Reviewerly/exHarmony huggingface dataset:
Survival Analysis of Battalion-Level Commanders(leaders) Using Big Data as Results of Brigade-Level KCTC Training - Focused on Infantry Battalion Defensive Operations -

Authors:
raw_author_name: Hoseok Moon

Abstract: In this study, we conducted a survival analysis on battalion-level commanders(leaders), focusing on infantry battalion defensive operations using the big data of brigade-level KCTC(Korea Combat Training Center) training results.Unlike previous studies, we utilized the brigade-level KCTC training results data for the first time to conduct a survival analysis, and the research subjects were battalion-level commanders(leaders), which can affect the battle. At this time, the battle results were defined, and through cluster analysis, infantry battalions were divided into excellent, average, and insufficient units, and the difference in the survival rate of the commanders was analyzed through the Kaplan-Meier survival analysis. This provided an opportunity to objectively compare the differences between excellent and insufficient units. Subsequently, factors affecting the survival of commanders were derived using the Cox proportional hazard model, and it was possible to confirm the influencing factors from various angles by also using the survival tree model. Significance and limitations confirmed in the research process were presented as policy suggestions and future research directions.
Extended Reality Technologies in Physical Fitness for Health Promotion

Authors:
raw_author_name: John Paul P. Miranda
raw_affiliation_string: Don Honorio Ventura State University, Philippines

Abstract: This chapter presents a bibliometric analysis of extended reality technologies (i.e., augmented, virtual, and mixed reality) concerning physical fitness by analyzing 251 published documents from Scopus-indexed sources. Results from the analysis indicated that medicine and computer science are the prominent subject areas, and the International Journal of Environmental Research and Public Health and Lecture Notes in Computer Science are the leading publishers in this domain. The analysis further found variability in terms of authorship patterns and international collaboration in 58 documents. In addition to this, the analysis found that there has been a steady increase in publications in this domain, with a substantial surge from 2019 onwards. The United States and China are the leading contributors, with several collaborative networks among countries, particularly among high-income, western nations. Furthermore, the keyword co-occurrences indicated that there is a focus on VR, gamification, and health issues related to older adults.
Extraction of information and facts from data mining of random sequences for undergraduate research

Authors:
raw_author_name: sunil Dehipawala
raw_affiliation_string: Queensborough Community College
raw_author_name: George Tremberger
raw_affiliation_string: CUNY Queensborough Community College
raw_author_name: Tak Cheung
raw_affiliation_string: CUNY Queensborough COmmunity College

Abstract: Abstract A general method to extract information and facts from data mining of random sequences in biology and astronomy has been developed. The random sequence analysis has been implemented in several NSF-REU projects using NIH and NASA databases. Examples of RNA sequence with reference to Shannon-entropy based bioinformatics and SDO magnetic topology analysis with reference to solar physics are presented. The contrast to Brookhaven Synchrotron and high energy physics data analysis is also discussed. The feedback of administrating REU projects to our physics teaching for engineering students was found to be valuable and the examples of magnetic reconnection and geomagnetic induced current are presented. Therefore community college REU project provides connectedness awareness in the linking of previous published reports, critical thinking in result interpretation, and career development when going onto a senior college REU program, the top three benefits of college education, according to a 2016 July Money Magazine "Value of College" survey.
Sparse Observation Assimilated Digital Image Correlation for High Fidelity Deformation Field Reconstruction

Authors:
raw_author_name: Bangyan Niu
raw_affiliation_string: State Key Laboratory of Intelligent Manufacturing and Equipment Technology, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Jingjing Ji
raw_affiliation_string: State Key Laboratory of Intelligent Manufacturing and Equipment Technology, Huazhong University of Science and Technology, Wuhan, China

Abstract: Digital image correlation (DIC) is an image-based deformation measurement method, which has been widely used in multiple cutting-edge fields, benefitted from its noncontact, low cost and high robustness. However, achieving higher accuracy and overcoming the tradeoff between resolution and measurement uncertainty have been recognized as bottleneck problems for DIC. This paper proposes a new method by assimilating sparse observations into DIC, constituting “Assimilated DIC”, to reconstruct deformation fields with high fidelity. In the proposed method, the subpixel registration process in the traditional DIC procedure is viewed as a dynamic model, which constitutes the basis for assimilation. Meanwhile, the sparse observation provides highly accurate guidance for correcting the dynamic model, thus facilitating the update of model data towards the ground-truth. In addition to effectively improving the measurement accuracy without lowering the computational efficiency, Assimilated DIC can obtain a higher resolution without compromising the accuracy performance. And the boundary regions of multi-connected structures, which are generally subject to large errors by traditional methods, can be precisely reconstructed by Assimilated DIC. In this work, the proposed method with two types of observations on strain and displacement has been experimentally validated under various deformation cases, including a rigid-body translation, a heterogeneous deformation, and an extension of a holed plate. These excellent properties of Assimilated DIC have been confirmed in comparison with those of a traditional DIC method.
AYOLOv5: Improved YOLOv5 based on attention mechanism for blood cell detection

Authors:
raw_author_name: Kexue Sun
raw_affiliation_string: College of Electronic and Optical Engineering & College of Flexible Electronics (Future Technology), Nanjing University of Posts and Telecommunications, 210023, China

Abstract: A crucial component of biological study and clinical trials is the microscopic examination of cells and tissues. Although tremendous progress has been made in computer-assisted microscopy cell detection techniques, especially recently based on deep learning, it still needs to be determined to accurately identify cell targets in the presence of dense and complex cell distribution. In this research, an improved YOLOv5 (AYOLOv5) based on the attention mechanism is suggested to address the issue of the low recognition rate of cell detection caused by this circumstance. Based on YOLOv5, the attention mechanism is introduced to improve the convolutional neural network's features in areas of the picture with a high density of features. The convolutional block attention module (CBAM) and the transformer encoder block are used in this study to develop the attention mechanism. The YOLOv5 integrated convolutional block attention module increases the weight of cell-dense regions in blood cell pictures and aids the network's ability to resist information other than cells. Additionally, the transformer block is introduced to YOLOv5′s processing of upper and lower feature data to improve the network's capacity to gather details about various cell properties, enabling AYOLOv5 to recognize and distinguish blood cells in cell-dense areas. The experiment was done on the dataset BCCD, and the mAP results for cell detection reached 93.3%, better than previously discovered. Also, the validation set's average recognition accuracy increased from 89% to 98%. The experimental results demonstrated that the suggested AYOLOv5 could extract the cells' feature information more effectively, considerably improving the cell pictures and recognition performance.
Unknown Threats Detection Methods of Smart Contracts

Authors:
raw_author_name: Daojing He
raw_affiliation_string: School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
raw_author_name: Sammy Chan
raw_affiliation_string: Department of Electrical Engineering, City University of Hong Kong, Hong Kong, China
raw_author_name: Mohsen Guizani
raw_affiliation_string: Machine Learning Department, Mohamed Bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE

Abstract: With the explosive growth of blockchain platforms and applications, security threats of blockchain also occur frequently. As a decentralized application deployed on the blockchain, smart contracts help the blockchain realize safe and efficient information storage, asset management, and value transfer. Therefore, smart contracts play a vital role in the security of the blockchain. In recent years, security threats against smart contracts have increased, not only causing huge economic losses but also impacting the credit system of the blockchain. Therefore, many researchers have carried out corresponding research on the security threats of smart contracts. Common threat detection methods include formal verification, symbolic execution, fuzzing, etc. Most of these methods are only for known threats, while there is not much work on detecting unknown threats. In order to better deal with unknown threats, we present a review of the typical smart contract security events in recent years, analyze the security threats from contract coding, Ethereum virtual machine, and blockchain characteristics. Further, we compare and summarize the latest unknown threat detection methods. Then, to address the problem that very few unknown threat samples are available, a detection method based on a few-shot learning is proposed.
Semantic Correlation Attention-Based Multiorder Multiscale Feature Fusion Network for Human Motion Prediction

Authors:
raw_author_name: Qin Li
raw_affiliation_string: School of Automation, Central South University, Changsha, China
raw_author_name: Yong Wang
raw_affiliation_string: School of Automation, Central South University, Changsha, China
raw_author_name: Fanbing Lv
raw_affiliation_string: Research and Development Department, Changsha Hisense Intelligent System Research Institute Company Ltd, Changsha, China

Abstract: Human motion prediction is to predict future human states based on the observed human states. However, current research ignores the semantic correlations between body parts (joints and bones) in the observed human states and motion time; thus, the prediction accuracy is limited. To address this issue, we propose a novel semantic correlation attention-based multiorder multiscale feature fusion network (SCAFF), which includes an encoder and a decoder. In the encoder, a multiorder difference calculation module (MODC) is designed to calculate the multiorder difference information of joint and bone attributes in the observed human states. Then, multiple semantic correlation attention-based graph calculation operators (SCA-GCOs) are stacked to extract the multiscale features of the multiorder difference information. Each SCA-GCO captures joint and bone dependencies of the multiorder difference information, refines them with a semantic correlation attention module (SCAM), and captures temporal dynamics of the refined joint and bone dependencies as the output features. Note that SCAM learns a semantic attention mask describing the semantic correlations between body parts and motion time for feature refinement. Afterward, multiple multiorder feature fusion modules (MOFFs) and multiscale feature fusion modules (MSFFs) are designed to fuse the multiscale features of the multiorder difference information extracted by multiple SCA-GCOs, thus obtaining the motion features of the observed human states. Based on the obtained motion features, the decoder recurrently recruits a composite gated recurrent module (CGRM) and multilayer perceptrons (MLPs) to predict future human states. As far as we know, this is the first attempt to consider the semantic correlations between body parts and motion time in human motion prediction. The results on public datasets demonstrate that SCAFF outperforms existing models.
Parameterized Convex Universal Approximators for Decision-Making Problems

Authors:
raw_author_name: Kim, Jinrae
raw_affiliation_string: Department of Aerospace Engineering, Seoul National University, Seoul 08826, Republic of Korea
raw_author_name: Kim, Youdan
raw_affiliation_string: Department of Aerospace Engineering, Institute of Advanced Aerospace Technology, Seoul National University, Seoul, Republic of Korea

Abstract: Parameterized max-affine (PMA) and parameterized log-sum-exp (PLSE) networks are proposed for general decision-making problems.The proposed approximators generalize existing convex approximators, namely, max-affine (MA) and log-sum-exp (LSE) networks, by considering function arguments of condition and decision variables and replacing the network parameters of MA and LSE networks with continuous functions with respect to the condition variable.The universal approximation theorem of PMA and PLSE is proven, which implies that PMA and PLSE are shape-preserving universal approximators for parameterized convex continuous functions.Practical guidelines for incorporating deep neural networks within PMA and PLSE networks are provided.A numerical simulation is performed to demonstrate the performance of the proposed approximators.The simulation results support that PLSE outperforms other existing approximators in terms of minimizer and optimal value errors with scalable and efficient computation for high-dimensional cases.
The <i>Transform-and-Perform</i> Framework: Explainable Deep Learning Beyond Classification

Authors:
raw_author_name: Ruud J G van Sloun
raw_affiliation_string: Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
raw_author_name: Stef van den Elzen
raw_affiliation_string: Department of Mathematics and Computer Science, Eindhoven University of Technology, AZ, Eindhoven, The Netherlands
raw_author_name: Anna Vilanova
raw_affiliation_string: Department of Mathematics and Computer Science, Eindhoven University of Technology, AZ, Eindhoven, The Netherlands
raw_author_name: Nicola Pezzotti
raw_affiliation_string: Department of Mathematics and Computer Science, Eindhoven University of Technology, AZ, Eindhoven, The Netherlands

Abstract: In recent years, visual analytics (VA) has shown promise in alleviating the challenges of interpreting black-box deep learning (DL) models. While the focus of VA for explainable DL has been mainly on classification problems, DL is gaining popularity in high-dimensional-to-high-dimensional (H-H) problems such as image-to-image translation. In contrast to classification, H-H problems have no explicit instance groups or classes to study. Each output is continuous, high-dimensional, and changes in an unknown non-linear manner with changes in the input. These unknown relations between the input, model and output necessitate the user to analyze them in conjunction, leveraging symmetries between them. Since classification tasks do not exhibit some of these challenges, most existing VA systems and frameworks allow limited control of the components required to analyze models beyond classification. Hence, we identify the need for and present a unified conceptual framework, the Transform-and-Perform framework (T&P), to facilitate the design of VA systems for DL model analysis focusing on H-H problems. T&P provides a checklist to structure and identify workflows and analysis strategies to design new VA systems, and understand existing ones to uncover potential gaps for improvements. The goal is to aid the creation of effective VA systems that support the structuring of model understanding and identifying actionable insights for model improvements. We highlight the growing need for new frameworks like T&P with a real-world image-to-image translation application. We illustrate how T&P effectively supports the understanding and identification of potential gaps in existing VA systems.
CGOF++: Controllable 3D Face Synthesis With Conditional Generative Occupancy Fields

Authors:
raw_author_name: Sun, Keqiang
raw_affiliation_string: Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR
raw_author_name: Wu, Shangzhe
raw_affiliation_string: Department of Engineering Science, University of Oxford, Oxford, U.K.
raw_author_name: Zhang, Ning
raw_affiliation_string: SenseTime Research, Beijing, China
raw_author_name: Huang, Zhaoyang
raw_affiliation_string: Department of Electronic Engineering, Chinese University of Hong Kong, Hong Kong SAR
raw_author_name: Wang, Quan
raw_affiliation_string: SenseTime Research, Beijing, China
raw_author_name: Li, Hongsheng
raw_affiliation_string: CUHK MMlab, Centre for Perceptual and Interactive Intelligence, Shanghai AI Laboratory, Shanghai, China

Abstract: Capitalizing on the recent advances in image generation models, existing controllable face image synthesis methods are able to generate high-fidelity images with some levels of controllability, e.g., controlling the shapes, expressions, textures, and poses of the generated face images. However, previous methods focus on controllable 2D image generative models, which are prone to producing inconsistent face images under large expression and pose changes. In this paper, we propose a new NeRF-based conditional 3D face synthesis framework, which enables 3D controllability over the generated face images by imposing explicit 3D conditions from 3D face priors. At its core is a conditional Generative Occupancy Field (cGOF++) that effectively enforces the shape of the generated face to conform to a given 3D Morphable Model (3DMM) mesh, built on top of EG3D [1], a recent tri-plane-based generative model. To achieve accurate control over fine-grained 3D face shapes of the synthesized images, we additionally incorporate a 3D landmark loss as well as a volume warping loss into our synthesis framework. Experiments validate the effectiveness of the proposed method, which is able to generate high-fidelity face images and shows more precise 3D controllability than state-of-the-art 2D-based controllable face synthesis methods.
Secure Task Distribution With Verifiable Re-Encryption in Mobile-Crowdsensing-Assisted Emergency IoT System

Authors:
raw_author_name: Liquan Jiang
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China
raw_author_name: Mamoun Alazab
raw_affiliation_string: College of Engineering, IT and Environment, Charles Darwin University, Darwin, NT, Australia
raw_author_name: Zhiguang Qin
raw_affiliation_string: School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, China

Abstract: Extreme events (such as earthquakes, hurricanes, etc.) pose a dual challenge to the reliability and serviceability of IoT systems. With regard to this challenge, by publishing some tasks and then encouraging the public to assist in real-time data collection through their mobile terminals (namely, the mobile crowdsourcing-assisted IoT systems), is expected to play an important role in secondary disaster prevention and personnel rescue in extreme events. However, it has weaknesses in terms of security, flexibility, and efficiency. As an elegant solution, identity-based broadcast proxy re-encryption (PR-IBBE) enables flexible access authorization sharing and efficient broadcast distribution of encrypted tasks via the cloud. However, their security relies on fully-trusted or semi-trusted cloud assumptions, which are hard to be implemented in real-world scenarios. And the cloud is more vulnerable in an emergency event since there is a lack of effective management. Motivated by that, we propose the verifiable PR-IBBE (VPR-IBBE) scheme, which realizes a cross-domain identity-based broadcast task file secure authorization access, and empowers the verifiability and reputability of re-encrypted ciphertext under the untrusted cloud setting. This mechanism ensures that the relevance between the re-encrypted ciphertext and the original ciphertext can be publically verified, so the cloud can defend itself if there is a malicious accusation of forging the re-encrypted ciphertext. Through rigorous formal security proofs, we demonstrate that VPR-IBBE attains the indistinguishability of ciphertext against selective identity chosen ciphertext attack (IND-sID-CPA), and is also resistant to the collusion attack between the untrusted cloud and the cooperative performer. Theoretical comparison and experimental results demonstrate the practicability of our VPR-IBBE scheme, as well as the superiority over representative related works.
Parallel Self-Attention and Spatial-Attention Fusion for Human Pose Estimation and Running Movement Recognition

Authors:
raw_author_name: Qingtian Wu
raw_affiliation_string: Department of Computer and Information Science, Faculty of Sciences and Technology, University of Macau, Macau, China
raw_author_name: Yu Zhang
raw_affiliation_string: Computer Science and Technology College, Shenyang University of Chemical Technology, Shenyang, China; Faculty of Sciences and Technology, University of Macau, Macau, China
raw_author_name: Liming Zhang
raw_affiliation_string: Department of Computer and Information Science, Faculty of Sciences and Technology, University of Macau, Macau, China
raw_author_name: Haoyong Yu
raw_affiliation_string: Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore

Abstract: Human pose estimation (HPE) is a fundamental yet promising visual recognition problem. Existing popular methods (e.g., Hourglass and its variants) either attempt to directly add local features element-wisely, or (e.g., vision transformers) try to learn the global relationships among different human parts. However, it remains an open problem to effectively integrate the local-global representations for accurate HPE. In this work, we design four feature fusion strategies on the hierarchical ResNet structure, including direct channel concatenation, element-wise addition and two parallel structures. Both two parallel structures adopt the naive self-attention encoder to model global dependencies. The difference between them is that one adopts the original ResNet BottleNeck while the other employs a spatial-attention module (named SSF) to learn the local patterns. Experiments on COCO Keypoint 2017 show that our SSF for HPE (named SSPose) achieves the best average precision with acceptable computational cost among the compared state-of-the-art methods. In addition, we build a lightweight running dataset to verify the effectiveness of SSPose. Based solely on the keypoints estimated by our SSPose, we propose a regression model to identify valid running movements without training any other classifiers. Our source codes and running dataset are publicly available.