Reviewerly/exHarmony huggingface dataset:
MS-NET: modular selective network

Abstract We propose a modular architecture of Deep Neural Network (DNN) for multi-class classification task. The architecture consists of two parts, a router network and a set of expert networks. In this architecture, for a C -class classification problem, we have exactly C experts. The backbone network for these experts and the router are built with simple and identical DNN architecture. For each class, the modular network has a certain number $$\rho$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> of expert networks specializing in that particular class, where $$\rho$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> is called the redundancy rate in this study. We demonstrate that $$\rho$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mi>ρ</mml:mi> </mml:math> plays a vital role in the performance of the network. Although these experts are light weight and weak learners alone, together they match the performance of more complex DNNs. We train the network in two phase wherein, first the router is trained on the whole set of training data followed by training each expert network enforced by a new stochastic objective function that facilitates alternative training on a small subset of expert data and the whole set of data. This alternative training provides an additional form of regularization and avoids over-fitting the expert network on subset data. During the testing phase, the router dynamically selects a fixed number of experts for further evaluation of the input datum. The modular nature and low parameter requirement of the network makes it very suitable in distributed and low computational environments. Extensive empirical study and theoretical analysis on CIFAR-10, CIFAR-100 and F-MNIST substantiate the effectiveness and efficiency of our proposed modular network.
3D seismic data compression with multi-resolution autoencoders

In this work, we propose an approach to tackle the problem of three-dimensional post-stack seismic data compression using multi-resolution deep autoencoders, by training one specific compression networ per seismic volume. We feed a residual convolution neural network with 2D slices extracted from 3D seismic blocks and combine multiple scales to generate a discrete latent representation of the input signal. The transformed latent map has its bits oriented in order to minimize both entropy and bit rate. The decoder performs upsampling followed by convolutions to generate the reconstructed image. With this approach, we achieved compressions at low bit rates, attaining a high-quality reconstruction. Experiments show that our method can effectively compress seismic datasets yielding PSNR values over 40dB at a rate of 33:1. Presentation Date: Tuesday, October 13, 2020 Session Start Time: 1:50 PM Presentation Time: 3:30 PM Location: Poster Station 1 Presentation Type: Poster
Improve Unseen Domain Generalization via Enhanced Local Color Transformation

Recent application of deep learning in medical image achieves expert-level accuracy. However, the accuracy often degrades greatly on unseen data, for example data from different device designs and population distributions. In this work, we consider a realistic problem of domain generalization in fundus image analysis: when a model is trained on a certain domain but tested on unseen domains. Here, the known domain data is taken from a single fundus camera manufacture, i.e. Canon. The unseen data are the image from different demographic population and with distinct photography styles. Specifically, the unseen images are taken from Topcom, Syseye and Crystalvue cameras. The model performance is evaluated by two objectives: age regression and diabetic retinopathy (DR) classification. We found that the model performance on unseen domain could decrease significantly. For example, the mean absolute error (MAE) of age prediction could increase by 57.7 %. To remedy this problem, we introduce an easy-to-use method, named enhanced domain transformation (EDT), to improve the performance on both seen and unseen data. The goal of EDT is to achieve domain adaptation without using labeling and training on unseen images. We evaluate our method comprehensively on seen and unseen data sets considering the factors of demographic distribution, image style and prediction task. All the results demonstrate that EDT improves the performance on seen and unseen data in the tasks of age prediction and DR classification. Equipped with EDT, the \(\mathrm{R}^2\) (coefficient of determination) of age prediction could be greatly improved from 0.599 to 0.765 (n = 29,577) on Crystalvue images, and AUC (area under curve) of DR classification increases from 0.875 to 0.922 (n = 1,015).
Doing Inclusive Design

How can user interface and user experience (UI/UX) professionals assess whether their software supports diverse users? And if they find problems, how can they fix them? We begin this keynote address with a summary of GenderMag, a systematic inspection method for finding and fixing "gender inclusivity bugs---biases against different genders in software interfaces and workflows. We then show what UI/UX professionals are doing with it in the real world, from their bias finds & fixes to their practices & pitfalls in using it. Finally, we present InclusiveMag, a meta-method that can be used by HCI researchers to generate systematic inclusiveness methods for other dimensions of diversity.
Asymmetric Convolutional Residual Network for AV1 Intra in-Loop Filtering

In video compression standards, in-loop filtering plays an important role in alleviating blocking, blurring and ringing artifacts caused by lossy compression, which enhances visual quality and benefits coding efficiency. The boom of neural network applications in super-resolution and image restoration brings insights into solutions of in-loop filtering in video codecs. In this paper, we design an asymmetric convolutional residual network (ACRN) for in-loop filtering in the state-of-the-art AV1 codec. With the asymmetric convolutional blocks, directional features can be extracted to restore textures and improve quality. The cascading structure of wide-activated residual blocks with pruned dense connections enables reflecting hierarchical coding unit (CU) partition characteristics of video coding without losing overall details. Experiments show that the proposed lightweight ACRN can bring up to 12.78% coding efficiency improvement in intra coding of AV1.
Trade-offs in the Design of Blockchain of Finite-Lifetime Blocks for Edge-IoT Applications

Unlike cryptocurrency transactions in bitcoin that are stored indefinitely, the data of certain applications like IoT have finite-lifetime. In this context, one of the recent research works proposed LiTiChain - a new architecture for Blockchain of finite-lifetime blocks with applications to Edge-IoT. The novelty of LiTiChain lies in ensuring the connectivity of the chain even after the expired blocks are deleted from the chain. To provide the same level of security as conventional blockchain, in LiTiChain, some blocks are stored longer than their lifetime, which incurs additional storage cost. This paper presents two new blockchain architectures i.e. p-LiTiChain and s-LiTiChain that are variants of LiTiChain. The proposed architectures offer a degree of freedom in the design of blockchain of finite-lifetime blocks in terms of a tradeoff between storage cost, security and computational cost. With extensive simulations and analysis, it is demonstrated that the proposed architectures have the potential to decrease the additional storage cost incurred by LiTiChain to zero and improve security at the expense of computational cost.
Comparative Study of Machine Learning Algorithms using a Breast Cancer Dataset

Cancer, in general, is considered to be one of the highest causes of death worldwide. According to the Global Cancer statistics, breast cancer, which is the leading cause of death for women overall, is the second most diagnosed cancer with 11.6% of all positive cases. Whenever a lump of mass is found in the chest area, it would be diagnosed as either a cancerous or a non-cancerous tumor, which are also known as malignant or benign, respectively. Proper diagnosis is vital in order for the patient to start a treatment plan and recover as soon as possible. In this paper, we compare different Machine learning algorithms that are used to classify a patient's tumor using a set of features provided. Diagnostic Wisconsin Breast Cancer Dataset is used to train and test the different models which are then compared with each other using different classification metrics to identify the most robust and accurate models and compare against the state-of-the-art results.
SecSched

Trusted execution environments (TEEs) are an integral part of modern processors because security has become a very important concern. However, many such environments are bedeviled by the high cost of context switches, particularly when there is a switch from secure mode to non-secure mode owing primarily to cache pollution and TLB-flushing overheads. State-of-the-art implementations create a secure shared memory channel between a thread running in secure mode and a thread running in non-secure mode, which invokes system calls on its behalf. We argue that this is inefficient, and it is possible to reduce the overheads significantly by efficiently storing the context of secure threads and intelligent scheduling. In this paper, we propose a new scheduling algorithm SecSched that uses Cuckoo filters to capture the context of a thread. We schedule threads with similar contexts on the same core to leverage the effects of the locality. Our algorithm requires minimal hardware enhancements that are limited to maintaining a Cuckoo filter per core and a thread with the addition of few performance counters per thread to keep track of the miss counts. We show that with these minimal changes we can increase the performance of a suite of OS-intensive workloads by 27.6% with a minimal area overhead (around 0.04%).
Single Image Dehazing using a Novel Histogram Tranformation Network

Images taken outdoor often experience degradation due to the influence of haze. A lot of algorithms have been proposed to solve this problem. One kind of algorithms are based on some hand-crafted features, which often work only in the situations where those hand-crafted features are valid. There are also some algorithms, which use deep learning-based methods to recover clear images, but they depend on the 2D images, and their run time increases rapidly when the size of images gets larger. Moreover, these models need a large dataset to be trained. In this study, we proposed a novel way based on deep learning and histogram matching to overcome these common problems. Firstly, we develop a network with 1D ResNet structure to predict the histogram of a recovered image. Secondly, we match the histograms of the inputs to the outputs of the model, which are processed patch by patch, to get a series of clear patches. Finally, we use an image-guided filter to overcome the unnatural transition between patches. Experiments on both synthetic and real-world hazy images show that our method performs about 3% better in terms of SSIM(structural similarity index) and 15% better in terms of CIEDE2000 than some state-of-the-art methods on a synthetic hazy image dataset. Furthermore, our model runs faster than other deep-learning-based algorithms in our experiments by about 187% to 382%.
Artificial Neural Network Pruning to Extract Knowledge

Artificial Neural Networks (NN) are widely used for solving complex problems from medical diagnostics to face recognition. Despite notable successes, the main disadvantages of NN are also well known: the risk of overfitting, lack of explainability (inability to extract algorithms from trained NN), and high consumption of computing resources. Determining the appropriate specific NN structure for each problem can help overcome these difficulties: Too poor NN cannot be successfully trained, but too rich NN gives unexplainable results and may have a high chance of overfitting. Reducing precision of NN parameters simplifies the implementation of these NN, saves computing resources, and makes the NN skills more transparent. This paper lists the basic NN simplification problems and controlled pruning procedures to solve these problems. All the described pruning procedures can be implemented in one framework. The developed procedures, in particular, find the optimal structure of NN for each task, measure the influence of each input signal and NN parameter, and provide a detailed verbal description of the algorithms and skills of NN. The described methods are illustrated by a simple example: the generation of explicit algorithms for predicting the results of the US presidential election.
Saliency-Driven Class Impressions For Feature Visualization Of Deep Neural Networks

In this paper, we propose a data-free method of extracting Impressions of each class from the classifier's memory. The Deep Learning regime empowers classifiers to extract distinct patterns (or features) of a given class from training data, which is the basis on which they generalize to unseen data. Before deploying these models on critical applications, it is very useful to visualize the features considered to be important for classification. Existing visualization methods develop high confidence images consisting of both background and foreground features. This makes it hard to judge what the important features of a given class are. In this work, we propose a saliency-driven approach to visualize discriminative features that are considered most important for a given task. Another drawback of existing methods is that, confidence of the generated visualizations is increased by creating multiple instances of the given class. We restrict the algorithm to develop a single object per image, which helps further in extracting features of high confidence, and also results in better visualizations. We further demonstrate the generation of negative images as naturally fused images of two or more classes. Our code is available at: https://giChub.com/val-iisc/Saliency-driven-Class-Impressions.
Using Differential Evolution in order to create a personalized list of recommended items

The recommendation systems are used to suggest new, still not discovered items to users. At the moment, in order to achieve the best quality of the generated recommendations, users and their choices in the system must be analyzed to create a certain profile of preferences for a given user in order to adjust the generated recommendation to his personal taste. This article will present a recommendation system, which based on the Differential Evolution (DE) algorithm will learn the ranking function while directly optimizing the average precision (AP) for the selected user in the system. To achieve that, items are represented through a feature vectors generated using user-item matrix factorization. The experiments have been conducted on a popular and widely available public dataset MovieLens, and show that our approach in certain situations can significantly improve the quality of the generated recommendations. Results of experiments are compared with other techniques.
Network Encrypted Traffic Classification Based on Secondary Voting Enhanced Random Forest

Nowadays, traffic classification plays a significant role in network behavior analysis, network planning, network anomaly detection and network traffic model construction. Since the Internet is indubitably moving towards the era of encryption, making traffic classification more and more challenging. In this paper, a novel classification model based on random forest is proposed, and public data set named ISCX VPN-NonVPN is adopted for validation. Compared with the general random forest, the classification process is divided into two parts: two-group test and secondary voting. The accuracy of prediction can be improved by secondary voting. Results prove that our method can achieve averagely 5% higher precision than comparison methods which includes the decision tree method, KNN and general random forest.
Safe flyable and energy efficient UAV missions via biologically inspired methods

In this article the offline Unmanned Aerial Vehicle (UAV) path planning problem is addressed for real environments whilst enhancing energy requirements. In particular, a two-step procedure is adopted for the determination of the energy efficient safe flyable path that satisfies a-priori defined criteria. By the former step, via principles of mechanics, safe flyable candidate paths are designed meeting functional/physical limitations of the aerial vehicle. By the latter step, those paths are fed in the Genetic Algorithm (GA) setup, and the best path that fulfils mission's objectives and satisfies constraints is determined. The efficacy of the approach is shown via simulation examples, where a UAV is deployed to safely navigate with the least energy demand in an a priori known environment whilst meeting mission objectives.
Interactive Contents with 360-Degree Panorama Virtual Reality for Soil and Water Conservation Outdoor Classroom

The paper proposes an interactive virtual reality (VR) 360-degree panorama application system which can be applied in education for soil and water conservation. In the development of the system, the interactive VR 360-degree panoramic videos are produced first. The contents of videos come from introductions of facilities built in soil and water conservation outdoor classrooms. The design of video contents and interactions between video contents and users is on the basis of the cognitive theory of multimedia learning. Additionally, the system offers an android app developed using VR technology to offer users with interactions between users and videos. Learners first download the app into a smart phone and then insert the phone into a cheap VR helmet. Subsequently, they play the app with wearing the VR helmet to watch VR 360-degree panoramic videos so as to achieve immersion on watching video in a 3D VR environment. Moreover, learners can move the interaction sign, a single-circle shape, by shaking their VR helmets on the specific marks on screen to trigger corresponding interactive actions such as selecting a video, exiting viewing video, answering questions of true-false items, etc. Experimental results show the way participants received the system has better interaction than that without the system. Furthermore, the interaction portfolios are also collected in the cloud space for future analysis. Finally, the system can be also applied in learning activities of performing flipped classroom strategy when people will visit soil and water conservation outdoor classrooms.
Blockchain-based Learning Credential Revision and Revocation Method

Many blockchain-based learning credential systems have been proposed to reduce fraud and improve verification efficiency. In addition to a method for issuing and verifying credentials, a solution is needed to support the revision and revocation of an issued credential record. For the case of learning credentials, depending on how a revocation policy affects credential use that occurred before the revocation date, an additional mechanism may be needed for credential revision. Current digital learning credential methods offer only a revocation mechanism. So they do not fully meet such unique requirement in the education context. In this paper, a blockchain-based method for learning credential revision and revocation is proposed. It makes use of the revision and revocation addresses assigned to each batch of issuing credentials. To revise (revoke) one or more credentials, the proposed method stores the revision (revocation) list as a message in the OP_RETURN field of the revision (revocation) transaction, with the revision (revocation) address as one of its outputs. The concept of a local credential id has been introduced to allow a revision (revocation) list to be efficiently stored on a blockchain system. It is based entirely on a blockchain system and does not require any centralized authority. It is also applicable to most blockchain systems. A comparative study of the proposed method against existing credential revocation methods is also provided.
Safe Human-Computer Interface Based On Efficient Image Processing Algorithm

With the growing digitalization of our society and the increasing development of Industry 4.0, the development of safe human-computer interfaces is of prime importance. For this purpose, we present a new solution for avoiding any physical contact between the human and the machine's display and/or its keyboard/pad. Hence, we developed an innovative image processing algorithm which runs on the live video stream of the remote interaction between the human and the machine such as a computer or an intelligent agent; the video stream being captured by an off-the-shelf camera which is embedded in the main machine. The resulting computer-vision system performs visual object detection and tracking of a marker the user holds in one hand and could wave it from a distance in order to remotely interact with the interface of the machine. This remote interaction system has been successfully tested in real-world operational conditions, and the performance of this image processing application has been evaluated in real time.
Bounding Edit Distance for similarity-based sequence classification on Structural Pattern Recognition

Pattern Recognition tasks in the structural domain generally exhibit high accuracy results, but their time efficiency is quite low. Furthermore, this low performance is more pronounced when dealing with instance-based classifiers, since, for each query, the entire corpus must be evaluated to find the closest prototype. In this work we address this efficiency issue for the Nearest Neighbor classifier when data are encoded as two-dimensional code sequences, and more precisely strings and sequences of vectors. For this, a set of bounds is proposed in the distance metric that avoid the calculation of unnecessary distances. Results obtained prove the effectiveness of the proposal as it reduces the classification time in percentages between 80% and 90% for string representations and between 60% and 80% for data codified as sequences of vectors with respect to their corresponding non-optimized version of the classifier.
HCH DEX: A Secure Cryptocurrency e-Wallet &amp; Exchange System with Two-way Authentication

Cryptocurrencies could potentially revolutionize the way our global economy functions. It presents us with enticing features that could give way to a secure and safe monetary transaction environment. Nonetheless, the present system provides a large room for improvement, and some could consider it merely a platform yet to be built upon. HCH DEX or Hot-Cold Hybrid Decentralized Exchange presents a method to locally store Cryptocurrency Wallet data in personal devices and process transactions between two personal devices without needing any common database or centralized server system to act as a broker or service provider; any licensed Local Broker can be used to facilitate a transaction in the Blockchain and register it in the distributed ledger. Additionally, the setup has been proposed that could be realized in the form of a smart card and could be designed to not be much thicker than the cards that are ubiquitous today. It is based on the conjunction of a secure two-way authentication system that enables robust handshaking mechanisms for e-Wallets and Lightweight DLT Nodes as local facilitators. It could arguably be a small step towards a fair and just economic system that is resistant to fraudulent and unethical policies and practices at different levels.