bluuebunny/crossref_metadata_2025 huggingface dataset:
Alevîlik-Bektaşîlik Araştırmalarında Yöntem Problemi Etrafında Bazı Düşünceler
Authors: Yıldız Harun
Published: July 2022

Son dönemlerde dikkat çekici biçimde yoğunlaştığı görülen Alevîlik-Bektaşîlik araştırmalarında karşılaşılan sorunların önemli bir kısmı, yöntem ve yaklaşım problemlerinden kaynaklanmaktadır. Yöntem problemi, her şeyden önce çalışılan konu, mesele ve sorunlarla ilgili olarak bilimsel anlamda doğru ve geçerli bilgiye ulaşabilme ve bu bilgileri doğru ve gerektiği biçimde analiz etme ve değerlendirebilme problemidir. Bu nedenle ciddi problemler yumağı içinde boğuşan Alevîlik-Bektaşîlik araştırmalarında bilimsel metodolojiyi önceleyen bir anlayış ile tarafsız bir yaklaşımı öne çıkarmak suretiyle ideolojik ve politik tutumlardan uzak durmak, olmazsa olmaz bir zorunluluktur. Ayrıca, konunun politik ya da ideolojik tartışmalara malzeme olmaktan kurtarılması için böyle bir yaklaşım, oldukça önemlidir. Aksi halde farklı birtakım etkenler ve ideolojik bakış açıları ile yapılan çalışmalar, konunun daha iyi anlaşılıp çözümlenmesini bir kenara bırakın, konuyu daha karmaşık ve içinden çıkılmaz bir duruma getirecektir. Alevîlik-Bektaşîlik konusunun çok yönlü olduğunu unutmadan hareket etmek gerektiği de ortadadır; zira bu konu, sosyal bilimlerin konusu olduğu kadar, aynı zamanda ilahiyat bilimlerinin de konusudur. Bu yüzden tarihsel, dinî, siyasal, sosyolojik ve güncel boyutlara da sahiptir. Bu çerçevede önemli bir tarihsel derinliğe sahip olan konunun tarihsel arka planını bilmek, bu süreci sağlam bir biçimde çözümlemek ve bu süreçte ortaya çıkan pek çok etkeni dikkate almak, bize büyük avantajlar sağlayacaktır.Bu makalede, ülkemizde yapılan Alevîlik-Bektaşîlik araştırmalarında genel hatlarıyla ortaya çıkan yöntemle ilgili problemler üzerinde durmaya ve bu problemler etrafında bazı düşünceler ortaya koyarak birtakım çözümler sunmaya çalışacağız. Bu çerçevede öncelikle literatürde karşılaşılan ana problemler ortaya konulacak, arkasından bu noktada üzerinde durulması gereken metodoloji merkezli ve konuyu daha iyi anlamaya yönelik öne çıkan araştırma alanları ve bu alanlara yönelik değişik yaklaşımlarla birlikte tartışmaların odağındaki isim olan Hacı Bektaş Velî üzerinde durulacaktır.
PERANCANGAN SISTEM INFORMASI BERBASIS WEBSITE PADA UPTD PUSKESMAS PUNGGUR LAMPUNG TENGAH
Authors: Rofiq Syahrul , Sudarmaji Sudarmaji , Hidayat Arif
Published: July 2022

Proses penyampaian informasi mengenai PROMKES (Promosi Kesehatan) ke masyarakat saatini melalui peyuluhan petugas dengan membagikan brousur/pamflet, poster yang diletakan disekitar Puskesamas. Layanan informasi berupa jenis pelayanan, tarif pelayanan, jam kerja danpelayanan) yang diletakan diarah ruang masuk, dan banner disekitar jalan raya sehingga untukmendapatkan informasi tersebut, masyarakat harus bekunjung terlebih dahulu ke Puskesmas. Haltersebut mengakibatkan daya sebar informasi kecil. Tujuan penelitian ini adalah membuatPerancangan Sistem Informasi Berbasis Website, untuk mempermudah penyampain informasisecara luas dan pihak UPTD Puskesmas Punggur untuk mengolah data berita layananperbagian/bidang. Jenis penelitian ini menggunakan Metode pengembangan sistem inimengunakan metode SLDC (System Development Life Cycle) dengan pengujian sistemmenggunakan teknik testing blackbox dan data berupa observasi, interview, dan dokumentasi.Sedangkan metode perancangan sistem informasi berbasis website menggunakan Bagan AlirDokumen, Data Flow Diagram, Entity Relationship Diagram. Berdasarkan hasil penelitian iniyaitu menghasilkan sistem informasi berbasis web yang dapat mengolah data berita layananperbagian/bidang maka akan mempermudah penyampaian informasi di UPTD PuskesmasPunggur sudah bersifat terbuka dan diberikan kemudahan akses untuk mendapatkan informasi.
APLIKASI PENGOLAHAN DATA KARYAWAN BMT MENTARI KOTAGAJAH LAMPUNG TENGAH BERBASIS JAVA
Authors: Henita Esy , Mustika Mustika , Dharmawan Dharmawan
Published: July 2022

Aset yang penting dalam sebuah perusahaan adalah sumber daya manusianya (karyawan)sehingga data-data yang berhubungan dengan karyawan merupakan suatu hal yang harus diolahdan didokumentasikan dengan baik mulai dari proses masuknya karyawan tersebut sampaidengan berakhirnya masa kerja karyawan. Pengolahan data karyawan di BMT Mentari KotaGajah belum menggunakan database, masih menggunakan cara memasukkan data karyawan satuper satu di setiap pengolahan data karyawan (tidak terintegrasi), proses menyimpan dokumendata karyawan dalam folder menyebabkan proses pencarian data karyawan menjadi lebihmerepotkan dan cenderung kurang efektif dan rentan terhadap kerusakan dan kehilangandokumen, proses perhitungan gaji juga belum otomatis. Selain itu, proses pelaporan sepertirekapitulasi data karyawan, rekapitulasi gaji karyawan belum otomatis. Metode penelitiandilakukan dengan observasi, wawancara, dokumentasi dan studi literatur. Sedangkan metodeperancangan aplikasi menggunakan bagan air dokumen (Flowchart) Data Flow Diagram (DFD),Entry Relationship Diagram (ERD). Perangkat lunak pendukung dalam membuat aplikasipemrosesan data karyawan menggunakan aplikasi NetBean IDE. 8. 2 bahasa pemrograman java,laporan menggunakan ireport, dan menggunakan database My SQL.Desain aplikasi pemrosesandata karyawan menghasilkan pembuatan aplikasi pemrosesan data karyawan ini untukmemfasilitasi petugas dalam memproses data karyawan menggunakan aplikasi pemrogramanJava Development Kids (JDK 7.0 atau lebih tinggi) dan Database Mysql.
PERANCANGAN SISTEM INFORMASI BERBASIS WEB PADA BMT MENTARI KOTA GAJAH LAMPUNG TENGAH
Authors: Azzuhroh Noviana Assa , Sudarmaji Sudarmaji , Irawan Dedi
Published: July 2022

Kondisi pada saat penelitian di BMT Mentari Kota Gajah. Khususnya bagian pelayananinformasi yang dilakukan oleh Admin BMT saat ini masih bersifat manual (konvensional), yaknipelayanan informasi kepada calon nasabah atau nasabah masih menggunakan brosur dankonsultasi secara langsung, yang dapat mengakibatkan menumpuknya antrian Nasabah yangingin berkonsultasi serta calon Nasabah yang ingin mengetahui informasi persyaratan untukmenjadi nasabah, selain itu bagi nasabah yang akan mencari informasi tentang BMT Mentari danpersyaratan simpan pinjam harus datang ke kantor BMT Mentari. Dalam perancangan sistemBMT Mentari Kota Gajah ini penulis menggunakan 2 metode yakni, yang pertama studilapangan yang terdiri dari observasi, wawancara, dan dokumentasi. Sedangkan yang kedua ialahstudii pustaka. Dalam perancangan sistem informasi penulis menggunakan aliran informasi, dataflow diagram (DFD), dan entity relationship diagram (ERD). Software pendukung dalam sisteminformasi ini adalah PHP, Dreamweaver CS6, dan Database MySQL. Perancangan Sisteminformasi pada BMT Mentari Kota Gajah menghasilkan informasi persyaratan simpan pinjamdan informasi seputar BMT Mentari.
Effects of B Addition on the Microstructure and Microhardness of Melt-Spun Al-7075 Alloy
Authors: Kilicaslan Muhammed Fatih , Elburni Saad Ibrahim , Yilmaz Yasin , Akkaş Mehmet
Published: July 2022

Main aim of this study is to examine change of microstructural and the mechanical properties of Al-7075 alloy depending on B content added with different proportions. Rapid solidification process used to produce the alloys was carried out with a single roll melt spinner via a wheel with a rotational speed of 25 m/s. For the microstructural characterization of the alloys, a scanning electron microscope and X-ray diffraction analyzes were used. According to obtained results, it can be said that B addition led to modification of dimensions and shapes of both α−Al and intermetallic phases occurred, it reduced average grain size from 0.45 μm to 0.34 μm in the microstructure. The B addition also led to dramatically increase in microhardness of the Al-7075 alloy. The microhardness of the alloy with 0.4 wt. % B is 0.19 GPa, this is clearly one times higher than that of the alloy without B addition. The microhardness of the alloy with 1 wt. % B is 0.21 GPa, this is also slightly higher than that of the alloy with 0.4 wt. % B.
Effect of Shop-Primer Coating on S235JR Steel on MAG Weldability
Authors: Çetinkaya Cemil , Akay Ali , Arabacı Uğur , Özdemir Uğur
Published: July 2022

In this study, S235JR structural steel samples in uncoated condition and coated (shop-primer) in different thicknesses were welded by MAG (Metal Active Gas) welding method, and the effects of these applications on the mechanical and microstructure properties of the material were investigated. In the experimental studies, the first specimen group were used without any sandblasting and coating application, the second group specimens were sandblasted at Sa 2 ½ degree, and 25 µm, 50 µm, and 75 µm coatings were applied to the specimens in the other group. Surface conditions and coating thicknesses were selected as the variable parameters. With the examination of the radiography films, it was observed that the surface conditions affected the welded joint. As a result of the study, it was observed that altered coating thicknesses caused defects in the welding zone. It was determined that the coating thickness partially affected the mechanical properties, and the highest hardness values occurred in the weld zone in all samples. Relatively low values were obtained in tensile, bending and Charpy impact tests performed on sample groups with 75 µm coating thickness. The results were verified by the surface fracture, SEM, and EDS studies.
Ethanolic extract of Edamame (Glycine max L. merril) enhance second degree burn wound healing trough modulating of hydroxiproline levels and increasing epithelial thickness
Authors: Sutejo Ika Rahmawati , Hasanah Arifah Nur , Sudarko Faizah Ramadhani
Published: July 2022

Objective: Burns has become a global health problem because it causes about 180.000 deaths worldwide every year. In developing countries, silver sulfadiazine cream is usually used as drug management for second-degree burns, but it is expensive, so several herbal treatments have developed recently. The seed of edamame has genistein compounds that can increase collagen synthesis. The antioxidant component also enhances the burn wound healing process. The study aims to prove the effectiveness of ethanolic extract of edamame’s seed in the second-degree burn wound healing process.

Methods: Burn wound was made by applying hot aluminum (70 °C) coin on the skin rat. The negative control group (C-) was given Na-CMC 0.5%, the positive control group (C+) was given cream silver sulfadiazine, the treatment groups T1, T2, T3, and T4 were given an ethanolic extract of edamame with the concentration of 20%, 40%, 60%, and 80% for 15 days. Hydroxyproline levels were evaluated by hydroxyproline biochemistry assay, the epithelial thickness was observed on histopathological preparations with HE staining.

Results: The results showed that the hydroxyproline levels on the 16th were modulated, while epithelial thickness was higher in the treatment group than in the control group (P<0.05).

Conclusions: We conclude that the certain concentration of ethanolic extract of edamame (Glycine max L. Merril) was effectively enhanced the second-degree burn wound healing by modulating hydroxyproline levels and increasing epithelial thickness.
Prosthetic management of acquired dentate maxillectomy defects: A clinical case series
Authors: Sharma Vineet , Paliwal Jyoti , Gurjar Balwant Singh , Meena Kamal Kumar
Published: July 2022

Acquired maxillectomy defects produce hypernasal speech, food, and liquid regurgitation into the nasal cavity, impaired deglutition and mastication, and cosmetic deformity. Furthermore, patients with acquired maxillary defects face psychosocial stigma, which has a negative impact on their quality of life. Prosthetic rehabilitation of such defects is required for stomatognathic system restoration and oroantral communication obturation. This case series discusses the fabrication of surgical, interim, and definitive obturator prostheses to restore the acquired dentate maxillectomy defects of three cancer patients. All patients had their treatment in the prosthodontics department of the RUHS College of Dental Sciences. The surgical obturator prosthesis was made before surgery, whereas the interim and definitive obturators were made one month and six months after surgery, respectively. The surgical obturator formed a shield between the surgical pack and the oral cavity. After the surgical obturator and packing were removed, an interim obturator was inserted for three to six months to allow the surgical site to heal. After the surgical site had healed, the fabrication of the definitive obturator began. Prosthetic rehabilitation with obturator prostheses sealed the acquired tissue defects of the palate and restored swallowing, speaking, chewing capacity, and cosmetic value, as well as significantly improved the quality of life of these patients.
Abdominal wall metastases due to a squamous cell carcinoma of the lung: Case report and literature review
Authors: Ioniță Corina Florica , Cojocaru Ioana Iulia , Gomotîrceanu Adriana , Suciu Bogdan Andrei , Milutin Doina , Butilcă Alexandru Ilie , Molnar Călin Dragoș , Halmaciu Ioana
Published: July 2022

Introduction: At the time of diagnosis, most patients with lung cancer are in an inoperable stage, with distant metastases. Most often, these patients have metastases to the brain, adrenal glands, liver, or bones. This article presents the case of a patient with non-small-cell lung cancer (NSCLC) metastases in the abdominal wall.

Case presentation: A 67-year-old patient came to our service reporting the existence of a tumor 5 cm in diameter, located at the level of the abdominal wall, without other clinical symptoms. Surgical excision of the tumor was performed. The postoperative evolution was favorable, with the patient discharged on the third postoperative day. Histological examination of the resected specimen revealed metastasis of squamous cell carcinoma of the lung.

Conclusions: The appearance of a tumor in the abdominal wall of patients with NSCLC may raise the suspicion of metastasis at this level.
Comparative analysis of mandibular changes after orthodontic treatment with and without extraction of four premolars: A digital cephalometric study
Authors: Alex Anithab , Shashidhar Keerthan , Kuttappa M N , Nayak Krishna U S , Menta Sai Kalyan , Anushree A
Published: July 2022

Objective: To compare and evaluate the cephalometric hard and soft tissue changes of mandible after orthodontic treatment with and without extraction of four first premolar.

Methodology:60 individuals with Class I skeletal pattern were selected as per inclusion criteria. They were divided into two groups: 30 in the extraction group and 30 in the non-extraction group. Each group consists of 15 males and 15 females. Pre-treatment and post-treatment lateral cephalograms of the selected subjects were obtained, analysed and compared to assess the changes might have occurred. Descriptive statistics of the explanatory and outcome variables were calculated by mean and standard deviation for quantitative variables. Paired t test was applied to check the statistical difference of pre-treatment and post-treatment cephalometric parameters within the group (Extraction and non-extraction). Unpaired t test was used to check the statistical difference between extraction and non-extraction groups. The level of significance is set at 5%.

Results: B point moved backward with lower incisor retraction. N-A-Pog shows a statistically significant decrease in the extraction group (P=0.003). B-NP shows a statistically significant decrease in extraction group (P=0.001). N- A’-Pog’ shows a statistically significant increase in non-extraction group (P=0.046). Soft tissue thickness in chin increases in extraction group and decreases in non-extraction group.

Conclusion: The results indicate that premolar extraction will have a flattening effect on the midface. With proper diagnosis and treatment planning, premolar extractions have high potential to improve the facial profile.
Supporting Industrial Implementation of R&D Results with Commercialisation Models
Authors: Poteralska Beata , Walasik Marzena
Published: July 2022

The paper aims to present commercialisation models for advanced technologies and services which can be applied in an R&D organisation. Against the background of the literature review on theoretical and practically verified commercialisation models, and marketing strategies and tools, the authors propose an original model, 7MWwBP, composed of several hybrid submodels. The model is intended to support commercialisation processes carried out at R&D organisations. The model is based on the input criteria (the type of innovation and the character of the innovation related to the market demand scale), and it indicates possible commercialisation paths (sale, licence, provision of a service, or a spin-off) and sets of dedicated marketing tools (BTL). The advantage of the model is that an R&D result (innovation) is its focal point, and the whole commercialisation process depends on it. The model aims to mitigate an empirical and research gap as regards commercialisation models suitable for R&D organisations. Furthermore, the model is distinguished by a holistic approach, which is not common for the commercialisation models described in the literature, as they do not comprehensively consider the relationship between the models and the type and character of a technological solution, commercialisation path, and the marketing tools used for commercialisation of R&D results. All the listed aspects are considered and included in the 7MWwBP model, which makes it suitable for the commercialisation of R&D results originating from research organisations, as presented in and substantiated by the example of an actual commercialisation process concerning R&D results in the field of optomechatronics.
Research Themes on the Quality of Public Services Exemplified By Healthcare Services — A Bibliometric Analysis
Authors: Yarmak Valiantsina , Rollnik-Sadowska Ewa
Published: July 2022

The growing competition for customers requires constant improvement of service quality, continuously raising the importance of this matter and the significance of the entire service sector. Essentially, service quality concerns not only the private but also the public sector. Service quality in the public sector has already been widely discussed in the literature. Among research themes in the frame of public service quality, the healthcare sector attracts particular attention from researchers. Therefore, this study aimed to identify research topics on the public service quality and healthcare quality discussed in the SCOPUS database from 2012 to 2022. A bibliometric technique and the VOSviewer software were used to analyse over 25 000 articles on public service and healthcare service published over ten years. As a result, a total of ten research themes were set up, five in the healthcare sector and five in the public sector, which were linked using keywords and presented on maps. The identified research themes demonstrate the most popular research directions and indicate research gaps related to the subject.
APPENDIX
Authors: Dikbud Jurnal
Published: July 2022

Indeks
Counterfactual Interpolation Augmentation (CIA): A Unified Approach to Enhance Fairness and Explainability of DNN
Authors: Qiang Yao , Li Chengyin , Brocanelli Marco , Zhu Dongxiao
Published: July 2022

Bias in the training data can jeopardize fairness and explainability of deep neural network prediction on test data. We propose a novel bias-tailored data augmentation approach, Counterfactual Interpolation Augmentation (CIA), attempting to debias the training data by d-separating the spurious correlation between the target variable and the sensitive attribute. CIA generates counterfactual interpolations along a path simulating the distribution transitions between the input and its counterfactual example. CIA as a pre-processing approach enjoys two advantages: First, it couples with either plain training or debiasing training to markedly increase fairness over the sensitive attribute. Second, it enhances the explainability of deep neural networks by generating attribution maps via integrating counterfactual gradients. We demonstrate the superior performance of the CIA-trained deep neural network models using qualitative and quantitative experimental results. Our code is available at: https://github.com/qiangyao1988/CIA
Distortion in Voting with Top-t Preferences
Authors: Borodin Allan , Halpern Daniel , Latifian Mohamad , Shah Nisarg
Published: July 2022

A fundamental question in social choice and multi-agent systems is aggregating ordinal preferences expressed by agents into a measurably prudent collective choice. A promising line of recent work views ordinal preferences as a proxy for underlying cardinal preferences. It aims to optimize distortion, the worst-case approximation ratio of the (utilitarian) social welfare. When agents rank the set of alternatives, prior work identifies near-optimal voting rules for selecting one or more alternatives. However, ranking all the alternatives is prohibitive when there are many alternatives.

In this work, we consider the setting where each agent ranks only her t favorite alternatives and identify almost tight bounds on the best possible distortion when selecting a single alternative or a committee of alternatives of a given size k. Our results also extend to approximating higher moments of social welfare. Along the way, we close a gap left open in prior work by identifying asymptotically tight distortion bounds for committee selection given full rankings.
Dynamic Domain Generalization
Authors: Sun Zhishu , Shen Zhifeng , Lin Luojun , Yu Yuanlong , Yang Zhifeng , Yang Shicai , Chen Weijie
Published: July 2022

Domain generalization (DG) is a fundamental yet very challenging research topic in machine learning. The existing arts mainly focus on learning domain-invariant features with limited source domains in a static model. Unfortunately, there is a lack of training-free mechanism to adjust the model when generalized to the agnostic target domains. To tackle this problem, we develop a brand-new DG variant, namely Dynamic Domain Generalization (DDG), in which the model learns to twist the network parameters to adapt to the data from different domains. Specifically, we leverage a meta-adjuster to twist the network parameters based on the static model with respect to different data from different domains. In this way, the static model is optimized to learn domain-shared features, while the meta-adjuster is designed to learn domain-specific features. To enable this process, DomainMix is exploited to simulate data from diverse domains during teaching the meta-adjuster to adapt to the agnostic target domains. This learning mechanism urges the model to generalize to different agnostic target domains via adjusting the model without training. Extensive experiments demonstrate the effectiveness of our proposed method. Code is available: https://github.com/MetaVisionLab/DDG
Spatiality-guided Transformer for 3D Dense Captioning on Point Clouds
Authors: Wang Heng , Zhang Chaoyi , Yu Jianhui , Cai Weidong
Published: July 2022

Dense captioning in 3D point clouds is an emerging vision-and-language task involving object-level 3D scene understanding. Apart from coarse semantic class prediction and bounding box regression as in traditional 3D object detection, 3D dense captioning aims at producing a further and finer instance-level label of natural language description on visual appearance and spatial relations for each scene object of interest. To detect and describe objects in a scene, following the spirit of neural machine translation, we propose a transformer-based encoder-decoder architecture, namely SpaCap3D, to transform objects into descriptions, where we especially investigate the relative spatiality of objects in 3D scenes and design a spatiality-guided encoder via a token-to-token spatial relation learning objective and an object-centric decoder for precise and spatiality-enhanced object caption generation. Evaluated on two benchmark datasets, ScanRefer and ReferIt3D, our proposed SpaCap3D outperforms the baseline method Scan2Cap by 4.94% and 9.61% in CIDEr@0.5IoU, respectively. Our project page with source code and supplementary files is available at https://SpaCap3D.github.io/.
Uncertainty-Guided Pixel Contrastive Learning for Semi-Supervised Medical Image Segmentation
Authors: Wang Tao , Lu Jianglin , Lai Zhihui , Wen Jiajun , Kong Heng
Published: July 2022

Recently, contrastive learning has shown great potential in medical image segmentation. Due to the lack of expert annotations, however, it is challenging to apply contrastive learning in semi-supervised scenes. To solve this problem, we propose a novel uncertainty-guided pixel contrastive learning method for semi-supervised medical image segmentation. Specifically, we construct an uncertainty map for each unlabeled image and then remove the uncertainty region in the uncertainty map to reduce the possibility of noise sampling. The uncertainty map is determined by a well-designed consistency learning mechanism, which generates comprehensive predictions for unlabeled data by encouraging consistent network outputs from two different decoders. In addition, we suggest that the effective global representations learned by an image encoder should be equivariant to different geometric transformations. To this end, we construct an equivariant contrastive loss to strengthen global representation learning ability of the encoder. Extensive experiments conducted on popular medical image benchmarks demonstrate that the proposed method achieves better segmentation performance than the state-of-the-art methods.
Boosting Multi-Label Image Classification with Complementary Parallel Self-Distillation
Authors: Xu Jiazhi , Huang Sheng , Zhou Fengtao , Huangfu Luwen , Zeng Daniel , Liu Bo
Published: July 2022

Multi-Label Image Classification (MLIC) appro-aches usually exploit label correlations to achieve good performance. However, emphasizing correlation like co-occurrence may overlook discriminative features and lead to model overfitting. In this study, we propose a generic framework named Parallel Self-Distillation (PSD) for boosting MLIC models. PSD decomposes the original MLIC task into several simpler MLIC sub-tasks via two elaborated complementary task decomposition strategies named Co-occurrence Graph Partition (CGP) and Dis-occurrence Graph Partition (DGP). Then, the MLIC models of fewer categories are trained with these sub-tasks in parallel for respectively learning the joint patterns and the category-specific patterns of labels. Finally, knowledge distillation is leveraged to learn a compact global ensemble of full categories with these learned patterns for reconciling the label correlation exploitation and model overfitting. Extensive results on MS-COCO and NUS-WIDE datasets demonstrate that our framework can be easily plugged into many MLIC approaches and improve performances of recent state-of-the-art approaches. The source code is released at https://github.com/Robbie-Xu/CPSD.
Learning to Generate Image Source-Agnostic Universal Adversarial Perturbations
Authors: Zhao Pu , Ram Parikshit , Lu Songtao , Yao Yuguang , Bouneffouf Djallel , Lin Xue , Liu Sijia
Published: July 2022

Adversarial perturbations are critical for certifying the robustness of deep learning models. A ``universal adversarial perturbation'' (UAP) can simultaneously attack multiple images, and thus offers a more unified threat model, obviating an image-wise attack algorithm. However, the existing UAP generator is underdeveloped when images are drawn from different image sources (e.g., with different image resolutions). Towards an authentic universality across image sources, we take a novel view of UAP generation as a customized instance of ``few-shot learning'', which leverages bilevel optimization and learning-to-optimize (L2O) techniques for UAP generation with improved attack success rate (ASR). We begin by considering the popular model agnostic meta-learning (MAML) framework to meta-learn a UAP generator. However, we see that the MAML framework does not directly offer the universal attack across image sources, requiring us to integrate it with another meta-learning framework of L2O. The resulting scheme for meta-learning a UAP generator (i) has better performance (50% higher ASR) than baselines such as Projected Gradient Descent, (ii) has better performance (37% faster) than the vanilla L2O and MAML frameworks (when applicable), and (iii) is able to simultaneously handle UAP generation for different victim models and data sources.
Inverting 43-step MD4 via Cube-and-Conquer
Authors: Zaikin Oleg
Published: July 2022

MD4 is a prominent cryptographic hash function proposed in 1990. The full version consists of 48 steps and produces a hash of size 128 bits given a message of an arbitrary finite size. In 2007, its truncated 39-step version was inverted via reducing to SAT and applying a CDCL solver. Since that time, several attempts have been made but the 40-step version still remains unbroken. In this study, 40-, 41-, 42-, and 43-step versions of MD4 are successfully inverted. The problems are reduced to SAT and solved via the Cube-and-Conquer approach. Two algorithms are proposed for this purpose. The first one generates inversion problems for MD4 by adding special constraints. The second one is aimed at finding a proper threshold for the cubing phase of Cube-and-Conquer. While the first algorithm is focused on inverting MD4 and similar cryptographic hash functions, the second one is not area specific and so is applicable to a variety of classes of hard SAT instances.
Towards Resolving Propensity Contradiction in Offline Recommender Learning
Authors: Saito Yuta , Nomura Masahiro
Published: July 2022

We study offline recommender learning from explicit rating feedback in the presence of selection bias. A current promising solution for dealing with the bias is the inverse propensity score (IPS) estimation. However, the existing propensity-based methods can suffer significantly from the propensity estimation bias. In fact, most of the previous IPS-based methods require some amount of missing-completely-at-random (MCAR) data to accurately estimate the propensity. This leads to a critical self-contradiction; IPS is ineffective without MCAR data, even though it originally aims to learn recommenders from only missing-not-at-random feedback. To resolve this propensity contradiction, we derive a propensity-independent generalization error bound and propose a novel algorithm to minimize the theoretical bound via adversarial learning. Our theory and algorithm do not require a propensity estimation procedure, thereby leading to a well-performing rating predictor without the true propensity information. Extensive experiments demonstrate that the proposed algorithm is superior to a range of existing methods both in rating prediction and ranking metrics in practical settings without MCAR data. Full version of the paper (including the appendix) is available at: https://arxiv.org/abs/1910.07295.
GRELEN: Multivariate Time Series Anomaly Detection from the Perspective of Graph Relational Learning
Authors: Zhang Weiqi , Zhang Chen , Tsung Fugee
Published: July 2022

System monitoring and anomaly detection is a crucial task in daily operation. With the rapid development of cyber-physical systems and IT systems, multiple sensors get involved to represent the system state from different perspectives, which inspires us to detect anomalies considering feature dependence relationship among sensors instead of focusing on individual sensor's behavior. In this paper, we propose a novel Graph Relational Learning Network (GReLeN) to detect multivariate time series anomalies from the perspective of between-sensor dependence relationship learning.

Variational AutoEncoder (VAE) serves as the overall framework for feature extraction and system representation. Graph Neural Network (GNN) and stochastic graph relational learning strategy are also imposed to capture the between-sensor dependence. Then a composite anomaly metric is established with the learned dependence structure explicitly.

The experiments on four real-world datasets show our superiority in detection accuracy, anomaly diagnosis, and model interpretation.
Parameterized Complexity of Hotelling-Downs with Party Nominees
Authors: Deligkas Argyrios , Eiben Eduard , Goldsmith Tiger-Lily
Published: July 2022

We study a generalization of the Hotelling-Downs model through the lens of parameterized complexity. In this model, there is a set of voters on a line and a set of parties that compete over them. Each party has to choose a nominee from a set of candidates with predetermined positions on the line, where each candidate comes at a different cost. The goal of every party is to choose the most profitable nominee, given the nominees chosen by the rest of the parties; the profit of a party is the number of voters closer to their nominee minus its cost. We examine the complexity of deciding whether a pure Nash equilibrium exists for this model under several natural parameters: the number of different positions of the candidates, the discrepancy and the span of the nominees, and the overlap of the parties. We provide FPT and XP algorithms and we complement them with a W[1]-hardness result.
Limits and Possibilities of Forgetting in Abstract Argumentation
Authors: Baumann Ringo , Berthold Matti
Published: July 2022

The topic of forgetting has been extensively studied in the field of knowledge representation and reasoning for many major formalisms. Quite recently it has been introduced to abstract argumentation. However, many already known as well as essential aspects about forgetting like strong persistence or strong invariance have been left unconsidered. We show that forgetting in abstract argumentation cannot be reduced to forgetting in logic programming. In addition, we deal with the more general problem of forgetting whole sets of arguments and show that iterative application of existing operators for single arguments does not necessarily yield a desirable result as it may not produce an informationally economic argumentation framework. As a consequence we provide a systematic and exhaustive study of forgetting desiderata and associated operations adapted to the intrinsics of abstract argumentation. We show the limits and shed light on the possibilities.
Explanations for Negative Query Answers under Inconsistency-Tolerant Semantics
Authors: Lukasiewicz Thomas , Malizia Enrico , Molinaro Cristian
Published: July 2022

Inconsistency-tolerant semantics have been proposed to provide meaningful query answers even in the presence of inconsistent knowledge. Recently, explainability has also become a prominent problem in different areas of AI. While the complexity of inconsistency-tolerant semantics is rather well-understood, not much attention has been paid yet to the problem of explaining query answers when inconsistencies may exist. Recent work on existential rules in the inconsistent setting has focused only on understanding why a query is entailed.

In this paper, we address another important problem, which is explaining why a query is not entailed under an inconsistency-tolerant semantics. In particular, we consider three popular semantics, namely, the ABox repair, the intersection of repairs, and the intersection of closed repairs. We provide a thorough complexity analysis for a wide range of existential rule languages and for several complexity measures.
Better Embedding and More Shots for Few-shot Learning
Authors: Chi Ziqiu , Wang Zhe , Yang Mengping , Guo Wei , Xu Xinlei
Published: July 2022

In few-shot learning, methods are enslaved to the scarce labeled data, resulting in suboptimal embedding. Recent studies learn the embedding network by other large-scale labeled data. However, the trained network may give rise to the distorted embedding of target data. We argue two respects are required for an unprecedented and promising solution. We call them Better Embedding and More Shots (BEMS). Suppose we propose to extract embedding from the embedding network. BE maximizes the extraction of general representation and prevents over-fitting information. For this purpose, we introduce the topological relation for global reconstruction, avoiding excessive memorizing. MS maximizes the relevance between the reconstructed embedding and the target class space. In this respect, increasing the number of shots is a pivotal but intractable strategy. As a creative method, we derive the bound of information-theory-based loss function and implicitly achieve infinite shots with negligible cost. A substantial experimental analysis is carried out to demonstrate the state-of-the-art performance. Compared to the baseline, our method improves by up to 10%+. We also prove that BEMS is suitable for both standard pre-trained and meta-learning embedded networks.
Can We Find Neurons that Cause Unrealistic Images in Deep Generative Networks?
Authors: Choi Hwanil , Chang Wonjoon , Choi Jaesik
Published: July 2022

Even though Generative Adversarial Networks (GANs) have shown a remarkable ability to generate high-quality images, GANs do not always guarantee the generation of photorealistic images. Occasionally, they generate images that have defective or unnatural objects, which are referred to as `artifacts'. Research to investigate why these artifacts emerge and how they can be detected and removed has yet to be sufficiently carried out. To analyze this, we first hypothesize that rarely activated neurons and frequently activated neurons have different purposes and responsibilities for the progress of generating images. In this study, by analyzing the statistics and the roles for those neurons, we empirically show that rarely activated neurons are related to the failure results of making diverse objects and inducing artifacts. In addition, we suggest a correction method, called `Sequential Ablation’, to repair the defective part of the generated images without high computational cost and manual efforts.
Insight into Voting Problem Complexity Using Randomized Classes
Authors: Fitzsimmons Zack , Hemaspaandra Edith
Published: July 2022

The first step in classifying the complexity of an NP problem is typically showing the problem in P or NP-complete. This has been a successful first step for many problems, including voting problems. However, in this paper we show that this may not always be the best first step. We consider the problem of constructive control by replacing voters (CCRV) introduced by Loreggia et al. [2015, https://dl.acm.org/doi/10.5555/2772879.2773411] for the scoring rule First-Last, which is defined by (1, 0, ..., 0, -1). We show that this problem is equivalent to Exact Perfect Bipartite Matching, and so CCRV for First-Last can be determined in random polynomial time. So on the one hand, if CCRV for First-Last is NP-complete then RP = NP, which is extremely unlikely. On the other hand, showing that CCRV for First-Last is in P would also show that Exact Perfect Bipartite Matching is in P, which would solve a well-studied 40-year-old open problem.

Considering RP as an option for classifying problems can also help classify problems that until now had escaped classification. For example, the sole open problem in the comprehensive table from Erdélyi et al. [2021, https://doi.org/10.1007/s10458-021-09523-9] is CCRV for 2-Approval. We show that this problem is in RP, and thus easy since it is widely assumed that P = RP.
SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities
Authors: Hu Pengbo , Li Xingyu , Zhou Yi
Published: July 2022

As deep learning advances, there is an ever-growing demand for models capable of synthesizing information from multi-modal resources to address the complex tasks raised from real-life applications. Recently, many large multi-modal datasets have been collected, on which researchers actively explore different methods of fusing multi-modal information. However, little attention has been paid to quantifying the contribution of different modalities within the proposed models.

In this paper, we propose the SHapley vAlue-based PErceptual (SHAPE) scores that measure the marginal contribution of individual modalities and the degree of cooperation across modalities. Using these scores, we systematically evaluate different fusion methods on different multi-modal datasets for different tasks. Our experiments suggest that for some tasks where different modalities are complementary, the multi-modal models still tend to use the dominant modality alone and ignore the cooperation across modalities. On the other hand, models learn to exploit cross-modal cooperation when different modalities are indispensable for the task. In this case, the scores indicate it is better to fuse different modalities at relatively early stages. We hope our scores can help improve the understanding of how the present multi-modal models operate on different modalities and encourage more sophisticated methods of integrating multiple modalities.
Data Augmentation for Learning to Play in Text-Based Games
Authors: Kim Jinhyeon , Kim Kee-Eung
Published: July 2022

Improving generalization in text-based games serves as a useful stepping-stone towards reinforcement learning (RL) agents with generic linguistic ability. Data augmentation for generalization in RL has shown to be very successful in classic control and visual tasks, but there is no prior work for text-based games. We propose Transition-Matching Permutation, a novel data augmentation technique for text-based games, where we identify phrase permutations that match as many transitions in the trajectory data. We show that applying this technique results in state-of-the-art performance in the Cooking Game benchmark suite for text-based games.
Multi-policy Grounding and Ensemble Policy Learning for Transfer Learning with Dynamics Mismatch
Authors: Lee Hyun-Rok , Sreenivasan Ram Ananth , Jeong Yeonjeong , Jang Jongseong , Shim Dongsub , Lee Chi-Guhn
Published: July 2022

We propose a new transfer learning algorithm between tasks with different dynamics. The proposed algorithm solves an Imitation from Observation problem (IfO) to ground the source environment to the target task before learning an optimal policy in the grounded environment. The learned policy is deployed in the target task without additional training. A particular feature of our algorithm is the employment of multiple rollout policies during training with a goal to ground the environment more globally; hence, it is named as Multi-Policy Grounding (MPG). The quality of final policy is further enhanced via ensemble policy learning. We demonstrate the superiority of the proposed algorithm analytically and numerically. Numerical studies show that the proposed multi-policy approach allows comparable grounding with single policy approach with a fraction of target samples, hence the algorithm is able to maintain the quality of obtained policy even as the number of interactions with the target environment becomes extremely small.
Cross-modal Representation Learning and Relation Reasoning for Bidirectional Adaptive Manipulation
Authors: Li Lei , Fan Kai , Yuan Chun
Published: July 2022

Since single-modal controllable manipulation typically requires supervision of information from other modalities or cooperation with complex software and experts, this paper addresses the problem of cross-modal adaptive manipulation (CAM). The novel task performs cross-modal semantic alignment from mutual supervision and implements bidirectional exchange of attributes, relations, or objects in parallel, benefiting both modalities while significantly reducing manual effort. We introduce a robust solution for CAM, which includes two essential modules, namely Heterogeneous Representation Learning (HRL) and Cross-modal Relation Reasoning (CRR). The former is designed to perform representation learning for cross-modal semantic alignment on heterogeneous graph nodes. The latter is adopted to identify and exchange the focused attributes, relations, or objects in both modalities. Our method produces pleasing cross-modal outputs on CUB and Visual Genome.
Game Redesign in No-regret Game Playing
Authors: Ma Yuzhe , Wu Young , Zhu Xiaojin
Published: July 2022

We study the game redesign problem in which an external designer has the ability to change the payoff function in each round, but incurs a design cost for deviating from the original game. The players apply no-regret learning algorithms to repeatedly play the changed games with limited feedback. The goals of the designer are to (i) incentivize players to take a specific target action profile frequently; (ii) incur small cumulative design cost. We present game redesign algorithms with the guarantee that the target action profile is played in T-o(T) rounds while incurring only o(T) cumulative design cost. Simulations on four classic games confirm the ef- fectiveness of our proposed redesign algorithms.
Self-paced Supervision for Multi-source Domain Adaptation
Authors: Wang Zengmao , Zhou Chaoyang , Du Bo , He Fengxiang
Published: July 2022

Multi-source domain adaptation has attracted great attention in machine learning community. Most of these methods focus on weighting the predictions produced by the adaptation networks of different domains. Thus the domain shifts between certain of domains and target domain are not effectively relieved, resulting in that these domains are not fully exploited and even may have a negative influence on multi-source domain adaptation task. To address such challenge, we propose a multi-source domain adaptation method to gradually improve the adaptation ability of each source domain by producing more high-confident pseudo-labels with self-paced learning for conditional distribution alignment. The proposed method first trains several separate domain branch networks with single domains and an ensemble branch network with all domains. Then we obtain some high-confident pseudo-labels with the branch networks and learn the branch specific pseudo-labels with self-paced learning. Each branch network reduces the domain gap by aligning the conditional distribution with its branch specific pseudo-labels and the pseudo-labels provided by all branch networks. Experiments on Office31, Office-Home and DomainNet show that the proposed method outperforms the state-of-the-art methods.
Self-Supervised Learning with Attention-based Latent Signal Augmentation for Sleep Staging with Limited Labeled Data
Authors: Lee Harim , Seong Eunseon , Chae Dong-Kyu
Published: July 2022

Sleep staging is an important task that enables sleep quality assessment and disorder diagnosis. Due to dependency on manually labeled data, many researches have turned from supervised approaches to self-supervised learning (SSL) for sleep staging. While existing SSL methods have made significant progress in terms of its comparable performance to supervised methods, there are still some limitations. Contrastive learning could potentially lead to false negative pair assignments in sleep signal data. Moreover, existing data augmentation techniques directly modify the original signal data, making it likely to lose important information. To mitigate these issues, we propose Self-Supervised Learning with Attention-aided Positive Pairs (SSLAPP). Instead of the contrastive learning, SSLAPP carefully draws high-quality positive pairs and exploits them in representation learning. Here, we propose attention-based latent signal augmentation, which plays a key role by capturing important features without losing valuable signal information. Experimental results show that our proposed method achieves state-of-the-art performance in sleep stage classification with limited labeled data. The code is available at: https://github.com/DILAB-HYU/SSLAPP