Reviewerly/exHarmony huggingface dataset:
An enhanced dynamic KC-slice model for privacy preserving data publishing with multiple sensitive attributes by inducing sensitivity

Privacy Preserving Data Publishing (PPDP) is an important aspect of real world scenarios. PPDP moves the researcher in the right direction by maintaining privacy and utility trade-off while publishing the data. This paper presents a concept on dynamic data publishing for multiple sensitive attributes by enhancing KC slice model. Our proposed KCi-slice method completes the data publishing process in two phases. First phase assigns the records into buckets based on the sensitiveness of the attributes, which considers different privacy thresholds on various sensitive attributes. It uses a semantic l-diversity approach to assign the records to the buckets to prevent similarity attacks. The privacy thresholds of all the sensitive attribute values in a bucket are verified. It splits the sensitive attributes into multiple sensitive tables according to the correlation among them. The later phase finds the correlation among quasi attributes. It groups the correlated quasi attributes and also concatenates the SIDs of sensitive attribute values with quasi attribute values. Finally it performs random permutations on the published quasi table. The proposed KCi-slice model enhances the utility and reduces the suppression of multiple sensitive attributes when compared to KC-slice approach.
Approach to 3D face reconstruction through local deep feature alignment

Here, the authors propose an end‐to‐end method based on deep learning to reconstruct three‐dimensional (3D) face models from given face images. In the training stage, the authors propose to extract the feature representations from the 3D sample faces and corresponding 2D sample images through the proposed local deep feature alignment (LDFA) algorithm, and estimate an explicit mapping from the 2D features to their 3D counterparts for each local neighbourhood, then the authors learn a feed‐forward deep neural network for each neighbourhood whose parameters are initialised with the parameters obtained in the locality‐aware learning process and the explicit mapping. In the testing stage, the authors only need to feed a given face image to the deep neural network corresponding to the nearest sample image and receive the outputted 3D face model. Extensive experiments have been conducted on both non‐face and face data sets. The authors find that the LDFA algorithm performs better than several popular unsupervised feature extraction algorithms, and the 3D reconstruction results obtained by the proposed method also outperform the comparison methods.
Using Kalman filter in the frequency domain for multi-frame scalable super resolution

Kalman filter (KF) as a linear estimator which is used in super-resolution (SR) problems, suffers from high computational costs and storage requirements. To gain appreciable success in the elimination of these two challenges, this paper advances a SR framework employing KF in the frequency domain, while no resort is made to any approximations or extra assumptions in the dynamic system modeling and statistical matrices. Generally, previous KF-based SR methods organized the system with huge-sized matrices in the spatial domain, following which they tried to reduce the system dimension using approximation and/or limitation on point spread function (PSF). In this study, first, several small-dimension dynamic systems are separately made in the frequency domain supporting space-invariant PSFs of an arbitrary form and size. Then, the acquired small-dimension KF estimators are applied rather than the traditional huge-dimension one. These will greatly reduce computational complexity, decrease storage requirements allowing parallel implementation as well. Furthermore, our proposed SR framework can be used to produce high resolution image of an expedient size, that is, a scalable SR. Experimental results with both simulated and real world sequences indicate that our proposed framework works more effectively than the other compared methods, especially in fine details restoration.
Multiview Laplacian semisupervised feature selection by leveraging shared knowledge among multiple tasks

Recently, considerable advancement has been achieved in semisupervised multitask feature selection methods, which they exploit the shared information from multiple related tasks. Besides, these algorithms have adopted manifold learning to leverage both the unlabeled and labeled data since its laborious to obtain adequate labeled training data. However, these semisupervised multitask selection feature algorithms are unable to naturally handle the multiview data since they are designed to deal single-view data. Existing studies have demonstrated that mining information enclosed in multiple views can drastically enhance the performance of feature selection. Multiview learning is capable of exploring the complementary and correlated knowledge from different views. In this paper, we incorporate multiview learning into semisupervised multitask feature selection framework and present a novel semisupervised multiview multitask feature selection framework. Our proposed algorithm is capable of exploiting complementary information from different feature views in each task while exploring the shared knowledge between multiple related tasks in a joint framework when the labeled training data is sparse. We develop an efficient iterative algorithm to optimize it since the objective function of the proposed method is non-smooth and difficult to solve. Experiment results on several multimedia applications have shown that the proposed algorithm is competitive compared with the other single-view feature selection algorithms.
What Else Is in an Evolved Name? Exploring Evolvable Specificity with SignalGP

Tags are evolvable labels that provide genetic programs a flexible mechanism for specification. Tags are used to label and refer to programmatic elements, such as functions or jump targets. However, tags differ from traditional, more rigid methods for handling labeling because they allow for inexact references; that is, a referring tag need not exactly match its referent. Here, we explore how adjusting the threshold for how what qualifies as a match affects adaptive evolution. Further, we propose broadened applications of tags in the context of a genetic programming (GP) technique called SignalGP. SignalGP gives evolution direct access to the event-driven paradigm. Program modules in SignalGP are tagged and can be triggered by signals (with matching tags) from the environment, from other agents, or due to internal regulation. Specifically, we propose to extend this tag based system to: (1) provide more fine-grained control over module execution and regulation (e.g., promotion and repression) akin to natural gene regulatory networks, (2) employ a mosaic of GP representations within a single program, and (3) facilitate major evolutionary transitions in individuality (i.e., allow hierarchical program organization to evolve de novo).
Associative memory and recall model with KID model for human activity recognition

Learning, memorizing and recalling knowledge are the basic functions of cognitive models. These models must prioritize which stimulants to respond to as well as package acquired knowledge in an easy to retrieve manner. The human brain is a cognitive model that derives information from sensor data such as vision, associates different patterns to create knowledge, and uses chunking mechanisms to package the acquired knowledge in manageable entities. The use of chunking mechanisms by the brain aids it to overcome its short-term memory (STM) capacity limitation. Through chunking, each entity held in the STM is a chunk containing more associations (knowledge) in it. By mimicking the human brain, this study proposes an associative memory and recall (AMR) model that stores associative knowledge from sensor data. Using chunking mechanisms, AMR can organize human activity knowledge in the manner that is efficient and effective to store and recall. The knowledge–information–data (KID) model is used for learning associative knowledge while the AMR continuously looks for associations among knowledge units and merges related units using merging mechanisms. The chunking mechanisms used in this study are inspired by the chunking mechanisms of the brain i.e. goal oriented chunking and automatic chunking.
Non-Markovianity detection with coherence measures based on the Tsallis relative<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" overflow="scroll" id="d1e451" altimg="si137.gif"><mml:mi>α</mml:mi></mml:math>entropies

In this paper, we use coherence measures based on the Tsallis relative α entropies (coherence α measures), with 0<α≤2, to detect the non-Markovianity of incoherent completely positive dynamical maps. At first, in a typical open quantum system, we study the differences between these quantum coherence (QC) measures (with different α) in reveling the non-Markovianity and meanwhile, we compare our results with the ones in Neto et al. (2016). We conclude that coherence α measures can detect non-Markovianity better than non-Markovianity measure which is based on entanglement. Then, in another example, we compare coherence α measures (with different α) with two other non-Markovianity criteria (trace distance and mutual information based criteria). The results show that generally, the coherence α measures can detect non-Markovianity at least as well as the two other criteria.
A framework for computationally efficient video quality assessment

Objective video quality assessment (VQA) methods are essentially algorithms that estimate video quality. Recent quality assessment methods aim to provide quality predictions that are well correlated with subjective quality scores. However, most of these methods are computationally costly, which limits their use in real-time applications. A possible solution to this problem is to decrease the video resolution (spatial, temporal or both) in order to reduce the amount of processed data. Although reducing the video resolution is a simple way of decreasing the running time of a VQA method, this approach might impact the prediction accuracy of the VQA method. In this paper, we analyze this impact. More specifically, we analyze the effects of resolution reduction on the performance of the VQA methods. Based on this analysis, we propose a framework that decreases the overall processing time of VQA methods, without decreasing significantly the performance accuracy. We test the framework using six different VQA methods and four different video quality databases. Results show that the proposed framework reduces the average runtime performance of the tested VQA methods, without considerably altering their performance accuracy.
Results in Workflow Resiliency

First proposed by Wang and Li in 2007, workflow resiliency is a policy analysis for ensuring that, even when an adversarial environment removes a subset of workers from service, a workflow can still be instantiated to satisfy all the security constraints. Wang and Li proposed three notions of workflow resiliency: static, decremental, and dynamic resiliency. While decremental and dynamic resiliency are both PSPACE-complete, Wang and Li did not provide a matching lower and upper bound for the complexity of static resiliency. The present work begins with proving that static resiliency is $¶i^p_2$-complete, thereby bridging a long-standing complexity gap in the literature. In addition, a fourth notion of workflow resiliency, one-shot resiliency, is proposed and shown to remain in the third level of the polynomial hierarchy. This shows that sophisticated notions of workflow resiliency need not be PSPACE-complete. Lastly, we demonstrate how to reduce static and one-shot resiliency to Answer Set Programming (ASP), a modern constraint-solving technology that can be used for solving reasoning tasks in the lower levels of the polynomial hierarchy. In summary, this work demonstrates the value of focusing on notions of workflow resiliency that reside in the lower levels of the polynomial hierarchy.
An interleaved method for constructing de Bruijn sequences

This paper presents an efficient method for constructing 2 2 n − 2 cyclic different de Bruijn sequences of order 2 n from the feedback function of a de Bruijn sequence of order n . This is done by extending an n -stage nonlinear feedback shift register (NFSR) to a 2 n -stage NFSR, and then recursively joining all cycles of the 2 n -stage NFSR to form a full cycle.
Cloud toolkit for Provider assessment, optimized Application Cloudification and deployment on IaaS

The deployment of a data-intensive application to a Cloud poses a number of serious challenges, mainly concerning the provider and resources selection process, based on the Quality of Service expected, as well as the management of the Virtual Machines in the provider premises. This work attempts to address those issues by providing a sophisticated toolkit assisting both the Cloud adopter and the Cloud Provider, in terms of application profiling and categorization, analyzing and predicting interference effects, benchmarking and exploiting Quality of Experience in Cloud computing infrastructures. Thus, a service oriented computing architecture is presented, realizing this toolkit in the context of the CloudPerfect framework, and a set of experiments is carried out, evaluating those tools in terms of effectiveness and efficiency. Finally, a specific Use Case scenario is analyzed, that involves an Infrastructure as a Service adoption for an EPR/CRM System.
Quantum filtering of accelerated qubit–qutrit system

The possibility of immunizing and improving the entanglement of accelerated systems via local filtering is discussed. The maximum bounds of entanglement depend on the dimensions of the accelerated and the filtered subsystems. If the small dimensional subsystem is accelerated and the large dimensional subsystem is filtered, one can get a long-lived entanglement. Moreover, if the larger subsystem is accelerated, then by filtering any subsystem, the upper bounds of entanglement of the filtered state are larger than that depicted for then non-filtered states. For any accelerated subsystem, the entanglement always increases as the filter strength of the large dimensional subsystem increases.
Deep Learning Concepts for Evolutionary Art

A deep convolutional neural network (CNN) trained on millions of images forms a very high-level abstract overview of an image. Our primary goal is to use this high-level content information a given target image to guide the automatic evolution of images using genetic programming. We investigate the use of a pre-trained deep CNN model as a fitness guide for evolution. Two different approaches are considered. Firstly, we developed a heuristic technique called Mean Minimum Matrix Strategy (MMMS) for determining the most suitable high-level CNN nodes to be used for fitness evaluation. This pre-evolution strategy determines the common high-level CNN nodes that show high activation values for a family of images that share an image feature of interest. Using MMMS, experiments show that GP can evolve procedural texture images that likewise have the same high-level feature. Secondly, we use the highest-level fully connected classifier layers of the deep CNN. Here, the user supplies a high-level classification label such as "peacock" or "banana", and GP tries to evolve an image that maximizes the classification score for that target label. Experiments evolved images that often achieved high confidence scores for the supplied labels. However, the images themselves usually display some key aspect of the target required for CNN classification, rather than the entire subject matter expected by humans. We conclude that deep learning concepts show much potential as a tool for evolutionary art, and future results will improve as deep CNN models are better understood.
The walker speaks its graph: global and nearly-local probing of the tunnelling amplitude in continuous-time quantum walks

We address continuous-time quantum walks on graphs, and discuss whether and how quantum-limited measurements on the walker may extract information on the tunnelling amplitude between the nodes of the graphs. For a few remarkable families of graphs, we evaluate the ultimate quantum bound to precision, i.e. we compute the quantum Fisher information (QFI), and assess the performances of incomplete measurements, i.e. measurements performed on a subset of the graph's nodes. We also optimize the QFI over the initial preparation of the walker and find the optimal measurement achieving the ultimate precision in each case. As the topology of the graph is changed, a non-trivial interplay between the connectivity and the achievable precision is uncovered.
Shiva's Rangoli

Shiva's Rangoli is a tangible interactive storytelling installation that allows readers to shape the emotional tone of a narrative by sculpting the ambience of their space. The system is designed to support meaningful interactions with the narrative that are decoupled from plot outcomes. This work is culturally rooted in Indian mythology and the traditional art form of Rangoli making. Readers interact with an interface that acts as a diegetic bridge between the world of the reader and the world of the fiction.
DevOps’ Shift-Left in Practice: An Industrial Case of Application

DevOps aims at unifying software development and operations to improve products and deliver value to customers. However, many organizations adopt DevOps mainly from a traditional perspective, that is, going forward from development to operations. In this paper we present a case of study that illustrates how Carvajal Technology and Services, a software development organization, improved the design of a family of its software products by exploiting operations data. This case of application constitutes a first incursion of the organization into DevOps, exemplifying how the community and companies in industry can also go backwards from operations to development and design, thus realizing the DevOps shift-left concept. The main contributions of this paper are: (i) the analysis of the industrial DevOps application, for which the deployment automation mechanism is crucial to realize the shift-left concept effectively; and (ii) Amelia, the DSL we developed for deploying the different (re)designs to put into operation and gather feedback data rapidly. To evaluate the approach, the organization analyzed this incursion in both directions: from development to operations, on the benefits of deployment automation; and from operations back to development, by improving the throughput of the original design by a factor of five.
Comparison of the tournament-based convection selection with the island model in evolutionary algorithms

Convection selection is an approach to multipopulational evolutionary algorithms where solutions are assigned to subpopulations based on their fitness values. Although it is known that convection selection can allow the algorithm to find better solutions than it would be possible with a standard single population, the convection approach was not yet compared to other, commonly used architectures of multipopulational evolutionary algorithms, such as the island model. In this paper we describe results of experiments which facilitate such a comparison, including extensive multi-parameter analyses. We show that approaches based on convection selection can obtain better results than the island model, especially for difficult optimization problems such as those existing in the area of evolutionary design. We also introduce and test a generalization of the convection selection which allows for adjustable overlapping of fitness ranges of subpopulations; the amount of overlapping influences the exploration vs. exploitation balance.
Multi-scale graph modeling and analysis of locomotion dynamics towards sensor-based dementia assessment

Dementia is a general neurodegenerative disorder beyond normal aging, which is not only overwhelming for the patients, but also negatively affects their caregivers and families. In the state of the art, paper-based survey methods such as the Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) are widely used for the assessment of dementia conditions. However, these methods require lab visits or administration from nurses, physicians and examiners, and are limited in the ability to track temporal degradation (or daily variations) of dementia conditions. With rapid advances in sensing technology, there is growing interest in the development of new, sensor-based methods that provide more flexibility in dementia monitoring and require minimal interventions from practitioners. In this article, we propose a new, sensor-based method that estimates dementia conditions with daily locomotion data. The proposed methodology is evaluated and validated with both simulation and real-world case studies. Experimental results show the compelling predictive accuracy, with both true positive and true negative rates above 85%. This article shows that sensor-based methods have great potential for real-time monitoring of temporal variations of dementia conditions from daily gait locomotion dynamics.
FRAIPA version 2: A fast recommendation approach based on self-adaptation and multi-thresholding

Finding relevant and interesting items on e-commerce websites is a major challenge in the information era. Recommender systems are widely used to help users deal with the information overload problem by giving them personalized recommendations. Furthermore, they help businesses make more profits. Collaborative filtering represents one of the most successful recommendation approaches. In general, several existing recommendation methods have demonstrated good performance in terms of prediction quality. However, they may require prohibitive computational times, and they often confront the sparsity problem, which negatively affects the efficiency of the system. In this paper, we propose a fast recommendation algorithm based on self-adaptation and multi-thresholding. It is designed to deal with the mentioned drawbacks, and improve the prediction quality. In addition, it is able to converge automatically. Extensive experiments on two real-world data sets demonstrate that our proposed method can achieve significantly better performance than other state-of-the-art methods. Particularly, it improves the MAE between 1.02% and 12.93%, and the computational time between 25.38% and 54.83%.
Subject analysis of LIS data archived in a Figshare using co-occurrence analysis

Purpose Based on the data from Figshare repositories, the purpose of this paper is to analyze which research data are actively produced and shared in the interdisciplinary field of library and information science (LIS). Design/methodology/approach Co-occurrence analysis was performed on keywords assigned to research data in the field of LIS, which were archived in the Figshare repository. By analyzing the keyword network using the pathfinder algorithm, the study identifies key areas where data production is actively conducted in LIS, and examines how these results differ from the conventional intellectual structure of LIS based on co-citation or bibliographic coupling analysis. Findings Four major domains – Open Access, Scholarly Communication, Data Science and Informatics – and 15 sub-domains were created. The keywords with the highest global influence appeared as follows, in descending order: “open access,” “scholarly communication” and “altmetrics.” Originality/value This is the first study to understand the key areas that actively produce and utilize data in the LIS field.