Reviewerly/exHarmony huggingface dataset:
Performance Analysis of different Task Scheduling Algorithms in Cloud Computing under Dynamic Environment

Cloud computing is one of the emerging technology in the field of computer science in which services are provided through the internet on-demand. It has various features like simple to use, minimum power consumption, and minimum cost. Cloud environment contains various virtualized computing resources, various development platforms, and users jobs. Task scheduling is one of the main essential parts of the cloud environment which is mainly concerned with minimizing energy consumption and maximizing resource utilization.
Sonum: Software-Defined Optimal Network Utilization Mechanism for Long Flow Scheduling in Data Center Networks

Long flow scheduling is an extremely important load balance problem in data center networks. In this paper, we propose a software-defined optimal network utilization mechanism called Sonum for long flow scheduling. Sonum minimizes the packet loss rate instead of maximizing the link utilization, and combines link resources and switch resources to network resources. Sonum translates load balancing into arranging minimum packet loss path for long flows. This paper also introduces a heuristic algorithm to solve the arranging problem. The experimental results show that compared with ECMP and Hedera, Sonum has a better performance on network throughput and flow completion time.
Context-Aware Design of Cyber-Physical Human Systems (CPHS)

Recently, it has been widely accepted by the research community that interactions between humans and cyber-physical infrastructures have played a significant role in determining the performance of the latter. The existing paradigm for designing cyber-physical systems for optimal performance focuses on developing models based on historical data. The impacts of context factors driving human system interaction are challenging and are difficult to capture and replicate in existing design models. As a result, many existing models do not or only partially address those context factors of a new design owing to the lack of capabilities to capture the context factors. This limitation in many existing models often causes performance gaps between predicted and measured results. We envision a new design environment, a cyber-physical human system (CPHS) where decision-making processes for physical infrastructures under design are intelligently connected to distributed resources over cyberinfrastructure such as experiments on design features and empirical evidence from operations of existing instances. The framework combines existing design models with context-aware design-specific data involving human-infrastructure interactions in new designs, using a machine learning approach to create augmented design models with improved predictive powers.
On Hardware-Aware Probabilistic Frameworks for Resource Constrained Embedded Applications

Edge reasoning attempts to mitigate latency and privacy shortcomings of cloud computing paradigms. However, it introduces additional challenges linked to the devices' resource constraints and the applications' dynamic conditions. To address these challenges, we have proposed a hardware-aware probabilistic framework that optimizes the target machine learning model under actual hardware constraints. This framework relies on tractable probabilistic models, as they facilitate efficient inference, while exhibiting a number of traits relevant to the application range of interest: robustness to missing data, joint prediction capabilities, explainability, and small data needs. In this work, we expand on this framework by introducing a discriminative-generative approach to model learning, which retains the robustness of a generative model under missing data but can potentially improve its discriminative performance. In addition, we demonstrate how the applicability of this framework goes beyond classification tasks, and can be used for density estimation tasks, relevant to applications such as mobile speaker verification.
A Multi-Layered Block Chain Network for Individual-Oriented Health-Care Records Storage and Management

In the background of the existing medical system, the medical information of most medical institutions in China is not Shared, which leads to individual health records scattered in different medical institutions so that it is difficult to realize the effective integration of personal health records and further use for follow-up medical services. This paper presents a multi-layered block chain network for individual-oriented health care records storage and management. It has features that includes decentralized, tamper resistant and multi nodes maintenance. It manages the relationship of each node and achieves the secure storage and sharing sparseness of health records through the unique consensus mechanism that is distinct from the common blockchain network, so as to resolve the existing health problems.
The Guardian Council

Systems security is becoming more challenging in the face of untrusted programs and system users. Safeguards against attacks currently in use, such as buffer overflows, control-flow integrity, side channels and malware, are limited. Software protection schemes, while flexible, are often too expensive, and hardware schemes, while fast, are too constrained or out-of-date to be practical.
Label Assignment and Sentimental Analysis for a Product Review on Twitter Data

The most popular microblogging social media is Twitter, where people express their point of view toward different things. They bought different products and give their unbiased opinion on Twitter. In this paper, a novel method is proposed to get the opinions of customers toward the product or company and analyze it. The product-related Tweets are gathered by Twitter with the help of the browser. This method is not using Twitter API. The proposed methods are defined for labeling Tweets in three emotions, that is, positive, negative, and neutral efficiently. Sentiment analysis models are used for classifying the opinions of the customer toward that product and toward individual features of the product. Using these results, it improves the future versions of the product. This research technique helps and improves the marketing approaches and high selling of product as well. It predicts the best and worst feature of the product with respect to customer opinion with an accuracy of 93.62%.
A Deep Research in Classic Classification Network

Abstract This article is a further study and reflection of the classification network after the completion of the article “The Comparison and Analysis of Classic Convolutional Neural Network in the Field of Computer Vision”. Different from previous work comparing network performance of VGGNet and ResNet, this paper compares and analyzes the importance of depth, breadth and residual learning of GoogLeNet and ResNet in convolutional neural networks. And considering that most of the time, the basic classification network nowadays appears in the form of backbone in the detection and segmentation network, this paper compares the advantages and disadvantages of several major classification networks as backbones.
Towards Practical Computer Vision in Teaching and Learning of Image Processing Theories

This research to practice full paper presents a methodology that leads students towards innovative solutions to complex computer vision real problems. The cornerstone of teaching-learning process of image processing theories is based on strong interdisciplinary concepts (e.g. calculus, logic, data structures, perception, graphs, etc). However, just this theoretical background is not sufficient to reach the right practical understanding of how each type of technique interferes in an image considering real problems. To address this issue, we proposed to focus on the application of the image processing theories in a real industrial problem, through a computer vision pipeline. To do so, we used the concept of non-zero-sum game where both players win (e.g. students and industry). To evaluate the effectiveness of our methodology, students answered a questionnaire about their satisfaction. Based on these answers we map how much it helps the undergraduate students to understand the implications of different image processing concepts in a real environment. We also analyzed the satisfaction rate and the students' scores to obtain a well defined analysis. Our methodology presents a strong level of satisfaction according to the students, reaching up to 91% of acceptance rate.
Automatic Speech Recognition Using Deep Neural Network

Sharmadha, S. Shivani, K. Shruthi, K. Bharathi, B. Kavitha, S. Automatic speech recognition acknowledges the spoken words and converts them to a machine-readable format of text. By converting spoken audio into text, this technology allows users to control digital devices by speaking instead of using conventional tools like keystrokes and buttons. The challenges in speech recognition are the improvisation of the accuracy, varying user responsiveness, performance, reliability and fault tolerance. The audio signal quality affects the recognition accuracy rate. Delayed speech recognition is used to overcome the issues by user responsiveness. This is because the pronunciation of a word differs when used under different contexts. Since the world is moving at a rapid pace towards digitisation, new technologies are being developed to make lives easy. Interactive Voice Response System is an example. The Interactive Voice Response System allows the computer to interact with human by using their voices. We have proposed an Interactive Voice Response System for railway reservation system. The proposed approach uses LSTM with CTC to recognise the spoken word. The methods used in the creation of this model outperform other models where testing is done to arrive at the resultant with a better accuracy.
Research on Remote Sensing Data Sharing Model Based on Blockchain Technology

Remote sensing data sharing will be an important part of the development of remote sensing. Remote sensing data is of great value, but the government is still the largest buyer behind the massive remote sensing data, and the commercial value of remote sensing has not been well discovered. In the future, data sharing will be one of the most effective methods in order to stimulate the commercial development of remote sensing data. Open sharing of data not only drives innovation in technology and industry, but also breaks down data barriers between various sectors of the economy, and generates great economic value for the country. According to this idea, this paper puts forward the remote sensing data sharing model which can conform to the development under the general trend of remote sensing data sharing. Remote sensing data often involves a series of security issues that need attention, such as land, so it is special. The remote sensing data involved in this paper is limited to commercially available data with the purpose of promoting the commercial development of remote sensing. The remote sensing data sharing envisaged in this paper involves remote sensing data that is built on the basis of being screened, not endangering or disclosing national geographic security information, and such data processing will help make people's life more convenient. Remote sensing data sharing faces three major problems, one is the data is large, and the resulting storage and processing problems, the second is that the data owner loses the control of the data, ownership, and the third is the real-time of remote sensing data, requiring the time of data processing must be timely and fast. We put forward a blockchain-based remote sensing data sharing model, which has the characteristics of cannot be tampered with, decentralized, secure, credible and collective maintenance, which is suitable for solving the security problems of data sharing of remote sensing institutions. We introduce concept cloud to solve data storage problems, and uses edge computing to alleviate data processing problems. This paper introduces the components of the model and the implementation principle, categorizes the existing remote sensing institutions, and uses the consensus mechanism to achieve fast and secure data sharing.
A Survey of In-Lined Reference Monitors: Policies, Applications and Challenges

This paper surveys the area of in-lined reference monitors (IRMs), a language-based security enforcement technology that has gained much popularity in the recent past. IRMs enforce given security policies in target applications by inserting dynamic security guards into these applications; the guards check for impending policy violations at runtime. IRMs keep track of security state and can thus enforce rich, history-based policies. This survey discusses IRMs for a variety of programming languages, application execution platforms, and security policy specification languages. The survey also discusses the benefits and importance of adding IRM certification, and the technical and managerial challenges of employing IRMs.
Grammar and Corpus Methodology

Abstract This chapter explores the potential of natural language corpora for grammatical research. The chapter distinguishes three main types of data (evidence) that a corpus can provide: factual evidence, frequency evidence, and interaction evidence. The chapter makes the research case for parsing a corpus completely, correcting the annotation by human linguists. It uses the cyclic ‘3A’ perspective (Nelson et al. 2002) to relate a series of exploratory algorithms and tools relevant to the grammatical researcher, including concordancing tools, grammatical exploration tools, and bottom-up generalization algorithms. The aim is not merely to describe what is found in a corpus but to perform systematic ‘natural experiments’. The rich grammatical analysis of a parsed corpus gains a new role: in reliably obtaining examples of grammatical units within which research may be conducted. The chapter concludes with a discussion of some simple experiments, and the methodological issues that arise in carrying them out.
Blockchain-Based Smart Contract Access Control System

Research has shown that smart contracts have become a significant and promising technology. However, the lack of mature and stable security mechanism, such as access control, makes smart contracts quite vulnerable. To mitigate this problem, we present an attributed-based access control system based on blockchain by building a hierarchical management mechanism of contract administrator and contract owner. After combining blockchain inherent synchronization function with our designed system-level smart contract, all peers can easily get access control rules timely. Moreover, we modify the original smart contract finite state machine to prevent malicious modification on the rules through system contract. In our evaluation, the system only introduces 2%-5% additional cost with assured security on Hyperledger.
The School of Squiggol

The Bird–Meertens Formalism, colloquially known as “Squiggol”, is a calculus for program transformation by equational reasoning in a function style, developed by Richard Bird and Lambert Meertens and other members of IFIP Working Group 2.1 for about two decades from the mid 1970s. One particular characteristic of the development of the Formalism is fluctuating emphasis on novel ‘squiggly’ notation: sometimes favouring notational exploration in the quest for conciseness and precision, and sometimes reverting to simpler and more rigid notational conventions in the interests of accessibility. This paper explores that historical ebb and flow.
On Applications of Bootstrap in Continuous Space Reinforcement Learning

In decision making problems for continuous state and action spaces, linear dynamical models are widely employed. Specifically, policies for stochastic linear systems subject to quadratic cost functions capture a large number of applications in reinforcement learning. Selected randomized policies have been studied in the literature recently that address the trade-off between identification and control. However, little is known about policies based on bootstrapping observed states and control inputs. In this work, we show that bootstrap-based policies achieve a square root scaling of regret with respect to time. We also obtain results on the accuracy of learning the model's dynamics. Corroborative numerical analysis that illustrates the technical results is also provided.
A root-and-pattern approach to word-formation in Chinese

Abstract This paper argues that compounding, the major source of word-formation in Chinese, and the root-and-pattern system in Hebrew involve fundamentally the same syntactic operations and observe the same locality constraints, despite the salient differences. More specifically, it addresses the well-known continuum that the coordinate and attributive compounds behave more like words, whereas resultative and subordinate compounds are much more like phrases. It puts forward the idea that this continuum can be accounted for by assuming that there is a distinction between word-formation from roots and word-formation from words, with the former giving rise to more lexical properties and the latter more phrasal properties. The paper also discusses some related issues, such as the correct formulation of word-level phases and the structure of the major types of compound words in Chinese.
A Comparative Approach to Mitigate Economic Denial of Sustainability (EDoS) in a Cloud Environment

In cloud computing, a new threat is introduced namely Economic Denial of Sustainability (EDoS) which is similar to Distributed Denial of Services (DDoS) attack that targets the vulnerabilities of the cloud server user along with its price model. EDoS attackers continually request bulk resources, such as virtual security devices and databases, virtual machines, virtual networking devices, that targets the auto scaling feature of cloud. As a result, there is a huge increase in consumer bill due to the illegitimate requests which results in bankruptcy. In this paper, various EDoS mitigation techniques are discussed with their different approaches. This paper proposed a new approach that uses Artificial Neural Network along with Genetic Algorithm. The classification of is done using Artificial Neural Network that that classify the cloud server consumer and may lessen the EDoS attacks in the cloud environment whereas Genetic Algorithm (GA) is deployed for optimization of the properties of each server using pertinent fitness function. The proposed technique's comparison with existing technique is taken under the considerations and expectations.
Background Modeling Algorithm for Multi-feature Fusion

In order to improve the accuracy of foreground target detection and establish a stable background model, this paper proposes a multi-feature fusion background modeling algorithm, which initializes the background model with the spatial correlation between the first frame pixel and the domain pixel, and quickly establishes the background. model. A multi-feature sample set consisting of pixel values, update frequency, update time, and adaptive dynamic coefficients is updated with temporal correlation of subsequent intra-pixels. According to the multi-feature sample set, the background complexity is adjusted to adjust the update speed of the model in different regions, which effectively improves the ghost phenomenon of the foreground target and reduces the false holes in the target and the false foreground in the background. The test results of multiple sets of data sets show that the proposed algorithm improves the adaptability and robustness of foreground target detection in scenarios with high dynamic changes.
Detecting Abnormal Event in Traffic Scenes using Unsupervised Deep Learning Approach

In today's digital age, detecting abnormal events in the traffic scene is a more significant problem in video surveillance. The proposed scheme is adopted for detecting abnormalities such as wrong side driving, people crossing the road illegally, vehicle moving in pedestrain pathway and which uses spatio-temporal attributes. Although numerous machine learning approaches are described in existing literature focusing on pixel-wise differnce with less performance in identifying the abnormalities in video traffic scenes. In this paper, a novel frame work for identifying abnormal detection using unsupervised deep learning algorithm. The scheme uses joint based methodology (ConvLSTM with kmeans) with a combination of reconstruction and clustering loss respectively. The convolutional neural network with LSTM is used to detect and recognize anomalies in traffic scenes with refernce of previous frame information. To identify the normal or abnormal events, the proposed work is composed of training and testing phase. The proposed model is implemented in real time and it is found that the accuracy rate for detecting abnormalities 93.02% abnormality than state-of-art technique.
Mixed Attention Mechanism for Small-Sample Fine-grained Image Classification

Fine-grained image Classification is an important task in computer vision. The main challenge of the task are that intra-class similarity is large and that training data points in each class are insufficient for training a deep neural network. Intuitively, if we can learn more discriminative features and more detailed features from fined-grained images, the classification performance can be improved. Considering that channel attention can learn more discriminative features, spatial attention can learn more detailed features, this paper proposes a new spatial attention mechanism by modifying Squeeze-and-Excitation block, and a new mixed attention by combining the channel attention and the proposed spatial attention. Experimental results on two small-sample fine-grained image classification datasets demonstrate that on both VGG16 network and ResNet-50 network, the proposed two attention mechanisms achieve good performance, and outperform other referred fine-grained image classification methods.
Towards Action Prediction Applying Deep Learning

Considering the incremental development future action prediction by video analysis task of computer vision where it is done based upon incomplete action executions. Deep learning is playing an important role in this task framework. Thus, this paper describes recently techniques and pertinent datasets utilized in human action prediction task.
Analysis of Mammography Techniques for Breast Cancer Detection

Breast cancer is positioned second among the main reasons of demise affecting females. Statistics have appeared one out of ten ladies is affected by breast cancer in their lifetime. Mammography is the best procedure for breast cancer screening and can be utilized for the early detection of masses or anomalies. Little clusters of micro calcifications showing up as a collection of white spots on mammograms hint at an early breast cancer. In digital mammography, wireless image of the breast is taken along with is put away straight in a processor. Be that as it may, untimely detection of breast cancer is reliant on both the radiologist’s capacity to peruse mammograms and the nature of mammogram images. The importance of this article is to conduct a survey of existing mammogram enhancement techniques.