OpenRaiser/Intern-Atlas huggingface dataset:
Deadlock-Free Typestate-Oriented Programming
Venue: arXiv cs.PL 2018
Year: 2018

Context. TypeState-Oriented Programming (TSOP) is a paradigm intended to help developers in the implementation and use of mutable objects whose public interface depends on their private state. Under this paradigm, well-typed programs are guaranteed to conform with the protocol of the objects they use. Inquiry. Previous works have investigated TSOP for both sequential and concurrent objects. However, an important difference between the two settings still remains. In a sequential setting, a well-typed program either progresses indefinitely or terminates eventually. In a concurrent setting, protocol conformance is no longer enough to avoid deadlocks, a situation in which the execution of the program halts because two or more objects are involved in mutual dependencies that prevent any further progress. Approach. In this work, we put forward a refinement of TSOP for concurrent objects guaranteeing that well-typed programs not only conform with the protocol of the objects they use, but are also deadlock free. The key ingredients of the type system are behavioral types, used to specify and enforce object protocols, and dependency relations, used to represent abstract descriptions of the dependencies between objects and detect circularities that might cause deadlocks. Knowledge. The proposed approach stands out for two features. First, the approach is fully compositional and therefore scalable: the objects of a large program can be type checked in isolation; deadlock freedom of an object composition solely depends on the types of the objects being composed; any modification/refactoring of an object that does not affect its public interface does not affect other objects either. Second, we provide the first deadlock analysis technique for join patterns, a high-level concurrency abstraction with which programmers can express complex synchronizations in a succinct and declarative form. Grounding. We detail the proposed typing discipline for a core programming language blending concurrent objects, asynchronous message passing and join patterns. We prove that the type system is sound and give non-trivial examples of programs that can be successfully analyzed. A Haskell implementation of the type system that demonstrates the feasibility of the approach is publicly available. Importance. The static analysis technique described in this work can be used to certify programs written in a core language for concurrent TSOP with proven correctness guarantees. This is an essential first step towards the integration and application of the technique in a real-world developer toolchain, making programming of such systems more productive and less frustrating.
Mapping Walls of Indoor Environment using RGB-D Sensor
Venue: arXiv cs.RO 2018
Year: 2018

Inferring walls configuration of indoor environment could help robot "understand" the environment better. This allows the robot to execute a task that involves inter-room navigation, such as picking an object in the kitchen. In this paper, we present a method to inferring walls configuration from a moving RGB-D sensor. Our goal is to combine a simple wall configuration model and fast wall detection method in order to get a system that works online, is real-time, and does not need a Manhattan World assumption. We tested our preliminary work, i.e. wall detection and measurement from moving RGB-D sensor, with MIT Stata Center Dataset. The performance of our method is reported in terms of accuracy and speed of execution.
Network Traffic Anomaly Detection Using Recurrent Neural Networks
Venue: arXiv cs.CY 2018
Year: 2018

We show that a recurrent neural network is able to learn a model to represent sequences of communications between computers on a network and can be used to identify outlier network traffic. Defending computer networks is a challenging problem and is typically addressed by manually identifying known malicious actor behavior and then specifying rules to recognize such behavior in network communications. However, these rule-based approaches often generalize poorly and identify only those patterns that are already known to researchers. An alternative approach that does not rely on known malicious behavior patterns can potentially also detect previously unseen patterns. We tokenize and compress netflow into sequences of "words" that form "sentences" representative of a conversation between computers. These sentences are then used to generate a model that learns the semantic and syntactic grammar of the newly generated language. We use Long-Short-Term Memory (LSTM) cell Recurrent Neural Networks (RNN) to capture the complex relationships and nuances of this language. The language model is then used predict the communications between two IPs and the prediction error is used as a measurement of how typical or atyptical the observed communication are. By learning a model that is specific to each network, yet generalized to typical computer-to-computer traffic within and outside the network, a language model is able to identify sequences of network activity that are outliers with respect to the model. We demonstrate positive unsupervised attack identification performance (AUC 0.84) on the ISCX IDS dataset which contains seven days of network activity with normal traffic and four distinct attack patterns.
Distributed Equilibrium-Learning for Power Network Voltage Control With a Locally Connected Communication Network
Venue: arXiv cs.SY 2018
Year: 2018

In current power distribution systems, one of the most challenging operation tasks is to coordinate the network- wide distributed energy resources (DERs) to maintain the stability of voltage magnitude of the system. This voltage control task has been investigated actively under either distributed optimization-based or local feedback control-based characterizations. The former architecture requires a strongly-connected communication network among all DERs for implementing the optimization algorithms, a scenario not yet realistic in most of the existing distribution systems with under-deployed communication infrastructure. The latter one, on the other hand, has been proven to suffer from loss of network-wide op- erational optimality. In this paper, we propose a game-theoretic characterization for semi-local voltage control with only a locally connected communication network. We analyze the existence and uniqueness of the generalized Nash equilibrium (GNE) for this characterization and develop a fully distributed equilibrium-learning algorithm that relies on only neighbor-to-neighbor information exchange. Provable convergence results are provided along with numerical tests which corroborate the robust convergence property of the proposed algorithm.
Modeling Customer Engagement from Partial Observations
Venue: arXiv cs.LG 2018
Year: 2018

It is of high interest for a company to identify customers expected to bring the largest profit in the upcoming period. Knowing as much as possible about each customer is crucial for such predictions. However, their demographic data, preferences, and other information that might be useful for building loyalty programs is often missing. Additionally, modeling relations among different customers as a network can be beneficial for predictions at an individual level, as similar customers tend to have similar purchasing patterns. We address this problem by proposing a robust framework for structured regression on deficient data in evolving networks with a supervised representation learning based on neural features embedding. The new method is compared to several unstructured and structured alternatives for predicting customer behavior (e.g. purchasing frequency and customer ticket) on user networks generated from customer databases of two companies from different industries. The obtained results show $4\%$ to $130\%$ improvement in accuracy over alternatives when all customer information is known. Additionally, the robustness of our method is demonstrated when up to $80\%$ of demographic information was missing where it was up to several folds more accurate as compared to alternatives that are either ignoring cases with missing values or learn their feature representation in an unsupervised manner.
Adversarial Binary Coding for Efficient Person Re-identification
Venue: arXiv cs.CV 2018
Year: 2018

Person re-identification (ReID) aims at matching persons across different views/scenes. In addition to accuracy, the matching efficiency has received more and more attention because of demanding applications using large-scale data. Several binary coding based methods have been proposed for efficient ReID, which either learn projections to map high-dimensional features to compact binary codes, or directly adopt deep neural networks by simply inserting an additional fully-connected layer with tanh-like activations. However, the former approach requires time-consuming hand-crafted feature extraction and complicated (discrete) optimizations; the latter lacks the necessary discriminative information greatly due to the straightforward activation functions. In this paper, we propose a simple yet effective framework for efficient ReID inspired by the recent advances in adversarial learning. Specifically, instead of learning explicit projections or adding fully-connected mapping layers, the proposed Adversarial Binary Coding (ABC) framework guides the extraction of binary codes implicitly and effectively. The discriminability of the extracted codes is further enhanced by equipping the ABC with a deep triplet network for the ReID task. More importantly, the ABC and triplet network are simultaneously optimized in an end-to-end manner. Extensive experiments on three large-scale ReID benchmarks demonstrate the superiority of our approach over the state-of-the-art methods.
Polynomial-Time Algorithms for Submodular Laplacian Systems
Venue: arXiv cs.DS 2018
Year: 2018

Let $G=(V,E)$ be an undirected graph, $L_G\in \mathbb{R}^{V \times V}$ be the associated Laplacian matrix, and $b \in \mathbb{R}^V$ be a vector. Solving the Laplacian system $L_G x = b$ has numerous applications in theoretical computer science, machine learning, and network analysis. Recently, the notion of the Laplacian operator $L_F:\mathbb{R}^V \to 2^{\mathbb{R}^V}$ for a submodular transformation $F:2^V \to \mathbb{R}_+^E$ was introduced, which can handle undirected graphs, directed graphs, hypergraphs, and joint distributions in a unified manner. In this study, we show that the submodular Laplacian system $L_F( x) \ni b$ can be solved in polynomial time. Furthermore, we also prove that even when the submodular Laplacian system has no solution, we can solve its regression form in polynomial time. Finally, we discuss potential applications of submodular Laplacian systems in machine learning and network analysis.
Computer-Assisted Text Analysis for Social Science: Topic Models and Beyond
Venue: arXiv cs.CL 2018
Year: 2018

Topic models are a family of statistical-based algorithms to summarize, explore and index large collections of text documents. After a decade of research led by computer scientists, topic models have spread to social science as a new generation of data-driven social scientists have searched for tools to explore large collections of unstructured text. Recently, social scientists have contributed to topic model literature with developments in causal inference and tools for handling the problem of multi-modality. In this paper, I provide a literature review on the evolution of topic modeling including extensions for document covariates, methods for evaluation and interpretation, and advances in interactive visualizations along with each aspect's relevance and application for social science research.
CobWeb - a toolbox for automatic tomographic image analysis based on machine learning techniques: application and examples
Venue: arXiv cs.HC 2018
Year: 2018

In this study, we introduce CobWeb 1.0 which is a graphical user interface tailored explicitly for accurate image segmentation and representative elementary volume analysis of digital rock images derived from high resolution tomography. The CobWeb code is a work package deployed as a series of windows executable binaries which use image processing and machine learning libraries of MATLAB. The user-friendly interface enables image segmentation and cross-validation employing K-means, Fuzzy C-means, least square support vector machine, and ensemble classification (bragging and boosting) segmentation techniques. A quick region of interest analysis including relative porosity trends, pore size distribution, and volume fraction of different phases can be performed on different geomaterials. Data can be exported to ParaView, DSI Studio (.fib), Microsoft Excel and MATLAB for further visualisation and statistical analysis. The efficiency of the new tool was verified using gas hydrate-bearing sediment samples and Berea sandstone, both from synchrotron tomography datasets, as well as Grosmont carbonate rock X-ray micro-tomographic dataset. Despite its high sub-micrometer resolution, the gas hydrate dataset was suffering from edge enhancement artefacts. These artefacts were primarily normalized by the dual filtering approach using both non-local means and anisotropic diffusion filtering. The desired automatic segmentation of the phases (brine, sand, and gas hydrate) was thus successfully achieved using the dual clustering approach.
Actor-Critic based Training Framework for Abstractive Summarization
Venue: arXiv cs.CL 2018
Year: 2018

We present a training framework for neural abstractive summarization based on actor-critic approaches from reinforcement learning. In the traditional neural network based methods, the objective is only to maximize the likelihood of the predicted summaries, no other assessment constraints are considered, which may generate low-quality summaries or even incorrect sentences. To alleviate this problem, we employ an actor-critic framework to enhance the training procedure. For the actor, we employ the typical attention based sequence-to-sequence (seq2seq) framework as the policy network for summary generation. For the critic, we combine the maximum likelihood estimator with a well designed global summary quality estimator which is a neural network based binary classifier aiming to make the generated summaries indistinguishable from the human-written ones. Policy gradient method is used to conduct the parameter learning. An alternating training strategy is proposed to conduct the joint training of the actor and critic models. Extensive experiments on some benchmark datasets in different languages show that our framework achieves improvements over the state-of-the-art methods.
Webcam-based Eye Gaze Tracking under Natural Head Movement
Venue: arXiv cs.CV 2018
Year: 2018

This manuscript investigates and proposes a visual gaze tracker that tackles the problem using only an ordinary web camera and no prior knowledge in any sense (scene set-up, camera intrinsic and/or extrinsic parameters). The tracker we propose is based on the observation that our desire to grant the freedom of natural head movement to the user requires 3D modeling of the scene set-up. Although, using a single low resolution web camera bounds us in dimensions (no depth can be recovered), we propose ways to cope with this drawback and model the scene in front of the user. We tackle this three-dimensional problem by realizing that it can be viewed as series of two-dimensional special cases. Then, we propose a procedure that treats each movement of the user's head as a special two-dimensional case, hence reducing the complexity of the problem back to two dimensions. Furthermore, the proposed tracker is calibration free and discards this tedious part of all previously mentioned trackers. Experimental results show that the proposed tracker achieves good results, given the restrictions on it. We can report that the tracker commits a mean error of (56.95, 70.82) pixels in x and y direction, respectively, when the user's head is as static as possible (no chin-rests are used). Furthermore, we can report that the proposed tracker commits a mean error of (87.18, 103.86) pixels in x and y direction, respectively, under natural head movement.
Energy-Efficient Transmission of Hybrid Array with Non-Ideal Power Amplifiers and Circuitry
Venue: arXiv cs.IT 2018
Year: 2018

This paper presents a new approach to efficiently maximizing the energy efficiency (EE) of hybrid arrays under a practical setting of non-ideal power amplifiers (PAs) and non-negligible circuit power, where coherent and non-coherent beamforming are considered. As a key contribution, we reveal that a bursty transmission mode can be energy-efficient to achieve steady transmissions of a data stream under the practical setting. This is distinctively different from existing studies under ideal circuits and PAs, where continuous transmissions are the most energy-efficient. Another important contribution is that the optimal transmit duration and powers are identified to balance energy consumptions in the non-ideal circuits and PAs, and maximize the EE. This is achieved by establishing the most energy-efficient structure of transmit powers, given a transmit duration, and correspondingly partitioning the non-convex feasible region of the transmit duration into segments with self-contained convexity or concavity. Evident from simulations, significant EE gains of the proposed approach are demonstrated through comparisons with the state of the art, and the superiority of the bursty transmission mode is confirmed especially under low data rate demands.
Detection of Structural Change in Geographic Regions of Interest by Self Organized Mapping: Las Vegas City and Lake Mead across the Years
Venue: arXiv cs.CY 2018
Year: 2018

Time-series of satellite images may reveal important data about changes in environmental conditions and natural or urban landscape structures that are of potential interest to citizens, historians, or policymakers. We applied a fast method of image analysis using Self Organized Maps (SOM) and, more specifically, the quantization error (QE), for the visualization of critical changes in satellite images of Las Vegas, generated across the years 1984-2008, a period of major restructuration of the urban landscape. As shown in our previous work, the QE from the SOM output is a reliable measure of variability in local image contents. In the present work, we use statistical trend analysis to show how the QE from SOM run on specific geographic regions of interest extracted from satellite images can be exploited to detect both the magnitude and the direction of structural change across time at a glance. Significantly correlated demographic data for the same reference time period are highlighted. The approach is fast and reliable, and can be implemented for the rapid detection of potentially critical changes in time series of large bodies of image data.
Learning Kinematic Descriptions using SPARE: Simulated and Physical ARticulated Extendable dataset
Venue: arXiv cs.CV 2018
Year: 2018

Next generation robots will need to understand intricate and articulated objects as they cooperate in human environments. To do so, these robots will need to move beyond their current abilities--- working with relatively simple objects in a task-indifferent manner--- toward more sophisticated abilities that dynamically estimate the properties of complex, articulated objects. To that end, we make two compelling contributions toward general articulated (physical) object understanding in this paper. First, we introduce a new dataset, SPARE: Simulated and Physical ARticulated Extendable dataset. SPARE is an extendable open-source dataset providing equivalent simulated and physical instances of articulated objects (kinematic chains), providing the greater research community with a training and evaluation tool for methods generating kinematic descriptions of articulated objects. To the best of our knowledge, this is the first joint visual and physical (3D-printable) dataset for the Vision community. Second, we present a deep neural network that can predit the number of links and the length of the links of an articulated object. These new ideas outperform classical approaches to understanding kinematic chains, such tracking-based methods, which fail in the case of occlusion and do not leverage multiple views when available.
AI Blue Book: Vehicle Price Prediction using Visual Features
Venue: arXiv cs.CV 2018
Year: 2018

In this work, we build a series of machine learning models to predict the price of a product given its image, and visualize the features that result in higher or lower price predictions. We collect two novel datasets of product images and their MSRP prices for this purpose: a bicycle dataset and a car dataset. We set baselines for price regression using linear regression on histogram of oriented gradients (HOG) and convolutional neural network (CNN) features, and a baseline for price segment classification using a multiclass SVM. For our main models, we train several deep CNNs using both transfer learning and our own architectures, for both regression and classification. We achieve strong results on both datasets, with deep CNNs significantly outperforming other models in a variety of metrics. Finally, we use several recently-developed methods to visualize the image features that result in higher or lower prices.
Caring robots are here to help
Venue: arXiv cs.CY 2018
Year: 2018

Robots, along with sensors and telemedicine, have been identified as technologies that can assist and prolong independent living for older people, with robots especially being used to help prevent social isolation and depression.
Phronesis of AI in radiology: Superhuman meets natural stupidity
Venue: arXiv cs.CY 2018
Year: 2018

Advances in AI in the last decade have clearly made economists, politicians, journalists, and citizenry in general believe that the machines are coming to take human jobs. We review 'superhuman' AI performance claims in radiology and then provide a self-reflection on our own work in the area in the form of a critical review, a tribute of sorts to McDermotts 1976 paper, asking the field for some self-discipline. Clearly there is an opportunity to replace humans, but there are better opportunities, as we have discovered to fit cognitive abilities of human and non-humans. We performed one of the first studies in radiology to see how human and AI performance can complement and improve each others performance for detecting pneumonia in chest X-rays. We question if there is a practical wisdom or phronesis that we need to demonstrate in AI today as well as in our field. Using this, we articulate what AI as a field has already and probably can in the future learn from Psychology, Cognitive Science, Sociology and Science and Technology Studies.
Understanding the Relationships between Information Architectures and Business Models
Venue: arXiv cs.CY 2018
Year: 2018

Based on the dualism of information architecture and business model by Kuk and Janssen (2011), the study proposed a theoretical framework to understanding the relationship among IoT and smart community which regards a path of smart community development as a configurations set including both information architecture factors and business model patterns.
Two-Stream Neural Networks for Tampered Face Detection
Venue: arXiv cs.CV 2018
Year: 2018

We propose a two-stream network for face tampering detection. We train GoogLeNet to detect tampering artifacts in a face classification stream, and train a patch based triplet network to leverage features capturing local noise residuals and camera characteristics as a second stream. In addition, we use two different online face swapping applications to create a new dataset that consists of 2010 tampered images, each of which contains a tampered face. We evaluate the proposed two-stream network on our newly collected dataset. Experimental results demonstrate the effectiveness of our method.