Reviewerly/exHarmony huggingface dataset:
The Present and Future Tasks of Community-Linked Education for Liberal Arts Education in College

The purpose of this study is to examine the characteristics and research results of community-linked education models, which have been receiving intensive attention in recent years due to the transition to competency-based education. This study also aims to discover the future directions and/or tasks for enhancing the effectiveness of community-linked education as liberal arts education. For this, firstly this study reviewed the literature review pertaining to the college-community linkage types and community-linked education models for liberal arts education. Secondly, this study conducted comparison analysis between 5 community-linked education models such as club activities, volunteer activities, out of class experiences, service-related subjects, and service learning, in order to derive implications for future community-linked education. As a result, the following 5 directions or tasks were proposed: First, in-class activities and community-related volunteer activities should be meaningfully connected through the subjects and students’research to increase the effectiveness of community-linked education. Second, elaborated learning models for community-linked education should be developed to maximize the improvement of the learners' core competencies and other educational effects through the curriculum or subject. Third, effective teaching models and strategies for operating this course should be specified, and textbooks and manuals should be developed and shared among the faculty. Fourth, a dedicated body, which is responsible for building networks for regional communities, and supporting and connecting faculty who facilitate this course, should be established and operated. Fifth, only highly responsible faculty members should be designated for this subject, so that they can devote themselves to this subject’s continuous improvement. Lastly, more research on community-linked education should be carried out and the results should be shared to establish a nationwide college-community-local government cooperative system, which improve not only students’ competency, but also community’s problem solving capacity.
Multi-modal Navigation Interaction Recommendation with a Driver Demand-Based Knowledge Graph

The personalized human-vehicle interaction system provides the driver with a way of interacting that suits them, allowing them to interact with the car system without interfering with normal driving activities, which is essential for driving safety. In general, drivers’ preferences or needs vary depending on the person, the scenario, and the task of the trip. This article takes navigation, a high-frequency human-vehicle interaction, as an example, and constructs a Driver demand Knowledge Graph (DKG) by using multi-modal and hierarchical thinking that focuses on the driver’s needs and consideration of multiple fine-grained factors. Ultimately, a multi-modal navigation interaction recommendation method through knowledge graphs is proposed, which represents nodes from multiple aspects and extracts a multi-relational graph structure for DKG.
Digital Twin for Immersive Exhibition Space Design

This research aimed to find an efficient method of responding to the various variables that may arise during the process of designing and constructing an immersive exhibition space. A digital twin of the exhibition space was generated, a real-time rendering method was applied using a game engine, and the optical phenomena of several typical devices used in immersive exhibitions were simulated. The functions simulated in this study are applicable to the generated digital twin to predict how the various devices installed in the exhibition space would operate in the actual exhibition. Thereby, the possibility that the simulation results of the core elements that constitute the exhibition content be immediately applied to the operation of the various devices in the exhibition space in real-time could be explored. Not all the devices and functions used in an immersive exhibition were simulated, so the model used in the paper must be modified according to the circumstances of the applications. Through additional research, the method of responding to various installations and devices must be expanded.
Dynamic Attributed Graph Prediction with Conditional Normalizing Flows

Graph representation learning aims at preserving structural and attributed information in latent representations. It has been studied mostly in the setting of static graph. In this work, we propose a novel approach for representation learning over dynamic attributed graph using the tool of normalizing flows for exact density estimation. Our approach has three components: (1) a time-aware graph neural component for aggregating graph information at each time step, (2) an adapted graph recurrent component for updating graph temporal contexts, and (3) a conditional normalizing flows component for capturing the evolution of node representations in latent space along time. Particularly, the third component has two sub-models of normalizing flows. One is used to capture the distribution of node representations of arbitrary complexity by considering graph temporal contexts as conditions. It learns invertible transformations to map node representations into simple priors conditioning on temporal contexts. The other one is dedicated to capture the evolutionary patterns of prior distributions. Extensive experiments demonstrate the proposed approach can outperform competitive baselines by a significant margin for dynamic link prediction on future graphs.
Adaptive Neuro-Fuzzy Model for Vehicle Theft Prediction and Recovery

Vehicle theft is continuously being reported as a global prevalent crime and the traditional mode of combating vehicle theft is faced with abnormalities hindering accurate, timely prediction and recovery of stolen vehicles from criminals. In this paper, we use Adaptive Neuro-Fuzzy Inference System (ANFIS) - a computational Artificial Intelligence (AI) technique to develop a model for minimizing investigation time and the number of deployed security operatives towards achieving a high success rate in the prediction, detection and recovery of stolen vehicles. A collection of vehicle theft and recovery data for (6) six consecutive years with fourteen (14) attributes collated by the Criminal Investigation Department of the Nigeria Police Force, Abeokuta, Ogun state were further analyzed through Dimensionality Reduction technique and Routine Activity Theory (RAT) approach to extract the most significant features. Datasets were sub-divided into 60%, 20% and 20% for training, testing and validating the model respectively. A significant result of 92.91% obtained with the Adaptive Neuro-Fuzzy Inference System (ANFIS) model showed that it is most efficient in predicting, detecting and recovering stolen vehicles as compared with other machine learning algorithms such as Random Tree, Naïve Bayes, J48 and Decision Rule of prediction accuracies of 86.51%, 71.24%, 67.68% and 55.73% respectively.
Efficient Semantic Web Services Development Approaches using REST and JSON

REST has an architectural set of postulates for composing web services emphasizing system resources and their resource states followed by their inscription and transferral over HTTP. REST architecture and JSON technologies have been extensively implemented in various web service domains - creating Knowledge Graphs, sending, and receiving requests over HTTP Protocol, Healthcare Information Management Web Applications, SPARQL-based Semantic Web Service, Mobile Application for universities, and so on. This report will focus and elaborate on the myriad of RESTful web service APIs designed, intricately discussing the working and execution leading to the functioning of the web services deployed. Furthermore, the report brushes on the relation of how the web along with the combination of RESTful web services becomes a novel software connector enabling coordination of distributed, stateful and autonomous software services and lastly, critically analyzing recommendations of emerging technologies which will subsequently support the development of said RESTful web services.
Context-Aware Augmented Reality Using Human–Computer Interaction Models

Augmented Reality is a technique that allows users to overlap digital information with their physical world. The Augmented Reality (AR) displays have an exceptional characteristic from the Human–Computer Interaction (HCI) perspective. Due to its increasing popularity and application in diverse domains, increasing user-friendliness and AR usage are critical. Context-aware is one approach since an AR application can adapt to the user, environment, needs and enhance ergonomic principles and functionality. This paper proposes the Intelligent Contextaware Augmented Reality Model (ICAARM) for Human–Computer Interaction systems. This study explores and reduces interaction uncertainty by semantically modeling user-specific interaction with context, allowing personalised interaction. Sensory information is captured from an AR device to understand user interactions and context. These depictions carry semantics to Augmented Reality applications about the user's intention to interact with a specific device affordance. Thus, this study describes personalised gesture interaction in VR/AR applications for immersive/intelligent environments.
Multi-granularity context semantic fusion model for Chinese event detection

Event detection is a key task in the field of information extraction, which is widely used in knowledge mapping, intelligent question answering, reading comprehension and other fields. Event detection model based on deep learning commomly regards event detection as a word classification task, but in Chinese, a language without natural separators, the error of word segmentation will lead to the mismatch between word and event trigger. In addition, the polysemy of Chinese words can make a word ambiguous in different contexts. In this paper, we propose a Chinese event detection model based on multi granularity context semantic fusion. Firstly, the semantic information in different word segmentation results is obtained through the Character-Word fusion gate mechanism to solve the problem of mismatch between Chinese words and event triggers. Then, the Character-Sentence fusion gate is designed to learn the semantic information of the whole sequence, and the self-attention mechanism is used to integrate the contextual semantic information to eliminate the ambiguity of Chinese words. Experiments on ACE2005 dataset show that our method can achieve better experimental results than the current mainstream Chinese event detection model.
Data Structures in XQuery

Complex algorithms need data structures that can be “updated”updated effectively. Here are some techniques and tips for using XQuery 3.1 maps for that purpose.
Systematic method for evaluating the performance of three-dimensional optical scanners by structured light projection applied to ballistic vests tests

Considering the recent use of three-dimensional digitalization equipment in ballistic vest tests to characterize trauma caused by projectiles, this study carried out a performance analysis of a 3D structured light scanner to measure trauma depths. Artifacts were manufactured and digitized by a 3D scanner and by an articulated arm coordinate measuring machine, which provided the reference values. A process was developed for estimating the depth from the point clouds, one of the main contributions of this work. Filtering and segmentation of the point clouds allowed the extraction of the trauma depths for later comparison. The systematic errors reported in the literature for structured light scanning equipment were confirmed, and it was statistically verified that the critical trauma measurement values are correctly measured, with a bias of 0.11 mm and standard measurement uncertainty of 0.12 mm. Finally, a real ballistic vest test shows the applicability in a practical scenario.
Improving transformer model translation for low resource South African languages using BERT

Language pre-training methods such as BERT have led to significant performance gains for a wide range of natural language processing tasks. This paper investigates the improvement in transformer model translation for low resource South African languages when pre-trained models are used in language translation. A South African low resource language multilingual BERT, dubbed SALR-mBERT is pre-trained from scratch using all nine of the low resource South African languages. SALR-mBERT performs better than mBERT and the transformer models for language corpora with approximately less than 600000 sentences. SALR-mBERT obtained the best results for Sesotho, Ndebele, Swati and Venda. SALR-mBERT outperforms multilingual BERT (mBERT) on language corpora with less than 600000 sentences by about 29% and also outperforms the transformer model by 50%. The transformer model translation obtained the best results compared to mBERT and the SALR-mBERT models for language corpora with more than 600000 sentences. The transformer obtained the best results followed by SALR-mBERT for Northern Sotho, Tsonga, Xhosa, Tswana and Zulu to English translations. The transformer model outperforms SALR-mBERT by 15% for Zulu which has the largest corpus size compared to other low resource South African languages. The main finding of this work is that the BERT type module is beneficial for machine translation if the corpus size is small and has less than approximately 600000 sentences, and further improvement can be gained when the BERT model is trained using languages of a similar nature like in the case of SALR-mBERT. However, transformer model translations outperform BERT model translations if the corpus size is greater than 600000 sentences.
Distributed Ensemble Feature Selection Framework for High-Dimensional and High-Skewed Imbalanced Big Dataset

The class-imbalance problem emerges when the class labels of a dataset have a skewed distribution. In this circumstance, the instances belonging to one class, which is exactly the principal purpose, are dominated thoroughly by the instances belonging to other classes. In recent years, feature selection for high-dimensional imbalanced data has become attraction research scope. This technique concerns selecting an informative feature set to improve the accuracy of the classification model. Moreover, as a subcategory of feature selection, the feature ranking technique has been deliberated to cope with high -dimensional datasets in the last decade. On the one hand, most traditional feature selection methods are not scalable, which is critical to cope with large-scale datasets. On the other hand, scalability is an intrinsic characteristic of the ensemble learning approach. This paper proposes a Distributed Ensemble Imbalanced feature selection framework, called DEIM, to deal with big imbalanced datasets. The DEIM, at first, transforms default data partitions to representative partitions in a single pass. Second, it applies a feature ranking method in a bagging approach upon each partition independently. Finally, It fuses intermediate feature rankings in a stacking strategy. In this paper, two traditional feature ranking algorithms, ReliefF and QPFS, are plugged into DEIM. Therefore, two methods DEIM-Relief and DEIM-QPFS, are produced. Experiments are accomplished on three big imbalanced datasets and upon a computer cluster. The empirical study depicts that the produced methods are scalable. Also, they have lower execution times, and their final results can induce better classification models than DiReliefF and DQPFS.
Multilevel controller‐assisted intrinsically modified ant colony optimization heuristic‐based load‐balancing model for mega cloud infrastructures

Summary The exponential rise in decentralized computing demands has made cloud computing an inevitable technical need; however, under exceedingly high load dynamism, ensuring service level agreement (SLA) has remained a challenge. Despite of the fact that heuristic‐driven VM migration or load‐balancing methods are effective towards quality of service (QoS) and reliable cloud services, the major existing solutions ignore the issues of local minima and convergence and hence undergo SLA violation (SLAV). Moreover, most of the existing methods are designed as a reactive scheduling concept that often results into delayed scheduling and hence SLAV or QoS compromise. To alleviate such issues, in this paper, a robust intrinsically modified evolutionary computing‐driven dynamic load‐balancing model (intrinsically modified ant colony system [IMACS]) is proposed for mega cloud infrastructures. To address aforesaid issues, IMACS employs stochastic prediction‐based overload detection, followed by SLA‐sensitive VM selection and IMACS for load balancing. In IMACS, the use of stochastic prediction‐based overload detection method enabled timely hotspot detection and hence improved proactive decision ability. Moreover, it applies minimum migration time‐sensitive VM selection concept that too helped in guaranteeing load balancing without undergoing downtime or SLAV. To improve VM migration, unlike classical heuristic methods, we designed IMACS with multitype population, adaptive pheromone update, adaptive pheromone diffusion, and coevolution concept, which not only helped in reducing the search space and allied computation but also enabled time‐efficient scheduling decision. The overall proposed model was realized in sync with two controllers named local controller and global controller. Here, the first controller collects the real‐time VM and VM manager (VMM) information which is used to detect overloaded host and the VM to be migrated. While the global controller is designed with IMACS to exploit the information from local controller to execute QoS‐sensitive VM migration. The proposed IMACS‐driven load‐balancing model was realized with CloudSim platform were simulating it over 800 number of independent VMs, each encompassing autonomous task, the performance was analyzed in terms of SLAV, number of migrations, SLA per active host, and energy consumption. Simulation results revealed that the proposed model exhibits 20% higher migration, while retaining almost 19% better SLA in comparison to the other state‐of‐art methods like genetic algorithm or classical ant colony system‐based load‐balancing methods. IMACS exhibited 30% higher host shutdown signifying better energy efficiency (15% lower power consumption).
Exploring Reflective Limitation of Behavior Cloning in Autonomous Vehicles

To become a standard part of our daily lives, autonomous vehicles must ensure human safety. This safety comes from knowing what will happen in the future. The most common approach in state-of-the-art methods for sensorimotor driving is behavior cloning. These models struggle to anticipate what will happen in the near future to better plan their actions. Humans do so by first observing what objects are present in the environment, and by studying their type and history, they can predict how they may evolve in the near future. Based on this observation, we first demonstrate the limitation of behavior cloning in making safe and reliable decisions. Then, we propose a hierarchical approach to teach an agent how to make safer decisions based on the plausible future. The key idea is instead of hand-picking future features we integrate a high-dimensional prediction module such as predicting future RGB/semantically segmented frames into our model to allow the model to learn the required features by itself. In the end, we demonstrate qualitatively and quantitatively that this approach yields safer decisions by the agent.
UGV Path Planning based on an Improved Continuous Ant Colony Optimisation Algorithm

Path planning has always been an essential component of autonomy for Unmanned Ground Vehicles. In this paper, we present an improved continuous ant colony optimisation algorithm with differential evolution operator and local search, namely LIACODE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> , to solve the path planning problem with improved accuracy. A transformed coordinate system is introduced, based on which a solution repair method is presented to accelerate the convergence speed and facilitate the search process for the most feasible and optimal path. Experiments are conducted in both abstract and physical environments to compare LIACODE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> to classical path planning algorithms such as A*, D* and other state-of-the-art swarm optimisation algorithms. The superior performance of LIACODE <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</inf> in terms of finding feasible path with lower cost are validated.
A Naive and Semantic Approach for Detecting Face Mask Region Based Convolutional Neural Networks (R-CNN)

Due to Covid-19, most of the health care organizations and governments has ordered their citizens to wear face mask to protect themself. This novel research presents a tactical methodology for rapid detection whether a person is wearing a face mask or not. It is entirely different from the existing system; the singular aim of the proposed system is to train the deep learning model with a minimum number of image samples and to operate face mask instance segmentation and along with object box detection. In this work, we proposed a novel and semantic pixel-to-pixel region based deep network which can detect no of face mask instances in different categories pixel wise to organize the segment bounding box and the confidence of the various categories for each pixel. This system experimental output demonstrate that this approach can effectively and precisely detect the face mask with multi-feature combination. It is also reported that our application performance outperforms the existing system.
Outlier-Robust Multi-View Subspace Clustering with Prior Constraints

Data may have multiple modalities, known as multi-view data. With the assumption that multi-view data often lie on a latent subspace, multi-view subspace clustering finds the underlying subspace by leveraging multiple views and clusters the data accordingly. Due to inevitable system errors, multi-view data may contain outliers and it may not therefore strictly follow subspace structure. Besides, prior information such as pairwise constraints describing relations between data instances is often available. These constraints provide a valuable guide on learning. Unfortunately, standard multi-view subspace clustering methods do not simultaneously exploit high order correlations among views and prior constraints with low computational complexity. In this paper, we propose a novel Robust Multi-View Subspace Clustering method, named as RMVSC, which is capable of taking advantage of high order correlations among views and prior constraints for outlier-robust multi-view subspace clustering with low computational complexity. The key idea is to use a low-rank tensor along with a constraint to integrate information from views and prior constraints for more comprehensive learning. We regard underlying clean subspace of singular vectors of views (leveraging views) which also represent projection coefficient of cluster membership vectors in data space (utilizing prior constraints) as a tensor. By decomposing singular vector of each view into its underlying clean subspace and a structured-sparse error (outlier) term, we characterize outliers explicitly. To solve the challenging optimization problem, we develop an algorithm based on Augmented Lagrangian Multiplier. Experimental results on real-world datasets show the superiority of the proposed method and its robustness against outliers.