OpenRaiser/Intern-Atlas huggingface dataset:
MAVIREC: ML-Aided Vectored IR-DropEstimation and Classification
Venue: arXiv cs.AR 2020
Year: 2020

Vectored IR drop analysis is a critical step in chip signoff that checks the power integrity of an on-chip power delivery network. Due to the prohibitive runtimes of dynamic IR drop analysis, the large number of test patterns must be whittled down to a small subset of worst-case IR vectors. Unlike the traditional slow heuristic method that select a few vectors with incomplete coverage, MAVIREC uses machine learning techniques -- 3D convolutions and regression-like layers -- for accurately recommending a larger subset of test patterns that exercise worst-case scenarios. In under 30 minutes, MAVIREC profiles 100K-cycle vectors and provides better coverage than a state-of-the-art industrial flow. Further, MAVIREC's IR drop predictor shows 10x speedup with under 4mV RMSE relative to an industrial flow.
SMART Frame Selection for Action Recognition
Venue: arXiv cs.CV 2020
Year: 2020

Action recognition is computationally expensive. In this paper, we address the problem of frame selection to improve the accuracy of action recognition. In particular, we show that selecting good frames helps in action recognition performance even in the trimmed videos domain. Recent work has successfully leveraged frame selection for long, untrimmed videos, where much of the content is not relevant, and easy to discard. In this work, however, we focus on the more standard short, trimmed action recognition problem. We argue that good frame selection can not only reduce the computational cost of action recognition but also increase the accuracy by getting rid of frames that are hard to classify. In contrast to previous work, we propose a method that instead of selecting frames by considering one at a time, considers them jointly. This results in a more efficient selection, where good frames are more effectively distributed over the video, like snapshots that tell a story. We call the proposed frame selection SMART and we test it in combination with different backbone architectures and on multiple benchmarks (Kinetics, Something-something, UCF101). We show that the SMART frame selection consistently improves the accuracy compared to other frame selection strategies while reducing the computational cost by a factor of 4 to 10 times. Additionally, we show that when the primary goal is recognition performance, our selection strategy can improve over recent state-of-the-art models and frame selection strategies on various benchmarks (UCF101, HMDB51, FCVID, and ActivityNet).
A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty
Venue: arXiv cs.CE 2020
Year: 2020

Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundamental limitation in multiscale models is the presence of uncertainty in the computational predictions delivered by them. In this work, a sequential multiscale model has been developed, incorporating discrete dislocation dynamics (DDD) simulations and a strain gradient plasticity (SGP) model to predict the size effect in plastic deformations of metallic micro-pillars. The DDD simulations include uniaxial compression of micro-pillars with different sizes and over a wide range of initial dislocation densities and spatial distributions of dislocations. An SGP model is employed at the continuum level that accounts for the size-dependency of flow stress and hardening rate. Sequences of uncertainty analyses have been performed to assess the predictive capability of the multiscale model. The variance-based global sensitivity analysis determines the effect of parameter uncertainty on the SGP model prediction. The multiscale model is then constructed by calibrating the continuum model using the data furnished by the DDD simulations. A Bayesian calibration method is implemented to quantify the uncertainty due to microstructural randomness in discrete dislocation simulations (density and spatial distributions of dislocations) on the macroscopic continuum model prediction (size effect in plastic deformation). The outcomes of this study indicate that the discrete-continuum multiscale model can accurately simulate the plastic deformation of micro-pillars, despite the significant uncertainty in the DDD results. Additionally, depending on the macroscopic features represented by the DDD simulations, the SGP model can reliably predict the size effect in plasticity responses of the micropillars with below 10% of error
A Systematic Mapping on the use of Visual Data Mining to Support the Conduct of Systematic Literature Reviews
Venue: arXiv cs.SE 2020
Year: 2020

A systematic literature review (SLR) is a methodology used to find and aggregate all relevant existing evidence about a specific research question of interest. Important decisions need to be made at several points in the review process, relating to search of the literature, selection of relevant primary studies and use of methods of synthesis. Visualization can support tasks that involve large collections of data, such as the studies collected, evaluated and summarized in an SLR. The objective of this paper is to present the results of a systematic mapping study (SM) conducted to collect and evaluate evidence on the use of a specific visualization technique, visual data mining (VDM), to support the SLR process. We reviewed 20 papers and our results indicate a scarcity of research on the use of VDM to help with conducting SLRs in the software engineering domain. However, most of the studies (16 of the 20 studies included in our mapping) have been conducted in the field of medicine and they revealed that the activities of data extraction and data synthesis, related to conducting the review phase of an SLR process, have more VDM support than other activities. In contrast, according to our SM, previous studies using VDM techniques with SLRs have not employed such techniques during the SLR's planning and reporting phases.
eTREE: Learning Tree-structured Embeddings
Venue: arXiv cs.LG 2020
Year: 2020

Matrix factorization (MF) plays an important role in a wide range of machine learning and data mining models. MF is commonly used to obtain item embeddings and feature representations due to its ability to capture correlations and higher-order statistical dependencies across dimensions. In many applications, the categories of items exhibit a hierarchical tree structure. For instance, human diseases can be divided into coarse categories, e.g., bacterial, and viral. These categories can be further divided into finer categories, e.g., viral infections can be respiratory, gastrointestinal, and exanthematous viral diseases. In e-commerce, products, movies, books, etc., are grouped into hierarchical categories, e.g., clothing items are divided by gender, then by type (formal, casual, etc.). While the tree structure and the categories of the different items may be known in some applications, they have to be learned together with the embeddings in many others. In this work, we propose eTREE, a model that incorporates the (usually ignored) tree structure to enhance the quality of the embeddings. We leverage the special uniqueness properties of Nonnegative MF (NMF) to prove identifiability of eTREE. The proposed model not only exploits the tree structure prior, but also learns the hierarchical clustering in an unsupervised data-driven fashion. We derive an efficient algorithmic solution and a scalable implementation of eTREE that exploits parallel computing, computation caching, and warm start strategies. We showcase the effectiveness of eTREE on real data from various application domains: healthcare, recommender systems, and education. We also demonstrate the meaningfulness of the tree obtained from eTREE by means of domain experts interpretation.
Computer Vision based Animal Collision Avoidance Framework for Autonomous Vehicles
Venue: arXiv cs.CV 2020
Year: 2020

Animals have been a common sighting on roads in India which leads to several accidents between them and vehicles every year. This makes it vital to develop a support system for driverless vehicles that assists in preventing these forms of accidents. In this paper, we propose a neoteric framework for avoiding vehicle-to-animal collisions by developing an efficient approach for the detection of animals on highways using deep learning and computer vision techniques on dashcam video. Our approach leverages the Mask R-CNN model for detecting and identifying various commonly found animals. Then, we perform lane detection to deduce whether a detected animal is on the vehicle's lane or not and track its location and direction of movement using a centroid based object tracking algorithm. This approach ensures that the framework is effective at determining whether an animal is obstructing the path or not of an autonomous vehicle in addition to predicting its movement and giving feedback accordingly. This system was tested under various lighting and weather conditions and was observed to perform relatively well, which leads the way for prominent driverless vehicle's support systems for avoiding vehicular collisions with animals on Indian roads in real-time.
Software, Attacker and Asset-centric Approach for Improving Security in System Development Process
Venue: arXiv cs.SE 2020
Year: 2020

Secure development process is a procedure taken by developers to ensure the programs developed are following the general security standards and will always be up to date so that the outcomes are well secured and obedient. As a software developer, it is very crucial to implement and develop a highly secured and reliable program for clients and users. In this current digital world where everything is advancing faster than we can ever think of, most of the old security policies can no longer be implemented alone. The consequences and impacts that could be brought upon a company are really huge if the software applications are not secured according to the modern trend. Therefore, in this paper research is done to asses the security integration in software development process. The concept and the purpose of this research is to provide insight about the current issues and challenges faced by most of the software developers in terms of secure software development. With a better and clearer explanation of these issues, challenges, and methodologies adopted to overcome them are discussed which can potentially provide a better and higher level of security along with better software programmers and client relationship. To comply with future demands and threats, security concerns need to be involved in all phases while developing a software system. Therefore, an effort is made to investigate and contribute to this domain through this paper.
Outliers Detection Is Not So Hard: Approximation Algorithms for Robust Clustering Problems Using Local Search Techniques
Venue: arXiv cs.DS 2020
Year: 2020

In this paper, we consider two types of robust models of the $k$-median/$k$-means problems: the outlier-version ($k$-MedO/$k$-MeaO) and the penalty-version ($k$-MedP/$k$-MeaP), in which we can mark some points as outliers and discard them. In $k$-MedO/$k$-MeaO, the number of outliers is bounded by a given integer. In $k$-MedP/$k$-MeaP, we do not bound the number of outliers, but each outlier will incur a penalty cost. We develop a new technique to analyze the approximation ratio of local search algorithms for these two problems by introducing an adapted cluster that can capture useful information about outliers in the local and the global optimal solution. For $k$-MeaP, we improve the best known approximation ratio based on local search from $25+\varepsilon$ to $9+\varepsilon$. For $k$-MedP, we obtain the best known approximation ratio. For $k$-MedO/$k$-MeaO, there exists only two bi-criteria approximation algorithms based on local search. One violates the outlier constraint (the constraint on the number of outliers), while the other violates the cardinality constraint (the constraint on the number of clusters). We consider the former algorithm and improve its approximation ratios from $17+\varepsilon$ to $3+\varepsilon$ for $k$-MedO, and from $274+\varepsilon$ to $9+\varepsilon$ for $k$-MeaO.
Multi-Head Linear Attention Generative Adversarial Network for Thin Cloud Removal
Venue: arXiv cs.CV 2020
Year: 2020

In remote sensing images, the existence of the thin cloud is an inevitable and ubiquitous phenomenon that crucially reduces the quality of imageries and limits the scenarios of application. Therefore, thin cloud removal is an indispensable procedure to enhance the utilization of remote sensing images. Generally, even though contaminated by thin clouds, the pixels still retain more or less surface information. Hence, different from thick cloud removal, thin cloud removal algorithms normally concentrate on inhibiting the cloud influence rather than substituting the cloud-contaminated pixels. Meanwhile, considering the surface features obscured by the cloud are usually similar to adjacent areas, the dependency between each pixel of the input is useful to reconstruct contaminated areas. In this paper, to make full use of the dependencies between pixels of the image, we propose a Multi-Head Linear Attention Generative Adversarial Network (MLAGAN) for Thin Cloud Removal. The MLA-GAN is based on the encoding-decoding framework consisting of multiple attention-based layers and deconvolutional layers. Compared with six deep learning-based thin cloud removal benchmarks, the experimental results on the RICE1 and RICE2 datasets demonstrate that the proposed framework MLA-GAN has dominant advantages in thin cloud removal.
Enabling Tangible Interaction through Detection and Augmentation of Everyday Objects
Venue: arXiv cs.HC 2020
Year: 2020

Digital interaction with everyday objects has become popular since the proliferation of camera-based systems that detect and augment objects "just-in-time". Common systems use a vision-based approach to detect objects and display their functionalities to the user. Sensors, such as color and depth cameras, have become inexpensive and allow seamless environmental tracking in mobile as well as stationary settings. However, object detection in different contexts faces challenges as it highly depends on environmental parameters and the conditions of the object itself. In this work, we present three tracking algorithms which we have employed in past research projects to track and recognize objects. We show, how mobile and stationary augmented reality can be used to extend the functionalities of objects. We conclude, how common items can provide user-defined tangible interaction beyond their regular functionality.
Mind the Gap: A Framework (BehaveFIT) Guiding The Use of Immersive Technologies in Behavior Change Processes
Venue: arXiv cs.HC 2020
Year: 2020

The design and evaluation of assisting technologies to support behavior change processes have become an essential topic within the field of human-computer interaction research in general and the field of immersive intervention technologies in particular. The mechanisms and success of behavior change techniques and interventions are broadly investigated in the field of psychology. However, it is not always easy to adapt these psychological findings to the context of immersive technologies. The lack of theoretical foundation also leads to a lack of explanation as to why and how immersive interventions support behavior change processes. The Behavioral Framework for immersive Technologies (BehaveFIT) addresses this lack by (1) presenting an intelligible categorization and condensation of psychological barriers and immersive features, by (2) suggesting a mapping that shows why and how immersive technologies can help to overcome barriers, and finally by (3) proposing a generic prediction path that enables a structured, theory-based approach to the development and evaluation of immersive interventions. These three steps explain how BehaveFIT can be used, and include guiding questions and one example for each step. Thus, the present paper contributes to guidance for immersive intervention design and evaluation, showing that immersive interventions support behavior change processes and explain and predict 'why' and 'how' immersive interventions can bridge the intention-behavior-gap.
Guidance Module Network for Video Captioning
Venue: arXiv cs.CV 2020
Year: 2020

Video captioning has been a challenging and significant task that describes the content of a video clip in a single sentence. The model of video captioning is usually an encoder-decoder. We find that the normalization of extracted video features can improve the final performance of video captioning. Encoder-decoder model is usually trained using teacher-enforced strategies to make the prediction probability of each word close to a 0-1 distribution and ignore other words. In this paper, we present a novel architecture which introduces a guidance module to encourage the encoder-decoder model to generate words related to the past and future words in a caption. Based on the normalization and guidance module, guidance module net (GMNet) is built. Experimental results on commonly used dataset MSVD show that proposed GMNet can improve the performance of the encoder-decoder model on video captioning tasks.
5G New Radio Unlicensed: Challenges and Evaluation
Venue: arXiv cs.NI 2020
Year: 2020

To meet the high demand for mobile data, the Third Generation Partnership Project (3GPP) established a set of standards known as 5G New Radio (5G NR). The architecture of 5G NR includes a flexible radio access network and a core network. 3GPP has also been working on a new radio access technology, called 5G NR Unlicensed (5G NR-U), which aims at extending 5G NR to unlicensed bands. In this paper, we give an overview of the most recent 5G NR-U design elements and discuss potential concerns, including fair coexistence with other unlicensed technologies such as Wi-Fi. We use simulations to study coexistence between Wi-Fi and 5G NR-U systems. Our evaluation indicates that NR-U often achieves higher throughput and lower delay than Wi-Fi (802.11ac). The two systems experience different buffer occupancies and spectrum utilization statistics. We also discuss the improvements that NR-U offers over LTE Licensed Assisted Access (LTE-LAA).
Biased Models Have Biased Explanations
Venue: arXiv cs.LG 2020
Year: 2020

We study fairness in Machine Learning (FairML) through the lens of attribute-based explanations generated for machine learning models. Our hypothesis is: Biased Models have Biased Explanations. To establish that, we first translate existing statistical notions of group fairness and define these notions in terms of explanations given by the model. Then, we propose a novel way of detecting (un)fairness for any black box model. We further look at post-processing techniques for fairness and reason how explanations can be used to make a bias mitigation technique more individually fair. We also introduce a novel post-processing mitigation technique which increases individual fairness in recourse while maintaining group level fairness.
Prove-It: A Proof Assistant for Organizing and Verifying General Mathematical Knowledge
Venue: arXiv cs.LO 2020
Year: 2020

We introduce Prove-It, a Python-based general-purpose interactive theorem-proving assistant designed with the goal of making formal theorem proving as easy and natural as informal theorem proving (with moderate training). Prove-It uses a highly-flexible Jupyter notebook-based user interface that documents interactions and proof steps using LaTeX. We review Prove-It's highly expressive representation of expressions, judgments, theorems, and proofs; demonstrate the system by constructing a traditional proof-by-contradiction that $\sqrt{2}\notin\mathbb{Q}$; and discuss how the system avoids inconsistencies such as Russell's and Curry's paradoxes. Extensive documentation is provided in the appendices about core elements of the system. Current development and future work includes promising applications to quantum circuit manipulation and quantum algorithm verification.
DeepKeyGen: A Deep Learning-based Stream Cipher Generator for Medical Image Encryption and Decryption
Venue: arXiv cs.CR 2020
Year: 2020

The need for medical image encryption is increasingly pronounced, for example to safeguard the privacy of the patients' medical imaging data. In this paper, a novel deep learning-based key generation network (DeepKeyGen) is proposed as a stream cipher generator to generate the private key, which can then be used for encrypting and decrypting of medical images. In DeepKeyGen, the generative adversarial network (GAN) is adopted as the learning network to generate the private key. Furthermore, the transformation domain (that represents the "style" of the private key to be generated) is designed to guide the learning network to realize the private key generation process. The goal of DeepKeyGen is to learn the mapping relationship of how to transfer the initial image to the private key. We evaluate DeepKeyGen using three datasets, namely: the Montgomery County chest X-ray dataset, the Ultrasonic Brachial Plexus dataset, and the BraTS18 dataset. The evaluation findings and security analysis show that the proposed key generation network can achieve a high-level security in generating the private key.
Improving unsupervised anomaly localization by applying multi-scale memories to autoencoders
Venue: arXiv cs.CV 2020
Year: 2020

Autoencoder and its variants have been widely applicated in anomaly detection.The previous work memory-augmented deep autoencoder proposed memorizing normality to detect anomaly, however it neglects the feature discrepancy between different resolution scales, therefore we introduce multi-scale memories to record scale-specific features and multi-scale attention fuser between the encoding and decoding module of the autoencoder for anomaly detection, namely MMAE.MMAE updates slots at corresponding resolution scale as prototype features during unsupervised learning. For anomaly detection, we accomplish anomaly removal by replacing the original encoded image features at each scale with most relevant prototype features,and fuse these features before feeding to the decoding module to reconstruct image. Experimental results on various datasets testify that our MMAE successfully removes anomalies at different scales and performs favorably on several datasets compared to similar reconstruction-based methods.
RC-chain: Reputation-based Crowdsourcing Blockchain for Vehicular Networks
Venue: arXiv cs.DC 2020
Year: 2020

As the commercial use of 5G technologies has grown more prevalent, smart vehicles have become an efficient platform for delivering a wide array of services directly to customers. The vehicular crowdsourcing service (VCS), for example, can provide immediate and timely feedback to the user regarding real-time transportation information. However, different sources can generate spurious information towards a specific service request in the pursuit of profit. Distinguishing trusted information from numerous sources is the key to a reliable VCS platform. This paper proposes a solution to this problem called "RC-chain", a reputation-based crowdsourcing framework built on a blockchain platform (Hyperledger Fabric). We first establish the blockchain-based platform to support the management of crowdsourcing trading and user-reputation evaluating activities. A reputation model, the Trust Propagation \& Feedback Similarity (TPFS), then calculates the reputation values of participants and reveals any malicious behavior accordingly. Finally, queueing theory is used to evaluate the blockchain-based platform and optimize the system performance. The proposed framework was deployed on the IBM Hyperledger Fabric platform to observe its real-world running time, effectiveness, and overall performance.