bluuebunny/crossref_metadata_2025 huggingface dataset:
Twitter Sentiment Analysis Using the Naive Bayes Algorithm in the Case of the Indosurya Savings and Loans Cooperative
Authors: Kiu Elma Rulfin Tiara , Sembiring Ornensya Br , Ngafidin Khairun Nisa Meiah
Published: August 2023

Kasus KSP Indosurya kembali mencuri perhatian saat ini karena terduga pelaku selaku pendiri KSP Indosurya divonis bebas oleh Hakim Ketua Pengadilan Negeri Jakarta Barat atas segala dakwaan pada dengan peryataan bahwa terdakwa terbukti melakukan perbuatan yang didakwakan tetapi bukan merupakan tindak pidana, melakukan perkara perdata yang kemudian menyita perhatian publik serta mengundang reaksi masyarakat diberbagai media sosial bahkan sempat menjadi tranding topik dimedia sosial twitter Indonesia. Oleh karena itu dilakukan penelitian ini guna mengetahui dan mengklasifikasikan sentimen masyarakat pada kasua KSP Indosurya menggunakan metode Naïve bayes. Dimana hasil tertinggi didapatkan oleh sentimen negative, sehingga didapatkan kesimpulan sentimen publik terhadap topik kasus KSP Indosurya di twitter Indonesia adalah negative dengan jumlah parameter 1200 berdasarkan analisis menggunakan metode Naïve bayes.The KSP Indosurya case has again stolen attention at this time because the alleged perpetrator Henry Surya as the founder of KSP Indosurya was acquitted by the Chief Judge of the West Jakarta District Court Syafrudin Ainor for all charges on Tuesday (24/1/2023) with a statement that the defendant Henry Surya was proven to have committed an act who was charged but did not constitute a crime, carried out a civil case which then seized the public's attention and invited public reaction on various social media and even became a trending topic on Indonesian Twitter social media Therefore, this research was carried out to find out and classify public sentiment in the KSP Indosurya case using the Naïve Bayes method. Where the highest results were obtained by negative sentiment, so it was concluded that public sentiment on the topic of the KSP Indosurya case on Twitter Indonesia was negative with a total of 1200 parameters based on analysis using the Naïve Bayes method.
Application of the MAUT Method to Determine Eligibility for Accredited School Libraries
Authors: Sukamto Sukamto , Fitriansyah Aidil
Published: August 2023

Pentingnya keberadaan perpustakaan sekolah dalam sebuah pendidikan, maka dilakukan pengakreditasian perpustakaan sekolah yang bertujuan untuk meningkatkan kualitas perpustakaan, serta meningkatkan kegemaran membaca. Dinas Perpustakaan dan Kearsipan Kota Pekanbaru (DISPUSIP) dalam menentukan suatu perpustakaan sekolah yang layak untuk diakreditasi masih dilakukan dengan sistem manual. Untuk itu diperlukan sistem pendukung keputusan (SPK). Adapun tujuan dari penelitian ini adalah membangun SPK untuk menentukan perpustakaan sekolah yang layak diakreditasi dengan menggunakan metode Multi Attribute Utility Theory (MAUT), dengan langkah-langkah membentuk matriks keputusan, normalisasi matriks keputusan, menentukan matriks utilitas, menghitung utilitas akhir, dan perangkingan. Alternatif yang digunakan adalah 9 sekolah jenjang SMP baik negeri maupun swasta. Kriteria yang digunakan mengacu pada instrument akreditasi perpustakaan sekolah terdiri dari 6 kriteria yaitu koleksi, sarana dan prasarana, pelayanan perpustakaan, tenaga perpustakaan, penyelenggaraan dan pengelolaan, dan penguatan. Hasil penelitian yang diperoleh dengan menggunakan metode MAUT adalah PS 4 (SMPN 27) dengan nilai 0,977; PS 5 (SMPIT Imam An-Nawawi) dengan nilai 0,873; dan PS 3 (SMPN 34) dengan nilai 0,861 merupakan tiga perpustakaan sekolah yang layak untuk diakreditasi.The importance of the existence of a school library in an education, the accreditation of the school library is carried out with the aim of improving the quality of the library, as well as increasing the love of reading. The Pekanbaru City Library and Archives Service (DISPUSIP) in determining a school library that is eligible for accreditation is still using a manual system. For that we need a decision support system (SPK). The purpose of this study is to build a SPK to determine which school libraries are eligible for accreditation using the Multi Attribute Utility Theory (MAUT) method, with the steps of forming a decision matrix, normalizing the decision matrix, determining the utility matrix, calculating the final utility, and ranking. The alternatives used were 9 junior high schools, both public and private. The criteria used refer to the school library accreditation instrument consisting of 6 criteria, namely collections, facilities and infrastructure, library services, library staff, administration and management, and strengthening. The research results obtained using the MAUT method were PS 4 (SMPN 27) with a value of 0.977; PS 5 (SMPIT Imam An-Nawawi) with a value of 0.873; and PS 3 (SMPN 34) with a value of 0.861 are three school libraries that are eligible for accreditation.
Web-Based Sales Application Using Selection Sort and Sequential Search Algorithms at Mba Tik Stores
Authors: Saputra Muhammad Hanafi , Kusniyati Harni
Published: August 2023

Toko Mba Tik merupakan salah satu toko yang melayani penjualan berbagai jenis kebutuhan kerumahan seperti beras, gula, jajanan, sabun dan lain-lain, serta beberapa jenis barang dalam skala besar dan juga kecil. Saat ini Toko Mba Tik memiliki sistem transaksi penjualan dan pembelian yang masih menggunakan sistem manual yaitu dengan melakukan pencatatan di dalam buku besar. Untuk sebagai pemecahnya, dirancangnya suatu aplikasi atau sistem informasi penjualan yang berbasis web yang diharapkan akan dapat membantu dalam memproses dan menyimpan data (laporan) dengan cepat, ringkas, dan rapih sekaligus menghemat waktu dan uang. Sistem Informasi Penjualan ini nantinya akan menggunakan Algoritma Selection Sort dan Sequential Search.  Penggunaan Algortima Selection Sort berfungsi untuk mengurutkan data dari nilai terkecil ke terbesar atau sebaliknya, sedangkan untuk penggunaan Algoritma Sequential Searching berfungsi untuk mendapatkan atau mencari suatu informasi data dapat lebih mudah dan cepat. Setelah dilakukan 2 kali percobaan pengujian pencarian data dengan metode Sequential Search, pengujian kecepatan pencarian dengan microtime menunjukkan bahwa Algoritma Sequential Search memiliki kinerja yang cepat, dengan rata-rata waktu pencarian sekitar 0,002 detik untuk jumlah data yang tidak terlalu besar atau terbatas.Mba Tik shop is one of the shops that sells various types of household needs such as rice, sugar, snacks, soap and others, as well as several types of goods on a large and small scale. Currently, Toko Mba Tik has a sales and purchase transaction system that still uses a manual system, namely by recording it in a ledger. As a solution, a computer-based sales information system is designed which is expected to be able to assist in processing and storing data (reports) quickly, concisely and neatly as well as saving time and costs. This Sales Information System will later use Selection Sort and Sequential Search Algorithms. The use of the Selection Sort Algorithm functions to sort data from the smallest to the largest value or vice versa, while the use of the Sequential Searching Algorithm functions to obtain or search for data information more easily and quickly. After conducting 2 trials of testing data search using the Sequential Search method, testing the search speed with the microtime function shows that the Sequential Search Algortima can perform searches quickly, namely an average of 0.002 seconds for data that is relatively limited or not too much.
Performance Comparison Analysis of Multi Prime RSA and Multi Power RSA
Authors: Talunohi Iwada Grawilser , Lubis Ibnu Jaki , Sutarman Sutarman , Candra Ade
Published: August 2023

Saat ini cukup banyak algoritma yang digunakan untuk pengamanan informasi dalam sistem atau perangkat lunak. Masing-masing algoritma tersebut memiliki tingkat waktu dalam key generate, enrkripsi dan dekripsi yang berbeda-beda. Dalam menggunakan algoritma tersebut harus mempertimbangkan waktu jika ingin menerapkan dalam suatu sistem atau perangkat lunak. Dalam penelitian ini, akan melakukan perbandingan kinerja dari dua algoritma asimetris yaitu Multi Power RSA dan Multi Prime RSA yang masing-masing merupakan varian dari RSA. Algoritma ini terdiri dari 2 kunci yaitu kunci publik dan kunci privat. Pengujian algoritma tersebut akan dilakukan dalam bahasa python. Kesimpulan yang didapat adalah algoritma Multi Prime RSA lebih cepat di bandingkan dengan Multi Power RSA dalam proses enkripsi dan dekripsi. Multi Power RSA lebih cepat dari Multi Prime RSA dalam proses key generate.Currently, quite a lot of algorithms are used to secure information in systems or software. Each of these algorithms has a different level of speed (time) in key generation, encryption and decryption. In using the algorithm must consider speed (time) if you want to implement it in a system or software. In this study, we will compare the performance of two asymmetric algorithms, namely Multi Power RSA and Multi Prime RSA, each of which is a variant of RSA. This algorithm consists of 2 keys, namely the public key and the private key. Testing the algorithm will be carried out in Python. The conclusion obtained is that the Multi Prime RSA algorithm is faster than the Multi Power RSA in the encryption and decryption process. Multi Power RSA is faster than Multi Prime RSA in the key generation process.
Household secondary attack rates and risk factors during periods of SARS-CoV-2 Delta and Omicron variant predominance in the Republic of Korea
Authors: Lee Jin , Ko Mijeong , Kim Seontae , Lim Dosang , Park Gemma , Lee Sang-Eun
Published: August 2023

Objectives: The household secondary attack rate (SAR) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an important indicator for community transmission. This study aimed to characterize transmission by comparing household SARs and identifying risk factors during the periods of Delta and Omicron variant predominance in Republic of Korea.Methods: We defined the period of Delta variant predominance (Delta period) as July 25, 2021 to January 15, 2022, and the period of Omicron variant predominance (Omicron period) as February 7 to September 3, 2022. The number of index cases included was 214,229 for the Delta period and 5,521,393 for the Omicron period. To identify the household SARs and risk factors for each period, logistic regression was performed to determine the adjusted odds ratio (aOR).Results: The SAR was 35.2% for the Delta period and 43.1% for the Omicron period. The aOR of infection was higher in 2 groups, those aged 0 to 18 years and ≥75 years, compared to those aged 19 to 49 years. Unvaccinated individuals (vs. vaccinated individuals) and individuals experiencing initial infection (vs. individuals experiencing a second or third infection) had an increased risk of infection with SARS-CoV-2.Conclusion: This study analyzed the household SARs and risk factors. We hope that the results can help develop age-specific immunization plans and responses to reduce the SAR in preparation for emerging infectious diseases or potential new variants of SARS-CoV-2.
David Gordon White. Myths of the Dog-Man. Chicago: University of Chicago Press, 1991.
Authors: Lorenzen David N.
Published: August 2023

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Evidencias tempranas de la religión tántrica
Authors: Lorenzen David N.
Published: August 2023

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La hegemonía de Chonger, Marqués de Jin
Authors: Page John
Published: August 2023

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John Whitney Hall. Japan. From Prehistory to Modern Times. Ann Arbor: The University of Michigan Press, 1991. (Reedición de la versión en inglés de 1970.)
Authors: Tanaka Michiko
Published: August 2023

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Shintaro Ishihara. Le Japon sans complexe. París: Dunod Éditeur, 1991.
Authors: Ramírez Bonilla Juan José
Published: August 2023

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Paul Demiéville (comp.). Anthologie de la poésie chinoise classique. París: Gallimand, 1988, 614 pp.
Authors: Maeth Ch. Russell
Published: August 2023

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La vida de Kabir en las leyendas
Authors: Lorenzen David N.
Published: August 2023

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Wang Shinfu. The Moon and the Zither: The Story of the Western Wing. Compilación, traducción e introducción de Stephen H. West y Wilt L. Idema. Berkeley, Los Ángeles, Oxford: University of California Press, 1991. XIII+503 pp.
Authors: Connelly Marisela
Published: August 2023

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Upaya Mengembangkan Kompetensi Guru Penggerak di Sekolah Dasar Pada Kurikulum Merdeka Belajar
Authors: Aditiya Novela , Fatonah Siti
Published: August 2023

Curriculum changes that occurred during the Covid-19 pandemic made the government reformulate the existing national education “Kurikulum 2013” to become “Merdeka Belajar” Curriculum (independent learning). The schools and teachers became the important institutions in the Independent Learning Curriculum that focused on the character of the Pancasila Students Profile. The teacher competencies need to be improved to achieve the learning objectives through the Sekolah dan Guru Penggerak (the activator schools and teachers). The role of the activator teachers was to  improve the teachers’ quality in implementing the Independent Learning Curriculum. The purpose of this study is to determine the efforts of the teachers in improving their competence through the predicate of the Activator Schools. The research was conducted referring to a descriptive qualitative approach. The research was carried out at one Activator School, which was the Elementary School 4 Koba in the Koba district. The purposive sampling technique was used. The primary data sources were obtained through observations and discussions with 7 female teachers in low and high grades. Meanwhile, the secondary data sources were obtained from the previous studies from international, Sinta 1, and Sinta 2 journal articles. The interview process was done in a semi-structured way by providing a form, then the data results were analyzed by reducing and concluding. The results of this study revealed that the activator school sought to develop learning activities that realizes the characters of Pancasila to the students. The components that needed improvements was the teachers’ ability to do innovation in the lesson plan that contains the characters of Pancasila. The concerns faced by the activator teachers was that the training activities were carried out online in a short time, that reduced the opportunity to train making indicators and the implementation. It is suggested the school look for information from relevant education agency sources.
EVALUASI PROGRAM BIMBINGAN DAN KONSELING BERBASIS CIPP PADA MASA PANDEMI COVID-19
Authors: Utomo Setyo Budi , Atma Elisabet Setia , Dwikurnaningsih Yari , Loekmono J.T Lobby
Published: August 2023

The purpose of evaluating the Guidance and Counseling (BK) program at SMP Negeri 2 Ambarawa during the pandemic is to evaluate the context, input, process and product of the guidance and counseling program during the pandemic. This research is evaluative research. The principal provides full support in implementing counseling services during the pandemic by providing a schedule and budget allocation. The ratio of counseling teachers to students is fulfilled by the presence of school counselors with a background in counseling education. Facilities and infrastructure are in good condition, but do not yet have a special counseling room. BK services are carried out with the help of applications, namely Google classroom, and google forms used by school counselors for delivery systems in service activities with asynchronous types and in synchronous types using WhatsApp, Zoom or Google Meet depending on the agreement between teachers and students. The problems experienced in the implementation of online counseling services are less than optimal since not all students have devices. The essential material compiled by the MGBK is less appropriate to use, it is more appropriate to use a needs assessment of students to develop a BK program. School counselors provide time for learners to consult with flexible time, either through in-person or device-assisted.
Aktualizacja GINA 2023 – fakty, które trzeba znać
Authors: Sybilski Adam J.
Published: August 2023

Astma charakteryzuje się przewlekłym stanem zapalnym dróg oddechowych, spowodowanym interakcją między różnymi komórkami, elementami komórkowymi i cytokinami. Postępowanie w astmie jest procesem ciągłym wymagającym stałego kontrolowania i redukowania ryzyka. Długoterminowymi celami leczenia astmy są uzyskanie dobrej kontroli objawów oraz zminimalizowanie ryzyka przyszłego remodelingu (przebudowy) i śmiertelności związanej z astmą, zaostrzeń, uporczywego ograniczenia przepływu powietrza i działań niepożądanych leczenia. Leczenie astmy powinno się opierać na współpracy lekarza z pacjentem. Światowa Inicjatywa na Rzecz Zwalczania Astmy (GINA, Global Initiative for Asthma) jest organizacją, której działalność polega na zwiększaniu świadomości personelu medycznego, pacjentów i społeczeństwa na temat astmy, jej przyczyn, przebiegu, leczenia oraz wpływu na funkcjonowanie. Swoje rekomendacje opiera na badaniach naukowych – ich sukcesywna aktualizacja dała podstawy do wydania w 2023 r. nowych wytycznych. Do najważniejszych zmian, modyfikacji i aktualizacji należą:∙ precyzyjne sformułowanie terminologii dotyczącej leków stosowanych w astmie∙ doprecyzowanie cyklu postępowania w astmie∙ modyfikacja schematów leczenia u dorosłych i młodzieży > 12. r.ż. – włączono jako Ścieżka 2. doraźnego stosowania wGKS + SABA∙ uwzględnienie aspektów środowiskowych przy wyborze inhalatora∙ zaktualizowanie zalecenia dotyczącego leczenia ciężkiej astmy∙ przedstawienie praktycznego przewodnika postępowania w zaostrzeniach astmy∙ stwierdzenie, że leczenie oparte na wyniku FeNO nie skutkowało mniejszą liczbą zaostrzeń∙ podkreślenie faktu, iż użycie cyfrowych narzędzi w celu przestrzegania zaleceń jest skuteczne∙ zaktualizowanie strategii postępowania niefarmakologicznego w astmie – zalecenie zachęcania chorych na astmę do aktywności fizycznej∙ zaktualizowanie danych dotyczących szczepień ochronnych.
Helmut Berve und die Alte Geschichte - Eine deutsche Biographie
Authors: Welte Jasmin
Published: August 2023

Helmut Berve (1896–1979) gehört zu den bedeutendsten deutschsprachigen Althistorikern des 20. Jahrhunderts und hat die Geschichte seines Fachs 40 Jahre lang geprägt. Vor und nach dem Zweiten Weltkrieg hatte er Lehrstühle sowie weitere akademische Funktionen in Leipzig, München und Erlangen inne.
Diese Biographie verfolgt auf der Grundlage seiner wissenschaftlichen Werke, seiner umfangreichen Korrespondenz und anderer Archivalien Berves wissenschaftliche, intellektuelle und politische Entwicklung. Das Augenmerk gilt seinen zentralen Konzepten und erkenntnisleitenden Ideen. Darüber hinaus werden die akademische Karriere rekonstruiert und seine wissenschaftlichen und politischen Netzwerke offengelegt. Das Buch versteht sich als Beitrag zu einer kritischen Fachgeschichte.
Die Schwierigkeit mit dem Wissen - Überlegungen ausgehend von Sokrates
Authors: Mosimann Martin
Published: August 2023

Sokrates’ Behauptung zu wissen, dass er nicht(s) wisse, mag als Muster einer «tiefen» philosophischen Aussage gelten, muss jedoch überprüft werden. So trifft sie in Bezug auf formales Wissen (wie es Mathematik und Logik herstellen) sicher nicht zu; dieses ist gewiss, aber inhaltsleer. Alltagswissen dagegen kann ohne Gewissheit erstaunlich brauchbar sein. Und materielles Wissen in Bezug auf die empirische Welt kann tatsächlich gewonnen werden, gilt aber immer nur auf Zusehen hin, statt gewiss zu sein. Und schliesslich gibt es Fragestellungen, die wirklich nicht beantwortet werden können.
GeNAS: Neural Architecture Search with Better Generalization
Authors: Jeong Joonhyun , Yu Joonsang , Park Geondo , Han Dongyoon , Yoo YoungJoon
Published: August 2023

Neural Architecture Search (NAS) aims to automatically excavate the optimal network architecture with superior test performance. Recent neural architecture search (NAS) approaches rely on validation loss or accuracy to find the superior network for the target data. In this paper, we investigate a new neural architecture search measure for excavating architectures with better generalization. We demonstrate that the flatness of the loss surface can be a promising proxy for predicting the generalization capability of neural network architectures. We evaluate our proposed method on various search spaces, showing similar or even better performance compared to the state-of-the-art NAS methods. Notably, the resultant architecture found by flatness measure generalizes robustly to various shifts in data distribution (e.g. ImageNet-V2,-A,-O), as well as various tasks such as object detection and semantic segmentation.
Guided Patch-Grouping Wavelet Transformer with Spatial Congruence for Ultra-High Resolution Segmentation
Authors: Ji Deyi , Zhao Feng , Lu Hongtao
Published: August 2023

Most existing ultra-high resolution (UHR) segmentation methods always struggle in the dilemma of balancing memory cost and local characterization accuracy, which are both taken into account in our proposed Guided Patch-Grouping Wavelet Transformer (GPWFormer) that achieves impressive performances. In this work, GPWFormer is a Transformer (T)-CNN (C) mutual leaning framework, where T takes the whole UHR image as input and harvests both local details and fine-grained long-range contextual dependencies, while C takes downsampled image as input for learning the category-wise deep context. For the sake of high inference speed and low computation complexity, T partitions the original UHR image into patches and groups them dynamically, then learns the low-level local details with the lightweight multi-head Wavelet Transformer (WFormer) network. Meanwhile, the fine-grained long-range contextual dependencies are also captured during this process, since patches that are far away in the spatial domain can also be assigned to the same group. In addition, masks produced by C are utilized to guide the patch grouping process, providing a heuristics decision. Moreover, the congruence constraints between the two branches are also exploited to maintain the spatial consistency among the patches. Overall, we stack the multi-stage process in a pyramid way. Experiments show that GPWFormer outperforms the existing methods with significant improvements on five benchmark datasets.
IMF: Integrating Matched Features Using Attentive Logit in Knowledge Distillation
Authors: Kim Jeongho , Lee Hanbeen , Woo Simon S.
Published: August 2023

Knowledge distillation (KD) is an effective method for transferring the knowledge of a teacher model to a student model, that aims to improve the latter's performance efficiently. Although generic knowledge distillation methods such as softmax representation distillation and intermediate feature matching have demonstrated improvements with various tasks, only marginal improvements are shown in student networks due to their limited model capacity. In this work, to address the student model's limitation, we propose a novel flexible KD framework, Integrating Matched Features using Attentive Logit in Knowledge Distillation (IMF). Our approach introduces an intermediate feature distiller (IFD) to improve the overall performance of the student model by directly distilling the teacher's knowledge into branches of student models. The generated output of IFD, which is trained by the teacher model, is effectively combined by attentive logit. We use only a few blocks of the student and the trained IFD during inference, requiring an equal or less number of parameters. Through extensive experiments, we demonstrate that IMF consistently outperforms other state-of-the-art methods with a large margin over the various datasets in different tasks without extra computation.
Character As Pixels: A Controllable Prompt Adversarial Attacking Framework for Black-Box Text Guided Image Generation Models
Authors: Kou Ziyi , Pei Shichao , Tian Yijun , Zhang Xiangliang
Published: August 2023

In this paper, we study a controllable prompt adversarial attacking problem for text guided image generation (Text2Image) models in the black-box scenario, where the goal is to attack specific visual subjects (e.g., changing a brown dog to white) in a generated image by slightly, if not imperceptibly, perturbing the characters of the driven prompt (e.g., ``brown'' to ``br0wn''). Our study is motivated by the limitations of current Text2Image attacking approaches that still rely on manual trials to create adversarial prompts. To address such limitations, we develop CharGrad, a character-level gradient based attacking framework that replaces specific characters of a prompt with pixel-level similar ones by interactively learning the perturbation direction for the prompt and updating the attacking examiner for the generated image based on a novel proxy perturbation representation for characters. We evaluate CharGrad using the texts from two public image captioning datasets. Results demonstrate that CharGrad outperforms existing text adversarial attacking approaches on attacking various subjects of generated images by black-box Text2Image models in a more effective and efficient way with less perturbation on the characters of the prompts.
Multi-Agent Intention Recognition and Progression
Authors: Dann Michael , Yao Yuan , Alechina Natasha , Logan Brian , Meneguzzi Felipe , Thangarajah John
Published: August 2023

For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.
WBFlow: Few-shot White Balance for sRGB Images via Reversible Neural Flows
Authors: Li Chunxiao , Kang Xuejing , Ming Anlong
Published: August 2023

The sRGB white balance methods aim to correct the nonlinear color cast of sRGB images without accessing raw values. Although existing methods have achieved increasingly better results, their generalization to sRGB images from multiple cameras is still under explored. In this paper, we propose the network named WBFlow that not only performs superior white balance for sRGB images but also generalizes well to multiple cameras. Specifically, we take advantage of neural flow to ensure the reversibility of WBFlow, which enables lossless rendering of color cast sRGB images back to pseudo raw features for linear white balancing and thus achieves superior performance. Furthermore, inspired by camera transformation approaches, we have designed a camera transformation (CT) in pseudo raw feature space to generalize WBFlow for different cameras via few shot learning. By utilizing a few sRGB images from an untrained camera, our WBFlow can perform well on this camera by learning the camera specific parameters of CT. Extensive experiments show that WBFlow achieves superior camera generalization and accuracy on three public datasets as well as our rendered multiple camera sRGB dataset. Our code is available at https://github.com/ChunxiaoLe/WBFlow.
Contact2Grasp: 3D Grasp Synthesis via Hand-Object Contact Constraint
Authors: Li Haoming , Lin Xinzhuo , Zhou Yang , Li Xiang , Huo Yuchi , Chen Jiming , Ye Qi
Published: August 2023

3D grasp synthesis generates grasping poses given an input object. Existing works tackle the problem by learning a direct mapping from objects to the distributions of grasping poses. However, because the physical contact is sensitive to small changes in pose, the high-nonlinear mapping between 3D object representation to valid poses is considerably non-smooth, leading to poor generation efficiency and restricted generality. To tackle the challenge, we introduce an intermediate variable for grasp contact areas to constrain the grasp generation; in other words, we factorize the mapping into two sequential stages by assuming that grasping poses are fully constrained given contact maps: 1) we first learn contact map distributions to generate the potential contact maps for grasps; 2) then learn a mapping from the contact maps to the grasping poses. Further, we propose a penetration-aware optimization with the generated contacts as a consistency constraint for grasp refinement. Extensive validations on two public datasets show that our method outperforms state-of-the-art methods regarding grasp generation on various metrics.
Local-Global Transformer Enhanced Unfolding Network for Pan-sharpening
Authors: Li Mingsong , Liu Yikun , Xiao Tao , Huang Yuwen , Yang Gongping
Published: August 2023

Pan-sharpening aims to increase the spatial resolution of the low-resolution multispectral (LrMS) image with the guidance of the corresponding panchromatic (PAN) image. Although deep learning (DL)-based pan-sharpening methods have achieved promising performance, most of them have a two-fold deficiency. For one thing, the universally adopted black box principle limits the model interpretability. For another thing, existing DL-based methods fail to efficiently capture local and global dependencies at the same time, inevitably limiting the overall performance. To address these mentioned issues, we first formulate the degradation process of the high-resolution multispectral (HrMS) image as a unified variational optimization problem, and alternately solve its data and prior subproblems by the designed iterative proximal gradient descent (PGD) algorithm. Moreover, we customize a Local-Global Transformer (LGT) to simultaneously model local and global dependencies, and further formulate an LGT-based prior module for image denoising. Besides the prior module, we also design a lightweight data module. Finally, by serially integrating the data and prior modules in each iterative stage, we unfold the iterative algorithm into a stage-wise unfolding network, Local-Global Transformer Enhanced Unfolding Network (LGTEUN), for the interpretable MS pan-sharpening. Comprehensive experimental results on three satellite data sets demonstrate the effectiveness and efficiency of LGTEUN compared with state-of-the-art (SOTA) methods. The source code is available at https://github.com/lms-07/LGTEUN.
VS-Boost: Boosting Visual-Semantic Association for Generalized Zero-Shot Learning
Authors: Li Xiaofan , Zhang Yachao , Bian Shiran , Qu Yanyun , Xie Yuan , Shi Zhongchao , Fan Jianping
Published: August 2023

Unlike conventional zero-shot learning (CZSL) which only focuses on the recognition of unseen classes by using the classifier trained on seen classes and semantic embeddings, generalized zero-shot learning (GZSL) aims at recognizing both the seen and unseen classes, so it is more challenging due to the extreme training imbalance. Recently, some feature generation methods introduce metric learning to enhance the discriminability of visual features. Although these methods achieve good results, they focus only on metric learning in the visual feature space to enhance features and ignore the association between the feature space and the semantic space. Since the GZSL method uses semantics as prior knowledge to migrate visual knowledge to unseen classes, the consistency between visual space and semantic space is critical. To this end, we propose relational metric learning which can relate the metrics in the two spaces and make the distribution of the two spaces more consistent. Based on the generation method and relational metric learning, we proposed a novel GZSL method, termed VS-Boost, which can effectively boost the association between vision and semantics. The experimental results demonstrate that our method is effective and achieves significant gains on five benchmark datasets compared with the state-of-the-art methods.
A Large-Scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement
Authors: Li Zinuo , Chen Xuhang , Wang Shuqiang , Pun Chi-Man
Published: August 2023

Film, a classic image style, is culturally significant to the whole photographic industry since it marks the birth of photography. However, film photography is time-consuming and expensive, necessitating a more efficient method for collecting film-style photographs. Numerous datasets that have emerged in the field of image enhancement so far are not film-specific. In order to facilitate film-based image stylization research, we construct FilmSet, a large-scale and high-quality film style dataset. Our dataset includes three different film types and more than 5000 in-the-wild high resolution images. Inspired by the features of FilmSet images, we propose a novel framework called FilmNet based on Laplacian Pyramid for stylizing images across frequency bands and achieving film style outcomes. Experiments reveal that the performance of our model is superior than state-of-the-art techniques. The link of our dataset and code is https://github.com/CXH-Research/FilmNet.
Sph2Pob: Boosting Object Detection on Spherical Images with Planar Oriented Boxes Methods
Authors: Liu Xinyuan , Xu Hang , Chen Bin , Zhao Qiang , Ma Yike , Yan Chenggang , Dai Feng
Published: August 2023

Object detection on panoramic/spherical images has been developed rapidly in the past few years, where IoU-calculator is a fundamental part of various detector components, i.e. Label Assignment, Loss and NMS. Due to the low efficiency and non-differentiability of spherical Unbiased IoU, spherical approximate IoU methods have been proposed recently. We find that the key of these approximate methods is to map spherical boxes to planar boxes. However, there exists two problems in these methods: (1) they do not eliminate the influence of panoramic image distortion; (2) they break the original pose between bounding boxes. They lead to the low accuracy of these methods. Taking the two problems into account, we propose a new sphere-plane boxes transform, called Sph2Pob. Based on the Sph2Pob, we propose (1) an differentiable IoU, Sph2Pob-IoU, for spherical boxes with low time-cost and high accuracy and (2) an agent Loss, Sph2Pob-Loss, for spherical detection with high flexibility and expansibility. Extensive experiments verify the effectiveness and generality of our approaches, and Sph2Pob-IoU and Sph2Pob-Loss together boost the performance of spherical detectors. The source code is available at https://github.com/AntXinyuan/sph2pob.
Bi-level Dynamic Learning for Jointly Multi-modality Image Fusion and Beyond
Authors: Liu Zhu , Liu Jinyuan , Wu Guanyao , Ma Long , Fan Xin , Liu Risheng
Published: August 2023

Recently, multi-modality scene perception tasks, e.g., image fusion and scene understanding, have attracted widespread attention for intelligent vision systems. However, early efforts always consider boosting a single task unilaterally and neglecting others, seldom investigating their underlying connections for joint promotion. To overcome these limitations, we establish the hierarchical dual tasks-driven deep model to bridge these tasks. Concretely, we firstly construct an image fusion module to fuse complementary characteristics and cascade dual task-related modules, including a discriminator for visual effects and a semantic network for feature measurement.

We provide a bi-level perspective to formulate image fusion and follow-up downstream tasks. To incorporate distinct task-related responses for image fusion, we consider image fusion as a primary goal and dual modules as learnable constraints. Furthermore, we develop an efficient first-order approximation to compute corresponding gradients and present dynamic weighted aggregation to balance the gradients for fusion learning. Extensive experiments demonstrate the superiority of our method, which not only produces visually pleasant fused results but also realizes significant promotion for detection and segmentation than the state-of-the-art approaches.
Semantic-Aware Generation of Multi-View Portrait Drawings
Authors: Ma Biao , Gao Fei , Jiang Chang , Wang Nannan , Xu Gang
Published: August 2023

Neural radiance fields (NeRF) based methods have shown amazing performance in synthesizing 3D-consistent photographic images, but fail to generate multi-view portrait drawings. The key is that the basic assumption of these methods -- a surface point is consistent when rendered from different views -- doesn't hold for drawings. In a portrait drawing, the appearance of a facial point may changes when viewed from different angles. Besides, portrait drawings usually present little 3D information and suffer from insufficient training data. To combat this challenge, in this paper, we propose a Semantic-Aware GEnerator (SAGE) for synthesizing multi-view portrait drawings. Our motivation is that facial semantic labels are view-consistent and correlate with drawing techniques. We therefore propose to collaboratively synthesize multi-view semantic maps and the corresponding portrait drawings. To facilitate training, we design a semantic-aware domain translator, which generates portrait drawings based on features of photographic faces. In addition, use data augmentation via synthesis to mitigate collapsed results. We apply SAGE to synthesize multi-view portrait drawings in diverse artistic styles. Experimental results show that SAGE achieves significantly superior or highly competitive performance, compared to existing 3D-aware image synthesis methods. The codes are available at https://github.com/AiArt-HDU/SAGE.
Invertible Residual Neural Networks with Conditional Injector and Interpolator for Point Cloud Upsampling
Authors: Mao Aihua , Duan Yaqi , Wen Yu-Hui , Du Zihui , Cai Hongmin , Liu Yong-Jin
Published: August 2023

Point clouds obtained by LiDAR and other sensors are usually sparse and irregular. Low-quality point clouds have serious influence on the final performance of downstream tasks. Recently, a point cloud upsampling network with normalizing flows has been proposed to address this problem. However, the network heavily relies on designing specialized architectures to achieve invertibility. In this paper, we propose a novel invertible residual neural network for point cloud upsampling, called PU-INN, which allows unconstrained architectures to learn more expressive feature transformations. Then, we propose a conditional injector to improve nonlinear transformation ability of the neural network while guaranteeing invertibility. Furthermore, a lightweight interpolator is proposed based on semantic similarity distance in the latent space, which can intuitively reflect the interpolation changes in Euclidean space. Qualitative and quantitative results show that our method outperforms the state-of-the-art works in terms of distribution uniformity, proximity-to-surface accuracy, 3D reconstruction quality, and computation efficiency.
Teaching What You Should Teach: A Data-Based Distillation Method
Authors: Shao Shitong , Chen Huanran , Huang Zhen , Gong Linrui , Wang Shuai , Wu Xinxiao
Published: August 2023

In real teaching scenarios, an excellent teacher always teaches what he (or she) is good at but the student is not. This gives the student the best assistance in making up for his (or her) weaknesses and becoming a good one overall. Enlightened by this, we introduce the "Teaching what you Should Teach" strategy into a knowledge distillation framework, and propose a data-based distillation method named "TST" that searches for desirable augmented samples to assist in distilling more efficiently and rationally. To be specific, we design a neural network-based data augmentation module with priori bias to find out what meets the teacher's strengths but the student's weaknesses, by learning magnitudes and probabilities to generate suitable data samples. By training the data augmentation module and the generalized distillation paradigm alternately, a student model is learned with excellent generalization ability. To verify the effectiveness of our method, we conducted extensive comparative experiments on object recognition, detection, and segmentation tasks. The results on the CIFAR-100, ImageNet-1k, MS-COCO, and Cityscapes datasets demonstrate that our method achieves state-of-the-art performance on almost all teacher-student pairs. Furthermore, we conduct visualization studies to explore what magnitudes and probabilities are needed for the distillation process.
Discrepancy-Guided Reconstruction Learning for Image Forgery Detection
Authors: Shi Zenan , Chen Haipeng , Chen Long , Zhang Dong
Published: August 2023

In this paper, we propose a novel image forgery detection paradigm for boosting the model learning capacity on both forgery-sensitive and genuine compact visual patterns. Compared to the existing methods that only focus on the discrepant-specific patterns (\eg, noises, textures, and frequencies), our method has a greater generalization. Specifically, we first propose a Discrepancy-Guided Encoder (DisGE) to extract forgery-sensitive visual patterns. DisGE consists of two branches, where the mainstream backbone branch is used to extract general semantic features, and the accessorial discrepant external attention branch is used to extract explicit forgery cues. Besides, a Double-Head Reconstruction (DouHR) module is proposed to enhance genuine compact visual patterns in different granular spaces. Under DouHR, we further introduce a Discrepancy-Aggregation Detector (DisAD) to aggregate these genuine compact visual patterns, such that the forgery detection capability on unknown patterns can be improved. Extensive experimental results on four challenging datasets validate the effectiveness of our proposed method against state-of-the-art competitors.
VGOS: Voxel Grid Optimization for View Synthesis from Sparse Inputs
Authors: Sun Jiakai , Zhang Zhanjie , Chen Jiafu , Li Guangyuan , Ji Boyan , Zhao Lei , Xing Wei
Published: August 2023

Neural Radiance Fields (NeRF) has shown great success in novel view synthesis due to its state-of-the-art quality and flexibility. However, NeRF requires dense input views (tens to hundreds) and a long training time (hours to days) for a single scene to generate high-fidelity images. Although using the voxel grids to represent the radiance field can significantly accelerate the optimization process, we observe that for sparse inputs, the voxel grids are more prone to overfitting to the training views and will have holes and floaters, which leads to artifacts. In this paper, we propose VGOS, an approach for fast (3-5 minutes) radiance field reconstruction from sparse inputs (3-10 views) to address these issues. To improve the performance of voxel-based radiance field in sparse input scenarios, we propose two methods: (a) We introduce an incremental voxel training strategy, which prevents overfitting by suppressing the optimization of peripheral voxels in the early stage of reconstruction. (b) We use several regularization techniques to smooth the voxels, which avoids degenerate solutions. Experiments demonstrate that VGOS achieves state-of-the-art performance for sparse inputs with super-fast convergence. Code will be available at https://github.com/SJoJoK/VGOS.
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++
Authors: Umapathi Barath Mohan , Chauhan Kushal , Shenoy Pradeep , Sridharan Devarajan
Published: August 2023

Reliable outlier detection is critical for real-world deployment of deep learning models. Although extensively studied, likelihoods produced by deep generative models have been largely dismissed as being impractical for outlier detection. First, deep generative model likelihoods are readily biased by low-level input statistics. Second, many recent solutions for correcting these biases are computationally expensive, or do not generalize well to complex, natural datasets. Here, we explore outlier detection with a state-of-the-art deep autoregressive model: PixelCNN++. We show that biases in PixelCNN++ likelihoods arise primarily from predictions based on local dependencies. We propose two families of bijective transformations -- ``stirring'' and ``shaking'' -- which ameliorate low-level biases and isolate the contribution of long-range dependencies to PixelCNN++ likelihoods. These transformations are inexpensive and readily computed at evaluation time. We test our approaches extensively with five grayscale and six natural image datasets and show that they achieve or exceed state-of-the-art outlier detection, particularly on datasets with complex, natural images. We also show that our solutions work well with other types of generative models (generative flows and variational autoencoders) and that their efficacy is governed by each model's reliance on local dependencies. In sum, lightweight remedies suffice to achieve robust outlier detection on image data with deep generative models.
Temporal Constrained Feasible Subspace Learning for Human Pose Forecasting
Authors: Wang Gaoang , Song Mingli
Published: August 2023

Human pose forecasting is a sequential modeling task that aims to predict future poses from historical motions. Most existing approaches focus on the spatial-temporal neural network model design for learning movement patterns to reduce prediction errors. However, they usually do not strictly follow the temporal constraints in the inference stage. Even though a small Mean Per Joint Position Error (MPJPE) is achieved, some of the predicted poses are not temporal feasible solutions, which disobeys the continuity of the body movement. In this paper, we consider the temporal constrained feasible solutions for human pose forecasting, where the predicted poses of input historical poses are guaranteed to obey the temporal constraints strictly in the inference stage. Rather than direct supervision of the prediction in the original pose space, a temporal constrained subspace is explicitly learned and then followed by an inverse transformation to obtain the final predictions. We evaluate the proposed method on large-scale benchmarks, including Human3.6M, AMASS, and 3DPW. State-of-the-art performance has been achieved with the temporal constrained feasible solutions.
A Dual Semantic-Aware Recurrent Global-Adaptive Network for Vision-and-Language Navigation
Authors: Wang Liuyi , He Zongtao , Tang Jiagui , Dang Ronghao , Wang Naijia , Liu Chengju , Chen Qijun
Published: August 2023

Vision-and-Language Navigation (VLN) is a realistic but challenging task that requires an agent to locate the target region using verbal and visual cues. While significant advancements have been achieved recently, there are still two broad limitations: (1) The explicit information mining for significant guiding semantics concealed in both vision and language is still under-explored; (2) The previously structured map method provides the average historical appearance of visited nodes, while it ignores distinctive contributions of various images and potent information retention in the reasoning process. This work proposes a dual semantic-aware recurrent global-adaptive network (DSRG) to address the above problems. First, DSRG proposes an instruction-guidance linguistic module (IGL) and an appearance-semantics visual module (ASV) for boosting vision and language semantic learning respectively. For the memory mechanism, a global adaptive aggregation module (GAA) is devised for explicit panoramic observation fusion, and a recurrent memory fusion module (RMF) is introduced to supply implicit temporal hidden states. Extensive experimental results on the R2R and REVERIE datasets demonstrate that our method achieves better performance than existing methods. Code is available at https://github.com/CrystalSixone/DSRG.