bluuebunny/crossref_metadata_2025 huggingface dataset:
COVID-19 in India: Breakthrough Infections in Delta Wave
Authors: Shervani Zameer , Nikhat Roma , Hasan Sadia , Qazi Umair Yaqub , Fatma Kehkeshan , Siddiquie Arif , Vuyyuru Venkata Phani Sai Reddy , Jamal Nudrat , Ibbrahim Aiman , Siddiqui Samar , Khan Adil Ahmed , Kumar Parangimalai Diwakar Madan , Purang Manseej , Agarwal Ankira
Published: July 2022

The breakthrough infections (BTI) during the Delta wave in the general population have been investigated. The BTI reported in the city of Gurugram (Haryana, India) in the month of August, September, and October 2021 were studied. The monthly BTI in August, September, and October 2021 were 26%, 41%, and 76%, which caused an increase in the monthly caseload to 167, 150, and 206 cases, respectively. Mostly, the patients who had comorbidities developed BTI. However, the BTI reported in the Delta wave did not cause a surge in new cases. The surge was noticed only after the emergence of the Omicron variant of the SARS- CoV-2 virus. In Gurugram, 78.3% sero prevalence recorded in September 2021 was due to vaccination and higher infection rate of 59,921 monthly cases reported in April 2021.
Respiratory Health of the Ultra-Endurance Cyclists and Runners–A Perspective
Authors: Rajasekhar Kali Venkata , Sree Meda Sandhya
Published: July 2022

High intensity sustained aerobic physical activities like ultra-endurance running and cycling may induce higher intensities of inflammatory and oxidative stress in the body and may cause disturbances in the general immunity and more specifically respiratory immunity temporarily and may also cause some health implications. Also, the chronic high intensity training of these athletes is another aspect that needs much attention. Respiratory tract gets affected severely during these high intensity sustained aerobic activities and may lead to the compromise in the bronchial epithelial integrity, leading to respiratory tract infections, which in turn could induce palpable reductions in the performances of these endurance athletes. Hence, health and performance are the two important factors that are to be considered during the training and competition of these ultra-endurance athletes. The training protocols of these athletes should consider the methods of tackling these inflammatory and oxidative stress both during practice and during competitions. A major recovery method for these athletes may be the proper nutrition program that can provide these ultra-endurance runners and cyclists with proper micronutrients. Special diets consisting of ample anti-oxidant and anti-inflammatory nutrients would certainly help these athletes in this regard. Enough quality protein and energy substrates may also help in the proper synthesis of the cell signaling cytokines and hormones that may protect the immune function of the body. Proper rest periods and proper consolidation of the anti-inflammatory and anti-oxidative capacities through sufficiently long duration training before taking part in such high intensity aerobic activities is highly recommended.
On the Generality of Morality: Accountability and Societal Impracticality of Particularism
Authors: Zhou Linghui
Published: July 2022

This paper focuses on the debate between Jonathan Dancy and Brad Hooker regarding the validity of moral particularism and investigates whether both philosophers have overlooked any important factors in their discussions. According to Jonathan Dancy’s ideas of particularism, morality which encompasses our moral thought, judgment, and the possibility of moral distinctions, is independent of the codification of moral principles. He appeals to holism in the theory of reasons to defend his belief that the same property or feature that is a reason in one case can count morally for or against action in different circumstances. Hooker, on the other hand, takes issue with Dancy’s account by suggesting that moral particularism is socially impractical, and Dancy’s provision of premises ought to be overwhelmingly plausible. Hooker argues that moral particularism fails to provide the moral assurance that is required of a shared commitment to morality that brings about mutually beneficial practices that we can generalize in consideration of the features of contexts in which they take place within the specification of reason(s) for belief and action. By comparing Hooker’s example involving theoretical particularist Patty and the Rossian generalist Gerry with Dancy’s rebuttal, this paper propounds that Dancy and Hooker both insufficiently address the conceptual relation between moral obligation and accountability within the moral domain. Morality as accountability does not exclusively relate to the valuable consequences of creating and following rules and principles. Rather, it entails our obligations conceptually to hold ourselves mutually accountable if and only if there are existing general rules and principles that are also accessible to all members within the community bounded by such common moral knowledge.
Kepercayaan Diri dan Kecemasan Atlet Judo Pada Masa Pandemik COVID-19
Authors: Purnamasari Ira , Febrianty Mona Fiametta , Novian Geraldi
Published: July 2022

Dalam konteks kejuaraan olahraga, Indonesia harus menunda Pekan Olahraga Nasional tahunan (selanjutnya disebut PON) di Papua dari tahun 2020 menjadi 2021. Akibatnya, para atlet pada awalnya diharuskan memiliki Training From Home (TFH) karena pencegahan penularan virus. Dalam kaitannya dengan latihan, kepercayaan diri dan kecemasan merupakan dua faktor yang masing-masing mempengaruhi kinerja atlet. Penelitian ini bertujuan untuk mengetahui bagaimana tingkat kepercayaan diri dan kecemasan atlet Judo Jawa Barat pasca TFH. Responden penelitian deskriptif ini terdiri dari seluruh atlet Judo Jawa Barat yang mengikuti PELATDA, program latihan provinsi, untuk persiapan PON Papua. Setelah menganalisis data, penelitian ini menemukan bahwa tingkat kepercayaan diri atlet adalah 86,30% dan tingkat kecemasan mereka adalah 40,40%. Ini berarti tingkat kepercayaan diri mereka tinggi dan kecemasan sedang setelah mereka mengikuti pelatihan dari rumah. Oleh karena itu, dapat disimpulkan bahwa pelatihan dari rumah mereka efektif karena tidak mengurangi kecemasan mereka secara signifikan. Karena rasa kepercayaan diri dan kecemasan akan mempengaruhi performa atlet elit, bahkan dalam situasi pandemi COVID-19, atlet elit harus mampu meningkatkan rasa kepercayaan diri dan menurunkan tingkat kecemasan agar performa dalam pertandingan tetap terjaga.
EDUCAÇÃO PROFISSIONAL E TECNOLÓGICA: considerações sobre a perspectiva inclusiva das pessoas com necessidades específicas
Authors: Magalhães Dayana Rocha Gonçalves de , Marques Welisson , Castaman Ana Sara
Published: July 2022

Este estudo apresenta-se como recorte de uma dissertação de mestrado, na qual tratou-se da temática Educação Inclusiva, tendo como lócus investigativo um do campus do Instituto Federal de Educação, Ciência e Tecnologia (IFTM). O objetivo deste artigo foi compreender em quais contextos se desenvolveu a educação das pessoas com necessidades específicas (NE) na EPT, apontando-se algumas interfaces entre a educação inclusiva e a formação integrada, além de abordar os documentos recentes, que dispõem sobre as atividades do Napne no âmbito do IFTM, sob o paradigma da inclusão. Desse modo, foi realizada uma revisão bibliográfica e documental referente à Educação Inclusiva e à Educação Profissional e Tecnológica (EPT), especificamente do IFTM. Verifica-se que a educação das pessoas com NE na EPT, se iniciou num contexto de cunho, meramente, social e assistencialista, desvinculado da formação científica. Contudo, é num cenário em que se objetiva oferecer à sociedade uma formação profissional integrada, politécnica e omnilateral, que se configura o processo de inclusão das pessoas com NE na EPT, sob a perspectiva da inclusão. Nessa lógica, percebeu-se, por meio dos documentos que regulamentam o Napne do IFTM, que esse núcleo busca promover a cultura da educação para inclusão, a fim de garantir o acesso, a permanência e o sucesso escolar dos estudantes com NE na EPT. Este estudo não contemplou a visão dos estudantes com NE, assim, recomenda-se a realização de pesquisas que possibilitem verificar suas percepções quanto a sua vivência escolar, sob a perspectiva da educação inclusiva, especialmente no contexto da EPT.
Current issues of professional development of specialists of the health care system
Authors:   Federal State Budgetary Educational Institution of Higher Education ", Berezina Z.I. ,   Rostov State Medical University",   of the Ministry of Health of the Russian Federation
Published: July 2022

The material is devoted to the features of additional professional education of medical specialists in modern conditions. The author considers the issues of transformation of the system of additional professional education in the context of digitalization. Special attention is paid to the role of continuous medical education in the system of advanced training of specialists.
Local Islam and Javanese Culture Penetration: Muslim Alif Rebo Wage (Aboge) Negotiations in Identity Formation
Authors: Muttaqin Ahmad , Noor Nina Mariani
Published: July 2022

This article aims to explain the efforts of Aboge Muslims as a group that gains penetration from mainstream culture to negotiate their identity. Aboge (Alif Rebo Wage) is a Muslim group in Java that has a Kejawen Islamic identity. In Banyumas District, Central Java, the Aboge identity was built based on the Penginyongan culture whose position is a subculture of Javanese. Using a critical paradigm with the theoretical approach of genetic structuralism developed by Pierre Bourdieu, this article shows that Aboge identity negotiations are carried out by capitalizing on the Penginyongan culture as a locality that has a strategy in the era of regional and village autonomy. The result of this study reveals that Aboge takes the space left by the Banyumasan community because it is inferior to standard Javanese. This is because Penginyongan culture, which includes language, tradition, and religion, tends to be positioned as marginal, assumed to be deviant, and misrepresented. Standard culture uses the instrument of power that is owned to maintain its dominance through the formation of a stratified structure. This structure is used to distribute other variations at different positions with the epicenter point filled by the standard culture. In the context of language, bandek Javanese (Yogyakarta and Solo) is the standard, outside of which includes the North Coast, East Java, and Banyumasan are dialects. Locality gained momentum during the period of regional and village autonomy which demanded its development to explore and accommodate local culture. Aboge with Penginyongan identity then becomes a manifestation that represents locality.
Nilai Religius Dalam Cerita Rakyat Nyi Mas Gamparan Ciseukeut dan Pemanfaatannya Sebagai Bahan Pembelajaran Apresiasi Sastra di SMA
Authors: Sauri Sopyan , Maryanah Maryanah
Published: July 2022

Penelitian ini bertujuan untuk mengetahui dan mendeskripsikan nilai religius pada cerita rakyat Nyi Mas Gamparan Ciseukeut Pandeglang Banten, dan pemanfaatannya sebagai bahan ajar bahasa Indonesia di SMA kelas X. Penelitian ini menggunakan metode deskriptif kualitatif. Sumber data dalam penelitian ini primer dan skunder. Primer berupa cerita rakyat Nyi Mas Gamparan Ciseukeut dan skunder berupa buku-buku atau jurnal. Teknik pengumpulan data menggunakan teknik survei, teknik wawancara dan teknik dokumentasi. Teknik analisis data menggunakan metode content analysis (analisis isi) dengan langkah penelitian kualitatif menggunakan model Milles dan Hubberman. Hasil temuan penelitian ini nilai religius terbagi menjadi tiga yaitu hubungan manusia dengan Tuhan, hubungan manusia dengan orang lain atau masyarakat dan alam serta hubungan manusia dengan diri sendiri. Hubungan manusia dengan Tuhan yang berindikator sembahyang berjumlah 21 kutipan, ritual lainnya berjumlah 1 kutipan. Kemudian, hubungan manusia dengan orang lain atau masyarakat dan alam yang berindikator gotong royong berjumlah 9 kutipan, cinta tanah air berjumlah 6 kutipan, musyawarah berjumlah 2 kutipan dan patuh pada adat berjumlah 1 kutipan. Selanjutnya, hubungan manusia dengan diri sendiri yang berindikator menentukan pandangan hidup berjumlah 1 kutipan dan sikap berprilaku berjumlah 3 kutipan. Implementasi cerita rakyat Nyi Mas Gamparan Ciseukeut sebagai bahan ajar Bahasa Indonesia SMA kelas X kurikulum 2013 yaitu kompetensi dasar 3.7 mengidentifikasi nilai- nilai dan isi yang terkandung dalam cerita rakyat (hikayat) baik lisan maupun tulis dan 4.7 menceritakan kembali isi cerita rakyat (hikayat) yang didengar dan dibaca. Hasil validasi pada penelitian ini menunjukan bahwa analisis nilai religius pada cerita rakyat Nyi Mas Gamparan Ciseukeut layak dijadikan sebagai bahan pembelajaran bahasa Indonesia di SMA.
A Descriptive Study to Assess the Childbirth Experiences among Primipara Mothers in Selected Hospitals of Dabri, New Delhi
Authors:   Lecturer, Kailash Institute of Nursing and Paramedical Sciences, Greater Noida, Uttar Pradesh, India., Kumari Krishna
Published: July 2022

Introduction: Primigravida’s experiences of childbirth is influenced by the knowledge and expectations of child birth is based on the information, she got from the antenatal clinic, intra natal and post-natal periods, the nursing staff, her mother, friends and family. Objectives of the study were to assess the childbirth experiences among Primipara mothers. To find associate the childbirth experiences among Primipara mothers with the selected demographic variables, and to identify the association between childbirth experiences and selected maternal outcome. Method: A non-experimental descriptive design was used on sample size consisted of 100 primipara mothers who were admitted in postnatal department by purposive sampling techniques. Data was collected by administered childbirth experiences questionnaire, demographic questionnaire and maternal outcome Performa by using interview schedule and analyzed by using SPSS software.Result: Result show that the mean score for the childbirth experience was 42.62 with a standard deviation of 7.13 and median with 44. The statistical outcomes of association between childbirth experience and socio demographic variables of age [C=8.75, df=2], education [C = 10.23, df=4], occupation [C=7.35, df=3], income [C=8.34, df=3], distance from health care facility [C=9.61, df=3], financial planning for birth [C=5.82, df=1], health centre for delivery [C=6.14, df=1] were found to be statistically significant with childbirth experience at 5% level i.e. P<0.05. The nature of family [C = 2.43, df=3], religion [C=6.93, df=3], variables were found to be statistically not significant at 5% level i.e., P>0.05. There was no significant relationship between childbirth experience and maternal outcome.Conclusion: The researcher found that majority of the primipara mothers had positive experience during child birth and labour.
Analysis of the Variation in Different Nondestructive Testing and Standards
Authors:   Associate Professor, Madan Bhandari Memorial Academy Nepal, Urlabari 3, Morang, Nepal., Mishra Anjay Kumar
Published: July 2022

Non Destructive Testing (NDT) methods are used to review or measure the materials or designs without annihilating their surface, item uprightness and future handiness. The overall objective of the research is to assess the strength of concrete in existing structures using Nondestructive Tests at Kachankawal Rural Municipality, Jhapa District of Nepal.Rebound hammer test and ultrasonic pulse velocity tests were used for determining the compressive strength of concrete. The existing Reinforced Concrete Cement culverts were used for 9 existing structures. Average rebound number were taken and calculated from each sample. From the average rebound number were taken to determine the grade of concrete along with corresponding compressive strength. Similarly, the Ultrasonic Pulse Velocity tests were done on same existing structures and path length and time were noted for assessing the corresponding compressive strength and the quality of concrete. Triangulation among rebound hammer test, ultrasonic pulse velocity test and proposed initial grading were validated using chi square test. Relation between compressive strength with rebound hammer and ultrasonic pulse velocity were analyzed using linear regression model. Chi square confirms variation in compressive strength between calculated compressive strength and standard compressive strength. The relation of compressive strength (y) at R² = 1 with rebound number (x) and ultrasonic pulse velocity (x) could be expressed as y = 0.9306x - 2.7233 and y = 2.904 x + 10.119 respectively. The study is a guiding tool for concern authority and professionals to make effective decision regarding further structural development and budget allocation of the existing structures.
Effect of Fine Aggregate Sources on Compressive Strength of Cement Concrete
Authors:   Post-Doctoral Research Scholar, Srinivas University, India and Associate Professor, Madan Bhandari Memorial Academy Nepal, Urlabari 3, Morang, Nepal., Mishra Anjay Kumar
Published: July 2022

The cause of failure of structure made from cement concrete is due to use of low-quality ingredient which is used directly without testing its properties and without studying its suitability in production of different grade of concrete mix. The aim of this study is to presents that the effect of fine aggregates from different sources on compressive strength of cement concrete. For this study four fine aggregate sources were taken. Altogether 48 concrete cubes of M20 grade were prepared from different sixteen combinations of these four fine aggregate sources. The different properties were founded by conducting different laboratory tests for fine aggregates from different sources and the compressive strength test of 48 cubes was conducted.The test results showed that properties of fine aggregate sources such as fineness modulus, loose bulk density, specific gravity, water absorption and Materials Finer than 75 μm were founded in the range of (3.17-3.88), (1508.33-1604.17) kg/m3, (2.57-2.66) %, (1.60-1.92) % and (2.74-7.47) respectively. Compressive strength test of all 48 samples were cross the minimum strength provided by M20 grade and it is ranges from 20.64 N/ mm2 to 32.47 N/ mm2. Different value of mean compressive strength was obtained for all sixteen combinations of aggregates form each other. The fine aggregate from Chisang source achieved more value of average compressive strength when mix with other four different coarse aggregate sample than other three sources. Significance difference in mean compressive strength of concrete made from different fine aggregates source was concluded by the Two-way ANOVA test results. This research work concluded that all four fine aggregate sources used in this study and concrete made from these sources can be used for residential building construction propose and other construction work which required strength provided by M20 grade of nominal mix concrete.
Book review of Petryna, A. 2022. Horizon work: at the edges of knowledge in an age of runaway climate change
Authors: Thomas Jordan
Published: July 2022

N/A
Convergence of Impact Measures and Impact Bundles
Authors: Egghe Leo
Published: July 2022

Purpose

A new point of view in the study of impact is introduced.

Design/methodology/approach

Using fundamental theorems in real analysis we study the convergence of well-known impact measures.

Findings

We show that pointwise convergence is maintained by all well-known impact bundles (such as the h-, g-, and R-bundle) and that the μ-bundle even maintains uniform convergence. Based on these results, a classification of impact bundles is given.

Research limitations

As for all impact studies, it is just impossible to study all measures in depth.

Practical implications

It is proposed to include convergence properties in the study of impact measures.

Originality/value

This article is the first to present a bundle classification based on convergence properties of impact bundles.
PEDOMAN PENULISAN DAN TEMPLATE ARTIKEL HASIL PENELITIAN JURNAL PENDIDIKAN DAN KEBUDAYAAN VOL 2, NO. 1 TAHUN 2017
Authors: Dikbud Jurnal
Published: July 2022

Pedoman Penulisan dan Template Artikel Hasil Penelitian Jurnal Pendidikan dan Kebudayaan Vol 2, No. 1 Tahun 2017
ДИСКУССИОННЫЕ АСПЕКТЫ КОНЦЕПЦИЙ ЗАПАДНЫХ АНТРОПОЛОГОВ О ДУМАЮЩЕЙ ПРИРОДЕ
Authors: Примак П.В.
Published: July 2022

Статья посвящена дискуссионным аспектам теоретических построений таких современных западных антропологов, как Ф. Дескола, Э. Кон и Э.В. де Кастру, относительно взаимоотношений человека и природы в архаических обществах. Для корректировки представления о научном приоритете сформулированных ими выводов автор статьи обращается к работам выдающихся российских этнографов – В.Г. Богораза, Л.Я. Штернберга, В.К. Арсеньева. Ключевые слова: западная антропология, российская этнография, архаические общества, думающая природа
Выборы в Госдуму-2021: итоги и перспективы
Authors: АЛЕКСЕЕВ Даниил Юрьевич
Published: July 2022

В статье анализируются итоги выборов в Госдуму РФ восьмого созыва, даётся оценка перспектив политических лидеров на будущих выборах Президента РФ. Использованы методы сравнительно-описательный, анализа электоральной статистики, анализа документов, метод сценариев и др. Главным результатом выборов стала убедительная победа "Единой России", сохранившей конституционное большинство в Госдуме, несмотря на то, что впервые в XXI веке в Госдуму прошли 5 партий, а не 4. В России закрепилась партийная система с доминирующей партией, в которой власть осуществляет наиболее сильная партия, занимающая особое положение, опережающая все другие на выборах с абсолютным большинством голосов Ключевые слова: выборы в Госдуму, политические партии, "Единая Россия", "Справедливая Россия", КПРФ, ЛДПР, "Новые люди"
Корейская диаспора Узбекистана: история и современность
Authors: НАЗАРОВ Равшан Ринатович
Published: July 2022

я. Корейцы, как заметная группа населения, проживают в Узбекистане с 1937 г., хотя отдельные контакты отмечались и ранее. На ранних этапах своего проживания в республике (1930-1950-е гг.) вклад корейской диаспоры заключался прежде всего в развитии сельского хозяйства, особенно – рисоводства. С 1960-х гг. происходит рост корейской интеллигенции, становится более заметным вклад диаспоры в развитие науки, культуры, спорта, общественной жизни. С середины 1980-х гг. возрастает процесс роста национального самосознания диаспоры, активизируется интерес к развитию национальной культуры, языка, ценностей, связей с исторической родиной. С начала 1990-х гг. диаспора выступает важным связующим звеном во взаимоотношениях между Республикой Узбекистан и Республикой Корея. Ключевые слова: корейцы, диаспора, Узбекистан, Республика Корея, история, современность
Post-Pandemic Reflections on Challenges and Opportunities for Marketing Research in the 21st Century
Authors: Hair Joseph F. , Harrison Dana , Risher Jeffrey
Published: July 2022

The role of marketing is evolving rapidly, and design and analysis methods used by marketing researchers are also changing. These changes are emerging from transformations in management skills, technological innovations, continuously evolving customer behavior, and most recently the Covid-19 pandemic. But perhaps the most substantial driver of these changes is the emergence of big data and the analytical methods used to examine and understand the data. To continue being relevant, marketing research must remain as dynamic as the markets themselves and adapt accordingly to the following:  data will continue increasing exponentially; data quality will improve; analytics will be more powerful, easier to use, and more widely used; management and customer decisions will increasingly be knowledge-based; privacy issues and challenges will be both a problem and an opportunity as organizations develop their analytics skills; data analytics will become firmly established as a competitive advantage, both in the marketing research industry and in academics; and for the foreseeable future, the demand for highly trained data scientists will exceed the supply.
Banking digital ecosystem creation
Authors: ALIEV Oleg M.
Published: July 2022

Subject. This article deals with the issues of development of digital ecosystems by banks. Objectives. The article aims to identify the motives for the formation of banking digital ecosystems and determine regional differences and prospects for their development. Methods. For the study, I used a content analysis, generalization, and the logical and comparative approaches. Results. The article defines the motives for creating banking ecosystems and formulates prospects for their further development in the Russian market. Relevance. The results obtained can be used by large banks, including the Bank of Russia, as well as by legislative bodies in the development of a regulatory framework governing the formation of banking digital ecosystems.
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
Authors: Abe Kenshi , Komiyama Junpei , Iwasaki Atsushi
Published: July 2022

This paper considers the capacity expansion problem in two-sided matchings, where the policymaker is allowed to allocate some extra seats as well as the standard seats. In medical residency match, each hospital accepts a limited number of doctors. Such capacity constraints are typically given in advance. However, such exogenous constraints can compromise the welfare of the doctors; some popular hospitals inevitably dismiss some of their favorite doctors. Meanwhile, it is often the case that the hospitals are also benefited to accept a few extra doctors. To tackle the problem, we propose an anytime method that the upper confidence tree searches the space of capacity expansions, each of which has a resident-optimal stable assignment that the deferred acceptance method finds. Constructing a good search tree representation significantly boosts the performance of the proposed method. Our simulation shows that the proposed method identifies an almost optimal capacity expansion with a significantly smaller computational budget than exact methods based on mixed-integer programming.
Event-driven Video Deblurring via Spatio-Temporal Relation-Aware Network
Authors: Cao Chengzhi , Fu Xueyang , Zhu Yurui , Shi Gege , Zha Zheng-Jun
Published: July 2022

Video deblurring with event information has attracted considerable attention. To help deblur each frame, existing methods usually compress a specific event sequence into a feature tensor with the same size as the corresponding video. However, this strategy neither considers the pixel-level spatial brightness changes nor the temporal correlation between events at each time step, resulting in insufficient use of spatio-temporal information. To address this issue, we propose a new Spatio-Temporal Relation-Attention network (STRA), for the specific event-based video deblurring. Concretely, to utilize spatial consistency between the frame and event, we model the brightness changes as an extra prior to aware blurring contexts in each frame; to record temporal relationship among different events, we develop a temporal memory block to restore long-range dependencies of event sequences continuously. In this way, the complementary information contained in the events and frames, as well as the correlation of neighboring events, can be fully utilized to recover spatial texture from events constantly. Experiments show that our STRA significantly outperforms several competing methods, e.g., on the HQF dataset, our network achieves up to 1.3 dB in terms of PSNR over the most advanced method. The code is available at https://github.com/Chengzhi-Cao/STRA.
Uncertainty-Aware Representation Learning for Action Segmentation
Authors: Chen Lei , Li Muheng , Duan Yueqi , Zhou Jie , Lu Jiwen
Published: July 2022

In this paper, we propose an uncertainty-aware representation Learning (UARL) method for action segmentation. Most existing action segmentation methods exploit continuity information of the action period to predict frame-level labels, which ignores the temporal ambiguity of the transition region between two actions. Moreover, similar periods of different actions, e.g., the beginning of some actions, will confuse the network if they are annotated with different labels, which causes spatial ambiguity. To address this, we design the UARL to exploit the transitional expression between two action periods by uncertainty learning. Specially, we model every frame of actions with an active distribution that represents the probabilities of different actions, which captures the uncertainty of the action and exploits the tendency during the action. We evaluate our method on three popular action prediction datasets: Breakfast, Georgia Tech Egocentric Activities (GTEA), and 50Salads. The experimental results demonstrate that our method achieves the performance with state-of-the-art.
Region-Aware Metric Learning for Open World Semantic Segmentation via Meta-Channel Aggregation
Authors: Dong Hexin , Chen Zifan , Yuan Mingze , Xie Yutong , Zhao Jie , Yu Fei , Dong Bin , Zhang Li
Published: July 2022

As one of the most challenging and practical segmentation tasks, open-world semantic segmentation requires the model to segment the anomaly regions in the images and incrementally learn to segment out-of-distribution (OOD) objects, especially under a few-shot condition. The current state-of-the-art (SOTA) method, Deep Metric Learning Network (DMLNet), relies on pixel-level metric learning, with which the identification of similar regions having different semantics is difficult. Therefore, we propose a method called region-aware metric learning (RAML), which first separates the regions of the images and generates region-aware features for further metric learning. RAML improves the integrity of the segmented anomaly regions. Moreover, we propose a novel meta-channel aggregation (MCA) module to further separate anomaly regions, forming high-quality sub-region candidates and thereby improving the model performance for OOD objects. To evaluate the proposed RAML, we have conducted extensive experiments and ablation studies on Lost And Found and Road Anomaly datasets for anomaly segmentation and the CityScapes dataset for incremental few-shot learning. The results show that the proposed RAML achieves SOTA performance in both stages of open world segmentation. Our code and appendix are available at https://github.com/czifan/RAML.
MNet: Rethinking 2D/3D Networks for Anisotropic Medical Image Segmentation
Authors: Dong Zhangfu , He Yuting , Qi Xiaoming , Chen Yang , Shu Huazhong , Coatrieux Jean-Louis , Yang Guanyu , Li Shuo
Published: July 2022

The nature of thick-slice scanning causes severe inter-slice discontinuities of 3D medical images, and the vanilla 2D/3D convolutional neural networks (CNNs) fail to represent sparse inter-slice information and dense intra-slice information in a balanced way, leading to severe underfitting to inter-slice features (for vanilla 2D CNNs) and overfitting to noise from long-range slices (for vanilla 3D CNNs). In this work, a novel mesh network (MNet) is proposed to balance the spatial representation inter axes via learning. 1) Our MNet latently fuses plenty of representation processes by embedding multi-dimensional convolutions deeply into basic modules, making the selections of representation processes flexible, thus balancing representation for sparse inter-slice information and dense intra-slice information adaptively. 2) Our MNet latently fuses multi-dimensional features inside each basic module, simultaneously taking the advantages of 2D (high segmentation accuracy of the easily recognized regions in 2D view) and 3D (high smoothness of 3D organ contour) representations, thus obtaining more accurate modeling for target regions. Comprehensive experiments are performed on four public datasets (CT\&MR), the results consistently demonstrate the proposed MNet outperforms the other methods. The code and datasets are available at: https://github.com/zfdong-code/MNet
D-DPCC: Deep Dynamic Point Cloud Compression via 3D Motion Prediction
Authors: Fan Tingyu , Gao Linyao , Xu Yiling , Li Zhu , Wang Dong
Published: July 2022

The non-uniformly distributed nature of the 3D Dynamic Point Cloud (DPC) brings significant challenges to its high-efficient inter-frame compression. This paper proposes a novel 3D sparse convolution-based Deep Dynamic Point Cloud Compression (D-DPCC) network to compensate and compress the DPC geometry with 3D motion estimation and motion compensation in the feature space. In the proposed D-DPCC network, we design a Multi-scale Motion Fusion (MMF) module to accurately estimate the 3D optical flow between the feature representations of adjacent point cloud frames. Specifically, we utilize a 3D sparse convolution-based encoder to obtain the latent representation for motion estimation in the feature space and introduce the proposed MMF module for fused 3D motion embedding. Besides, for motion compensation, we propose a 3D Adaptively Weighted Interpolation (3DAWI) algorithm with a penalty coefficient to adaptively decrease the impact of distant neighbours. We compress the motion embedding and the residual with a lossy autoencoder-based network. To our knowledge, this paper is the first work proposing an end-to-end deep dynamic point cloud compression framework. The experimental result shows that the proposed D-DPCC framework achieves an average 76% BD-Rate (Bjontegaard Delta Rate) gains against state-of-the-art Video-based Point Cloud Compression (V-PCC) v13 in inter mode.
Region-Aware Temporal Inconsistency Learning for DeepFake Video Detection
Authors: Gu Zhihao , Yao Taiping , Chen Yang , Yi Ran , Ding Shouhong , Ma Lizhuang
Published: July 2022

The rapid development of face forgery techniques has drawn growing attention due to security concerns. Existing deepfake video detection methods always attempt to capture the discriminative features by directly exploiting static temporal convolution to mine temporal inconsistency, without explicit exploration on the diverse temporal dynamics of different forged regions. To effectively and comprehensively capture the various inconsistency, in this paper, we propose a novel Region-Aware Temporal Filter (RATF) module which automatically generates corresponding temporal filters for different spatial regions. Specifically, we decouple the dynamic temporal kernel into a set of region-agnostic basic filters and region-sensitive aggregation weights. And different weights guide the corresponding regions to adaptively learn temporal inconsistency, which greatly enhances the overall representational ability. Moreover, to cover the long-term temporal dynamics, we divide the video into multiple snippets and propose a Cross-Snippet Attention (CSA) to promote the cross-snippet information interaction. Extensive experiments and visualizations on several benchmarks demonstrate the effectiveness of our method against state-of-the-art competitors.
SatFormer: Saliency-Guided Abnormality-Aware Transformer for Retinal Disease Classification in Fundus Image
Authors: Jiang Yankai , Xu Ke , Wang Xinyue , Li Yuan , Cui Hongguang , Tao Yubo , Lin Hai
Published: July 2022

Automatic and accurate retinal disease diagnosis is critical to guide proper therapy and prevent potential vision loss. Previous works simply exploit the most discriminative features while ignoring the pathological visual clues of scattered subtle lesions. Therefore, without a comprehensive understanding of features from different lesion regions, they are vulnerable to noise from complex backgrounds and suffer from misclassification failures. In this paper, we address these limitations with a novel saliency-guided abnormality-aware transformer which explicitly captures the correlation between different lesion features from a global perspective with enhanced pathological semantics. The model has several merits. First, we propose a saliency enhancement module (SEM) which adaptively integrates disease related semantics and highlights potentially salient lesion regions. Second, to the best of our knowledge, this is the first work to explore comprehensive lesion feature dependencies via a tailored efficient self-attention. Third, with the saliency enhancement module and abnormality-aware attention, we propose a new variant of Vision Transformer models, called SatFormer, which outperforms the state-of-the-art methods on two public retinal disease classification benchmarks. Ablation study shows that the proposed components can be easily embedded into any Vision Transformers via a plug-and-play manner and effectively boost the performance.
PRNet: Point-Range Fusion Network for Real-Time LiDAR Semantic Segmentation
Authors: Li Xiaoyan , Zhang Gang , Jiang Tao , Cai Xufen , Wang Zhenhua
Published: July 2022

Accurate and real-time LiDAR semantic segmentation is necessary for advanced autonomous driving systems. To guarantee a fast inference speed, previous methods utilize the highly optimized 2D convolutions to extract features on the range view (RV), which is the most compact representation of the LiDAR point clouds. However, these methods often suffer from lower accuracy for two reasons: 1) the information loss during the projection from 3D points to the RV, 2) the semantic ambiguity when 3D points labels are assigned according to the RV predictions. In this work, we introduce an end-to-end point-range fusion network (PRNet) that extracts semantic features mainly on the RV and iteratively fuses the RV features back to the 3D points for the final prediction. Besides, a novel range view projection (RVP) operation is designed to alleviate the information loss during the projection to the RV, and a point-range convolution (PRConv) is proposed to automatically mitigate the semantic ambiguity during transmitting features from the RV back to 3D points. Experiments on the SemanticKITTI and nuScenes benchmarks demonstrate that the PRNet pushes the range-based methods to a new state-of-the-art, and achieves a better speed-accuracy trade-off.
Toward Policy Explanations for Multi-Agent Reinforcement Learning
Authors: Boggess Kayla , Kraus Sarit , Feng Lu
Published: July 2022

Advances in multi-agent reinforcement learning (MARL) enable sequential decision making for a range of exciting multi-agent applications such as cooperative AI and autonomous driving. Explaining agent decisions is crucial for improving system transparency, increasing user satisfaction, and facilitating human-agent collaboration. However, existing works on explainable reinforcement learning mostly focus on the single-agent setting and are not suitable for addressing challenges posed by multi-agent environments. We present novel methods to generate two types of policy explanations for MARL: (i) policy summarization about the agent cooperation and task sequence, and (ii) language explanations to answer queries about agent behavior. Experimental results on three MARL domains demonstrate the scalability of our methods. A user study shows that the generated explanations significantly improve user performance and increase subjective ratings on metrics such as user satisfaction.
Cost Ensemble with Gradient Selecting for GANs
Authors: Liu Minghui , Deng Jiali , Yang Meiyi , Cheng Xuan , Liu Nianbo , Liu Ming , Wang Xiaomin
Published: July 2022

Generative Adversarial Networks(GANs) are powerful generative models on numerous tasks and datasets but are also known for their training instability and mode collapse. The latter is because the optimal transportation map is discontinuous, but DNNs can only approximate continuous ones. One way to solve the problem is to introduce multiple discriminators or generators. However, their impacts are limited because the cost function of each component is the same. That is, they are homogeneous. In contrast, multiple discriminators with different cost functions can yield various gradients for the generator, which indicates we can use them to search for more transportation maps in the latent space. Inspired by this, we have proposed a framework to combat the mode collapse problem, containing multiple discriminators with different cost functions, named CES-GAN. Unfortunately, it may also lead to the generator being hard to train because the performance between discriminators is unbalanced, according to the Cannikin Law. Thus, a gradient selecting mechanism is also proposed to pick up proper gradients. We provide mathematical statements to prove our assumptions and conduct extensive experiments to verify the performance. The results show that CES-GAN is lightweight and more effective for fighting against the mode collapse problem than similar works.
Learning Implicit Body Representations from Double Diffusion Based Neural Radiance Fields
Authors: Yao Guangming , Wu Hongzhi , Yuan Yi , Li Lincheng , Zhou Kun , Yu Xin
Published: July 2022

In this paper, we present a novel double diffusion based neural radiance field, dubbed DD-NeRF, to reconstruct human body geometry and render the human body appearance in novel views from a sparse set of images. We first propose a double diffusion mechanism to achieve expressive representations of input images by fully exploiting human body priors and image appearance details at two levels. At the coarse level, we first model the coarse human body poses and shapes via an unclothed 3D deformable vertex model as guidance. At the fine level, we present a multi-view sampling network to capture subtle geometric deformations and image detailed appearances, such as clothing and hair, from multiple input views. Considering the sparsity of the two level features, we diffuse them into feature volumes in the canonical space to construct neural radiance fields. Then, we present a signed distance function (SDF) regression network to construct body surfaces from the diffused features. Thanks to our double diffused representations, our method can even synthesize novel views of unseen subjects. Experiments on various datasets demonstrate that our approach outperforms the state-of-the-art in both geometric reconstruction and novel view synthesis.
RAPQ: Rescuing Accuracy for Power-of-Two Low-bit Post-training Quantization
Authors: Yao Hongyi , Li Pu , Cao Jian , Liu Xiangcheng , Xie Chenying , Wang Bingzhang
Published: July 2022

We introduce a Power-of-Two post-training quantization( PTQ) method for deep neural network that meets hardware requirements and does not call for long-time retraining. PTQ requires a small set of calibration data and is easier for deployment, but results in lower accuracy than Quantization-Aware Training( QAT). Power-of-Two quantization can convert the multiplication introduced by quantization and dequantization to bit-shift that is adopted by many efficient accelerators. However, the Power-of-Two scale has fewer candidate values, which leads to more rounding or clipping errors. We propose a novel Power-of-Two PTQ framework, dubbed RAPQ, which dynamically adjusts the Power-of-Two scales of the whole network instead of statically determining them layer by layer. It can theoretically trade off the rounding error and clipping error of the whole network. Meanwhile, the reconstruction method in RAPQ is based on the BN information of every unit. Extensive experiments on ImageNet prove the excellent performance of our proposed method. Without bells and whistles, RAPQ can reach accuracy of 65% and 48% on ResNet-18 and MobileNetV2 respectively with weight INT2 activation INT4. We are the first to propose PTQ for the more constrained but hardware-friendly Power-of-Two quantization and prove that it can achieve nearly the same accuracy as SOTA PTQ method. The code will be released.
VidyutVanika21: An Autonomous Intelligent Broker for Smart-grids
Authors: Chandlekar Sanjay , Pedasingu Bala Suraj , Subramanian Easwar , Bhat Sanjay , Paruchuri Praveen , Gujar Sujit
Published: July 2022

An autonomous broker that liaises between retail customers and power-generating companies (GenCos) is essential for the smart grid ecosystem. The efficiency brought in by such brokers to the smart grid setup can be studied through a well-developed simulation environment. In this paper, we describe the design of one such energy broker called VidyutVanika21 (VV21) and analyze its performance using a simulation platform called PowerTAC (PowerTrading Agent Competition). Specifically, we discuss the retail (VV21–RM) and wholesale market (VV21–WM) modules of VV21 that help the broker achieve high net profits in a competitive setup. Supported by game-theoretic analysis, the VV21–RM designs tariff contracts that a) maintain a balanced portfolio of different types of customers; b) sustain an appropriate level of market share, and c) introduce surcharges on customers to reduce energy usage during peak demand times. The VV21–WM aims to reduce the cost of procurement by following the supply curve of the GenCo to identify its lowest ask for a particular auction which is then used to generate suitable bids. We further demonstrate the efficacy of the retail and wholesale strategies of VV21 in PowerTAC 2021 finals and through several controlled experiments.
A Probabilistic Code Balance Constraint with Compactness and Informativeness Enhancement for Deep Supervised Hashing
Authors: Zhang Qi , Hu Liang , Cao Longbing , Shi Chongyang , Wang Shoujin , Liu Dora D.
Published: July 2022

Building on deep representation learning, deep supervised hashing has achieved promising performance in tasks like similarity retrieval. However, conventional code balance constraints (i.e., bit balance and bit uncorrelation) imposed on avoiding overfitting and improving hash code quality are unsuitable for deep supervised hashing owing to their inefficiency and impracticality of simultaneously learning deep data representations and hash functions. To address this issue, we propose probabilistic code balance constraints on deep supervised hashing to force each hash code to conform to a discrete uniform distribution. Accordingly, a Wasserstein regularizer aligns the distribution of generated hash codes to a uniform distribution. Theoretical analyses reveal that the proposed constraints form a general deep hashing framework for both bit balance and bit uncorrelation and maximizing the mutual information between data input and their corresponding hash codes. Extensive empirical analyses on two benchmark datasets further demonstrate the enhancement of compactness and informativeness of hash codes for deep supervised hash to improve retrieval performance (code available at: https://github.com/mumuxi/dshwr).
C3-STISR: Scene Text Image Super-resolution with Triple Clues
Authors: Zhao Minyi , Wang Miao , Bai Fan , Li Bingjia , Wang Jie , Zhou Shuigeng
Published: July 2022

Scene text image super-resolution (STISR) has been regarded as an important pre-processing task for text recognition from low-resolution scene text images. Most recent approaches use the recognizer's feedback as clues to guide super-resolution. However, directly using recognition clue has two problems: 1) Compatibility. It is in the form of probability distribution, has an obvious modal gap with STISR - a pixel-level task; 2) Inaccuracy. it usually contains wrong information, thus will mislead the main task and degrade super-resolution performance. In this paper, we present a novel method C3-STISR that jointly exploits the recognizer's feedback, visual and linguistical information as clues to guide super-resolution. Here, visual clue is from the images of texts predicted by the recognizer, which is informative and more compatible with the STISR task; while linguistical clue is generated by a pre-trained character-level language model, which is able to correct the predicted texts. We design effective extraction and fusion mechanisms for the triple cross-modal clues to generate a comprehensive and unified guidance for super-resolution. Extensive experiments on TextZoom show that C3-STISR outperforms the SOTA methods in fidelity and recognition performance. Code is available in https://github.com/zhaominyiz/C3-STISR.