Reviewerly/exHarmony huggingface dataset:
Distributed Fuzzy Clustering Based Association Rule Mining: Design, Deployment and Implementation

With the explosive growth of transactional big data in various scenarios such as manufacturing chain and supply chain etc., fuzzy association rule mining (FARM) algorithms as a practical method in data analysis and decision making are confronted with unprecedented challenges. Commonly used centralized FARM algorithms are no longer suitable for current numerical big data. In this paper, a distributed fuzzy clustering based association rule mining (DFARM) framework is proposed where outside-layer and inside-layer distribution are employed to realize the parallel operation of the whole FARM algorithm. In order to implement the proposed framework, we specifically design an implementation algorithm by the ‘MapReduce’ paradigm. Through the proposed algorithm, distributed association rule mining can be carried out for any form of data such as Boolean data or numerical data with various volumes. Furthermore, various experiments are conducted to validate that our algorithm outperforms the centralized one in terms of device utilization and time performance.
Authorship Attribution Using Customized Associative Classification

In this chapter, Associative Classification (AC) [139] is employed, based on association rule discovery techniques, for authorship identification. The developed classification model consists of patterns that represent the respective author’s most prominent combinations of writing style features.
Stereo Matching Algorithm Based on CGAN

Most of the current end-to-end stereo matching algorithms use 3D convolution, which leads to high computational cost. Therefore, this paper proposes a algorithm based on CGAN to solve this problem. Because the result of prediction is presented in the way of disparity map, the task of disparity prediction can be regarded as the task of image generation. The generator in CGAN converts the pairs of images into the disparity maps. The generated image or the ground truth will be input to the discriminator in CGAN to identify whether the input image is a generated sample or the ground truth. The errors calculated from the generated results and the discriminant results guide the training of the generator and the discriminator to form a adversarial process until both are optimal. Our proposed method does not use 3D convolution, which not only simplifies the realization process of stereo matching, but also reduces the calculation cost on the premise of ensuring certain accuracy. We evaluated our model on the Scene Flow dataset and achieved a good result by comparing different architectures.
Evaluating Trust in an Uncertain and Multisource Environment

Recently, questions are emerging such as "how much" can one trust an Artificial Intelligence (AI) solution, how can the user interpret its outcomes, or how can the system explain the results provided? In the context of information fusion, the specific aspects of uncertainty representation and reasoning should be questioned the same way, as important components of the inference engine. This paper presents the impact from a subset of information quality dimensions on trust through the post-analysis of two serious games. Two scenarios are considered: Military Operations in Urban Terrain (MOUT) and Maritime Security Operations (MSO), in which individual players have to decide based on imperfect information from sources, alternatively human or artificial. The paper establishes a common framework for analyzing trust for situational awareness (or belief assessment) and decision making. On the one hand, in the CMRE Risk Game (MSO scenario), trust is measured through degrees of belief change, while in the GMU Game (MOUT scenario), trust is measured by the effective decision made. Results analyze and quantify the impact of source reputation and reliability, but the quality dimensions of confidence and specificity on trust. We finally provide next steps for future studies.
Quantum steganography

Quantum steganography is the art of secretly transmitting quantum information while disguising the fact that any secret communication is taking place. Like classical steganography, this involves embedding the hidden communication within a seemingly innocent “covertext,” which in the quantum case is a quantum state. The best-developed protocol for quantum steganography uses an innocent cover state encoded in a quantum error-correcting code and transmitted over a quantum channel. The sender (Alice) hides a quantum state, a string of M quantum bits (qubits), in the N-qubit codeword by disguising the message as channel noise in such a way that the resulting state is indistinguishable from the innocent state with typical errors from the channel. The receiver (Bob) is able to decode the secret state. In general, quantum steganography requires Alice and Bob to share a secret key in advance, either as a string of random bits or a string of entangled qubit pairs (ebits). If the eavesdropper (Eve) has imperfect knowledge of the channel—perhaps because Alice and Bob have been systematically making it seem noisier than it really is—then Alice and Bob can communicate steganographically at a nonzero asymptotic rate. If Eve has exact knowledge of the channel, then Alice and Bob can still communicate secretly, but only sublinearly in the number of channel uses. This chapter reviews the basic quantum steganography protocol, describes different encoding methods, and proves bounds on the asymptotic rate of quantum steganographic communication and the rate of secret key consumption.
Optimal Bandwidth and Delay of Video Streaming Traffic in Cloud Data Center Server Racks

The task of streaming video files or sources in data centers is challenging since data streaming must be implemented at a constant rate without any interruption. The task of streaming requires proper storage space, network bandwidth and efficient algorithms that tackle the changing environmental conditions. In this paper, we conduct research on the optimality of incurring delay and consuming bandwidth when video streaming is established in data center network (DCN) architectures. We first observe the behavior of such traffic patterns in eight server rack and sixteen server rack architectures and compare the simulation results. We then expand our study to observe the behavior of higher complexity data center network. In our study, we attempted to include realistic situations such as appropriate Proxy servers, adaptive bit rate streaming (ABS) feature, and virtual private network (VPN) security feature. We also conduct a study on high-resolution video and multicast traffic in such environments.
Meaning Negotiation

Nowadays, the web is becoming highly used technology, in our community. This technology allows us to work in collaboration and to share knowledge. The pragmatic web represents the most recent extension of the web (semantic web); which facilitates the exploitation and the interpretation of the data by the machine. This web is based on three important components, the context, the community, and the meaning negotiation. The Meaning negotiation is the most important component of the pragmatic web on which we will fix our attention. It plays an important role in the exchanges and resolves conflicts in people cooperation activities. The knowledge (context) of each part in the community of users is heterogeneous; this will make the meaning negotiation complicated. This paper realizes a meaning negotiation scenario based on the ontologies merging into the geopolitical domain. This will reduce and simplifies the process, and improves the semantics of data.
Introduction

column Share on Introduction Authors: Raquel Robinson University of Saskatchewan University of SaskatchewanView Profile , Katherine Isbister University of California, Santa Cruz University of California, Santa CruzView Profile Authors Info & Claims InteractionsVolume 27Issue 1January - February 2020 pp 36–39https://doi.org/10.1145/3371297Published:26 December 2019Publication History 1citation2,136DownloadsMetricsTotal Citations1Total Downloads2,136Last 12 Months432Last 6 weeks12 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Digital Curation

The majority of recorded geographic information is produced digitally, whether born digital or made digital through scanning or manual entry. Just as care is taken with nondigital recorded information, such as papers, maps, and photographs, to ensure this information is accessible and useable into the future, so too is care required to ensure digital information remains accessible, useable and reusable. The phrase digital curation, emerged in the early 2000s to describe the applied, lifecycle management of digital resources of scholarly and scientific interest over time for current and future generations of users and in anticipation of technological obsolescence. Digital curation is a broad term encompassing data curation, data management, digital archiving, digital preservation, digital stewardship, and access and use aspects of digital libraries. Digital curation components include all the value-added activities and features that make digital content (either born digital or made digital) understandable, renderable, trusted, secured, and available in perpetuity. The policies, strategies, and actions throughout the lifecycle inform roles and models to enable digital content to be findable, accessible, interoperable, and reusable.
A framework for ensemble classification and sensitivity analysis in privacy preserving data mining

The perturbation mechanism for data streams is a challenging task. In the emerging world, data is erupting from various sources. The core applications need care on the data streams for further analysis and experimentation. As the micro data available with the core applications shall not be revealed to the public without taking any chance of breach, the perturbation challenges the analysis to get through the like results as of on the original datasets. In this paper, we have applied a concept of Perlin noise to distract the original data from the eyes of the analysts, however allowing them to perform their activities well. Applying security dynamically on such data is a challenging task. This paper deals about the concepts of generation of smooth noise and syntactic perturbation mechanism on the selective tuples as selective perturbation.
VulChecker: Achieving More Effective Taint Analysis by Identifying Sanitizers Automatically

The automatic detection of vulnerabilities in Web applications using taint analysis is a hot topic. However, existing taint analysis methods for sanitizers identification are too simple to find available taint transmission chains effectively. These methods generally use pre-constructed dictionaries or simple keywords to identify, which usually suffer from large false positives and false negatives. No doubt, it will have a greater impact on the final result of the taint analysis. To solve that, we summarise and classify the commonly used sanitizers in Web applications and propose an identification method based on semantic analysis. Our method can accurately and completely identify the sanitizers in the target Web applications through static analysis. Specifically, we analyse the natural semantics and program semantics of existing sanitizers, use semantic analysis to find more in Web applications. Besides, we implemented the method prototype in PHP and achieved a vulnerability detection tool called VulChecker. Then, we experimented with some popular open-source CMS frameworks. The results show that Vulchecker can accurately identify more sanitizers. In terms of vulnerability detection, VulChecker also has a lower false positive rate and a higher detection rate than existing methods. Finally, we used VulChecker to analyse the latest PHP applications. We identified several new suspicious taint data propagation chains. Before the paper was completed, we have identified four unreported vulnerabilities. In general, these results show that our approach is highly effective in improving vulnerability detection based on taint analysis.
Privacy Protection in Federated Learning Based on Differential Privacy and Mutual Information

Federated learning is a distributed machine learning framework. On the premise of ensuring the legitimacy and compliance of data and the security of user privacy, it realizes the joint modeling of multi-party computer groups, in order to improve the accuracy of model fitting. Aiming at the privacy security of Federated learning user data, this paper uses Laplace distributed noise and weighted aggregation algorithm with mutual information as scoring mechanism to protect user data privacy. During the experiment, the research group realized the protection algorithm proposed in the paper based on the fat platform and obtained the optimal noise parameters through multiple groups of experiments, so as to minimize the impact of the protection mechanism on the model fitting accuracy and protect the privacy and security of the data to the greatest extent. In addition, the research group reproduces the previous attack algorithm and defense Algorithm for comparative experiments, so as to verify the security and accuracy of this algorithm.
Cloud Technology Drives Superior Video Encoding

New codecs such as AV1 will enable broadcasters and service providers to deliver higher quality, including 4K and 8K VR, but with increased complexity. Since adaptive streaming requires multi-resolution synchronized encoding, the delivery of next-generation video becomes even more challenging. For live applications, the amount of complexity is unmanageable with current CPU or GPU on-premises technologies. This paper will examine the limitations of on-premises server-based encoding and how cloud computing can solve these issues. It will outline how broadcasters and service providers can take advantage of new applications like event-based encoding to successfully deliver superior-quality video on every screen.
No-Reference Image Quality Assessment using Meta-SGD

Deep learning-based no-reference image quality assessment (NR-IQA) algorithm requires massive training data and labels, but data scarcity usually exists in practical applications. Most of the previous metrics used pre-trained networks to solve this problem by fine-tuning on the target tasks. Unfortunately, the network trained on other tasks cannot be directly applied to IQA tasks and failed when encountering complex distortions. To reduce dependence on labeled data and improve generalization ability of unknown distortion types, a NR-IQA metric based on Meta-SGD is proposed, which can learn the quick adaptability of humans and obtain general meta-knowledge for distortion evaluation. Specifically, we collected a great many NR-IQA tasks with different distortion types to pre-train the model; then a meta-learning metric based on optimization, Meta-SGD, is proposed to acquire the meta-knowledge when evaluating images quality with various known distortions; finally, the obtained pre-trained meta-model can be directly applied to the new NR-IQA tasks only by fine-tuning a few images. Experiments on the TID2013 database demonstrate that the model after meta-learning obviously outperforms than some existing methods, and the average SRCC value for all types of distortion performance tests can reach 0.91.
Software Development Process Standards for Very Small Companies

For very small software development companies, the quality of their software products is a key to competitive advantage. However, the usage of software engineering standards is extremely low amongst such very small software companies. A primary reason cited by many such companies for this lack of quality standards adoption is the perception that they have been developed for large multi-national software companies and not with small and very small organizations in mind and are therefore not suitable for their specific needs. This chapter describes the design and development of the software process lifecycle standard for very small entities. This chapter presents a unique insight from the perspective of some of the standards authors on the development of the ISO/IEC 29110 standard, including the rationale behind its development and the innovative design of implementation guides to assist very small companies in adopting the standards, as well outlining a pilot project scheme for usage in early trials of this standard.
A Brief Review on Deep Learning and Types of Implementation for Deep Learning

In recent years, there's been a resurgence in the field of Artificial Intelligence and deep learning is gaining a lot of attention. Deep learning is a branch of machine learning based on a set of algorithms that can be used to model high-level abstractions in data by using multiple processing layers with complex structures, or otherwise composed of multiple non-linear transformations. Estimation of depth in a Neural Network (NN) or Artificial Neural Network (ANN) is an integral as well as complicated process. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. This chapter describes the motivations for deep architecture, problem with large networks, the need for deep architecture and new implementation techniques for deep learning. At the end, there is also an algorithm to implement the deep architecture using the recursive nature of functions and transforming them to get the desired output.
Copy-Move Forgery Detection Using DyWT

In this paper, we proposed a passive method for copy-move region duplication detection using dyadic wavelet transform (DyWT). DyWT is better than discrete wavelet transform (DWT) for data analysis as it is shift invariant. Initially we decompose the input image into approximation (LL1) and detail (HH1) sub-bands. Then LL1 and HH1 sub-bands are divided into overlapping sub blocks and find the similarity between the blocks. In LL1 sub-band the copied and moved blocks have high similarity rate than the HH1 sub-band, this is just because, there is noise inconsistency in the moved blocks. Then we sort the LL1 sub-band blocks pair based on high similarity and in HH1 blocks are sorted based on high dissimilarity. Then we apply threshold to get the copied moved blocks. Here we also applied some post processing operations to check the robustness of our method and we get the satisfactory results to validate the copy move forgery detection.
Dissemination of Cultural Heritage: Design and Implementation of a VR environment for the preservation of art and culture in Pujilí - Ecuador

Technology through time has been a fundamental mean in the development and cultural evolution, changing the perspective of the human being with the world in relation to the reconstruction of memory and the historical past. In this context, Ecuador seeks to adapt to the rapid changes of the technological era and solve the problems caused by the pandemic caused by the outbreak of COVID-19 in early 2020, in the interruption of cultural and economic activities. At the local level, Pujilí, Land of the DANZANTE, considered intangible cultural heritage of humanity, has not been exempt from this situation, being the Cultural area the most affected. For this reason, the current research, PUJILÍ MUSEUM, proposes a means of dissemination and preservation of Art and Culture, adapted to current changes, through a Virtual Reality mobile application that integrates 3D modeling technologies and reconstruction by Photogrammetry, to improve the quality and immersion, allowing visitors to immerse themselves in a world full of history, art and culture, music, and tradition. The application was tested by 30 users/visitors who had mid/high range mobile devices, applying the convenience sampling technique, in order to know the levels of satisfaction and learning of the different interactive areas. The experimental results showed a positive effect on visitors and a high level of acceptance when using the virtual environment. This has been corroborated by the number of downloads, ratings, and reviews.
Privacy Protection for Data-Driven Smart Manufacturing Systems

The Industrial Internet of Things (IIoT) is a new industrial ecosystem that combines intelligent and autonomous machines, advanced predictive analytics, and machine-human collaboration to improve productivity, efficiency and reliability. The integration of industry and IoT creates various attack surfaces and new opportunities for data breaches. In the IIoT context, it will often be the case that data is considered sensitive. This is because data will encapsulate various aspects of industrial operation, including highly sensitive information about products, business strategies, and companies. The transition to more open network architectures and data sharing of IoT poses challenges in manufacturing and industrial markets. The loss of sensitive information can lead to significant business loss and cause reputational damage. In this paper, the authors discuss emerging issues that are related to IIoT data sharing, investigate possible technological solutions to hide sensitive information and discuss some privacy management techniques in smart manufacturing systems.
Performance

Abstract A musical improvisation, as well as the performance of a musical work, can be understood recursively as a sequence of choices and the successful or unsuccessful implementation of those choices in the light of the implementation of previous choices in the sequence. These sequences take on a different character, depending on whether the music is being improvised or performed in accordance with a pre-existing musical work. In both cases there is potential for artistic creation, and potential for showmanship and charlatanry. The paper advocates a philosophical approach to writing about music that is tempered by an acquaintance with musicology, music criticism, empirical research and music history, and that acknowledges the imperfections in the real world of music-making.
Investigation on Deep Learning Approach for Big Data

In recent years, big data analytics is the major research area where the researchers are focused. Complex structures are trained at each level to simplify the data abstractions. Deep learning algorithms are one of the promising researches for automation of complex data extraction from large data sets. Deep learning mechanisms produce better results in machine learning, such as computer vision, improved classification modelling, probabilistic models of data samples, and invariant data sets. The challenges handled by the big data are fast information retrieval, semantic indexing, extracting complex patterns, and data tagging. Some investigations are concentrated on integration of deep learning approaches with big data analytics which pose some severe challenges like scalability, high dimensionality, data streaming, and distributed computing. Finally, the chapter concludes by posing some questions to develop the future work in semantic indexing, active learning, semi-supervised learning, domain adaptation modelling, data sampling, and data abstractions.
IT Service Management Architectures

IT service providers tend to view their services as quasi-embedded in the client organization infrastructure. Therefore, IT service providers lack a full picture of being an organization with its own enterprise architecture. By systematically developing an enterprise architecture using the unification operating model, IT service providers can much more efficient develop relevant service catalogues with connected reporting services related to SLAs and KPIs based on ITIL and newer frameworks like SIAM.