Joint Supervision Research Articles

Two-stream network with phase map for few-shot classification

As an important research direction of deep learning, few-shot classification mainly solves problems of classification when training samples are scarce. Under this circumstance, how to effectively use the information of existing samples has become the key to solve few-shot classification problems. We propose a method to fully mine existing samples information, which can be implemented in the training and testing stages. Specifically, we introduce a two-stream neural network as an embedding model to extract features of the original image and its phase map, and perform fine-grained adaptive feature fusion to them. The algorithm can learn and extract more fully-informed fusion features under the network. Besides, we also introduce Circle Loss in loss function, which has a non-linear decision boundary. Under the joint supervision of Soft-max Loss and Circle Loss, the distinction between different classes of features in embedding space increases. We conduct experiments on miniImageNet, tieredImageNet, and CIFAR-FS, respectively. The accuracy rate is increased by 0.99%, 0.96%, and 0.91%, which proves the effectiveness of our algorithm.

Deep Discrete Cross-Modal Hashing with Multiple Supervision

Deep hashing has been widely used for large-scale cross-modal retrieval benefited from the low storage cost and fast search speed. However, most existing deep supervised methods only preserve the instance-pairwise relationship supervised by the semantic similarity matrix, which always inufficient heterogeneous correlation. Thus, we propose the Deep Discrete Cross-Modal Hashing with Multiple Supervision (DDCHms) to further enhance the semantic consistency of heterogeneous modalities. It improves the performance of semantic information retrieval with the joint supervision of instance-pairwise, instance-labeled and class-wise similarities. Specifically, we firstly utilize the instance-pairwise similarity matrix to supervise the learning process of heterogeneous networks and it keeps the pairwise correlation from the perspective of instance-instance. Specially, we design a semantic network to fully exploit the semantic information implicated in labels, which is also used to supervise multi-modal networks on instance-label level. Furthermore, we propose the class-wise hash codes to cooperate with the intrinsic label matrix as the prototypes, and it guides the hash learning and further ensures the precision and compactness of the learned hash codes. In addition, we design different discrete optimization strategies to optimize the class-wise hash codes and unified hash codes, respectively. That avoids the optimization errors and ensures the high-quality of learned hash codes. Experiments on three popular datasets indicate that our method outperforms other state-of-the-art methods in terms of cross-modal retrieval.

Integrating PrEP delivery in public health family planning clinics: a protocol for a pragmatic stepped wedge cluster randomized trial in Kenya

BackgroundAdolescent girls and young women account for a disproportionate fraction of new HIV infections in Africa and are a priority population for HIV prevention, including provision of pre-exposure prophylaxis (PrEP). Anchoring PrEP delivery to care settings like family planning (FP) services that women already access routinely may offer an efficient platform to reach HIV at-risk women. However, context-specific implementation science evaluation is needed.MethodsThe Family Planning Plus Project is a prospective, pragmatic implementation evaluation, designed as a stepped wedge, cluster randomized trial, at 12 clinics in Kenya. In collaboration with the Kenya Ministry of Health and Kisumu County Department of Health, we will introduce integration of HIV risk screening and PrEP delivery in public health FP clinics. The core multifaceted implementation strategies to integrate PrEP in FP clinics will include: (1) PrEP delivery by existing FP clinic staff, (2) health provider training, (3) PrEP technical assistance to coach and mentor providers, (4) joint supervision with Kisumu County health officials, and (5) stakeholder engagement. All core components of PrEP delivery—including screening for HIV risk, HIV testing, dispensing, adherence and risk reduction counseling, assessment of side effects, and provision of refills, or safety assessment—will be conducted by existing FP clinic staff as part of a standard care service package. The goal is to catalyze sustainable scale-up within existing infrastructures beyond the project. We will rigorously evaluate implementation outcomes and impact, using the RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) framework, and we will use Organizational Readiness for Implementing Change (ORIC) and the Consolidated Framework for Implementation Science Research (CFIR) to assess readiness to implement and contextual enablers and barriers of implementation, including how clinics innovate efficient delivery systems.DiscussionAnchoring PrEP delivery to existing FP systems and staffing has tremendous potential to address barriers that women face in accessing HIV prevention and PrEP care, including lack of time, cost, and stigma of visiting a facility solely for HIV prevention. The FP Plus Project will initiate preparation for full-scale and sustainable model of integration of comprehensive HIV prevention services, including PrEP implementation, in public health FP clinics in low-income settings.Trial registrationRegistered with ClinicalTrials.gov on December 14, 2020: NCT04666792

Real time facial expression and gender recognition with feature integrated CNN

ABSTRACT For social communication, facial expression recognition is crucial; further improvement with additional facial attributes like gender can boost the performance of intelligent applications. Recently, the incorporation of deep learning methods extended the research of facial expressions and gender recognition. The deep learning method gives a better detection rate but incurs high computation costs and memory requirements. In this regard, a feature-integrated convolution neural network, namely FusedEGNET, has been designed with fewer parameters to deploy in real-world environments. Further, it utilizes joint supervision with softmax and centre loss to learn the discriminative features of facial expressions. Besides, evaluations of five publicly available facial expression datasets, namely FER-2013, JAFFE, CK+, KDEF, and FERPlus, along with the Adience and CK + datasets for gender recognition, have also been carried out. Finally, to examine the efficiency of the proposed method, an application has been developed and applied to detect real-time facial expressions and gender.

A study on the effect of joint supervision between auditors and sponsors: evidence from China

AbstractThis study examines the joint supervision of auditors and sponsors in a sample of private placements on the Shanghai and Shenzhen stock markets from 2006 to 2019. We observe variation in the inhibitory effect of earnings management and inefficient investment based on the combination of intermediaries with different reputations. We find that auditors exert a strong inhibitory effect on earnings management while sponsors are best positioned to suppress inefficient investment. Furthermore, this study enriches the existing supervisory literature and provides insights to further improve the relationship between auditors and sponsors in the context of China’s unique registration system.

School and Business Partnerships to Support Job Training for Students with Developmental and Other Disabilities: Employers' Perspectives.

This study examines partnerships between schools and businesses that are intended to foster training and employment opportunities for secondary students with developmental and other disabilities in Saudi Arabia. A survey was developed to investigateemployers' perspectives on students' skills, practical strategies fortraining, andtheir contribution.Fifty-two employers participated in the survey. The findings revealedthe most valued skills on job sites, such as self- determination, social, and job skills, and believed that coordination, job matching, and joint supervision are successful practices that improve training. The employers concluded that some of their contributions to such partnerships include participation in curriculum development, training students in their workplaces under their supervision, and offering financial support.

The Fusion and Construction Strategy of Smart Sports and Traditional Sports Teaching Mode in College and Universities

Use expert interviews, literature and other methods to summarize and analyze the pros and cons of smart sports and traditional sports, combine traditional sports with smart education, and create a more scientific and effective smart education model for colleges and universities, so as to promote the joint participation or supervision of schools, teachers, parents, and students to improve the quality of physical education, so as to achieve physical education for students and enhance the time and intensity of students’ physical fitness.

X‐ray Spectrometry Best Referee awards 2021

X‐ray Spectrometry Best Referee awards 2021

Simulation Research on Participation Willingness of Industrial Company on Green Finance Policies based on Tripartite Game

The green finance policy is an important embodiment of the basic national policy of environmental protection in the financial system, and also an important guarantee for company to achieve green development. Based on the heterogeneous interest pursuit of the central state, local provinces and industrial companies, the article builds an evolutionary model of three game partners, analyzes the strategic choice of the stakeholders in the implementation process of the green finance policies, and simulates the dynamical track of evolution stability strategies of three partners. The results show that the management from the central state is very important to the implementation of the policy, and the supervision cost and punishment will also affect the final stability strategy; the policy incentive from the central state can effectively promote the enthusiasm of the local provinces to implement the green finance policies, but the implementation cost will seriously affect the willingness of the local government to help industrial companies; under the joint supervision effect of the central state, the local government’s policy incentives and the complete market mechanism, industrial companies will enhance their willingness to participate in green finance policies.

MFNet‐LE: Multilevel fusion network with Laplacian embedding for face presentation attacks detection

Face detection is playing a pivotal role for crowd counting and abnormal events detection. However, it is vulnerable to face presentation attacks by printed photos, videos, and 3D masks of real human faces. Although numerous detection techniques based on deep learning have been employed to address the problem of face presentation attacks, there are still several weaknesses in these approaches, such as high algorithm complexity and a lack of detection ability. To overcome these weaknesses, a method based on a multilevel fusion network with Laplacian embedding (MFNet-LE) for the detection of face presentation attacks is proposed. First, a shallow network that contains just three layers was developed, which makes the model faster. Then, an optimised multilevel fusion strategy was developed to combine the input with the output of all previous layers to improve the detection ability of the method. Finally, a Laplacian embedding algorithm is introduced to maintain the inter-class discrimination and penalise the intra-class distance. Under the joint supervision of Laplacian loss and softmax loss, the proposed approach can obtain more discriminative features, which enhance the accuracy of attack detection. Experiments were conducted with three public databases for face presentation attacks: CASIA FASD, Idiap Replay Attack database and MSU USSA. The results demonstrate that the MFNet-LE model can outperform the state-of-the-art methods.

Metric learning for image-based flower cultivars identification

BackgroundThe study of plant phenotype by deep learning has received increased interest in recent years, which impressive progress has been made in the fields of plant breeding. Deep learning extremely relies on a large amount of training data to extract and recognize target features in the field of plant phenotype classification and recognition tasks. However, for some flower cultivars identification tasks with a huge number of cultivars, it is difficult for traditional deep learning methods to achieve better recognition results with limited sample data. Thus, a method based on metric learning for flower cultivars identification is proposed to solve this problem.ResultsWe added center loss to the classification network to make inter-class samples disperse and intra-class samples compact, the script of ResNet18, ResNet50, and DenseNet121 were used for feature extraction. To evaluate the effectiveness of the proposed method, a public dataset Oxford 102 Flowers dataset and two novel datasets constructed by us are chosen. For the method of joint supervision of center loss and L2-softmax loss, the test accuracy rate is 91.88%, 97.34%, and 99.82% across three datasets, respectively. Feature distribution observed by T-distributed stochastic neighbor embedding (T-SNE) verifies the effectiveness of the method presented above.ConclusionsAn efficient metric learning method has been described for flower cultivars identification task, which not only provides high recognition rates but also makes the feature extracted from the recognition network interpretable. This study demonstrated that the proposed method provides new ideas for the application of a small amount of data in the field of identification, and has important reference significance for the flower cultivars identification research.

Recent Advances in the Treatment of Gliomas: The Multimodal Care Therapy

BACKGROUND: Glioblastoma (GBM) is the most devastating primary malignancy of the central nervous system in adults. At present, standard treatment consists of maximal safe surgical resection followed by radiotherapy (60 Gray) with concomitant daily temozolomide chemotherapy. Low-grade gliomas constitute approximately 15% of the nearly primary brain tumors diagnosed in adults each year. Extent of tumor resection has become a strong predictor of patient outcomes, alongside patient age, performance status, tumor histology, and molecular genetics (isocitrate dehydrogenase-1 and 1p/19q codeletion status). Over the past two decades, surgeons have emphasized the importance of maximizing extent of resection and its impact on overall survival, progression-free survival, and time to malignant transformation. AIM: We aimed to present recent advances in the treatment of gliomas. METHODS: This is a prospective analysis of 50 patients diagnosed with gliomas which are enrolled in a joint supervision between Kasr Al Aini Hospital, Cairo University, Egypt, and Coventry University Hospitals, England. RESULTS: The study included 50 patients, 31 males and 19 females, ages ranged from 21 to 75 years (mean age 47.5 years). Gross total resection was achieved in 28 patients (56%). The most common surgical complication in our series was post-operative transient weakness in 4 patients (8%). Mean true survival of low-grade glioma patients was 40.5 months while the mean true survival for anaplastic astrocytoma (Grade 3) patients was 38 months and that of GBM (Grade 4) patients was 18.8 months. CONCLUSION: Despite persistent limitations in the quality of data, mounting evidence suggests that more extensive surgical resection is associated with longer life expectancy for both low- and high-grade gliomas.

Blockchain-based approach to achieve credible traceability of agricultural product transactions

The traditional traceability system for agricultural product transactions is susceptible to information alteration and damage, which may lead to issues regarding product quality and food safety. And blockchain is a technology that boasts tamper-proofing, complete traceability and time-stamped storage. Considering the above, this study proposes a new blockchain-based approach to the quality management of agricultural products and introduces Solidity-based prototype smart contracts for agricultural product transactions. Test results show that the new traceability system can offer good performance in terms of data upload and block response time. The method proposed in this paper can be used as a solution for quality management of agricultural product that boasts whole process transparency, full-link reliability and joint supervision by all nodes in the system.

Practices and strategies for enhancing learning through collaboration between vocational teacher training institutions and workplaces

The study aimed to identify and understand practices and strategies for enhancing learning through collaboration among a master’s degree in vocational pedagogy (MVP) program, vocational teacher training institutions (VTIs), and workplaces. Using in-depth semi-structured individual and focus group interviews, data were obtained from administrators, mentors, supervisors, students, teachers, officers/managers of the MVP, two VTIs, and four workplaces from central and eastern parts of Uganda. The data analysis was based on Engestrom’s cultural-historical activity theory (CHAT) particularly the concept of expansive learning for resolving contradictions within human activity systems. The findings revealed a need for involving actors in timely planning and disseminating the activity plans, increasing duration for collaborative activities as well as involving the students in the tracking of MVP activity record in fostering the institutional capacity to plan and implement collaborative activities. To strengthen the institutional capacity to supervise learning under collaborative activities, findings indicated a need to engaging workplace mentors and facilitators in learning at the MVP as well as joint supervision and collaborative development of supervision guidelines. To foster the communication between partners, the findings revealed a need to institute a collaboration focal person, providing feedback to collaborating actors and government support on a policy encouraging workplaces’ involvement in vocational training. Relationship issues revealed a need to initiate collaboration based on a signed memorandum of understanding as well as organising workshops and symposiums to equip and orient actors to MVP work methods and practices. Due to contradicting learning cultures and traditions amongst the activity systems, some of the suggested strategies required renegotiating the system especially the university before being implemented to minimise further challenges.

Cluster Preface: Radicals – by Young Chinese Organic Chemists

(left) received his B.S. degree from Xiamen University in 2003 under the supervision of Prof. Pei-Qiang Huang, and his Ph.D. degree from the Shanghai Institute of Organic Chemistry in 2008 under the supervision of Prof. Guo-Qiang Lin. After postdoctoral research at ­Gakushuin University, Japan with Prof. Takahiko Akiyama, he moved to the University of Texas Southwestern Medical Center, working as a postdoctoral fellow with Prof. John R. Falck and Prof. Chuo Chen. He was appointed as a professor at Soochow University, China in December 2013. He is currently the Head of the Organic Chemistry Department at Soochow University. His current research interests include radical-mediated transformations, in particular radical ­rearrangements, and their applications in the construction of natural products and biologically active compounds. Xin-Yuan Liu (right) obtained his B.S. degree from Anhui Normal University (AHNU) in 2001. He continued his research studies at both the Shanghai Institute of Organic Chemistry (SIOC), CAS and AHNU under the joint supervision of Prof. Dr. Shizheng Zhu and Prof. Dr. Shaowu Wang, obtaining his master’s degree in 2004. After a one-year stint in Prof. Gang Zhao’s laboratory at SIOC, he joined Prof. Dr. Chi-Ming Che’s group at The University of Hong Kong (HKU) and earned his Ph.D. degree in 2010. He subsequently undertook postdoctoral studies in Prof. Che’s group at HKU and in Prof. Carlos F. Barbas III’s group at The Scripps Research Institute. At the end of 2012, he began his independent academic career at the Southern University of Science and Technology (SUSTech) and was promoted to a tenured Full Professor of SUSTech in 2018. His research interests are directed towards the design of novel chiral anionic ligands to solve radical-involved asymmetric reactions.

РЕВМАТИЧЕСКИЕ ЗАБОЛЕВАНИЯ И БЕРЕМЕННОСТЬ

. The problem of pregnancy in rheumatic diseases does not lose its relevance. The most studied is the course of pregnancy in patients with rheumatoid arthritis, systemic lupus erythematosus and axial spondyloarthritis. Observations in other systemic connective tissue diseases are episodic and do not allow us to draw reliable conclusions. The course of the disease itself, as well as the therapy received by patients, can affect pregnancy, starting with the possibility of conception and ending with unfavorable pregnancy outcomes for the mother and fetus. The onset of pregnancy should be planned at the time of remission or minimal activity of the disease. Of particular interest is drug therapy in patients with rheumatic diseases during gestation. This issue has been and remains complex and has not been fully studied due to the fact that any conclusions about the effect of drugs on the fetus are made retrospectively and cannot be the result of a deliberately initiated study. Currently allowed, though with some reservations, during pregnancy drugs are glucocorticoids, non-steroidal anti-inflammatory drugs, hydroxychloroquine and sulfasalazine. The development of genetic engineering has raised the question of the possibility of using genetic engineering basic therapy during gestation. Currently, the largest number of observations is available for TNF-α inhibitors, the use of which is recognized as possible during pregnancy. The presence of rheumatic disease in itself is not an absolute indication for a caesarean section. The question of the method of delivery is decided individually. Pregnant patients with rheumatic diseases should be under regular joint supervision of an obstetrician-gynecologist, rheumatologist and neonatologist

Preface

The 5th International Conference on Advanced Technology and Applied Science (ICATAS) 2020 and the 6th Malaysia-Japan Joint International Conference (MJJIC) 2020 were successfully hosted virtually by the Malaysia-Japan International Institute of Technology (MJIIT), UTM Kuala Lumpur due to the COVID-19 pandemic. MJIIT is fortunate to have 29 Japanese universities, known as the JUC, participating not only in this conference but also in the development of MJIIT, which is the Malaysian and Japanese governments huge undertaking to provide the first Japanese-style engineering education at Bachelor, Masters and PhD level in Malaysia.This virtual conference aims at promoting cooperation between MJIIT, JUC and partners from the Arab Academy for Science Technology & Maritime Transport (AASTMT) which eventually would culminate into student exchange activities, joint supervision at postgraduate level and joint research undertakings. It is important for young researchers and postgraduate students to take the opportunity to be involved in such activities, deliberating on their research findings. The conference is also an excellent platform to network between researchers and students towards sustainable research collaboration.The conference has covered the major areas of focus, namely in Track 1: Industrial Revolution 4.0 and Track 2: Sustainable Technology, Energy and Environment. Three keynote addresses and over 130 research papers were presented virtually at this remarkable event, spanning from nine countries. All papers have been reviewed by the researchers and experts in the field appointed by the conference technical committee.The guest editors would like to sincerely thank the publication team for their efforts in preparing this proceeding. The publication committee wishes to thank all the authors for their contributions to ICATAS-MJJIC 2020 proceedings and we look forward to welcoming more of your contributions to the ICATAS-MJJIC 2021. We also would like to take the opportunity to thank the researchers and postgraduate students for their contribution and dedication to this event. Gratitudes to all the hardworking committee members and the secretariat for materializing this joint conference. Last but not least, our appreciation to the Japan International Cooperation Agency (JICA) and AASTMT and other sponsors for giving their strong support for this conference.List of Editorial Board, INTERNATIONAL SCIENTIFIC COMMITTEE, ORGANISING COMMITTEE and this titles are available in this pdf.

The Philosophical Attitude of Eugene Hütz, Singer of Gogol Bordello

The Philosophical Attitude of Eugene Hütz, Singer of Gogol Bordello

Attribute-Guided Multi-Scale Prototypical Network for Few-Shot SAR Target Classification

Few-shot synthetic aperture radar (SAR) target classification has received more and more attention in recent years, where most of the existing methods have applied existing networks designed for natural images to SAR images, ignoring the special characteristics of SAR data. Therefore, in this paper, we propose an attribute guided multi-scale prototypical network (AG-MsPN) combined with subband decomposition for few-shot SAR target classification, aiming to learn more discriminative features from a few labeled data. Since the SAR images are essentially complex-valued images containing both amplitude and phase information, we employ the subband decomposition on complex-valued SAR images to explore the backscattering behavior variation of targets to get more complete descriptions of targets. Then, considering the complementary information extracted by different convolutional layers, based on prototypical network, a multi-scale prototypical network is proposed to aggregate the features of different layers to enhance the discrimination of feature representations, thus relieving the problem of intraclass diversity and inter-class similarity. Besides, we devise the prior binary attributes of SAR targets and present the attribute classification module to further strengthen the performance of multi-scale prototypical network under the joint supervision of the label and prior attribute information. We demonstrate the effectiveness of our proposed AG-MsPN on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark dataset and our method surpasses many other existing methods in the few-shot cases.

A Deep Feature Fusion Network Based on Multiple Attention Mechanisms for Joint Iris-Periocular Biometric Recognition

Joint iris-periocular recognition based on feature fusion can overcome some inherent drawbacks of unimodal biometrics, but most of the prior works are limited by conventional feature extraction approaches and fixed fusion schemes. To achieve more accurate and adaptive recognition, an end-to-end deep feature fusion network for joint iris-periocular recognition is proposed in this paper. Multiple attention mechanisms including self-attention and co-attention mechanisms are integrated into the network. Specifically, two forms of self-attention mechanisms, spatial attention and channel attention, are inserted into the feature extraction module, aiming to effectively learn the most important features and suppress unnecessary ones. Also, co-attention mechanism is introduced in the feature fusion module, which can adaptively fuse features to obtain more representative iris-periocular features. Additionally, in order to further enhance the discriminative power of the learned features, the proposed network is trained with a joint supervision of softmax loss and center loss. On two publicly available datasets, the proposed network with a small number of parameters outperforms unimodal biometrics and several iris-periocular recognition approaches.