OpenRaiser/Intern-Atlas huggingface dataset:
Resource Logics with a Diminishing Resource
Venue: arXiv cs.LO 2018
Year: 2018

Model-checking resource logics with production and consumption of resources is a computationally hard and often undecidable problem. We introduce a simple and realistic assumption that there is at least one diminishing resource, that is, a resource that cannot be produced and every action has a non-zero cost on this resource. An example of such resource is time. We show that, with this assumption, problems that are undecidable even for the underlying Alternating Time Temporal Logic, such as model-checking under imperfect information and perfect recall, become decidable for resource logics with a diminishing resource.
Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints
Venue: arXiv cs.LG 2018
Year: 2018

Classifiers can be trained with data-dependent constraints to satisfy fairness goals, reduce churn, achieve a targeted false positive rate, or other policy goals. We study the generalization performance for such constrained optimization problems, in terms of how well the constraints are satisfied at evaluation time, given that they are satisfied at training time. To improve generalization performance, we frame the problem as a two-player game where one player optimizes the model parameters on a training dataset, and the other player enforces the constraints on an independent validation dataset. We build on recent work in two-player constrained optimization to show that if one uses this two-dataset approach, then constraint generalization can be significantly improved. As we illustrate experimentally, this approach works not only in theory, but also in practice.
Josephine: Using JavaScript to safely manage the lifetimes of Rust data
Venue: arXiv cs.PL 2018
Year: 2018

This paper is about the interface between languages which use a garbage collector and those which use fancy types for safe manual memory management. Garbage collection is the traditional memory management scheme for functional languages, whereas type systems are now used for memory safety in imperative languages. We use existing techniques for linear capabilities to provide safe access to copyable references, but the application to languages with a tracing garbage collector, and to data with explicit lifetimes is new. This work is related to mixed linear/non-linear programming, but the languages being mixed are Rust and JavaScript.
Measuring and comparing the scaling behaviour of a high-performance CFD code on different supercomputing infrastructures
Venue: arXiv cs.PF 2018
Year: 2018

Parallel code design is a challenging task especially when addressing petascale systems for massive parallel processing (MPP), i.e. parallel computations on several hundreds of thousands of cores. An in-house computational fluid dynamics code, developed by our group, was designed for such high-fidelity runs in order to exhibit excellent scalability values. Basis for this code is an adaptive hierarchical data structure together with an efficient communication and (numerical) computation scheme that supports MPP. For a detailled scalability analysis, we performed several experiments on two of Germany's national supercomputers up to 140,000 processes. In this paper, we will show the results of those experiments and discuss any bottlenecks that could be observed while solving engineering-based problems such as porous media flows or thermal comfort assessments for problem sizes up to several hundred billion degrees of freedom.
Modeling Friends and Foes
Venue: arXiv cs.AI 2018
Year: 2018

How can one detect friendly and adversarial behavior from raw data? Detecting whether an environment is a friend, a foe, or anything in between, remains a poorly understood yet desirable ability for safe and robust agents. This paper proposes a definition of these environmental "attitudes" based on an characterization of the environment's ability to react to the agent's private strategy. We define an objective function for a one-shot game that allows deriving the environment's probability distribution under friendly and adversarial assumptions alongside the agent's optimal strategy. Furthermore, we present an algorithm to compute these equilibrium strategies, and show experimentally that both friendly and adversarial environments possess non-trivial optimal strategies.
Improved Techniques for Learning to Dehaze and Beyond: A Collective Study
Venue: arXiv cs.CV 2018
Year: 2018

Here we explore two related but important tasks based on the recently released REalistic Single Image DEhazing (RESIDE) benchmark dataset: (i) single image dehazing as a low-level image restoration problem; and (ii) high-level visual understanding (e.g., object detection) of hazy images. For the first task, we investigated a variety of loss functions and show that perception-driven loss significantly improves dehazing performance. In the second task, we provide multiple solutions including using advanced modules in the dehazing-detection cascade and domain-adaptive object detectors. In both tasks, our proposed solutions significantly improve performance. GitHub repository URL is: https://github.com/guanlongzhao/dehaze
Information Retrieval in the Cloud
Venue: arXiv cs.IR 2018
Year: 2018

There has been a recent trend to migrate IT infrastructure into the cloud. In this paper, we discuss the impact of this trend on searching for textual and other data, i.e. the distributed indexing and retrieval of information, from an organizational context. Keywords: information retrieval (IR); federated search; cloud search.
Autonomous Deep Learning: A Genetic DCNN Designer for Image Classification
Venue: arXiv cs.CV 2018
Year: 2018

Recent years have witnessed the breakthrough success of deep convolutional neural networks (DCNNs) in image classification and other vision applications. Although freeing users from the troublesome handcrafted feature extraction by providing a uniform feature extraction-classification framework, DCNNs still require a handcrafted design of their architectures. In this paper, we propose the genetic DCNN designer, an autonomous learning algorithm can generate a DCNN architecture automatically based on the data available for a specific image classification problem. We first partition a DCNN into multiple stacked meta convolutional blocks and fully connected blocks, each containing the operations of convolution, pooling, fully connection, batch normalization, activation and drop out, and thus convert the architecture into an integer vector. Then, we use refined evolutionary operations, including selection, mutation and crossover to evolve a population of DCNN architectures. Our results on the MNIST, Fashion-MNIST, EMNISTDigit, EMNIST-Letter, CIFAR10 and CIFAR100 datasets suggest that the proposed genetic DCNN designer is able to produce automatically DCNN architectures, whose performance is comparable to, if not better than, that of stateof- the-art DCNN models
Exponential Convergence of the Deep Neural Network Approximation for Analytic Functions
Venue: arXiv cs.LG 2018
Year: 2018

We prove that for analytic functions in low dimension, the convergence rate of the deep neural network approximation is exponential.
Xcel-RAM: Accelerating Binary Neural Networks in High-Throughput SRAM Compute Arrays
Venue: arXiv cs.ET 2018
Year: 2018

Deep neural networks are a biologically-inspired class of algorithms that have recently demonstrated state-of-the-art accuracies involving large-scale classification and recognition tasks. Indeed, a major landmark that enables efficient hardware accelerators for deep networks is the recent advances from the machine learning community that have demonstrated aggressively scaled deep binary networks with state-of-the-art accuracies. In this paper, we demonstrate how deep binary networks can be accelerated in modified von-Neumann machines by enabling binary convolutions within the SRAM array. In general, binary convolutions consist of bit-wise XNOR followed by a population-count (popcount). We present a charge sharing XNOR and popcount operation in 10 transistor SRAM cells. We have employed multiple circuit techniques including dual-read-worldines (Dual-RWL) along with a dual-stage ADC that overcomes the inaccuracies of a low precision ADC, to achieve a fairly accurate popcount. In addition, a key highlight of the present work is the fact that we propose sectioning of the SRAM array by adding switches onto the read-bitlines, thereby achieving improved parallelism. This is beneficial for deep networks, where the kernel size grows and requires to be stored in multiple sub-banks. As such, one needs to evaluate the partial popcount from multiple sub-banks and sum them up for achieving the final popcount. For n-sections per sub-array, we can perform n convolutions within one particular sub-bank, thereby improving overall system throughput as well as the energy efficiency. Our results at the array level show that the energy consumption and delay per-operation was 1.914pJ and 45ns, respectively. Moreover, an energy improvement of 2.5x, and a performance improvement of 4x was achieved by using the proposed sectioned-SRAM, compared to a non-sectioned SRAM design.
Human Satisfaction as the Ultimate Goal in Ridesharing
Venue: arXiv cs.MA 2018
Year: 2018

Transportation services play a crucial part in the development of modern smart cities. In particular, on-demand ridesharing services, which group together passengers with similar itineraries, are already operating in several metropolitan areas. These services can be of significant social and environmental benefit, by reducing travel costs, road congestion and co2 emissions. The deployment of autonomous cars in the near future will surely change the way people are traveling. It is even more promising for a ridesharing service, since it will be easier and cheaper for a company to handle a fleet of autonomous cars that can serve the demands of different passengers. We argue that user satisfaction should be the main objective when trying to find the best assignment of passengers to vehicles and the determination of their routes. Moreover, the model of user satisfaction should be rich enough to capture the traveling distance, cost, and other factors as well. We show that it is more important to capture a rich model of human satisfaction than peruse an optimal performance. That is, we developed a practical algorithm for assigning passengers to vehicles, which outperforms assignment algorithms that are optimal, but use a simpler satisfaction model. To the best of our knowledge, this is the first paper to exclusively concentrate on a rich and realistic function of user satisfaction as the objective, which is (arguably) the most important aspect to consider for achieving a widespread adaption of ridesharing services.
Machine learning 2.0 : Engineering Data Driven AI Products
Venue: arXiv cs.AI 2018
Year: 2018

ML 2.0: In this paper, we propose a paradigm shift from the current practice of creating machine learning models - which requires months-long discovery, exploration and "feasibility report" generation, followed by re-engineering for deployment - in favor of a rapid, 8-week process of development, understanding, validation and deployment that can executed by developers or subject matter experts (non-ML experts) using reusable APIs. This accomplishes what we call a "minimum viable data-driven model," delivering a ready-to-use machine learning model for problems that haven't been solved before using machine learning. We provide provisions for the refinement and adaptation of the "model," with strict enforcement and adherence to both the scaffolding/abstractions and the process. We imagine that this will bring forth the second phase in machine learning, in which discovery is subsumed by more targeted goals of delivery and impact.
Dynamic Swarm Dispersion in Particle Swarm Optimization for Mining Unsearched Area in Solution Space (DSDPSO)
Venue: arXiv cs.NE 2018
Year: 2018

Premature convergence in particle swarm optimization (PSO) algorithm usually leads to gaining local optimum and preventing from surveying those regions of solution space which have optimal points in. In this paper, by applying special mechanisms, suitable regions were detected and then swarm was guided to them by dispersing part of particles on proper times. This process is called dynamic swarm dispersion in PSO (DSDPSO) algorithm. In order to specify the proper times and to rein the evolutionary process alternating between exploring and exploiting behaviors, we used a diversity measuring approach and implemented the dispersion mechanism. To promote the performance of DSDPSO algorithm, three different policies including particle relocation, velocity settings of dispersed particles and parameters setting were applied. We compared the promoted algorithm with similar new approaches and according to the numerical results, the proposed algorithm outperformed the basic GPSO, LPSO, DMS-PSO, CLPSO and APSO in most of the 12 standard benchmark problems with different properties taken in this study.
ColdRoute: Effective Routing of Cold Questions in Stack Exchange Sites
Venue: arXiv cs.AI 2018
Year: 2018

Routing questions in Community Question Answer services (CQAs) such as Stack Exchange sites is a well-studied problem. Yet, cold-start -- a phenomena observed when a new question is posted is not well addressed by existing approaches. Additionally, cold questions posted by new askers present significant challenges to state-of-the-art approaches. We propose ColdRoute to address these challenges. ColdRoute is able to handle the task of routing cold questions posted by new or existing askers to matching experts. Specifically, we use Factorization Machines on the one-hot encoding of critical features such as question tags and compare our approach to well-studied techniques such as CQARank and semantic matching (LDA, BoW, and Doc2Vec). Using data from eight stack exchange sites, we are able to improve upon the routing metrics (Precision$@1$, Accuracy, MRR) over the state-of-the-art models such as semantic matching by $159.5\%$,$31.84\%$, and $40.36\%$ for cold questions posted by existing askers, and $123.1\%$, $27.03\%$, and $34.81\%$ for cold questions posted by new askers respectively.
A SAT Encoding for the $n$-Fractions Problem
Venue: arXiv cs.DM 2018
Year: 2018

This note describes a SAT encoding for the $n$-fractions puzzle which is problem 041 of the CSPLib. Using a SAT solver we obtain a solution for two of the six remaining open instances of this problem.
A Broader View on Bias in Automated Decision-Making: Reflecting on Epistemology and Dynamics
Venue: arXiv cs.LG 2018
Year: 2018

Machine learning (ML) is increasingly deployed in real world contexts, supplying actionable insights and forming the basis of automated decision-making systems. While issues resulting from biases pre-existing in training data have been at the center of the fairness debate, these systems are also affected by technical and emergent biases, which often arise as context-specific artifacts of implementation. This position paper interprets technical bias as an epistemological problem and emergent bias as a dynamical feedback phenomenon. In order to stimulate debate on how to change machine learning practice to effectively address these issues, we explore this broader view on bias, stress the need to reflect on epistemology, and point to value-sensitive design methodologies to revisit the design and implementation process of automated decision-making systems.
Simplifying Urban Data Fusion with BigSUR
Venue: arXiv cs.GR 2018
Year: 2018

Our ability to understand data has always lagged behind our ability to collect it. This is particularly true in urban environments, where mass data capture is particularly valuable, but the objects captured are more varied, denser, and complex. To understand the structure and content of the environment, we must process the unstructured data to a structured form. BigSUR is an urban reconstruction algorithm which fuses GIS data, photogrammetric meshes, and street level photography, to create clean representative, semantically labelled, geometry. However, we have identified three problems with the system i) the street level photography is often difficult to acquire; ii) novel façade styles often frustrate the detection of windows and doors; iii) the computational requirements of the system are large, processing a large city block can take up to 15 hours. In this paper we describe the process of simplifying and validating the BigSUR semantic reconstruction system. In particular, the requirement for street level images is removed, and greedy post-process profile assignment is introduced to accelerate the system. We accomplish this by modifying the binary integer programming (BIP) optimization, and re-evaluating the effects of various parameters. The new variant of the system is evaluated over a variety of urban areas. We objectively measure mean squared error (MSE) terms over the unstructured geometry, showing that BigSUR is able to accurately recover omissions from the input meshes. Further, we evaluate the ability of the system to label the walls and roofs of input meshes, concluding that our new BigSUR variant achieves highly accurate semantic labelling with shorter computational time and less input data.
Path Finding for the Coalition of Co-operative Agents Acting in the Environment with Destructible Obstacles
Venue: arXiv cs.AI 2018
Year: 2018

The problem of planning a set of paths for the coalition of robots (agents) with different capabilities is considered in the paper. Some agents can modify the environment by destructing the obstacles thus allowing the other ones to shorten their paths to the goal. As a result the mutual solution of lower cost, e.g. time to completion, may be acquired. We suggest an original procedure to identify the obstacles for further removal that can be embedded into almost any heuristic search planner (we use Theta*) and evaluate it empirically. Results of the evaluation show that time-to-complete the mission can be decreased up to 9-12 % by utilizing the proposed technique.
Representation Mapping: A Novel Approach to Generate High-Quality Multi-Lingual Emotion Lexicons
Venue: arXiv cs.CL 2018
Year: 2018

In the past years, sentiment analysis has increasingly shifted attention to representational frameworks more expressive than semantic polarity (being positive, negative or neutral). However, these richer formats (like Basic Emotions or Valence-Arousal-Dominance, and variants therefrom), rooted in psychological research, tend to proliferate the number of representation schemes for emotion encoding. Thus, a large amount of representationally incompatible emotion lexicons has been developed by various research groups adopting one or the other emotion representation format. As a consequence, the reusability of these resources decreases as does the comparability of systems using them. In this paper, we propose to solve this dilemma by methods and tools which map different representation formats onto each other for the sake of mutual compatibility and interoperability of language resources. We present the first large-scale investigation of such representation mappings for four typologically diverse languages and find evidence that our approach produces (near-)gold quality emotion lexicons, even in cross-lingual settings. Finally, we use our models to create new lexicons for eight typologically diverse languages.
An energy-based generative sequence model for testing sensory theories of Western harmony
Venue: arXiv cs.SD 2018
Year: 2018

The relationship between sensory consonance and Western harmony is an important topic in music theory and psychology. We introduce new methods for analysing this relationship, and apply them to large corpora representing three prominent genres of Western music: classical, popular, and jazz music. These methods centre on a generative sequence model with an exponential-family energy-based form that predicts chord sequences from continuous features. We use this model to investigate one aspect of instantaneous consonance (harmonicity) and two aspects of sequential consonance (spectral distance and voice-leading distance). Applied to our three musical genres, the results generally support the relationship between sensory consonance and harmony, but lead us to question the high importance attributed to spectral distance in the psychological literature. We anticipate that our methods will provide a useful platform for future work linking music psychology to music theory.
Pragmatic approach to structured data querying via natural language interface
Venue: arXiv cs.CL 2018
Year: 2018

As the use of technology increases and data analysis becomes integral in many businesses, the ability to quickly access and interpret data has become more important than ever. Information retrieval technologies are being utilized by organizations and companies to manage their information systems and processes. Despite information retrieval of a large amount of data being efficient organized in relational databases, a user still needs to master the DB language/schema to completely formulate the queries. This puts a burden on organizations and companies to hire employees that are proficient in DB languages/schemas to formulate queries. To reduce some of the burden on already overstretched data teams, many organizations are looking for tools that allow non-developers to query their databases. Unfortunately, writing a valid SQL query that answers the question a user is trying to ask isn't always easy. Even seemingly simple questions, like "Which start-up companies received more than $200M in funding?" can actually be very hard to answer, let alone convert into a SQL query. How do you define start-up companies? By size, location, duration of time they have been incorporated? This may be fine if a user is working with a database they're already familiar with, but what if users are not familiar with the database. What is needed is a centralized system that can effectively translate natural language queries into specific database queries for different customer database types. There is a number of factors that can dramatically affect the system architecture and the set of algorithms used to translate NL queries into a structured query representation.
CoCalc as a Learning Tool for Neural Network Simulation in the Special Course "Foundations of Mathematic Informatics"
Venue: arXiv cs.CY 2018
Year: 2018

The role of neural network modeling in the learning content of the special course "Foundations of Mathematical Informatics" was discussed. The course was developed for the students of technical universities - future IT-specialists and directed to breaking the gap between theoretic computer science and it's applied applications: software, system and computing engineering. CoCalc was justified as a learning tool of mathematical informatics in general and neural network modeling in particular. The elements of technique of using CoCalc at studying topic "Neural network and pattern recognition" of the special course "Foundations of Mathematic Informatics" are shown. The program code was presented in a CoffeeScript language, which implements the basic components of artificial neural network: neurons, synaptic connections, functions of activations (tangential, sigmoid, stepped) and their derivatives, methods of calculating the network's weights, etc. The features of the Kolmogorov-Arnold representation theorem application were discussed for determination the architecture of multilayer neural networks. The implementation of the disjunctive logical element and approximation of an arbitrary function using a three-layer neural network were given as an examples. According to the simulation results, a conclusion was made as for the limits of the use of constructed networks, in which they retain their adequacy. The framework topics of individual research of the artificial neural networks is proposed.