Reviewerly/exHarmony huggingface dataset:
Towards the use of Data Engineering, Advanced Visualization techniques and Association Rules to support knowledge discovery for public policies

Education and employment are key aspects of a country’s well-being. Governments expend valuable resources on designing education plans and employment programs. These two aspects are usually analysed separately, although, as they are closely related, considering them together might improve their efficacy. The problem lies, at least in part, in the fact that different public entities manage their own data with their own isolated systems, and do not develop joint educational and employment policies. In order to facilitate working towards this goal, in this manuscript, we make use of Data Engineering, Data Visualization, and Intelligent Data Analytics methods to create a decision support system for the Government of Extremadura. Extremadura is a European Union Objective 1 region in Spain with high rates of unemployment and secondary school drop-out. Data Engineering is used to create a Data Warehouse that unifies the different data sources into a central repository for quick access and control. This allows dealing with the challenge of transforming, processing, storing and accessing the data. Data Visualization techniques are applied to create an interactive dashboard that assists users in analysing and interpreting the data in the Data Warehouse repository. Thus, charts, diagrams, and maps are created specifically to help technical or political decision-makers. Finally, Intelligent Data Analytics techniques are used to incorporate Association Rules into the visualization dashboard. Its goal is to identify associations, relationships, and patterns in data that, at least in plain sight, are not readable or interpretable by humans. It does this by inferring knowledge that humans cannot pick out by themselves. As a result, a complete system was defined and implemented to support public administrations in their decision-making and definition of precise evidence-based policies in the areas of education and employment. In particular, it allows the definition of unified strategies to reduce the unemployment rate.
A Systematic Characterization of Sampling Algorithms for Open-ended Language Generation

This work studies the widely adopted ancestral sampling algorithms for auto-regressive language models. We use the quality-diversity (Q-D) trade-off to investigate three popular sampling methods (top-k, nucleus and tempered sampling). We focus on the task of open-ended language generation, and first show that the existing sampling algorithms have similar performance. By carefully inspecting the transformations defined by different sampling algorithms, we identify three key properties that are shared among them: entropy reduction, order preservation, and slope preservation. To validate the importance of the identified properties, we design two sets of new sampling methods: one set in which each algorithm satisfies all three properties, and one set in which each algorithm violates at least one of the properties. We compare their performance with existing algorithms, and find that violating the identified properties could lead to drastic performance degradation, as measured by the Q-D trade-off. On the other hand, we find that the set of sampling algorithms that satisfy these properties performs on par with the existing sampling algorithms.
Performance Analysis of Resampling Algorithms of Parallel/Distributed Particle Filters

Particle filters have been widely used in various fields due to their advantages in dealing with non-linear and/or non-Gaussian systems. A large number of particles are needed to guarantee the convergence of particle filters for the state estimation, especially for large-scale complex systems. Therefore, parallel/distributed particle filters were adopted to improve the performance, in which different paradigms of resampling were proposed, including the centralized resampling, the decentralized resampling, and the hybrid resampling. To ease their adoptions, we analyze time consumptions and speedup factors of parallel/distributed particle filters with various resampling algorithms, state sizes, system complexities, numbers of processing units, and model dimensions in this study. The experimental results indicate that the decentralized resampling achieves the highest speedup factors due to the local transfer of particles, the centralized resampling always has the lowest speedup factors because of the global transfer of particles, and the hybrid resampling attains the speedup factors between. Moreover, we define the complexity-state ratio, as the ratio between the system complexity and the system state size to study how it impacts the speedup factor. The experiments show that the higher complexity-state ratio results in the increase of the speedup factors. This is one of the earliest attempts to analyze and compare the performance of parallel/distributed particle filters with different resampling algorithms. The analysis can provide potential solutions for further performance improvements and guide the appropriate selection of the resampling algorithm for parallel/distributed particle filters.
Dual-Channel Contrast Prior for Blind Image Deblurring

In this article, a dual-channel contrast prior (Dual-CP) is proposed for blind image deblurring. The prior is motivated by the observation that image contrast will significantly degenerate after the blurring process, which is proved in both mathematically and empirically. Based on this inherent property of the blurring process, we analyze the variation of contrast influenced by blur and research the feasibility for using contrast prior to estimate blur kernel. We model the contrast by the difference between the dark channel and the bright channel. By maximizing the contrast in the local patch, we can obtain a reliable result which contains sharp edges and is beneficial for kernel estimation. To solve this non-convex nonlinear problem, we develop an efficient optimization method with the auxiliary variable idea and alternate direction minimization. Extensive experiments on real and synthetic blurry sets demonstrate that the proposed algorithm has good performance and exhibits competitiveness compared with state-of-the-art methods. Besides, we show that the proposed method can be applied to non-uniform deblurring.
Fuzzy production network model for quality assessment of an information system based on microservices

This article describes the analysis of the quality of microservice architectures, which are one of the main approaches to the creation and maintenance of modern information systems capable of quickly respond to changes in business demands. The implementation of continuous delivery of software components for dynamic business processes of information systems can be carried out by various sets of microservices, the optimal choice of which is a complex multi-alternative task. The paper presents a review of existing approaches to solving the problem, which showed that the development of models for assessing the quality of microservices of information systems requires further elaboration in terms of accounting for uncertainty in the initial data and modes of operation. The authors have proposed an approach to solving the problem of analyzing the quality of a microservice architecture which is implemented on the basis of a fuzzy production network model. The model allows for comprehensive accounting of various parameters (qualitative and quantitative). The article shows the implementation process of the fuzzy production network that was developed to analyze the functional quality of the microservice architecture for processing customer orders using fuzzy modeling software. The results of the analysis will allow managers and system architects to make an informed choice of the microservice architecture of the information system, as well as use it in their reports when arguing the need for scaling the system and increasing the availability of microservices.
Factors that Influence PRNU-Based Camera-Identification via Videos

The Photo Response Non-Uniformity pattern (PRNU-pattern) can be used to identify the source of images or to indicate whether images have been made with the same camera. This pattern is also recognized as the “fingerprint” of a camera since it is a highly characteristic feature. However, this pattern, identically to a real fingerprint, is sensitive to many different influences, e.g., the influence of camera settings. In this study, several previously investigated factors were noted, after which three were selected for further investigation. The computation and comparison methods are evaluated under variation of the following factors: resolution, length of the video and compression. For all three studies, images were taken with a single iPhone 6. It was found that a higher resolution ensures a more reliable comparison, and that the length of a (reference) video should always be as high as possible to gain a better PRNU-pattern. It also became clear that compression (i.e., in this study the compression that Snapchat uses) has a negative effect on the correlation value. Therefore, it was found that many different factors play a part when comparing videos. Due to the large amount of controllable and non-controllable factors that influence the PRNU-pattern, it is of great importance that further research is carried out to gain clarity on the individual influences that factors exert.
Relation Specific Transformations for Open World Knowledge Graph Completion

We propose an open-world knowledge graph completion model that can be combined with common closed-world approaches (such as ComplEx) and enhance them to exploit text-based representations for entities unseen in training.Our model learns relation-specific transformation functions from text-based embedding space to graph-based embedding space, where the closedworld link prediction model can be applied.We demonstrate state-of-the-art results on common open-world benchmarks and show that our approach benefits from relation-specific transformation functions (RST), giving substantial improvements over a relation-agnostic approach.
Total Quality Management and Business Excellence

All companies around the world aim to reach excellence in their work. Thus, successful companies keep following scientific and technological advancement to improve their business. Thus, new notions must be presented, mainly through vast companies that usually have been inactive and vulnerable to international competition, and are located in states of constant transition. Moreover, companies have to adopt an effective management system that can enhance the company and carry it to sustainability. A system takes into account the quality and stakeholder’s satisfaction. Total quality management (TQM) is deemed as a strategic procedure permitting the accomplishment of business excellence (BE). The authors conducted a research on 354 Palestinian companies, by using a questionnaire filled by the managers, to exploring the relationship between TQM and business excellence through employing the structural equation modeling utilizing a statistical package of smart PLS. Findings demonstrate a positive linkage between applying the fundamentals of TQM and business excellence. This result indicates that the execution of TQM principles in a company will improve the performance of the company and increase the ability to excellence their business.
Geometric Multimodal Learning Based on Local Signal Expansion for Joint Diagonalization

Multimodal learning, also known as multi-view learning, data integration, or data fusion, is an emerging field in signal processing, machine learning, and pattern recognition domains. It aims at building models, learned from several related and complementary modalities, in order to increase the generalization performances of a predictive learning model. Multimodal manifold learning extends spectral or diffusion geometry-aware data analysis to multiple modalities. This can be performed through the definition of undirected graph Laplacian matrices in different modalities. However, finding common eigenbasis of multiple Laplacians is not always a relevant solution for multimodal manifold learning problems. As a matter of fact, the Laplacians of all modalities are not simultaneously diagonalizable in many real-world problems due to the major differences between the different modalities. In this paper, we propose a multimodal manifold learning approach based on intrinsic local tangent spaces of underlying data manifolds in order to discover the local geometrical structure around matching and mismatching samples in different modalities in sparse diagonalization problems. This approach searches for approximate common eigenbasis of Laplacian matrices by expanding the signal of limited existing information about matching and mismatching samples of different modalities to their on-manifold neighbors. Experiments on synthetic and real-world datasets in supervised, unsupervised, and semi-supervised problems demonstrate the superiority of our proposed approach over existing state-of-the-art related methods.
Face Expression Recognition using Convolution Neural Network (CNN) Models

This paper proposes the design of a Facial Expression Recognition (FER) system based on deep convolutional neural network by using three model. In this work, a simple solution for facial expression recognition that uses a combination of algorithms for face detection, feature extraction and classification is discussed. The proposed method uses CNN models with SVM classifier and evaluates them, these models are Alex-net model, VGG-16 model and Res-Net model. Experiments are carried out on the Extended Cohn-Kanada (CK+) datasets to determine the recognition accuracy for the proposed FER system. In this study the accuracy of AlexNet model compared with Vgg16 model and ResNet model. The result show that AlexNet model achieved the best accuracy (88.2%) compared to other models.
Personalized recommendation by using fused user preference to construct smart library

In online digital library, it makes people be convenient to visit latest books or magazines via digitization. Online digital library becomes the platform for people to search and browse latest books or magazines. From digital library to smart library, the key is how to recommend interesting items from personalized preference according to visiting history records, comments, and discussions. This paper introduces user preference into digital library recommendation system. Generally, if two users have similar preference, they also like the books or magazines from the same or related topics. First, the topics is estimated by a Latent Dirichlet Allocation (LDA) model; second, the topics with high user preference score is recommended to the user. Thus, the user can receive the item list which are close to the topics which he or she is interested in. The experimental results on a university digital library show the effectiveness of the user preference based recommendation system for smart library.
Shapley explainability on the data manifold

Explainability in AI is crucial for model development, compliance with regulation, and providing operational nuance to predictions. The Shapley framework for explainability attributes a model’s predictions to its input features in a mathematically principled and model-agnostic way. However, general implementations of Shapley explainability make an untenable assumption: that the model’s features are uncorrelated. In this work, we demonstrate unambiguous drawbacks of this assumption and develop two solutions to Shapley explainability that respect the data manifold. One solution, based on generative modelling, provides flexible access to data imputations; the other directly learns the Shapley value-function, providing performance and stability at the cost of flexibility. While “off-manifold” Shapley values can (i) give rise to incorrect explanations, (ii) hide implicit model dependence on sensitive attributes, and (iii) lead to unintelligible explanations in higher-dimensional data, on-manifold explainability overcomes these problems.
The Effectiveness of Core Stability Exercises on Increasing Core Muscle Strength for Junior Swimming Athletes

Swimming is a way to maintain a streamlined body position, which requires core muscle strength; the core muscles must be strong to carry out their function in maintaining body position during swimming.Core muscle strength correlates with swimmers' buoyancy and performance.Thus, training such as core stability exercises is needed to increase the strength of the core muscles.Therefore, this study aims to determine the effectiveness of core stability exercises using a Swiss ball to increase core muscle strength in junior swimmers.An experimental method was employed with one group only pretest and posttest design.The research instrument utilized to measure the core muscle strength of junior swimmers was the sit-up test.50 members of the Taksaka Swimming Club were determined as the population of the study while only 30 of them were involved as the sample of this study.Out of the 30 people, 15 of them were given treatment and the rest 15 people were grouped in the control group.They were divided using a purposive sampling technique.The results showed that core stability exercise using a Swiss ball was effective in increasing core muscle strength in junior swimmers as indicated by a t-score of 7.5 at the significance level of α = 0.05.Therefore, it can be concluded that core stability exercises using a Swiss ball can be used to increase the strength of the core muscles to enable the stability around the lumbar spine and abdominal muscles might bring about biomechanical changes that allow swimmers to swim faster in a more efficient way.
Estimating Lipschitz constants of monotone deep equilibrium models

Several methods have been proposed in recent years to provide bounds on the Lipschitz constants of deep networks, which can be used to provide robustness guarantees, generalization bounds, and characterize the smoothness of decision boundaries. However, existing bounds get substantially weaker with increasing depth of the network, which makes it unclear how to apply such bounds to recently proposed models such as the deep equilibrium (DEQ) model, which can be viewed as representing an infinitely-deep network. In this paper, we show that monotone DEQs, a recently-proposed subclass of DEQs, have Lipschitz constants that can be bounded as a simple function of the strong monotonicity parameter of the network. We derive simple-yet-tight bounds on both the input-output mapping and the weight-output mapping defined by these networks, and demonstrate that they are small relative to those for comparable standard DNNs. We show that one can use these bounds to design monotone DEQ models, even with e.g. multi-scale convolutional structure, that still have constraints on the Lipschitz constant. We also highlight how to use these bounds to develop PAC-Bayes generalization bounds that do not depend on any depth of the network, and which avoid the exponential depth-dependence of comparable DNN bounds.
Data-Driven Algorithm Design

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IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression

In this paper we analyse and improve integer discrete flows for lossless compression. Integer discrete flows are a recently proposed class of models that learn invertible transformations for integer-valued random variables. Their discrete nature makes them particularly suitable for lossless compression with entropy coding schemes. We start by investigating a recent theoretical claim that states that invertible flows for discrete random variables are less flexible than their continuous counterparts. We refute this claim with a proof for integer discrete flows. Furthermore, we zoom in on the effect of gradient bias due to the straight-through estimator in integer discrete flows, and demonstrate that its influence is highly dependent on architecture choices and less prominent than previously thought. Finally, we show how different modifications to the architecture improve the performance of this model class for lossless compression.
Blockchain-based Micro-credentials: Design, Implementation, Evaluation and Adoption

This study examines a blockchain-based microcredential system implementation with a particular focus on understanding user perceptions.While blockchain technology has become increasingly popular, its applications extend far beyond finance and cryptocurrency.In particular, blockchain enables the generation and management of verifiable digital certificates which possess several system-level advantages when compared to current solutions.Still, does the utilisation of blockchain add value to the issuers and recipients of micro-credentials?Applying a design science approach, we design, implement and evaluate a blockchain-based microcredential management system within a business school's executive education unit.Qualitative evaluation reveals that such systems can decrease the overall cost and administrative workload.While issuers perceive the implementation as useful and low risk, the general knowledge regarding blockchain and its advantages, especially in the context of microcredential management, is insufficient.We discuss this amongst other challenges that must be addressed before widespread adoption of blockchain-based micro-credentials can be achieved.1.To design and implement an independently managed blockchain-based, micro-credential system within a university.2. To evaluate the implementation from the perspectives of the certificate issuer and recipient.To carry out this study, we adopt the Design Science Research Methodology (DSRM) [6].DSRM is a popular approach to conduct design science research which involves six key steps: (1) Problem
Implicit Convex Regularizers of CNN Architectures: Convex Optimization of Two- and Three-Layer Networks in Polynomial Time

We study training of Convolutional Neural Networks (CNNs) with ReLU activations and introduce exact convex optimization formulations with a polynomial complexity with respect to the number of data samples, the number of neurons, and data dimension. More specifically, we develop a convex analytic framework utilizing semi-infinite duality to obtain equivalent convex optimization problems for several two- and three-layer CNN architectures. We first prove that two-layer CNNs can be globally optimized via an l2 norm regularized convex program. We then show that three-layer CNN training problems are equivalent to an l1 regularized convex program that encourages sparsity in the spectral domain. We also extend these results to multi-layer CNN architectures including three-layer networks with two ReLU layers and deeper circular convolutions with a single ReLU layer. Furthermore, we present extensions of our approach to different pooling methods, which elucidates the implicit architectural bias as convex regularizers.
Web infrastructure for data management, storage and computation

The Data Storage for Computation and Integration (DSCI) proposes management innovations for web-based secure data storage, algorithms deployment, and task execution. Its architecture allows uploading, browsing, sharing, and task execution in remote computing grids. Here, we demonstrate the DSCI implementation and the deployment of Image processing tools (TMJSeg), machine learning algorithms (MandSeg, DentalModelSeg), and advanced statistical packages (Multivariate Functional Shape Data Analysis, MFSDA), with data transfer and task execution handled by the clusterpost plug-in. Due to its comprehensive web-based design, local software installation is no longer required. The DSCI aims to enable and maintain distributed computing and collaboration environment across multi-site clinical centers for the data processing of multisource features such as clinical, biological markers, volumetric images, and 3D surface models.