Reviewerly/exHarmony huggingface dataset:
The Digital Teacher in a Mobile and Always-on World

This chapter focuses on the question of how the education system has to change to meet the needs of learners in a connected and always-on world. Teachers of the future need to function in a digital world where learners are already comfortable using technologies and are always connected. They will become digital teachers using technology to design, deliver and support learners regardless of the learners’ location, status or education level. The power of the digital technology will allow the digital teacher to cater for learners’ individual needs and allow access to learning materials from anywhere and anytime for lifelong learning. Also, their role will change from teaching to facilitating. However, for these to take place, the education system must be transformed to integrate the use of digital technology for teaching and learning. Teachers should view the use of digital technology as a way to reach the students who were unreachable in the old education system and to provide education for all. The use of emerging digital technologies will meet the needs of the current and upcoming generations of students who are comfortable using technologies. More research is needed to develop digital learning technologies that are seamless for teachers to be an effective facilitator of learning. With emerging technologies and artificial intelligence, the education system will become smarter to address individual learner’s needs so that education can be provided to all.
Analysis of the rock stratum in a mining area in China with virtual reality technology

Virtual reality (VR) technology can promote the development of mining activities. A mining area in Henan, China, was taken as an example, and a VR system of the mining area was established through the VR-Platform software. Mechanical data of the rock stratum in the mining area were analysed with a rock-fracture-process analysis system. The fracture of the mine roadway was taken as an example to analyse the rock stratum fracture of a circular roadway, a rectangular roadway and the roadway support. It was found that roadways with different shapes had different fracture situations. The cracks occurred randomly, and a closed or non-closed near-annular fracture zone might appear. The roadway support could effectively reduce the fracture of the rock stratum and improve the stability of the roadway. This study proved the important value of VR technology in the mineral industry, which is conducive to providing a scientific guidance for the planning of the mining of the mining area and promoting the orderly development of mining work.
gap between users and cataloguers

This paper provides some insight into cataloguers’ and end users’ perception of the importance of bibliographic data for fiction for two purposes: required reading for school and leisure reading. To obtain information about opinion of cataloguers and users, we used multiple data collection methods: observation, think aloud protocol and in-depth interviews. Four studies were conducted: among 30 cataloguers in June 2014 and 32 cataloguers in February and March 2016, and among 105 high school students in November 2011 and 108 adults in July 2012. The focus was on Slovenian catalogers and users. The results revealed similarities between users’ and cataloguers’ view of the most important bibliographic data for fiction. Finally, we propose attributes, considered as important from cataloguers’ as well as users’ standpoint. Most of them could be included in the bibliographic record, regardless of the cataloguing rules and practice.
Computing Service Scheme with Idle Virtual Machine Based on OpenStack

Virtual machine provides cloud computing services that offer computing resources to users through the Internet based on virtualization technology. Generally, supercomputing or grid computing has been used to process a large scale job in scientific, technology, and engineering application problems. Currently, services for large scale parallel processing through idle virtual machines in cloud computing are not provided. Previously, the utilization rate of computing resources in cloud computing was low when users do not use virtual machines anymore or for a long period of time since all the rights in relation to the use of virtual machine are given to users. This study proposes a scheme that increase resource utilization of idle virtual machines and process a large scale job through the idle virtual machines. Basically, idle virtual machines are identified based on virtual machines created through OpenStack, and idle virtual machine-computing service (IVM-CS) is proposed.
Documents, Topics, and Authors: Text Mining of Online News

The goal of recommender systems is, in essence, to help people to discover items they might like, i.e., items that fit their preferences, personality, and needs. Depending on the respective domain, those items can be books, movies, music, hotels, and much more. Typically, recommendations are based on past user interactions (e.g., movies a user saw, hotels a user booked, etc.). This work in progress paper focuses on news recommender systems. Because of the nature of news (e.g., constantly new items, short item lifetime, etc.), recommendations based on past interactions are especially hard to make. Hence, news recommender systems heavily rely on the actual content of news. While previous work mainly considers one aspect of the content of news articles, we jointly analyse and discuss in this work a given corpora of news articles on three different levels (i.e., document-level, topic-level, and author-level). The overall aim is to set to provide the basis for a comprehensive news recommender system, which reaches beyond accuracy and considers also diversity and serendipity. We demonstrate that relevant information can be extracted out of a given corpora, and differences in author, time, and topic can be shown. Furthermore, the author-level analysis shows that documents can be clustered based on the writing style of authors. Finally, our findings show that author-level analysis has the potential to recommend the most diverse items compared to the other approaches.
SPARQL Queries over Ontologies Under the Fixed-Domain Semantics

Fixed-domain reasoning over OWL ontologies is adequate in certain closed-world scenarios and has been shown to be both useful and feasible in practice. However, the reasoning modes hitherto supported by available tools do not include querying. We provide the formal foundations of querying under the fixed domain semantics, based on the principle of certain answers, and show how fixed-domain querying can be incorporated in existing reasoning methods using answer set programming (ASP).
Vocabulary Domain Prediction for Pathological Report Analysis Using ICD-O3

Pathology is a basic medical field of diagnosing diseases and describing their conditions that are presented in a medical report form. It is important to understand the medical domain from various medical terms in the reports, such as observation parts, disease conditions and names, and measuring units. In this paper, we apply various machine learning algorithms to predict the domain of untrained terms used in real pathological reports. Here, we focus on the oncology section with ICD-O3. The analysis result shows the possibility and potential usefulness of the domain prediction using medical terms.
Enhancing real-time parameters estimation precision by quantum jump-based feedback control

Real-time quantum parameter estimation is the basis of quantum feedback control and adaptive control. In this paper, the possibility of real-time estimating the feedback coefficient and dissipation rate is discussed by means of quantum Fisher information. We find that: (1) whether the feedback coefficient λ or dissipation rate γ⁡, an effective way to estimate is adjusting the initial state of system to 0 (θ=0); (2) for a given initial state 0, the estimation precision of feedback coefficient depends on the feedback coefficient itself. If it is bounded in ⁡⁡λ≤π/2, then the closer the feedback coefficient is near ⁡⁡λ=π/2, the more accurate the estimation of the feedback coefficient is; (3) when estimating the dissipation parameter, we should avoid setting the feedback parameter to λ=⁡⁡π/2 in order to improve the precision of estimation. At this time, there are some optimized values of feedback parameter, and under these values, the information the system dissipates is exactly equal to that the feedback compensates, the system reaches a dynamic balance. Quantum Fisher information can also be maintained at a high value for a long time so as to estimate the dissipation parameter with a high accuracy.
Estimating the Noise Level Function with the Tree of Shapes and Non-parametric Statistics

The knowledge of the noise level within an image is a valuable information for many image processing applications. Estimating the noise level function (NLF) requires the identification of homogeneous regions, upon which the noise parameters are computed. Sutour et al. have proposed a method to estimate this NLF based on the search for homogeneous regions of square shape. We generalize this method to the search for homogeneous regions with arbitrary shape thanks to the tree of shapes representation of the image under study, thus allowing a more robust and precise estimation of the noise level function.
A Global-Matching Framework for Multi-View Stereopsis

As deep neural network demonstrated its success on various Computer Vision problems, a number of approaches have been proposed for applying it to multi-view stereopsis. Most of these approaches train networks over small cropped image patches so that the requirements on GPU’s processing power and memory space are manageable. The limitation of such approaches, however, is that the networks cannot effectively learn global information and hence have trouble handling large textureless regions. In addition, when testing on different datasets, these networks often need to be retrained to achieve optimal performances. To address this incompetency, we present in this paper a robust framework that is trained on high-resolution ( $$1280 \times 1664$$ ) stereo images directly. It is therefore capable of learning global information and enforcing smoothness constraints across the whole image. To reduce the memory space requirement, the network is trained to output the matching scores of different pixels under each depth hypothesis at a time. A novel loss function is designed to properly handle the unbalanced distribution of matching scores. Finally, trained over binocular stereo datasets only, we show that the network can directly handle the DTU multi-view stereo dataset and generate results comparable to existing state-of-the-art approaches.
CNN Support to Diagnostics in Sjögren’s Syndrome

Sjögren’s Syndrome is a systemic disease. Its diagnosis can be supported by histopathological examination of minor salivary glands. The value of the focus score equal or greater than one ensures the diagnosis of this disease. Human estimation is inherently subjective, which often leads to diverging results during the process of diagnosis. The paper proposes to use U-net CNN to find area of inflammation in WSI to support pathologist in the process of diagnosis by selecting foci for detailed inspection. Proposed neural network was trained based on tiles of size $$256\times 256$$ pixels in magnification 400x from 13 digital slides stained with hematoxylin and eosin from patients with labial minor salivary gland biopsies. The ground truth was established by manual annotations done by a pathology resident. The accuracy and recall of proposed neural network model on testing dataset show the potential of machine learning for classification problem solving in this field.
Hand Gesture Classification using Non-Audible Sound

Recognizing and distinguishing the behavior and gesture of a user has become important owing to an increase in the use of wearable devices, such as a smartwatch. This study aims to propose a method for classifying hand gestures by creating sound in the non-audible frequency range using a smartphone and reflected signal. The proposed method converts the sound data, which has been reflected and recorded, into an image within a short time using short-time Fourier transform, and the obtained data are applied to a convolutional neural network (CNN) model to classify hand gestures. The results showed classification accuracy for 8 hand gestures with an average of 87.75%. Additionally, it is confirmed that the suggested method has a higher classification accuracy than other machine learning classification algorithms.
Small-Scale Data Classification Based on Deep Forest

Developing effective and efficient small-scale data classification methods is very challenging in the digital age. Recent researches have shown that deep forest achieves a considerable increase in classification accuracy compared with general methods, especially when the training set is small. However, the standard deep forest may experience over-fitting and feature vanishing in dealing with small sample size. In this paper, we tackle this problem by proposing a skip connection deep forest (SForest), which can be viewed as a modification of the standard deep forest model. It leverages multi-class-grained scanning method to train multiple binary forest from different training sub-dataset of classes to encourage the diversity of ensemble and solve the class-imbalance problem. To expand the diversity of each layer in cascade forest, five different classifiers are employed. Meanwhile, the fitting quality of each classifiers is taken into consideration in representation learning. In addition, we propose a skip connection strategy to augment the feature vector, and use Gradient Boosting Decision Tree (GBDT) as the final classifier to improve the overall performance. Experiments demonstrated the proposed model achieved superior performance than the-state-of-the-art deep forest methods with almost the same parameter.
Deep Learning and Sustainable Telemedicine

The need for healthcare is increasing on a global scale. The lack of medical professionals available to fill this need has increased interest in deep learning and sustainable telemedicine technologies. Telemedicine has been shown to be financially beneficial to both patients and healthcare facilities, provided that government regulations and insurance companies recognize them as a reimbursable expense. Advancements in cloud computing, deep learning, and telemedicine are creating a global standard for healthcare, and at the same time increasing the need for these services.
Face Detection Based on Template Matching and Neural Network

Face detection technique is a branch of the Artificial Intelligence field. Most of the classic detection algorithms have better accuracy, but the speed is in general. The rise of Deep Learning has made neural network achieve remarkable speed in the field of detection, but there is imbalance between accuracy and speed. So in this paper, face detection based on template matching and neural network is proposed, which is committed to implementing the consequent that speed and accuracy are obtained the balance simultaneously. The experiment indicates that the algorithm is better than other traditional detection methods on speed and accuracy in the same time.
Characterizing the Expressivity of Game Description Languages

Bisimulations are a key notion to study the expressive power of a modal language. This paper studies the expressiveness of Game Description Language (GDL) and its epistemic extension EGDL through a bisimulations approach. We first define a notion of bisimulation for GDL and prove that it coincides with the indistinguishability of GDL-formulas. Based on it, we establish a characterization of the definability of GDL in terms of k-bisimulations. Then we define a novel notion of bisimulation for EGDL, and obtain a characterization of the expressive power of EGDL. In particular, we show that a special case of the bisimulation for EGDL can be used to characterize the expressivity of GDL. These characterizations not only justify the notions of bisimulation are appropriate for game description languages, but also provide a powerful tool to identify their expressive power.
Herbrand Constructivization for Automated Intuitionistic Theorem Proving

We describe a new method to constructivize proofs based on Herbrand disjunctions by giving a practically effective algorithm that converts (some) classical first-order proofs into intuitionistic proofs. Together with an automated classical first-order theorem prover such a method yields an (incomplete) automated theorem prover for intuitionistic logic. Our implementation of this prover approach, Slakje, performs competitively on the ILTP benchmark suite for intuitionistic provers: it solves 1674 out of 2670 problems (1290 proofs and 384 claims of non-provability) with Vampire as a backend, including 800 previously unsolved problems.
Certified Equational Reasoning via Ordered Completion

Abstract On the one hand, equational reasoning is a fundamental part of automated theorem proving with ordered completion as a key technique. On the other hand, the complexity of corresponding, often highly optimized, automated reasoning tools makes implementations inherently error-prone. As a remedy, we provide a formally verified certifier for ordered completion based techniques. This certifier is code generated from an accompanying Isabelle/HOL formalization of ordered rewriting and ordered completion incorporating an advanced ground joinability criterion. It allows us to rigorously validate generated proof certificates from several domains: ordered completion, satisfiability in equational logic, and confluence of conditional term rewriting.
Exploiting Tri-types of Information for Attributed Network Embedding

With a surge of network data, attributed networks are widely applied for various applications. Recently, how to embed an attributed network into a low-dimensional representation space has gained a lot of attention. Noting that nodes in attributed networks not only have structural information, but also often contain attribute and label information. Actually, these types of information could help to learn effective node representations since they can strengthen the similarities of nodes. However, most existing embedding methods consider either network structure only or both of the structural and attribute information, ignoring node labels. They merely produce suboptimal results as these methods fail to leverage all of these information. To address this issue, we propose a novel method called EANE that is Exploiting tri-types of information (i.e., network structure, node attributes and labels) for learning an effective Attributed Network Embedding. Specifically, EANE consists of three modules that separately encode these three information while preserving their correlations. Experimental results on three real-world datasets show that EANE outperforms the state-or-art embedding algorithms.
An Iterative Algorithm of Key Feature Selection for Multi-class Classification

In this paper, we propose an iterative algorithm of key feature selection for multi-class classification problems, where the data includes a large number of features but the amount of data is limited. For efficient classification, the proposed algorithm first extracts a set of key feature candidates based on Boruta algorithm and then iteratively adopts conventional machine learning based classification algorithms to determine key features. Simulation results show that the proposed algorithm can effectively determine key features, leading to improved classification accuracy compared to direct adoption of multi-class classification algorithms.
When Computers Advise Us: How to Represent the Types of Knowledge Users Seek for Expert Advice

Building on research in artificial intelligence, instructional design, cognitive psychology, and the learning sciences, this article introduces a new knowledge representation, called expert advice, that is used for developing digital advisors that discern the knowledge human users seek in fulfilling their responsibilities in the workplace.
The Influence of Organizational Enforcement on the Attitudes of Employees towards Information Security Compliance

The increasing importance of combating information security crimes has prompted individual organizations to enforce information security compliance. How such enforcement affects the attitude of individual employees towards information security compliance, however, is unclear. With the insights from the organizational theory literature, this study tests and validates a conceptual model that explores the impact of organizational enforcement on the attitude of employees using structural equation modelling based on the data collected from a survey of 294 employees in organizations. The study shows that both organizational security culture and enforcement processes have a positive impact on the attitude of employees. The study further reveals that there is a positive relationship between organizational security culture, security technologies, and enforcement processes in enforcing security compliance. Such relationships form a mutual force for continually fostering a positive attitude of employees toward information security compliance in organizations.
The Adaptive Model Driven Approach for Enhancing Usability of User Interface Design

This paper focus to narrative review the adaptive user interface using model-driven approach to overcome the usability problem. Several UI designs suffered from the usability problem that might cause some applications hardly to deliver the function intended to the user. Therefore, the adaptive user interface design was introduced to acknowledge the usability problem that caused by the traditional UI which cannot satisfy context-of-use (user, device, and environment). In this paper, we describe model-driven approach of adaptive UI using modelling language to create the models of adaptive UI. Model-Driven Engineering (MDE) can raise the level of abstraction in software application, so it will be the basis for devising adaptive UIs because it has the possibility to apply numerous types of adaptions to the various levels of abstraction. The evaluation of usability has its own requirement that different from the usability evaluation of other system application. At the end of this paper we will conclude our finding that the adaptive UI could minimize the usability problem in web application using model-driven approach.
A Study on Push Technology of Intelligent Agriculture Service Information Based on CART Algorithm

In this paper, the information push service will be studied to realize the customization of messages according to precise space-time and crop message, and the meteorological message, monitoring data and decision-making model of the designated area in a specific time will be combined to decide whether to apply fertilizer and the amount of fertilizer. The push technology will be combined according to the decision tree based on CART algorithm for sample set to verify and make decision on information and the push server based on XMPP protocol will be built to recommend decision information for users automatically and intelligently. The farm operation database is established to push the farm operation information to different regions on a time scale, which directly guides the actual production of users and ensures the timeliness of information and high efficiency of management.