OpenRaiser/Intern-Atlas huggingface dataset:
Inference of Recyclable Objects with Convolutional Neural Networks
Venue: arXiv cs.CV 2021
Year: 2021

Population growth in the last decades has resulted in the production of about 2.01 billion tons of municipal waste per year. The current waste management systems are not capable of providing adequate solutions for the disposal and use of these wastes. Recycling and reuse have proven to be a solution to the problem, but large-scale waste segregation is a tedious task and on a small scale it depends on public awareness. This research used convolutional neural networks and computer vision to develop a tool for the automation of solid waste sorting. The Fotini10k dataset was constructed, which has more than 10,000 images divided into the categories of 'plastic bottles', 'aluminum cans' and 'paper and cardboard'. ResNet50, MobileNetV1 and MobileNetV2 were retrained with ImageNet weights on the Fotini10k dataset. As a result, top-1 accuracy of 99% was obtained in the test dataset with all three networks. To explore the possible use of these networks in mobile applications, the three nets were quantized in float16 weights. By doing so, it was possible to obtain inference times twice as low for Raspberry Pi and three times as low for computer processing units. It was also possible to reduce the size of the networks by half. When quantizing the top-1 accuracy of 99% was maintained with all three networks. When quantizing MobileNetV2 to int-8, it obtained a top-1 accuracy of 97%.
A Comparative Analysis of Machine Learning and Grey Models
Venue: arXiv cs.LG 2021
Year: 2021

Artificial Intelligence (AI) has recently shown its capabilities for almost every field of life. Machine Learning, which is a subset of AI, is a `HOT' topic for researchers. Machine Learning outperforms other classical forecasting techniques in almost all-natural applications. It is a crucial part of modern research. As per this statement, Modern Machine Learning algorithms are hungry for big data. Due to the small datasets, the researchers may not prefer to use Machine Learning algorithms. To tackle this issue, the main purpose of this survey is to illustrate, demonstrate related studies for significance of a semi-parametric Machine Learning framework called Grey Machine Learning (GML). This kind of framework is capable of handling large datasets as well as small datasets for time series forecasting likely outcomes. This survey presents a comprehensive overview of the existing semi-parametric machine learning techniques for time series forecasting. In this paper, a primer survey on the GML framework is provided for researchers. To allow an in-depth understanding for the readers, a brief description of Machine Learning, as well as various forms of conventional grey forecasting models are discussed. Moreover, a brief description on the importance of GML framework is presented.
Symmetric Continuous Subgraph Matching with Bidirectional Dynamic Programming
Venue: arXiv cs.DB 2021
Year: 2021

In many real datasets such as social media streams and cyber data sources, graphs change over time through a graph update stream of edge insertions and deletions. Detecting critical patterns in such dynamic graphs plays an important role in various application domains such as fraud detection, cyber security, and recommendation systems for social networks. Given a dynamic data graph and a query graph, the continuous subgraph matching problem is to find all positive matches for each edge insertion and all negative matches for each edge deletion. The state-of-the-art algorithm TurboFlux uses a spanning tree of a query graph for filtering. However, using the spanning tree may have a low pruning power because it does not take into account all edges of the query graph. In this paper, we present a symmetric and much faster algorithm SymBi which maintains an auxiliary data structure based on a directed acyclic graph instead of a spanning tree, which maintains the intermediate results of bidirectional dynamic programming between the query graph and the dynamic graph. Extensive experiments with real and synthetic datasets show that SymBi outperforms the state-of-the-art algorithm by up to three orders of magnitude in terms of the elapsed time.
Low Dose Helical CBCT denoising by using domain filtering with deep reinforcement learning
Venue: arXiv cs.CV 2021
Year: 2021

Cone Beam Computed Tomography(CBCT) is a now known method to conduct CT imaging. Especially, The Low Dose CT imaging is one of possible options to protect organs of patients when conducting CT imaging. Therefore Low Dose CT imaging can be an alternative instead of Standard dose CT imaging. However Low Dose CT imaging has a fundamental issue with noises within results compared to Standard Dose CT imaging. Currently, there are lots of attempts to erase the noises. Most of methods with artificial intelligence have many parameters and unexplained layers or a kind of black-box methods. Therefore, our research has purposes related to these issues. Our approach has less parameters than usual methods by having Iterative learn-able bilateral filtering approach with Deep reinforcement learning. And we applied The Iterative learn-able filtering approach with deep reinforcement learning to sinograms and reconstructed volume domains. The method and the results of the method can be much more explainable than The other black box AI approaches. And we applied the method to Helical Cone Beam Computed Tomography(CBCT), which is the recent CBCT trend. We tested this method with on 2 abdominal scans(L004, L014) from Mayo Clinic TCIA dataset. The results and the performances of our approach overtake the results of the other previous methods.
FT-BLAS: A High Performance BLAS Implementation With Online Fault Tolerance
Venue: arXiv cs.DC 2021
Year: 2021

Basic Linear Algebra Subprograms (BLAS) is a core library in scientific computing and machine learning. This paper presents FT-BLAS, a new implementation of BLAS routines that not only tolerates soft errors on the fly, but also provides comparable performance to modern state-of-the-art BLAS libraries on widely-used processors such as Intel Skylake and Cascade Lake. To accommodate the features of BLAS, which contains both memory-bound and computing-bound routines, we propose a hybrid strategy to incorporate fault tolerance into our brand-new BLAS implementation: duplicating computing instructions for memory-bound Level-1 and Level-2 BLAS routines and incorporating an Algorithm-Based Fault Tolerance mechanism for computing-bound Level-3 BLAS routines. Our high performance and low overhead are obtained from delicate assembly-level optimization and a kernel-fusion approach to the computing kernels. Experimental results demonstrate that FT-BLAS offers high reliability and high performance -- faster than Intel MKL, OpenBLAS, and BLIS by up to 3.50%, 22.14% and 21.70%, respectively, for routines spanning all three levels of BLAS we benchmarked, even under hundreds of errors injected per minute.
On the computation of asymptotic critical values of polynomial maps and applications
Venue: arXiv cs.SC 2021
Year: 2021

Let $\mathbf{f} = \left(f_1, \dots, f_p\right) $ be a polynomial tuple in $\mathbb{Q}[z_1, \dots, z_n]$ and let $d = \max_{1 \leq i \leq p} °f_i$. We consider the problem of computing the set of asymptotic critical values of the polynomial mapping, with the assumption that this mapping is dominant, $\mathbf{f}: z \in \mathbb{K}^n \to (f\_1(z), \dots, f\_p(z)) \in \mathbb{K}^p$ where $\mathbb{K}$ is either $\mathbb{R}$ or $\mathbb{C}$. This is the set of values $c$ in the target space of $\mathbf{f}$ such that there exists a sequence of points $(\mathbf{x}_i)_{i\in \mathbb{N}}$ for which $\mathbf{f}(\mathbf{x}_i)$ tends to $c$ and $\|\mathbf{x}_i\| κ{\rm d} \mathbf{f}(\mathbf{x}_i))$ tends to $0$ when $i$ tends to infinity where ${\rm d} \mathbf{f}$ is the differential of $\mathbf{f}$ and $κ$ is a function measuring the distance of a linear operator to the set of singular linear operators from $\mathbb{K}^n$ to $\mathbb{K}^p$. Computing the union of the classical and asymptotic critical values allows one to put into practice generalisations of Ehresmann's fibration theorem. This leads to natural and efficient applications in polynomial optimisation and computational real algebraic geometry. Going back to previous works by Kurdyka, Orro and Simon, we design new algorithms to compute asymptotic critical values. Through randomisation, we introduce new geometric characterisations of asymptotic critical values. This allows us to dramatically reduce the complexity of computing such values to a cost that is essentially $O(d^{2n(p+1)})$ arithmetic operations in $\mathbb{Q}$. We also obtain tighter degree bounds on a hypersurface containing the asymptotic critical values, showing that the degree is at most $p^{n-p+1}(d-1)^{n-p}(d+1)^{p}$. Next, we show how to apply these algorithms to unconstrained polynomial optimisation problems and the problem of computing sample points per connected component of a semi-algebraic set defined by a single inequality/inequation. We report on the practical capabilities of our implementation of this algorithm. It shows how the practical efficiency surpasses the current state-of-the-art algorithms for computing asymptotic critical values by tackling examples that were previously out of reach.
Landmarks Augmentation with Manifold-Barycentric Oversampling
Venue: arXiv cs.CV 2021
Year: 2021

The training of Generative Adversarial Networks (GANs) requires a large amount of data, stimulating the development of new augmentation methods to alleviate the challenge. Oftentimes, these methods either fail to produce enough new data or expand the dataset beyond the original manifold. In this paper, we propose a new augmentation method that guarantees to keep the new data within the original data manifold thanks to the optimal transport theory. The proposed algorithm finds cliques in the nearest-neighbors graph and, at each sampling iteration, randomly draws one clique to compute the Wasserstein barycenter with random uniform weights. These barycenters then become the new natural-looking elements that one could add to the dataset. We apply this approach to the problem of landmarks detection and augment the available annotation in both unpaired and in semi-supervised scenarios. Additionally, the idea is validated on cardiac data for the task of medical segmentation. Our approach reduces the overfitting and improves the quality metrics beyond the original data outcome and beyond the result obtained with popular modern augmentation methods.
Some Combinatorial Problems in Power-law Graphs
Venue: arXiv cs.CG 2021
Year: 2021

The power-law behavior is ubiquitous in a majority of real-world networks, and it was shown to have a strong effect on various combinatorial, structural, and dynamical properties of graphs. For example, it has been shown that in real-life power-law networks, both the matching number and the domination number are relatively smaller, compared with homogeneous graphs. In this paper, we study analytically several combinatorial problems for two power-law graphs with the same number of vertices, edges, and the same power exponent. For both graphs, we determine exactly or recursively their matching number, independence number, domination number, the number of maximum matchings, the number of maximum independent sets, and the number of minimum dominating sets. We show that power-law behavior itself cannot characterize the combinatorial properties of a heterogenous graph. Since the combinatorial properties studied here have found wide applications in different fields, such as structural controllability of complex networks, our work offers insight in the applications of these combinatorial problems in power-law graphs.
On Securing Cloud-hosted Cyber-physical Systems Using Trusted Execution Environments
Venue: arXiv cs.CR 2021
Year: 2021

Recently, cloud control systems have gained increasing attention from the research community as a solution to implement networked cyber-physical systems (CPSs). Such an architecture can reduce deployment and maintenance costs albeit at the expense of additional security and privacy concerns. In this paper, first, we discuss state-of-the-art security solutions for cloud control systems and their limitations. Then, we propose a novel control architecture based on Trusted Execution Environments (TEE). We show that such an approach can potentially address major security and privacy issues for cloud-hosted control systems. Finally, we present an implementation setup based on Intel Software Guard Extensions (SGX) and validate its effectiveness on a testbed system.
Human Biases Preventing The Widespread Adoption Of Self-Driving Cars
Venue: arXiv cs.HC 2021
Year: 2021

Self-driving cars offer a plethora of safety advantages over our accustomed human-driven ones, yet many individuals feel uneasy sharing the road with these machines and entrusting their lives to their driving capabilities. Thus, bringing about a widespread adoption of autonomous cars requires overcoming these compulsions through careful planning and forethought. Here we break down the three primary psychological barriers that may hamstring or even wholly prevent their widespread adoption as well as how to tackle them.
Limiting Tags Fosters Efficiency
Venue: arXiv cs.CY 2021
Year: 2021

Tagging facilitates information retrieval in social media and other online communities by allowing users to organize and describe online content. Researchers found that the efficiency of tagging systems steadily decreases over time, because tags become less precise in identifying specific documents, i.e., they lose their descriptiveness. However, previous works did not answer how or even whether community managers can improve the efficiency of tags. In this work, we use information-theoretic measures to track the descriptive and retrieval efficiency of tags on Stack Overflow, a question-answering system that strictly limits the number of tags users can specify per question. We observe that tagging efficiency stabilizes over time, while tag content and descriptiveness both increase. To explain this observation, we hypothesize that limiting the number of tags fosters novelty and diversity in tag usage, two properties which are both beneficial for tagging efficiency. To provide qualitative evidence supporting our hypothesis, we present a statistical model of tagging that demonstrates how novelty and diversity lead to greater tag efficiency in the long run. Our work offers insights into policies to improve information organization and retrieval in online communities.
Plot2API: Recommending Graphic API from Plot via Semantic Parsing Guided Neural Network
Venue: arXiv cs.SE 2021
Year: 2021

Plot-based Graphic API recommendation (Plot2API) is an unstudied but meaningful issue, which has several important applications in the context of software engineering and data visualization, such as the plotting guidance of the beginner, graphic API correlation analysis, and code conversion for plotting. Plot2API is a very challenging task, since each plot is often associated with multiple APIs and the appearances of the graphics drawn by the same API can be extremely varied due to the different settings of the parameters. Additionally, the samples of different APIs also suffer from extremely imbalanced. Considering the lack of technologies in Plot2API, we present a novel deep multi-task learning approach named Semantic Parsing Guided Neural Network (SPGNN) which translates the Plot2API issue as a multi-label image classification and an image semantic parsing tasks for the solution. In SPGNN, the recently advanced Convolutional Neural Network (CNN) named EfficientNet is employed as the backbone network for API recommendation. Meanwhile, a semantic parsing module is complemented to exploit the semantic relevant visual information in feature learning and eliminate the appearance-relevant visual information which may confuse the visual-information-based API recommendation. Moreover, the recent data augmentation technique named random erasing is also applied for alleviating the imbalance of API categories. We collect plots with the graphic APIs used to drawn them from Stack Overflow, and release three new Plot2API datasets corresponding to the graphic APIs of R and Python programming languages for evaluating the effectiveness of Plot2API techniques. Extensive experimental results not only demonstrate the superiority of our method over the recent deep learning baselines but also show the practicability of our method in the recommendation of graphic APIs.
IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Task
Venue: arXiv cs.CL 2021
Year: 2021

This paper describes our contribution to SemEval 2021 Task 1: Lexical Complexity Prediction. In our approach, we leverage the ELECTRA model and attempt to mirror the data annotation scheme. Although the task is a regression task, we show that we can treat it as an aggregation of several classification and regression models. This somewhat counter-intuitive approach achieved an MAE score of 0.0654 for Sub-Task 1 and MAE of 0.0811 on Sub-Task 2. Additionally, we used the concept of weak supervision signals from Gloss-BERT in our work, and it significantly improved the MAE score in Sub-Task 1.
Information Geometry and Classical Cramér-Rao Type Inequalities
Venue: arXiv cs.IT 2021
Year: 2021

We examine the role of information geometry in the context of classical Cramér-Rao (CR) type inequalities. In particular, we focus on Eguchi's theory of obtaining dualistic geometric structures from a divergence function and then applying Amari-Nagoaka's theory to obtain a CR type inequality. The classical deterministic CR inequality is derived from Kullback-Leibler (KL)-divergence. We show that this framework could be generalized to other CR type inequalities through four examples: $α$-version of CR inequality, generalized CR inequality, Bayesian CR inequality, and Bayesian $α$-CR inequality. These are obtained from, respectively, $I_α$-divergence (or relative $α$-entropy), generalized Csiszár divergence, Bayesian KL divergence, and Bayesian $I_α$-divergence.
What Taggers Fail to Learn, Parsers Need the Most
Venue: arXiv cs.CL 2021
Year: 2021

We present an error analysis of neural UPOS taggers to evaluate why using gold standard tags has such a large positive contribution to parsing performance while using predicted UPOS tags either harms performance or offers a negligible improvement. We evaluate what neural dependency parsers implicitly learn about word types and how this relates to the errors taggers make to explain the minimal impact using predicted tags has on parsers. We also present a short analysis on what contexts result in reductions in tagging performance. We then mask UPOS tags based on errors made by taggers to tease away the contribution of UPOS tags which taggers succeed and fail to classify correctly and the impact of tagging errors.
Defending Against Image Corruptions Through Adversarial Augmentations
Venue: arXiv cs.CV 2021
Year: 2021

Modern neural networks excel at image classification, yet they remain vulnerable to common image corruptions such as blur, speckle noise or fog. Recent methods that focus on this problem, such as AugMix and DeepAugment, introduce defenses that operate in expectation over a distribution of image corruptions. In contrast, the literature on $\ell_p$-norm bounded perturbations focuses on defenses against worst-case corruptions. In this work, we reconcile both approaches by proposing AdversarialAugment, a technique which optimizes the parameters of image-to-image models to generate adversarially corrupted augmented images. We theoretically motivate our method and give sufficient conditions for the consistency of its idealized version as well as that of DeepAugment. Our classifiers improve upon the state-of-the-art on common image corruption benchmarks conducted in expectation on CIFAR-10-C and improve worst-case performance against $\ell_p$-norm bounded perturbations on both CIFAR-10 and ImageNet.
Developing a New Autism Diagnosis Process Based on a Hybrid Deep Learning Architecture Through Analyzing Home Videos
Venue: arXiv cs.CV 2021
Year: 2021

Currently, every 1 in 54 children have been diagnosed with Autism Spectrum Disorder (ASD), which is 178% higher than it was in 2000. An early diagnosis and treatment can significantly increase the chances of going off the spectrum and making a full recovery. With a multitude of physical and behavioral tests for neurological and communication skills, diagnosing ASD is very complex, subjective, time-consuming, and expensive. We hypothesize that the use of machine learning analysis on facial features and social behavior can speed up the diagnosis of ASD without compromising real-world performance. We propose to develop a hybrid architecture using both categorical data and image data to automate traditional ASD pre-screening, which makes diagnosis a quicker and easier process. We created and tested a Logistic Regression model and a Linear Support Vector Machine for Module 1, which classifies ADOS categorical data. A Convolutional Neural Network and a DenseNet network are used for module 2, which classifies video data. Finally, we combined the best performing models, a Linear SVM and DenseNet, using three data averaging strategies. We used a standard average, weighted based on number of training data, and weighted based on the number of ASD patients in the training data to average the results, thereby increasing accuracy in clinical applications. The results we obtained support our hypothesis. Our novel architecture is able to effectively automate ASD pre-screening with a maximum weighted accuracy of 84%.
PhyAug: Physics-Directed Data Augmentation for Deep Sensing Model Transfer in Cyber-Physical Systems
Venue: arXiv cs.SD 2021
Year: 2021

Run-time domain shifts from training-phase domains are common in sensing systems designed with deep learning. The shifts can be caused by sensor characteristic variations and/or discrepancies between the design-phase model and the actual model of the sensed physical process. To address these issues, existing transfer learning techniques require substantial target-domain data and thus incur high post-deployment overhead. This paper proposes to exploit the first principle governing the domain shift to reduce the demand on target-domain data. Specifically, our proposed approach called PhyAug uses the first principle fitted with few labeled or unlabeled source/target-domain data pairs to transform the existing source-domain training data into augmented data for updating the deep neural networks. In two case studies of keyword spotting and DeepSpeech2-based automatic speech recognition, with 5-second unlabeled data collected from the target microphones, PhyAug recovers the recognition accuracy losses due to microphone characteristic variations by 37% to 72%. In a case study of seismic source localization with TDoA fngerprints, by exploiting the frst principle of signal propagation in uneven media, PhyAug only requires 3% to 8% of labeled TDoA measurements required by the vanilla fingerprinting approach in achieving the same localization accuracy.