Reviewerly/exHarmony huggingface dataset:
A fast and accurate brain extraction method for CT head images

Abstract Background Brain extraction is an essential prerequisite for the automated diagnosis of intracranial lesions and determines, to a certain extent, the accuracy of subsequent lesion recognition, location, and segmentation. Segmentation using a fully convolutional neural network (FCN) yields high accuracy but a relatively slow extraction speed. Methods This paper proposes an integrated algorithm, FABEM, to address the above issues. This method first uses threshold segmentation, closed operation, convolutional neural network (CNN), and image filling to generate a specific mask. Then, it detects the number of connected regions of the mask. If the number of connected regions equals 1, the extraction is done by directly multiplying with the original image. Otherwise, the mask was further segmented using the region growth method for original images with single-region brain distribution. Conversely, for images with multi-region brain distribution, Deeplabv3 + is used to adjust the mask. Finally, the mask is multiplied with the original image to complete the extraction. Results The algorithm and 5 FCN models were tested on 24 datasets containing different lesions, and the algorithm’s performance showed MPA = 0.9968, MIoU = 0.9936, and MBF = 0.9963, comparable to the Deeplabv3+. Still, its extraction speed is much faster than the Deeplabv3+. It can complete the brain extraction of a head CT image in about 0.43 s, about 3.8 times that of the Deeplabv3+. Conclusion Thus, this method can achieve accurate brain extraction from head CT images faster, creating a good basis for subsequent brain volume measurement and feature extraction of intracranial lesions.
Parallel-filter-based observer control for unknown multiple narrow-band disturbance rejection

An observer-based control method with parallel Q-filter is proposed to reject unknown multiple narrow-band disturbances for line of sight stabilization in telescope systems. In general, wide-band or multiple narrow-band disturbances rejection of observer-based control can be transformed into designing Q-filter, which could be too high-order to achieve fully-accuracy digital implementation, resulting in degrading the closed-loop performance or even causing instability. Therefore, a new parallel type of Q-filters is proposed to flexibly adjust the number of Q-filter in the sensitivity function and effectively reduce the computational complexity. A sufficient condition of the effectiveness for the parallel Q-filter and the closed-loop stability is derived and verified through simulations. Furthermore, disturbance frequencies can be on-line identified to adaptively optimize the Q-filter parameters. The effectiveness of the proposed method is validated on the image-based stabilization subsystem of a segmented lightweight large-scaled diffractive telescope.
Healthcare 2030. A Design Studio on the Future of a Distributed System in Singapore

Taking Singapore as a laboratory for thinking and designing future healthcare spaces, services, and deliveries, architecture, and industrial design students worked together to develop innovative and scalable solutions for a distributed healthcare system in the year 2030. The projects are collaborative, multi-scalar, and multi-stakeholder in approach. They considered near-to-far-future scenarios for a distributed system across different sites, touchpoints, and experiences by harnessing digital technologies, deploying connected care platforms, and being supported by grassroots organisations, social agencies, and communities. The paper will share the critical reflections and outcomes from the studio led by an architect, experience, and industrial designers working in academia and the industry. The paper argues that designing a system-based healthcare future demands a re-tooling of current architecture education to one that is multidisciplinary, collaborative, and empathetic.
Using game design to enhance the learning motivation of programming courses

The ability of computational thinking can be improved through learning programming design and practicing logical thinking and problem-solving skills. However, the cognitive load of programming courses can be heavy, and students from various majors may not have sufficient knowledge about computer science. To address this, innovative teaching methods and content should be considered to reduce learning anxiety and improve motivation and performance. This study aims to redesign the Python programming course. In the first stage, a traditional lecture will be used to teach the basic concepts of programming. The CodeCombat platform will be introduced in the second stage, utilizing game-based learning strategies to teach Python programming. In the third stage, the curriculum will integrate the Pygame module for game design and implementation. After each stage of implementation, students will be given a questionnaire on learning motivation. The questionnaire will collect data on students' motivations, student work, and student thoughts for statistical analysis. According to the statistical results, the overall average score of students' learning motivation in the third stage is the highest, while the first stage has the lowest score. Therefore, it can be concluded that the teaching method of learning programming through game design is most effective in improving students' motivation to learn.
Relationship Research between Positive Psychological Qualities and Meaning of Life

Middle school students; Positive psychological qualities; Meaning of life
Hierarchical Feature Pooling Transformer for Efficient UAV Object Tracking

Recently, owing to the long-range feature dependencies, transformers have achieved considerable progress in the field of visual tracking. One of the most challenging problems in transformer-based tracking is that the large feature length of tokens leads to high computational cost. Meanwhile, only the single feature map from the last layer of convolutional neural networks (CNN) is employed as the input of transformer, which reduces the tracking accuracy and robustness in complex scenarios. Thus, in this letter, we present an efficient and effective hierarchical feature pooling transformer (HFPT) for UAV object tracking, which is able to inherit the merits from both CNN and transformer architectures. Firstly, we introduce a single pooling operation into multi-head self-attention (MHSA) in transformer to build a new backbone network for reducing the concatenated feature length and capturing rich contextual information. Secondly, hierarchical feature maps generated by multi-level convolutional layers are fed into the pooling transformer to learn interdependencies between high-resolution features and low-resolution features. Thirdly, a feature correction layer is designed to enrich the encoded detailed information for handling the small targets. Finally, we evaluate our proposed method in three well-known UAV benchmarks such as DTB70, UAV20L and UAV123@10fps. Numerous experimental results demonstrate that our HFPT method is able to achieve better performance than the current top-performing trackers with an average speed of 30.3fps on the edge platform of Nvidia Jetson AGX Orin.
Group Frenet Frame CAV Path Planning on Highways

Connected autonomous vehicle (CAV) systems could bring considerable benefits to our daily lives, and possibly outperform single autonomous vehicles. Nevertheless, the real-time determination of the optimal route for each CAV within a continuous space presents a considerable challenge. This difficulty arises from the exponential growth of potential motion combinations for CAVs, considering the diverse road geometries they encounter. This paper proposed a CAV group planning framework to overcome this challenge. The framework works hierarchically. The global and local controllers play a crucial role in generating long-term reference paths for each CAV by employing a versatile road geometry model capable of accommodating diverse road shapes. Initially, waypoints are extracted utilizing this generalized road geometric model. Subsequently, potential combinations of waypoints are generated by considering the CAV group as a fleet. Finally, optimal waypoint combinations are assigned to each CAV by considering the CAVs’ own benefit and road usage. Reference paths for each CAV are generated using the selected waypoints and are passed on to the CAVs and roadside units (RSUs) layer. The CAVs and RSUs generate short-term motion, given the reference paths. This is operated in the Frenet frame, and the optimal motion for each CAV is selected in the aspect of the entire CAV fleet. The proposed framework is tested in simulation and has shown the ability to generate safe and sound paths under various road geometries with obstacles and in mixed traffics in real-time.
Reasoning over the Air: A Reasoning-based Implicit Semantic-Aware Communication Framework

Semantic-aware communication is a novel paradigm that draws inspiration from human communication focusing on the delivery of the meaning of messages. It has attracted significant interest recently due to its potential to improve the efficiency and reliability of communication and enhance users’ quality-of-experience (QoE). Most existing works focus on transmitting and delivering the explicit semantic meaning that can be directly identified from the source signal. This paper investigates the implicit semantic-aware communication in which the hidden information, e.g., hidden relations, concepts and implicit reasoning mechanisms of users, that cannot be directly observed from the source signal must be recognized and interpreted by the intended users. To this end, a novel implicit semantic-aware communication (iSAC) architecture is proposed for representing, communicating, and interpreting the implicit semantic meaning between source and destination users. A graph-inspired structure is first developed to represent the complete semantics, including both explicit and implicit, of a message. A projection-based semantic encoder is then proposed to convert the high-dimensional graphical representation of explicit semantics into a low-dimensional semantic constellation space for efficient physical channel transmission. To enable the destination user to learn and imitate the implicit semantic reasoning process of source user, a generative adversarial imitation learning-based solution, called G-RML, is proposed. Different from existing communication solutions, the source user in G-RML does not focus only on sending as much of the useful messages as possible; but, instead, it tries to guide the destination user to learn a reasoning mechanism to map any observed explicit semantics to the corresponding implicit semantics that are most relevant to the semantic meaning. By applying G-RML, we prove that the destination user can accurately imitate the reasoning process of the source user and automatically generate a set of implicit reasoning paths following the same probability distribution as the expert paths. Compared to the existing solutions, our proposed G-RML requires much less communication and computational resources and scales well to the scenarios involving the communication of rich semantic meanings consisting of a large number of concepts and relations. Numerical results show that the proposed solution achieves up to 92% accuracy of implicit meaning interpretation.
Regularized Optimal Transport Layers for Generalized Global Pooling Operations

Global pooling is one of the most significant operations in many machine learning models and tasks, which works for information fusion and structured data (like sets and graphs) representation. However, without solid mathematical fundamentals, its practical implementations often depend on empirical mechanisms and thus lead to sub-optimal, even unsatisfactory performance. In this work, we develop a novel and generalized global pooling framework through the lens of optimal transport. The proposed framework is interpretable from the perspective of expectation-maximization. Essentially, it aims at learning an optimal transport across sample indices and feature dimensions, making the corresponding pooling operation maximize the conditional expectation of input data. We demonstrate that most existing pooling methods are equivalent to solving a regularized optimal transport (ROT) problem with different specializations, and more sophisticated pooling operations can be implemented by hierarchically solving multiple ROT problems. Making the parameters of the ROT problem learnable, we develop a family of regularized optimal transport pooling (ROTP) layers. We implement the ROTP layers as a new kind of deep implicit layer. Their model architectures correspond to different optimization algorithms. We test our ROTP layers in several representative set-level machine learning scenarios, including multi-instance learning (MIL), graph classification, graph set representation, and image classification. Experimental results show that applying our ROTP layers can reduce the difficulty of the design and selection of global pooling — our ROTP layers may either imitate some existing global pooling methods or lead to some new pooling layers fitting data better. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/SDS-Lab/ROT-Pooling</uri> .
A Survey of Security Protection Methods for Deep Learning Model

In recent years, deep learning (DL) models have attracted widespread concern. Due to its own characteristics, DL has been successfully applied in the fields of object detection, super-resolution reconstruction, speech recognition, natural language processing, etc., bringing high efficiency to industrial production and daily life. With the Internet of Things, 6 G and other new technologies have been proposed, leading to an exponential growth in data volume. DL models currently suffer from some security issues, such as privacy issues during data collection, defense issues during model training and deployment, etc. The sensitive data of users and special institutions that are directly used as training data of DL models may lead to information leakage and serious privacy problems. In addition, DL models have encountered many malicious attacks in the real world, such as poisoning attack, exploratory attack, adversarial attack, etc., which caused model security problems. Therefore, this paper discusses ways of ensuring the security and data privacy of DL models under diversified attack methods, and the ways of ensuring the privacy security of edge mobile devices equipped with pre-trained deep neural networks (DNNs). Alternatively, this paper analyzes the privacy security of DL models for typical deployment platforms such as server/cloud, edge mobile device and web browser, and then summarizes future research direction.
التلوث البكتيري للعملات النقدية اليمنية المتداولة في مدينة رداع/البيضاء

تتلوث العملات النقدية الورقية بكثير من الممرضات كالبكتيريا, الفطريات والفيروسات الضارة بصحة الإنسان, هدف هذا البحث إلى تحديد معدل التلوث البكتيري للعملات المتداولة في مدينة رداع وجمعت 25 عينة من فئات مختلفة (50 ريال، 100 ريال، 250 ريال، 500 ريال، 1000 ريال يمني) بطريقة عشوائية ومن أماكن مختلفة، أظهرت النتائج أن نسبة التلوث بالبكتيريا 100%, بطريقة العزل المباش, في حين لم يعزل اي نوع بكتيري من عينات النقود المتحصل عليها من البنك, واتضح ان البكتيريا الموجبة لصبغة جرام هي السائدة بنسبة 84% في حين البكتيريا السالبة لصبغة جرام كانت 16%, وكانت البكتيريا الموجبة الأكثر إنتشاراًCoagulase negative staphylococcus وبنسبة 28.94%, بينما البكتيريا السالبة كانتKlebsiella sp. E.coli sp. وبلغت نسبة العزل 5,26% لكلً منهما, وجد إن فئة 50 ريال مثلت أعلى نسبة تلوث وبنسبة 25% تليها فئة 500 ريال بنسبة 21,9%, بينما فئة 100ريال, 250 ريال سجلت نسبة 18,75% لكلاً منهما في حين سجلت فئة 1000 ريال أقل نسبة وبواقع 15,6%, كما أظهرت النتائج أن متوسط أعداد البكتيريا في الأماكن المختلفة بمدينة رداع تختلف من مكان لأخر حسب كثافة تواجد أفراد المجتمع. الكلمات المفتاحية: العملات الورقية-العزل البكتيري – البكتيريا السالبة والموجبة –صبغة جرام. Abstract Banknotes get contaminated with several sickness causes such as bacteria, fungi and viruses harmful to human health. This study aims to determine the rate of bacterial contamination of banknotes circulating in the city of Rada'a. 25 samples were collected from different denominations (including 50 rials, 100 rials, 250 rials, 500 rials, and 1000 Yemeni rials) from different places, The results showed that the percentage of contamination with bacteria was 100%, by the direct isolation method, while no bacterial type was isolated from the money samples obtained from the bank. It is also revealed that the Gram-positive bacteria were the prevalent bacteria 84%, while Gram-negative bacteria were 16%. The most widespread was Coagulase negative staphylococcus, with a rate of 28.94%.,while Isolated negative bacteria widespread was Klebsiella sp. E.coli sp. The percentage of isolation was 5.26% for each of them, It was found that the category of 50 rials represented the highest contamination rate with a rate of 25%, followed by the category of 500 rials with a rate of 21.9%, while the category of 100 rials and 250 rials recorded a rate of 18.75% for each of them, while the category of 1000 rials recorded the lowest percentage with a rate of 15.6%.The results also showed that the average number of bacteria in different places in Rada'a Town differs from one place to another according to the density of the presence of community members. Key words: Paper currency – Bacterial isolation -positive and negative bacteria
The Impact of Scientific Research on Economic Growth: An Empirical Study Based on Data from 20 Countries in 2000-2018

This article aims to reveal the relationship between scientific and technological development and economic growth, which is essential in figuring out what ultimately decides economic development. This paper uses the empirical method and adopts data about the economy, technology, and education of the 20 most significant economic communities from 2000 to 2018 to analyze how science and technology creativity will influence economic growth. A meaningful finding may be that scientific research and high education are not always favorable to economic growth. Only after a country's research and education level reaches a point can they play a positive role in the economy.
Temporal Down-sampling based Video Coding with Frame-Recurrent Enhancement

In many digital systems, the transmission bandwidth, as well as storage capacity, are usually very limited. This introduces challenges for both video transmission and video storage. To seek lower bit rates and further obtain high-quality up-sampled videos, this paper proposes a temporal down-sampling based video coding system and a frame-recurrent enhancement based video upsampling strategy. The structure of our proposed method is shown in Fig. 1. Unlike the existing work [1], instead of downsampling all video frames, only the intermediate frames are downsampled and two frames remain with high quality on the video coding system. Then, these two high-quality frames are used to iteratively enhance the quality of the low-bitrate low-quality frames through a deep-learned enhancement network. Compared to the latest video coding standard Versatile Video Coding (VVC), our work can obtain a BD-rate reduction from $39.261 {\%} \sim 85. 455$ % in All-Intra and Low-Delay-P configurations on the downsampled frames. A temporal down-sampling based video coding framework (TDS) is proposed. It can be combined with all the existing coding standards including HEVC/H.265 and VVC/H.266. A method of super-resolution with frame recurrent image enhancement (SRFR) is applied to up-sampling the frames by the neighboring high resolution frame. The temporal information from high resolution frames can be fully used to improve the video quality through frame recurrent.
Analysis of the Influencing Factors of the Garbage Sorting Behavior on Campus in the Post-Epidemic Period

With the rapid development of the economy and the rise of peoples con-sumption level, the problem of garbage is becoming a more and more severe challenge. Meanwhile, colleges, as a place with a high concentration of stu-dents, have also more serious garbage sorting problems. Therefore, this paper will be devoted to analyzing the factors that influence college students gar-bage sorting behavior in the post-epidemic period. To start with, a question-naire based on the Theory of Planned Behavior (TPB) model was made to collect students data. Then the KMO tests and AMOS Structural Equation Fitting were conducted to verify the validity of the data and structure. After confirming that the data is effective, the data will be analyzed by building models including the Measurement Model of Empirical Analysis Framework, Overall model fitting, Theory of Reasoned Action (TRA) model and original Model. Lastly, with the optimization of the mediation effect and confirmatory factor analysis, the conclusion can be drawn that the student participants tend to adopt garbage-sorting behavior in both willingness and action. And the results show that they also have a positive attitude, good subjective norms, and strong perceived behavior control for the garbage sorting behavior on campus.
Classification of Punches in Olympic Boxing Using Static RGB Cameras

Cameras in sports continuously track athletes, recognize their activities, and monitor performance. This capability is ensured by sophisticated computer vision systems with machine learning algorithms and massive computation power. Combat sports are rather challenging because punches happen rather quickly. This paper provides comprehensive research on approaches to measuring the performance of athletes in combat sports. We use RGB cameras to measure athletes’ activity from a distance without interfering with their equipment, in contrast to the approach which uses wearable sensors. The aim of this paper is to provide a solution to classify punches in Olympic boxing based on static RGB cameras opposite the boxing ring. The proposed solution classifies three types of punches and the best classifier obtained sequentially 94%, 84%, and 81% of the F1 score for them. Finally, we measured the impact of the data augmentation process on classification performance and provide future works.
Configuration of Project Team Members’ Competences: A Proactive and Reactive Approach

To implement IT, business, educational, scientific, etc. projects, it is required to have a team of employees with specific competences. A very important aspect is also to guarantee the availability of these employees during the implementation of individual stages of the project. From the point of view of project management, it is crucial to provide an answer e.g. to the question: Do the competences of team members guarantee the completion of the project on time? If constraints related to the absence of team members, costs, limited working time, etc. are additionally considered, then the problem becomes non-trivial. The paper proposes a MILP (Mixed Integer Linear Programming) model for the configuration of project team members, which makes it possible to find answers to many questions related to project management in the context of the project team. The model can be used to make decisions both proactively and reactively. The implementation of the model using mathematical programming and the author’s procedure for reducing the size of the modeled problem were also presented.