Reviewerly/exHarmony huggingface dataset:
Attention-based Cross-Modal Unification of Visualized Text and Image Features

The attentional analysis on graphical user interfaces (GUIs) is shifting from Areas-of-Interest (AOIs) to Data-of-Interest (DOI). However, the heterogeneity data modalities on GUIs hinder the DOI-based analyses. To overcome this limitation, we present a Canonical Correlation Analysis (CCA) based approach to unify the heterogeneous modalities (text and images) concerning user attention. Especially, the influence of interface and user idiosyncrasies in establishing the cross-modal correlation is studied. The performance of the proposed approach is analyzed for free-viewing eye-tracking experiments conducted on bi-modal webpages. The results reveal: (i) Cross-modal text and image visual features are correlated when the interface idiosyncrasies, alone or along with user idiosyncrasies, are constrained. (ii) The font-families of text are comparable to color histogram visual features of images in drawing the users’ attention. (iii) Text and image visual features can delineate the attention of each other. Our approach finds applications in user-oriented webpage rendering and computational attention modeling.
The research progress of human organoids and the prospects of application in stomatology

The organoid is a multicellular mass with the ability of self-renewal and self-organization in artificial extracellular matrix, which is constructed by three-dimensional culture in vitro, and it maintains the physiological structure and function of its source tissues. As a new research model, the organoid has the advantages of both cell lines and animal models. The easily material construction, high culturing efficiency and short time-consuming make it good application prospects in disease research model construction, clinical drug sensitivity test and regenerative medicine application. The organoid models can be divided into three types of cell sources: embryonic stem cells/induced pluripotent stem cells, adult stem cells and cancer cells. In this review, organoid from three cell sources will be elaborated separately, and mainly focused on the current status of human organoid research progress and the prospect of application in stomatology.Key words: Organoid; Embryonic stem cells; Induced pluripotent stem cells; Adult stem cells; Neoplasms
Can We Design Software as We Talk?

In the context of digital transformation, speeding up the time-to-market of high-quality software products is a big challenge. Main challenges. Software quality correlates with the success of requirements engineering (RE) sessions. RE sessions demand software analysts to collect all relevant material usually specified on written notes, flip charts, pictures, etc. Afterwards comprehensible requirements need to be specified for software implementation and testing. These activities are mostly performed manually, which causes process delays and software quality attributes like reliability, usability, comprehensibility, etc., are diminished causing software devaluation. Innovative aspects. This research idea paper proposes a framework for automating the tasks of requirements specification. The proposed framework involves computational mechanisms to enable the automatic generation of software design while requirements are discussed. The innovative aspect of this research comes from digitally transforming the software development life cycle (SDLC) where requirements are generated “on the fly” and virtual reality systems are in place. Potential to make change. The proposed framework has the potential to renovate the role of software analysts, which can experience substantial reduction of manual tasks, more efficient communication, dedication to more analytical tasks, and assurance of software quality from conception phases. This research idea paper introduces the framework for automating the task of requirements specification, and report our progress. We conclude the paper by outlining lessons learnt and future lines of work.
Lightweight Super-resolution Learning Model for Extremely Exposed Images

Video surveillance system adopting wireless sensor network (WSN) becomes more and more popular. To achieve energy efficiency and low transmitting bandwidth, low-cost and low-resolution video camera may be used. However, captured image/video with low resolution may cause information loss; for example, suspicious objects such as a bomb, and emergent events such as fire emergency. Moreover, it is getting deteriorated in case an extremely exposed scene is presented. In this paper, a lightweight learning-based super-resolution (LLBSR) image reconstruction algorithm is proposed for the control center of surveillance system to recover information details from low-resolution images with extremely exposed scenes. The captured video sequences were processed via a simplified difference residual network (DRN) to improve contrast first. Then the pre-processed video sequences were scaled up via a lightweight SR neural network (LSRNN). Experimental results show that the proposed algorithm can achieve a comparable PSNR performance using a simple neural network as compared with a famous prior work with very deep neural network
Agile Implementation and Expansive Learning: Identifying Contradictions and Their Resolution Using an Activity Theory Perspective

A key challenge organisations face when transitioning to agile delivery methods is that of quickly and effectively learning new ways of working. This study posits that fundamental historical, cultural and behavioural aspects affect the transition and contribute to the poor performance of many agile implementations. In order to address such factors, this study applies a modified Activity Theory (AT) based framework to a case study agile implementation within a large public sector organisation. An activity is closely defined, and six generic activities associated with all agile implementations are identified. These are validated against the agile maturity model literature and a set of evaluation criteria of contradictions, congruences and collaboration is established. Evidence is gathered from participant interviews and the framework is used to surface learning and development obstacles and issues within an expansive learning cycle. The study argues that analysis via this modified AT framework brings original insight. Initial findings indicate that there are relatively few learning and development issues associated with the use of agile tools and techniques themselves and that most problems arise at the interface where the “changed” (more agile) delivery teams meet the organisation’s behavioural norms and practices.
Implementation of Bayesian Mixture Models in identifying subpopulation of breast cancer patients based on blood test measurements

Abstract A complete blood test is one of a series of initial examinations of cancer patients that is relatively easy. The use of blood measurement components in analysing patient conditions is commonly used. However, it is not the case for the ratio and inter-ratio components of blood measurements, and this is what is proposed in this study. The built hypothesis is that the ratio and inter-ratio components of blood tests that can explain the condition of cancer patients are better than the blood test’s own components. An analysis will also be conducted to develop a patient profile based on these measurements, and those that can clearly distinguish between patient groups will be identified. The Finite Mixture Model is a method for modelling heterogeneous data that may originate from different subpopulations, where subpopulations represent groups of patients based on a particular latent condition. This model takes the form of a superposition of several distributions, which in this study, a Gaussian distribution is used. The parameter estimation used in the Bayesian method, which determines the prior distribution of the model parameters, and it is combined with the likelihood which will produce a posterior distribution. Then, the Markov Chain Monte Carlo-Gibbs Sampler is used to draw samples on the parameters of the posterior distribution. By using the breast cancer patient blood test data from the Oncology Department of a hospital in Jakarta, with 100,000 iterations as burn-in, and 200,000 iterations for sampling, based on Deviance Information Criterion values, the optimal grouping is two subpopulations using blood ratio and inter-ratio measurements. Two subpopulations were identified, with the first population is characterized by low distribution value and the second subpopulation with the opposite characteristics. The explanatory factors of ratio data are ratio neutrophils to lymphocytes, ratio platelets to lymphocytes, and ratio lymphocytes to monocytes.
Rapid Prototyping for AR/VR Experiences

This course will provide an introduction to techniques for rapid prototyping for Augmented Reality (AR) and Virtual Reality (VR) experiences. With the rise of consumer head mounted displays and powerful mobile phones, AR and VR are becoming increasingly popular. However, until recently, developing AR and VR experiences required strong programming skills. In this course participants will learn how to rapidly prototype AR/VR experiences without the need for programming. Using a mixture of lecture and hands-on activities participants will learn about methods for quickly creating their own AR/VR interfaces. The course will use a mixture of traditional prototyping tools such as sketching, as well as easy to use, free tools for creating AR/VR experiences. This is an ideal course for people who was to quickly prototype and test the core elements of AR/VR experiences before developing their final applications.
Application of pedagogical theories, concepts and teaching methods for intelligent learning systems design

The analysis of intelligent learning systems efficiency revealed a number of contradictions between the capabilities of software-integrated computer hardware facilities and low didactic efficiency of their use. Therefore, when designing intelligent learning systems, the task of researching and analyzing the existing psycho-pedagogical theories, concepts and training methods is still urgent today. The purpose of the paper is to study existing theories, concepts and learning methods for adapting them to automated software-integrated hardware learning facilities. The paper proposes variants of teacher and students adaptation to intelligent learning systems capabilities. Meanwhile, in the process of users adaptation to intelligent learning system by changing the color presentation of information blocks, its possible to gain a chance to influence students psycho-emotional state favorably. The author considers various fonts (words image) application which influence the efficiency of perception and capture of visual information; the author takes into account psycho physiological capacities of human capture of visual information and considers functionality of informing software-integrated hardware facilities, namely: volume and speed of visual information processed; - increasing the sizes of graphic pictures by which a certain person cannot analyze information presented. The methodological basis of the research is a complex application of mutually supportive approaches and methods based on the principles of general scientific methodology. The main approaches were - Person-oriented, system-working and competence-oriented approaches. The analysis of existing theories and concepts has shown that each of the studied educational concepts has its values. The paper considers shortcomings of basic theories and educational concepts from the position of a complex approach to the solution of educational tasks. Thereunder, any of basic applied theories and concepts can be adopted as a single universal educational basis for educational process structuring. A specific emphasis in the paper is aimed at the advantages and disadvantages of the concept of personal-oriented education, which is the most progressive in learning process structuring in authors opinion and which causes more discords of opponents. Using the results of theories and education concepts analysis the author gives recommendations how to design technical requirements to intelligent learning systems. The conditions of feasibility of intellectual learning systems and their didactic values have been formulated. The results of the research can be used in the design of intelligent learning systems for the purpose of selecting a complex of didactic educational tools.
Two-dimensional Phase Unwrapping with Priori Boundary Condition

Phase unwrapping is essential for many applications such as Interferometric synthetic aperture radar, MRI, and X-ray phase imaging etc. In these applications, the phase is determined only in the principal value range of [-π,π). The unwrapping that recover true phase method becomes difficult when the measured object possesses big discontinuity. In these minimum norm methods, for unwrapping, it is needed to minimize the difference between the gradient of the wrapped phase and that of the unwrapped phase using the Lp norm. Many authors suggest that the goal of phase unwrapping should be minimizing the Lo norm problem. However, previous methods have reached its limits as this is a nonconvex problem. To solve this difficulty, in this paper we used boundary information as the priori condition which is suitable in many applications. Then the unwrapped phase is given by solving a Lp (p ≥ 1) norm minimization problem that belongs to convex optimization. The simulation results demonstrate that our method is effective.
TPEGADP: improvement of EGADP based on topology potential

With the development of hardware equipment and storage technology, people are facing more and more problems about how to protect and publish private datasets. Therefore, many perturbation methods are proposed to cover the confidential information containing in the datasets. However, these methods are usually applied to the whole datasets, which means extreme value would lead to bias. Therefore, this study surveys the TPEGADP, a data perturbation method, combining the topology potential and EGADP. According to the topology potential in different areas, we can calculate the density of k neighborhood, so that TPEGADP or calculating mean can be used to produce perturbated data, covering the information. What's more, the cluster features as well as statistics can be maintained. TPEGADP also shows a good result in the experiment compared with GADP and NeNDS, and also shows good stability in different clustering methods, including K-means, Birch and DBSCAN.
A. GMM-Based intrusion detection for perimeter security system

As the exploitation of shale gas has increased in recent years, the safety of perimeter protection for shale gas wells has become increasingly important. Today, there are a number of problems with algorithms that build models based on foreground signals for intrusion detection. The biggest problem is that the use of foreground modeling requires a large number of intrusion signals, but often in the actual environment, the intrusion signal is relatively small, the signal will be in a normal state without interference for a long time, and the signal waveform generated by different intrusion methods is also different. This can result in low intrusion detection rates and scattered models. Therefore, in this paper, based on the background modeling method, the perimeter detection of wellsite intrusion detection is studied. By collecting data in the field, preprocessing the data and designing the algorithm to verify the feasibility of the method. Experiments show that the proposed background intrusion detection rate based on background modeling can reach 90.56%, which greatly meets the basic requirements for intrusion detection.
Automatic generation of methodological materials in the study of technical and engineering texts in a foreign language

Abstract Masters, postgraduate students, engineers and scientists in Russia should study new areas of knowledge in a foreign language, for example, in English. But they have a double task arises: 1) learning English for a specific professional field; 2) studying the professional field using the acquired knowledge of the English language. The main requirement here is the minimum time for training. The purpose of the training process under consideration is to develop knowledge and skills for reading texts in a specific area of knowledge. At this stage the problem of active English language proficiency, i. e. writing your own articles or speaking is not considered. The student does not have the similar material in Russian. The material studied contains new concepts that are not known to the student. In standard university textbooks simple material is used that is already known to the student in Russian. Therefore, the student can understand the meaning of the content which significantly simplifies the learning process. But in this case the situation is different. Automation of training material’ design within the framework of the situation is carried out in two stages: the analysis of the studied materials and the synthesis of training materials. At this stage the existing software such as Microsoft Word and Excel together with Visual Basic for Application (VBA) can be used. The corresponding algorithms are applied for each operation. The automated development of training materials’ proposed technology for mastering a foreign language in a narrowly specialized field of knowledge enables to increase the effectiveness of subsequent training in a foreign language.
The Theory and Practice of Colleges and University Mass Maker Space Co-building

This paper summarizes the origin of the Mass Maker Space and the development of the Mass Maker Space in colleges and universities and discuss the necessity of co-building Mass Maker Space in colleges and universities from many aspects, such as the local government support, fund and financial institution support, cooperation with professional Mass Maker Space companies, cooperation with enterprises, cooperation with scientific research institutions, etc. This paper then introduces the specific cases of the cooperation of the Mass Maker Space in colleges and universities, including South China University of Technology, Guangdong University of Technology, South China Agricultural University, Shenzhen University, Guangzhou University of Science and Technology, Guangdong College of Business and Technology, and so on. Last but not the least, this paper analyzes the existing problems and general countermeasures of Mass Maker Space in colleges and universities, and proposes to improve the whole process of the innovation service "Mass Maker Space + Incubator + Accelerator".
A Parallel Gene Expression Clustering Algorithm Based on Producer-Consumer Model

Clustering is one of the important tasks of machine learning. Gene Expression Programming (GEP) is used to solve clustering problems because of its strong global searching ability. In order to solve the limitation of lower rate of convergence and easy falling into optimal local solution in the traditional GEP clustering process, this paper proposes a parallel GEP clustering algorithm based on the producer-consumer model (PGEPC/PCM), which parallelizes the time-consuming operations such as fitness calculation, recombination, and mutation in GEP clustering analysis to speed up, improves the calculation method of fitness function to enable it to cluster automatically. This algorithm can fast calculate accurate clustering center points in parallel. Extensive experiments on four widely used benchmark Iris, Wine, Soybean and Seeds from the UCI machine learning data sets are conducted to investigate the influence of algorithmic component and results are compared with traditional GEP clustering algorithm. These comparisons demonstrate the competitive efficiency of the proposed algorithm.
Demo of PolySense: How to Make Electrically Functional Textiles

We demonstrate a simple and accessible method for enhancing textiles with custom piezo-resistive properties. Based on in-situ polymerization, our method offers seamless integration at the material level, preserving a textile's haptic and mechanical properties. We demonstrate how to enhance a wide set of fabrics and yarns using only readily available tools. During each demo session, conference attendees may bring textile samples which will be polymerized in a shared batch. Attendees may keep these samples. While the polymerization is happening, attendees can inspect pre-made samples and explore how these might be integrated in functional circuits. Examples objects created using polymerization include rapid manufacturing of on-body interfaces, tie-dyed motion-capture clothing, and zippers that act as potentiometers.
Application of Bayesian Mixture Models to Satellite Images and Estimating the Risk of Fire-Ant Incursion in the Identified Geographical Cluster

Bayesian non-parametric mixture models have found great success in the statistical practice of identifying latent clusters in data. However, fitting such models can be computationally intensive and of less practical use when it comes to tall datasets, such as Landsat imagery. To overcome this issue, we propose to obtain multiple samples from data using stratified random sampling to enforce adequate representation in each sample from sub-populations that may exist in data. The non-parametric model is then fitted to each sample dataset independently to obtain posterior estimates. Label correspondence across multiple estimates is achieved using multivariate component densities of a chosen reference partition followed by pooling multiple posterior estimates to form a consensus posterior inference. The labels for pixels in the entire image are inferred using the conditional posterior distribution given pooled estimates, thereby substantially reducing the computational time and memory requirement. The method is tested on Landsat images from the Brisbane region in Australia, which were compiled as a part of the national program for the eradication of the imported red fire-ant that was launched in September 2001 and which continues to the present date. The aim is to estimate the risk of fire-ant incursion in each of the identified geographical cluster so that the eradication program focuses on high risk areas.
A Novel User Interface for Knowledge Base Browsing

Abstract Intuitive user interfaces have been of great concern for GUI developers. The current research, who deals with their designing, faces the term intuitive constantly. The main question is how can the Interface be intuitive? For the moment, the researchers try to provide a very intuitive generic user interface that can be used in a variety of applications. In this paper we provide a solution that can model any applied ontology into a honeycomb menu. The hexagonal shape of the honeycomb has attracted the attention of humans for centuries. As a relevant consequence, the final user can browse any knowledge base very easily with the aid of this interface. Another useful feature is that programmers can take full advantage of semantic web technologies which can tailor results based on any knowledge base that is feed as input, without any need for code change, thus leading towards a panacea system.