connections, visualization tools like VINE that offer fresh insight into the underlying dynamics of evolution become increasingly valuable and important for inspiring new innovations and applications.
Why do Users Kill HPC Jobs?
Venue: arXiv cs.DC 2018
Year: 2018

Given the cost of HPC clusters, making best use of them is crucial to improve infrastructure ROI. Likewise, reducing failed HPC jobs and related waste in terms of user wait times is crucial to improve HPC user productivity (aka human ROI). While most efforts (e.g.,debugging HPC programs) explore technical aspects to improve ROI of HPC clusters, we hypothesize non-technical (human) aspects are worth exploring to make non-trivial ROI gains; specifically, understanding non-technical aspects and how they contribute to the failure of HPC jobs. In this regard, we conducted a case study in the context of Beocat cluster at Kansas State University. The purpose of the study was to learn the reasons why users terminate jobs and to quantify wasted computations in such jobs in terms of system utilization and user wait time. The data from the case study helped identify interesting and actionable reasons why users terminate HPC jobs. It also helped confirm that user terminated jobs may be associated with non-trivial amount of wasted computation, which if reduced can help improve the ROI of HPC clusters.
Label Embedding with Partial Heterogeneous Contexts
Venue: arXiv cs.CV 2018
Year: 2018

Label embedding plays an important role in many real-world applications. To enhance the label relatedness captured by the embeddings, multiple contexts can be adopted. However, these contexts are heterogeneous and often partially observed in practical tasks, imposing significant challenges to capture the overall relatedness among labels. In this paper, we propose a general Partial Heterogeneous Context Label Embedding (PHCLE) framework to address these challenges. Categorizing heterogeneous contexts into two groups, relational context and descriptive context, we design tailor-made matrix factorization formula to effectively exploit the label relatedness in each context. With a shared embedding principle across heterogeneous contexts, the label relatedness is selectively aligned in a shared space. Due to our elegant formulation, PHCLE overcomes the partial context problem and can nicely incorporate more contexts, which both cannot be tackled with existing multi-context label embedding methods. An effective alternative optimization algorithm is further derived to solve the sparse matrix factorization problem. Experimental results demonstrate that the label embeddings obtained with PHCLE achieve superb performance in image classification task and exhibit good interpretability in the downstream label similarity analysis and image understanding task.
Learning Pretopological Spaces to Model Complex Propagation Phenomena: A Multiple Instance Learning Approach Based on a Logical Modeling
Venue: arXiv cs.LG 2018
Year: 2018

This paper addresses the problem of learning the concept of "propagation" in the pretopology theoretical formalism. Our proposal is first to define the pseudo-closure operator (modeling the propagation concept) as a logical combination of neighborhoods. We show that learning such an operator lapses into the Multiple Instance (MI) framework, where the learning process is performed on bags of instances instead of individual instances. Though this framework is well suited for this task, its use for learning a pretopological space leads to a set of bags exponential in size. To overcome this issue we thus propose a learning method based on a low estimation of the bags covered by a concept under construction. As an experiment, percolation processes (forest fires typically) are simulated and the corresponding propagation models are learned based on a subset of observations. It reveals that the proposed MI approach is significantly more efficient on the task of propagation model recognition than existing methods.
Multi-task Learning of Cascaded CNN for Facial Attribute Classification
Venue: arXiv cs.CV 2018
Year: 2018

Recently, facial attribute classification (FAC) has attracted significant attention in the computer vision community. Great progress has been made along with the availability of challenging FAC datasets. However, conventional FAC methods usually firstly pre-process the input images (i.e., perform face detection and alignment) and then predict facial attributes. These methods ignore the inherent dependencies among these tasks (i.e., face detection, facial landmark localization and FAC). Moreover, some methods using convolutional neural network are trained based on the fixed loss weights without considering the differences between facial attributes. In order to address the above problems, we propose a novel multi-task learning of cas- caded convolutional neural network method, termed MCFA, for predicting multiple facial attributes simultaneously. Specifically, the proposed method takes advantage of three cascaded sub-networks (i.e., S_Net, M_Net and L_Net corresponding to the neural networks under different scales) to jointly train multiple tasks in a coarse-to-fine manner, which can achieve end-to-end optimization. Furthermore, the proposed method automatically assigns the loss weight to each facial attribute based on a novel dynamic weighting scheme, thus making the proposed method concentrate on predicting the more difficult facial attributes. Experimental results show that the proposed method outperforms several state-of-the-art FAC methods on the challenging CelebA and LFWA datasets.
Machine learning regression on hyperspectral data to estimate multiple water parameters
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.
Hatching for 3D prints: line-based halftoning for dual extrusion fused deposition modeling
Venue: arXiv cs.GR 2018
Year: 2018

This work presents a halftoning technique to manufacture 3D objects with the appearance of continuous grayscale imagery for Fused Deposition Modeling (FDM) printers. While droplet-based dithering is a common halftoning technique, this is not applicable to FDM printing, since FDM builds up objects by extruding material in semi-continuous paths. The line-based halftoning principle called 'hatching' is applied to the line patterns naturally occuring in FDM prints, which are built up in a layer-by-layer fashion. The proposed halftoning technique isn't limited by the challenges existing techniques face; existing FDM coloring techniques greatly influence the surface geometry and deteriorate with surface slopes deviating from vertical or greatly influence the basic parameters of the printing process and thereby the structural properties of the resulting product. Furthermore, the proposed technique has little effect on printing time. Experiments on a dual-nozzle FDM printer show promising results. Future work is required to calibrate the perceived tone.
Multi-Source Fusion Operations in Subjective Logic
Venue: arXiv cs.AI 2018
Year: 2018

The purpose of multi-source fusion is to combine information from more than two evidence sources, or subjective opinions from multiple actors. For subjective logic, a number of different fusion operators have been proposed, each matching a fusion scenario with different assumptions. However, not all of these operators are associative, and therefore multi-source fusion is not well-defined for these settings. In this paper, we address this challenge, and define multi-source fusion for weighted belief fusion (WBF) and consensus & compromise fusion (CCF). For WBF, we show the definition to be equivalent to the intuitive formulation under the bijective mapping between subjective logic and Dirichlet evidence PDFs. For CCF, since there is no independent generalization, we show that the resulting multi-source fusion produces valid opinions, and explain why our generalization is sound. For completeness, we also provide corrections to previous results for averaging and cumulative belief fusion (ABF and CBF), as well as belief constraint fusion (BCF), which is an extension of Dempster's rule. With our generalizations of fusion operators, fusing information from multiple sources is now well-defined for all different fusion types defined in subjective logic. This enables wider applicability of subjective logic in applications where multiple actors interact.
Dimensional emotion recognition using visual and textual cues
Venue: arXiv cs.AI 2018
Year: 2018

This paper addresses the problem of automatic emotion recognition in the scope of the One-Minute Gradual-Emotional Behavior challenge (OMG-Emotion challenge). The underlying objective of the challenge is the automatic estimation of emotion expressions in the two-dimensional emotion representation space (i.e., arousal and valence). The adopted methodology is a weighted ensemble of several models from both video and text modalities. For video-based recognition, two different types of visual cues (i.e., face and facial landmarks) were considered to feed a multi-input deep neural network. Regarding the text modality, a sequential model based on a simple recurrent architecture was implemented. In addition, we also introduce a model based on high-level features in order to embed domain knowledge in the learning process. Experimental results on the OMG-Emotion validation set demonstrate the effectiveness of the implemented ensemble model as it clearly outperforms the current baseline methods.
InceptB: A CNN Based Classification Approach for Recognizing Traditional Bengali Games
Venue: arXiv cs.CV 2018
Year: 2018

Sports activities are an integral part of our day to day life. Introducing autonomous decision making and predictive models to recognize and analyze different sports events and activities has become an emerging trend in computer vision arena. Albeit the advances and vivid applications of artificial intelligence and computer vision in recognizing different popular western games, there remains a very minimal amount of efforts in the application of computer vision in recognizing traditional Bangladeshi games. We, in this paper, have described a novel Deep Learning based approach for recognizing traditional Bengali games. We have retrained the final layer of the renowned Inception V3 architecture developed by Google for our classification approach. Our approach shows promising results with an average accuracy of 80% approximately in correctly recognizing among 5 traditional Bangladeshi sports events.
CYCLOSA: Decentralizing Private Web Search Through SGX-Based Browser Extensions
Venue: arXiv cs.DC 2018
Year: 2018

By regularly querying Web search engines, users (unconsciously) disclose large amounts of their personal data as part of their search queries, among which some might reveal sensitive information (e.g. health issues, sexual, political or religious preferences). Several solutions exist to allow users querying search engines while improving privacy protection. However, these solutions suffer from a number of limitations: some are subject to user re-identification attacks, while others lack scalability or are unable to provide accurate results. This paper presents CYCLOSA, a secure, scalable and accurate private Web search solution. CYCLOSA improves security by relying on trusted execution environments (TEEs) as provided by Intel SGX. Further, CYCLOSA proposes a novel adaptive privacy protection solution that reduces the risk of user re- identification. CYCLOSA sends fake queries to the search engine and dynamically adapts their count according to the sensitivity of the user query. In addition, CYCLOSA meets scalability as it is fully decentralized, spreading the load for distributing fake queries among other nodes. Finally, CYCLOSA achieves accuracy of Web search as it handles the real query and the fake queries separately, in contrast to other existing solutions that mix fake and real query results.