Reviewerly/exHarmony huggingface dataset:
DIV8K: DIVerse 8K Resolution Image Dataset

Super-resolution methods in literature has in recent years been dominated by convolutional neural networks (CNNs), aiming to learn a direct mapping from a low to high resolution image. Although successful, these methods rely on large-scale and high-quality datasets, to learn more powerful models. The scale of the existing super-resolution datasets are limiting the performance of current deep and highly complex architectures. Moreover, current datasets are severely limited in terms of resolution, prohibiting the move towards more extreme conditions with high upscaling factors. In this paper, we introduce the DIVerse 8K resolution image dataset (DIV8K). The dataset contains a over 1500 images with a resolution up to 8K. It highly covers diverse scene contents. It is therefore the ideal dataset for training and benchmarking super-resolution approaches, applicable to extreme upscaling factors of 32x and beyond. The dataset was employed for the AIM 2019 Image Extreme Super Resolution Challenge.
Accelerometer

At global user population scale, important microservices in warehouse-scale data centers can grow to account for an enormous installed base of servers. With the end of Dennard scaling, successive server generations running these microservices exhibit diminishing performance returns. Hence, it is imperative to understand how important microservices spend their CPU cycles to determine acceleration opportunities across the global server fleet. To this end, we first undertake a comprehensive characterization of the top seven microservices that run on the compute-optimized data center fleet at Facebook.
Sigmoid Activation Function in Selecting the Best Model of Artificial Neural Networks

Abstract The aim of the research is to make predictions from the best architectural model of backpropagation neural networks. In determining the outcome in the form of a prediction model, the activation function in the artificial neural network is useful to transform an input into a certain output. In this study the activation function used is sigmoid. The study uses the case of population density in Indonesia considering that for developing countries population growth has many negative impacts such as increased poverty, unemployment and crime rates. This study uses data from Badan Pusat Statistic Indonesia for population categories in 2003-2015. The process of determining the architectural model is carried out 2 stages including: the training stage and the testing stage. The two architectural models used (5-2-1 and 5-10-1), the selection of architectural models is done by looking at several assessment parameters such as epoch, accuracy, MSE training and MSE testing. The process of training and testing of data is done by using the help of the Matlab application. The results of the study obtained that the architectural model 5-2-1 is the best model for predicting with an accuracy of 65%, MSE Training 0,0009997254, MSE Testing 0.0014897214 and Epoch 28548.
Bypassing Detection of URL-based Phishing Attacks Using Generative Adversarial Deep Neural Networks

The URL components of web addresses are frequently used in creating phishing detection techniques. Typically, machine learning techniques are widely used to identify anomalous patterns in URLs as signs of possible phishing. However, adversaries may have enough knowledge and motivation to bypass URL classification algorithms by creating examples that evade classification algorithms. This paper proposes an approach that generates URL-based phishing examples using Generative Adversarial Networks. The created examples can fool Blackbox phishing detectors even when those detectors are created using sophisticated approaches such as those relying on intra-URL similarities. These created instances are used to deceive Blackbox machine learning-based phishing detection models. We tested our approach using actual phishing datasets. The results show that GAN networks are very effective in creating adversarial phishing examples that can fool both simple and sophisticated machine learning phishing detection models.
Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning

The diagnosis of breast cancer in early stage is essential for successful treatment. Detection can be performed in several ways, the most common being through mammograms. The projections acquired by this type of examination are directly affected by the composition of the breast, which density can be similar to the suspicious masses, being a challenge the identification of malignant lesions. In this article, we propose a computer-aided detection (CAD) system to aid in the diagnosis of masses in digitized mammograms using a model based in the U-Net, allowing specialists to monitor the lesion over time. Unlike most of the studies, we propose the use of an entire base of digitized mammograms using normal, benign, and malignant cases. Our research is divided into four stages: (1) pre-processing, with the removal of irrelevant information, enhancement of the contrast of 7989 images of the Digital Database for Screening Mammography (DDSM), and obtaining regions of interest. (2) Data augmentation, with horizontal mirroring, zooming, and resizing of images; (3) training, with tests of six-based U-Net models, with different characteristics; (4) testing, evaluating four metrics, accuracy, sensitivity, specificity, and Dice Index. The tested models obtained different results regarding the assessed parameters. The best model achieved a sensitivity of 92.32%, specificity of 80.47%, accuracy of 85.95% Dice Index of 79.39%, and AUC of 86.40%. Even using a full base without case selection bias, the results obtained demonstrate that the use of a complete database can provide knowledge to the CAD expert.
Finding Hash Collisions with Quantum Computers by Using Differential Trails with Smaller Probability than Birthday Bound

In this paper we spot light on dedicated quantum collision attacks on concrete hash functions, which has not received much attention so far. In the classical setting, the generic complexity to find collisions of an n-bit hash function is $$O(2^{n/2})$$ , thus classical collision attacks based on differential cryptanalysis such as rebound attacks build differential trails with probability higher than $$2^{-n/2}$$ . By the same analogy, generic quantum algorithms such as the BHT algorithm find collisions with complexity $$O(2^{n/3})$$ . With quantum algorithms, a pair of messages satisfying a differential trail with probability p can be generated with complexity $$p^{-1/2}$$ . Hence, in the quantum setting, some differential trails with probability up to $$2^{-2n/3}$$ that cannot be exploited in the classical setting may be exploited to mount a collision attack in the quantum setting. In particular, the number of attacked rounds may increase. In this paper, we attack two international hash function standards: AES-MMO and Whirlpool. For AES-MMO, we present a 7-round differential trail with probability $$2^{-80}$$ and use it to find collisions with a quantum version of the rebound attack, while only 6 rounds can be attacked in the classical setting. For Whirlpool, we mount a collision attack based on a 6-round differential trail from a classical rebound distinguisher with a complexity higher than the birthday bound. This improves the best classical attack on 5 rounds by 1. We also show that those trails are optimal in our approach. Our results have two important implications. First, there seems to exist a common belief that classically secure hash functions will remain secure against quantum adversaries. Indeed, several second-round candidates in the NIST post-quantum competition use existing hash functions, say SHA-3, as quantum secure ones. Our results disprove this common belief. Second, our observation suggests that differential trail search should not stop with probability $$2^{-n/2}$$ but should consider up to $$2^{-2n/3}$$ . Hence it deserves to revisit the previous differential trail search activities.
Efficient classical simulation of noisy random quantum circuits in one dimension

Understanding the computational power of noisy intermediate-scale quantum (NISQ) devices is of both fundamental and practical importance to quantum information science. Here, we address the question of whether error-uncorrected noisy quantum computers can provide computational advantage over classical computers. Specifically, we study noisy random circuit sampling in one dimension (or 1D noisy RCS) as a simple model for exploring the effects of noise on the computational power of a noisy quantum device. In particular, we simulate the real-time dynamics of 1D noisy random quantum circuits via matrix product operators (MPOs) and characterize the computational power of the 1D noisy quantum system by using a metric we call MPO entanglement entropy. The latter metric is chosen because it determines the cost of classical MPO simulation. We numerically demonstrate that for the two-qubit gate error rates we considered, there exists a characteristic system size above which adding more qubits does not bring about an exponential growth of the cost of classical MPO simulation of 1D noisy systems. Specifically, we show that above the characteristic system size, there is an optimal circuit depth, independent of the system size, where the MPO entanglement entropy is maximized. Most importantly, the maximum achievable MPO entanglement entropy is bounded by a constant that depends only on the gate error rate, not on the system size. We also provide a heuristic analysis to get the scaling of the maximum achievable MPO entanglement entropy as a function of the gate error rate. The obtained scaling suggests that although the cost of MPO simulation does not increase exponentially in the system size above a certain characteristic system size, it does increase exponentially as the gate error rate decreases, possibly making classical simulation practically not feasible even with state-of-the-art supercomputers.
Federated Learning with Autotuned Communication-Efficient Secure Aggregation

Federated Learning enables mobile devices to collaboratively learn a shared inference model while keeping all the training data on a user's device, decoupling the ability to do machine learning from the need to store the data in the cloud. Existing work on federated learning with limited communication demonstrates how random rotation can enable users' model updates to be quantized much more efficiently, reducing the communication cost between users and the server. Meanwhile, secure aggregation enables the server to learn an aggregate of at least a threshold number of device's model contributions without observing any individual device's contribution in unaggregated form. In this paper, we highlight some of the challenges of setting the parameters for secure aggregation to achieve communication efficiency, especially in the context of the aggressively quantized inputs enabled by random rotation. We then develop a recipe for auto-tuning communication-efficient secure aggregation, based on specific properties of random rotation and secure aggregation - namely, the predictable distribution of vector entries post-rotation and the modular wrapping inherent in secure aggregation. We present both theoretical results and initial experiments.
Multi-label feature selection with shared common mode

Multi-label feature selection plays an indispensable role in multi-label learning, which eliminates irrelevant and redundant features while retaining relevant features. Most of existing multi-label feature selection methods employ two strategies to construct feature selection models: extracting label correlations to guide feature selection process and maintaining the consistency between the feature matrix and the reduced low-dimensional feature matrix. However, the data information is described by two data matrices: the feature matrix and the label matrix. Previous methods devote attention to either of the two data matrices. To address this issue, we propose a novel feature selection method named Feature Selection considering Shared Common Mode between features and labels (SCMFS). First, we utilize Coupled Matrix Factorization (CMF) to extract the shared common mode between the feature matrix and the label matrix, considering the comprehensive data information in the two matrices. Additionally, Non-negative Matrix Factorization (NMF) is adopted to enhance the interpretability for feature selection. Extensive experiments are implemented on fifteen real-world benchmark data sets for multiple evaluation metrics, the experimental results demonstrate the classification superiority of the proposed method.
CANTransfer

In-vehicle communications, due to simplicity and reliability, a Controller Area Network (CAN) bus is widely used as the de facto standard to provide serial communications between Electronic Control Units (ECUs). However, prior research exhibits several network-level attacks can be easily performed and exploited in the CAN bus. Additionally, new types of intrusion attacks are discovered very frequently. However, unless we have a large amount of data about an intrusion, developing an efficient deep neural network-based detection mechanism is not easy. To address this challenge, we propose CANTransfer, an intrusion detection method using Transfer Learning for CAN bus, where a Convolutional LSTM based model is trained using known intrusion to detect new attacks. By applying one-shot learning, the model can be adaptable to detect new intrusions with a limited amount of new datasets. We performed extensive experimentation and achieved a performance gain of 26.60% over the best baseline model for detecting new intrusions.
Machine Learning Aspects and its Applications Towards Different Research Areas

Machine learning is a branch of artificial intelligence that aims at enabling machines to perform their jobs skillfully by using intelligent software. The statistical learning methods constitute the backbone of intelligent software that is used to develop machine intelligence. Now a Day, a huge increase in demand for machine learning has been seen with the great number of available datasets. The knowledge acquisition mechanizing from experience improvement using computational methods comes under machine learning. There is need of knowledge specific to the domain by expert performance and number of AI expert systems has been produced by knowledge engineering. Its regular use has been seen in the industry in different domains. Due to the increase in use and applicability of machine learning, the systematic review on various aspects related to it has been presented in this paper. The paper started with giving a brief description of machine learning, and the use of different models of machine learning. The various types of machine learning algorithms that are used for various purposes like data mining, predictive analytics, image processing etc. has also presented in the comprehensive review. We have also given a review of different work done by various researchers in different application areas. It covered the use of machine learning in medical, social media, Travelling and robotics. The primary purpose behind its popularity in the different application is its ability to learn once and then it works automatically for any same type of data or input given to it.
Application deployment using containers with auto-scaling for microservices in cloud environment

A microservice-based application is composed of a set of small services that run within their own processes and communicate with a lightweight mechanism. Processing the microservices efficiently with minimum processing time and cost, while utilizing the computing resources efficiently, is a challenging task in a cloud environment. To address this challenge, in this paper, we propose a new container-aware application scheduling strategy with an auto-scaling policy. The proposed strategy deploys the requested applications on the best-fit lightweight containers, with minimum deployment time, based on the resource requirements. Another important issue of the container-aware cloud environment is the cold start effect, which is solved using a rule-based policy in the proposed work for minimizing deployment time and cost of the applications. Furthermore, a dynamic bin-packing strategy is designed for deploying the applications to the minimum number of physical machines (PMs) with efficient utilization of the computing resources. Finally, a heuristic-based auto-scaling policy has been designed for minimizing the wastage of the computing resources in the cloud data center. Through numerical evaluation, we have shown the superiority of the proposed method over the existing state-of-the-art algorithms in terms of processing time, processing cost, resource utilization, and required numbers of PMs.
Effects of Social Grooming on Incivility in COVID-19

This study implements a computer-assisted content analysis to identify which social grooming factors reduce social media users' incivility when commenting or posting about the COVID-19 situation in South Korea. In addition, this study conducts semantic network analysis to interpret qualitatively how people express their thoughts. The findings suggest that social network size is a negative predictor of incivility. Moreover, Twitter users who have built larger networks and gained positive responses from others are less likely to use uncivil language. Lastly, linguistic choice among users is different depending on the size of their social network.
Joint Operation Mechanism of Distributed Photovoltaic Power Generation Market and Carbon Market Based on Cross-Chain Trading Technology

The rise of blockchain technology has injected new vitality into the energy market.At present, blockchain projects for distributed photovoltaic power generation and carbon trading are rapidly emerging, but due to the barrier of two markets and the independent operation of such projects in two chains, the two market values cannot be two-way circulation, which is not conducive to the expansion of power trading business and the scale of carbon market.This paper proposed a joint operation mechanism of cross-chain trading, combined distributed photovoltaic power generation market and the carbon market by the blockchain technology.The novelty is to construct two chains which includes the mainchain and sidechain that enables the two markets to share data and circulate value, and to design a two-way anchoring method that achieves equating between carbon trading and electricity trading by cryptocurrency.The simulation studies took the improved IEEE 33 system as an example to simulate transactions on the Ethereum platform, and technically verify the feasibility of cross-chain transactions in these two markets, and use Ether as the settlement currency.
SGM-Net: Skeleton-guided multimodal network for action recognition

Single-modality human action recognition on RGB or skeleton has been extensively studied. Each of these two modalities has its own advantages as well as limitations, because they depict action from different perspectives. The feature of different modalities can complement each other for describing actions. Therefore, it is meaningful to fuse these two modalities using their complementarity for action recognition. However, existing multimodal methods fail to fully exploit the complementarity of RGB and skeleton modalities. In this paper, we propose a Skeleton-Guided Multimodal Network (SGM-Net) for human action recognition. The proposed method takes full use of the complementarity of these two modalities at semantic feature level. From the technical perspective, we introduce a guided block, the key component of SGM-Net. It enables skeleton feature to guide on RGB feature, so that the important RGB information strongly related to the action is enhanced. Moreover, in the guided block, two schemes of correlation operation are explored. We perform a series of ablation experiments to verify the effectiveness of the guided block. The experimental results show that our approach achieves state-of-the-art performance over the existing methods on NTU and Sub-JHMDB datasets.
Late Acceptance Hill Climbing Based Social Ski Driver Algorithm for Feature Selection

Feature selection (FS) is mainly used as a pre-processing tool to reduce dimensionality by eliminating irrelevant or redundant features to be used for a machine learning or data mining algorithm. In this paper, we have introduced binary variant of a recently proposed meta-heuristic algorithm called Social Ski Driver (SSD) optimization. To the best of our knowledge, SSD has not been used yet in the domain of FS. Two binary variants of SSD are proposed using S-shaped and V-shaped transfer functions. Besides, the exploitation ability of SSD is improved by using a local search method, called Late Acceptance Hill Climbing (LAHC). The hybrid meta-heuristic is then converted to binary version by using said transfer functions. The proposed methods are applied on 18 standard UCI datasets and compared with 15 state-of-the-art FS methods. Also to check the robustness of the proposed method, we have applied it to 3 high dimensional microarray datasets and compared with 6 state-of-the-art methods. Achieved results confirm the superiority of the proposed methods compared to other meta-heuristic wrapper based FS methods considered here. Source code of this work is available at https://github.com/consigliere19/SSD-LAHC.
Systematic Literature Review of Smart Home Monitoring Technologies Based on IoT for the Elderly

Smart home technology implementation remains an essential aspect of Internet of Things (IoT). It provides needed living support and convenience for elderly people in society. Despite the remarkable achievements in smart home monitoring technology studies, a systematic literature review (SLR) on smart home technology implementation is lacking. There is a limited number of SLR studies on smart home monitoring technology. Therefore, the current study assesses the literature to collect the evidence regarding studies on smart home monitoring technology implantation. An SLR method involving a manual search was applied to review the articles published from January 2010 to December 2019. To carefully classify these articles accordingly, we applied certain quality assessment criteria. Out of 73 relevant primary studies, only 3% were identified to have applied comprehensive SLR guidelines. Seven percent of the relevant studies were identified to have applied the SLR guidelines in a strongly moderately acceptable manner, but they were not completely comprehensive, whereas 8% of the primary studies applied the SLR guideline in a fairly acceptable manner. This manuscript therefore encourages researchers working on SLR studies on smart home technology to apply comprehensive SLR guidelines that takes into account the quality standard.
Chart Mining: A Survey of Methods for Automated Chart Analysis

Charts are useful communication tools for the presentation of data in a visually appealing format that facilitates comprehension. There have been many studies dedicated to chart mining, which refers to the process of automatic detection, extraction and analysis of charts to reproduce the tabular data that was originally used to create them. By allowing access to data which might not be available in other formats, chart mining facilitates the creation of many downstream applications. This paper presents a comprehensive survey of approaches across all components of the automated chart mining pipeline, such as (i) automated extraction of charts from documents; (ii) processing of multi-panel charts; (iii) automatic image classifiers to collect chart images at scale; (iv) automated extraction of data from each chart image, for popular chart types as well as selected specialized classes; (v) applications of chart mining; and (vi) datasets for training and evaluation, and the methods that were used to build them. Finally, we summarize the main trends found in the literature and provide pointers to areas for further research in chart mining.