OpenRaiser/Intern-Atlas huggingface dataset:
Computing Optimal Assignments in Linear Time for Approximate Graph Matching
Venue: arXiv cs.LG 2019
Year: 2019

Finding an optimal assignment between two sets of objects is a fundamental problem arising in many applications, including the matching of `bag-of-words' representations in natural language processing and computer vision. Solving the assignment problem typically requires cubic time and its pairwise computation is expensive on large datasets. In this paper, we develop an algorithm which can find an optimal assignment in linear time when the cost function between objects is represented by a tree distance. We employ the method to approximate the edit distance between two graphs by matching their vertices in linear time. To this end, we propose two tree distances, the first of which reflects discrete and structural differences between vertices, and the second of which can be used to compare continuous labels. We verify the effectiveness and efficiency of our methods using synthetic and real-world datasets.
Representation theorems for extended contact algebras based on equivalence relations
Venue: arXiv cs.LO 2019
Year: 2019

The aim of this paper is to give new representation theorems for extended contact algebras. These representation theorems are based on equivalence relations.
Minimax-optimal decoding of movement goals from local field potentials using complex spectral features
Venue: arXiv cs.NE 2019
Year: 2019

We consider the problem of predicting eye movement goals from local field potentials (LFP) recorded through a multielectrode array in the macaque prefrontal cortex. The monkey is tasked with performing memory-guided saccades to one of eight targets during which LFP activity is recorded and used to train a decoder. Previous reports have mainly relied on the spectral amplitude of the LFPs as a feature in the decoding step to limited success, while neglecting the phase without proper theoretical justification. This paper formulates the problem of decoding eye movement intentions in a statistically optimal framework and uses Gaussian sequence modeling and Pinsker's theorem to generate minimax-optimal estimates of the LFP signals which are later used as features in the decoding step. The approach is shown to act as a low-pass filter and each LFP in the feature space is represented via its complex Fourier coefficients after appropriate shrinking such that higher frequency components are attenuated; this way, the phase information inherently present in the LFP signal is naturally embedded into the feature space. The proposed complex spectrum-based decoder achieves prediction accuracy of up to $94\%$ at superficial electrode depths near the surface of the prefrontal cortex, which marks a significant performance improvement over conventional power spectrum-based decoders.
Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for Temporal Logic Planning
Venue: arXiv cs.AI 2019
Year: 2019

Problems arise when using reward functions to capture dependencies between sequential time-constrained goal states because the state-space must be prohibitively expanded to accommodate a history of successfully achieved sub-goals. Also, policies and value functions derived with stationarity assumptions are not readily decomposable, leading to a tension between reward maximization and task generalization. We demonstrate a logic-compatible approach using model-based knowledge of environment dynamics and deadline information to directly infer non-stationary policies composed of reusable stationary policies. The policies are constructed to maximize the probability of satisfying time-sensitive goals while respecting time-varying obstacles. Our approach explicitly maintains two different spaces, a high-level logical task specification where the task-variables are grounded onto the low-level state-space of a Markov decision process. Computing satisfiability at the task-level is made possible by a Bellman-like equation which operates on a tensor that links the temporal relationship between the two spaces; the equation solves for a value function that can be explicitly interpreted as the probability of sub-goal satisfaction under the synthesized non-stationary policy, an approach we term Constraint Satisfaction Propagation (CSP).
MgNet: A Unified Framework of Multigrid and Convolutional Neural Network
Venue: arXiv cs.CV 2019
Year: 2019

We develop a unified model, known as MgNet, that simultaneously recovers some convolutional neural networks (CNN) for image classification and multigrid (MG) methods for solving discretized partial differential equations (PDEs). This model is based on close connections that we have observed and uncovered between the CNN and MG methodologies. For example, pooling operation and feature extraction in CNN correspond directly to restriction operation and iterative smoothers in MG, respectively. As the solution space is often the dual of the data space in PDEs, the analogous concept of feature space and data space (which are dual to each other) is introduced in CNN. With such connections and new concept in the unified model, the function of various convolution operations and pooling used in CNN can be better understood. As a result, modified CNN models (with fewer weights and hyper parameters) are developed that exhibit competitive and sometimes better performance in comparison with existing CNN models when applied to both CIFAR-10 and CIFAR-100 data sets.
A structured approach for the implementation of distributed manufacturing simulation
Venue: arXiv cs.MA 2019
Year: 2019

Manufacturing has been changing from a mainly inhouse effort to a distributed style in order to meet new challenges owing to globalization of markets and worldwide competition. Distributed simulation provides an attractive solution to construct cross enterprise simulations to evaluate the viability of the proposed distributed manufacturing enterprises. However, due to its complexity and high cost distributed simulation failed to gain a wide acceptance from industrial users. The main objective of this paper is to address these issues and present a new structured approach to implement distributed simulation with cost effective and easy to implementable tools. A simplified approach for model partitioning for distributed simulation is also included in the proposed approach. The implementation of distributed manufacturing simulation is illustrated with Arena, Microsoft Message Queue (MSMQ) and Visual Basic for Applications (VBA).
An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders
Venue: arXiv cs.CY 2019
Year: 2019

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.
The Computer-Linguistic Analysis of Socio-Demographic Profile of Virtual Community Member
Venue: arXiv cs.CY 2019
Year: 2019

This article considers the current problem of investigation and development of computerlinguistic analysis of socio-demographic profile of virtual community member. Webmembers' socio-demographic characteristics' profile validation based on analysis of sociodemographic characteristics. The topicality of the paper is determined by the necessity to identify the web-community member by means of computer-linguistic analysis of their information track. The formal model of basic socio-demographic characteristics of virtual communities' member is formed. The structural model of lingvo-communicative indicators of socio-demographic characteristics of the web-members and common algorithm of the formation of lingvo-communicative indicators based on processing training sample are developed. Types of the computer-linguistic analysis of indicative characteristics are studied and classifications of lingvo-communicative indicators of gender, age and sphere of activities of web-community member is established. Also, the formal model of the basic socio-demographic characteristics of web-communities' member is introduced.
A Review on Energy, Environmental, and Sustainability Implications of Connected and Automated Vehicles
Venue: arXiv cs.CY 2019
Year: 2019

Connected and automated vehicles (CAVs) are poised to reshape transportation and mobility by replacing humans as the driver and service provider. While the primary stated motivation for vehicle automation is to improve safety and convenience of road mobility, this transformation also provides a valuable opportunity to improve vehicle energy efficiency and reduce emissions in the transportation sector. Progress in vehicle efficiency and functionality, however, does not necessarily translate to net positive environmental outcomes. Here we examine the interactions between CAV technology and the environment at four levels of increasing complexity: vehicle, transportation system, urban system, and society. We find that environmental impacts come from CAV-facilitated transformations at all four levels, rather than from CAV technology directly. We anticipate net positive environmental impacts at the vehicle, transportation system, and urban system levels, but expect greater vehicle utilization and shifts in travel patterns at the society level to offset some of these benefits. Focusing on the vehicle-level improvements associated with CAV technology is likely to yield excessively optimistic estimates of environmental benefits. Future research and policy efforts should strive to clarify the extent and possible synergetic effects from a systems level in order to envisage and address concerns regarding the short- and long-term sustainable adoption of CAV technology.
Rare geometries: revealing rare categories via dimension-driven statistics
Venue: arXiv cs.LG 2019
Year: 2019

In many situations, classes of data points of primary interest also happen to be those that are least numerous. A well-known example is detection of fraudulent transactions among the collection of all financial transactions, the vast majority of which are legitimate. These types of problems fall under the label of `rare-category detection.' There are two challenging aspects of these problems. The first is a general lack of labeled examples of the rare class and the second is the potential non-separability of the rare class from the majority (in terms of available features). Statistics related to the geometry of the rare class (such as its intrinsic dimension) can be significantly different from those for the majority class, reflecting the different dynamics driving variation in the different classes. In this paper we present a new supervised learning algorithm that uses a dimension-driven statistic, called the kappa-profile, to classify whether unlabeled points belong to a rare class. Our algorithm requires very few labeled examples and is invariant with respect to translation so that it performs equivalently on both separable and non-separable classes.
Enhanced Minimal Scheduling Function for IEEE802.15.4e TSCH Networks
Venue: arXiv cs.NI 2019
Year: 2019

MAC layer protocol design in a WSN is crucial due to the limitations on processing capacities and power of wireless sensors. The latest version of the IEEE 802.15.4, referenced to as IEEE 802.15.4e, was released by IEEE and outlines the mechanism of the Time Slotted Channel Hopping (TSCH). Hence, 6TiSCH working group has released a distributed algorithm for neighbour nodes to agree on a communication pattern driven by a minimal scheduling function. A slotframe contains a specific number of time slots, which are scheduled based on the application requirements and the routing topology. Sensors nodes use the schedule to determine when to transmit or to receive data. However, IEEE 802.15.4e TSCH does not address the specifics on planning time slot scheduling. In this paper, we propose a distributed Enhanced Minimal Scheduling Function (EMSF) based on the minimal scheduling function, which is compliant with 802.15.4e TSCH. In this vein, we introduce a distributed algorithm based on a Poisson process to predict the following schedule requirements. Consequently, the negotiation operations between pairs of nodes to agree about the schedule will be reduced. As a result, EMSF decreases the exchanged overhead, the end-to-end latency and the packet queue length significantly. Preliminary simulation results have confirmed that EMSF outperforms the 802.15.4e TSCH MSF scheduling algorithm.
On the Credibility of Information Flows in Real-time Wireless Networks
Venue: arXiv cs.NI 2019
Year: 2019

This paper considers a wireless network where multiple flows are delivering status updates about their respective information sources. An end user aims to make accurate real-time estimations about the status of each information source using its received packets. As the accuracy of estimation is most impacted by events in the recent past, we propose to measure the credibility of an information flow by the number of timely deliveries in a window of the recent past, and say that a flow suffers from a loss-of-credibility (LoC) if this number is insufficient for the end user to make an accurate estimation. We then study the problem of minimizing the system-wide LoC in wireless networks where each flow has different requirement and link quality. We show that the problem of minimizing the system-wide LoC requires the control of temporal variance of timely deliveries for each flow. This feature makes our problem significantly different from other optimization problems that only involves the average of control variables. Surprisingly, we show that there exists a simple online scheduling algorithm that is near-optimal. Simulation results show that our proposed algorithm is significantly better than other state-of-the-art policies.
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector
Venue: arXiv cs.RO 2019
Year: 2019

Training a deep object detector for autonomous driving requires a huge amount of labeled data. While recording data via on-board sensors such as camera or LiDAR is relatively easy, annotating data is very tedious and time-consuming, especially when dealing with 3D LiDAR points or radar data. Active learning has the potential to minimize human annotation efforts while maximizing the object detector's performance. In this work, we propose an active learning method to train a LiDAR 3D object detector with the least amount of labeled training data necessary. The detector leverages 2D region proposals generated from the RGB images to reduce the search space of objects and speed up the learning process. Experiments show that our proposed method works under different uncertainty estimations and query functions, and can save up to 60% of the labeling efforts while reaching the same network performance.
Enhanced Variational Inference with Dyadic Transformation
Venue: arXiv cs.LG 2019
Year: 2019

Variational autoencoder is a powerful deep generative model with variational inference. The practice of modeling latent variables in the VAE's original formulation as normal distributions with a diagonal covariance matrix limits the flexibility to match the true posterior distribution. We propose a new transformation, dyadic transformation (DT), that can model a multivariate normal distribution. DT is a single-stage transformation with low computational requirements. We demonstrate empirically on MNIST dataset that DT enhances the posterior flexibility and attains competitive results compared to other VAE enhancements.
On Multiterminal Communication over MIMO Channels with One-bit ADCs at the Receivers
Venue: arXiv cs.IT 2019
Year: 2019

The fundamental limits of communication over multiple-input multiple-output (MIMO) networks are considered when a limited number of one-bit analog to digital converters (ADC) are used at the receiver terminals. Prior works have mainly focused on point-to-point communications, where receiver architectures consisting of a concatenation of an analog processing module, a limited number of one-bit ADCs with non-adaptive thresholds, and a digital processing module are considered. In this work, a new receiver architecture is proposed which utilizes adaptive threshold one-bit ADCs - where the ADC thresholds at each channel-use are dependent on the channel outputs in the previous channel-uses - to mitigate the quantization rate-loss. Coding schemes are proposed for communication over the point-to-point and broadcast channels, and achievable rate regions are derived. In the high SNR regime, it is shown that using the proposed architectures and coding schemes leads to the largest achievable rate regions among all receiver architectures with the same number of one-bit ADCs.
An Actor-Critic Reinforcement Learning Method for Computation Offloading with Delay Constraints in Mobile Edge Computing
Venue: arXiv cs.NI 2019
Year: 2019

In this paper, we consider a mobile edge computing system that provides computing services by cloud server and edge server collaboratively. The mobile edge computing can both reduce service delay and ease the load on the core network. We model the problem of maximizing the average system revenues with the average delay constraints for different priority service as a constrained semi-Markov decision process (SMDP). We propose an actor-critic algorithm with eligibility traces to solve the constrained SMDP. We use neural networks to train the policy parameters and the state value function's parameters to continuously improve the system performance.
Doubly Sparse: Sparse Mixture of Sparse Experts for Efficient Softmax Inference
Venue: arXiv cs.LG 2019
Year: 2019

Computations for the softmax function are significantly expensive when the number of output classes is large. In this paper, we present a novel softmax inference speedup method, Doubly Sparse Softmax (DS-Softmax), that leverages sparse mixture of sparse experts to efficiently retrieve top-k classes. Different from most existing methods that require and approximate a fixed softmax, our method is learning-based and can adapt softmax weights for a better inference speedup. In particular, our method learns a two-level hierarchy which divides entire output class space into several partially overlapping experts. Each expert is sparse and only contains a subset of output classes. To find top-k classes, a sparse mixture enables us to find the most probable expert quickly, and the sparse expert enables us to search within a small-scale softmax. We empirically conduct evaluation on several real-world tasks, including neural machine translation, language modeling and image classification, and demonstrate that significant computation reductions can be achieved at no performance loss.
LU factorization with errors *
Venue: arXiv cs.SC 2019
Year: 2019

We present new algorithms to detect and correct errors in the lower-upper factorization of a matrix, or the triangular linear system solution, over an arbitrary field. Our main algorithms do not require any additional information or encoding other than the original inputs and the erroneous output. Their running time is softly linear in the dimension times the number of errors when there are few errors, smoothly growing to the cost of fast matrix multiplication as the number of errors increases. We also present applications to general linear system solving.
Autonomous Cars: Vision based Steering Wheel Angle Estimation
Venue: arXiv cs.CV 2019
Year: 2019

Machine learning models, which are frequently used in self-driving cars, are trained by matching the captured images of the road and the measured angle of the steering wheel. The angle of the steering wheel is generally fetched from steering angle sensor, which is tightly-coupled to the physical aspects of the vehicle at hand. Therefore, a model-agnostic autonomous car-kit is very difficult to be developed and autonomous vehicles need more training data. The proposed vision based steering angle estimation system argues a new approach which basically matches the images of the road captured by an outdoor camera and the images of the steering wheel from an onboard camera, avoiding the burden of collecting model-dependent training data and the use of any other electromechanical hardware.
Malicious cryptocurrency miners: Status and Outlook
Venue: arXiv cs.CY 2019
Year: 2019

In this study, we examine the behavior and profitability of modern malware that mines cryptocurrency. Unlike previous studies, we look at the cryptocurrency market as a whole, rather than just Bitcoin. We not only consider PCs, but also mobile phones, and IoT devices. In the past few years, criminals have attacked all these platforms for the purpose of cryptocurrency mining. The question is: how much money do they make? It is common knowledge that mining Bitcoin is now very difficult, so why do the criminals even target low-end devices for mining purposes? By analyzing the most important families of malicious cryptocurrency miners that were active between 2014 and 2017, we are able to report how they work, which currency they mine, and how profitable it is to do so. We will see that the evolution of the cryptocurrency market with many new cryptocurrencies that are still CPU minable and offer better privacy to criminals and have contributed to making mining malware attractive again -- with attackers generating a continuous stream of profit that in some cases may reach in the millions.
Automated Skin Lesion Classification Using Ensemble of Deep Neural Networks in ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection Challenge
Venue: arXiv cs.CV 2019
Year: 2019

In this paper, we studied extensively on different deep learning based methods to detect melanoma and skin lesion cancers. Melanoma, a form of malignant skin cancer is very threatening to health. Proper diagnosis of melanoma at an earlier stage is crucial for the success rate of complete cure. Dermoscopic images with Benign and malignant forms of skin cancer can be analyzed by computer vision system to streamline the process of skin cancer detection. In this study, we experimented with various neural networks which employ recent deep learning based models like PNASNet-5-Large, InceptionResNetV2, SENet154, InceptionV4. Dermoscopic images are properly processed and augmented before feeding them into the network. We tested our methods on International Skin Imaging Collaboration (ISIC) 2018 challenge dataset. Our system has achieved best validation score of 0.76 for PNASNet-5-Large model. Further improvement and optimization of the proposed methods with a bigger training dataset and carefully chosen hyper-parameter could improve the performances. The code available for download at https://github.com/miltonbd/ISIC_2018_classification