Reviewerly/exHarmony huggingface dataset:
Tradition in Transition: Technology and Change in Archaeological Visualisation Practice

Abstract Archaeologists are the mediators between fragmented, and often contested, pasts and the momentary present. To record, organise, interpret, and reconstruct complex narratives of the past and to communicate these to present-day peers and the public, they use a wide range of visualisation methods. As such, visualisation methods form an intrinsic part of the representation of practical and intellectual findings, being crucial to knowledge production in archaeology. The adoption and adaptation of digital visualisation technology changes the way archaeologists shape new knowledge. However, for a discipline that is particularly concerned with how technology had an effect on past societies, for example, the impact of the potter’s wheel on local ceramic production strategies, archaeologists have a remarkably limited awareness of how current (digital) technology has an impact on their own visualisation practice and the subsequent knowledge production. This study presents the conceptual framework “tradition in transition,” which integrates technological and visualisation methodologies, and aims to provide a framework to analyse the underlying processes and mechanisms that shape and change the practice of creating visualisations.
Emotion Class-Wise Aware Loss for Image Emotion Classification

With the increasing number of images containing rich emotional information in social media and the urgent demand for faster and more accurate image emotional information mining, some researchers have begun to pay attention to image emotion classification research. However, most of the work focuses on the complex model design, neglecting the proper consideration of the loss function, which is common in the research of image emotion classification task. Simultaneously, the widely used loss function, such as the Softmax Loss, ignores the difference in the concentration of the inner-class features in image emotion and object classification, which causes the problem of lacking inner-class feature distance converging data imbalance leading to more misclassifications of affective images. We explored the problem of inner-class feature constraints in the loss function design for image emotion classification tasks. Based on the existing loss improvement, we propose a method with the Emotion Class-wise Aware (ECWA) loss to get better accuracy and robustness on more occasions. Results show that the method we proposed is more effective in the image emotion classification task, especially in the emotion category with few samples.
Real-time Maneuver Command Generation and Tracking For a Miniature Fixed-Wing Drone with a Ducted-Fan Unit

Due to the limitations of actuators, physical components, and onboard computers, autonomously performing aggressive maneuvers is challenging for miniature fixed-wing unmanned aerial vehicles(UAVs), especially for one with a ducted fan. This paper proposed an integrated maneuver command generation and tracking control(MCGTC) scheme that enables fixed-wing UAVs to finish agile acrobatics. Our algorithm has two essential parts: 1)A maneuver command generator learned from a few demonstrations. The geometric characteristics of the pilot’s demonstrated trajectories are preserved through a new, coordinate-free invariant paradigm. 2)A tracking control structure consists of an incremental dynamic inversion(INDI) based airspeed controller and a nonlinear dynamic inversion(NDI) based attitude controller. The feasibility and effectiveness of our proposed MCGTC scheme are verified through flight tests. Using a 2.5kg fixed-wing drone with a 70mm ducted fan, we can realize an agile Immelman maneuver in the physical world.
SVIoT: A Secure Visual-IoT Framework for Smart Healthcare

The advancement of the Internet of Things (IoT) has transfigured the overlay of the physical world by superimposing digital information in various sectors, including smart cities, industry, healthcare, etc. Among the various shared information, visual data are an insensible part of smart cities, especially in healthcare. As a result, visual-IoT research is gathering momentum. In visual-IoT, visual sensors, such as cameras, collect critical multimedia information about industries, healthcare, shopping, autonomous vehicles, crowd management, etc. In healthcare, patient-related data are captured and then transmitted via insecure transmission lines. The security of this data are of paramount importance. Besides the fact that visual data requires a large bandwidth, the gap between communication and computation is an additional challenge for visual IoT system development. In this paper, we present SVIoT, a Secure Visual-IoT framework, which addresses the issues of both data security and resource constraints in IoT-based healthcare. This was achieved by proposing a novel reversible data hiding (RDH) scheme based on One Dimensional Neighborhood Mean Interpolation (ODNMI). The use of ODNMI reduces the computational complexity and storage/bandwidth requirements by 50 percent. We upscaled the original image from M × N to M ± 2N, dissimilar to conventional interpolation methods, wherein images are upscaled to 2M × 2N. We made use of an innovative mechanism, Left Data Shifting (LDS), before embedding data in the cover image. Before embedding the data, we encrypted it using an AES-128 encryption algorithm to offer additional security. The use of LDS ensures better perceptual quality at a relatively high payload. We achieved an average PSNR of 43 dB for a payload of 1.5 bpp (bits per pixel). In addition, we embedded a fragile watermark in the cover image to ensure authentication of the received content.
Practices of Using Blockchain Technology in e-Learning

The COVID-19 pandemic has led online learning mode to be extremely popular all over the world. It is conceivable that people will gradually adapt to the convenience and low-cost advantages of e-learning mode, even for a long time after the end of the pandemic. However, the e-learning mode has relatively low credibility in the traditional academic and degree certification education system. Applying blockchain technology on e-learning system can help to solve the lack of credit system in the education field. We propose a blockchain-enhanced e-learning ecosystem that can provide functions such as learning behavior recording, degree certificate verificating, courseware resource protecting, transaction book tracking, and cross-institutional recognizing of academic qualifications, which can effectively help build an online education credit system, for some major educational application scenarios, such as inter-institutional lifelong education, inter-disciplinary education, and modern vocational education system.
PLDANet: Reasonable Combination of PCA and LDA Convolutional Networks

Integrating deep learning with traditional machine learning methods is an intriguing research direction. For example, PCANet and LDANet adopts Principal Component Analysis (PCA) and Fisher Linear Discriminant Analysis (LDA) to learn convolutional kernels separately. It is not reasonable to adopt LDA to learn filter kernels in each convolutional layer, local features of images from different classes may be similar, such as background areas. Therefore, it is meaningful to adopt LDA to learn filter kernels only when all the patches carry information from the whole image. However, to our knowledge, there are no existing works that study how to combine PCA and LDA to learn convolutional kernels to achieve the best performance. In this paper, we propose the convolutional coverage theory. Furthermore, we propose the PLDANet model which adopts PCA and LDA reasonably in different convolutional layers based on the coverage theory. The experimental study has shown the effectiveness of the proposed PLDANet model.
Crowdsmelling: A preliminary study on using collective knowledge in code smells detection

Code smells are seen as major source of technical debt and, as such, should be detected and removed. However, researchers argue that the subjectiveness of the code smells detection process is a major hindrance to mitigate the problem of smells-infected code. This paper presents the results of a validation experiment for the Crowdsmelling approach proposed earlier. The latter is based on supervised machine learning techniques, where the wisdom of the crowd (of software developers) is used to collectively calibrate code smells detection algorithms, thereby lessening the subjectivity issue. In the context of three consecutive years of a Software Engineering course, a total "crowd" of around a hundred teams, with an average of three members each, classified the presence of 3 code smells (Long Method, God Class, and Feature Envy) in Java source code. These classifications were the basis of the oracles used for training six machine learning algorithms. Over one hundred models were generated and evaluated to determine which machine learning algorithms had the best performance in detecting each of the aforementioned code smells. Good performances were obtained for God Class detection (ROC= 0.896 for Naive Bayes) and Long Method detection (ROC= 0.870 for AdaBoostM1), but much lower for Feature Envy (ROC= 0.570 for Random Forrest). The results suggest that Crowdsmelling is a feasible approach for the detection of code smells. Further validation experiments based on dynamic learning are required to a comprehensive coverage of code smells to increase external validity.
Visual-Language Navigation Pretraining via Prompt-based Environmental Self-exploration

Vision-language navigation (VLN) is a challenging task due to its large searching space in the environment.To address this problem, previous works have proposed some methods of fine-tuning a large model that pretrained on large-scale datasets.However, the conventional fine-tuning methods require extra human-labeled navigation data and lack self-exploration capabilities in environments, which hinders their generalization of unseen scenes.To improve the ability of fast cross-domain adaptation, we propose Prompt-based Environmental Self-exploration (ProbES), which can self-explore the environments by sampling trajectories and automatically generates structured instructions via a large-scale cross-modal pretrained model (CLIP).Our method fully utilizes the knowledge learned from CLIP to build an in-domain dataset by self-exploration without human labeling.Unlike the conventional approach of fine-tuning, we introduce prompt-based learning to achieve fast adaptation for language embeddings, which substantially improves the learning efficiency by leveraging prior knowledge.By automatically synthesizing trajectoryinstruction pairs in any environment without human supervision and efficient prompt-based learning, our model can adapt to diverse visionlanguage navigation tasks, including VLN and REVERIE.Both qualitative and quantitative results show that our ProbES significantly improves the generalization ability of the navigation model * .
Virtual reality for ambulance simulation environment

Abstract Simulations are beneficial in evaluating clinicians’ empirical competencies through practical skills, prioritizing, and decision-making as part of patient care scenarios generally run in a full-scale physical context. However, such simulations require physical space, manufacturing, and replacement of damaged or used equipment. On the other hand, virtual reality (VR) computerized simulators are comparatively modern instruments for use in practical training. VR can be employed to simulate real-world situations without the actual need for physical devices. This work presents an ambulance patient compartment VR simulation that can be used by emergency medical services (EMS) staff to customize the configuration of the ambulance patient compartment according to their preference as well as for vehicle orientation or training purposes. The proposed simulation can be used repeatedly enabling the paramedics to access equipment in a fully immersive and safe environment. The user studies have demonstrated the usability and perceived effectiveness of the proposed simulation.
Application of Virtual Reality Technology in Mental Health Education

With the advent of the 5G era, virtual reality technology is widely used in various fields. The traditional mental health education is limited by time, space and form, so it is difficult for students to immerse themselves. VR technology can make up for the shortcomings in traditional mental health education. According to the increasingly individualized and diversified needs of students, virtual reality technology is used to construct virtual scenes and carry out virtual psychological training. For example, the training of fear of heights can enhance students' immersive experience; developing VR picture books and VR educational games can stimulate students' interest in learning; providing reference for future teaching methods can help students grow up mentally healthily. And using a questionnaire survey method to conduct a VR mental health education course expectation survey of 100 college students, and found that 88% of the students would take this course. This study will discuss the advantages of virtual reality technology in mental health education, and the specific application of VR in mental health education at this stage.
The Nexus between Top Management Support on Change Management, Cloud ERP Implementation, and Performance of SMEs

Top management support (TMS) is a critical success factor in cloud computing (CC) implementation and firm performance. However, extant empirical research on these relationships has obtained mixed findings due to earlier studies focusing on a one-dimensional TMS construct with diverse measurement items that muddle up different behaviours of top management. TMS is typically a multi-dimensional construct, such as TMS change management (TMSCM), TMS resource provision (TMSR), and TMS vision sharing (TMSV). The aim of this study is to examine a specific TMS dimension, TMSCM, on cloud ERP implementation and, further, to measure the effect of TMSCM on the financial performance of manufacturing small and medium-sized enterprises (SMEs) through the mediating effect of cloud ERP implementation. The proposed research model was validated using partial least squares structural equation modelling (PLS-SEM) with a sample of 204 Malaysian manufacturing SMEs. Results reveal that TMSCM significantly impacts cloud ERP implementation and financial performance. Moreover, cloud ERP implementation is a significant driver of financial performance, and it partially mediates the relationship between TMSCM and financial performance. Contributing to disentangling the mixed findings of TMS impact on cloud ERP implementation and firm performance, this study also generates insights for practitioners that they need to balance TMSCM and cloud ERP implementation to improve the financial performance of SMEs.&#x0D; &#x0D; Received: 17 December 2021 / Accepted: 21 February 2022 / Published: 5 May 2022
Exploiting Reasoning Chains for Multi-hop Science Question Answering

We propose a novel Chain Guided Retrieverreader (CGR) framework to model the reasoning chain for multi-hop Science Question Answering.Our framework is capable of performing explainable reasoning without the need of any corpus-specific annotations, such as the ground-truth reasoning chain, or humanannotated entity mentions.Specifically, we first generate reasoning chains from a semantic graph constructed by Abstract Meaning Representation of retrieved evidence facts.A Chain-aware loss, concerning both local and global chain information, is also designed to enable the generated chains to serve as distant supervision signals for training the retriever, where reinforcement learning is also adopted to maximize the utility of the reasoning chains.Our framework allows the retriever to capture step-by-step clues of the entire reasoning process, which is not only shown to be effective on two challenging multi-hop Science QA tasks, namely OpenBookQA and ARC-Challenge, but also favors explainability.
Survey on different methods in image compression of Brain Images

The survey of brain and medical image compression methods. Reduce the size of image as image compression. Necessity and importance of compression of an image has been discussed. Application of the lossy compression technique is multimedia data. Various compression approaches are discussed for two categories. Also brain image compression techniques are highlighted, in addition with, quantitative comparisons between different compression methods. Also advantages and disadvantages of each method are discussed.
Splicing forgery localisation using colour illumination inconsistencies

In the digital imaging era, people used to deliberately distort images or videos for fun or misleading others. Image splicing is one of the methods of manipulation by copying an image from one photograph and pasting it into another. Typically, those two photographs were captured in different environments from various image sources. In this paper, we proposed a simple statistical-based learning-free approach to reveal this type of splicing forgeries using illumination inconsistencies with the assumption that the original images may have uniform illumination. The image first segmented into irregular objects as superpixels and colour illumination is estimated for each superpixel using greyness index in rg-chromaticity space. For each pair of superpixels, the dissimilarity is then estimated. A superpixel region growing algorithm is proposed to extract automatically all the tampered superpixels to localise the spliced region without human involvement. The results of the experiment show that the proposed method effectively localises splicing forgery than the state of art.
<i>Body RemiXer</i>: Extending Bodies to Stimulate Social Connection in an Immersive Installation

Body RemiXer is a mixed reality installation that connects immersants across the virtual/actual divide through emergent tactility and abstract embodiment. Using a virtual reality headset, Kinect and projections, the installation explores the potential of immersive technology to create copresent experiences that foster intercorporeal relationships between immersants wearing a headset and those using the projections. Immersants’ bodies are at the center of the installation, activated as a site for social exchange. Body RemiXer has been exhibited at an art festival and at several smaller events. The authors’ observations during these exhibits reveal Body RemiXer's capacity to disrupt social norms and stimulate new connections.
Machine learning-based optimization of pre-symptomatic COVID-19 detection through smartwatch

Abstract Patients with weak or no symptoms accelerate the spread of COVID-19 through various mutations and require more aggressive and active means of validating the COVID-19 infection. More than 30% of patients are reported as asymptomatic infection after the delta mutation spread in Korea. It means that there is a need for a means to more actively and accurately validate the infection of the epidemic via pre-symptomatic detection, besides confirming the infection via the symptoms. Mishara et al. (Nat Biomed Eng 4, 1208–1220, 2020) reported that physiological data collected from smartwatches could be an indicator to suspect COVID-19 infection. It shows that it is possible to identify an abnormal state suspected of COVID-19 by applying an anomaly detection method for the smartwatch’s physiological data and identifying the subject’s abnormal state to be observed. This paper proposes to apply the One Class-Support Vector Machine (OC-SVM) for pre-symptomatic COVID-19 detection. We show that OC-SVM can provide better performance than the Mahalanobis distance-based method used by Mishara et al. (Nat Biomed Eng 4, 1208–1220, 2020) in three aspects: earlier (23.5–40% earlier) and more detection (13.2–19.1% relative better) and fewer false positives. As a result, we could conclude that OC-SVM using Resting Heart Rate (RHR) with 350 and 300 moving average size is the most recommended technique for COVID-19 pre-symptomatic detection based on physiological data from the smartwatch.
On the single networked links between classifications of sci-tech information. (Part 1. "The converters" between classifications. Methodological approaches to comparing classifications)

The Russian National Public Library for Science and Technology has been developing the single network of links between the classifications of various types of scientific subjects with the core GRNTI (State Rubricator of Sci-tech In-formation, The Rubricator) as the backbone classifier of the State System of Sci-tech Information (GSNTI). The key universal approaches toward science data rep-resentation to support compatibility and integration of information resources classified within different systems are defined. The procedure for building the network of matching headings and subjects in sci-tech classifications is described. Based on intellectual analysis method and the principle of classification synonymy through semantic comparison of hierarchy branches and conceptual comparison of subject numbers of individual thematic headings, the tables of interreflections of the Rubricator headings and codes in other classifications are generated. The UDC and Rubricator are matched on the first level of the Rubricator classes. Totally, 144 semantic matches for the Rubricator thematic classes are found. Matching the Rubricator to HAC classifier (Higher Attestation Commission), OECD international classifier, WoS subject domains and categories is accomplished for all three Rubricator levels. Through matching the Rubricator and HAC classifier, approx. 7,000 matches are identified; the Rubricator and OECD classifier – about 11,500 matches, and the Rubricator and WoS classification – over 18,000 match-es identified.
A hybrid method based on semi-supervised learning for relation extraction in Chinese EMRs

Building a large-scale medical knowledge graphs needs to automatically extract the relations between entities from electronic medical records (EMRs) . The main challenges are the scarcity of available labeled corpus and the identification of complexity semantic relations in text of Chinese EMRs. A hybrid method based on semi-supervised learning is proposed to extract the medical entity relations from small-scale complex Chinese EMRs.The semantic features of sentences are extracted by a residual network and the long dependent information is captured by bidirectional gated recurrent unit. Then the attention mechanism is used to assign weights for the extracted features respectively, and the output of two attention mechanisms is integrated for relation prediction. We adjusted the training process with manually annotated small-scale relational corpus and bootstrapping semi-supervised learning algorithm, and continuously expanded the datasets during the training process.We constructed a small corpus of Chinese EMRs relation extraction based on the EMR datasets released at the China Conference on Knowledge Graph and Semantic Computing. The experimental results show that the best F1-score of the proposed method on the overall relation categories reaches 89.78%, which is 13.07% higher than the baseline CNN.