Reviewerly/exHarmony huggingface dataset:
Influence of the Warning Area Division on the Effect of the Propagation of Local Undamped Motion (PLUM) Method

It is possible to obtain a detailed estimates of the expected vibration by dividing the early warning area, to achieve an optimized early warning effect. This paper studies the influence of the warning area division on the warning effect of the the Propagation of Local Undamped Motion (PLUM) method. Japan is divided into grids with side lengths ranging from 30 km to 120 km, and each grid is considered an early warning area. Using the data recorded by the National Research Institute for Earth Science and Disaster Resilience (NIED), we studied the effects of different grid sizes on PLUM warnings by simulating the "311 earthquake" in Japan. The simulation results obtained with the grid divisions are compared with the simulation results obtained using predefined warning areas, and the influence and applicability of the warning area division method on the warning effect are analyzed. The calculation results show that an increased grid size leads to an expanded warning range, but at the same time, unnecessary warnings are sent to some areas. Under the conditions of the "311 earthquake" in Japan, the prediction scores and precision of the 60 km and 70 km grid are higher, and the mean warning times is longer than those of other grid sizes; the overall warning results of these grids are the closest to those obtained with the predefined warning areas in Japan.
‘I’m your automatic colour’ La colorazione automatica delle immagini in antropologia

Colour photographs now represent almost all the images produced with the new reality capture tools, mobile phones, which in 2020 ‘took’ 90% of all photos of that year. Black and white is relegated to artistic expression, even newspapers have converted to colour for some years. In the history of photography, although research on colour is attempted from the early stages, it is necessary to wait until 1861 with the experiences of James Clerk Maxwell who created a stable colour image. However, it is from the fifties of the twentieth century that the use of colour becomes ‘popular’ even in a more aesthetic dimension than an objective reproduction of reality. Part of the ethnographic, anthropological, archaeological and field research, on the other hand still makes use of consolidated and inexpensive black and white for a long time. On these images largely available online and open source you can conduct automatic colouring experiences. The procedure, managed with artificial intelligence algorithms with deep learning processes, is always more widely used with free applications and allows to obtain qualitatively more and more relevant results, even if some critical analysis is still necessary. This article presents the state of the art to 2021 of automatic colouring, with the comparison between algorithms developed since 2016 and showing with experimental examples both the possibilities of rendering and even the critical issues that emerged with the application in anthropological photographs, with the aim of extracting information that is not very evident in the originals in black and white.
Research on VR Data Visualization Design Based on Usability Principle

With the development of VR technology, people began to display the visually processed data under the virtual reality scene, users can more easily extract data information and get an interactive experience through this immersive way. This article relies on the ten usability principles of human–computer interaction proposed by Nielsen, combined with the current data visualization design cases in the VR scene, the line-of-sight design and the data information design level. Analyze, put forward design ideas for its development from the aspects of light, motion, color, sound and other audiovisual methods through comparison and induction.
Modulation Recognition Method of MAPSK Signal

The automatic modulation recognition technology plays a crucial role in non-cooperative communication, especially in the recommended MAPSK modulation. However, existing methods suffer from the great challenge due to the diversity on the MAPSK modulation and influence of frequency offset. In this paper, a novel recognition method based on the amplitude histogram and the spectrum of the high-order moment of the signal constructed by the received signal phase is proposed, which has low computational complexity. The simulation results show that if the signal-to-noise ratio is greater than 0dB, the successful recognition probability of the novel method reaches 98% with the residual carrier frequency offset. The robustness and successful recognition probability of our method outperform high-order cumulant and deep learning state-of-the-art automatic modulation recognition methods.
Transductive Learning for BI-RADS Knowledge Graph based on Knowledge Tensor Factorization

The advantage of Knowledge Graph (KG) can greatly prompt the interpretability of the artificial intelligence diagnosis. For breast ultrasound, the KG can be built through BI-RADS semantic descriptions, and the diagnosis can be achieved by link reconstruction between patients and outcomes. However, the existing KG analysis methods consider only the linked neighbors of the entities and relations during embedding, but not the whole entities and relations in KG, which reduces the link reconstruction power for diagnosis in the case of only a small fraction of labeled patients. In this paper, we present a transductive learning based Knowledge Tensor Factorization (KTF) method, which can effectively represent the KG data through a core tensor of interactions among all entities and relations and their embedding vectors. KTF demonstrates distinct diagnosis performance even if there is only a small fraction of labeled patients. Through experiments of assessments, KTF shows distinct superior performance in diagnosis for KG data of BI-RADS with a small fraction of known outcomes of patients.
Cyanobacteria Bloom Identification Method in Remote Sensing Image Based on ER Rule

The effective identification of cyanobacteria bloom in remote sensing image is of great significance to the protection of water ecosystem. At present, the commonly used identification methods have some problems, such as strong subjectivity, insufficient fusion of available information and so on. Dempster-Shafer (D-S) evidence theory is used to deal with the subjectivity of remote sensing image interpretation, fuse multi-source data and introduce expert experience. However, the calculation process of existing researches is complex, and the theory itself has the problem of combination explosion, which is not conducive to fuse too much data. The evidence reasoning rule (ER Rule) proposed on the basis of D-S theory can better overcome this problem. Based on the existing ER Rule classifier, a cyanobacteria bloom identification method is proposed. The input of the method is the calculated vegetation index, and particle swarm optimization (PSO) algorithm is used to optimize the initial model. Finally, taking Chaohu Lake remote sensing image as an example to carry out experiments, the applicability and effectiveness of the method are verified. This study provides a new solution to the identification of cyanobacteria bloom, and is of reference significance for the monitoring and prediction of it.
Impact of Multi-size Small Anchors and Penalized IoU on Face Mask Detection

Wearing masks as one of the most effective ways to diminish the transmission of COVID-19 increases the demand for automatic face mask detection in all countries. Face masks belong to the small objects category in images, thus introducing the challenge of training a robust face mask detector, particularly for small object detection. Feature Pyramids derived from deep convolutional neural networks are commonly used to achieve scale-invariant object detection; however, it does not reach the same level of performance in detecting face masks as in detecting larger objects. This work proposed two methods: fully utilizing the feature map extract from the neural network by adding small multiscale anchors on the last feature map, which contains the highest resolution information. The other is to replace the standard IoU calculation with a tolerant strategy for small objects. Using these two methods, we improve the accuracy of small object detection while increasing the general average precision.
Identifying Constraints Factors of Supporting Policy for Seafood Processing Companies in Gyeongsangbuk-do

The purpose of this study is to analyze the factors hindering the support system for seafood food processing companies through individual in-depth interviews. In this study, it was found that various impeding factors be occurred in the policy process related to the support system for seafood processing companies. The main obstacles are lack of understanding corporate policy demands, lack of communication channels between stakeholders, lack of trust, the difference of perspectives(short-term, long-term) between public officals and companies, quantitative performance-oriented support system, the lack of professionalism and continuity due to frequent personnel changes of the person in charge, and difficulties in responding to supporting project documents and manpower. As a way to improve the support system for seafood food processing companies, a survey was conducted on the policy demand for seafood food processing companies, establishment of communication channels with stakeholders, simplification of the support process, and formation of long-term partnerships between policy providers and seafood companies.
The Use of Digital Technologies in the Institute of Higher Education

The article presents a description of modern digital tools that can be used in the educational process for more activating content in the formation of the necessary professional competencies in students. The use of the Internet board "Miro" allows students to master such concepts as "conflict" and "conflict resistance" while studying the discipline "Conflictology"; the digital tool "Nearpod" while studying the topic "Conflict Management in the Organization" made it possible to present a presentation and, after studying its content, conduct online testing on the "Test Wizard" platform, using tasks of varying complexity; the use of artificial intelligence is also possible as a new tool for resolving interpersonal conflicts - on the platform of the TELEGRAM social network.
An Enhanced Version of K-means Algorithm

Abstract- Recently, the world has seen significant advancements, particularly in the field of information technology. Over the last decades, massive amount of data has been accumulated; such massive data include implicit knowledge that cannot be discovered using typical data analysis technologies. The availability of data and the urgent need to transform it into knowledge has called for the emergence of new field called Knowledge Discovery in Databases (KDD). The Data Mining (DM) step is one of the KDD process' steps that employees intelligent methods in the discovery of patterns which are the essence of knowledge. Cluster analysis is one of the well-known descriptive DM functionalities. The topic of this paper is a study of cluster analysis, with emphasis on the K-means algorithm. The contribution of this work is to develop computer system software to combine two modified versions of the K-means algorithm into one to benefit from the proposed modifications. Our study was concluded with analysis and discussion of the experiments’ results on the basis of several criteria, which are shown that our modified version mostly was better than the other versions.
AI-Assisted Dynamic Modeling for Data Management in a Distributed System

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. Distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure’s functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.
Embedding Emojis to Redefine Gaze-Point and Scan-path in a Visual Centre field

Gaze point reflects the eyes' fixation on a visual field but doesn't express the meaning of a fixation point and scan-path on the same area of interest as much as text-based expression. Text-based expressions are the most common type of emoticons. Emojis are derived from emotion and icons. In an instance when one is feeling happy, not-happy, overjoyed, or upset emoticons can help express the most basic form of these feelings in ways that word or speech cannot. An important function of emoji as a form of the communicative symbol is to convey emotional content from a sender to a receiver in a computer-mediated form of communication. Here we tend to redefine fixations points and scan-path using emoji and physiological response to convey user emotional response to a static webpage by a synchronous assembly of emoji coordinates and gaze points based on the following concepts: (1) correlating gaze points with emotional emoji (2) redefine eye movement behaviour with emoji bubbles (3) integrate physiological correlates for synchronious mapping of emoji characters. From the analysis, result shows that stress and neutral mood are the basic form of emotion expressed by users while dealing with specific form of web content, this reflects the dynamic changes in their pupil dilation.
Implementing object tracking for seeding quality on Android devices

The recent increase in processing power of mobile devices opens up a wide range of opportunities for the development of innovative applications. Tracking combines object recognition with video processing technologies to detect objects in a scene and label them uniquely. In this work, we present an application for evaluating seeding quality, which is an important factor for achieving higher production numbers. The application developed in this work is implemented on Android platform and uses SORT algorithm as a basis for seed tracking. This paper proposes modifications to the tracking algorithm to increase precision and performance based on the conditions and constraints of the case study. In the experiments, the application running on a real device achieved a performance of 27 frames per second to detect and track five seeds in the scene.
Visualizing the Evolution of Information Retrieval via the ACM Computer Classification Codes

The Association for Computing Machinery (ACM) “provides the computing field's premier Digital Library and serves its members and the computing profession with leading-edge publications, conferences, and career resources” [1]. As part of the submission process to the digital library, each document is tagged with both controlled and uncontrolled vocabulary terms by the author to aid in that document's retrieval by other researchers.
A support vector approach based on penalty function method

Support vector machine (SVM) models are usually trained by solving the dual of a quadratic programming, which is time consuming. Using the idea of penalty function method from optimization theory, this paper combines the objective function and the constraints in the dual, obtaining an unconstrained optimization problem, which could be solved by a generalized Newton method, yielding an approximate solution to the original model. Extensive experiments on pattern classification were conducted, and compared to the quadratic programming-based models, the proposed approach is much more computationally efficient (tens to hundreds of times faster) and yields similar performance in terms of receiver operating characteristic curve. Furthermore, the proposed method and quadratic programming-based models extract almost the same set of support vectors.
Nonlinear stochastic filtration of satellite measurements

Abstract Existing methods for processing satellite measurements are based on the use of either the least squares method in different versions, or with the known model of motion of an object – various modifications of the Kalman filter. At the same time, the Kalman approach is more accurate, since it takes into account the dynamics of the movement of the object and the history of estimates, but its significant drawback is the need for a priori knowledge of the equations of motion of the object. In this regard, a new approach is proposed to assess the navigation parameters of the object by satellite measurements. On the one hand, this approach takes into account the dynamic nature of motion parameters and the history of estimates, and on the other hand, free from restriction in the form of accurate knowledge of the equations of motion of an object. The effectiveness of the considered approach in comparison with the existing traditional methods of processing satellite measurements is confirmed by the results of numerical modeling.
A Streaming Feature Extraction Accelerator using DPCM Image Compression Technique for SLAM Applications

The extraction of feature points plays a significant role in simultaneous localization and mapping (SLAM) applications. However, in the streaming architecture of the feature extraction, sizable row buffers are required to store data, usually occupying a large proportion of the hardware area. To ameliorate this problem, in this paper, we propose a streaming feature extraction architecture with narrower row buffers, combined with the differential pulse-code modulation (DPCM) image compression technique. Meanwhile, we improve the data flow to omit the compressions and decompressions in the critical data path by employing a novel strategy of transposing DPCM decompression and linear operation (TDDLO). Moreover, the calculations are further simplified by introducing an approximate algorithm of the rotation calculation. Consequently, the hardware costs are notably saved, while the impact of DPCM compression on power, latency, and accuracy is mitigated. The experimental results reveal at least a 32% reduction in memory compared with state-of-the-art architectures. Simulated by TSMC 28nm CMOS technology, the proposed architecture can process full-HD (1920×1080) images at 241 fps and consume only 52.7 mW power, while the normalized absolute trajectory error increases slightly by 0.2% on the TUM dataset.
A Realization Method of Aircraft Cabin Based on Mixed Reality Technology

This article introduces a cabin realization method to design the cabin of a civil aircraft, more specifically, a method that combines both virtual and physical designing techniques. In this framework, we need one segment of a simply-constructed physical cabin, a set of mixed reality eyeglasses, and a data synchronization server system. The simply-constructed cabin comes from the upper half barrel section of the fuselage (excluding the passenger cabin), and the cabin length is capable of accommodating about 3 rows of seats, and the main structure is composed of the aircraft truss, frame, skin and floor. The interior of the cabin is composed of several essential physical components such as interior trim panels, luggage compartments, seats, and porthole video systems. The MR (mixed reality) eyeglasses set consists of 3-4 pairs of eyeglasses, which are used to present virtual scenes. The data synchronization server system functions as rendering and transmitting virtual scene data streams, synchronization data, etc. This method integrates the advantages of physical and virtual compartments, and each gives full play to their strengths, balancing various factors such as cost, cycle, and practicality, so as to achieve the best overall efficiency.
Cryptanalysis on Lin and Wu’s Undeniable (t, n)-Threshold Signature Scheme with Cheater Identification

In this manuscript, we show the weakness of the undenialbe (t, n)-threshold signature scheme with cheater identification proposed by Lin and Wu. In our attack method, if cheater in the signer group is able to violate the correctness of the signature without being identified.
Monitoring Wheelchair Users in Care Facilities with BLE Beacons Attached to Wheels

As the human life span continues to increase due, the shortage of caregivers in nursing homes has become critical. In elderly nursing homes, health is maintained by good nutrition, physical rehabilitation, and the monitoring of many daily tasks that require assistance. Various methods were proposed in previous human-monitoring studies, including acceleration sensors and changes in power consumption. Unfortunately, these techniques do not apply to wheelchair users, and the costs of introducing and operating them are high. In this research, we develop a low-cost monitoring method for wheelchair users that uses BLE beacons that determine the amount of wheel rotations and whether the user is being self-propelled or pushed. Since the radio waves transmitted by BLE beacons are weak, the radio wave strength can be easily changed by the distance and direction of the receiver. Using this receiver feature, we installed a BLE beacon to the wheels of a wheelchair and the uniforms of caregivers and estimated their movements from the changes in the strength of the received radio waves. Using the proposed method, we estimated the number of wheel rotations with 100% accuracy and distinguished between self-propelling and being pushed with 93.0% accuracy.
On De-Interlacing and Sub-Pixel Precision Tracking

Video cameras with interlaced scan sensors find applications in a variety of tasks such as object tracking due to their lower overhead in terms of memory and the higher sensitivity in comparison to their counterparts that employ progressive scan sensors. Such cameras, however, suffer from noticeable interlacing artefacts that need to be corrected with appropriate de-interlacing methods before the target in the video can be accurately tracked. Despite this, the effect of de-interlacing methods on the object tracking accuracy has not yet been widely studied. In this work, the first comprehensive comparison of different de-interlacing methods is carried out in the context human computer interaction studies where precise finger tracking is required. Furthermore, we propose a semiautomatic sub-pixel annotation scheme to create precise ground truth for fingertip location, allowing the analysis of the impact of de-interlacing filters on tracking at sub-pixel level. The experimental part of the work showed that the de-interlacing filter by Pixop outperformed other filters that were evaluated. Moreover, the plausible benefits of sub-pixel precise tracking over pixel precise tracking in trajectory analysis were demonstrated.