Reviewerly/exHarmony huggingface dataset:
An Efficient Technique for Virtual Machine Clustering and Communications Using Task-Based Scheduling in Cloud Computing

Cloud computing models use virtual machine (VM) clusters for protecting resources from failure with backup capability. Cloud user tasks are scheduled by selecting suitable resources for executing the task in the VM cluster. Existing VM clustering processes suffer from issues like preconfiguration, downtime, complex backup process, and disaster management. VM infrastructure provides the high availability resources with dynamic and on-demand configuration. The proposed methodology supports VM clustering process to place and allocate VM based on the requesting task size with bandwidth level to enhance the efficiency and availability. The proposed clustering process is classified as preclustering and postclustering based on the migration. Task and bandwidth classification process classifies tasks with adequate bandwidth for execution in a VM cluster. The mapping of bandwidth to VM is done based on the availability of the VM in the cluster. The VM clustering process uses different performance parameters like lifetime of VM, utilization of VM, bucket size, and task execution time. The main objective of the proposed VM clustering is that it maps the task with suitable VM with bandwidth for achieving high availability and reliability. It reduces task execution and allocated time when compared to existing algorithms.
Steganographic Secret Sharing With GAN-Based Face Synthesis and Morphing for Trustworthy Authentication in IoT

Trust and security are fundamental to the successful adoption of the Internet of Things (IoT). This paper proposes a secure message authentication scheme based on steganographic secret sharing for building trust in IoT systems. In our scheme, the message is split and distributed to two participants by a dealer, and it can be revealed only when the two authorized participants grant their consents. Neither of the participants can disclose the message without the consent of the other. To avoid malicious cyberattacks in IoT communications, each share of message is concealed in the form of a human face image, referred to as the shadow image, via a generative adversarial network (GAN). For each participant, a convolutional neural network (CNN) is trained to extract the share of message from the shadow image generated with the participant’s key. Distortions and alterations to the shadow images may occur during the transmission from the dealer to the participant. As a tamper-evident design, each shadow image is morphed with the participant’s source image under the participant’s customized morphing parameter. Cheater detection is also crucial for involved participants to identify fake shadow images during the secret retrieval process. As a cheating countermeasure, the shadow image for one participant is morphed with that for the other and then morphed further with the given source image. The proposed scheme enables multi-factor authentication in the sense that the message is protected by the keys, source images, and morphing parameters of two participants.
Universal Cross-Domain Retrieval: Generalizing Across Classes and Domains

In this work, for the first time, we address the problem of universal cross-domain retrieval, where the test data can belong to classes or domains which are unseen during training. Due to dynamically increasing number of categories and practical constraint of training on every possible domain, which requires large amounts of data, generalizing to both unseen classes and domains is important. Towards that goal, we propose SnMpNet (Semantic Neighbourhood and Mixture Prediction Network), which incorporates two novel losses to account for the unseen classes and domains encountered during testing. Specifically, we introduce a novel Semantic Neighborhood loss to bridge the knowledge gap between seen and unseen classes and ensure that the latent space embedding of the unseen classes is semantically meaningful with respect to its neighboring classes. We also introduce a mix-up based supervision at image-level as well as semantic-level of the data for training with the Mixture Prediction loss, which helps in efficient retrieval when the query belongs to an unseen domain. These losses are incorporated on the SE-ResNet50 backbone to obtain SnMpNet. Extensive experiments on two large-scale datasets, Sketchy Extended and DomainNet, and thorough comparisons with state-of-the-art justify the effectiveness of the proposed model.
Governing Techno-Futures: OECD Anticipation of Automation and the Multiplication of Managerialism

How do international organisations (IOs) govern the present based on claims about the coming impacts of technological change? Drawing on primary documents and participant observation, this article traces how the Organisation of Economic Cooperation and Development (OECD) anticipates automation emanating from the growing integration of blockchain technologies in global governance. We find that promises of radical, rapid, and reckless automation advanced by promoters of Bitcoin and other ‘defiant’ applications of the technology are steered by this IO towards more incremental and carefully managed forms of automation. The OECD relies on two managerialist practices to anticipate “reckless automation” through the promotion of what we identify as “responsible disruption”. In combination, OECD practices of scenario building and shared orientation framework construction deepen and extend managerial forms of global governance today whose technocratic and expert-led nature limits democratic possibilities and perpetuates global inequalities.
Scalable and Flexible Two-Phase Ensemble Algorithms for Causality Discovery

Causality study investigates cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience. To discover causal relationships, many data-driven causality discovery methods, e.g., Granger causality, PCMCI and Dynamic Bayesian Network, have been proposed. Many of these causality discovery approaches mine time-series data and generate a directed causality graph where each graph edge denotes a cause-effect relationship between the two connected graph nodes. Our benchmarking of different causality discovery approaches with real-world climate data show these approaches often generate quite different causality results with the same input dataset due to their internal learning mechanism differences. Meanwhile, there are ever-increasing available data in virtually every discipline, which makes it more and more difficult to use existing causality discovery algorithms to produce causality results within reasonable time. To address these two challenges, this paper utilizes data partitioning and ensemble techniques, and proposes a flexible two-phase causality ensemble framework. The framework first conducts phase 1 ensemble for partitioned data and then conducts phase 2 ensemble from phase 1 ensemble results. Based on the framework, we develop two ensemble approaches: i) data ensemble at phase 1 and algorithm ensemble at phase 2, and ii) algorithm ensemble at phase 1 and data ensemble at phase 2. To achieve scalability, we further parallelize the ensemble approaches via the Spark big data analytics engine. The proposed ensemble approaches are evaluated by synthetic and real-world datasets. Our experiments show that the proposed approaches achieve good accuracy through ensemble and high scalability through data-parallelization in distributed computing environments.
Periocular Recognition using CNN based Feature Extraction and Classification

Due to the COVID-19 pandemic, there has been an increased focus on contactless biometric systems for authentication. With face masks covering half of the face, it becomes significantly difficult for existing face recognition algorithms to work with the same accuracy (as for non-occluded faces). The main objective of this paper is to successfully build an authentication system that can detect and recognize a user based only on the periocular region of their face. The UBIPr - ubiris dataset has been analyzed and by using transfer learning a CNN model based on VGG-16 has been built for feature extraction. Later, these features have been passed through an ANN for fine-tuned classification. Two such ANN models have been maintained depending on the eye position i.e., separate models for left and right eyes and their outputs have then been combined for classification. A traditional split of 70-30 for training and testing has been followed. The accuracy for the test set is found to be 99.4%. The overall reduction in the loss for the test set is 0.09%. The average confidence for true positives is 0.78 and false positives is 0.11. This provides security to the system from false positives by setting the confidence threshold higher as well as minimizes the chances of physical contact. Currently, the system works only on still images.
Feature‐driven systems engineering procedure for standardized product‐line development

Abstract Numerous systems engineering (SE) methods for the model‐based and textual specification of systems focus on managing complexity solely by partitioning the system based on physical structures or by defining different views of the system and therefore reach their limits in agile development. The increasing demand for an agile system development requires an agile systems engineering procedure for the model‐based and textual top‐down specification of systems. Although function‐based development, variant management, and product line development are well established in software engineering, previous work has failed to introduce methods for the agile specification of systems by combining established methods from systems and software engineering. For that purpose, this paper demonstrates a new SE methodology, which for the first time combines conventional SE methods with the agile development procedure of feature‐driven development. The methodology is systematically developed based on theoretical analyses and its suitability for the application‐specific definition of feature‐driven development processes is demonstrated using the example of reference architectures for XiL simulation models of electric vehicles. By applying feature‐driven development, the resulting CUBE methodology enhances collaboration in interdisciplinary development teams and enables companies to adapt development processes to a more agile top‐down specification of systems.
Investigating Italian Citizens’ Attitudes Towards Immuni, the Italian Contact Tracing App

This research investigates Italian citizens’ attitudes towards the contact tracing app Immuni, promoted by the Italian government to track the spread of Covid19 and prevent possible future out-breaks. More specifically, this paper tries to uncover the factors that have motivated individuals to uptake the app or fail to do so. We have used a variety of qualitative methods: firstly, a virtual ethnography has been conducted, analyzing thematically 3013 tweets with the hashtag #Immuni. To further investigate the motivation behind either the adoption or the refusal of Immuni, twenty semi-structured interviews have been carried out with potential and actual users. A doctor and a health official have been also interviewed as secondary users. The results show that multiple factors shape users’ attitudes towards Immuni and influence their willingness to adopt it. The most relevant have to do with the perceived benefits, perceived barriers, perceived efficacy of the app and the cues to action. The usability of the overall app is also discussed with a focus on some of the key issues identified by the users.
Improving Textual Emotion Recognition Based on Intra- and Inter-Class Variations

Textual Emotion Recognition (TER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications. Prior research has tackled the automatic classification of emotion expressions in text by maximising the probability of the correct emotion class using cross-entropy loss. However, this approach does not account for intra- and inter-class variations within and between emotion classes. To overcome this problem, we introduce a variant of triplet centre loss as an auxiliary task to emotion classification. This allows TER models to learn compact and discriminative features. Furthermore, we introduce a method for evaluating the impact of intra- and inter-class variations on each emotion class. Experiments performed on three data sets demonstrate the effectiveness of our method when applied to each emotion class in comparison to previous approaches. Finally, we present analyses that illustrate the benefits of our method in terms of improving the prediction scores as well as producing discriminative features.
A Novel Classification Model SA-MPCNN for Power Equipment Defect Text

The text classification of power equipment defect is of great significance to equipment health condition evaluation and power equipment maintenance decisions. Most of the existing classification methods do not sufficiently consider the semantic relation between words in the same sentence and cannot extract deep semantic features. To tackle those problems, this article proposes a novel classification method by combining the self-attention mechanism and multi-channel pyramid convolution neural networks. We utilize the bidirectional gated recurrent unit to model the text sequence and, on this basis, improve self-attention layer to dot multiplication on the forward and backward features to obtain the global attention score. Thereby, effective features are enhanced, invalid features are weakened, and important text representation vectors are obtained. To solve the problem that the shallow network structure cannot extract deep semantic features, we design a multi-channel pyramid convolution network, which first extracts deep text features from the channels of different windows and then fuses the text features of each channel. By comparing with the state-of-the-art methods, the model in this article has better performance in text classification of power equipment defects.
Automatic generation of structured radiology reports for volumetric computed tomography images using question-Specific deep feature extraction and learning

Background: In today's modern medicine, the use of radiological imaging devices has spread at medical centers. Therefore, the need for accurate, reliable, and portable medical image analysis and understanding systems has been increasing constantly. Accompanying images with the required clinical information, in the form of structured reports, is very important, because images play a pivotal role in detect, planning, and diagnosis of different diseases. Report-writing can be exposure to error, tedious and labor-intensive for physicians and radiologists; to address these issues, there is a need for systems that generate medical image reports automatically and efficiently. Thus, automatic report generation systems are among the most desired applications. Methods: This research proposes an automatic structured-radiology report generation system that is based on deep learning methods. Extracting useful and descriptive image features to model the conceptual contents of the images is one of the main challenges in this regard. Considering the ability of deep neural networks (DNNs) in soliciting informative and effective features as well as lower resource requirements, tailored convolutional neural networks and MobileNets are employed as the main building blocks of the proposed system. To cope with challenges such as multi-slice medical images and diversity of questions asked in a radiology report, our system develops volume-level and question-specific deep features using DNNs. Results: We demonstrate the effectiveness of the proposed system on ImageCLEF2015 Liver computed tomography (CT) annotation task, for filling in a structured radiology report about liver CT. The results confirm the efficiency of the proposed approach, as compared to classic annotation methods. Conclusion: We have proposed a question-specific DNN-based system for filling in structured radiology reports about medical images.
Open-set Recognition with Supervised Contrastive Learning

Open-set recognition is a problem in which classes that do not exist in the training data can be presented at test time. Existing methods mostly take a multitask approach that integrates N-class classification and self-supervised pretext tasks, and they detect outliers by examining the distance to each class center in the feature space. Instead of relying on the learning through reconstruction, this paper explicitly uses distance learning to obtain the feature space for the open-set problem. In addition, although existing methods concatenate features from multiple tasks to measure the abnormality, we calculate it in each task-specific space independently and merge the results later. In experiments, the proposed method partially outperforms the state-of-the-art methods with significantly fewer parameters.
Noiseless Privacy: Definition, Guarantees, and Applications

In this article, we define noiseless privacy, as a non-stochastic rival to differential privacy, requiring that the outputs of a mechanism (i.e., function composition of a privacy-preserving mapping and a query) attain only a few values while varying the data of an individual (the logarithm of the number of the distinct values is bounded by the privacy budget). Therefore, the output of the mechanism is not fully informative of the data of the individuals in the dataset. We prove several guarantees for noiselessly-private mechanisms. The information content of the output about the data of an individual, even if an adversary knows all the other entries of the private dataset, is bounded by the privacy budget. The zero-error capacity of memory-less channels using noiselessly private mechanisms for transmission is upper bounded by the privacy budget. The performance of a non-stochastic hypothesis-testing adversary is bounded again by the privacy budget. Assuming that an adversary has access to a stochastic prior on the dataset, we prove that the estimation error of the adversary for individual entries of the dataset is lower bounded by a decreasing function of the privacy budget. In this case, we also show that the maximal leakage is bounded by the privacy budget. In addition to privacy guarantees, we prove that noiselessly-private mechanisms admit composition theorem and post-processing does not weaken their privacy guarantees. We prove that quantization or binning can ensure noiseless privacy if the number of quantization levels is appropriately selected based on the sensitivity of the query and the privacy budget. Finally, we illustrate the privacy merits of noiseless privacy using multiple datasets in energy, transport, and finance.
Haskell⁻¹: automatic function inversion in Haskell

We present an approach for automatic function inversion in Haskell. The inverse functions we generate are based on an extension of Haskell's computational model with non-determinism and free variables. We implement this functional logic extension of Haskell via a monadic lifting of functions and type declarations. Using inverse functions, we additionally show how Haskell's pattern matching can be augmented with support for functional patterns, which enable arbitrarily deep pattern matching in data structures. Finally, we provide a plugin for the Glasgow Haskell Compiler to seamlessly integrate inverses and functional patterns into the language, covering almost all of the Haskell2010 language standard.
A novel imbalanced data classification approach for suicidal ideation detection on social media

Suicide has become a serious social health issue in modern society. Suicidal ideation is people’s thoughts about committing or planning suicide. Many factors, such as long-term exposure to negative feelings or life events, can lead to suicidal ideation and suicide attempts. Among these approaches to suicide prevention, early detection of suicidal ideation is one of the most effective ways. Using social networking services provides a platform for people to express their sufferings and feelings in the real world, which provides a source for a deeper investigation into models and approaches for the detection of suicidal intent to enable prevention. This paper addresses the early detection of suicide ideation through the associative classification approach applied to Twitter social media. However, since the number of suicide intention tweets is tiny compared to the number of all the tweets, this leads us to an imbalanced classification problem, in which, the minority class (suicide intention) is more important than the majority class (no suicide intention). In such a situation, classical classifiers usually yield very inaccurate results regarding minor classes, since they can easily discover rules predicting the majority class and overlook those related to the minor. This paper aims to contribute to this line of research by introducing a new interestingness measure to enhance the classification process. This measure highlights the two classes regardless of their imbalanced distribution. Carried out experiments proved that the adapted CBA outweighs in terms of prediction accuracy the original one, and other pioneering baseline classification approaches.
An Accelerated Maximally Split ADMM for a Class of Generalized Ridge Regression

Ridge regression (RR) has been commonly used in machine learning, but is facing computational challenges in big data applications. To meet the challenges, this article develops a highly parallel new algorithm, i.e., an accelerated maximally split alternating direction method of multipliers (A-MS-ADMM), for a class of generalized RR (GRR) that allows different regularization factors for different regression coefficients. Linear convergence of the new algorithm along with its convergence ratio is established. Optimal parameters of the algorithm for the GRR with a particular set of regularization factors are derived, and a selection scheme of the algorithm parameters for the GRR with general regularization factors is also discussed. The new algorithm is then applied in the training of single-layer feedforward neural networks. Experimental results on performance validation on real-world benchmark datasets for regression and classification and comparisons with existing methods demonstrate the fast convergence, low computational complexity, and high parallelism of the new algorithm.
Age Regression with Specific Facial Landmarks by Dual Discriminator Adversarial Autoencoder

Facial age conversion is to generate faces of different age groups from the input face and retain the characteristics of the original face. Most of the existing methods are exploring the aging of the face, while ignoring the rejuvenation. In addition to improving aging, we will also explore the regression of human faces. Due to the lack of images of the same person in a longer age range, it becomes a challenging task. Since the generated faces are relatively unreal, we developed a novel model based on Conditional Adversarial Autoencoder (CAAE). This model uses two discriminators to generate a more realistic image. Furthermore, by considering specific facial landmarks where the face shape has changed greatly in different age groups, the face shape belonging to the corresponding age group can be obtained. Moreover, the collected database is divided into different races for training to improve the age development of different races.