bluuebunny/crossref_metadata_2025 huggingface dataset:
ВКЛАД Б.А. КАЛОЕВА В РАЗВИТИЕ ИСТОРИОГРАФИЧЕСКОГО КАВКАЗОВЕДЕНИЯ
Authors: ГОСТИЕВА Л.К.
Published: August 2021

Статья посвящена деятельности Б.А. Калоева в области историографического кавказоведения. Рассмотрены его монографии и многочисленные статьи по историографии Кавказа, в которых автор нашел много замечательных мыслей, по-новому ставивших проблемы кавказоведения. В монографии «В.Ф. Миллер - кавказовед (Исследование и материалы)» Калоев подробно осветил многогранную личность выдающегося ученого и направления его кавказоведческой и, в том числе, осетиноведческой деятельности. Особый акцент сделан на включение в монографию писем представителей осетинской интеллигенции к В.Ф. Миллеру. Проанализирована монография Калоева «М.М. Ковалевский и его исследования горских народов Кавказа», в которой особое внимание уделено осетиноведческим трудам Ковалевского. Отмечено изучение ученым этногенеза осетин, их общественного строя и обычного права, материальной и духовной культуры, семейного и общественного быта. Указано на использование в фундаментальном труде Ковалевского «Современный обычай и древний закон» полевых материалов, собранных в Осетии. Подвергнута разбору монография Калоева «Василий Иванович Абаев и вопросы этнографии в его трудах». Акцентировано внимание на публикуемый в приложении к книге дневник, который вел Калоев в ходе многолетнего общения с Абаевым. Разобрана статья Калоева «А.Ф. Кони о Кавказе», в которой автор попытался оценить вклад в кавказоведение выдающегося юриста-оратора, ученого, литературоведа, публициста и педагога. Изучен историографический очерк Калоева «А.И. Шёгрен - этнограф-кавказовед», в котором оценены заслуги академика Шёгрена в области этнографического кавказоведения. Рассмотрены статьи Калоева об известных кавказоведах, с которыми ему довелось сотрудничать: В.К. Гарданове, Л.И. Лаврове, М.О. Косвене, Е.Г. Пчелиной. В статьях охарактеризованы научные исследования ученых и их идеи на фоне состояния развития науки рассматриваемого периода. Сделан вывод о значительном вкладе Калоева в развитие историографического кавказоведения.
A Periodic Boundary Value Problem for a Fourth Order Differential Operator with a Summable Potential
Authors: Митрохин С.И.
Published: August 2021

Работа посвящена изучению дифференциального оператора четвертого порядка с суммируемым потенциалом и периодическими граничными условиями. Метод изучения операторов с суммируемым потенциалом является развитием метода изучения операторов с кусочно-гладкими коэффициентами. Краевые задачи такого рода возникают при изучении колебаний балок и мостов, склеенных из материалов различной плотности. Решение дифференциального уравнения, задающего дифференциальный оператор, сведено к решению интегрального уравнения Вольтерры. Интегральное уравнение решается методом последовательных приближений Пикара.
Целью исследования интегрального уравнения является получение асимптотических формул и оценок для решений дифференциального уравнения, задающего дифференциальный оператор. Вопросы геофизики, квантовой механики, кинетики, газодинамики и теории колебаний стержней, балок и мембран требуют развития асимптотических методов на случай негладких коэффициентов дифференциальных уравнений. Асимптотические методы продолжают развиваться, несмотря на бурное развитие численных методов, связанное с появлением мощных суперкомпьютеров, в настоящее время асимптотические и численные методы дополняют друг друга.
В статье при больших значениях спектрального параметра получена асимптотика решений дифференциального уравнения, задающего дифференциальный оператор. Асимптотические оценки решений устанавливаются аналогично асимптотическим оценкам решений дифференциального оператора второго порядка с гладкими коэффициентами. Изучение периодических граничных условий приводит к изучению корней функции, представленной в виде определителя четвёртого порядка. Для получения корней этой функции изучена индикаторная диаграмма. Корни этого уравнения находятся в четырех секторах бесконечно малого раствора, определяемых индикаторной диаграммой. В статье исследовано поведение корней этого уравнения в каждом из секторов индикаторной диаграммы. Найдена асимптотика собственных значений изучаемого дифференциального оператора. Полученные формулы для асимптотики собственных значений позволяют изучить спектральные свойства собственных функций исследуемого дифференциального оператора. Если потенциал оператора будет не суммируемой функцией, а только кусочно гладкой, то полученных формул для асимптотики собственных значений достаточно для вывода формулы первого регуляризованного следа изучаемого дифференциального оператора.
Drug used in controlling chickens diseases, the withdrawal periods, and the threats of drugs residues in food chain. A review
Authors: Ngongolo Kelvin , Omary Kitojo , Andrew Chota
Published: August 2021

Drugs have been used by many farmers for the treatment of most infectious diseases in chicken regardless of their residue effects to human health if proper management has not adhered. This paper focuses on finding out the common chicken diseases, their prevalence, drugs used for treatment, and their residue implication in the chicken products and by-products. A systematic literature-review was used to synthesis the information from soft and hard copy sources of information. In this paper, it was noted that different infectious diseases were affecting chicken in Tanzania and other countries including Newcastle (Prevalence 7-90 percent percent per flock), Infectious bursal disease (Prevalence 7-100 percent per flock), Fowl Cholera (Prevalence 1-60 percent per flock), and Fowl Typhoid (Prevalence 0-20 percent per flock). As a remedy, drugs such as Amprolium, oxytetracycline, and sulphanilamide were used for treatment which could prompt antibiotic residues in chicken products and by-products. The antibiotic residue was reported in meat, eggs, liver, and kidneys of the chicken which are considered edible to a human thus posing public health challenges. In this regard, there is a need to create awareness to the farmers on the proper use of the drugs. In this case, the understanding of the withdrawal period for the applied drugs can minimize the risk of drug residues in chicken products.
PENGARUH PENERAPAN METODE PEER TUTORING TERHADAP PEMAHAMAN MATEMATIS SISWA MTs
Authors: Mahmudah Yuni , Saniah Lia
Published: August 2021

Penelitian ini bertujuan untuk: 1). Mengetahui peningkatan pemecahan masalah terhadap matematik dengan menggunakan model pembelajaran investigasi kelompok; 2). Mengetahui respon siswa kelas VIIIF dalam pembelaran investigasi kelompok di SMP Negeri 4 Karangtengah. Penelitian ini termasuk kedalam Penelitian Tindakan Kelas (PTK) Kurt Lewis yang dilakukan dari Siklus I, Siklus II dan Siklus III. Teknik analisis data yang digunakan dalam pengujian hipotesis adalah adanya tes formatif untuk melihat persentase tingkat keberhasilan pemecahan masalah matematik, sedangakan dalam pengujian jurnal siswa dengan melihat persentase jawaban siswa untuk mengetahui positif atau negatifnya respon siswa. Hasil penelitian menunjukan bahwa adanya penigkatan pada setiap langakah pemecahan masalah dari Siklus I, Siklus II, dan Siklus III. Dari hasil penelitian dapat disimpulkan bahwa pembelajaran dengan model investigasi kelompok dapat meningkatkan meningkatkan kemampuan pemecahan masalah matematik. Sikap siswa positif terhadap pembelajaran investigasi kelompok.
Kata Kunci: Investigasi kelompok, Kemampuan pemecahan masalah mateamatik, sikap.
Maxillofacial Surgery Specialization in Ukraine: A New Order and Step in the Growth of the Specialty: Analysis of Qualification Categories
Authors: Tymofieiev Oleksii , Ushko Natalia , Fesenko Ievgen , Cherniak Olha
Published: August 2021

Oral and maxillofacial surgery (OMS) and its
training systems continue to evolve around the
globe.1 Review of Kumar emphasized that a wide
diversity of dental/stomatology/medical background
requirements and training pathways exist across the
states that lead to a degree in OMS.1 Nevertheless,
to our knowledge the issue of OMS education in
Ukraine is not highlighted in the English-language
literature. Moreover, the relevance of coverage of
this issue has increased this month. As on April 23, 2021 a more than a historical order for the whole oral and maxillofacial community in Ukraine was
approved.2 Order #799 of the Ministry of Healthcare
of Ukraine “On Approval of Amendments to the
Handbook of Qualification Characteristics of
Workers' Professions.2 Issue 78 “Healthcare”.” This
much-needed ministerial document orders to add
a new “Maxillofacial Surgeon Doctor” (MFSD)
position to the Section “Professionals in the Field of
Stomatology.”2
IMPLEMENTASI SISTEM PENGAWASAN CCTV LALU LINTAS DI KOTA TANJUNGPINANG
Authors: Aini Zaharatul , Hutapea Fransiska , Ramadhanie Nurul
Published: August 2021

The problem of traffic and public order on the Tanjungpinang City highway is in the spotlight by the government and society in general. Monitoring and supervision needs to be carried out in order to create public order and convenience. One of the surveillance systems that has been implemented in several areas of Tanjungpinang City is to use CCTV. But in reality there are still traffic problems such as traffic violations, whether in the form of a red light breakthrough, stop passing the stop line when a red light, not wearing a helmet and others, as well as traffic jams at some intersection points that are congested with vehicles. In addition there are still many people who do not know how the use and application of CCTV in traffic needs. This study aims to determine (1) Types of traffic surveillance CCTVs in Tanjungpinang City, and (2) implementation of traffic surveillance systems in Tanjungpinang City. This research was conducted at the Tanjungpinang City Transportation Department Office with the type of research using qualitative methods, while the data collection techniques were interview, documentation and observation. The results of this study indicate that the implementation of the traffic surveillance system in Tanjungpinang City has not been implemented optimally. The implementation of this policy is aimed at the public, especially public road users with an indirect monitoring and supervision system or inside the CCTV monitoring room of the Tanjungpinang City Transportation Department.
EVALUASI IMPLEMENTASI PROGRAM PEMBANGUNAN DAN PEMBERDAYAAN MASYARAKAT DESA (P3MD) DALAM MEWUJUDKAN DESA MANDIRI DI DESA KUALU NENAS KECAMATAN TAMBANG KABUPATEN KAMPAR
Authors: S.Sos Basir , Alkadafi Muammar , Fithriyyah Mustiqowati Ummul
Published: August 2021

The Village Community Development and Empowerment Program (P3MD) is a strategic government program. The implementation of the program in Kualu Nenas Village has not had a significant impact on changing the status of the village, from a developing village to a developed and independent village. The research objective was to determine program implementation and to determine the impact of the program in Kualu Nenas Village. The research method used a qualitative approach, with evaluative research type. Sources of data obtained through observations at the target location of program activities, in-depth interviews, with key informants; Village Head, Village Officials, BPD, LKMD, community leaders and community group representatives, village assistants. Data was also obtained through distributing questionnaires to 30 informants who were selected by purposive sampling. Research result. The implementation of the Program covers 4 (four) areas of activity, namely activities of village administration, village development, village community empowerment, village communities. The implementation of activities has not been fully carried out properly, not all program activities are carried out in accordance with the activity items, according to the program implementation instructions and the specific objectives of the program. There has been no innovation (change) that has led to the achievement of a change in the status of the village from being left behind to becoming a developed and independent village. The programs that have been implemented have not been fully felt by the community, especially in the aspect of developing community productive economic facilities in the agricultural and plantation sectors, and increasing community capacity.
Intensification of the development of regional economic systems in the conditions of the creative economy formation
Authors: Dergaliuk Marta , Khanin Semen , Popelo Olha , Tulchynska Svitlana , Pakhota Nataliia
Published: August 2021

This article is devoted to a detailed analysis and formation of guidelines for intensifying the development of regional economic systems in the formation of the theory of creative economy under the influence of its characteristic intellectual and innovative determinants. A brief description of the studied theoretical concept is given, on the basis of which a list of key features that characterize economic systems built on the principles of creative economy is identified.  The key features of the implementation of the basic provisions of the theory of creative economy in the existing regional economic systems are formed.  The main subjective levels of realization of economic relations at which creativity manifests itself are identified, namely the personal level, entrepreneurial level, regional level and the level of the national economy.
ON A NEW GENERALIZED BETA FUNCTION DEFINED BY THE GENERALIZED WRIGHT FUNCTION AND ITS APPLICATIONS
Authors: ABUBAKAR UMAR MUHAMMAD , Patel Soraj
Published: August 2021

Various extensions of classical gamma, beta, Gauss hypergeometric and confluent hypergeometric functions have been proposed recently by many researchers. In this paper, we further generalized extended beta function with some of its properties such as symmetric properties, summation formulas, integral representations, connection with some other special functions such as classical beta, error, Mittag – Leffler, incomplete gamma, hypergeometric, classical Wright, Fox – Wright, Fox H and Meijer G – functions. Furthermore, the generalized beta function is used to generalize classical and other extended Gauss hypergeometric, confluent hypergeometric, Appell’s and Lauricella’s functions.
Penguatan Peran UMKM melalui Pendampingan Pembuatan Laporan Keuangan
Authors: Damayanti Rhosalina , Rompis Adrian Izaak
Published: August 2021

UMKM diharapkan mampu menunjang pertumbuhan perekonomian di Indonesia. Harapan ini harus mampu dijawab UMKM melalui penyediaan indikator penilaian kinerja berupa laporan keuangan. Pengabdian masyarakat ini dilakukan di Waroeng Pitoe, salah satu UMKM yang belum sepenuhnya mampu melakukan pencatatan akuntansi dan pelaporan keuangan. Tujuan dari pengabdian masyarakat ini adalah bentuk partisipasi aktif akademisi untuk meningkatkan pemahaman Waroeng Pitoe tentang pentingnya laporan keuangan dan menghasilkan laporan keuangan. Pengabdian masyarakat ini bermanfaat untuk membantu Waroeng Pitoe dalam penyediaan laporan keuangan sebagai salah satu alat penilaian kinerja keuangan dan membantu pengambilan keputusan bisnis. Pengabdian masyarakat dilakukan dengan membantu penyusunan template laporan keuangan, penyuluhan dan pelatihan kepada direktur dan karyawan Waroeng Pitoe. Hasil pengabdian masyarakat ini adalah meningkatkan pemahaman direktur dan karyawan Waroeng Pitoe terkait pentingnya ketersediaan laporan keuangan, membantu proses pencatatan akuntansi, dan penyusunan laporan keuangan sederhana.
Morality, Religion and Capitalism in Jane Austen’s ‘Pride and Prejudice’
Authors:   [Eng.], Vidya Publications, Patna (Bihar), Priydarshi Ashok Kumar
Published: August 2021

The rise and development of English novel, like any other phenomenon in literature, can be seen as a part of a history or the process of the individual development. Romantic novels are non-realistic and considered as the aristocratic literature of feudalism. They are non-realistic in sense that their underlying intention is not to help people cope in a positive way. These novels, express and recommend the attitudes of the aristocratic class to which it was ideally supposed to sustain. The genre, developed, however, as a reaction to the aristocratic romance, and grows with the middle class a new art form that centres on a new middle class values, rather than aristocratic patronage. Thus the period after the Restoration of the 16th to 17th century opened up other discourses, thereby breaking the frontier by allowing social mobility and making female writing possible. This allowed Jane Austen to write on realistic and naturalistic themes as morality, religion, captalism, etc. and ‘Pride and Prejudice’ is its fine example.
Jane Austen’s Comic Vision and Her Use of Irony
Authors:   Author [English], Vidya Publications, Patna, Bihar, India, Priydarshi Ashok Kumar
Published: August 2021

Irony in its broadest sense, is a rhetorical device, literary technique, or event in which what on the surface appears to be the case or to be expected differs radically from what is actually the case. In other words, the basic feature of irony is a contrast between reality and appearance. It can be categorized into different types, including verbal irony, dramatic irony and situational irony. These types of ironies are often used for emphasis in the assertion of a truth. Jane Austen uses all these ironies in her novels to show the comic vision of her life. She has used it as a neutral discoverer and explorer of incongruities.
Double-stage discretization approaches for biomarker-based bladder cancer survival modeling
Authors: Nascimben Mauro , Venturin Manolo , Rimondini Lia
Published: August 2021

Bioinformatic techniques targeting gene expression data require specific analysis pipelines with the aim of studying properties, adaptation, and disease outcomes in a sample population. Present investigation compared together results of four numerical experiments modeling survival rates from bladder cancer genetic profiles. Research showed that a sequence of two discretization phases produced remarkable results compared to a classic approach employing one discretization of gene expression data. Analysis involving two discretization phases consisted of a primary discretizer followed by refinement or pre-binning input values before the main discretization scheme. Among all tests, the best model encloses a sequence of data transformation to compensate skewness, data discretization phase with class-attribute interdependence maximization algorithm, and final classification by voting feature intervals, a classifier that also provides discrete interval optimization.
Prognostic value of cytokines in breast cancer: Correlation with positive hormonal status and obesity
Authors: Ibrahim Noha Yehia , Talima Soha Mohamed , Kaldas David Fadl , Kassem Hebatallah , Kassem Neemat
Published: August 2021

Background

The relation of interleukin 6 (IL6) and molecular subtypes as well as body mass index is not well settled. Little is known about interferon gamma (IFγ) and prognosis of breast cancer.

Patients and methods

Serum level of IL6 and IFγ was assessed by enzyme-linked immunosorbent assay (ELISA) and correlated with the TNM staging, molecular subtypes, and body mass index.

Results

Among 78 patients, the median age was 54 years. The majority of the cases were T2 (62.8%), N1 (38.5%), and M0 (89.74%) with stage II being the most common (47.4%). Most females were estrogen receptor (97.9%) and progesterone receptor positive (96.9%) with high Ki67 ≥ 20 (61.5%). Her2 neu positive presented 16.7%. Luminal A and luminal B presented 29.5% and 53.8%, respectively. Obese patients presented by far the majority (82.1%).

The median level of IL6 and IFγ was 56.20 ± 28.715 and 76.37 ± 41.54, respectively. IL6 was significantly correlated with tumor size (P = 0.001), nodal involvement (P = >0.0001), the presence of metastasis (P = 0.008), and the stage (P = >0.0001). High level of IL6 was associated with positive estrogen receptor, Her2 neu positive, luminal A, and being obese (P = 0.09, 0.07, 0.06, and 0.05, respectively).

High IFγ was only associated with lower nodal burden being significantly higher in N1 than in N3 (118.15 ± 31.07 vs 76.37 ± 44.46, P = 0.01) and early stage (P = 0.02).

Conclusions

IL6 level was correlated to the initial staging, hormonal status, being Her2 positive, and obesity. The IFγ level was inversely correlated IL6 regarding the nodal status (P = 0.05).
Can Elite Australian Football Player’s Game Performance Be Predicted?
Authors: Fahey-Gilmour J. , Heasman J. , Rogalski B. , Dawson B. , Peeling P.
Published: August 2021

In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.
Considerations in the Use of Work-From-Home (WFH) for Post-Pandemic Planning and Management
Authors: Manko Barbara
Published: August 2021

after widespread use of work-from-home (a.k.a. ‘remote work’) during the COVID-19 pandemic, many organizations intend to continue or expand its use into the future. This paper presents a literature review and results from an original survey, both of which point to key considerations for planners and managers. Subjects that are highlighted include: giving remote workers access to job-related information they need; recognizing that certain types of tasks may be more challenging to manage in WFH, such as loosely specified tasks and those involving substantial change or newness; and issues that arise in regard to morale and motivation.
PIANO: A Parametric Hand Bone Model from Magnetic Resonance Imaging
Authors: Li Yuwei , Wu Minye , Zhang Yuyao , Xu Lan , Yu Jingyi
Published: August 2021

Hand modeling is critical for immersive VR/AR, action understanding, or human healthcare. Existing parametric models account only for hand shape, pose, or texture, without modeling the anatomical attributes like bone, which is essential for realistic hand biomechanics analysis. In this paper, we present PIANO, the first parametric bone model of human hands from MRI data. Our PIANO model is biologically correct, simple to animate, and differentiable, achieving more anatomically precise modeling of the inner hand kinematic structure in a data-driven manner than the traditional hand models based on the outer surface only. Furthermore, our PIANO model can be applied in neural network layers to enable training with a fine-grained semantic loss, which opens up the new task of data-driven fine-grained hand bone anatomic and semantic understanding from MRI or even RGB images. We make our model publicly available.
A Multi-Constraint Similarity Learning with Adaptive Weighting for Visible-Thermal Person Re-Identification
Authors: Ling Yongguo , Luo Zhiming , Lin Yaojin , Li Shaozi
Published: August 2021

The challenges of visible-thermal person re-identification (VT-ReID) lies in the inter-modality discrepancy and the intra-modality variations. An appropriate metric learning plays a crucial role in optimizing the feature similarity between the two modalities. However, most existing metric learning-based methods mainly constrain the similarity between individual instances or class centers, which are inadequate to explore the rich data relationships in the cross-modality data. Besides, most of these methods fail to consider the importance of different pairs, incurring an inefficiency and ineffectiveness of optimization. To address these issues, we propose a Multi-Constraint (MC) similarity learning method that jointly considers the cross-modality relationships from three different aspects, i.e., Instance-to-Instance (I2I), Center-to-Instance (C2I), and Center-to-Center (C2C). Moreover, we devise an Adaptive Weighting Loss (AWL) function to implement the MC efficiently. In the AWL, we first use an adaptive margin pair mining to select informative pairs and then adaptively adjust weights of mined pairs based on their similarity. Finally, the mined and weighted pairs are used for the metric learning. Extensive experiments on two benchmark datasets demonstrate the superior performance of the proposed over the state-of-the-art methods.
Self-boosting for Feature Distillation
Authors: Pei Yulong , Qu Yanyun , Zhang Junping
Published: August 2021

Knowledge distillation is a simple but effective method for model compression, which obtains a better-performing small network (Student) by learning from a well-trained large network (Teacher). However, when the difference in the model sizes of Student and Teacher is large, the gap in capacity leads to poor performance of Student. Existing methods focus on seeking simplified or more effective knowledge from Teacher to narrow the Teacher-Student gap, while we address this problem by Student's self-boosting. Specifically, we propose a novel distillation method named Self-boosting Feature Distillation (SFD), which eases the Teacher-Student gap by feature integration and self-distillation of Student. Three different modules are designed for feature integration to enhance the discriminability of Student's feature, which leads to improving the order of convergence in theory. Moreover, an easy-to-operate self-distillation strategy is put forward to stabilize the training process and promote the performance of Student, without additional forward propagation or memory consumption. Extensive experiments on multiple benchmarks and networks show that our method is significantly superior to existing methods.
Learning with Selective Forgetting
Authors: Shibata Takashi , Irie Go , Ikami Daiki , Mitsuzumi Yu
Published: August 2021

Lifelong learning aims to train a highly expressive model for a new task while retaining all knowledge for previous tasks. However, many practical scenarios do not always require the system to remember all of the past knowledge. Instead, ethical considerations call for selective and proactive forgetting of undesirable knowledge in order to prevent privacy issues and data leakage. In this paper, we propose a new framework for lifelong learning, called Learning with Selective Forgetting, which is to update a model for the new task with forgetting only the selected classes of the previous tasks while maintaining the rest. The key is to introduce a class-specific synthetic signal called mnemonic code. The codes are "watermarked" on all the training samples of the corresponding classes when the model is updated for a new task. This enables us to forget arbitrary classes later by only using the mnemonic codes without using the original data. Experiments on common benchmark datasets demonstrate the remarkable superiority of the proposed method over several existing methods.
Structure Guided Lane Detection
Authors: Su Jinming , Chen Chao , Zhang Ke , Luo Junfeng , Wei Xiaoming , Wei Xiaolin
Published: August 2021

Recently, lane detection has made great progress with the rapid development of deep neural networks and autonomous driving. However, there exist three mainly problems including characterizing lanes, modeling the structural relationship between scenes and lanes, and supporting more attributes (e.g., instance and type) of lanes. In this paper, we propose a novel structure guided framework to solve these problems simultaneously. In the framework, we first introduce a new lane representation to characterize each instance. Then a top-down vanishing point guided anchoring mechanism is proposed to produce intensive anchors, which efficiently capture various lanes. Next, multi-level structural constraints are used to improve the perception of lanes. In the process, pixel-level perception with binary segmentation is introduced to promote features around anchors and restore lane details from bottom up, a lane-level relation is put forward to model structures (i.e., parallel) around lanes, and an image-level attention is used to adaptively attend different regions of the image from the perspective of scenes. With the help of structural guidance, anchors are effectively classified and regressed to obtain precise locations and shapes. Extensive experiments on public benchmark datasets show that the proposed approach outperforms state-of-the-art methods with 117 FPS on a single GPU.
Temporal Induced Self-Play for Stochastic Bayesian Games
Authors: Chen Weizhe , Zhou Zihan , Wu Yi , Fang Fei
Published: August 2021

One practical requirement in solving dynamic games is to ensure that the players play well from any decision point onward. To satisfy this requirement, existing efforts focus on equilibrium refinement, but the scalability and applicability of existing techniques are limited. In this paper, we propose Temporal-Induced Self-Play (TISP), a novel reinforcement learning-based framework to find strategies with decent performances from any decision point onward. TISP uses belief-space representation, backward induction, policy learning, and non-parametric approximation. Building upon TISP, we design a policy-gradient-based algorithm TISP-PG. We prove that TISP-based algorithms can find approximate Perfect Bayesian Equilibrium in zero-sum one-sided stochastic Bayesian games with finite horizon. We test TISP-based algorithms in various games, including finitely repeated security games and a grid-world game. The results show that TISP-PG is more scalable than existing mathematical programming-based methods and significantly outperforms other learning-based methods.
MatchVIE: Exploiting Match Relevancy between Entities for Visual Information Extraction
Authors: Tang Guozhi , Xie Lele , Jin Lianwen , Wang Jiapeng , Chen Jingdong , Xu Zhen , Wang Qianying , Wu Yaqiang , Li Hui
Published: August 2021

Visual Information Extraction (VIE) task aims to extract key information from multifarious document images (e.g., invoices and purchase receipts). Most previous methods treat the VIE task simply as a sequence labeling problem or classification problem, which requires models to carefully identify each kind of semantics by introducing multimodal features, such as font, color, layout. But simply introducing multimodal features can't work well when faced with numeric semantic categories or some ambiguous texts. To address this issue, in this paper we propose a novel key-value matching model based on a graph neural network for VIE (MatchVIE). Through key-value matching based on relevancy evaluation, the proposed MatchVIE can bypass the recognitions to various semantics, and simply focuses on the strong relevancy between entities. Besides, we introduce a simple but effective operation, Num2Vec, to tackle the instability of encoded values, which helps model converge more smoothly. Comprehensive experiments demonstrate that the proposed MatchVIE can significantly outperform previous methods. Notably, to the best of our knowledge, MatchVIE may be the first attempt to tackle the VIE task by modeling the relevancy between keys and values and it is a good complement to the existing methods.
Norm-guided Adaptive Visual Embedding for Zero-Shot Sketch-Based Image Retrieval
Authors: Wang Wenjie , Shi Yufeng , Chen Shiming , Peng Qinmu , Zheng Feng , You Xinge
Published: August 2021

Zero-shot sketch-based image retrieval (ZS-SBIR), which aims to retrieve photos with sketches under the zero-shot scenario, has shown extraordinary talents in real-world applications. Most existing methods leverage language models to generate class-prototypes and use them to arrange the locations of all categories in the common space for photos and sketches. Although great progress has been made, few of them consider whether such pre-defined prototypes are necessary for ZS-SBIR, where locations of unseen class samples in the embedding space are actually determined by visual appearance and a visual embedding actually performs better. To this end, we propose a novel Norm-guided Adaptive Visual Embedding (NAVE) model, for adaptively building the common space based on visual similarity instead of language-based pre-defined prototypes. To further enhance the representation quality of unseen classes for both photo and sketch modality, modality norm discrepancy and noisy label regularizer are jointly employed to measure and repair the modality bias of the learned common embedding. Experiments on two challenging datasets demonstrate the superiority of our NAVE over state-of-the-art competitors.
Deep Unified Cross-Modality Hashing by Pairwise Data Alignment
Authors: Wang Yimu , Xue Bo , Cheng Quan , Chen Yuhui , Zhang Lijun
Published: August 2021

With the increasing amount of multimedia data, cross-modality hashing has made great progress as it achieves sub-linear search time and low memory space. However, due to the huge discrepancy between different modalities, most existing cross-modality hashing methods cannot learn unified hash codes and functions for modalities at the same time. The gap between separated hash codes and functions further leads to bad search performance. In this paper, to address the issues above, we propose a novel end-to-end Deep Unified Cross-Modality Hashing method named DUCMH, which is able to jointly learn unified hash codes and unified hash functions by alternate learning and data alignment. Specifically, to reduce the discrepancy between image and text modalities, DUCMH utilizes data alignment to learn an auxiliary image to text mapping under the supervision of image-text pairs. For text data, hash codes can be obtained by unified hash functions, while for image data, DUCMH first maps images to texts by the auxiliary mapping, and then uses the mapped texts to obtain hash codes. DUCMH utilizes alternate learning to update unified hash codes and functions. Extensive experiments on three representative image-text datasets demonstrate the superiority of our DUCMH over several state-of-the-art cross-modality hashing methods.
HifiFace: 3D Shape and Semantic Prior Guided High Fidelity Face Swapping
Authors: Wang Yuhan , Chen Xu , Zhu Junwei , Chu Wenqing , Tai Ying , Wang Chengjie , Li Jilin , Wu Yongjian , Huang Feiyue , Ji Rongrong
Published: August 2021

In this work, we propose a high fidelity face swapping method, called HifiFace, which can well preserve the face shape of the source face and generate photo-realistic results. Unlike other existing face swapping works that only use face recognition model to keep the identity similarity, we propose 3D shape-aware identity to control the face shape with the geometric supervision from 3DMM and 3D face reconstruction method. Meanwhile, we introduce the Semantic Facial Fusion module to optimize the combination of encoder and decoder features and make adaptive blending, which makes the results more photo-realistic. Extensive experiments on faces in the wild demonstrate that our method can preserve better identity, especially on the face shape, and can generate more photo-realistic results than previous state-of-the-art methods. Code is available at: https://johann.wang/HifiFace
Local Representation is Not Enough: Soft Point-Wise Transformer for Descriptor and Detector of Local Features
Authors: Wang Zihao , Li Xueyi , Li Zhen
Published: August 2021

Significant progress has been witnessed for the descriptor and detector of local features, but there still exist several challenging and intractable limitations, such as insufficient localization accuracy and non-discriminative description, especially in repetitive- or blank-texture regions, which haven't be well addressed. The coarse feature representation and limited receptive field are considered as the main issues for these limitations. To address these issues, we propose a novel Soft Point-Wise Transformer for Descriptor and Detector, simultaneously mining long-range intrinsic and cross-scale dependencies of local features. Furthermore, our model leverages the distinct transformers based on the soft point-wise attention, substantially decreasing the memory and computation complexity, especially for high-resolution feature maps. In addition, multi-level decoder is constructed to guarantee the high detection accuracy and discriminative description. Extensive experiments demonstrate that our model outperforms the existing state-of-the-art methods on the image matching and visual localization benchmarks.
Segmenting Transparent Objects in the Wild with Transformer
Authors: Xie Enze , Wang Wenjia , Wang Wenhai , Sun Peize , Xu Hang , Liang Ding , Luo Ping
Published: August 2021

This work presents a new fine-grained transparent object segmentation dataset, termed Trans10K-v2, extending Trans10K-v1, the first large-scale transparent object segmentation dataset.

Unlike Trans10K-v1 that only has two limited categories, our new dataset has several appealing benefits. (1) It has 11 fine-grained categories of transparent objects, commonly occurring in the human domestic environment, making it more practical for real-world application.

(2) Trans10K-v2 brings more challenges for the current advanced segmentation methods than its former version.

Furthermore, a novel Transformer-based segmentation pipeline termed Trans2Seg is proposed.

Firstly, the Transformer encoder of Trans2Seg provides the global receptive field in contrast to CNN's local receptive field, which shows excellent advantages over pure CNN architectures.

Secondly, by formulating semantic segmentation as a problem of dictionary look-up, we design a set of learnable prototypes as the query of Trans2Seg's Transformer decoder, where each prototype learns the statistics of one category in the whole dataset.

We benchmark more than 20 recent semantic segmentation methods, demonstrating that Trans2Seg significantly outperforms all the CNN-based methods, showing the proposed algorithm's potential ability to solve transparent object segmentation.Code is available in https://github.com/xieenze/Trans2Seg.
Coupling Intent and Action for Pedestrian Crossing Behavior Prediction
Authors: Yao Yu , Atkins Ella , Johnson-Roberson Matthew , Vasudevan Ram , Du Xiaoxiao
Published: August 2021

Accurate prediction of pedestrian crossing behaviors by autonomous vehicles can significantly improve traffic safety. Existing approaches often model pedestrian behaviors using trajectories or poses but do not offer a deeper semantic interpretation of a person's actions or how actions influence a pedestrian's intention to cross in the future. In this work, we follow the neuroscience and psychological literature to define pedestrian crossing behavior as a combination of an unobserved inner will (a probabilistic representation of binary intent of crossing vs. not crossing) and a set of multi-class actions (e.g., walking, standing, etc.). Intent generates actions, and the future actions in turn reflect the intent. We present a novel multi-task network that predicts future pedestrian actions and uses predicted future action as a prior to detect the present intent and action of the pedestrian. We also designed an attention relation network to incorporate external environmental contexts thus further improve intent and action detection performance. We evaluated our approach on two naturalistic driving datasets, PIE and JAAD, and extensive experiments show significantly improved and more explainable results for both intent detection and action prediction over state-of-the-art approaches. Our code is available at: https://github.com/umautobots/pedestrian_intent_action_detection
Removing Foreground Occlusions in Light Field using Micro-lens Dynamic Filter
Authors: Zhang Shuo , Shen Zeqi , Lin Youfang
Published: August 2021

Foreground occlusion removal task aims to automatically detect and remove foreground occlusions and recover background objects. Since for Light Fields (LFs), background objects occluded in some views may be seen in other views, the foreground occlusion removal task for LFs is easy to achieve. In this paper, we propose a learning-based method combining ‘seeking’ and ‘generating’ to recover occluded background. Specifically, the micro-lens dynamic filters are proposed to ‘seek’ occluded background points in shifted micro-lens images and remove occlusions using angular information. The shifted images are then combined to further ‘generate’ background regions to supplement more background details using spatial information. By fully exploring the angular and spatial information in LFs, the dense and complex occlusions can be easily removed. Quantitative and qualitative experimental results show that our method outperforms other state-of-the-arts methods by a large margin.
What If We Could Not See? Counterfactual Analysis for Egocentric Action Anticipation
Authors: Zhang Tianyu , Min Weiqing , Yang Jiahao , Liu Tao , Jiang Shuqiang , Rui Yong
Published: August 2021

Egocentric action anticipation aims at predicting the near future based on past observation in first-person vision. While future actions may be wrongly predicted due to the dataset bias, we present a counterfactual analysis framework for egocentric action anticipation (CA-EAA) to enhance the capacity. In the factual case, we can predict the upcoming action based on visual features and semantic labels from past observation. Imagining one counterfactual situation where no visual representation had been observed, we would obtain a counterfactual predicted action only using past semantic labels. In this way, we can reduce the side-effect caused by semantic labels via a comparison between factual and counterfactual outcomes, which moves a step towards unbiased prediction for egocentric action anticipation. We conduct experiments on two large-scale egocentric video datasets. Qualitative and quantitative results validate the effectiveness of our proposed CA-EAA.
Improved CP-Based Lagrangian Relaxation Approach with an Application to the TSP
Authors: Boudreault Raphaël , Quimper Claude-Guy
Published: August 2021

CP-based Lagrangian relaxation (CP-LR) is an efficient optimization technique that combines cost-based filtering with Lagrangian relaxation in a constraint programming context. The state-of-the-art filtering algorithms for the WeightedCircuit constraint that encodes the traveling salesman problem (TSP) are based on this approach. In this paper, we propose an improved CP-LR approach that locally modifies the Lagrangian multipliers in order to increase the number of filtered values. We also introduce two new algorithms based on the latter to filter WeightedCircuit. The experimental results on TSP instances show that our algorithms allow significant gains on the resolution time and the size of the search space when compared to the state-of-the-art implementation.
Backdoor DNFs
Authors: Ordyniak Sebastian , Schidler Andre , Szeider Stefan
Published: August 2021

We introduce backdoor DNFs, as a tool to measure the theoretical hardness of CNF formulas. Like backdoor sets and backdoor trees, backdoor DNFs are defined relative to a tractable class of CNF formulas. Each conjunctive term of a backdoor DNF defines a partial assignment that moves the input CNF formula into the base class. Backdoor DNFs are more expressive and potentially smaller than their predecessors backdoor sets and backdoor trees. We establish the fixed-parameter tractability of the backdoor DNF detection problem. Our results hold for the fundamental base classes Horn and 2CNF, and their combination. We complement our theoretical findings by an empirical study. Our experiments show that backdoor DNFs provide a significant improvement over their predecessors.
The Parameterized Complexity of Connected Fair Division
Authors: Deligkas Argyrios , Eiben Eduard , Ganian Robert , Hamm Thekla , Ordyniak Sebastian
Published: August 2021

We study the Connected Fair Division problem (CFD), which generalizes the fundamental problem of fairly allocating resources to agents by requiring that the items allocated to each agent form a connected subgraph in a provided item graph G. We expand on previous results by providing a comprehensive complexity-theoretic understanding of CFD based on several new algorithms and lower bounds while taking into account several well-established notions of fairness: proportionality, envy-freeness, EF1 and EFX. In particular, we show that to achieve tractability, one needs to restrict both the agents and the item graph in a meaningful way. We design (XP)-algorithms for the problem parameterized by (1) clique-width of G plus the number of agents and (2) treewidth of G plus the number of agent types, along with corresponding lower bounds. Finally, we show that to achieve fixed-parameter tractability, one needs to not only use a more restrictive parameterization of G, but also include the maximum item valuation as an additional parameter.
Federated Learning with Sparsification-Amplified Privacy and Adaptive Optimization
Authors: Hu Rui , Gong Yanmin , Guo Yuanxiong
Published: August 2021

Federated learning (FL) enables distributed agents to collaboratively learn a centralized model without sharing their raw data with each other. However, data locality does not provide sufficient privacy protection, and it is desirable to facilitate FL with rigorous differential privacy (DP) guarantee. Existing DP mechanisms would introduce random noise with magnitude proportional to the model size, which can be quite large in deep neural networks. In this paper, we propose a new FL framework with sparsification-amplified privacy. Our approach integrates random sparsification with gradient perturbation on each agent to amplify privacy guarantee. Since sparsification would increase the number of communication rounds required to achieve a certain target accuracy, which is unfavorable for DP guarantee, we further introduce acceleration techniques to help reduce the privacy cost. We rigorously analyze the convergence of our approach and utilize Renyi DP to tightly account the end-to-end DP guarantee. Extensive experiments on benchmark datasets validate that our approach outperforms previous differentially-private FL approaches in both privacy guarantee and communication efficiency.