Reviewerly/exHarmony huggingface dataset:
Work-in-progress—Design of LMS for the Shared Campus in Metaverse Learning Environment

This study is a Metaverse education framework in higher education for a shared learning environment at a consortium university. Here is a case of a consortium university in Korea, which has established a shared learning environment that can entail various advantages by forming a consortium between local governments, universities, and companies. In this work in progress, a Metaverse virtual campus was opened to overcome the distance between regions, and an LMS system was used for learning management. Despite the different attributes of the website and Metaverse, suggestions on how to build a new learning system were described by combining mutual advantages.
Bringing Humans at the Epicenter of Artificial Intelligence: A Confluence of AI, HCI and Human Centered Computing

Artificial Intelligence (AI) and Human Computer Interaction (HCI) often intersect under the purview of different technologies in a plethora of application areas, wherein it becomes pivotal to identify and interpret the exact roles of both the disciplines. Stakeholders involved in the fusion of AI and HCI range from engineers, scientists, philosophers and technologists to psychologists, cognitive scientists, graphic designers and researchers across a wide spectrum of realms. While it is important to develop intelligent interactive systems owing to the ubiquitous and pervasive nature of data ushering in the next era of AI, it is all the more important to develop these systems in-tandem with the aforementioned stakeholders, and in a way that users exercise appropriate control over the AI that is been developed. Human-in-the-loop systems and operator assistive technologies, especially in the context of Industry 4.0 are examples of a fabulous fusion of AI-HCI, wherein the technology that is being used works by keeping the humans in the loop, thereby paving the way for a robust, transparent, ethical, intelligent and interactive mechanism for the co-existence of AI and HCI systems. In this paper, we aim to bank upon the state-of-the-art literature that has been written previously in this regard and derive though provoking research questions. We also put forth challenges, case studies, application areas and come up with solutions, along with few recommendations that can be viably used in developing frameworks and mechanisms for the synergic co-existence of these two challenging, yet interesting fields of AI and HCI.
Information security risk management models for cloud hosted systems: A comparative study

Cloud hosting approach is becoming a popular choice for management. Having the information hosted in the cloud increase the risks and threats. A strong Security Model is required to maximize the security and treat risks efficiently. This research aims to analysis the most popular Security Model Methodologies and provide recommendation on the best fit methodology for cloud hosting approach. This research used a comparison model to compare the Security Model methodologies, which are ISO27005, NIST SP 800-30, CRAMM, CORAS, OCTAVE Allegro, and COBIT 5, based on three areas: Applicability, adaptability, and Involvement of the models for a cloud-based hosting approach. The research recommends OCTAVE Allegro as the preferred model for cloud hosting approach, as well as COBIT 5 and CORAS with some tuning. The previous models handle the CIA Triad and focus on the information storage, Processing, and transmission. The ISO27005, NIST SP 800-30, and CRAMM are all describing the risk management and assessment as an abstract and might not provide clear guidelines for cloud risks evaluation and assessment. There are more than 200 security models in the management studies, few are suitable for fast growing and continuously changing environment like the cloud approach, the research value on highlighting the best fit approaches to consider when using a cloud hosting platform, securing the information with a proper model.
The Combination of Serious Gaming and Immersive Virtual Reality through the Constructivist Approach: An Application to Teaching Architecture

Immersive virtual reality (IVR) has proven to be a technology that can benefit the dissemination of cultural content. In 2019 was the five hundredth anniversary of the death of Leonardo Da Vinci. Given the few works that develop IVR technologies to explain the genius of the master, we decided to take advantage of the opportunity to learn about the master through the use of new technologies. To build an IVR application that aims to spread knowledge, it is necessary to define an educational paradigm and the type of application. Given the domain of the application and the need to convey complex/novel topics, the IVR application developed in this study is based on the constructivist framework and creates a serious game (SG). In order to explain Leonardo Da Vinci’s thinking and design approach, we decided to focus on urban planning and architecture studies by explaining the projects envisioned by Leonardo da Vinci. This paper investigates whether an IVR-SG application maintains the fundamental characteristics underlying disclosure processes, such as immersivity and a sense of presence. Two secondary school classes experienced this by evaluating the application through a psychometric questionnaire. The results show that immersivity and a sense of presence were evaluated positively.
A Graph-Neural-Network-Based Social Network Recommendation Algorithm Using High-Order Neighbor Information

Social-network-based recommendation algorithms leverage rich social network information to alleviate the problem of data sparsity and boost the recommendation performance. However, traditional social-network-based recommendation algorithms ignore high-order collaborative signals or only consider the first-order collaborative signal when learning users’ and items’ latent representations, resulting in suboptimal recommendation performance. In this paper, we propose a graph neural network (GNN)-based social recommendation model that utilizes the GNN framework to capture high-order collaborative signals in the process of learning the latent representations of users and items. Specifically, we formulate the representations of entities, i.e., users and items, by stacking multiple embedding propagation layers to recursively aggregate multi-hop neighborhood information on both the user–item interaction graph and the social network graph. Hence, the collaborative signals hidden in both the user–item interaction graph and the social network graph are explicitly injected into the final representations of entities. Moreover, we ease the training process of the proposed GNN-based social recommendation model and alleviate overfitting by adopting a lightweight GNN framework that only retains the neighborhood aggregation component and abandons the feature transformation and nonlinear activation components. The experimental results on two real-world datasets show that our proposed GNN-based social recommendation method outperforms the state-of-the-art recommendation algorithms.
On the weightwise nonlinearity of weightwise perfectly balanced functions

In this article we perform a general study on the criterion of weightwise nonlinearity for the functions which are weightwise perfectly balanced (WPB). First, we investigate the minimal value this criterion can take over WPB functions, deriving theoretic bounds, and exhibiting the first values. We emphasize the link between this minimum and weightwise affine functions, and we prove that for n≥8 no n-variable WPB function can have this property. Then, we focus on the distribution and the maximum of this criterion over the set of WPB functions. We provide theoretic bounds on the latter and algorithms to either compute or estimate the former, together with the results of our experimental studies for n up to 8. Finally, we present two new constructions of WPB functions obtained by modifying the support of linear functions for each set of fixed Hamming weight. This provides a large corpus of WPB function with proven weightwise nonlinearity, and we compare the weightwise nonlinearity of these constructions to the average value, and to the parameters of former constructions in 8 and 16 variables.
Semantic Web-Based Interoperability for Intelligent Agents with PSyKE

Modern distributed systems require communicating agents to agree on a shared, formal semantics for the data they exchange and operate upon. The Semantic Web offers tools to encode semantics in the form of ontologies, where data is represented in the form of knowledge graphs (KG). Applying such tools to intelligent agents equipped with machine learning (ML) capabilities is of particular interest, as it may enable a higher degree of interoperability among heterogeneous agents. Indeed, inputs and outputs of ML models can be formalised through ontologies, while the data they operate upon can be represented as KG. In this paper we explore the combination of Semantic Web tools with knowledge extraction—that is, a research line aimed at extracting intelligible rules mimicking the behaviour of ML predictors, with the purpose of explaining their behaviour. Along this line, we study whether and to what extent ontologies and KG can be exploited as both the source and the outcome of a rule extraction procedure. In other words, we investigate the extraction of semantic rules out of sub-symbolic predictors trained upon data as KG—possibly adhering to some ontology. In doing so, we extend our PSyKE framework for rule extraction with Semantic Web support. In practice, we make PSyKE able to (i) train ML predictors out of OWL ontologies and RDF knowledge graphs, and (ii) extract semantic knowledge out of them, in the form of SWRL rules. A discussion among the major benefits and issues of our approach is provided, along with a description of the overall workflow.
Active Automata Learning as Black-Box Search and Lazy Partition Refinement

We present a unifying formalization of active automata learning algorithms in the MAT model, including a new, efficient, and simple technique for the analysis of counterexamples during learning: $$L^{\!\lambda }$$ is the first active automata learning algorithm that does not add sub-strings of counterexamples to the underlying data structure for observations but instead performs black-box search and partition refinement. We analyze the worst case complexity in terms of membership queries and equivalence queries and evaluate the presented learning algorithm on benchmark instances from the Automata Wiki, comparing its performance against efficient implementations of some learning algorithms from LearnLib.
Efficient and Reliable Overlay Networks for Decentralized Federated Learning

We propose near-optimal overlay networks based on $d$-regular expander graphs to accelerate decentralized federated learning (DFL) and improve its generalization. In DFL a massive number of clients are connected by an overlay network, and they solve machine learning problems collaboratively without sharing raw data. Our overlay network design integrates spectral graph theory and the theoretical convergence and generalization bounds for DFL. As such, our proposed overlay networks accelerate convergence, improve generalization, and enhance robustness to client failures in DFL with theoretical guarantees. Also, we present an efficient algorithm to convert a given graph to a practical overlay network and maintain the network topology after potential client failures. We numerically verify the advantages of DFL with our proposed networks on various benchmark tasks, ranging from image classification to language modeling using hundreds of clients.
A Flexible and Energy-Efficient BLAKE-256/2s Co-Processor for Blockchain-based IoT Applications

Developing flexible and energy-efficient BLAKE-256/2s hardware has recently become necessary since BLAKE-256 and BLAKE2s are important cryptographic hash functions in reliability and security enhancement for blockchain-based IoT applications. However, previous BLAKE-256/2s architectures are challenging in achieving high flexibility and energy efficiency. Therefore, this paper proposes the BLAKE-256/2s co-processor to achieve high flexibility and energy efficiency for blockchain-based IoT applications. The proposed BLAKE-256/2s accelerator has three novel optimization techniques to achieve those goals. First, a configurable hashing core is proposed to enhance flexibility. Second, a pipelined permutation and compression architecture are developed to improve the throughput and hardware efficiency. Third, a mining transmission mechanism is introduced to optimize the performance of our co-processor at the system-on-chip level. The proposed co-processor is implemented and verified on a Xilinx Zynq <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{UltraScale}+$</tex> MPSoC ZCU102 FPGA. Accordingly, the power and energy efficiency of the co-processor on the ZCU102 FPGA is significantly better than the Intel i9 10940X CPU and the RTX 3090 GPU. Moreover, experimental results on several FPGAs prove that the proposed co-processor is considerably higher throughput, area efficiency, and flexibility than FPGA-based related works.
Improving the Performance and Explainability of Indoor Human Activity Recognition in the Internet of Things Environment

Traditional indoor human activity recognition (HAR) has been defined as a time-series data classification problem and requires feature extraction. The current indoor HAR systems still lack transparent, interpretable, and explainable approaches that can generate human-understandable information. This paper proposes a new approach, called Human Activity Recognition on Signal Images (HARSI), which defines the HAR problem as an image classification problem to improve both explainability and recognition accuracy. The proposed HARSI method collects sensor data from the Internet of Things (IoT) environment and transforms the raw signal data into some visual understandable images to take advantage of the strengths of convolutional neural networks (CNNs) in handling image data. This study focuses on the recognition of symmetric human activities, including walking, jogging, moving downstairs, moving upstairs, standing, and sitting. The experimental results carried out on a real-world dataset showed that a significant improvement (13.72%) was achieved by the proposed HARSI model compared to the traditional machine learning models. The results also showed that our method (98%) outperformed the state-of-the-art methods (90.94%) in terms of classification accuracy.
Sequential clustering and classification using deep learning technique and multi-objective sine-cosine algorithm

This study introduced a novel data analytics-based sequential clustering and classification (SCC) approach. The proposed approach named deep MOSCA-SCC integrates a multi-objective sine–cosine algorithm (MOSCA), deep clustering technique, and classification algorithms to exploit data structure before implementing the prediction model. Herein, the autoencoder combined with the K-means algorithm was utilized for the deep clustering to reveal the data pattern. Regarding classification, support vector machine, back-propagation neural network, and decision tree classification were implemented to explore the correlated factors with the revealed patterns. To evaluate the performance of the proposed method, a comparison was conducted between the proposed deep MOSCA-SCC and other benchmark algorithms, including the NSGAII-SCC and the regular MOSCA-SCC. The deep MOSCA-SCC achieved better performance than other algorithms in terms of clustering sum of squared error and classification accuracy. In addition, deep MOSCA-SCC with a decision tree yielded the best accuracies with 92.6% and 87% when the numbers of clusters were equal to 3 and 4, respectively.
An efficient method for autoencoder based outlier detection

Unsupervised Learning is widely used approach for outlier detection because non-availability of training dataset in various domains (especially, in evolving domains). Approaches like clustering-based, distance-based, density-based outlier detection methods have been proposed over the last several years. Recently, outlier detection using deep learning has drawn attention of researchers. Deep learning-based unsupervised techniques (autoencoder) minimize the reconstruction error using each data instance in the dataset and subsequently, data points with higher reconstruction error are treated as outlier points. However, autoencoder based model overestimates the reconstruction error for normal points whereas it is underestimated for outlier points. As a result, genuine outliers are missed by this approach. We propose two techniques to address the issue of reconstruction error stated earlier. Main idea of our techniques is to compute reconstruction error only using ‘normal points’. In the proposed techniques, we identify probable outliers utilizing the clustering approaches intelligently and subsequently, we do not include them in the minimization process of reconstruction error. We exploit recently recognized clustering approach Density Peak Clustering (DPC) to identify the probable outlier points based on density and distance to the higher density points. However, DPC has inherent drawback of setting threshold which plays important role in deciding density. Therefore, Self Organizing Map (SOM) is exploited as another clustering approach in this article. Subsequently, we conducted experiments on synthetic as well as real world datasets and the results show that the proposed technique outperforms the popular existing deep learning model like RandNet, Boosting-based Autoencoder Ensemble method (BAE), One Class Support Vector Machine (OCSVM) and density-based algorithms like LOF, LDOF, INFLO, and RDOS.
Real-time high quality font generation with Conditional Font GAN

Designing and generating novels fonts manually is a laborious and time-consuming process owing to the large number and complexity of characters in the majority of language systems. Recent advancements in generative adversarial networks (GANs) have significantly improved font generation. These GAN-based approaches either handle the font generation as a vanilla GAN problem (that is, by synthesizing characters from a uniform latent vector) or an image-to-image translation problem. While the former approach has no limitation in generating diverse font styles, the generated fonts contain artifacts and can operate only on low-resolution images, thus impairing their usability. The latter approach generates high-quality font images for previously observed fonts, but the quality degrades during the inference phase while designing novel fonts. Furthermore, additional fine-tuning steps are required to achieve photorealistic results, which is computationally expensive and time-consuming. To address the shortcomings of these approaches, we propose a font generation method that employs the vanilla GAN approach to generate an infinite number of font styles but focuses on the real-time generation of photo-realistic font images. Additionally, we strive to create high-resolution images that can be used in practical applications. To accomplish this, we propose a conditional font GAN (CFGAN) with a sophisticated network architecture that is designed to generate novel style-consistent diverse font character sets. We control the generated characters in the proposed network using a non-trainable fixed character vector, while the style variation sampled from a Gaussian distribution is fused at all blocks of the generator through adaptive instance normalization (AdaIN) operation. Thus, the generator architecture can simultaneously generate an infinite number of font styles with style consistency and diversity during inference. We conducted various quantitative and qualitative experiments to demonstrate the effectiveness of the proposed model in terms of both image quality and computational cost.
Applying Inter-Rater Reliability and Agreement in collaborative Grounded Theory studies in software engineering

The qualitative research on empirical software engineering that uses Grounded Theory is increasing (GT). The trustworthiness, rigor, and transparency of GT qualitative data analysis can benefit, among others, when multiple analysts juxtapose diverse perspectives and collaborate to develop a common code frame based on a consensual and consistent interpretation. Inter-Rater Reliability (IRR) and/or Inter-Rater Agreement (IRA) are commonly used techniques to measure consensus, and thus develop a shared interpretation. However, minimal guidance is available about how and when to measure IRR/IRA during the iterative process of GT, so researchers have been using ad hoc methods for years. This paper presents a process for systematically measuring IRR/IRA in GT studies, when appropriate, which is grounded in a previous systematic mapping study on collaborative GT in the field of software engineering. Meta-science guided us to analyze the issues and challenges of collaborative GT and formalize a process to measure IRR/IRA in GT. This process guides researchers to incrementally generate a theory while ensuring consensus on the constructs that support it, improving trustworthiness, rigor, and transparency, and promoting the communicability, reflexivity, and replicability of the research. The application of this process to a GT study seems to support its feasibility. In the absence of further confirmation, this would represent the first step in a de facto standard to be applied to those GT studies that may benefit from IRR/IRA techniques.
AHU-MultiNet: Adaptive loss balancing based on homoscedastic uncertainty in multi-task medical image segmentation network

Medical image segmentation is an important field in medical image analysis and a vital part of computer-aided diagnosis. Due to the challenges in acquiring image annotations, semi-supervised learning has attracted high attention in medical image segmentation. Despite their impressive performance, most existing semi-supervised approaches lack attention to ambiguous regions (e.g., some edges or corners around the organs). To achieve better performance, we propose a novel semi-supervised method called Adaptive Loss Balancing based on Homoscedastic Uncertainty in Multi-task Medical Image Segmentation Network (AHU-MultiNet). This model contains the main task for segmentation, one auxiliary task for signed distance, and another auxiliary task for contour detection. Our multi-task approach can effectively and sufficiently extract the semantic information of medical images by auxiliary tasks. Simultaneously, we introduce an inter-task consistency to explore the underlying information of the images and regularize the predictions in the right direction. More importantly, we notice and analyze that searching an optimal weighting manually to balance each task is a difficult and time-consuming process. Therefore, we introduce an adaptive loss balancing strategy based on homoscedastic uncertainty. Experimental results show that the two auxiliary tasks explicitly enforce shape-priors on the segmentation output to further generate more accurate masks under the adaptive loss balancing strategy. On several standard benchmarks, the 2018 Atrial Segmentation Challenge and the 2017 Liver Tumor Segmentation Challenge, our proposed method achieves improvements and outperforms the new state-of-the-art in semi-supervised learning.