Reviewerly/exHarmony huggingface dataset:
Training Convolutional Neural Networks with Differential Evolution using Concurrent Task Apportioning on Hybrid CPU-GPU Architectures

The core algorithm for training of Artificial Neural Nets (ANNs) continues to remain the back-propagation (BP) algorithm - to an extent that it is now considered as a paradigm of Deep Learning (DL). Many important facets of DL, like hierarchical construction of features across layers in image recognition, vanishing gradients, etc., are taken for granted without recognizing that these may implicitly be induced by BP itself. Evolutionary Algorithms (EAs) perform global optimization in contrast to localized gradient descent of BP. If used extensively for ANN training, they can potentially disrupt these assumed facets of DL - and construct alternative and interesting perspectives. But they are severely constrained by the need for large computational resources, as they work concurrently on a population of candidate solutions. The bulk of processing occurs in the forward pass through the ANN of thousands of data samples - which can be efficiently parallelized on GPUs. However, the candidates themselves can be launched in small groups on different CPU cores - by exploiting their natural concurrency. Here we explore the possibility of launching training of ANNs with EAs on hybrid CPU-GPU systems with candidates split across CPUs and samples across GPU threads. We conduct a series of experiments from which we synthesize a successful mechanism for orders-of-magnitude speedup of EAs through efficient apportioning of multi-level computing tasks onto different classes of processing elements. This enables analysis of DL using EAs empowering alternative interpretations of the above facets.
A Practical Approach to Navigating the Tradeoff Between Privacy and Precise Utility

Due to the recent popularity of online social networks, coupled with people’s propensity to disclose personal information in an effort to achieve certain gratifications, the problem of navigating the tradeoff between privacy and utility attracted a lot of recent interest and generated a rich body of research. A critical prerequisite to solving the problem is to appropriately capture the privacy and the utility aspects in the problem formulation. Most of the existing works focus on the notion of privacy, while utility loss is often treated as the undesirable but necessary distortion of the true data, introduced by the privacy mechanism. By contrast, we are interested in modeling utility differently, by associating it with specific attributes of a user, just like privacy is associated with specific private attributes in the literature. Our model of utility facilitates a better and more precise privacy mechanism. We further incorporate into our problem formulation a practical constraint on acceptable loss in utility per unit gain in privacy, which allows users to customize the privacy mechanisms in order to account for the relative values that each user associates with their own privacy and utility. This paper discusses the intricacies of our utility model and the corresponding privacy-utility tradeoff, introduces a heuristic greedy algorithm to solve the problem and presents experimental results.
SNR: Network-aware Geo-Distributed Stream Analytics

Emerging applications such as those running on the Internet of Things (IoT) devices produce constant data streams that need to be processed in real-time. Distributed stream processing systems (DSPs), with geographically distributed cluster networks interconnected via wide area network (WAN) links, have recently gained interest in handling these applications. How-ever, these applications have stringent requirements such as low-latency and high bandwidth that must be guaranteed to ensure the quality of service (QoS). These application requirements raise fundamental DSPs resource management and scheduling challenge. In this paper, we formulate the problem of placement of worker nodes on a geo-distributed DSPs cluster network as a multi-criteria decision-making problem and propose an additive weighting-based approach to solve it. The proposed solution finds the trade-off among different network parameters and allows executing the tasks according to the desired performance metrics. We evaluated the proposed approach using the Yahoo! streaming benchmark on a testbed and compare it against mechanisms deployed in Apache Spark, Apache Storm, and Apache Flink. The results of the evaluation show that our approach improves the performance of Spark up to 2.2x-7.2x, Storm up to 1.2x-3.4x, and Flink up to 1.4x-3.3x compared to other approaches, which makes our approach useful for use in practical environments.
A Comprehensive Study of Various Techniques for Hostile Drone Detection and Their Classification

Today's atmosphere is being increasingly congested due to the proliferation of airborne objects such as small airplanes, helicopters, and unmanned aerial vehicles (drones) of varying forms and sizes. The UAVs can also become a major threat as they can be utilized in smuggling, industrial spy, robbery, and terrorism, besides enabling access to new technologies. Owing to its speed, agility, and altitude, it is hard to defend against a UAV incursion. Aerial defence systems are integrated with a wide range of sensors to efficiently track aircraft. There are several methods and techniques discussed in this paper that are ideal for this specific application. This paper also discusses the paradigm of drone detection using Radar and Imaging techniques and the classification of drones based on their size, features, flying height, and Unique Identification Number (UIN).
Limits on the Adaptive Security of Yao’s Garbling

Yao's garbling scheme is one of the most fundamental cryptographic constructions. Lindell and Pinkas (Journal of Cryptograhy 2009) gave a formal proof of security in the selective setting where the adversary chooses the challenge inputs before seeing the garbled circuit assuming secure symmetric-key encryption (and hence one-way functions). This was followed by results, both positive and negative, concerning its security in the, stronger, adaptive setting. Applebaum et al. (Crypto 2013) showed that it cannot satisfy adaptive security as is, due to a simple incompressibility argument. Jafargholi and Wichs (TCC 2017) considered a natural adaptation of Yao's scheme (where the output mapping is sent in the online phase, together with the garbled input) that circumvents this negative result, and proved that it is adaptively secure, at least for shallow circuits. In particular, they showed that for the class of circuits of depth $$\delta $$ , the loss in security is at most exponential in $$\delta $$ . The above results all concern the simulation-based notion of security. In this work, we show that the upper bound of Jafargholi and Wichs is basically optimal in a strong sense. As our main result, we show that there exists a family of Boolean circuits, one for each depth $$\delta \in \mathbb {N}$$ , such that any black-box reduction proving the adaptive indistinguishability of the natural adaptation of Yao's scheme from any symmetric-key encryption has to lose a factor that is exponential in $$\sqrt{\delta }$$ . Since indistinguishability is a weaker notion than simulation, our bound also applies to adaptive simulation. To establish our results, we build on the recent approach of Kamath et al. (Eprint 2021), which uses pebbling lower bounds in conjunction with oracle separations to prove fine-grained lower bounds on loss in cryptographic security.
A Novel Approach in Cyber Security for securing the Workplace of the Future in large Industry Setups

Cyber Security as a topic has been a major course of concern for any product offering. Digitization has been on the rise and with the impact of the COVID19 scenario, the need for realizing business requirements over a virtual setup has become critical. This has led to massive cyber-attacks that leads to stolen assets, lost reputation, lost business, financial losses, impact on the stock market, and theft of intellectual property so on. It is essential to build a security strategy to navigate through challenging times, recover quickly and continue to deliver the intended outcome. Security efficiency can be achieved by balancing and optimizing the relationships between people, processes, and technology. Investing in sustainable security culture is necessary as it transforms security from a one-time event into a lifecycle that generates security returns forever. In this paper, we explore an approach to achieve security culture changes among people that results in the change of process & technology for a secure environment. This proposed paper also explores the impacts of people, processes, and technology and how each of these pillars contributes to a creation of a secure workplace of the future.
On the Performance of PBFT-based Permissioned Blockchain Networks in Constraint Environments

In this paper we propose and develop a simple analytical model to study the performance of permissioned blockchain networks in a constraint environment with intermittent connectivity and limited resources. We assume that a Practical Byzantine Fault Tolerence (PBFT) consensus process is used as the endorsement policy. We use the model to study several important performance metrics such as the average number of attempts to reach consensus, the average amount of time to reach consensus, and the average number of messages transferred before a transaction is committed. We also present a non-exhaustive set of numerical results that can provide valuable insights into the design of permissioned blockchain platforms, and establish a link between the performance and system parameters including size of network, packet loss rates, and average link bandwidth.
Blind Faith: Privacy-Preserving Machine Learning using Function Approximation

Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption of cloud-based services. As a result, various solutions have been proposed in which the machine learning models run on a remote cloud provider. However, when such a model is deployed on an untrusted cloud, it is of vital importance that the users' privacy is preserved. To this end, we propose Blind Faith - a machine learning model in which the training phase occurs in plaintext data, but the classification of the users' inputs is performed on homomorphically encrypted ciphertexts. To make our construction compatible with homomorphic encryption, we approximate the activation functions using Chebyshev polynomials. This allowed us to build a privacy-preserving machine learning model that can classify encrypted images. Blind Faith preserves users' privacy since it can perform high accuracy predictions by performing computations directly on encrypted data.
Using electronic health records to accurately predict COVID-19 health outcomes through a novel machine learning pipeline

Current COVID-19 predictive models primarily focus on predicting the risk of mortality, and rely on COVID-19 specific medical data such as chest imaging after COVID-19 diagnosis. In this project, we developed an innovative supervised machine learning pipeline using longitudinal Electronic Health Records (EHR) to accurately predict COVID-19 related health outcomes including mortality, ventilation, days in hospital or ICU. In particular, we developed unique and effective data processing algorithms, including data cleaning, initial feature screening, vector representation, and feature normalization. Then we trained models using state-of-the-art machine learning strategies combined with different parameter settings and feature selection. Based on routinely collected EHR, our machine learning pipeline not only consistently outperformed those developed by other research groups using the same data set, but also achieved similar mortality prediction accuracy as those trained on medical data available only after COVID-19 diagnosis. In addition, we identified top COVID-19 risk factors, which are consistent with epidemiologic findings.
Classification of Various Scheduling Approaches for Resource Management System in Cloud Computing

Cloud computing has shown up as a generally acknowledged computing worldview for offering different types of assistance to Industry 4.0. Booking resources in a productive way assumes a crucial job behind the achievement of cloud computing and the expansion of Industry 4.0. There are different calculations for planning the undertaking with resources. The virtual machine, stockpiling, RAM, and CPU time are the various resources that should be overseen proficiently. By utilizing effective planning calculation, we can oversee resources productively which will be useful for specialist co-op just as purchaser. This section presents the central calculations utilized for planning of resource management framework alongside their need in Industry 4.0. The need for planning calculation, their characterization on a different scale, advantages, and disadvantages are additionally examined in detail. Various apparatuses and test systems accessible for research in the booking are likewise talked about. The diverse ideal techniques for distributing the resources utilized by the significant cloud suppliers in the business are canvassed in detail. With the approach of fourth modern unrest, numerous nature-motivated enhancement calculations have now discovered their way of giving ideal resource distribution in the cloud. The understanding of these calculations is additionally drawn here.
An Adversarial Approach for Explaining the Predictions of Deep Neural Networks

Machine learning models have been successfully applied to a wide range of applications including computer vision, natural language processing, and speech recognition. A successful implementation of these models however, usually relies on deep neural networks (DNNs) which are treated as opaque black-box systems due to their incomprehensible complexity and intricate internal mechanism. In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning. Our approach identifies the relative importance of input features in relation to the predictions based on the behavior of an adversarial attack on the DNN. Our algorithm has the advantage of being fast, consistent, and easy to implement and interpret. We present our detailed analysis that demonstrates how the behavior of an adversarial attack, given a DNN and a task, stays consistent for any input test data point proving the generality of our approach. Our analysis enables us to produce consistent and efficient explanations. We illustrate the effectiveness of our approach by conducting experiments using a variety of DNNs, tasks, and datasets. Finally, we compare our work with other well-known techniques in the current literature.
HyperChannel: A Secure Layer-2 Payment Network for Large-Scale IoT Ecosystem

For the future large-scale IoT ecosystem, the number and frequency of micro-payments will increase dramatically. However, the mainstream of cryptocurrencies such as Bitcoin and Ethereum fail to meet the need for a large-scale IoT ecosystem due to limit transaction throughput and high transaction fee. Although Layer-2 solutions such as Lightning Network (LN) increases the throughput of cryptocurrencies by allowing participants to conduct off-chain transactions, LN still suffers from two main limitations: participants need to access the Blockchain within a short bounded time, and a payment channel can only accommodate two participants. To overcome these limitations, we propose HyperChannel, a novel distributed layer-2 payment network designed specifically for the IoT ecosystem which outsources the transaction processing task safely to a group of Intel Software Guard Extensions (SGXs) run by for-profit selfish third parties. Clients such as IoT devices and IoT service providers who often trade with each other will be assigned to a channel to conduct high-frequency in-channel transactions while being allowed to conduct crosschannel transactions in a fee-saving fashion. Compared with existing SGX-based layer-2 payment framework, HyperChannel achieves maximum throughput, addresses both limitations of LN, and further lightens the burden of participants so that IoT devices can conduct layer-2 transactions without running an SGX by themselves.
Feature-Importance Feature-Interactions (FIFI) graph: A graph-based Novel Visualization for Interpretable Machine Learning

A majority of the complex Machine Learning (ML) models lack in human interpretability. This lacking makes it difficult for human users to interpret why a particular prediction outcome has been made by an ML model. During recent years, a renewed interested has been observed among ML research community to confer human interpretability to ML models. Humans are inherently good at interpreting visualizations and have been using visualizations as a medium for explaining a phenomenon or process to others. Therefore, the output of most of the approaches towards conferring human interpretability to ML models has been usually a visualization. These visualizations aim to explain ML model behaviour to human users by highlighting the most contributing features. Studying interactions between features is equally important to facilitate knowledge discovery and providing new insights about the underlying physical phenomena. Existing visualizations mainly focusing on feature importance. This paper proposes a FIFI graph, a novel visualization for interpreting ML models. The intuitive idea is to plot the relative importance of features and relative strength of interactions between features into a single plot. The proposed visualization has been modelled as a network graph. It has been demonstrated using two datasets. Being a graph enables using existing research and software tools on network analysis for interpreting ML model behaviour. Also, a comparison of the FIFI graph with a knowledge graph has been made.
State-of-the-art Multivariate Regression with a General N<sub>k</sub> Hidden Multi-Layer Feed Forward Neural Network Model

We have introduced a novel multivariate regression model, called General N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> Hidden Multi-Layer Feed Forward Neural Network Model. The model prediction power comes essentially from a combination of Universal Approximation theorem, the Stochastic Gradient Descent convergence, and finally the use of ADAM optimizer which is also (at least) locally convergent. This N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> -architecture is proposed to fit any multivariate regression task, as well as any classification task when a softmax activation gate function is applied to the output layer nodes. The model can easily be augmented to thousands of possible layers without loss of predictive power, and has the potential to overcome our difficulties simultaneously in building a model that has a good fit on the test data, and don’t overfit. Its hyper-parameters, the learning rate, the batch size, the number of training times (epochs), the size of each layer, the number of hidden layers, all can be chosen experimentally with cross-validation methods. We have run some experiments with the Mulan Project Datasets [29] to illustrate the performance of the model against Random Forest with a number of estimators from 5 to 10, and a maximum depth from 10 to 30. Not only has the model surpasses the Random Forest model in all tested configurations, but, we have also found this General N <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</inf> Hidden Multi-layer Feed Forward Neural Network to be so effective as it reaches state-of-the-art performance for multivariate regression in terms of Mean Squared Error.
Understanding Block and Transaction Logs of Permissionless Blockchain Networks

Public blockchain records are widely studied in various aspects such as cryptocurrency abuse, anti-money-laundering, and monetary flow of businesses. However, the final blockchain records, usually available from block explorer services or querying locally stored data of blockchain nodes, do not provide abundant and dynamic event logs that are only visible from a live large-scale measurement. In this paper, we collect the network logs of three popular permissionless blockchains, that is, Bitcoin, Ethereum, and EOS. The discrepancy between observed events and the public block data is studied via a noble analysis model provided with the soundness of measurement. We share our key findings including a false universal assumption of previous mining-related studies and the block/transaction arrival characteristics.
Robust Image-to-Image Color Transfer Using Optimal Inlier Maximization

In this paper we target the color transfer estimation problem, when we have pixel-to-pixel correspondences. We present a feature-based method, that robustly fits color transforms to data containing gross outliers. Our solution is based on an optimal inlier maximization algorithm that maximizes the number of inliers in polynomial time. We introduce a simple feature detector and descriptor based on the structure tensor that gives the means for reliable matching of the color distributions in two images. Using combinatorial methods from optimization theory and a number of new minimal solvers, we can enumerate all possible stationary points to the inlier maximization problem. In order for our method to be tractable we use a decoupling of the intensity and color direction for a given RGB-vector. This enables the intensity transformation and the color direction transformation to be handled separately. Our method gives results comparable to state-of-the-art methods in the presence of little outliers, and large improvement for moderate or large amounts of outliers in the data. The proposed method has been tested in a number of imaging applications.
Measuring Distance between Quantum States by Fuzzy Similarity Operators

This paper introduces a study on fuzzy-based approaches aimed at addressing a crucial task in quantum computation: the evaluation of the similarity between quantum states. A quantum state is a mathematical entity that provides a probability distribution for the outcomes of each possible measurement of a quantum algorithm. Because quantum computers are still characterized by high noise in computation, output quantum states generated by quantum algorithms could be very far to be close to the ideal output quantum state computed by a noiseless quantum computer. As a consequence, there is a strong emergence for measures capable of assessing the similarity level of two quantum states, one ideal and the other real, to infer the quality of a quantum device in performing precise calculations and design appropriate quantum error correction schemes. This research proves that fuzzy methods are fully suitable to face this crucial challenge in a pioneering scenario such as that of quantum computing, as proved by their application on well-known quantum algorithms, such as Bernstein-Vazirani and Grover's algorithm.
Optical metrology: methodological analogy and duality revisited

Analogy and duality help systematic and unified understanding of the seemingly different principles of optical metrology. As an ode honoring Jim Wyant and his achievements in optical metrology, I would like to present a review that revisits the time-space analogy and signal-frequency duality in optical metrology.