Reviewerly/exHarmony huggingface dataset:
RESI: A Region-Splitting Imputation method for different types of missing data

A certain degree of data loss seriously affects the accuracy and availability of data, especially on the effects of the subsequent in-depth data analysis and mining. It is of great value in practical applications to construct a data imputation model, which is suitable for completing different types of missing data, including numerical only, categorical only and mixed-type data, and has strong capability of generalization. To address this issue, this paper defines a new metric, mean integrity rate, to measure the missing degree of a dataset, and proposes RESI, a novel tuple-based REgion-Splitting Imputation model, to impute different type missing data. We first select features and assign weights to each attribute by using the entropy weight method, and then partition the tuples into a subset of complete tuples and several subsets of incomplete tuples based on their integrity rate, which is formulated with the weights of attributes and the missing degree of tuples. The model performs training iterations on the complete tuple subset. In each iteration, the trained model is used to impute the next missing subset, and the computed subset is merged into the complete subset for training the next model. To improve the imputation accuracy, we leverage k-fold cross validation to correct errors. Besides imputing diverse types of missing data, extensive experimental results have shown that our model, RESI, significantly outperforms the state-of-the-art methods in the sensitivity to missing rate and accuracy of imputed data.
Incremental Learning from Low-labelled Stream Data in Open-Set Video Face Recognition

Deep Learning approaches have brought solutions, with impressive performance, to general classification problems where wealthy of annotated data are provided for training. In contrast, less progress has been made in continual learning of a set of non-stationary classes, mainly when applied to unsupervised problems with streaming data. Here, we propose a novel incremental learning approach which combines a deep features encoder with an Open-Set Dynamic Ensembles of SVM, to tackle the problem of identifying individuals of interest (IoI) from streaming face data. From a simple weak classifier trained on a few video-frames, our method can use unsupervised operational data to enhance recognition. Our approach adapts to new patterns avoiding catastrophic forgetting and partially heals itself from miss-adaptation. Besides, to better comply with real world conditions, the system was designed to operate in an open-set setting. Results show a benefit of up to 15% F1-score increase respect to non-adaptive state-of-the-art methods.
KAT: A Knowledge Adversarial Training Method for Zero-Order Takagi–Sugeno–Kang Fuzzy Classifiers

While input or output-perturbation-based adversarial training techniques have been exploited to enhance the generalization capability of a variety of nonfuzzy and fuzzy classifiers by means of dynamic regularization, their performance may perhaps be very sensitive to some inappropriate adversarial samples. In order to avoid this weakness and simultaneously ensure enhanced generalization capability, this work attempts to explore a novel knowledge adversarial attack model for the zero-order Tagaki-Sugeno-Kang (TSK) fuzzy classifiers. The proposed model is motivated by exploiting the existence of special knowledge adversarial attacks from the perspective of the human-like thinking process when training an interpretable zero-order TSK fuzzy classifier. Without any direct use of adversarial samples, which is different from input or output perturbation-based adversarial attacks, the proposed model considers adversarial perturbations of interpretable zero-order fuzzy rules in a knowledge-oblivion and/or knowledge-bias or their ensemble to mimic the robust use of knowledge in the human thinking process. Through dynamic regularization, the proposed model is theoretically justified for its strong generalization capability. Accordingly, a novel knowledge adversarial training method called KAT is devised to achieve promising generalization performance, interpretability, and fast training for zero-order TSK fuzzy classifiers. The effectiveness of KAT is manifested by the experimental results on 15 benchmarking UCI and KEEL datasets.
Mathematical Modeling Approaches for Blockchain Technology

These days blockchain innovation has become a significant issue as of late and greatly affected the business. Even though blockchain innovations was reliable for security issues, advantageous administrations, and difficulties for procedure assumes a significant key job that we have to break down. To have a superior observation about this square chain innovation, we will find out about the essential security highlights like-expanded limit, better security, unchanging nature, quicker settlement, and decentralized framework. The range of blockchain applications limits from budgetary, human services, vehicle, chance administration, Internet of things to open and social administrations. In this paper, by examining its structure to various agreement calculations just as difficulties and openings, we endeavor to lead an expansive going review on blockchain innovation and also the few mathematical models behind this blockchain technology.
Federated Learning Under Importance Sampling

Federated learning encapsulates distributed learning strategies that are managed by a central unit. Since it relies on using a selected number of agents at each iteration, and since each agent, in turn, taps into its local data, it is only natural to study optimal sampling policies for selecting agents and their data in federated learning implementations. Usually, only uniform sampling schemes are used. However, in this work, we examine the effect of importance sampling and devise schemes for sampling agents and data non-uniformly guided by a performance measure. We find that in schemes involving sampling without replacement, the performance of the resulting architecture is controlled by two factors related to data variability at each agent, and model variability across agents. We illustrate the theoretical findings with experiments on simulated and real data and show the improvement in performance that results from the proposed strategies.
Network analytics and social BIM for managing project unstructured data

Unstructured data, mainly text in project documents and final evaluation or summary reports, is a container of tacit knowledge. We present an exploratory case study to use semantic network analysis to help capture and formalize elements of this knowledge. Text from project documents and chats by project participants over an interactive BIM platform were collected and arranged in the form of concept networks. With that, we used the rich literature in network science to formally study the networks. We illustrate the proposed approach by analyzing five concept networks. The case study illustrates the benefits of the approach—mainly, to develop a project-specific map of key concepts. Using network measures, such as centrality, identified the project's key issues and how they relate. Clustering measures identified possible knowledge constructs (interrelated concepts). Measures for quantifying the overall structure of the network can also be used to contrast projects.
Innovation Capabilities and Authentic Leadership: Do They Really Matter to Firm Performance?

The purpose of this article was to examine the impact of innovation capabilities and authentic leadership on firm performance in the banking sector. A quantitative approach was employed in this study for collecting data from 220 employees of several banks in the east coast of Malaysia using a survey method. To analyze the data and reach conclusions, SPSS and structural equation modeling (AMOS) were used. The results showed that product innovation and service innovation have significant positive effects on firm performance. Furthermore, the outcomes confirmed that process and marketing innovations play important roles in affecting firm performance. Finally, the results revealed that authentic leadership has a significant positive effect on firm performance. The outcome increases our understanding with regards to the role of innovation capabilities and authentic leadership in affecting firm performance in the banking sector.
PRNU-Based Content Forgery Localization Augmented With Image Segmentation

The advances in image editing and retouching technology have enabled an unskilled person to easily produce visually indistinguishable forged images.To detect and localize such forgeries, many image forensic tools rely on visually imperceptible clues, e.g. the subtle traces or artifacts introduced during image acquisition and processing, and slide a regular, typically square, detection window across the image to search for discrepancies of specific clues.Such a sliding-window paradigm confines the explorable neighborhood to a regular grid and inevitably limits its capability in localizing forgeries in a broad range of shapes.While image segmentation that generates segments adhering to object boundaries might be a promising alternative to the sliding window-based approach, it is generally believed that the potential of the segmentation-based detection scheme is hindered by object removal forgeries where meaningful segments are often unavailable.In this work, we take forgery localization based on photo-response non-uniformity (PRNU) noise as an example and propose a segmentationbased forgery localization scheme that exploits the local homogeneity of visually indiscernible clues to mitigate the limitations of existing segmentation approaches that are merely based on visually perceptible content.We further propose a multi-orientation localization scheme that integrates the forgery probabilities obtained with image segmentation and multi-orientated detection windows.The multi-orientation scheme aggregates the complementary strengths of image segmentation and multi-oriented detection window in localizing the object insert and object removal forgeries.Experimental results on a public realistic tampering image dataset demonstrate that the proposed segmentation-based and multi-orientation forgery localization schemes outperform existing state-of-the-art PRNU-based forgery localizers in terms of both region and boundary F 1 scores.
Annotation-Assisted Clustering of Player Profiles in Cultural Games: A Case for Tensor Analytics in Julia

Computer games play an increasingly important role in cultural heritage preservation. They keep tradition alive in the digital domain, reflect public perception about historical events, and make history, and even legends, vivid, through means such as advanced storytelling and alternative timelines. In this context, understanding the respective underlying player base is a major success factor as different game elements elicit various emotional responses across players. To this end, player profiles are often built from a combination of low- and high-level attributes. The former pertain to ordinary activity, such as collecting points or badges, whereas the latter to the outcome of strategic decisions, such as participation in in-game events such as tournaments and auctions. When available, annotations about in-game items or player activity supplement these profiles. In this article, we describe how such annotations may be integrated into different player profile clustering schemes derived from a template Simon–Ando iterative process. As a concrete example, the proposed methodology was applied to a custom benchmark dataset comprising the player base of a cultural game. The findings are interpreted in the light of Bartle taxonomy, one of the most prominent player categorization. Moreover, the clustering quality is based on intra-cluster distance and cluster compactness. Based on these results, recommendations in an affective context for maximizing engagement are proposed for the particular game player base composition.
An Efficient and Robust Aggregation Algorithm for Learning Federated CNN

Federated learning is a privacy-protected way of decentralized machine learning. In a Federated Learning system, the server uses the aggregation algorithm to obtain a global model from clients. The traditional aggregation method called FedAvg is a simple arithmetic average, but it has never been proved efficient. Moreover, there are many potential attacks and network instability in Federated Learning systems. This paper aims to achieve two goals by replacing the server-side aggregation strategy: 1) Accelerate global model's convergence speed 2) Make global model reliable when facing network instability and offline attacks. Considering different clients' contributions have different impacts, we try to use gaussian distribution to weight clients' potential contributions. To make the aggregation process more modality to different neural network architecture, we try to solve the above problems on Convolution Neural Network as a representation. To work well on different functional units in neural networks, we also propose layer-wise optimizing steps. We have experimented on some representative tasks of Federated Learning, and the results show that our method exceeds FedAvg a lot on convergence speed. When simulating attack situations, our algorithm could be proved to maintain a reliable global model.
Hard and Soft EM in Bayesian Network Learning from Incomplete Data

Incomplete data are a common feature in many domains, from clinical trials to industrial applications. Bayesian networks (BNs) are often used in these domains because of their graphical and causal interpretations. BN parameter learning from incomplete data is usually implemented with the Expectation-Maximisation algorithm (EM), which computes the relevant sufficient statistics (“soft EM”) using belief propagation. Similarly, the Structural Expectation-Maximisation algorithm (Structural EM) learns the network structure of the BN from those sufficient statistics using algorithms designed for complete data. However, practical implementations of parameter and structure learning often impute missing data (“hard EM”) to compute sufficient statistics instead of using belief propagation, for both ease of implementation and computational speed. In this paper, we investigate the question: what is the impact of using imputation instead of belief propagation on the quality of the resulting BNs? From a simulation study using synthetic data and reference BNs, we find that it is possible to recommend one approach over the other in several scenarios based on the characteristics of the data. We then use this information to build a simple decision tree to guide practitioners in choosing the EM algorithm best suited to their problem.
Industry 4.0 Visual Tools for Digital Twin System Design

This work analyses methods of digital model development and uses electronics production as an example. It focuses on the analysis of the visual tools of a digital manufacturing system design. The digital model development process is divided into a sequence of tasks, to solve which a number of models, methods and tools is proposed. These procedures can be used to perform simulation modelling, performance prediction, promising option selection, parameters optimisation of both the device itself and its manufacturing mechanisms along with other lifecycle components, without the expensive sample tests, field tests or introduction of any changes to the already established manufacturing, selling, service and other infrastructures. Digitalisation aspects are covered by creating interrelated digital models of the device and the digital manufacturing process model that take all the technical and economic aspects into account. This approach, called Digital Twin (of the actual production), underlies the modern Industry 4.0 concept that suggests the introduction of different cyber-physical systems in the manufacturing systems. The proposed method of the visual system design uses a complex of software tools to produce digital system models of an electronics manufacturing system. The resulting digital twin of the actual manufacturing is representative of both the produced device and its manufacturing process design features. And it can be used for preparation, analysis and manufacturing improvement purposes.
A Hybrid Greedy Sine Cosine Algorithm with Differential Evolution for Global Optimization and Cylindricity Error Evaluation

Sine-cosine algorithm (SCA) has found a widespread application in various engineering optimization problems. However, SCA suffers from premature convergence and insufficient exploitation. Cylindricity error evaluation is a typical engineering optimization problem related to the quality of cylindrical parts. A hybrid greedy sine-cosine algorithm with differential evolution (HGSCADE) is developed in this paper to solve optimization problems and evaluate cylindricity error. HGSCADE integrates the SCA with the opposition-based population initialization, the greedy search, the differential evolution (DE), the success history-based parameter adaptation, and the Levy flight-based local search. HGSCADE is tested on the CEC2014 benchmark functions and is employed in cylindricity error evaluation. The results show the superiority of HGSCADE to other state-of-the-art algorithms for the benchmark functions and cylindricity error evaluation.
Multi-label thresholding for cost-sensitive classification

Multi-label classification associates each instance with a set of labels which reflects the nature of a wide range of real-world applications. However, existing approaches assume that all labels have the same misclassification cost, whereas in real-world problems different types of misclassification errors have different costs, which are generally unknown in the training context or might change from one context to another. Thus, there is a demand for cost-sensitive classification methods that minimise the average misclassification cost rather than error rates or counts. In this paper, we adopt a simple yet general method, called thresholding, which applies to most classification algorithms to adapt them to cost-sensitive multi-label classification. This paper investigates current threshold choice approaches for multi-label classification. It explores the choice of single and multiple thresholds and extends some of the current techniques to support multi-label problems. Moreover, it proposes cost curves and scatter diagrams for performance evaluation in the multi-label setting. Experimental evaluation on 13 multi-label datasets demonstrates that there is no significant loss by adjusting a global threshold rather than a per-label threshold considering different misclassification costs across labels. Although tuning multiple thresholds is the obvious solution, the global threshold can also be valid.
Wikipedia Knowledge Graph for Explainable AI

Explainable artificial intelligence (XAI) requires domain information to explain a system's decisions, for which structured forms of domain information like Knowledge Graphs (KGs) or ontologies are best suited. As such, readily available KGs are important to accelerate progress in XAI. To facilitate the advancement of XAI, we present the cycle-free Wikipedia Knowledge Graph (WKG) based on information from English Wikipedia. Each Wikipedia article title, its corresponding category, and the category hierarchy are transformed into different entities in the knowledge graph. Along with cycle-free version we also provide the original knowledge graph as it is. We evaluate whether the WKG is helpful to improve XAI compared with existing KGs, finding that WKG is better suited than the current state of the art. We also compare the cycle-free WKG with the Suggested Upper Merged Ontology (SUMO) and DBpedia schema KGs, finding minimal to no information loss.
Gain A New Perspective: Towards Exploring Multi-View Alignment in Mixed Reality

Manufacturing, maintenance, assembly, and training tasks represent some of the human activities that have captured special interest for Mixed Reality (MR) applications. For most of these scenarios, accurate object alignment constitutes a requirement to ensure the desired outcome. This task has proved to be especially challenging in egocentric approaches, frequently leading to estimation errors in depth. While traditional MR methods provide virtual guides such as text, arrows, or animations to assist users during alignment, this work explores the feasibility of using additional views generated by virtual cameras and mirrors. Presenting additional views from different perspectives can help to mitigate the estimation errors and show information that is not directly visible to users.To explore the benefits of using additional views for alignment tasks, and to collect reliable data and diminish external factors, we conducted a user study in a controlled virtual environment where participants aligned objects supported by additional views from a top-down camera and virtual mirrors. Data regarding alignment error, time to completion, user's interaction and attention, distance traveled, average head velocity, usability, and mental effort were collected. Our results show that using additional views reduces the mental effort and distance traveled by users and increases acceptance without negatively affecting the alignment accuracy. Therefore, we believe that users will also benefit from integrating these techniques during alignment tasks in MR environments.
Approaching Change with and in Design

Design practice is intrinsically about change and changing the world. Over the past two decades several new areas have emerged within design, such as service design, design thinking, speculative design, transition design and social design. What constitutes a desirable design outcome has also evolved. New design processes exist to enable those outcomes, and broader, more diverse communities often contribute to collective changemaking. Despite this, we still have a fragmented understanding of how to use design to create change, with a limited number of frameworks to guide its implementation in public and private settings. This article provides a brief overview of the recent developments in and through design that relate to how and why the usage of design has changed and how we design change. I look at the products, processes, and people related to change in design, and how design practice has fostered new roles in the field. In depth interviews with expert designers, and their insights about these roles, have been used to open up the discussion of what questions designers are aiming to define and answer, with whom, and how.
Subject-Independent Slow Fall Detection with Wearable Sensors via Deep Learning

One of the major healthcare challenges is elderly fallers. A fall can lead to disabilities and even mortality. With the current Covid-19 pandemic, insufficient resources could be provided for the care of elderlies, and care workers often may not be able to visit them. Therefore, a fall may get undetected or delayed leading to serious harm or consequences. Automatic fall detection systems could provide the necessary detection and warnings for timely intervention. Although many sensor-based fall detection systems have been proposed, most systems focus on the sudden fall and have not considered the slow fall scenario, a typical fall instance for elderly fallers. In this paper, a robust activity (RA) and slow fall detection system is proposed. The system consists of a waist-worn wearable sensor embedded with an inertial measurement unit (IMU) and a barometer, and a reference ambient barometer. A deep neural network (DNN) is developed for fusing the sensor data and classifying fall events. The results have shown that the IMU-barometer design yield better detection of fall events and the DNN approach (90.33% accuracy) outperforms traditional machine learning algorithms.