Reviewerly/exHarmony huggingface dataset:
Applying machine learning for tag classification in a collaborative knowledge system

The use of technological resources to enhance learning has grown exponentially in recent decades, mainly with the advent of mobile communication devices that increased the interactivity of users who collaborate to form a network of collective intelligence. In this technology environment that uses the Web, there is a large volume of data that is commonly disorganized, which is a challenge to use this data in the learning process in a continuous way to complete the knowledge. This study proposes using metrics capable of measuring knowledge aggregated in metadata shared in a collaborative system to be used in a recommendation system in the tagging process in continuous learning.
Role-Based Network Embedding via Quantum Walk with Weighted Features Fusion

Role-based network embedding aims to embed role-similar nodes into a similar embedding space, which is widely used in graph mining tasks such as role classification and detection. Roles are sets of nodes in graph networks with similar structural patterns and functions. However, the role-similar nodes may be far away or even disconnected from each other. Meanwhile, the neighborhood node features and noise also affect the result of the role-based network embedding, which are also challenges of current network embedding work. In this paper, we propose a Role-based network Embedding via Quantum walk with weighted Features fusion (REQF), which simultaneously considers the influence of global and local role information, node features, and noise. Firstly, we capture the global role information of nodes via quantum walk based on its superposition property which emphasizes the local role information via biased quantum walk. Secondly, we utilize the quantum walk weighted characteristic function to extract and fuse features of nodes and their neighborhood by different distributions which contain role information implicitly. Finally, we leverage the Variational Auto-Encoder (VAE) to reduce the effect of noise. We conduct extensive experiments on seven real-world datasets, and the results show that REQF is more effective at capturing role information in the network, which outperforms the best baseline by up to 14.6% in role classification, and 23% in role detection on average.
Intermediary RRT*-PSO: A Multi-Directional Hybrid Fast Convergence Sampling-Based Path Planning Algorithm

Path planning is a prevalent process that helps mobile robots find the most efficient pathway from the starting position to the goal position to avoid collisions with obstacles. In this paper, we propose a novel path planning algorithm–Intermediary RRT*-PSO-by utilizing the exploring speed advantages of Rapidly exploring Random Trees and using its solution to feed to a metaheuristic-based optimizer, Particle swarm optimization (PSO), for fine-tuning and enhancement. In Phase 1, the start and goal trees are initialized at the starting and goal positions, respectively, and the intermediary tree is initialized at a random unexplored region of the search space. The trees were grown until one met the other and then merged and re-initialized in other unexplored regions. If the start and goal trees merge, the first solution is found and passed through a minimization process to reduce unnecessary nodes. Phase 2 begins by feeding the minimized solution from Phase 1 as the global best particle of PSO to optimize the path. After simulating two special benchmark configurations and six practice configurations with special cases, the results of the study concluded that the proposed method is capable of handling small to large, simple to complex continuous environments, whereas it was very tedious for the previous method to achieve.
Reviewing Metaverse: Applications, Challenges, and Integration with Other Technologies

Metaverse has recently become a buzzword. With all the IT giants making large investments in securing their position in metaverse-based applications and researchers working even harder at inventing new technologies and integrating existing ones to make the metaverse even more realistic, responsive, and immersive; it is not just a concept now but a reality that beholds our future. In this paper, we examine the metaverse from its inception to its contemporary implementations, noting how all the big IT firms and startups are working to make it more broadly applicable. We analyzed the current technology trends to sort out some of the vulnerabilities and challenges that are faced by the technology in its course of development. We also studied some of the current field in which the metaverse technology is booming rapidly.
MGTIPM: A Multi-granularity Time Intention Prediction Model for Next Point-of-Interest Recommendation

Next Point-of-Interest (POI) recommendation aims at ranking several POIs that the user tends to visit next time based on the user’s historical trajectories. Most of the previous studies directly predict the next check-in POIs. However, the user’s time intention and revisit preference play important roles in the effective recommendation and can be excavated based on the strong relation between check-in time and location. Therefore, in this study, a new model named MGTIPM is discussed. Specifically, MGTIPM designs a multi-granularity time intention prediction module to effectively predict the next check-in time within a period of one day. Moreover, a balancing mechanism integrates the time intention into the check-in sequence POI recommendation model so that MGTIPM can recognize the users’ intention to revisit POIs. Experiments are conducted on two datasets and the results show that the proposed MGTIPM outperforms baselines regarding recommendation performance.
On the Equivalence between the Welch–Berlekamp Algorithm and the Modular Approach Algorithm

The Welch-Berlekamp (WB) algorithm and the modular approach (MA) algorithm are two distinct algorithms that solve the same rational interpolation problem behind the Reed-Solomon decoding. The MA algorithm is more suitable for hardware implementation due to its parallel structure. Even though both algorithms look different, this paper establishes the equivalence between them.
Selective Visual Attention Revealed by Integrating FMRI and Eye-Tracking

Selective visuo-spatial attention (SVSA), consisting of both bottom-up and top-down processes, prioritizes relevant visual information while filtering out the rest. The research interests in SVSA tend to shift from isolating the two processes to depicting the interplay between them. Existing studies have highlighted the combination of computational, behavioral, and naturalistic paradigm neuroimaging enables the study of SVSA in ecologically valid contexts. However, several critical issues need to be revisited. First, the computational visual saliency model that bridges external video stimuli and brain activities in previous studies are designed for static images rather than dynamic videos. Second, both the computational saliency maps and the eye-gaze heatmaps could be noisy. Third, the participant cohort is relatively small. In this study, we investigate the SVSA using the large-scale movie-watching functional magnetic resonance imaging (fMRI) data in the Human Connectome Project (HCP), and by integrating the potential solutions to the limitations discussed above. Our experimental results highlight the importance of visual and auditory interactions in forming the bottom-up visual attention, as well as the engagement of high-order visual cortices and the ventral frontoparietal network in the bottom-up modulatory effect in naturalistic conditions.
Personalization Approaches for Cultural Heritage Study

In this paper, we review different approaches of providing personalization for cultural heritage content – several static and adaptive approaches, and recommendation systems. The purpose of our research is to compare these methods, evaluate their feasibility for certain types of applications in the field. We outline the needs for personalized experience involving cultural heritage content and describe how those needs are addressed by the different approaches, and point the challenges, benefits, and disadvantages of each of these methods.
The Implementation of A Multiliteracy Pedagogy Framework for Teaching Critical Teaching

The Multiliteracy Pedagogy Framework has been promoted to improve education in many countries; however, it is not popular yet in Indonesia. The framework relates to enhancing students' thinking skills. The other research indicated that the MPF did not positively affect students' critical thinking since the teacher did not have good knowledge about it; the teacher did not completely implement the four framework syntax in the teaching-learning process. The research, therefore, intends to reveal the implementation of the MPF for teaching critical thinking in the reading class of English Language Education (PBI) students. The research subjects are a lecture on the Reading subject and 20 students in the third semester at PBI Nahdlatul Ulama University of Sidoarjo (UNUSIDA). It is descriptive qualitative research using observation and questionnaire techniques to collect data. Descriptive statistics were used to generate summaries. The first implementation process is changing the lesson plan based on the multiliteracy pedagogy framework dimension. The activities are arranged in order from the cognitive functions in Multiliteracy; experiencing, conceptualizing, analyzing, and applying. The lecturer followed the syntax easily and completely. The result of students' assignments proves they can think critically about a text by giving comments supported with reasons, recommendations, data, conclusions, and evaluations. Besides, the students enjoyed the instruction and responded well to the questionnaire, with 96,5% on average.
Paragraph-Level Interaction Discourse Relation Recognition Model via Neural Tensor Network

In Natural Language Processing (NLP), if separated from other parts of the paragraph, the semantics of the sentence or clause cannot be fully understood. The semantic understanding of a sentence or clause also depends on all discourse relations and the whole paragraph-level discourse structure. To better understand the semantics of sentences by improving the performance of discourse relation classification, the Paragraph-level Interaction model via Neural Tensor Network (PINTN) is constructed to model the interdependence between Discourse Unit (DU) and the continuity and pattern of discourse relations. On the basis of the discourse representation, the Neural Tensor Network (NTN) is further used to explore deeper semantic interaction. So as to better recognize discourse relations. The experimental results on PDTB dataset show that the PINTN model by constructed is effective. Its performance in implicit and explicit discourse relation recognition is superior to other models. The experimental result shows that the recognition of implicit discourse relations is affected by the recognition of contiguous explicit discourse relations.
Short-Term Photovoltaic Power Forecasting Via Sparse Coding and Dictionary Based Techniques

In recent years, extensive research has been conducted on the issue of photovoltaic power forecasting due to its significance in grid management and the integration of large-scale PV systems. To forecast short-term photovoltaic power, this paper presents a novel approach based on sparse coding and dictionary-based techniques. Sparse coding utilizes a vast dictionary comprising potential basis vectors. In this approach, a given vector is represented as a sparse linear combination of the vectors in the dictionary, where the combination consists of only a few non-zero coefficients. It has gained significant attention and found applications in various domains, such as data compression and image processing, within the signal processing field. This study aims to extend the utility of sparse coding by applying it to the field of time series forecasting. The suggested method employs a local learning framework, wherein the query point is expressed as a sparse linear combination of atoms from the training dictionary. This representation is subsequently utilized to forecast the target value of the query point by applying the same sparse combination to the target vectors of the training atoms in the dictionary. The paper presents an empirical investigation that evaluates different sparse coding algorithms. Specifically, experiments are conducted using data obtained from the Australia Solar Centre (DKASC). The results demonstrate the remarkable performance of the sparse methods, surpassing that of K-nearest neighbor (KNN) regression.
Uncertainty-Aware Multi-Parametric Magnetic Resonance Image Information Fusion for 3D Object Segmentation

Multi-parametric magnetic resonance (MR) imaging is an indispensable tool in the clinic. Automatic volume-of-interest segmentation based on multi-parametric MR imaging is crucial for computer-aided disease diagnosis, treatment planning, and prognosis monitoring. Despite the extensive studies conducted in deep learning-based medical image analysis, further investigations are still required to effectively exploit the information provided by different imaging parameters. How to fuse the information is a key question in this field. Here, we propose an uncertainty-aware multi-parametric MR image feature fusion method to fully exploit the information for enhanced 3D image segmentation. Uncertainties in the independent predictions of individual modalities are utilized to guide the fusion of multi-modal image features. Extensive experiments on two datasets, one for brain tissue segmentation and the other for abdominal multi-organ segmentation, have been conducted, and our proposed method achieves better segmentation performance when compared to existing models.
Graph Joint Representation Clustering via Penalized Graph Contrastive Learning

Graph clustering based on graph contrastive learning (GCL) is one of the dominant paradigms in the current graph clustering research field. However, those GCL-based methods often yield false negative samples, which can distort the learned representations and limit clustering performance. In order to alleviate this issue, we propose the idea of maintaining mutual information (MI) between the representations and the inputs to mitigate the loss of semantic information of false negative samples. We demonstrate the validity of this proposal through relevant experiments. Since maximizing MI can be approximately replaced by minimizing reconstruction error, we further propose a graph clustering method based on GCL penalized by reconstruction error, in which our carefully designed reconstruction decoder, as well as reconstruction error term, improve the clustering performance. In addition, we use a pseudo-label-guided strategy to improve the GCL process and further alleviate the problem of false negative samples. Our experiment results demonstrate the superiority and great potential of our proposed graph clustering method compared with state-of-the-art algorithms.
LDCNet: A Lightweight Multi-Scale Convolutional Neural Network Using Local Dense Connectivity for Image Recognition

Deep convolutional neural networks (DCNNs) have made great contributions to the development of computer vision. Since the trained DCNN models require a large amount of computing and storage resources to achieve high performance, it is usually difficult to deploy them on resource-limited systems. To address this problem, we propose a novel local dense connectivity (LDC) module to generate feature maps from cheap convolution operations. The LDC module constructs hierarchical and locally dense connections within a single layer. This construction promotes the reuse of features in network layers. As a result, it leads to the generation of multi-scale feature maps and increases the receptive fields for each network layer. A basic architecture block called LDCBlock was designed based on the LDC module. By stacking this kind of block, we propose a kind of lightweight and efficient multi-scale residual network named LDCNet. Moreover, to model the interdependencies between the channels of the LDC module and enhance the informativeness of diversified features, we also design a sparse Squeeze-Excitation (SE) module, which has fewer parameters and computations. Finally, the experiments based on CIFAR datasets, ImageNet dataset and a defect detection dataset demonstrate that our LDCNet achieves competitive performance compared with the state-of-the-art models focusing on model compression.
Blockchain-Based Framework for Indian Retail Market in SMEs

Conceptually, blockchain is a distributed ledger which has bought a paradigm shift in the industry with respect to decentralized and centralized aspects of regulation. In this work, blockchain is used to provide a solution for Small and Medium scale Enterprises (SMEs) and retailers who face unique challenges in the current scenario. Due to resistive factors, these retailers are suffering in isolation all across the country. We provide a solution using blockchain which can centralize the interests of retailers while decentralizing the supply chain management aspects. A careful balance of both is achieved using the proposed framework. We use smart contracts to handle the legal aspects of conducted transactions. In future, we aim to work toward more granular challenges faced by retailers and incorporate the solutions by updating the proposed smart contracts.
Wearable Wireless Sensor Network for Mitigating COVID-19 Transmission Through Physical Distancing

The global community is currently facing a critical situation with the widespread transmission of the Covid-19 virus. The rapid increase in Covid-19 cases has necessitated immediate action and treatment. As the World Health Organization (WHO) recommended, maintaining a minimum distance of one meter between individuals is an effective preventive measure to avoid transmission through respiratory droplets. To address this issue, this paper introduces a cost-effective design and implementation of a wearable wireless sensor network using the ESP32 microcontroller and Bluetooth Low-Energy (BLE) technology that promotes physical distancing and contact tracing to mitigate the spread of COVID-19. In this research, we have determined that the safe distance between individuals is 1.5 meters. Proximity testing has indicated that an RSSI value of -62 dBm corresponds to a distance below 1.5 meters. Hence, we set -62 dBm as the distance threshold between wearable devices to ensure safe physical distancing. These wearable wireless sensor network devices are intended to assist individuals in maintaining a safe distance from others, thereby reducing the risk of COVID-19 transmission.
Barcode positioning method based on line detection under non-uniform illumination

Abstract The positioning problem of the barcode is easily affected by factors such as uneven illumination and complex background. Based on the EDLines line detection algorithm, a positioning algorithm that can deal with the barcode positioning problem under non-uniform lighting conditions is proposed. First, the EDLines algorithm is used to detect the straight-line segment of the image, and the midpoint coordinates of all line segments are extracted. Since the midpoint coordinates near the barcode are relatively densely distributed, the density-based HDBSCAN point clustering algorithm is used for all midpoint coordinates for density clustering. Then a filtering strategy based on the consistency of line segment direction is proposed to preliminarily filter the clustering results, and one or more point clustering results that may belong to the barcode area are obtained. Then the RANSAC algorithm is used to perform line fitting on the point clustering results. The accuracy of the straight-line fitting results is judged by direction consistency screening, and the intersection of the fitted straight line and the straight-line segment to which the centroid belongs is calculated for the results that meet the expectations, and the width of the barcode area is obtained. Finally, the straight-line segment obtained by screening is calculated. The average length and the height of the barcode area are got, to accurately locate the barcode position. Compared with the previous algorithms on the public barcode dataset and the self-made barcode dataset, the algorithm has good positioning accuracy and running speed.
Representation of Knowledge in Taxonomies and Ontologies and their Application in Advance Analysis to Cognitive Computing

This chapter focuses on how ontologies and semantic web technology can be used in artificial intelligence or systems engineering. Technological trends imply that future digital technology approaches and tools will use AI and ML technology. Logic based reasoning and semantic modeling assist in classification, customization, and relationship detection but struggle to describe how decisions are made. Knowledge acquisition plays a vital role in using this form of AI. Ontologies are methods for mode modeling of reasoning domains required for digital fields instantiated in Digital System Models (DSM). They grow as digital twins and evolve with the physical instantiations of a DSM over time. Semantic innovations and ontologies codify knowledge of systems engineering as a prerequisite for reasoning using interoperable ontologies. This chapter explores the technologies behind advanced analytics and how they can be leveraged in a knowledge-driven cognitive environment. Advanced analytics help gaining deeper insights and predict outcomes more accurately and insightfully.
Data Visualisation and Data Analytics in HCI

This chapter uses Light Detection and Ranging (LIDAR) technology field data to assess efficient terrain visualization. There are two algorithms for detailed computer rendering. The test results for the productivity of the two these algorithms or techniques are presented in the subsequent sections. In visual-spatial perception, the assessment of the results is ultimately examined. In this chapter, the model then uses the information to select the optimal level of details (LOD) to prevent visible changes in representation. The relationship between computer processing power and mental representation is critical for understanding these cognitive processes.
HCI Learning From Cognitive Web

A cognitive framework is suggested in this article to monitor learning processes based on the combination of human-computer interaction. The observation is founded on the interaction of elements between humans and the computer. The adaptive architecture of cognitive learning is introduced for interaction between humans and machines. The authors have also chosen a topology tree as the hierarchical model of a low-dimensional educational space to perform online observations. In addition, the methodology for the BSM (coupling-manifold brain human cognitive scenario) is provided for the coupling morphism. It proposes that things be observed in a mental or learning diverse way. Finally, this chapter suggests developing new tools and implementing different functionalities integrating intelligent data analysis techniques. An area that still needs further work is the cognitive area, particularly towards helping build more accurate mental model.
GRAPH TANGGA DAN SIFAT-SIFATNYA

Ladder Graph is one type of connected and simple graph that has its own uniqueness. The aim of this research is to find the k-deficiency of points in the spanning tree in the ladder graph. Calculation of k-deficiency is carried out for each additional step, starting from the second ladder ( L2), up to the nth Ladder (Ln). Calculations are based on a representative spanning tree on each ladder. In the end, we get a number pattern from the second ladder onwards which is then proven by mathematical induction, for the calculation of each step up the ladder. In this study, the pattern of k-deficiency points on the ladder graph was obtained.