OpenRaiser/Intern-Atlas huggingface dataset:
Extending the DEVS Formalism with Initialization Information
Venue: arXiv cs.SE 2018
Year: 2018

DEVS is a popular formalism to model system behaviour using a discrete-event abstraction. The main advantages of DEVS are its rigourous and precise specification, as well as its support for modular, hierarchical construction of models. DEVS frequently serves as a simulation "assembly language" to which models in other formalisms are translated, either giving meaning to new (domain-specific) languages, or reproducing semantics of existing languages. Despite this rigourous definition of its syntax and semantics, initialization of DEVS models is left unspecified in both the Classic and Parallel DEVS formalism definition. In this paper, we extend the DEVS formalism by including an initial total state. Extensions to syntax as well as denotational (closure under coupling) and operational semantics (abstract simulator) are presented. The extension is applicable to both main variants of the DEVS formalism. Our extension is such that it adds to, but does not alter the original specification. All changes are illustrated by means of a traffic light example.
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow
Venue: arXiv cs.CV 2018
Year: 2018

Wood-composite materials are widely used today as they homogenize humidity related directional deformations. Quantification of these deformations as coefficients is important for construction and engineering and topic of current research but still a manual process. This work introduces a novel computer vision approach that automatically extracts these properties directly from scans of the wooden specimens, taken at different humidity levels during the long lasting humidity conditioning process. These scans are used to compute a humidity dependent deformation field for each pixel, from which the desired coefficients can easily be calculated. The overall method includes automated registration of the wooden blocks, numerical optimization to compute a variational optical flow field which is further used to calculate dense strain fields and finally the engineering coefficients and their variance throughout the wooden blocks. The methods regularization is fully parameterizable which allows to model and suppress artifacts due to surface appearance changes of the specimens from mold, cracks, etc. that typically arise in the conditioning process.
Sentence Boundary Detection for French with Subword-Level Information Vectors and Convolutional Neural Networks
Venue: arXiv cs.CL 2018
Year: 2018

In this work we tackle the problem of sentence boundary detection applied to French as a binary classification task ("sentence boundary" or "not sentence boundary"). We combine convolutional neural networks with subword-level information vectors, which are word embedding representations learned from Wikipedia that take advantage of the words morphology; so each word is represented as a bag of their character n-grams. We decide to use a big written dataset (French Gigaword) instead of standard size transcriptions to train and evaluate the proposed architectures with the intention of using the trained models in posterior real life ASR transcriptions. Three different architectures are tested showing similar results; general accuracy for all models overpasses 0.96. All three models have good F1 scores reaching values over 0.97 regarding the "not sentence boundary" class. However, the "sentence boundary" class reflects lower scores decreasing the F1 metric to 0.778 for one of the models. Using subword-level information vectors seem to be very effective leading to conclude that the morphology of words encoded in the embeddings representations behave like pixels in an image making feasible the use of convolutional neural network architectures.
Polar-Coded Forward Error Correction for MLC NAND Flash Memory Polar FEC for NAND Flash Memory
Venue: arXiv cs.AR 2018
Year: 2018

With the ever-growing storage density, high-speed, and low-cost data access, flash memory has inevitably become popular. Multi-level cell (MLC) NAND flash memory, which can well balance the data density and memory stability, has occupied the largest market share of flash memory. With the aggressive memory scaling, however, the reliability decays sharply owing to multiple interferences. Therefore, the control system should be embedded with a suitable error correction code (ECC) to guarantee the data integrity and accuracy. We proposed the pre-check scheme which is a multi-strategy polar code scheme to strike a balance between reasonable frame error rate (FER) and decoding latency. Three decoders namely binary-input, quantized-soft, and pure-soft decoders are embedded in this scheme. Since the calculation of soft log-likelihood ratio (LLR) inputs needs multiple sensing operations and optional quantization boundaries, a 2-bit quantized hard-decision decoder is proposed to outperform the hard-decoded LDPC bit-flipping decoder with fewer sensing operations. We notice that polar codes have much lower computational complexity compared to LDPC codes. The stepwise maximum mutual information (SMMI) scheme is also proposed to obtain overlapped boundaries without exhausting search. The mapping scheme using Gray code is employed and proved to achieve better raw error performance compared to other alternatives. Hardware architectures are also given in this paper.
Deep Learning with Apache SystemML
Venue: arXiv cs.LG 2018
Year: 2018

Enterprises operate large data lakes using Hadoop and Spark frameworks that (1) run a plethora of tools to automate powerful data preparation/transformation pipelines, (2) run on shared, large clusters to (3) perform many different analytics tasks ranging from model preparation, building, evaluation, and tuning for both machine learning and deep learning. Developing machine/deep learning models on data in such shared environments is challenging. Apache SystemML provides a unified framework for implementing machine learning and deep learning algorithms in a variety of shared deployment scenarios. SystemML's novel compilation approach automatically generates runtime execution plans for machine/deep learning algorithms that are composed of single-node and distributed runtime operations depending on data and cluster characteristics such as data size, data sparsity, cluster size, and memory configurations, while still exploiting the capabilities of the underlying big data frameworks.
Attention based Sentence Extraction from Scientific Articles using Pseudo-Labeled data
Venue: arXiv cs.IR 2018
Year: 2018

In this work, we present a weakly supervised sentence extraction technique for identifying important sentences in scientific papers that are worthy of inclusion in the abstract. We propose a new attention based deep learning architecture that jointly learns to identify important content, as well as the cue phrases that are indicative of summary worthy sentences. We propose a new context embedding technique for determining the focus of a given paper using topic models and use it jointly with an LSTM based sequence encoder to learn attention weights across the sentence words. We use a collection of articles publicly available through ACL anthology for our experiments. Our system achieves a performance that is better, in terms of several ROUGE metrics, as compared to several state of art extractive techniques. It also generates more coherent summaries and preserves the overall structure of the document.
Examining the Tip of the Iceberg: A Data Set for Idiom Translation
Venue: arXiv cs.CL 2018
Year: 2018

Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. Although state-of-the-art NMT systems are generating progressively better translations, idiom translation remains one of the open challenges in this field. Idioms, a category of multiword expressions, are an interesting language phenomenon where the overall meaning of the expression cannot be composed from the meanings of its parts. A first important challenge is the lack of dedicated data sets for learning and evaluating idiom translation. In this paper we address this problem by creating the first large-scale data set for idiom translation. Our data set is automatically extracted from a widely used German-English translation corpus and includes, for each language direction, a targeted evaluation set where all sentences contain idioms and a regular training corpus where sentences including idioms are marked. We release this data set and use it to perform preliminary NMT experiments as the first step towards better idiom translation.
A Concurrent Constraint Programming Interpretation of Access Permissions
Venue: arXiv cs.LO 2018
Year: 2018

A recent trend in object oriented (OO) programming languages is the use of Access Permissions (APs) as an abstraction for controlling concurrent executions of programs. The use of AP source code annotations defines a protocol specifying how object references can access the mutable state of objects. Although the use of APs simplifies the task of writing concurrent code, an unsystematic use of them can lead to subtle problems. This paper presents a declarative interpretation of APs as Linear Concurrent Constraint Programs (lcc). We represent APs as constraints (i.e., formulas in logic) in an underlying constraint system whose entailment relation models the transformation rules of APs. Moreover, we use processes in lcc to model the dependencies imposed by APs, thus allowing the faithful representation of their flow in the program. We verify relevant properties about AP programs by taking advantage of the interpretation of lcc processes as formulas in Girard's intuitionistic linear logic (ILL). Properties include deadlock detection, program correctness (whether programs adhere to their AP specifications or not), and the ability of methods to run concurrently. By relying on a focusing discipline for ILL, we provide a complexity measure for proofs of the above mentioned properties. The effectiveness of our verification techniques is demonstrated by implementing the Alcove tool that includes an animator and a verifier. The former executes the lcc model, observing the flow of APs and quickly finding inconsistencies of the APs vis-a-vis the implementation. The latter is an automatic theorem prover based on ILL. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).
Subtyping for Hierarchical, Reconfigurable Petri Nets
Venue: arXiv cs.DM 2018
Year: 2018

Hierarchical Petri nets allow a more abstract view and reconfigurable Petri nets model dynamic structural adaptation. In this contribution we present the combination of reconfigurable Petri nets and hierarchical Petri nets yielding hierarchical structure for reconfigurable Petri nets. Hierarchies are established by substituting transitions by subnets. These subnets are themselves reconfigurable, so they are supplied with their own set of rules. Moreover, global rules that can be applied in all of the net, are provided.
Automatic thread painting generation
Venue: arXiv cs.GR 2018
Year: 2018

ThreadTone is an NPR representation of an input image by half-toning using threads on a circle. Current approaches to create ThreadTone paintings greedily draw the chords on the circle. We introduce the concept of chord space, and design a new algorithm to improve the quality of the thread painting. We use an optimization process that estimates the fitness of every chord in the chord space, and an error-diffusion based sampling process that selects a moderate number of chords to produce the output painting. We used an image similarity measure to evaluate the quality of our thread painting and also conducted a user study. Our approach can produce high quality results on portraits, sketches as well as cartoon pictures.
Automated Reporting of GUI Design Violations for Mobile Apps
Venue: arXiv cs.SE 2018
Year: 2018

The inception of a mobile app often takes form of a mock-up of the Graphical User Interface (GUI), represented as a static image delineating the proper layout and style of GUI widgets that satisfy requirements. Following this initial mock-up, the design artifacts are then handed off to developers whose goal is to accurately implement these GUIs and the desired functionality in code. Given the sizable abstraction gap between mock-ups and code, developers often introduce mistakes related to the GUI that can negatively impact an app's success in highly competitive marketplaces. Moreover, such mistakes are common in the evolutionary context of rapidly changing apps. This leads to the time-consuming and laborious task of design teams verifying that each screen of an app was implemented according to intended design specifications. This paper introduces a novel, automated approach for verifying whether the GUI of a mobile app was implemented according to its intended design. Our approach resolves GUI-related information from both implemented apps and mock-ups and uses computer vision techniques to identify common errors in the implementations of mobile GUIs. We implemented this approach for Android in a tool called GVT and carried out both a controlled empirical evaluation with open-source apps as well as an industrial evaluation with designers and developers from Huawei. The results show that GVT solves an important, difficult, and highly practical problem with remarkable efficiency and accuracy and is both useful and scalable from the point of view of industrial designers and developers. The tool is currently used by over one-thousand industrial designers and developers at Huawei to improve the quality of their mobile apps.
Local Descent For Temporal Logic Falsification of Cyber-Physical Systems (Extended Technical Report)
Venue: arXiv cs.SY 2018
Year: 2018

One way to analyze Cyber-Physical Systems is by modeling them as hybrid automata. Since reachability analysis for hybrid nonlinear automata is a very challenging and computationally expensive problem, in practice, engineers try to solve the requirements falsification problem. In one method, the falsification problem is solved by minimizing a robustness metric induced by the requirements. This optimization problem is usually a non-convex non-smooth problem that requires heuristic and analytical guidance to be solved. In this paper, functional gradient descent for hybrid systems is utilized for locally decreasing the robustness metric. The local descent method is combined with Simulated Annealing as a global optimization method to search for unsafe behaviors.
Random Linear Network Coding for 5G Mobile Video Delivery
Venue: arXiv cs.NI 2018
Year: 2018

An exponential increase in mobile video delivery will continue with the demand for higher resolution, multi-view and large-scale multicast video services. Novel fifth generation (5G) 3GPP New Radio (NR) standard will bring a number of new opportunities for optimizing video delivery across both 5G core and radio access networks. One of the promising approaches for video quality adaptation, throughput enhancement and erasure protection is the use of packet-level random linear network coding (RLNC). In this review paper, we discuss the integration of RLNC into the 5G NR standard, building upon the ideas and opportunities identified in 4G LTE. We explicitly identify and discuss in detail novel 5G NR features that provide support for RLNC-based video delivery in 5G, thus pointing out to the promising avenues for future research.
Learning via social awareness: Improving a deep generative sketching model with facial feedback
Venue: arXiv cs.LG 2018
Year: 2018

In the quest towards general artificial intelligence (AI), researchers have explored developing loss functions that act as intrinsic motivators in the absence of external rewards. This paper argues that such research has overlooked an important and useful intrinsic motivator: social interaction. We posit that making an AI agent aware of implicit social feedback from humans can allow for faster learning of more generalizable and useful representations, and could potentially impact AI safety. We collect social feedback in the form of facial expression reactions to samples from Sketch RNN, an LSTM-based variational autoencoder (VAE) designed to produce sketch drawings. We use a Latent Constraints GAN (LC-GAN) to learn from the facial feedback of a small group of viewers, by optimizing the model to produce sketches that it predicts will lead to more positive facial expressions. We show in multiple independent evaluations that the model trained with facial feedback produced sketches that are more highly rated, and induce significantly more positive facial expressions. Thus, we establish that implicit social feedback can improve the output of a deep learning model.
Understanding Membership Inferences on Well-Generalized Learning Models
Venue: arXiv cs.CR 2018
Year: 2018

Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model. Prior work has shown that the attack is feasible when the model is overfitted to its training data or when the adversary controls the training algorithm. However, when the model is not overfitted and the adversary does not control the training algorithm, the threat is not well understood. In this paper, we report a study that discovers overfitting to be a sufficient but not a necessary condition for an MIA to succeed. More specifically, we demonstrate that even a well-generalized model contains vulnerable instances subject to a new generalized MIA (GMIA). In GMIA, we use novel techniques for selecting vulnerable instances and detecting their subtle influences ignored by overfitting metrics. Specifically, we successfully identify individual records with high precision in real-world datasets by querying black-box machine learning models. Further we show that a vulnerable record can even be indirectly attacked by querying other related records and existing generalization techniques are found to be less effective in protecting the vulnerable instances. Our findings sharpen the understanding of the fundamental cause of the problem: the unique influences the training instance may have on the model.
Analysing and Patching SPEKE in ISO/IEC
Venue: arXiv cs.CR 2018
Year: 2018

Simple Password Exponential Key Exchange (SPEKE) is a well-known Password Authenticated Key Exchange (PAKE) protocol that has been used in Blackberry phones for secure messaging and Entrust's TruePass end-to-end web products. It has also been included into international standards such as ISO/IEC 11770-4 and IEEE P1363.2. In this paper, we analyse the SPEKE protocol as specified in the ISO/IEC and IEEE standards. We identify that the protocol is vulnerable to two new attacks: an impersonation attack that allows an attacker to impersonate a user without knowing the password by launching two parallel sessions with the victim, and a key-malleability attack that allows a man-in-the-middle (MITM) to manipulate the session key without being detected by the end users. Both attacks have been acknowledged by the technical committee of ISO/IEC SC 27, and ISO/IEC 11770-4 revised as a result. We propose a patched SPEKE called P-SPEKE and present a formal analysis in the Applied Pi Calculus using ProVerif to show that the proposed patch prevents both attacks. The proposed patch has been included into the latest revision of ISO/IEC 11770-4 published in 2017.
Molecular Structure Extraction From Documents Using Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Chemical structure extraction from documents remains a hard problem due to both false positive identification of structures during segmentation and errors in the predicted structures. Current approaches rely on handcrafted rules and subroutines that perform reasonably well generally, but still routinely encounter situations where recognition rates are not yet satisfactory and systematic improvement is challenging. Complications impacting performance of current approaches include the diversity in visual styles used by various software to render structures, the frequent use of ad hoc annotations, and other challenges related to image quality, including resolution and noise. We here present end-to-end deep learning solutions for both segmenting molecular structures from documents and for predicting chemical structures from these segmented images. This deep learning-based approach does not require any handcrafted features, is learned directly from data, and is robust against variations in image quality and style. Using the deep-learning approach described herein we show that it is possible to perform well on both segmentation and prediction of low resolution images containing moderately sized molecules found in journal articles and patents.
Prophit: Causal inverse classification for multiple continuously valued treatment policies
Venue: arXiv cs.LG 2018
Year: 2018

Inverse classification uses an induced classifier as a queryable oracle to guide test instances towards a preferred posterior class label. The result produced from the process is a set of instance-specific feature perturbations, or recommendations, that optimally improve the probability of the class label. In this work, we adopt a causal approach to inverse classification, eliciting treatment policies (i.e., feature perturbations) for models induced with causal properties. In so doing, we solve a long-standing problem of eliciting multiple, continuously valued treatment policies, using an updated framework and corresponding set of assumptions, which we term the inverse classification potential outcomes framework (ICPOF), along with a new measure, referred to as the individual future estimated effects ($i$FEE). We also develop the approximate propensity score (APS), based on Gaussian processes, to weight treatments, much like the inverse propensity score weighting used in past works. We demonstrate the viability of our methods on student performance.
Optimal Fairness-Aware Time and Power Allocation in Wireless Powered Communication Networks
Venue: arXiv cs.IT 2018
Year: 2018

In this paper, we consider the sum $α$-fair utility maximization problem for joint downlink (DL) and uplink (UL) transmissions of a wireless powered communication network (WPCN) via time and power allocation. In the DL, the users with energy harvesting receiver architecture decode information and harvest energy based on simultaneous wireless information and power transfer. While in the UL, the users utilize the harvested energy for information transmission, and harvest energy when other users transmit UL information. We show that the general sum $α$-fair utility maximization problem can be transformed into an equivalent convex one. Tradeoffs between sum rate and user fairness can be balanced via adjusting the value of $α$. In particular, for zero fairness, i.e., $α=0$, the optimal allocated time for both DL and UL is proportional to the overall available transmission power. Tradeoffs between sum rate and user fairness are presented through simulations.