OpenRaiser/Intern-Atlas huggingface dataset:
Probing Representations Learned by Multimodal Recurrent and Transformer Models
Venue: arXiv cs.CL 2019
Year: 2019

Recent literature shows that large-scale language modeling provides excellent reusable sentence representations with both recurrent and self-attentive architectures. However, there has been less clarity on the commonalities and differences in the representational properties induced by the two architectures. It also has been shown that visual information serves as one of the means for grounding sentence representations. In this paper, we present a meta-study assessing the representational quality of models where the training signal is obtained from different modalities, in particular, language modeling, image features prediction, and both textual and multimodal machine translation. We evaluate textual and visual features of sentence representations obtained using predominant approaches on image retrieval and semantic textual similarity. Our experiments reveal that on moderate-sized datasets, a sentence counterpart in a target language or visual modality provides much stronger training signal for sentence representation than language modeling. Importantly, we observe that while the Transformer models achieve superior machine translation quality, representations from the recurrent neural network based models perform significantly better over tasks focused on semantic relevance.
On Efficient Data Transfers Across Geographically Dispersed Datacenters
Venue: arXiv cs.NI 2019
Year: 2019

As applications become more distributed to improve user experience and offer higher availability, businesses rely on geographically dispersed datacenters that host such applications more than ever. Dedicated inter-datacenter networks have been built that provide high visibility into the network status and flexible control over traffic forwarding to offer quality communication across the instances of applications hosted on many datacenters. These networks are relatively small, with tens to hundreds of nodes and are managed by the same organization that operates the datacenters which make centralized traffic engineering feasible. Using coordinated data transmission from the services and routing over the inter-datacenter network, one can optimize the network performance according to a variety of utility functions that take into account data transfer deadlines, network capacity consumption, and transfer completion times. In this dissertation, we study techniques and algorithms for fast and efficient data transfers across geographically dispersed datacenters over the inter-datacenter networks. We discuss different forms and properties of inter-datacenter transfers and present a generalized optimization framework to maximize an operator selected utility function. Next, in the several chapters that follow, we study, in detail, the problems of admission control for transfers with deadlines and inter-datacenter multicast transfers. For the admission control problem, our solutions offer significant speed up in the admission control process while offering almost identical performance in the total traffic admitted into the network. For the bulk multicasting problem, our techniques enable significant performance gain in receiver completion times with low computational complexity, which makes them highly applicable to inter-datacenter networks.
KBSET -- Knowledge-Based Support for Scholarly Editing and Text Processing
Venue: arXiv cs.AI 2019
Year: 2019

KBSET supports a practical workflow for scholarly editing, based on using LaTeX with dedicated commands for semantics-oriented markup and a Prolog-implemented core system. Prolog plays there various roles: as query language and access mechanism for large Semantic Web fact bases, as data representation of structured documents and as a workflow model for advanced application tasks. The core system includes a LaTeX parser and a facility for the identification of named entities. We also sketch future perspectives of this approach to scholarly editing based on techniques of computational logic.
Active Learning for UAV-based Semantic Mapping
Venue: arXiv cs.RO 2019
Year: 2019

Unmanned aerial vehicles combined with computer vision systems, such as convolutional neural networks, offer a flexible and affordable solution for terrain monitoring, mapping, and detection tasks. However, a key challenge remains the collection and annotation of training data for the given sensors, application, and mission. We introduce an informative path planning system that incorporates novelty estimation into its objective function, based on research for uncertainty estimation in deep learning. The system is designed for data collection to reduce both the number of flights and of annotated images. We evaluate the approach on real world terrain mapping data and show significantly smaller collected training dataset compared to standard lawnmower data collection techniques.
PULP-NN: Accelerating Quantized Neural Networks on Parallel Ultra-Low-Power RISC-V Processors
Venue: arXiv cs.NE 2019
Year: 2019

We present PULP-NN, an optimized computing library for a parallel ultra-low-power tightly coupled cluster of RISC-V processors. The key innovation in PULP-NN is a set of kernels for Quantized Neural Network (QNN) inference, targeting byte and sub-byte data types, down to INT-1, tuned for the recent trend toward aggressive quantization in deep neural network inference. The proposed library exploits both the digital signal processing (DSP) extensions available in the PULP RISC-V processors and the cluster's parallelism, achieving up to 15.5 MACs/cycle on INT-8 and improving performance by up to 63x with respect to a sequential implementation on a single RISC-V core implementing the baseline RV32IMC ISA. Using PULP-NN, a CIFAR-10 network on an octa-core cluster runs in 30x and 19.6x less clock cycles than the current state-of-the-art ARM CMSIS-NN library, running on STM32L4 and STM32H7 MCUs, respectively. The proposed library, when running on GAP-8 processor, outperforms by 36.8x and by 7.45x the execution on energy efficient MCUs such as STM32L4 and high-end MCUs such as STM32H7 respectively, when operating at the maximum frequency. The energy efficiency on GAP-8 is 14.1x higher than STM32L4 and 39.5x higher than STM32H7, at the maximum efficiency operating point.
Exploiting Temporality for Semi-Supervised Video Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

In recent years, there has been remarkable progress in supervised image segmentation. Video segmentation is less explored, despite the temporal dimension being highly informative. Semantic labels, e.g. that cannot be accurately detected in the current frame, may be inferred by incorporating information from previous frames. However, video segmentation is challenging due to the amount of data that needs to be processed and, more importantly, the cost involved in obtaining ground truth annotations for each frame. In this paper, we tackle the issue of label scarcity by using consecutive frames of a video, where only one frame is annotated. We propose a deep, end-to-end trainable model which leverages temporal information in order to make use of easy to acquire unlabeled data. Our network architecture relies on a novel interconnection of two components: a fully convolutional network to model spatial information and temporal units that are employed at intermediate levels of the convolutional network in order to propagate information through time. The main contribution of this work is the guidance of the temporal signal through the network. We show that only placing a temporal module between the encoder and decoder is suboptimal (baseline). Our extensive experiments on the CityScapes dataset indicate that the resulting model can leverage unlabeled temporal frames and significantly outperform both the frame-by-frame image segmentation and the baseline approach.
Multivariate Spatial Data Visualization: A Survey
Venue: arXiv cs.HC 2019
Year: 2019

Multivariate spatial data plays an important role in computational science and engineering simulations. The potential features and hidden relationships in multivariate data can assist scientists to gain an in-depth understanding of a scientific process, verify a hypothesis and further discover a new physical or chemical law. In this paper, we present a comprehensive survey of the state-of-the-art techniques for multivariate spatial data visualization. We first introduce the basic concept and characteristics of multivariate spatial data, and describe three main tasks in multivariate data visualization: feature classification, fusion visualization, and correlation analysis. Finally, we prospect potential research topics for multivariate data visualization according to the current research.
On exact asymptotics of the error probability in channel coding: symmetric channels
Venue: arXiv cs.IT 2019
Year: 2019

The exact order of the optimal sub-exponentially decaying factor in the classical bounds on the error probability of fixed-length codes over a Gallager-symmetric discrete memoryless channel with and without ideal feedback is determined. Regardless of the availability of feedback, it is shown that the order of the optimal sub-exponential factor exhibits a dichotomy. Moreover, the proof technique is used to establish the third-order term in the normal approximation for symmetric channels, where a similar dichotomy is shown to exist.
A New Method for Employing Feedback to Improve Coding Performance
Venue: arXiv cs.IT 2019
Year: 2019

We introduce a novel mechanism, called timid/bold coding, by which feedback can be used to improve coding performance. For a certain class of DMCs, called compound-dispersion channels, we show that timid/bold coding allows for an improved second-order coding rate compared with coding without feedback. For DMCs that are not compound dispersion, we show that feedback does not improve the second-order coding rate. Thus we completely determine the class of DMCs for which feedback improves the second-order coding rate. An upper bound on the second-order coding rate is provided for compound-dispersion DMCs. We also show that feedback does not improve the second-order coding rate for very noisy DMCs. The main results are obtained by relating feedback codes to certain controlled diffusions.
Mobile Cloud Computing in Healthcare Using Dynamic Cloudlets for Energy-Aware Consumption
Venue: arXiv cs.DC 2019
Year: 2019

Mobile cloud computing (MCC) has increasingly been adopted in healthcare industry by healthcare professionals (HCPs) which has resulted in the growth of medical software applications for these platforms. There are different applications which help HCPs with many important tasks. Mobile cloud computing has helped HCPs in better decision making and improved patient care. MCC enables users to acquire the benefit of cloud computing services to meet the healthcare demands. However, the restrictions posed by network bandwidth and mobile device capacity has brought challenges with respect to energy consumption and latency delays. In this paper we propose dynamic energy consumption mobile cloud computing model (DEMCCM) which addresses the energy consumption issue by healthcare mobile devices using dynamic cloudlets.
Class-Based Styling: Real-time Localized Style Transfer with Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

We propose a Class-Based Styling method (CBS) that can map different styles for different object classes in real-time. CBS achieves real-time performance by carrying out two steps simultaneously. While a semantic segmentation method is used to obtain the mask of each object class in a video frame, a styling method is used to style that frame globally. Then an object class can be styled by combining the segmentation mask and the styled image. The user can also select multiple styles so that different object classes can have different styles in a single frame. For semantic segmentation, we leverage DABNet that achieves high accuracy, yet only has 0.76 million parameters and runs at 104 FPS. For the style transfer step, we use a popular real-time method proposed by Johnson et al. [7]. We evaluated CBS on a video of the CityScapes dataset and observed high-quality localized style transfer results for different object classes and real-time performance.
IoT based Smart Access Controlled Secure Smart City Architecture Using Blockchain
Venue: arXiv cs.CR 2019
Year: 2019

Standard security protocols like SSL, TLS, IPSec etc. have high memory and processor consumption which makes all these security protocols unsuitable for resource constrained platforms such as Internet of Things (IoT). Blockchain (BC) finds its efficient application in IoT platform to preserve the five basic cryptographic primitives, such as confidentiality, authenticity, integrity, availability and non-repudiation. Conventional adoption of BC in IoT platform causes high energy consumption, delay and computational overhead which are not appropriate for various resource constrained IoT devices. This work proposes a machine learning (ML) based smart access control framework in a public and a private BC for a smart city application which makes it more efficient as compared to the existing IoT applications. The proposed IoT based smart city architecture adopts BC technology for preserving all the cryptographic security and privacy issues. Moreover, BC has very minimal overhead on IoT platform as well. This work investigates the existing threat models and critical access control issues which handle multiple permissions of various nodes and detects relevant inconsistencies to notify the corresponding nodes. Comparison in terms of all security issues with existing literature shows that the proposed architecture is competitively efficient in terms of security access control.
Small Obstacle Avoidance Based on RGB-D Semantic Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a novel obstacle avoidance system for road robots equipped with RGB-D sensor that captures scenes of its way forward. The purpose of the system is to have road robots move around autonomously and constantly without any collision even with small obstacles, which are often missed by existing solutions. For each input RGB-D image, the system uses a new two-stage semantic segmentation network followed by the morphological processing to generate the accurate semantic map containing road and obstacles. Based on the map, the local path planning is applied to avoid possible collision. Additionally, optical flow supervision and motion blurring augmented training scheme is applied to improve temporal consistency between adjacent frames and overcome the disturbance caused by camera shake. Various experiments are conducted to show that the proposed architecture obtains high performance both in indoor and outdoor scenarios.
High Performance Scalable FPGA Accelerator for Deep Neural Networks
Venue: arXiv cs.DC 2019
Year: 2019

Low-precision is the first order knob for achieving higher Artificial Intelligence Operations (AI-TOPS). However the algorithmic space for sub-8-bit precision compute is diverse, with disruptive changes happening frequently, making FPGAs a natural choice for Deep Neural Network inference, In this work we present an FPGA-based accelerator for CNN inference acceleration. We use {\it INT-8-2} compute (with {\it 8 bit} activation and {2 bit} weights) which is recently showing promise in the literature, and which no known ASIC, CPU or GPU natively supports today. Using a novel Adaptive Logic Module (ALM) based design, as a departure from traditional DSP based designs, we are able to achieve high performance measurement of 5 AI-TOPS for {\it Arria10} and project a performance of 76 AI-TOPS at 0.7 TOPS/W for {\it Stratix10}. This exceeds known CPU, GPU performance and comes close to best known ASIC (TPU) numbers, while retaining the versatility of the FPGA platform for other applications.
Rethinking Irregular Scene Text Recognition
Venue: arXiv cs.CV 2019
Year: 2019

Reading text from natural images is challenging due to the great variety in text font, color, size, complex background and etc.. The perspective distortion and non-linear spatial arrangement of characters make it further difficult. While rectification based method is intuitively grounded and has pushed the envelope by far, its potential is far from being well exploited. In this paper, we present a bag of tricks that prove to significantly improve the performance of rectification based method. On curved text dataset, our method achieves an accuracy of 89.6% on CUTE-80 and 76.3% on Total-Text, an improvement over previous state-of-the-art by 6.3% and 14.7% respectively. Furthermore, our combination of tricks helps us win the ICDAR 2019 Arbitrary-Shaped Text Challenge (Latin script), achieving an accuracy of 74.3% on the held-out test set. We release our code as well as data samples for further exploration at https://github.com/Jyouhou/ICDAR2019-ArT-Recognition-Alchemy
Adaptively Sparse Transformers
Venue: arXiv cs.CL 2019
Year: 2019

Attention mechanisms have become ubiquitous in NLP. Recent architectures, notably the Transformer, learn powerful context-aware word representations through layered, multi-headed attention. The multiple heads learn diverse types of word relationships. However, with standard softmax attention, all attention heads are dense, assigning a non-zero weight to all context words. In this work, we introduce the adaptively sparse Transformer, wherein attention heads have flexible, context-dependent sparsity patterns. This sparsity is accomplished by replacing softmax with $α$-entmax: a differentiable generalization of softmax that allows low-scoring words to receive precisely zero weight. Moreover, we derive a method to automatically learn the $α$ parameter -- which controls the shape and sparsity of $α$-entmax -- allowing attention heads to choose between focused or spread-out behavior. Our adaptively sparse Transformer improves interpretability and head diversity when compared to softmax Transformers on machine translation datasets. Findings of the quantitative and qualitative analysis of our approach include that heads in different layers learn different sparsity preferences and tend to be more diverse in their attention distributions than softmax Transformers. Furthermore, at no cost in accuracy, sparsity in attention heads helps to uncover different head specializations.
Quantity doesn't buy quality syntax with neural language models
Venue: arXiv cs.CL 2019
Year: 2019

Recurrent neural networks can learn to predict upcoming words remarkably well on average; in syntactically complex contexts, however, they often assign unexpectedly high probabilities to ungrammatical words. We investigate to what extent these shortcomings can be mitigated by increasing the size of the network and the corpus on which it is trained. We find that gains from increasing network size are minimal beyond a certain point. Likewise, expanding the training corpus yields diminishing returns; we estimate that the training corpus would need to be unrealistically large for the models to match human performance. A comparison to GPT and BERT, Transformer-based models trained on billions of words, reveals that these models perform even more poorly than our LSTMs in some constructions. Our results make the case for more data efficient architectures.
Software-Defined Network-Based Vehicular Networks: A Position Paper on Their Modeling and Implementation
Venue: arXiv cs.NI 2019
Year: 2019

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks' challenges handled by SDN-based vehicular networks.
Conditions for Unnecessary Logical Constraints in Kernel Machines
Venue: arXiv cs.LO 2019
Year: 2019

A main property of support vector machines consists in the fact that only a small portion of the training data is significant to determine the maximum margin separating hyperplane in the feature space, the so called support vectors. In a similar way, in the general scheme of learning from constraints, where possibly several constraints are considered, some of them may turn out to be unnecessary with respect to the learning optimization, even if they are active for a given optimal solution. In this paper we extend the definition of support vector to support constraint and we provide some criteria to determine which constraints can be removed from the learning problem still yielding the same optimal solutions. In particular, we discuss the case of logical constraints expressed by Lukasiewicz logic, where both inferential and algebraic arguments can be considered. Some theoretical results that characterize the concept of unnecessary constraint are proved and explained by means of examples.
Multiple Object Tracking with Motion and Appearance Cues
Venue: arXiv cs.CV 2019
Year: 2019

Due to better video quality and higher frame rate, the performance of multiple object tracking issues has been greatly improved in recent years. However, in real application scenarios, camera motion and noisy per frame detection results degrade the performance of trackers significantly. High-speed and high-quality multiple object trackers are still in urgent demand. In this paper, we propose a new multiple object tracker following the popular tracking-by-detection scheme. We tackle the camera motion problem with an optical flow network and utilize an auxiliary tracker to deal with the missing detection problem. Besides, we use both the appearance and motion information to improve the matching quality. The experimental results on the VisDrone-MOT dataset show that our approach can improve the performance of multiple object tracking significantly while achieving a high efficiency.
Model-free Visual Control for Continuum Robot Manipulators via Orientation Adaptation
Venue: arXiv cs.RO 2019
Year: 2019

We present an orientation adaptive controller to compensate for the effects of highly constrained environments on continuum manipulator actuation. A transformation matrix updated using optimal estimation techniques from optical flow measurements captured by the distal camera is composed with any Jacobian estimation or kinematic model to compensate for these effects. By utilizing domain knowledge to define the structure of this matrix, fewer parameters need to be estimated and a stable controller can be guaranteed. The algorithm is tested on a custom robotic catheter and convergence is shown both empirically and theoretically.