Reviewerly/exHarmony huggingface dataset:
ICT application in CBDRM in Karangtengah village Bantul Regency

Abstract This research was conducted in Karangtengah Village, Imogiri District, Bantul Regency, Special Region of Yogyakarta. The landslide incident that occurred in the Karangtengah Village area on November 28, 2017 then prompted several groups to take the initiative to install an early warning system and several other supporting tools at the scene. This study aims to identify what Information and Communication Technology tools already exist in the Karangtengah Village area that can support landslide hazard monitoring activities, and see how residents manage these tools. This research was conducted with a qualitative method using a Participatory Rural Appraisal tool. The results of this study show that the presence of Information and Communication Technology tools requires the community to have the capacity to synergize with existing tools and manage them independently so that there is no failure of the role of Information and Communication Technology in supporting the work of Community-Based Disaster Risk Management in Karangtengah Village. Access to data generated by existing Information and Communication Technology tools will also be useful for residents to monitor phenomena or conditions around them.
Research on Improved Multi-Sensor Data Fusion Algorithm Based on D-S Evidence Theory

The data fusion of multi-sensor enhances the intrinsic relationship of data between sensors, reduces the workload of information processing, and will not miss the important information features. This paper points out the main problems in the application of D-S evidence theory, analyzes and compares the existing improved methods. When the evidence conflict is large, the traditional D-S evidence theory synthesis formula is inconsistent with the actual situation and the result is invalid. The priority factor is introduced to reallocate the basic probability assignment of the evidence conflict part. And the method is used for map construction simulation. From the constructed ring map, it can be seen that the obstacle points of each decision are very close to the obstacle points in the simulation. The reliability and accuracy of the method of introducing priority factor in drawing construction are explained.
Experimental Study on Instrument Pointer Detection Based on Hough Transform and RANSAC Algorithm

Because of the complex working environment in most factory workshops, the measurement of pointer instrument relies on manual reading. At present, in the field of instrument detection, most algorithms are based on improved image preprocessing and ignore the accuracy, stability and robustness of automatic pointer selection. In order to improve the anti-noise performance of meter pointer selection, RANSAC (random sampling consistent) algorithm was combined with Hough transform linear detection, and the meter pointer in the binary edge graph was straight-line fitting. The idea of improvement is to use SURF algorithm to extract feature points first and separate points distributed near different lines. Further, RANSAC algorithm is used to assume the linear model data, given a reasonable inner group, and remove the invalid outlier data. Finally, the Hough transform algorithm is used to fit the instrument pointer line. The linear fitting calibration image verification of the instrument detection pointer shows that the combined algorithm has better fitting effect on the instrument detection pointer, reduces a lot of noise interference and improves the accuracy.
A New LWE-based Homomorphic Encryption Algorithm over Integer

The design of public-key cryptography algorithm based on LWE hard problem is a hot topic in the field of post-quantum cryptography. In this paper, we design a new homomorphic encryption algorithm based on LWE problem. Firstly, to solve the problem that the existing encryption algorithms can only encrypt a single 0 or 1 bit, a new encryption algorithm based on LWE over integer is proposed, and its correctness and security are proved by theoretical analysis. Secondly, an additive homomorphism algorithm is constructed based on the algorithm, and the correctness of the algorithm is proved. The homomorphism algorithm can carry out multi-level homomorphism addition under certain parameters. Finally, the public key cryptography algorithm and homomorphic encryption algorithm are simulated through experiments, which verifies the correctness of the algorithm again, and compares the efficiency of the algorithm with existing algorithms. The experimental data shows that the algorithm has certain efficiency advantages.
In-Situ 3d Shape Measurement System for High-Temperature Object in Plasma Wind Tunnel Via Dmd-Projecting Laser Structured Light and Adaptive Speckle Filtering

Evaluation of object 3D shape change during ablation in wind tunnel test is critical for optimization of thermal protection system. However, strong thermal radiation and harsh environment bring challenges to the traditional measurement methods. A single camera DMD-projector structured laser light system is proposed for in-situ 3D shape measurement in wind tunnel test. A customized Digital Micro-Mirror Device (DMD) combined with laser source is devised for projecting concentrated full-field high-power structured light stripes. To filter laser speckle noise in projected chessboard images for model calibration, an auto-window Kuwahara adaptive filter is put forward. 3D shape of a blunt-head cylinder was in-situ measured in a plasma wind tunnel which maximum temperature reached 1950 ℃ . Diameter reconstruction error was within 0.85% (0.17/20.03mm). With projected laser light and multi-step filters, ablation process of front face of a dome-top cylinder at 1950 ℃ was clearly observed at a long working distance over 1.5m.
Survey of the VR Environment for Deep Learning Model Development

As deep learning research develops and models become larger and more complex, there are increasing concerns to deep learning about its’ low ability of explanations to humans and its black box characteristics. Model visualization research of DL (Deep Learning) has been attracting attention for a decade for a solution to this concern. VR (Virtual Reality) interaction research between humans and models is a practical means of model visualization research with great potential but is still in the early stages. The purpose of our study is to propose new methods for VR technology to contribute to the development of deep learning models by investigating and implementing the visualization technology of deep learning and VR research projects. In this paper, we also report our two experimental results. One is a web survey using demo images of a PC application and a VR goggles application, and the other is an evaluation experiment using VR goggles.
Digital Twins in Real Economy: Unobvious Effects

This work considers the transition of methods and approaches from the realm of science into the real economy as exemplified by digital twins. The authors review the transition from the use of the digital twin to the digital footprint of a real physical object. The importance of the educational component in matters concerning the use of digital twins is growing. The authors note the increasing role of generally understood algorithms and their connection to moral and ethical principles. They formulate the idea of the transformation of laws concerning scientific and digital technologies. Social inequality and alienation are increasing due to the introduction of digital technologies in everyday life. The authors discuss the necessity of a detailed assessment of moral and ethical risks and challenges caused by the rapid digitalization in the academic sector and its transfer into everyday life. There is an idea that only the presence of three efficient components—“real object” (e.g., a mega-science facility), digital footprint, and users (researchers working with the data)—can provide the connections between the model and the algorithm and the real world.
Unsupervised Feature Learning for Temporal Segmentation of Auroral Image Sequences

Optical observation of aurora can obtain extensive information about magnetosphere and solar-terrestrial electromagnetic activities. Temporal segmentation of auroral image sequences is the basis for detection and retrieval of auroral events and further analysis of statistical features (such as occurrence time, lifetime and periodic, etc.). Different from other classification tasks, there is no definite semantic labels in the temporal segmentation of auroral image sequences. In this study, we propose an unsupervised learning method to characterize the appearance and dynamic features of auroral image sequences. Then, the clustering algorithm is used to segment long sequences. Experimental results on auroral image sequences obtained from all-sky image observations at Yellow River Station (YRS), show that the segmentation result is consistent with that of human visual perception.
A Directed Graph Link Prediction Method Combined with Higher Order Structure Information

Link prediction is one of the common tasks in complex network research, which aims to predict the missing edge or the edge that may be generated in the future. The key to link prediction is to obtain the features with a strong representation for nodes. Modeling and exploring high-order structures, also called network motifs, are essential for understanding the fundamental structures that control and mediate the behavior of various complex systems. But the existing link prediction methods fail to take full account of the high- order structure. In addition, many of the existing link prediction methods do not pay attention to the directed network, ignoring the potential directionality, which limits a lot of applications in real life. This paper extends the graph auto encoder (GAE) and variational auto encoder (VAE) frameworks to address link prediction in directed networks. First (Node Embedding): We propose a node embedding method that considers the higher- order structure of the network, that can solve the link prediction problem of directed networks by exploiting higher-order structure information. Second (Link Prediction): We propose a method to predict the direction of links. This method uses node centrality and node representation to predict links. Third (Model and Experiment), We propose a link prediction model for directed networks called Motif-Based Graph Auto Encoder (MGAE), and extend it by proposing a Motif-Based Variational Graph Auto Encoder (MVGAE). Meanwhile, we apply our proposed model to solve the real-world problems on various types of networks, such as citation networks, web networks, and social networks. Experiments show that our method is superior to the existing link prediction methods in terms of prediction accuracy and recall rate. It is proved that our model has better experimental results than the baseline in different types of complex networks.
Word Per Minute (WPM) Lampung Script Keyboard

Users of Font Lampung in previous studies have difficulty in typing because the layout of the Lampung script is complicated to understand. Re-designing the Lampung alphabet keyboard layout allows users to be able to type text in characters more easily. The purpose of this research is to create a Lampung alphabet keyboard layout for more effective typing of the Lampung alphabet. The methods used are UX (User Experience) that are, requirements gathering, alternative design, prototype, evaluation, and result report generation. The layout design is created by eliminating the use of the SHIFT key and regrouping parent letters, the child letters in a more effective composition. The layout design of the Lampung alphabet was printed using the Laser Engraver tool on the keyboard. Through the test, the typing speed increased to 208% of the average value of 62 seconds to 32 seconds. In addition to the results of WPM (Word Per Minute) on keyboard layouts with 8.7 WPM which is faster than previous WPM research but still slower than QWERTY keyboard WPM. Testing of character quality, readability, and difficulty level to know the flaws in the Lampung RaTaYa keyboard layout with good results. This research concludes that the keyboard layout of Lampung RaTaYa is more effective than using the font Lampung script.
A semantic model to publish open source software on the web of data

Purpose The purpose of the paper is to propose a semantic model for describing open source software (OSS) in a machine–human understandable format. The model is extracted to support source code reusing and revising as the two primary targets of OSS through a systematic review of related documents. Design/methodology/approach Conducting a systematic review, all the software reusing criteria are identified and introduced to the web of data by an ontology for OSS (O4OSS). The software semantic model introduced in this paper explores OSS through triple expressions in which the O4OSS properties are predicates. Findings This model improves the quality of web data by describing software in a structured machine–human readable profile, which is linked to the related data that was previously published on the web. Evaluating the OSS semantic model is accomplished through comparing it with previous approaches, comparing the software structured metadata with profile index of software in some well-known repositories, calculating the software retrieval rank and surveying domain experts. Originality/value Considering context-specific information and authority levels, the proposed software model would be applicable to any open and close software. Using this model to publish software provides an infrastructure of connected meaningful data and helps developers overcome some specific challenges. By navigating software data, many questions which can be answered only through reading multiple documents can be automatically responded on the web of data.
Eye professional selection of port movers

From the beginning of 1933, professional selection of loaders of the Odessa port was included in the tasks of the psychophysiological station of water transport.&#x0D; The eye office was then faced with the question of the rules of eye professional selection for port movers. This question made itself felt especially because not a single relevant guideline, not a single official resolution contained specific instructions on the rules for the eye professional selection of loaders in general and port loaders in particular. Therefore, the eye department of the Unified Dispensary and Station took the lead in developing the above rules.
Fast Super-Resolution Algorithm for Real-Time Communication

Video super-resolution aims to restore a high-resolution video frame from multiple low-resolution frames which can effectively improve the perceived quality of the video and enhance the user's visual experience in real-time video communication. Current video super-resolution algorithms pay more attention to super-resolution performance rather than the inference speed. Most of them adopt computationally expensive alignment and fusion module, which leads to high inference time cost and hinders the real-world deployment. Therefore, it is necessary to achieve a balance between inference speed and super-resolution performance. In this paper, we propose a fast video super-resolution network which is achieved through three lightweight alignment methods and implement it on the video restoration algorithm with enhanced deformable convolutional networks (EDVR). We trained the model through the Vimeo-90K training dataset, and tested the algorithm through the Vid4 and Vimeo-90K-T test datasets. The experimental results show that the inference time of the network with our alignment methods can be nearly 38% shorter than original EDVR.
Visual Analytics for Predicting Disease Outcomes Using Laboratory Test Results

Laboratory tests play an essential role in the early and accurate diagnosis of diseases. In this paper, we propose SUNRISE, a visual analytics system that allows the user to interactively explore the relationships between laboratory test results and a disease outcome. SUNRISE integrates frequent itemset mining (i.e., Eclat algorithm) with extreme gradient boosting (XGBoost) to develop more specialized and accurate prediction models. It also includes interactive visualizations to allow the user to interact with the model and track the decision process. SUNRISE helps the user probe the prediction model by generating input examples and observing how the model responds. Furthermore, it improves the user’s confidence in the generated predictions and provides them the means to validate the model’s response by illustrating the underlying working mechanism of the prediction models through visualization representations. SUNRISE offers a balanced distribution of processing load through the seamless integration of analytical methods with interactive visual representations to support the user’s cognitive tasks. We demonstrate the usefulness of SUNRISE through a usage scenario of exploring the association between laboratory test results and acute kidney injury, using large provincial healthcare databases from Ontario, Canada.
Dynamic Grouping Design System of Leisure Sports Based on Computer Intelligent Algorithm

With the development of economy and the improvement of people's living standards, leisure sports have developed rapidly in China. As a sports project with strong interest, high participation, high flexibility and certain ornamental value, leisure sports have attracted more and more attention and gradually become an important part of the national fitness plan. Therefore, it is of great significance to study the dynamic grouping design of leisure sports based on computer intelligent algorithm. This paper first introduces the background and significance of leisure sports dynamic selection, and then expounds the requirements of dynamic grouping design. According to the application of computer intelligent algorithm, a dynamic grouping system based on computer intelligent algorithm is designed. Finally, the model is tested to study the possible errors in the process of dynamic grouping. The test results show that the random matching result error generated by the system in dynamic grouping is very small, which shows that it can meet the needs of users.
Management System for Records in the Context of Business Analysis with BABOK for Native Organizations in Latin America

The aim of this research was to design a management system for records in the context of business analysis with BABOK extrapolatable for native organizations in Latin America. Methodologically, it was firstly made a comprehension about what a native organization and its context is like following activities from BABOK standard. Then, the management system for records was designed based on the DIRSK methodology from ISO 15489 for a representative native organization. Subsequently, it was implemented in a project. The relevance of this research will allow documentation of the information of any native organization with a focus on processes. It will be possible to have the design of a specific management system for records for native communities; and a solution-oriented work strategy based on business analysis. The main results indicate that there is an improvement on satisfaction about how document management is carried out according to three aspects: document processing, from 43.75% to 96.28%; workflow, from 45.43% to 91.66%; and, information availability, with an improvement from 51.36% to 95%.
Obstacle Awareness System of An Indoor UAV with Multi-Sensor Fusion Algorithm

A Flying Ad-hoc network (FANET) emerges recently due to its flexibility in terms of flying tracks and movements. As one type of Unmanned Aerial Vehicles (UAVs), a drone can be considered as the low-cost platform to implement the FANET. In a particular case, the flying tracks and movements of a drone can encounter inevitable obstacles such as building construction and any random objects. Thus, this paper focused on the obstacle issue in drone’s movements and proposed the feasibility of Sensor Fusion algorithm to distinguish the obstacle in the indoor environment. Under two conditions: single and multiple obstacles scenarios, the autonomous drone implementing Kalman Filter in Sensor Fusion experienced the real time response linearly as the distance increases.
Application of Intelligent Equipment and Physiology Online System Mode in Practice of Medical Students

This paper studies the application of intelligent equipment and physiology online teaching mode in medical students’ practice. Through the real experience of the teaching practice process, the advantages of online teaching are summarized: the high integration of teaching resources, the repeated learning mode anytime and anywhere, the network and dataization of the supervision of the teaching process, etc.; the teacher's PBL evaluation of the students is in the “pertinence of the speech” and “improving comprehensive ability” are statistically significant (P<0.05); the difference in the scores of the seven subjects of basic medicine in the curriculum reform class is not statistically significant (P>0.05).
Social Distancing Crowd Segmentation, Estimation and Visualisation

This research shows a modern crowd counting solution which alters typical prediction solutions into a segmentation of individuals based on a distance threshold, allowing for better visualisation and results. The study proposes using YOLOv4-normal and YOLOv4-tiny models, which have shown great results throughout calibration with an MAE of 14 and 36 respectively. However it did present some issues of accuracy degradation when trained on head annotations at any level of crowd density. As for visualisation, perspective transformation was used which directly helped in providing the distance calculation that was absent from standard transformation. If any variants of YOLOv4 are to be used, the main argument is the choice between speed over accuracy while relying on native implementations. In the case of distance regulation, any transformation that maps itself onto the region of interest, such as perspective transformation should be used to precisely determine distances from a camera to the region of interest itself.
The experience of modern cinema interpretation of classics: A.P. Chekhov’s «Three sisters» in Sergei Solovyov’s and Yuri Grymov’s film adaptations

This article is devoted to versions of Chekhovs play Three Sisters in film at the turn of the twenty-first century. Analyzed are interpretations of the play by Sergei Solovev (1994) and Iuri Grymov (2017). Both films appeared in crisis periods, at times of rethinking historical experience and re-evaluating beliefs; each responds to the challenges of its time. The aim of this article is to reveal the originality of these films, and to consider them in relation to the text of the Chekhovs play, to the established tradition of reading it, and, finally, with each other. This analysis allows us to understand how each of the directors sees the world and what historical problems seem to them the most important. The author examines not only the originality of the interpretations by two directors belonging to different generations, but also specific ways of visualizing Chekhov in cinema.