Reviewerly/exHarmony huggingface dataset:
Pedagogical Intelligence in Virtual Reality Environments

With the availability of virtual reality, new possibilities of learning are created. However, intelligent pedagogical support is needed to engage the learners and add to the effectiveness of learning. Varying types of artificial intelligence could be added, but attaining pedagogical goals is essential. The pedagogical-aware utilization of such intelligence in an immersive rich environment meets the concept of intelligent pedagogical agents. The chapter sheds light on the concept of pedagogical intelligence and intelligent pedagogical agents from different angles in a way that shapes its requirements and expectations in the current state of the art and in the future given eruptive surrounding technologies and methods. The chapter discusses various forms of intelligence for education in virtual learning environments, illustrates expandable learning scenarios with intelligent pedagogical agent in an immersive environment. Supporting pedagogical-aware intelligence is given through discussed supporting models.
The Use of Augmented Reality as an Aid in the Perception and Learning of Human Body Anatomical Structures by Students of Medical Schools

The study of the human body and human anatomy has great importance for future physicians. The use of cadavers to study the body has been one of the most traditional ways to learn about this subject, however it involves several practical, ethical and moral issues. In this work, an integrative literature review was carried out on the use of augmented reality for learning and understanding the human body in the field of medicine. This literature review has the following research question: How is Augmented Reality transforming learning and facilitating understanding about the human body in the field of medicine? And was adopted a methodology that encompasses the following steps: definition of the scope of the work and formulation of the research question of the review (stated above); establishment of exclusion and inclusion criteria for literature search in the databases; identification of pre-selected studies with a careful reading of its titles, abstracts and keywords in order to verify their adequacy to the exclusion and inclusion criteria; categorization of studies in order to summarize and document the information extracted from the papers; and finally, the analysis and interpretation of the data obtained with the papers. A lot of research is being done about the subject and augmented reality is changing the traditional way that students learning human anatomy—books, usage of guinea pigs and cadavers are being replaced by simulations, avatars and different devices. Many studies still have shown that augmented reality appears to have greater learning potential than traditional methods. The knowledge retention is improved when students are actively involved in their learning experience. Active, problem-oriented learning stimulates students’ interest and helps to solve problems. In the field of medicine, critical thinking and long-term knowledge retention are very important for identifying anatomically the structures of the body. So, educational tools enhanced by technology, in special Augmented Reality, can greatly enhance student involvement and the learning of human anatomy.
Towards a Recommender System Based on Community Detection and Performed Activities in the Context of Social Learning

Social online learning is gaining prominence with the emergence of social media and networks. In the presence of recommendation systems, the learner has access to several educational contents and elements proposed according to his interaction, profile, dynamics and other parameters. Our proposal revolves around an intelligent recommender system for generating recommendations in social learning environments, in particular social networks, based on the learner's activities, falling into the category of hybrid recommender systems and integrating the concept of community detection in the generation of recommendations. The proposed global approach is composed of several recommender systems: the correlation and co-occurrence based recommender system aims at measuring the degree of connectivity between learners' activities in order to generate relevant recommendations in the light of learners' implicit feedback, the community detection based recommender system through friendships. This system is an important part of our overall approach as it combines the hybrid recommendation system with the community detection based on friendship links between learners and the recommendation system based on the degree of interactivity of learners with the available learning objects. This system also consists in detecting communities, which group learners with similar preferences and orientations. The results obtained prove the potential of combining the two notions of connectivity and the integration of community detection based on several indicators to produce more relevant recommendations, while offering recommendations by group of learners by applying the concept of continuous improvement.
Use of Instructional Design, Instructional Strategy, and Learning Theory in Mobile Learning Application Development

Nowadays, with the advancement of technology, mobile learning is becoming increasingly popular in higher education learning and teaching systems. However, the issue of integration between technical and pedagogical factors in mobile learning applications remains one of the main concerns for researchers. The aim of this systematic literature review was to investigate mobile application development, especially in light of pedagogical aspects in the context of mobile learning applications, such as instructional design, instructional strategy, and learning theory. The method used in this study was Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) involving two databases, namely Scopus and Web of Science (WOS). The study found 19 articles relevant to the objectives of the study. The main findings showed the variety of instructional design models used in mobile learning application development, with the most common model being the ADDIE model. However, most studies neither adopt any instructional strategies nor state the learning theories in their mobile application. While instructional design models and instruction strategies provide systematic procedures for developing mobile applications for educational contexts and the organisation of learning activities in mobile applications, learning theory is fundamental to both. This indicates that pedagogical aspects and their integration with technological aspects in mobile applications are still overlooked, especially in the learning contexts where the studies have been conducted. Several recommendations were proposed, namely to conduct reviews in more databases, to research on how to implement pedagogical aspects in mobile learning applications, and to assess the impact of mobile applications on students’ learning. The study concludes that instructors need specific interventions to increase their level of teaching expertise in Technology, Pedagogy, and Content (TPACK) as instructional designers so that they will be able to design learning materials effectively tailored to their students’ needs.
Augmented Reality as a Teaching Resource in Higher Education

The motivation of the students is an essential component in their good performance and in the assimilation of the contents of the various disciplines they receive during their career. Teachers, in order to awaken the interest of students and as part of their methodological work, constantly seek new didactic resources that enrich the teaching–learning experience. Augmented Reality, as a didactic resource, has contributed to awakening true interest among students. This statement has been corroborated by different international studies that show high levels of satisfaction when students use this technology, in addition to the significant increase in levels of motivation when students are immersed in training activities with it. Along with motivation, it has been shown that the use of Augmented Reality enhances more motivating, collaborative and interactive training scenarios and helps a more open and creative education. Therefore, the objective of this research was to increase the motivation of the students, as well as the training of competences in the Computer Architecture course of the Computer Engineering career through a teaching aid that made use of Augmented Reality. This tool was applied to the second year of the aforementioned degree and after a comparative study of the results obtained by the students during 3 academic years, including the current one, increases in motivation and appropriation of competences were evidenced, expressed in the evaluations of performance obtained.
Multi-Class Positive and Unlabeled Learning for High Dimensional Data Based on Outlier Detection in a Low Dimensional Embedding Space

Positive and unlabeled (PU) learning targets a binary classifier on labeled positive data and unlabeled data containing data samples of positive and unknown negative classes, whereas multi-class positive and unlabeled (MPU) learning aims to learn a multi-class classifier assuming labeled data from multiple positive classes. In this paper, we propose a two-step approach for MPU learning on high dimensional data. In the first step, negative samples are selected from unlabeled data using an ensemble of k-nearest neighbors-based outlier detection models in a low dimensional space which is embedded by a linear discriminant function. We present an approach for binary prediction which determines whether a data sample is a negative data sample. In the second step, the linear discriminant function is optimized on the labeled positive data and negative samples selected in the first step. It alternates between updating the parameters of the linear discriminant function and selecting reliable negative samples by detecting outliers in a low-dimensional space. Experimental results using high dimensional text data demonstrate the high performance of the proposed MPU learning method.
Learning Balance Feature for Object Detection

In the field of studying scale variation, the Feature Pyramid Network (FPN) replaces the image pyramid and has become one of the most popular object detection methods for detecting multi-scale objects. State-of-the-art methods have FPN inserted into a pipeline between the backbone and the detection head to enable shallow features with more semantic information. However, FPN is insufficient for object detection on various scales, especially for small-scale object detection. One of the reasons is that the features are extracted at different network depths, which introduces gaps between features. That is, as the network becomes deeper and deeper, the high-level features have more semantics but less content description. This paper proposes a new method that includes a multi-scale receptive fields extraction module, a feature constructor module, and an attention module to improve the detection efficiency of FPN for objects of various scales and to bridge the gap in content description and semantics between different layers. Together, these three modules make the detector capable of selecting the most suitable feature for objects. Especially for the attention module, this paper chooses to use a parallel structure to simultaneously extract channel and spatial attention from the same features. When we use Adopting Adaptive Training Sample Selection (ATSS) and FreeAnchor as the baseline and ResNet50 as the backbone, the experimental results on the MS COCO dataset show that our algorithm can enhance the mean average precision (mAP) by 3.7% and 2.4% compared to FPN, respectively.
LanCeX: A Versatile and Lightweight Defense Method against Condensed Adversarial Attacks in Image and Audio Recognition

Convolutional Neural Networks (CNNs) are widely deployed in various embedded recognition applications. However, they demonstrate a considerable vulnerability to adversarial attacks, which leverage the well-designed perturbations to mislead the recognition results. Recently, for easier perturbation injection and higher attack effectiveness, the adversarial perturbations have been concentrated into a small area with various types and different data modalities. When defending such condensed adversarial attacks on the embedded recognition scenarios, most of the existing defense works highlight two critical issues. First, they are particularly designed for each individual condensed attack scenario, lacking enough versatility to accommodate attacks with different data modalities. Second, they rely on computation-intensive preprocessing techniques, which is impractical for time-sensitive embedded recognition scenarios. In this article, we propose LanCeX –a versatile and lightweight CNN defense solution against condensed adversarial attacks. By examining the CNN’s intrinsic vulnerability, we first identify the common attacking mechanism behind condensed adversarial attacks across different data modalities. Based on this mechanism, LanCeX can defend against various condensed attacks with the optimal computation workload in different recognition scenarios. Experiments show that LanCeX can achieve an average 91%, 85%, and 90% detection success rate and optimal adversarial mitigation performance in three recognition scenarios, respectively: image classification, object detection, and audio recognition. Moreover, LanCeX is at most 3× faster compared with the state-of-the-art defense methods, making it feasible to use with resource-constrained embedded systems.
Thoughts for the Knowledgebase

Library knowledgebases (kbs) have improved generation by generation, product by product over the last three or four decades. Functionality, completeness, integration with other library systems, and the standardization of useful data elements have all increased particularly via the Knowledgebases and Related Tools (KBART) Recommended Practice (RP), KBART Automation RP, and the upcoming NISO recommendation for package identifiers. However, several more fields and limiters would greatly enhance kbs for staff users. These include additional date fields, indicators for consortia- or region-specific packages, curator/responsible party, and more.
Semi-supervised Multi-source Domain Adaptation in Wearable Activity Recognition

The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.
Research on Text-based Q&amp;A System Technology for Medical Aesthetic Field

Medical aesthetics is the process of re-homing the shape of various parts of the body using medical means in order to meet the subjective definition of beauty. A text-based question and answer system in the field of medical aesthetics can provide quick answers to patients’ various medical search needs and help them to consult online and get professional explanations of their medical aesthetic problems. We propose the GPT2 Medchat model as an important part of the system and need to set up the following aspects to complete the system: 1. We prepare a new dataset in the medical aesthetics domain, which covers a wide range of questions and answers with strong professionalism. 2. In the pre-training phase, high-quality dialogue texts in the medical aesthetics domain are used as training materials. The system is built based on the pre-training phase. The medical text is used as training material, and the GPT2 Medchat model is trained and fine-tuned by relying on the pkuseg professional medical model to segment the medical text. The optimized segmentation results are used as a medical dictionary. 3. Sampling strategy to ensure the quality and diversity of the quizzes under the specified medical aesthetic domain questions. Both objective and subjective evaluations show that the GPT2 Medchat model has achieved a high practical and reference value for quizzing in the medical aesthetics domain.
OSHA: A General-purpose and Next Generation One-way Secure Hash Algorithm

Secure hash functions are widely used in cryptographic algorithms to secure against diverse attacks. A one-way secure hash function is used in the various research fields to secure, for instance, blockchain. Notably, most of the hash functions provide security based on static parameters and publicly known operations. Consequently, it becomes easier to attack by the attackers because all parameters and operations are predefined. The publicly known parameters and predefined operations make the oracle regenerate the key even though it is a one-way secure hash function. Moreover, the sensitive data is mixed with the predefined constant where an oracle may find a way to discover the key. To address the above issues, we propose a novel one-way secure hash algorithm, OSHA for short, to protect sensitive data against attackers. OSHA depends on a pseudo-random number generator to generate a hash value. Particularly, OSHA mixes multiple pseudo-random numbers to produce a secure hash value. Furthermore, OSHA uses dynamic parameters, which is difficult for adversaries to guess. Unlike conventional secure hash algorithms, OSHA does not depend on fixed constants. It replaces the fixed constant with the pseudo-random numbers. Also, the input message is not mixed with the pseudo-random numbers; hence, there is no way to recover and reverse the process for the adversaries.
Research on A Classification Algorithm of the Power System Related Objects Based on YOLOv4

Recently, drones have been widely used in the power system. However, interpreting the image data of drones through the traditional human-based method is time-consuming, labor-intensive, and inefficient. Thus, in this paper, an innovative method based on deep learning for identification and classification of objects related to power system is proposed, which has achieved the satisfied result. Experiment in this paper shows that the data collected from drones could be processed easily and automatically, besides, the proposed algorithm greatly improves the work efficiency of drone inspections and saves inspection time. Therefore, people can extract and identify building signs effectively with the proposed method in this paper.
Construction and Application of the Knowledge Graph in Endangered Plants

The current network information of endangered plants is scattered, making acquiring and reusing relevant knowledge difficult. The endangered plants' knowledge graph ePlantKG constructed in this paper can form a visual semantic information network. The data sources in this paper include China Rare and Endangered Plant Information Network, Baidu Encyclopedia, Wikipedia, and Kuaiming Encyclopedia. Firstly, we use a Python crawler to obtain network data and preprocess them. Then we use the obtained structured data combined with previously constructed plant ontology to define and build new ontologies. Next, through the rule-based method, we extract triples from semi-structured and unstructured data, fuse knowledge between heterogeneous data, and store them in the Neo4j database to form ePlantKG. Finally, we design and build a knowledge service platform to illustrate knowledge graphs and intelligent question answering. The intelligent question answering algorithm extracts features from the user's input text with TF-IDF and classifies questions with Naive Bayes. After realizing the similarity matching between entities and relations, we retrieve answers with the returned Cypher statement. The ePlantKG records 1926 species of endangered plants and 37860 species of common plants, and the image filling rate of endangered plants is more than 99 %. The platform implements several functions, e.g., graph display, entity recognition, and intelligent question answering. This paper realizes the information sharing and reuses on endangered plants, providing a method reference for applying knowledge graph in forestry intelligent question answering system and forestry big data analysis.
A Blockchain-driven Elastic Firmware Deployment Platform for TR-069 Compatible Appliances

The computing capability of the embedded systems and bandwidth of the home network increase rapidly due to the rapid development of information and communication technologies. Many home appliances such as TVs, refrigerators, or air conditioners are now connected to the internet, then, the controlling firmware modules are automatically updatable via the network. TR-069 is a widely adopted standard for automatic appliance management and firmware update. Maintaining a TR-069 network usually involves the design and deployment of the overall security and trust infrastructure, the update file repository and the update audit mechanisms. Thus, maintaining a dedicated TR-069 network is a heavy burden for the vendors of home appliances. Blockchain is an emerging technology that provides a secure and trust infrastructure based on distributed consensus. This paper reports the results of our initial attempt to design a prototype of a multitenant TR-069 platform based on the blockchain. The core idea is to reify each automatic deployment task as a smart contract instance whose transactions are recorded in the append-only distributed ledger and verified by the peers. Also, the overall design should be transparent to the original TR-069 entities. We have built a prototype based on the proposed architecture to verify the feasibility in three key scenarios. The experimental results show that the proposed approach is feasible and is able to scale linearly in proportion to the number of managed devices.