Reviewerly/exHarmony huggingface dataset:
SLAM: A Framework for SLA Management in Multicloud ecosystem using Blockchain

The shift towards cloud marketplaces and API-centric services is enabling enterprises to rapidly develop and deliver their IT solutions and services. Enterprises are increasingly adopting a multicloud approach to leverage the latest innovations and unique capabilities of different cloud providers. This is resulting in multicloud systems that utilize independent services, which in turn may be dependent on other services. Clients rely on Service Level Agreements (SLAs) to ensure a minimum quality of service. However, monitoring service quality becomes difficult in a multi-level and multi-provider system due to decentralized control and lack of visibility in the entire system hierarchy. SLA violations often go undetected as multicloud systems lack automated means to monitor SLAs and conduct root cause analysis of violations. We propose SLAM, an autonomous and trusted framework for continuous SLA monitoring in a multicloud ecosystem. It is based on blockchain and leverages smart contracts to detect SLA violations and determine their root cause across the hierarchical system structure.
WITHDRAWN: Correlation-based high distinction feature selection in digital mammogram

For any classification algorithm to be flourishing, attribute choice from the given dataset is the most significant work that determines the whole process and classes of classified data. Feature assortment strategies to extract the most related features, and to drive out the inappropriate attributes with respect to given decision attribute. In this proposed work, the textural features of digital mammographic images are extracted from the Regions of Interest (ROIs) using Gray Level Co-occurrence Matrix. An optimal feature selection technique, namely Correlation-based High Distinction Feature Selection (CHDFS) is attempted to select the most significant features alone to classify the ROIs as Normal, Benign and Malignant. These selected features employed for categorization. With Support Vector Machine (SVM) and Artificial Neural Network (ANN) classifications algorithms, the validation was carried out. The investigational results illustrate that the proposed method be capable of producing better classification performance than the existing feature selection techniques like Correlation-dependent Feature Selection (CFS) and Principle Component Analysis (PCA).
A Multivariate Approach to Lexical Diversity in Constrained Language

Empirical Translation Studies have recently extended the scope of research to other forms of constrained and mediated communication, including bilingual communication, editing, and intralingual translation. Despite the diversity of factors accounted for so far, this new strand of research is yet to take the leap into intermodal comparisons. In this paper we look at Lexical Diversity (LD), which under different guises, has been studied both within Translation Studies (TS) and Second Language Acquisition (SLA). LD refers to the rate of word repetition, and vocabulary size and depth, and previous research indicates that translated and non-native language tends to be less lexically diverse. There is, however, no study that would investigate both varieties within a uniﬁed methodological framework. The study reported here looks at LD in spoken and written modes of constrained and non-constrained language. In a two-step analysis involving Exploratory Factor Analysis and linear mixed-effects regression models we ﬁnd interpretations to be least lexically diverse and written non-constrained texts to be most diverse. Speeches delivered impromptu are less diverse than those read out loud and the non-constrained texts are more sensitive to such delivery-related differences than the constrained ones.
On the Exploitation of Blockchain for Distributed File Storage

Distributed file storage aims to support credible access to data on distributed nodes. There are some application scenarios, for example, data centers, peer-to-peer (P2P) storage systems, and storage in wireless networks. Nevertheless, among these applications, data blocks are inevitably replaced and inaccessible when there exists nodes failure. As a result, data integrity and credibility is absent. To overcome such a challenge, blockchain is explored to protect the distributed data. Through analysis and evaluation, we demonstrate that blockchain advocates data integrity and credibility for distributed file storage, as well as the application of blockchain technology for distributed file storage.
Lifting wheelchair for limbless people

Abstract He impaired are denied of all open doors for social and monetary turn of events. They have an absence of essential offices like wellbeing, instruction, and business. One of the crippled classifications is individuals who don’t have appendages. Limbless individuals face numerous issues like moving starting with one spot then onto the next, climbing steps, taking things from the rack or racks, and so forth. Our task intention is to answer the limbless individuals who are confronting issues in taking the things from the racks at the most extreme stature. We met our locale accomplice concerning this task talked about the issues looked by them. Because they collaborated with their issues, we thought of the most ideal arrangement which comforts and fulfills the client needs. Our item serves the genuinely impeded individuals in the general public in the most effective way.
Analysing the Benefit of Selfish Mining with Multiple Players

Many current mainstream blockchain systems, including Bitcoin, adopt Proof-of-Work (PoW) as their consensus protocol. Such a system faces various cryptoeconomic attacks, such as selfish mining. Previous studies have shown that with over 25% of the overall mining power, a selfish miner can benefit by gaining a proportion of rewards higher than its mining power in Bitcoin. This threshold is still higher than any current mining pool, and hence, selfish mining is not considered likely in Bitcoin. Unfortunately, this threshold is only applicable for a single attacker scenario, whereas in a realistic setting, multiple mining pools with significant hash power could perform selfish mining simultaneously. In this paper, we address this research gap by analyzing selfish mining scenarios with multiple independent attackers. Through extensive simulation studies, we show that when the number of selfish miners increases, each of them requires less mining power to gain an advantage, but that the range of mining power such that each selfish miner benefits becomes narrower and thus less sustainable. Our work is the first to show that in practice, there are scenarios where it is enough to have 12% mining power to benefit from selfish mining but also that having more than 7 selfish miners which benefit simultaneously is highly unlikely. We also infer that it is always beneficial for selfish miners to collude and build a more powerful mining pool than to mine independently. Additionally, we propose a safe limit for the size of mining pools in Bitcoin to avoid multi-player attacks. Finally, we extend our studies to Ethereum which uses a different reward model based on uncle blocks.
A Distance-based Genetic Algorithm for Robust Data-intensive Web Service Composition in Dynamic Bandwidth Environment

With the complex needs of the companies that cannot be met by a single service, Data-intensive Web Service Composition (DWSC) is required to compose multiple services in a distributed service environment. Compositions must satisfy functional specifications and non-functional requirements, i.e. Quality of Service (QoS). Existing approaches on DWSC make the underlying assumption that the participating Web services and communication networks are static so that their QoS and bandwidth seldom change. However, those approaches are impractical since network failures or dynamic bandwidth changes cause violations of user agreements. Additionally, they ignore the distribution of services in general, and therefore, variations in network attributes are not taken into account. In this paper, we address the problem of dynamic distributed DWSC (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -DWSC), design a simulation model for bandwidth patterns, and propose an algorithm to generate robust solutions for D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -DWSC which can cope with the changes in dynamic environments. Experimental results verify the effectiveness of our method.
Enhancing the security of financial transactions in Blockchain by using machine learning techniques: towards a sophisticated security tool for banking and finance

Blockchain is an innovative technology that can be used for processing and sharing data in a safe manner, via an unreliable network. This paper starts with a background of blockchains. We give some principal concepts of this new technology. Then we deal with machine learning as intelligent techniques that may be used for analyzing huge datasets and detecting eventual malicious transactions that can be occured in the untrusted network. We show the importance of such intelligent techniques when combining together to make good decisions in banking and finance. Our idea is applied to the Bitcoin system where the public Elliptic dataset from Kaggle is used as a benchmark. Since the latter is not all labeled, we use the k-means algorithm to partition the unlabeled data into two main clusters while the labeled data are moved to their corresponding clusters. Then, four machine learning techniques are used to classify all the data. The proposed system shows promising results in particular when combining k-means with the random forest classifier.
3D Face Image Inpainting with Generative Adversarial Nets

In the process of face recognition, face acquisition data is seriously distorted. Many face images collected are blurred or even missing. Faced with so many problems, the traditional image inpainting was based on structure, while the current popular image inpainting method is based on deep convolutional neural network and generative adversarial nets. In this paper, we propose a 3D face image inpainting method based on generative adversarial nets. We identify two parallels of the vector to locate the planer positions. Compared with the previous, the edge information of the missing image is detected, and the edge fuzzy inpainting can achieve better visual match effect. We make the face recognition performance dramatically boost.
Improving the Data Quality for Credit Card Fraud Detection

Label imbalance and data missing are two major challenges in the problem of credit card fraud detection. However, existing matrix completion algorithms are generally difficult and cannot be easily applied to real-world credit card fraud detection since the scale of the normally used dataset is oversized. In this paper, we develop a spectral regularization algorithm to complete the large-scale sparse matrices, and further utilize an over-sampling algorithm to tackle the problem of the imbalance between positive and negative samples. Experimental results on a real-world dataset demonstrate that our model can outperform the state-of-the-art baseline methods. The proposed method could also be extended to other large-scale scenarios where data is missing or labels are imbalanced.
Artificial Intelligence and Innovation: The End of Patent Law As We Know It

With the advent of artificial intelligence (AI), the end of patent law is near. Though it may not happen today or tomorrow, the system’s decline is underway. Groundbreaking innovations in AI technology have made inventions “made by AI” a reality. Today, AI is able to “invent” not only new materials and machines but also manufacturing processes, pharmaceutical drugs, and household products. Soon, our life will be replete with artificial artifacts. In a sense, humans no longer stand at the center of the creative universe — we are no longer the masters of innovation. Despite this upheaval on the horizon, the consequences for legal doctrine remain widely unexplored. The most fundamental disruption we are about to witness is the emergence of scenarios where the inventive input is made by AI — and where no human “fire of genius” is involved. The problem here is that patent doctrine is still founded on the idea that only human beings can be inventors. Accordingly, such “inventions without an inventor” will fail before patent offices and courts. It does not take much imagination to see that with the increase in artificial inventiveness, the raison d’être of strict anthropocentrism will dwindle away. Indeed, the emergence of autonomously acting “artificial inventors” dissolves the law’s very foundations — its doctrine, policies, and practice. Unless this vacuum is filled with a new architecture, a system meltdown is inevitable. We desperately need a patent update for the era of artificial inventions.This paper analyzes the status quo of artificial innovation — i.e., inventions “made by AI” — and its regulation (or, rather, non-regulation) in the United States and explores relevant doctrinal debate and economic foundations. On this basis, I offer suggestions how to re-conceptualize the law and how to re-calibrate legal doctrine and practice in order to keep the patent system operative.
Hard-ODT: Hardware-Friendly Online Decision Tree Learning Algorithm and System

Decision trees are machine learning models commonly used in various application scenarios. In the era of big data, traditional decision tree induction algorithms are not suitable for learning large-scale datasets due to their stringent data storage requirement. Online decision tree learning algorithms have been devised to tackle this problem by concurrently training with incoming samples and providing inference results. However, even the most up-to-date online tree learning algorithms still suffer from either high memory usage or high computational intensity with dependency and long latency, making them challenging to implement in hardware. To overcome these difficulties, we introduce a new quantile-based algorithm to improve the induction of the Hoeffding tree, one of the state-of-the-art online learning models. The proposed algorithm is lightweight in terms of both memory and computational demand, while still maintaining high generalization ability. A series of optimization techniques dedicated to the proposed algorithm have been investigated from the hardware perspective, including coarse-grained and fine-grained parallelism, dynamic and memory-based resource sharing, pipelining with data forwarding. Following this, we present Hard-ODT, a high-performance, hardware-efficient and scalable online decision tree learning system on a field-programmable gate array (FPGA) with system-level optimization techniques. Performance and resource utilization are modeled for the complete learning system for early and fast analysis of the tradeoff between various design metrics. Finally, we propose a design flow in which the proposed learning system is applied to FPGA run-time power monitoring as a case study. Experimental results show that our proposed algorithm outperforms the state-of-the-art Hoeffding tree learning method, leading to 0.05% to 12.3% improvement in inference accuracy. Real implementation of the complete learning system on the FPGA demonstrates a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$384\times $ </tex-math></inline-formula> to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1581\times $ </tex-math></inline-formula> speedup in execution time over the state-of-the-art design. The power modeling strategy with Hard-ODT achieves an average power prediction error within 4.93% of a commercial gate-level power estimation tool.
The things of the Internet of Musical Things: defining the difficulties to standardize the behavior of these devices

Musical practice has always been susceptible to technological changes, adapting to the novelties that each era provided, such as, for example, the replacement of mechanical devices by electric ones or even the adhesion of magnetic tapes in the recording process. Nowadays, these technological transformations continue to influence music, especially when using concepts from the Internet of Things (IoT), creating a field called Internet of Musical Things (IoMusT). Although IoMusT helps with remote rehearsals, streamlines the recording process and allows the creation of participatory concerts, there is still no clear definition of how an object connected to this network should behave. In this context, this paper proposes software, hardware and communication protocols standards that these devices must follow, in order to improve the user experience and make this field more widespread and easier to understand. Issues that prevent this standardization are also discussed.
Tree regression models using statistical testing and mixed integer programming

Regression analysis is a statistical procedure that fits a mathematical function to a set of data in order to capture the relationship between dependent and independent variables. In tree regression, tree structures are constructed by repeated splits of the input space into two subsets, creating if-then-else rules. Such models are popular in the literature due to their ability to be computed quickly and their simple interpretations. This work introduces a tree regression algorithm that exploits an optimisation model of an existing literature method called Mathematical Programming Tree (MPtree) to optimally split nodes into subsets and applies a statistical test to assess the quality of the partitioning. Additionally, an approach of splitting nodes using multivariate decision rules is explored in this work and compared in terms of performance and computational efficiency. Finally, a novel mathematical model is introduced that performs subset selection on each node in order to select an optimal set of variables to considered for splitting, that improves the computational performance of the proposed algorithm.
Design of NCA Filters for Tracking Maneuvering Targets

When tracking maneuvering targets with a nearly constant acceleration (NCA) Kalman filter with discrete white noise acceleration (DWNA), the selection of the process noise variance is complicated by the fact that the process noise errors are modeled as white Gaussian, while target maneuvers are deterministic or highly correlated in time. In recent years for nearly constant velocity (NCV) Kalman filters, the deterministic tracking index was introduced and used to develop a relationship between the maximum acceleration of the target and the process noise variance that minimizes the maximum mean squared error (MMSE) in position. A lower bound on the process noise variance was also expressed in terms of the maximum acceleration and deterministic tracking index. In this paper, the design methods for NCV Kalman filters with DWNA are extended to develop design methods for NCA Kalman filters with DWNA tracking maneuvering targets. The effectiveness of the design methods are illustrated via Monte Carlo simulations.
Deep High-Resolution Network With Double Attention Residual Blocks for Human Pose Estimation

To improve the accuracy of human pose estimation, a novel method based on the deep highresolution network (HRNet) and equipped with double attention residual blocks is proposed.Firstly, the channel attention and spatial attention modules are added to the residual block of feature extraction, resulting in the network paying more attention to the target area which needs to be extracted important information and suppressed unimportant information.Moreover, this paper proposes a novel module, Parallel Residual Attention Block (PRAB), which parallels the 3x3 group convolution of ResNeXt to the 3x3 convolution layer in the Bottleneck of ResNet, and then adds channel attention and spatial attention modules to these two branches respectively.In this way, the network can further improve the accuracy of human keypoint detection without significantly increasing the computation overhead.To demonstrate the effectiveness of our method, a series of comparative experiments are conducted on the MPII Human Pose dataset and the COCO2017 keypoint detection dataset.Experimental results illustrate that the attention mechanism is effective to improve the accuracy of human pose estimation and the proposed PRAB obtained the best results 90.5% on MPII which outperforms the existing methods.
Multiple-Hypothesis Group Tracking

This paper generalizes the track-oriented multiple-hypothesis formalism to consider a time-varying number of groups, each composed of a time-varying number of targets. Additionally, the paper develops a tractable multi-stage tracking approach that performs target-level tracking under suitable statistics in the first stage, followed by group-level tracking under the generalized recursion in the second stage. Our approach is relevant to group tracking problems, with the restriction that objects do not change from one group to another. It is also relevant to repeated-measurement problems for extended targets.
Quantitative Analysis of Deep CNNs for Multilingual Handwritten Digit Recognition

Indian subcontinent is a birthplace of multilingual people, where documents such as job application form, passport, number plate identification, and so forth are composed of text contents written in different languages or scripts. These scripts consist of different Indic numerals in a single document page. Recently, deep convolutional neural networks (CNN) have achieved favorable result in computer vision problems, especially in recognizing handwritten digits but most of the works focuses on only one language, i.e., English or Hindi or Bangla, etc. However, developing a language-invariant method is very important as we live in a global village now. In this work, we have examined the performance of the ten state-of-the-art deep CNN methods for the recognition of handwritten digits using four most common languages in the Indian sub-continent that creates the foundation of a script invariant handwritten digit recognition system. Among the deep CNNs, Inception-v4 performs the best based on accuracy and computation time. Besides, it discusses the limitations of existing techniques and shows future research directions.
Generative Adversarial Training for Weakly Supervised Nuclei Instance Segmentation

Nuclei segmentation occupies an important position in medical image analysis, which helps to predict and diagnose diseases. With the further research of deep learning, the task of nuclei segmentation has been automated. However, most existing methods require a great deal of manually marked full masks for training, which is time-consuming and labor-intensive, and can only be done by professional personnel. For the purpose of reducing the cost of labeling, we propose a weakly supervised method using generative adversarial training for segmentation of nucleus. In the case of no boundary, but only the centroid of the nucleus, the proposed method segmented the nucleus region with blurred boundaries. We first use the generative adversarial network(GAN) to generate the likelihood map of the nuclear centroid, then use Guided Backpropagation to visualize the pixels that contributes to the detection of the centroid of each nucleus, and finally obtain the segmentation mask of the nucleus by graph-cut. In addition, for the purpose of training the network better, we performed stain normalization on each pathological image. We have verified the proposed method on a multi-organ nuclei dataset. The final experiment results show that our advanced method achieves better segmentation performance than other weakly supervised methods, and can even reach the level of full supervision.