Reviewerly/exHarmony huggingface dataset:
Efficient network with ghost tied block for heart segmentation

Heart segmentation plays an important role in accurate diagnosis and treatment of cardiovascular disease. More recently, deep convolutional neural networks (CNN) are predominant to many medical image analysis applications including 3D heart segmentation. For example, 3D UNet with U-shape encoder-decoder architecture performs well in volumetric segmentation. However, standard convolution, the building block of 3D CNN network usually contains a large number of parameters. In this paper, our objective is to investigate a light convolution module to build an efficient network. Inspired by 2D Tied Block Convolution (TBC), we introduce a Tied Block 3D Convolution (TBC-3D) operator which reuses a small number of convolution filters across each channel group. To this end, TBC-3D requires fewer parameters and is able to obtain more feature maps with high performance. Furthermore, we combine TBC-3D with the Ghost-3D module to construct Ghost Tied Block (GTB). Specifically, Ghost module employs standard convolution (OP1) with few filters to obtain intrinsic feature maps, and then generates more features by cheap linear operation like depth-wise convolution (OP2). TBC-3D is applied in both OP1 and OP2 in the Ghost-3D module. Compared to state-of-the-art solutions using 3D UNet-like architecture, our model with GTB achieves competitive performance on the MM-WHS whole heart segmentation Challenge 2017 datasets with 2.31x less parameters and 1.93x fewer FLOPs.
Research on Recognition Method of Truck Colors Based on Convolutional Neural Network Model

Truck color is one of the important information of vehicle ownership in intelligent transportation system. Aiming at the problem that truck color recognition is easy to be affected by complex scene and illumination changes, this paper studied the truck recognition method based on convolutional neural network, constructed the truck color image set, transformed truck vehicle image samples into RGB, HSV, and LAB color spaces. A truck color recognition method based on convolutional neural network model is proposed and experimental research is proposed. The experimental results show that the performance of the proposed convolutional neural network model based on LAB color space is better than that of proposed convolutional neural network model based on HSV color space, and achieved a verification accuracy at 96.36&#x0025;.
Detection and Verification for Deepfake Bypassed Facial Feature Authentication

Facial features have become one of the leading standards for biometric authentication. With the growing demand for the Internet of Things, it has become imperative to have systems to detect any data breach used for authentication. New deep fake technologies have made it possible to compromise the database, making real-time authentication inefficient. The counterfeit to the real-time data can be challenging to detect. We have developed a deep fake image detection utilizing ResNet50 and Spatial Pyramidal Pooling to tackle this problem. Our model detects manipulation in real-time data, irrespective of its size and background, at an accuracy of 94%. This model will allow us to detect bypass in the database for facial biometric authentication and improve the system’s security.
Economic reform and political stagnation: The inconsistent patterns of institutional change

Abstract This study examines whether convergence in institutional quality distinguished by economic and political dimensions of institutions has occurred across countries. The study uses data from 81 countries over the period of 1985 to 2018. The study tests the convergence hypothesis by using three different measures of institutional quality capturing political and economic dimensions of institutions. The empirical results show that convergence in economic institutional quality has occurred and most countries with weak economic institutions have a higher rate of change compared with countries with strong economic institutions. In contrast, for political institutions, the convergence process is short‐lived.
Measures to improve curriculum through analysis of the recognition and demand of education college students on creativity convergence education

Recently, a crucial issue of college innovation in Korea is creativity convergence education. Fostering education college students with creativity convergence competency is emerging as an important aspect. However, the current curriculum operation to implement a creativity convergence education for the education of college students is insufficient. Accordingly, an analysis of the recognition and demand of education college students on creativity convergence education was done. A survey was conducted on the recognition and demand of creativity convergence education for 326 education college students, and the differences were compared. As a result of this study, various differences were revealed according to gender, grade, and detailed majors in the education college. These results show that a creativity convergence education should be implemented considering the background variables of education college students and more opportunities for various career paths and choices should be provided. Therefore, it implies that the creativity convergence education that reflects the recognition and demand of education college students should be more actively conducted in the education colleges.
An Accelerated and Flexible SIFT Parallel-Computing Approach Based on the General Multi-Core Platform

Visual retrieval has been a significant technology in the computer vision task. Visual feature descriptors are the key to the visual retrieval. The famous local feature descriptor is called the Scale Invariant Feature Transform (SIFT), which can keep invariant mapping for the scale, rotate and simulate images. To utilize effectively the SIFT feature descriptor for visual matching on different hardware platforms, this paper proposes an accelerated SIFT algorithm based on the SIFT feature computing principle of the general multi-core platform. First, our multi-core task allocation method introduces the WFM theory into task assignment for each core to improve the core computing resource utilization for high-efficient parallel computing. Then, to improve the efficiency of picture matching, we introduce global geometric constraints condition to optimal picture matching for the multi-core parallelization approach. Experimental results show that the proposed approach can save on average 87.31% on the Intel X86 platform, compared to the single-core time. Also, our approach can save on average 33.79% on the Raspberry Pi platform, compared to the single-core time.
A Generic Contact Tracing Framework

This paper aims at implementing a generic contact tracing framework for efficiently identifying people at risk during any pandemic. It makes use of wireless networks and QR code checkpoints to record close contacts between individuals using their smartphones. It also uses smart cameras to detect special face masks consisting of QR codes in real-time, using the YOLOv5 algorithm. The face masks are then decoded to uniquely identify people wearing them. Moreover, distances are estimated from network signal strengths and the triangle inequality theorem is used to identify close contacts. All information is stored in a graph database for fast analysis. Parallel processing is used to reduce the time taken for identifying and alerting people who were possibly in contact with an infected person. In addition, a web application enables administrators to visualise infection chains and user displacements in an interactive map. The system has been thoroughly evaluated and the results demonstrate that it is highly effective and customisable for any pandemic. Its privacy-oriented aspect also enables a high adoption rate among users. Lastly, the smart camera system enables facemask-driven contact tracing during the Covid-19 pandemic.
Design of Interactive System for Autonomous Learning of Business English Majors Based on Deep Learning Algorithms

In recent years, breakthroughs have been made in the fields of image processing, natural language understanding, speech recognition, and online advertising. Integrate deep learning into the recommendation system, study the construction of self-service learning ability for business English majors based on heterogeneous data, and verify the experimental hypothesis that “reflection can promote the achievement of deep learning goals” through the analysis of the difference between written tests and work evaluations., Through the analysis of the difference between the written test and the work evaluation, verify the experimental hypothesis that “reflection can promote the achievement of deep learning goals.”
Material characterization of pagerjurang clay as a raw material for pottery

Abstract Various functional and decorative pottery articles are produced of Pagerjurang, Klaten. The articles are made of raw material which are processed, shaped, dried, and burned in traditional kilns. In this study, a raw material of Pagerjurang Clay was characterized using XRD (X-ray Diffractometer) and SEM/EDX (Scanning Electron Microscope- Energy Dispersive X-ray ). The results showed that Pagerjurang Clay was contained bentonite (24,87 %), quartz (37.08 %), feldspar (9.46 %), and illite (28.57 %), that consist silica 46.51% (w/w) and alumina 17.34 % (w/w), respectively. Pagerjurang Clay is red-browns color; very fine size; easy to break while dry, but very tough and sticky while wet; and easily made into the desired shape and elastic (not easily to be broken)
Arabic Automatic Essay Scoring Systems: An Overview Study

Assessing a large number of student essays is a challenging task for teachers. It requires a lot of time and effort. Therefore, the automation of this work has become essential. For a decade, Automatic Essay Scoring (AES) systems have represented a very important and complicated research axis in the field of natural language processing. In literature, various methods of automatic essay scoring (AES) have been proposed. However, most of them have concentrated on European languages, while works dealing with Arabic language are very limited. This paper presents an overview of Arabic Automatic Essay Scoring systems. The purpose of this study is to present a state of the art of the existing methods and models of Automatic Essay Scoring (AES) systems in Arabic language. We describe some models then we compare and discuss the achieved results. Finally, we give our perspectives and challenges to propose our future work in this context.
GraceOutZip: Graceful labeled Graph summarization approach with Percolated anomalous discovery

Voluminous graph data management is a daunting problem in every real world application of a kind, besides the advancements in computation and storage technology. Efficient graph summarization techniques were contributed to achieve the substantial need for preserving novelty in social graphs. A GraceOutZip compression friendly graph reordering technique using graceful labeling strategy is adopted. User defined probabilistic selection method that provides unique labels for every identified outlier for potential use. Proposed method exploits unicycle-star based community representation rendering assignment of both node and edge labels based on graceful property. A novel mathematical programming model GraceOutZip is proposed to perform lossless compression with graph decomposition and unique label arrangement with intention for futuristic graph reconstruction. The experimental study on different real world network datasets demonstrates that GraceOutZip shows better scalable performance in perspective of interactive large-scale visual analytics and query optimization with 4 times better compression than the state-of-the-art representative method.
Sparse capsule networks for informative representation learning in digital pathology

Digital pathology involves the digitization of high quality tissue biopsies on microscope slides to be used by physicians for patient diagnosis and prognosis. These slides have become exciting avenues for deep learning applications to improve care. Despite this, labels are difficult to produce and thus remain rare. In this work, we create a sparse capsule network with a spatial broadcast decoder to perform representation learning on segmented nuclei patches extracted from the BreastPathQ dataset. This was able to produce disentangled latent space for categories such as rotations, and logistic regression classifiers trained on the latent space performed well.
Enhanced Connectivity Validity Measure Based on Outlier Detection for Multi-Objective Metaheuristic Data Clustering Algorithms

Data clustering algorithms experience challenges in identifying data points that are either noise or outlier. Hence, this paper proposes an enhanced connectivity measure based on the outlier detection approach for multi-objective data clustering problems. The proposed algorithm aims to improve the quality of the solution by utilising the local outlier factor method (LOF) with the connectivity validity measure. This modification is applied to select the neighbour data point’s mechanism that can be modified to eliminate such outliers. The performance of the proposed approach is assessed by applying the multi-objective algorithms to eight real-life and seven synthetic two-dimensional datasets. The external validity is evaluated using the F-measure, while the performance assessment matrices are employed to assess the quality of Pareto-optimal sets like the coverage and overall non-dominant vector generation. Our experimental results proved that the proposed outlier detection method has enhanced the performance of the multi-objective data clustering algorithms.
Robert Johnson and spectral timbre: what we hear, what we construct

Abstract The myth of the deal with the devil at the crossroads frames the reception of Robert Johnson's corpus, particularly lyrics that directly or indirectly reference otherworldly forces. Setting aside the myth, I analyse the uncanny sonic qualities in his performances that evoke a spectral presence. A close examination of vocal and guitar timbre that considers harmonic resonance, chimera effect and other ‘eerie’ qualities of the music breaks down musical elements other than lyrics that evoke the myth of supernatural power. Humming, mumbling and talking, corner loading, falsetto, simultaneous rhythm and lead, among other acoustic effects, contribute to a complex soundscape with spectral resonances. Taking a lead from Johnson's mentor Ike Zimmerman and his fondness for practising in the cemetery, my analysis of timbral quality informed by Freud's concept of the uncanny seeks to explore what we hear, how it is produced and the origin that we construct for the sounds.
Seismology Perspectives on Integrated, Coordinated, Open, Networked (ICON) Science

Abstract Seismology focuses on the study of earthquakes and associated phenomena to characterize seismic sources and Earth structure, which both are of immediate relevance to society. This article is composed of two independent views on the state of the integrated, coordinated, open, networked (ICON) principles (Goldman et al., 2021, https://doi.org/10.1029/2021eo153180 ) in seismology and reflects on the opportunities and challenges of adopting them from a different angle. Each perspective focuses on a different topic. Section 1 deals with the integration of multiscale and multidisciplinary observations, focusing on integrated and open approaches, whereas Section 2 discusses computing and open‐source algorithms, reflecting coordinated, networked, and open principles. In the past century, seismology has benefited from two co‐existing technological advancements—The emergence of new, more capable sensory systems and affordable and distributed computing infrastructure. Integrating multiple observations is a crucial strategy to improve the understanding of earthquake hazards. However, current efforts in making big datasets available and manageable lack coherence, which makes it challenging to implement initiatives that span different communities. Building on ongoing advancements in computing, machine learning algorithms have been revolutionizing the way of seismic data processing and interpretation. A community‐driven approach to code management offers open and networked opportunities for young scholars to learn and contribute to a more sustainable approach to seismology. Investing in new sensors, more capable computing infrastructure, and open‐source algorithms following the ICON principles will enable new discoveries across the Earth sciences.
Mobilization of Au and Ag during Supergene Processes in the Linglong Gold Deposit: Evidence from SEM and LA–ICP–MS Analyses of Sulfides

Precious metals can be mobilized during supergene processes, which are important for the formation of high-grade or high-purity ores. The world-class Linglong gold deposit has high-grade ores that have undergone supergene processes in the near-surface zone. Under which conditions the supergene modification occurred and how Au and Ag behaved during the supergene processes have been poorly studied in this deposit. Here, we performed scanning electron microscope (SEM) and laser ablation–inductively coupled plasma–mass spectrometry (LA–ICP–MS) analyses on samples from the supergene enrichment zone of the Linglong gold deposit. The results show that secondary minerals were formed sequentially from magnetite-goethite-limonite to marcasite-acanthite, and finally to siderite after the primary minerals of pyrite-pyrrhotite-chalcopyrite. These mineral assemblages and variations indicate that the supergene modification by groundwater occurred under oxidative and weakly acidic conditions in the near-surface zone and evolved to reductive and near neutral conditions in the supergene enrichment zone. The newly formed marcasite has much higher Au (0.003–23.5 ppm, mean of 1.33 ppm) and Ag (81.7–6021 ppm, mean of 1111 ppm) concentrations than those of the primary pyrite (Au of 0.004–0.029 ppm and Ag of 0.22–4.14 ppm), which together with the formation of independent Ag–S mineral (acanthite), indicates that Au and Ag were significantly mobilized and fractionated during the supergene processes. These processes improved the Au and Ag grades in the supergene enrichment zone and thus facilitate their extraction.
Research on object-oriented data model technology in tourism information management

The tourism information management system design mainly adopts the object-oriented data model, where the technical key points of the system design can be found. Through ORDBS, the maintenance and modification of the information management system are easier. With the establishment of the tourism information management data model and transforming the model into a relational database, the tourism information management system can be better built.
Experience of Digital Transformation in a Small University, 2021

In Fukuyama University, we have an educational affairs system and a course management system for education for more correct work and increasing efficiency of educational affairs and education. However, we do not have a common computer system for our office work except Office 365. Most procedures for office work are currently processed using pieces of paper and seals, which are time consuming and annoying. There is a tendency to promote digital transformation in Japan in the 2020s, but it is not easy to transform previous paperwork to effective digitalized work in a small university. This paper discusses the experience of digital transformation in Fukuyama University. The president of our university asked us to digitalize one of our procedures. We made a prototype system for the work using Office 365, discussing with officers who are in charge of the work. The system is going to be deployed shortly. Our educational affairs system is not perfect. There are many things to deal with by the faculties’ hands. In order to reduce such chores, we showed some examples of Python programs using Selenium, which is a tool for automating the handling of a web page, to a group of the faculty.
Spectral characteristics and petrology of Sinai Mesozoic basaltic rocks: Gebel Farsh El Azraq-Gebel Himayir area, Um Bogma, southwestern Sinai, Egypt

Sinai Mesozoic basalts are restricted in its southwestern portion at Um Bogma area, where they occur as intra-continental fissure-fed sills, dykes, plugs and lava flows, following mainly the older structural and crustal weaknesses planes. During Late Paleozoic - Middle to Late Triassic, Egypt had witnessed sporadic tectonic disturbances and faulting activities accompanied by volcanisms. Faulting activity continued during Early to Middle Jurassic and was associated with crustal stretching and basaltic volcanism. At Um Bogma area, southwestern Sinai Peninsula, Middle Triassic, and Early Jurassic basaltic exposures intruded/extruded the Carboniferous sandstones. The main Mesozoic basalts outcrop at Gebel Farsh El Azraq - Gebel Himayir area, where they were successfully discriminated from their surrounding country rocks using Landsat-8 dataset. The Mesozoic basaltic rocks are the least reflective rocks and exhibit a characteristic spectral curve, compared to their country rocks. Landsat-8 bands 7, 5, 3 in RGB and Principal Component Analyses (PCA) technique were used in lithological discrimination of the Mesozoic basalts and their surrounding Late Neoproterozoic basement rocks and Phanerozoic sedimentary succession. Accordingly, a new geological map was produced for the study area. Petrography and geochemistry of these basalts were addressed. Characteristically they contain substantial MgO contents (22.24 to 7.09 wt%) and exhibit low TiO2 (1.56–0.77 wt%) and P2O5 (0.30–0.15 wt%) contents. Geochemically, they pertain to the low P2O5 –TiO2 continental tholeiitic to transitional basalts and point out to enriched MORB magma sources. They suggest derivation from spinel-garnet lherzolite source by moderate non-modal partial melting (ca 20–30%). Their petrogenesis was discussed and fractional crystallization modeling was performed as a plausible mechanism to be accounted for their evolutions.
Estimating Context-Specific Subjective Content Descriptions using BERT

An agent in pursuit of a task may work with a corpus containing text documents associated with Subjective Content Descriptions (SCDs) [1]. SCDs provide additional location-specific data for documents and add value in the context of the agent's task. On the pursuit of new documents to add to the corpus, the agent may come across documents without associated SCDs or documents where content and SCDs are interleaved. Therefore, this paper presents approaches estimating SCDs using the well-known BERT [2] language model. Furthermore, the paper presents approaches separating SCDs and actual content interleaved in a document. An evaluation compares the performance of BERT with approaches from Kuhr et al. [1] and Bender et al. [3], which use an SCD-word distribution represented by an SCD matrix <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta(\mathcal{D})$</tex> . The evaluation uses text documents annotated with additional textual definitions.
Abstract Reservoir Computing

Noise of any kind can be an issue when translating results from simulations to the real world. We suddenly have to deal with building tolerances, faulty sensors, or just noisy sensor readings. This is especially evident in systems with many free parameters, such as the ones used in physical reservoir computing. By abstracting away these kinds of noise sources using intervals, we derive a regularized training regime for reservoir computing using sets of possible reservoir states. Numerical simulations are used to show the effectiveness of our approach against different sources of errors that can appear in real-world scenarios and compare them with standard approaches. Our results support the application of interval arithmetics to improve the robustness of mass-spring networks trained in simulations.