OpenRaiser/Intern-Atlas huggingface dataset:
Frugal Optimization for Cost-related Hyperparameters
Venue: arXiv cs.LG 2020
Year: 2020

The increasing demand for democratizing machine learning algorithms calls for hyperparameter optimization (HPO) solutions at low cost. Many machine learning algorithms have hyperparameters which can cause a large variation in the training cost. But this effect is largely ignored in existing HPO methods, which are incapable to properly control cost during the optimization process. To address this problem, we develop a new cost-frugal HPO solution. The core of our solution is a simple but new randomized direct-search method, for which we prove a convergence rate of $O(\frac{\sqrt{d}}{\sqrt{K}})$ and an $O(dε^{-2})$-approximation guarantee on the total cost. We provide strong empirical results in comparison with state-of-the-art HPO methods on large AutoML benchmarks.
An Adaptive Diffserv Approach To Support QoS In Network Mobility Nemo Environment
Venue: arXiv cs.NI 2020
Year: 2020

Network mobility basic support (NEMO BS) protocol (RFC 3963) is an extension of Mobile IPv6. The NEMO BS embraced by the IETF working group to permit any node in the portable network to be accessible to the Internet despite the fact the network itself is roaming. This protocol likewise Mobile IPv6 does not deliver any kind of Quality of Service (QoS) guarantees to its clients. It can barely offer the same level of services (i.e. Best-Effort) to all the users without obligation to the needs of applications. These propositions a challenge to real-time applications that demand a precise level of QoS pledge. The Differentiated Services have recently come to be the most widely used QoS support technology in IP networks due to its relative simplicity and scalability benefits. This paper proposes a new scheme to provide QoS to mobile network nodes within the NEMO context. The proposed scheme intends to reduce handover latency for the users of MNN as well as alleviates packet losses. The feasibility of the proposed enhancement is assessed by measuring its performance against the native NEMO BS standard protocol using the NS-2 simulator. The obtained results in the simulation study have demonstrated that the proposed scheme outperforms the standard NEMO BS protocol.
HOG, LBP and SVM based Traffic Density Estimation at Intersection
Venue: arXiv cs.CV 2020
Year: 2020

Increased amount of vehicular traffic on roads is a significant issue. High amount of vehicular traffic creates traffic congestion, unwanted delays, pollution, money loss, health issues, accidents, emergency vehicle passage and traffic violations that ends up in the decline in productivity. In peak hours, the issues become even worse. Traditional traffic management and control systems fail to tackle this problem. Currently, the traffic lights at intersections aren't adaptive and have fixed time delays. There's a necessity of an optimized and sensible control system which would enhance the efficiency of traffic flow. Smart traffic systems perform estimation of traffic density and create the traffic lights modification consistent with the quantity of traffic. We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time. The proposed methodology takes pictures of traffic at junction to estimate the traffic density. We use Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for traffic density estimation. The strategy is computationally inexpensive and can run efficiently on raspberry pi board. Code is released at https://github.com/DevashishPrasad/Smart-Traffic-Junction.
Learning-based Tracking of Fast Moving Objects
Venue: arXiv cs.CV 2020
Year: 2020

Tracking fast moving objects, which appear as blurred streaks in video sequences, is a difficult task for standard trackers as the object position does not overlap in consecutive video frames and texture information of the objects is blurred. Up-to-date approaches tuned for this task are based on background subtraction with static background and slow deblurring algorithms. In this paper, we present a tracking-by-segmentation approach implemented using state-of-the-art deep learning methods that performs near-realtime tracking on real-world video sequences. We implemented a physically plausible FMO sequence generator to be a robust foundation for our training pipeline and demonstrate the ease of fast generator and network adaptation for different FMO scenarios in terms of foreground variations.
A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017
Venue: arXiv cs.CY 2020
Year: 2020

Framing is an indispensable narrative device for news media because even the same facts may lead to conflicting understandings if deliberate framing is employed. Therefore, identifying media framing is a crucial step to understanding how news media influence the public. Framing is, however, difficult to operationalize and detect, and thus traditional media framing studies had to rely on manual annotation, which is challenging to scale up to massive news datasets. Here, by developing a media frame classifier that achieves state-of-the-art performance, we systematically analyze the media frames of 1.5 million New York Times articles published from 2000 to 2017. By examining the ebb and flow of media frames over almost two decades, we show that short-term frame abundance fluctuation closely corresponds to major events, while there also exist several long-term trends, such as the gradually increasing prevalence of the ``Cultural identity'' frame. By examining specific topics and sentiments, we identify characteristics and dynamics of each frame. Finally, as a case study, we delve into the framing of mass shootings, revealing three major framing patterns. Our scalable, computational approach to massive news datasets opens up new pathways for systematic media framing studies.
Understanding Scanned Receipts
Venue: arXiv cs.CL 2020
Year: 2020

Tasking machines with understanding receipts can have important applications such as enabling detailed analytics on purchases, enforcing expense policies, and inferring patterns of purchase behavior on large collections of receipts. In this paper, we focus on the task of Named Entity Linking (NEL) of scanned receipt line items; specifically, the task entails associating shorthand text from OCR'd receipts with a knowledge base (KB) of grocery products. For example, the scanned item "STO BABY SPINACH" should be linked to the catalog item labeled "Simple Truth Organic Baby Spinach". Experiments that employ a variety of Information Retrieval techniques in combination with statistical phrase detection shows promise for effective understanding of scanned receipt data.
Fine-grained Financial Opinion Mining: A Survey and Research Agenda
Venue: arXiv cs.CL 2020
Year: 2020

Opinion mining is a prevalent research issue in many domains. In the financial domain, however, it is still in the early stages. Most of the researches on this topic only focus on the coarse-grained market sentiment analysis, i.e., 2-way classification for bullish/bearish. Thanks to the recent financial technology (FinTech) development, some interdisciplinary researchers start to involve in the in-depth analysis of investors' opinions. In this position paper, we first define the financial opinions from both coarse-grained and fine-grained points of views, and then provide an overview on the issues already tackled. In addition to listing research issues of the existing topics, we further propose a road map of fine-grained financial opinion mining for future researches, and point out several challenges yet to explore. Moreover, we provide possible directions to deal with the proposed research issues.
Helly-gap of a graph and vertex eccentricities
Venue: arXiv cs.DM 2020
Year: 2020

A new metric parameter for a graph, Helly-gap, is introduced. A graph $G$ is called $α$-weakly-Helly if any system of pairwise intersecting disks in $G$ has a nonempty common intersection when the radius of each disk is increased by an additive value $α$. The minimum $α$ for which a graph $G$ is $α$-weakly-Helly is called the Helly-gap of $G$ and denoted by $α(G)$. The Helly-gap of a graph $G$ is characterized by distances in the injective hull $\mathcal{H}(G)$, which is a (unique) minimal Helly graph which contains $G$ as an isometric subgraph. This characterization is used as a tool to generalize many eccentricity related results known for Helly graphs ($α(G)=0$), as well as for chordal graphs ($α(G)\le 1$), distance-hereditary graphs ($α(G)\le 1$) and $δ$-hyperbolic graphs ($α(G)\le 2δ$), to all graphs, parameterized by their Helly-gap $α(G)$. Several additional graph classes are shown to have a bounded Helly-gap, including AT-free graphs and graphs with bounded tree-length, bounded chordality or bounded $α_i$-metric.
Generating Thermal Image Data Samples using 3D Facial Modelling Techniques and Deep Learning Methodologies
Venue: arXiv cs.CV 2020
Year: 2020

Methods for generating synthetic data have become of increasing importance to build large datasets required for Convolution Neural Networks (CNN) based deep learning techniques for a wide range of computer vision applications. In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models. For the proposed research work we have used tufts datasets for generating 3D varying face poses by using a single frontal face pose. The system works by refining the existing image quality by performing fusion based image preprocessing operations. The refined outputs have better contrast adjustments, decreased noise level and higher exposedness of the dark regions. It makes the facial landmarks and temperature patterns on the human face more discernible and visible when compared to original raw data. Different image quality metrics are used to compare the refined version of images with original images. In the next phase of the proposed study, the refined version of images is used to create 3D facial geometry structures by using Convolution Neural Networks (CNN). The generated outputs are then imported in blender software to finally extract the 3D thermal facial outputs of both males and females. The same technique is also used on our thermal face data acquired using prototype thermal camera (developed under Heliaus EU project) in an indoor lab environment which is then used for generating synthetic 3D face data along with varying yaw face angles and lastly facial depth map is generated.
P2ExNet: Patch-based Prototype Explanation Network
Venue: arXiv cs.AI 2020
Year: 2020

Deep learning methods have shown great success in several domains as they process a large amount of data efficiently, capable of solving complex classification, forecast, segmentation, and other tasks. However, they come with the inherent drawback of inexplicability limiting their applicability and trustworthiness. Although there exists work addressing this perspective, most of the existing approaches are limited to the image modality due to the intuitive and prominent concepts. Conversely, the concepts in the time-series domain are more complex and non-comprehensive but these and an explanation for the network decision are pivotal in critical domains like medical, financial, or industry. Addressing the need for an explainable approach, we propose a novel interpretable network scheme, designed to inherently use an explainable reasoning process inspired by the human cognition without the need of additional post-hoc explainability methods. Therefore, class-specific patches are used as they cover local concepts relevant to the classification to reveal similarities with samples of the same class. In addition, we introduce a novel loss concerning interpretability and accuracy that constraints P2ExNet to provide viable explanations of the data including relevant patches, their position, class similarities, and comparison methods without compromising accuracy. Analysis of the results on eight publicly available time-series datasets reveals that P2ExNet reaches comparable performance when compared to its counterparts while inherently providing understandable and traceable decisions.
Establishing Baselines for Text Classification in Low-Resource Languages
Venue: arXiv cs.CL 2020
Year: 2020

While transformer-based finetuning techniques have proven effective in tasks that involve low-resource, low-data environments, a lack of properly established baselines and benchmark datasets make it hard to compare different approaches that are aimed at tackling the low-resource setting. In this work, we provide three contributions. First, we introduce two previously unreleased datasets as benchmark datasets for text classification and low-resource multilabel text classification for the low-resource language Filipino. Second, we pretrain better BERT and DistilBERT models for use within the Filipino setting. Third, we introduce a simple degradation test that benchmarks a model's resistance to performance degradation as the number of training samples are reduced. We analyze our pretrained model's degradation speeds and look towards the use of this method for comparing models aimed at operating within the low-resource setting. We release all our models and datasets for the research community to use.
Finding the maximum-a-posteriori behaviour of agents in an agent-based model
Venue: arXiv cs.MA 2020
Year: 2020

In this paper we consider the problem of finding the most probable set of events that could have led to a set of partial, noisy observations of some dynamical system. In particular, we consider the case of a dynamical system that is a (possibly stochastic) time-stepping agent-based model with a discrete state space, the (possibly noisy) observations are the number of agents that have some given property and the events we're interested in are the decisions made by the agents (their ``expressed behaviours'') as the model evolves. We show that this problem can be reduced to an integer linear programming problem which can subsequently be solved numerically using a standard branch-and-cut algorithm. We describe two implementations, an ``offline'' algorithm that finds the maximum-a-posteriori expressed behaviours given a set of observations over a finite time window, and an ``online'' algorithm that incrementally builds a feasible set of behaviours from a stream of observations that may have no natural beginning or end. We demonstrate both algorithms on a spatial predator-prey model on a 32x32 grid with an initial population of 100 agents.
An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control
Venue: arXiv cs.HC 2020
Year: 2020

In the 34 developed and 156 developing countries, there are about 132 million disabled people who need a wheelchair constituting 1.86% of the world population. Moreover, there are millions of people suffering from diseases related to motor disabilities, which cause inability to produce controlled movement in any of the limbs or even head.The paper proposes a system to aid people with motor disabilities by restoring their ability to move effectively and effortlessly without having to rely on others utilizing an eye-controlled electric wheelchair. The system input was images of the users eye that were processed to estimate the gaze direction and the wheelchair was moved accordingly. To accomplish such a feat, four user-specific methods were developed, implemented and tested; all of which were based on a benchmark database created by the authors.The first three techniques were automatic, employ correlation and were variants of template matching, while the last one uses convolutional neural networks (CNNs). Different metrics to quantitatively evaluate the performance of each algorithm in terms of accuracy and latency were computed and overall comparison is presented. CNN exhibited the best performance (i.e. 99.3% classification accuracy), and thus it was the model of choice for the gaze estimator, which commands the wheelchair motion. The system was evaluated carefully on 8 subjects achieving 99% accuracy in changing illumination conditions outdoor and indoor. This required modifying a motorized wheelchair to adapt it to the predictions output by the gaze estimation algorithm. The wheelchair control can bypass any decision made by the gaze estimator and immediately halt its motion with the help of an array of proximity sensors, if the measured distance goes below a well-defined safety margin.
Quality Guided Sketch-to-Photo Image Synthesis
Venue: arXiv cs.CV 2020
Year: 2020

Facial sketches drawn by artists are widely used for visual identification applications and mostly by law enforcement agencies, but the quality of these sketches depend on the ability of the artist to clearly replicate all the key facial features that could aid in capturing the true identity of a subject. Recent works have attempted to synthesize these sketches into plausible visual images to improve visual recognition and identification. However, synthesizing photo-realistic images from sketches proves to be an even more challenging task, especially for sensitive applications such as suspect identification. In this work, we propose a novel approach that adopts a generative adversarial network that synthesizes a single sketch into multiple synthetic images with unique attributes like hair color, sex, etc. We incorporate a hybrid discriminator which performs attribute classification of multiple target attributes, a quality guided encoder that minimizes the perceptual dissimilarity of the latent space embedding of the synthesized and real image at different layers in the network and an identity preserving network that maintains the identity of the synthesised image throughout the training process. Our approach is aimed at improving the visual appeal of the synthesised images while incorporating multiple attribute assignment to the generator without compromising the identity of the synthesised image. We synthesised sketches using XDOG filter for the CelebA, WVU Multi-modal and CelebA-HQ datasets and from an auxiliary generator trained on sketches from CUHK, IIT-D and FERET datasets. Our results are impressive compared to current state of the art.
A Space-Efficient Dynamic Dictionary for Multisets with Constant Time Operations
Venue: arXiv cs.DS 2020
Year: 2020

We consider the dynamic dictionary problem for multisets. Given an upper bound $n$ on the total cardinality of the multiset (i.e., including multiplicities) at any point in time, the goal is to design a data structure that supports multiplicity queries and allows insertions and deletions to the multiset (i.e., the dynamic setting). The data structure must be space-efficient (the space is $1+o(1)$ times the information-theoretic lower bound) and support all operations in constant time with high probability. In this paper, we present the first dynamic dictionary for multisets that achieves these performance guarantees. This answers an open problem of Arbitman, Naor and Segev (FOCS 2010). The previously best-known construction of Pagh, Pagh and Rao (SODA 2005) supports membership in constant time, multiplicity queries in $O(\log n)$ time in the worst case, and insertions and deletions in constant expected amortized time. The main technical component of our solution is a strategy for efficiently storing variable-length binary counters using weighted balls-into-bins experiments in which balls have logarithmic weights. We also obtain a counting filter that approximates multiplicity queries with a one sided error, using the reduction of Carter et al. (STOC 1978). Counting filters have received significant attention over the years due to their applicability in practice.We present the first counting filter with constant time operations.
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods
Venue: arXiv cs.CV 2020
Year: 2020

Detection of wheat heads is an important task allowing to estimate pertinent traits including head population density and head characteristics such as sanitary state, size, maturity stage and the presence of awns. Several studies developed methods for wheat head detection from high-resolution RGB imagery. They are based on computer vision and machine learning and are generally calibrated and validated on limited datasets. However, variability in observational conditions, genotypic differences, development stages, head orientation represents a challenge in computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse and well-labelled dataset, the Global Wheat Head detection (GWHD) dataset. It contains 4,700 high-resolution RGB images and 190,000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD is publicly available at http://www.global-wheat.com/ and aimed at developing and benchmarking methods for wheat head detection.
AutoTune: Automatically Tuning Convolutional Neural Networks for Improved Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

Transfer learning enables solving a specific task having limited data by using the pre-trained deep networks trained on large-scale datasets. Typically, while transferring the learned knowledge from source task to the target task, the last few layers are fine-tuned (re-trained) over the target dataset. However, these layers are originally designed for the source task that might not be suitable for the target task. In this paper, we introduce a mechanism for automatically tuning the Convolutional Neural Networks (CNN) for improved transfer learning. The pre-trained CNN layers are tuned with the knowledge from target data using Bayesian Optimization. First, we train the final layer of the base CNN model by replacing the number of neurons in the softmax layer with the number of classes involved in the target task. Next, the pre-trained CNN is tuned automatically by observing the classification performance on the validation data (greedy criteria). To evaluate the performance of the proposed method, experiments are conducted on three benchmark datasets, e.g., CalTech-101, CalTech-256, and Stanford Dogs. The classification results obtained through the proposed AutoTune method outperforms the standard baseline transfer learning methods over the three datasets by achieving $95.92\%$, $86.54\%$, and $84.67\%$ accuracy over CalTech-101, CalTech-256, and Stanford Dogs, respectively. The experimental results obtained in this study depict that tuning of the pre-trained CNN layers with the knowledge from the target dataset confesses better transfer learning ability. The source codes are available at https://github.com/JekyllAndHyde8999/AutoTune_CNN_TransferLearning.