Reviewerly/exHarmony huggingface dataset:
Retraction: The Construction of Network Teaching System for Nursing Specialty Based on Computer Technology (J. Phys.: Conf. Ser. 1574 012043)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
Retraction: Study in the Big Data Talent Training Oriented Reform of Information Management and Information System Major under the Management Category (J. Phys.: Conf. Ser. 1616 012025)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. The authors of the article were given opportunity to present evidence that they were the original and genuine creators of the work, however the peer review and data they presented were assessed and were found to not meet the standards of the journal. IOP Publishing agrees there are enough indicators to cause doubts over the validity of the work and see no evidence reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. As such, IOP Publishing agree this article should be retracted. The authors have neither confirmed whether they agree or disagree to this retraction. Retraction published: 23 September 2022
Retraction: The Integration of Environmental Art Design Based on Artificial Intelligence (J. Phys.: Conf. Ser. 1574 012093)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
Retraction: Research on the Integration of Chinese and Russian Original Ecological Dance Elements and Modern Elements Based on Computer Big Data Analysis (J. Phys.: Conf. Ser. 1578 012077)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
Retraction: Research on Application of Artificial Intelligence in Computer Network Technology under the Background of Big Data (J. Phys.: Conf. Ser. 1607 012093)

This article has been retracted by IOP Publishing following an allegation that raises concerns this article may have been created, manipulated, and/or sold by a commercial entity. In addition, IOP Publishing has seen no evidence that reliable peer review was conducted on this article, despite the clear standards expected of and communicated to conference organisers. The authors of the article have been given opportunity to present evidence that they were the original and genuine creators of the work, however at the time of publication of this notice, IOP Publishing has not received any response. IOP Publishing has analysed the article and agrees there are enough indicators to cause serious doubts over the legitimacy of the work and agree this article should be retracted. The authors are encouraged to contact IOP Publishing Limited if they have any comments on this retraction. Retraction published: 23 September 2022
Iteration

AbstractThis chapter introduces tail recursion, iteration, and functional abstraction over tail-recursive list-processing functions.
A Fast, Practical and Simple Shortest Path Protocol for Multiparty Computation

We present a simple and fast protocol to securely solve the (single source) Shortest Path Problem, based on Dijkstra’s algorithm over Secure Multiparty Computation. Our protocol improves the current state of the art by Aly et al. [FC 2013 & ICISC 2014] and can offer perfect security against both semi-honest and malicious adversaries. Furthermore, it is the first data oblivious protocol to achieve quadratic complexity in the number of communication rounds. Moreover, our protocol can be easily adapted as subroutine in other combinatorial mechanisms. Our focus is usability; hence, we provide an open source implementation and exhaustive benchmarking under different adversarial settings and players setups.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing Publishing. • Type of peer review: Double Anonymous • Conference submission management system: Morressier • Number of submissions received: 77 • Number of submissions sent for review: 74 • Number of submissions accepted: 60 • Acceptance Rate (Submissions Accepted / Submissions Received × 100): 77.9 • Average number of reviews per paper: 2.05 • Total number of reviewers involved: 39 • Contact person for queries: Name: Dhimas Widhi Handani Email: dhimas@ne.its.ac.id Affiliation: Institut Teknologi Sepuluh Nopember (ITS)
Probabilistic Biomass Estimation with Conditional Generative Adversarial Networks

Biomass is an important variable for our understanding of the terrestrial carbon cycle, facilitating the need for satellite-based global and continuous monitoring. However, current machine learning methods used to map biomass can often not model the complex relationship between biomass and satellite observations or cannot account for the estimation’s uncertainty. In this work, we exploit the stochastic properties of Conditional Generative Adversarial Networks for quantifying aleatoric uncertainty. Furthermore, we use generator Snapshot Ensembles in the context of epistemic uncertainty and show that unlabeled data can easily be incorporated into the training process. The methodology is tested on a newly presented dataset for satellite-based estimation of biomass from multispectral and radar imagery, using lidar-derived maps as reference data. The experiments show that the final network ensemble captures the dataset’s probabilistic characteristics, delivering accurate estimates and well-calibrated uncertainties.
Distributed Sequential Likelihood Ratio Test for Track Confirmation in Non-Linear Scenarios

In the research literature there are numerous publications on Track-to-Track fusion (T2TF) and Track-to-Track association (T2TA). These algorithms assume that the problem of target existence is solved on a per sensor basis. More sophisticated detection methods incorporate the full knowledge of a distributed system by making this decision on a global level. Statistical models that reflect false decisions of individual sensors based on their local data can yield optimal results under linear Gaussian conditions. In particular it has been shown that the Distributed Kalman Filter (DKF) is able to reconstruct the global Sequential Likelihood Ratio Test (SLRT). This, however, is hindered by nonlinear models in many applications. In this paper, this approach is applied to the Federated Kalman filter and to the general setting for T2TA/T2TF such that it can also be applied in non-linear scenarios.
Spatio-Frequency Decoupled Weak-Supervision for Face Reconstruction

3D face reconstruction has witnessed considerable progress in recovering 3D face shapes and textures from in-the-wild images. However, due to a lack of texture detail information, the reconstructed shape and texture based on deep learning could not be used to re-render a photorealistic facial image since it does not work in harmony with weak supervision only from the spatial domain. In the paper, we propose a method of spatio-frequency decoupled weak-supervision for face reconstruction, which applies the losses from not only the spatial domain but also the frequency domain to learn the reconstruction process that approaches photorealistic effect based on the output shape and texture. In detail, the spatial domain losses cover image-level and perceptual-level supervision. Moreover, the frequency domain information is separated from the input and rendered images, respectively, and is then used to build the frequency-based loss. In particular, we devise a spectrum-wise weighted Wing loss to implement balanced attention on different spectrums. Through the spatio-frequency decoupled weak-supervision, the reconstruction process can be learned in harmony and generate detailed texture and high-quality shape only with labels of landmarks. The experiments on several benchmarks show that our method can generate high-quality results and outperform state-of-the-art methods in qualitative and quantitative comparisons.
Spam filter based on geographical location of the sender

Abstract Spam annoys users and poses a security threat. This article proposes a spam filter based on geographical location of the sender determined by IP geolocation. This filter was implemented as a plugin to the SpamAssassin anti-spam software. The plugin allows to define a penalty score for specific countries sending spam. The proposed filter was tested on a dataset of 1500 e-mails consisting of 1200 spam and 300 legitimate e-mails. The Matthews correlation coefficient of the filter has a value of 0.222. This indicates that the proposed spam filter contributes to the correct spam filtering.
Right to remix: ideas and original forms of musical works

The modern stage of development of creativity is characterized by an increase in the manufacturability of this type of activity. The art of making popular music fits into this trend, highlighting its advantages and disadvantages most vividly. The peculiarity of popular music production is the repetition of standard expressive means, which have proven to be attractive. The copying of popular fragments and spectacular musical techniques became widespread, resulting in the formation of sound remix culture. Against this background, the number of disputes over the boundaries of legal borrowing of musical material has increased in the courts both in Russia and abroad. In the media sphere, examples of unlawful appropriation of fragments from popular songs that violate the rights of their composers, performers, and producers of phonograms are discussed. To resolve such disputes, specialized procedures have been developed, the results of which are criticized. This article discusses various approaches to the legal qualification of copying musical material. The phenomenon of the formation of musical standards, repetitive elements, techniques, and clichés, which are proposed to be classified as unguarded musical ideas at the doctrinal level, is investigated. The methods used to establish copyright infringement, to identify the facts of unlawful appropriation of musical material, and significant similarity of works are analyzed. Our attention is drawn to the test of the unique quality of a musical work and its justification from the point of view of the principles of copyright. To make decisions on music copying cases, we propose to use data on the quantitative ratio of protected and unprotected material in the compared works, as well as on the sources of borrowing that can be obtained using computer music recognition tools
Requirements Engineering

This chapter discusses requirements engineering and discusses activities such as requirements gathering, requirements elicitation, requirements analysis, requirements management, and requirements verification and validation.
Frequent Pattern Mining in Big Data Using Integrated Frequent Pattern (IFP) Growth Algorithm

Day-by-day, a huge volume of data are generated through electronic gadgets via recent technologies. The data are generated in different forms and thereby its complex in nature. Therefore, big data analytics techniques are introduced to analyse the complex dataset efficiently in different perspectives. Among the different perspectives, the finding of frequent pattern matching in the given dataset is addressed in this work. However, researchers introduced various algorithms for finding frequent pattern matching. But, the existing algorithms have less accuracy for predicting frequent pattern matching and take more retrieval time. In addition to that, the existing algorithms do not preprocess the dataset. Therefore, the main objective of the proposed work is to increase the accuracy by preprocessing the dataset and minimize the response time by parallel processing the dataset. In order to achieve the above objectives, this work introduces an integrated frequent pattern (IFP) growth algorithm to find the distributed frequent pattern effectively from the large dataset. Therefore, IFP growth algorithm deploys in a Hadoop platform. In Hadoop, the Hadoop distributed file system (HDFS) processes the dataset using a multiprocessor. Henceforth, the IFP growth algorithm improves the accuracy and also minimizes the prediction time. Thus, the proposed work chooses a supermarket dataset as a case study.
A Deep Learning Framework for Real-Time Indian Sign Language Gesture Recognition and Translation to Text and Audio

Indian Sign Language (ISL) is used in the deaf community all over India. Development of the ISL recognition system is an active area to aid this community. In ISL, most of the signs are two-handed signs, and thus, it differs from another commonly used American Sign Language (ASL) and seems complex. In this paper, the design and implementation of a system to recognize ISL signs is reported. Building such a system can help specially abled person/people, by providing a medium to communicate with others without human interpreters. The proposed system is built using a deep convolutional neural network (CNN), which performs both feature extraction and classification, preceded by an image preprocessing step. A real-time input (live signs captured from webcam) is given to this system, and the output is delivered in the form of text and audio. Proposed CNN architecture has achieved an accuracy of 98% for a given dataset which comprises 56 items (1–10 digits, A-Z letters, and 20 general words).
Data-Driven Decision Management from a Dashboard Perspective

Organisations are eager to use and develop decisions by managing data in the most efficient way. To become more competitive and better meet their current and future needs, data-rich information enables managers to prospect the market, use the information for analysis and make decisions that could keep their businesses one step ahead of the rest. The scarcity of such data creates gaps in research information for business intelligence. Although algorithms and artificial intelligence are introductory concepts of data-driven decision management, the dashboard can be a useful tool that organisations can use both to present useful data and to make good decisions effectively. This paper describes the concept of using and managing data for decision-making in areas other than information technology. Using a conceptual framework to describe the importance of data-driven decision management, it argues that the creation of dashboards could be applied effectively the Université des Mascareignes (UdM) where it could meet the needs of users and decision-makers willing to get a clear picture of the problem affecting them. Rather than just disseminating information, the dashboard could provide important information that improves decisions and actions that can be initiated more efficiently and productively.
Thinking and Playing Music: Intentional Strategies for Optimal Practice and Performance

"Thinking and Playing Music: Intentional Strategies for Optimal Practice and Performance." Music Reference Services Quarterly, ahead-of-print(ahead-of-print), pp. 1–2
Adoption of Artificial Intelligence in Romania: Innovative Policies to Overpass Vulnerabilities with TRIZ and Deep-Thinking Tools

Adoption of artificial intelligence (AI) in all aspects of society and the economy is a major desiderate at the EU level. Nevertheless, this demarch is influenced by a series of contexts and endowments, such as talents, sophistication, and specificity of the business models, maturity of public processes, social mindset, etc. This paper analyzes opportunities and threats associated with AI adoption in Romania, possible measures to improve the predicted outcome together with lessons learned at the global level. The research methodology includes analysis of the current situation and formulation of innovative solutions to overpass various barriers and constraints. Based on TRIZ and deep-thinking approaches we formulate novel policies to overpass the status of evolution and create a new foundation for AI development and adoption in the Romanian ecosystem, with implications at the EU level. TRIZ and the deep-thinking systematic approach reveal several healthy patterns of evolution to deviate the current course of actions toward better results and outcomes. Critical findings relevant to AI at the EU level are also underlined in relation to the conclusions at the national level.
Applying Text Analytics to Insider Risk Analysis: A Case Study on Keyword Generation

Recent advancements in text analytics demonstrate significant gains in many natural language processing tasks, taking advantage of newer neural network architectures and transfer learning. In this article, we present findings from a literature review and exploratory study which investigated a specific application of text analytics for insider risk analysis. Results from our literature review find that recent advancements in text analytics greatly augment capabilities to exploit unstructured text commonly collected for insider risk analysis, offering new abilities to extract and generate intelligence to further support, standardize, and automate workflows. Results from our exploratory study suggest that the curation of keyword lists for insider threat detection can be augmented by automated text analytics capabilities, finding evidence that the manual process of managing threat keyword lists can be augmented with automated text analytics approaches. As a takeaway, the insider risk community should investigate further applications of text analytics to support and automate insider risk analysis workflows.
Octobot - Web Scarping towards retrieving Google Scholar Data

Web crawling is widely used these days in many different applications which can efficiently crawl the internet to provide the user with relevant results by utilizing its core algorithm. Google Scholar is one of the very important websites that contains a huge data about researchers and their research output along with the different citation counts. In this paper we try to utilize we crawling to retrieve the Google scholar data for Applied Science University-Bahrain by developing a searching engine tailored for this purpose. To achieve that goal, several sorts of web crawlers, and their architecture is presented briefly. Moreover, looking through numerous existing web crawlers to study their benefits and drawbacks in order to determine which criteria this web crawler should address. In addition, assess reducing online crawling techniques. Based on prior work and accepted software comparison measures, an assessment framework for comparing online crawling solutions is designed and used to aid the process. The implemented Web Crawler (OCTOBOT) is presented and different usage scenarios were shown.
Feature Extraction of News Text based on MMR Algorithm

Aiming at the problem that the current text classification neural network cannot fully extract the semantic structure feature information between words and sentences and sentences, a text feature extraction based on MMR algorithm is proposed. First, the MMR is used to extract the words and words, the sentence and sentence features information of the text respectively; secondly, the layered attention mechanism network layer is used to select the important words and sentences in the text respectively; finally, the MMR algorithm will be one by one. The text features obtained by layer extraction are used for text classification. The results show that the MMR algorithm can effectively extract the features of news text.
Fusion Method of Power Customer Data Model and Power Grid Asset Equipment Business Data Model

With the development of information technology, the amount of data and information we face has become more and more enormous. The technology that emerged at the historic moment is data mining, but traditional mining techniques show many deficiencies for incomplete information systems. Fully implement the group management of customer service data, correctly understand, meet and guide customer needs, pay attention to customer needs, formulate diversified service strategies, and provide customers with safe, reliable, efficient and convenient services. This is the power supply company to establish "good service", Good management, good image "three good service image inevitable requirements. Based on the experimental research method, this paper conducts corresponding research on the electricity customer data, and draws the following conclusions to describe the business characteristics of the electricity value contribution model to reflect the characteristics of different customer groups. The value contribution model is divided into 5 groups: high value contribution group, accounting for 2%; medium value contribution group, accounting for 10%; medium value high overdue risk group, accounting for 10%; ordinary group, accounting for 71%; Low-value contribution group, accounting for 3%.