Reviewerly/exHarmony huggingface dataset:
Statistical analysis of an improved tuning method for optimizing performances of Hadoop applications

Hadoop is one among upcoming and most promising technologies for handling immense volume of data, generated from a variety of sources. It supports parallel processing among multiple jobs, and when such jobs are being executed on Hadoop, it is of paramount importance to know if Hadoop cluster resources are being completely utilized or not, which in turn tells us about efficiency in resource utilization for the purpose of effective completion of jobs. It could happen that Hadoop cluster resources are not being completely utilized, and because of which there arises a need for performance tuning. Fortunately, Hadoop framework equips us with several parameters to do the same and fine-tune aforesaid jobs. Performance tuning comprises of four main components: CPU utilization, Memory consumption, Disk I/O and Network traffic. In the proposed work, authors have presented several important relative parameters in relation to aforesaid components, along with how to fine-tune these parameters with appropriate values so as to optimize Hadoop execution.
Alexa, know your limits: developing a framework for the accepted and desired degree of product smartness for digital voice assistants

Abstract This research investigates the conditions for the acceptance of digital voice user interfaces focusing on the accepted and desired degree of product smartness. We argue that digital voice assistants (DVAs) are different from other smart products because DVAs are not self-contained products. Smart products also work without DVAs. Therefore, the decision to buy and use a DVA is different. DVAs are not designed to work in isolation. Of course, they can be used only to talk to, but that greatly restricts what the assistants are capable of. The existing literature lacks research on the critical characteristics and properties of DVAs, as well as a categorization of their smartness in the light of the advances in artificial intelligence. The qualitative research design is based on interviews with users and non-users of DVAs. Using a qualitative content analysis, a category system for the degree of product smartness (PS) of DVAs is developed. This paper contributes to the existing literature by exploring the attributes that influence the perception of DVAs and providing a graduated framework for organizing the accepted and desired degree of smartness for DVAs. The framework suggests four gradations each representing an advanced application of artificial intelligence. Red lines appear for some applications, indicating that they are technically feasible but, at least currently, rejected. Rejection relates to the device’s autonomous decision-making and privacy control capabilities, as well as the style of interaction, when the DVA acts as though it was a friend. Future research should quantitatively investigate the relationships between user profiles and acceptance. For designers, the model provides guidance for offering user-customized settings for DVAs, according to user preferences.
Fake Agile: What Is It and How to Avoid It?

During the past few years, the interpretation of the term “agile” has become so overburdened that it sometimes offers a wrong view of what agility is. This disharmony is called Fake Agile because there is not a complete alignment with the principles and values of agile. In this article, we deal with the causes and early symptoms that make a difference between true agile and Fake Agile. We identify some reasons for its emergence, we present some implications of practicing Fake Agile, and we discuss how to mitigate it.
Deep Graph Matching for Partial Label Learning

Partial Label Learning (PLL) aims to learn from training data where each instance is associated with a set of candidate labels, among which only one is correct. In this paper, we formulate the task of PLL problem as an ``instance-label'' matching selection problem, and propose a DeepGNN-based graph matching PLL approach to solve it. Specifically, we first construct all instances and labels as graph nodes into two different graphs respectively, and then integrate them into a unified matching graph by connecting each instance to its candidate labels. Afterwards, the graph attention mechanism is adopted to aggregate and update all nodes state on the instance graph to form structural representations for each instance. Finally, each candidate label is embedded into its corresponding instance and derives a matching affinity score for each instance-label correspondence with a progressive cross-entropy loss. Extensive experiments on various data sets have demonstrated the superiority of our proposed method.
Runtime Assessment of the Parameter Utilisation in Adaptive Systems

A pervasive computing system that provides users with a specific functionality often needs to adapt to dynamic conditions and changing requirements. In particular, if it is equipped with autonomous learning capabilities and consists of a large number of individual units, this leads to considerable complexity in monitoring and controlling the system&#x2019;s behaviour at the macro level. Building on recent work on a measurement and evaluation framework for properties of such systems, in this paper we introduce a new metric for quantifying the parameter utilisation. The idea is to monitor the degree of freedom used and thus be able to make statements about the expected system behaviour. We analyse the characteristics of this metric using two simulated scenarios from the fields of surveillance networks and traffic control.
Secured framework for privacy preserving healthcare based on blockchain

Healthcare has become one of the most important aspects of people’s lives, resulting in a surge in medical big data. Healthcare providers are increasingly using Internet of Things (IoT)-based wearable technologies to speed up diagnosis and treatment. In recent years, Through the Internet, billions of sensors, gadgets, and vehicles have been connected. One such example is for the treatment and care of patients, technology—remote patient monitoring—is already commonplace. However, these technologies also offer serious privacy and data security problems. Data transactions are transferred and logged. These medical data security and privacy issues might ensue from a pause in therapy, putting the patient’s life in jeopardy. We planned a framework to manage and analyse healthcare large data in a safe manner based on blockchain. Our model’s enhanced privacy and security characteristics are based on data sanitization and restoration techniques. The framework shown here make data and transactions more secure.
Whale optimization algorithm based on hybrid adaptive strategy

Whale optimization algorithm (WOA) is characterized by fewer parameters, simple structure, and stronger optimization seeking ability compared with traditional optimization algorithms, but in practical applications there are problems such as sluggish convergence speed and easily falling into local optimal solutions. This work proposes MAWOA, a whale optimization algorithm based on hybrid adaptive strategy, introducing a method to adaptively adjust the weights with the iterative situation of the population to accelerate the convergence of the algorithm; designing an adaptive adjustment threshold, and individuals select a random search method according to the value of the threshold to enhance the global search ability of the population and circumventing local values; introducing an adaptive nonlinear convergence factor to strengthen the algorithm in initial exploration breadth and later local development process. Twelve different morphological benchmark functions and a MAWOA-BP wine quality classification model were used to optimize the experiment. The results shows that MAWOA has stronger performance in terms of convergence speed and optimization-seeking accuracy, and the classification results are significantly improved compared with traditional classification models such as KNN, decision trees and BP neural networks.
New Media Public Relations Regulation Strategy Model Based on Generative Confrontation Network

The rapid development of new media weakens the control of traditional news media on information. At the same time, under the impact of the rapid development of new media, it has brought new challenges to the regulation of public relations. In the new media environment, this paper constructs a text emotion generation model based on GAN to support the application of new media public relations regulation strategy. Aiming at the problem of insufficient constraint information of keywords in text generation, this paper uses the confrontation generation model based on reinforcement learning to supplement sentence components around keywords, so as to generate the text with the highest quality. At the same time, in order to extend GAN from continuous space to discrete space, the differentiable function based on Softmax transformation is adopted to approach the original nondifferentiable function. In this paper, LTP word segmentation system is used to select 356742 pieces of data with a length less than 20 for the experiment. Compared with Seqtoseq+attenion and Transform models, this model has higher similarity of real text distribution and higher text diversity. The retention degree of the main content of the input text is as high as 96.17%, which is higher than that of Seqtoseq+attenion model of 8.49% and Transform model of 6.11%. It provides effective support for the regulation of new media public relations.
Learning state-variable relationships for improving POMCP performance

We address the problem of learning state-variable relationships across different episodes in Partially Observable Markov Decision Processes (POMDPs) to improve planning performance. Specifically, we focus on Partially Observable Monte Carlo Planning (POMCP) and we represent the acquired knowledge with Markov Random Fields (MRFs). We propose three different methods to compute MRF parameters while the agent acts in the environment. Our techniques acquire information from agent action outcomes, and from the belief of the agent, which summarizes the knowledge acquired from observations. We also propose a stopping criterion to determine when the MRF is accurate enough and the learning process can be stopped. Results show that the proposed approach allows to effectively learn state-variable probabilistic constraints and to outperform standard POMCP with no computational overhead.
A Platform to Develop and Apply Digital Methods for Empirical Bioethics Research: Mixed Methods Design and Development Study

The rise of digital methods and computational tools has opened up the possibility of collecting and analyzing data from novel sources, such as discussions on social media. At the same time, these methods and tools introduce a dependence on technology, often resulting in a need for technical skills and expertise. Researchers from various disciplines engage in empirical bioethics research, and software development and similar skills are not usually part of their background. Therefore, researchers often depend on technical experts to develop and apply digital methods, which can create a bottleneck and hinder the broad use of digital methods in empirical bioethics research.This study aimed to develop a research platform that would offer researchers the means to better leverage implemented digital methods, and that would simplify the process of developing new methods.This study used a mixed methods approach to design and develop a research platform prototype. I combined established methods from user-centered design, rapid prototyping, and agile software development to iteratively develop the platform prototype. In collaboration with two other researchers, I tested and extended the platform prototype in situ by carrying out a study using the prototype.The resulting research platform prototype provides three digital methods, which are composed of functional components. This modular concept allows researchers to use existing methods for their own experiments and combine implemented components into new methods.The platform prototype illustrates the potential of the modular concept and empowers researchers without advanced technical skills to carry out experiments using digital methods and develop new methods. However, more work is needed to bring the prototype to a production-ready state.
A Highly Adaptive Oversampling Approach to Address the Issue of Data Imbalance

Data imbalance is a serious problem in machine learning that can be alleviated at the data level by balancing the class distribution with sampling. In the last decade, several sampling methods have been published to address the shortcomings of the initial ones, such as noise sensitivity and incorrect neighbor selection. Based on the review of the literature, it has become clear to us that the algorithms achieve varying performance on different data sets. In this paper, we present a new oversampler that has been developed based on the key steps and sampling strategies identified by analyzing dozens of existing methods and that can be fitted to various data sets through an optimization process. Experiments were performed on a number of data sets, which show that the proposed method had a similar or better effect on the performance of SVM, DTree, kNN and MLP classifiers compared with other well-known samplers found in the literature. The results were also confirmed by statistical tests.
Residual size is not enough for anomaly detection

Unsupervised anomaly detection is commonly performed by identifying unusual data samples (or anomalies) from the residual size produced by machine learning algorithms based on normal data (e.g., the residuals of regression models or reconstruction errors of autoencoder models), assuming that anomalies cause large residuals. Unfortunately, anomalies do not always cause large residuals. Anomaly detection algorithms based on residual size can miss anomalies that cause only small or noisy residuals for each variable in a multivariate time-series. To overcome this issue, we propose "neighbors to residuals" (N2RE), a novel anomaly scoring function based on residual similarity using nearest neighbor distance (NND). Even if residuals of anomalies are small, they show patterns that are different from those of residuals of normal data. Using N2RE can improve anomaly detection performance and reduce the variation in anomaly detection performance due to threshold changes. Experiments with various models on three cyber-physical system datasets verify that N2RE can achieve 19% higher anomaly detection performance than previous approaches without changes to the models.
Automatic disease vector mosquitoes identification via hierarchical data stream classification

Vector-borne diseases (VBDs), such as Dengue or Malaria, are one of the main concerns of public health agencies and governments. These diseases are mainly spread by mosquitoes acting as vectors by transmitting infected blood between humans. Machine learning can be used to design and improve control strategies of VBDs by providing models able to recognize disease vector mosquitoes and automatically capture or kill harmful species. The automatic identification of disease vector mosquitoes was not yet addressed concerning the hierarchical classification of data streams. Thus, reliable information has not been used to improve learning models, such as mosquitoes' hierarchical taxonomy. In this study, we propose a framework for the automatic identification of disease vector mosquitoes in the context of the hierarchical classification of data streams area. To this end, we propose a hierarchical adaptation of a disease vector mosquitoes' dataset to include their taxonomy and introduce kNC and Dribble, two novel classification methods fitted to hierarchical data streams representing the mosquitoes. Results depicted that our framework, using summarization techniques, achieves significantly better prediction and processing speed rates when compared to existing state-of-the-art models.
PoSP: A Novel Proof of Service Placement in Blockchain-based Edge Computing

Edge computing provides computing resources for deploying delay-sensitive services. Edge servers are controlled by different owners where mutual distrust may exist among them, and users also hesitate to trust edge service providers. Most of the works related to service placement in edge computing follow centralized architecture, vulnerable to a single-point failure, and assume that the resource allocation method is run by a trusted third party, who may change the allocation results. This work-in-progress proposes a new consensus mechanism, i.e., Proof of Service Placement (PoSP), for a blockchain-based edge computing system to address the issues mentioned above. Experiments show the effectiveness of the work.
Investigating the Performance of Various Deep Neural Networks-based Approaches Designed to Identify Game Events in Gameplay Footage

Video games, in addition to representing an extremely relevant field of entertainment and market, have been widely used as a case study in artificial intelligence for representing a problem with a high degree of complexity. In such studies, the investigation of approaches that endow player agents with the ability to retrieve relevant information from game scenes stands out, since such information can be very useful to improve their learning ability. This work proposes and analyses new deep learning-based models to identify game events occurring in Super Mario Bros gameplay footage. The architecture of each model is composed of a feature extractor convolutional neural network (CNN) and a classifier neural network (NN). The extracting CNN aims to produce a feature-based representation for game scenes and submit it to the classifier, so that the latter can identify the game event present in each scene. The models differ from each other according to the following elements: the type of the CNN; the type of the NN classifier; and the type of the game scene representation at the CNN input, being either single frames, or chunks, which are n-sequential frames (in this paper 6 frames were used per chunk) grouped into a single input. The main contribution of this article is to demonstrate the greater performance reached by the models which combines the chunk representation for the game scenes with the resources of the classifier recurrent neural networks (RNN).
Aspect based sentiment analysis in music

Nowadays, there are more and more social networks and Web platforms that give their users the opportunity to share their opinions and tastes on items of different types. This inevitably led to a growth of data relating to the subjective sphere of each individual. This information is extremely useful for several purposes, such as providing personalized recommendation services or understanding opinions conveyed through text. Sentiment Analysis provides helpful methods to analyze these textual opinions (e.g. reviews) from a global point of view. In case we want a more detailed representation of the opinion represented in a text, Aspect-based Sentiment Analysis identifies a valuable option thanks to its fine-grained level of text analysis.
Dual hybridization method for the classification of ultrasound breast tumors

Automation of breast tumor classification has become of great importance in terms of replacing the inefficiency of manual diagnosis by individual radiologists. Most findings in literature are heavily relied on supervised learning, and such methods typically entails myriad of labelling processes. To alleviate this burden, we present an automated unsupervised learning method via double reconstruction which has the benefit of omitting data annotation step. The purpose of dual reconstruction is to compress different latent variables into a single representation in order to generate the salient attributes between lesion groups, instead of directly using each latent variable itself. Also, different types of preprocessed data are separately provided to the network as an input. Each output is expected to seize global correlation and localized variations, particularly in the tumor domain. Both representations are then hybridized as our final features to enhance the tumor discrimination. To maintain unsupervised scheme, the features are provided to the k-means clustering algorithm. The cross-validation analysis demonstrates that our method, afforded by strictly unsupervised fashion, derives significant improvements than single use of latent variables and existing pretrained supervised deep learning models Further, we affirmed the applicability of the unsupervised learning approach to this topic, and the value of sensitivity, specificity, and mean-accuracy is obtained by 93.4%, 92.26%, and 96.71%, respectively.
Adversarial Attacks on an Optical Neural Network

Adversarial attacks have been extensively investigated for machine learning systems including deep learning in the electronic domain. However, the adversarial attacks on optical neural networks (ONNs) have been seldom considered previously. In this work, we first construct an accurate image classifier with an ONN using a mesh of interconnected Mach–Zehnder interferometers (MZIs). Then a corresponding adversarial attack scheme is proposed for the first time. The attacked images are visually very similar to the original ones but the ONN system becomes malfunctioned and generates wrong classification results in most time. The results indicate that adversarial attack is also a significant issue for optical machine learning systems.
DyWCP

As Internet of Things (IoT) is more and more pervasive and deployed in critical applications, it's becoming increasingly important to preserve the confidentiality of sensitive data when IoT devices communicate with each other. However, traditional cryptography is usually time and energy consuming. It may not be applicable to IoT devices with limited computational capability or limited power. In this paper, we propose a lightweight encryption scheme named Dynamic Wireless Channel P ad (DyWCP) inspired by one-time pad encryption. One-time pad encryption achieves perfect secrecy but has been rarely used in practice due to the inconvenience of key negotiation. Our research discovers that in the wireless context it is possible to design a one-time pad encryption scheme without key negotiation. Towards the realization of DyWCP, we create techniques to utilize the additive feature of wireless channel to encrypt messages, to integrate modular operations at wireless physical layer, and to defend against multiple eavesdroppers. We implement a prototype of the proposed scheme using Universal Software Defined Radio Peripherals (USRP), and conduct a suite of experiments to evaluate the performance of the proposed scheme.
Object Oriented Programming

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Are tags 'it?'

User queries on StackOverflow commonly suffer from either inadequate length or inadequate clarity with regards to the languages and/or tools they are meant for. Although the site makes use of a tagging system for classifying questions, tags are used minimally (if at all). To investigate the impact of tags in the quality of results returned by the queries, in this research we propose a new query expansion solution. Our technique assigns tags to queries based on how well they match the queries' topics. To evaluate our solution, we examined our technique on eight sets of queries categorized by overall length and programming language. We examined the retrieval results by adding various number of tags to the queries and monitor the recall and precision rates. Our results indicate that queries yield considerably higher recall and precision rates with extra tags than without. We further conclude that tags are particularly an effective means of enhancement when the original queries do not already return sufficient yields to begin with.