Hasil untuk "Manners and customs (General)"

Mingrui Zhang, Hamid Bagheri, Lisong Xu

General-purpose congestion control algorithms (CCAs) are designed to achieve general congestion control goals, but they may not meet the specific requirements of certain users. Customized CCAs can meet certain users' specific requirements; however, non-expert users often lack the expertise to implement them. In this paper, we present an exploratory non-expert customized CCA framework, named NECC, which enables non-expert users to easily model, implement, and deploy their customized CCAs by leveraging Large Language Models and the Berkeley Packet Filter (BPF) interface. To the best of our knowledge, we are the first to address the customized CCA implementation problem. Our evaluations using real-world CCAs show that the performance of NECC is very promising, and we discuss the insights that we find and possible future research directions.

Nur Khasanatun Ni'mah, Ken Widyatwati, Muhammad Suryadi

This study aims to describe the manners of Javanese nobility in the Kartini film. Using qualitative methods, with a sociology of literature approach based on Ian Watt's theory and semiotics according to Charles Sanders Peirce. Data collection techniques include observing and taking notes. Primary data was obtained from the film in the form of dialogue, visual expressions, and interactions between characters, while secondary data came from various supporting literature. Data analysis was carried out through the stages of data reduction, data presentation, interpretation of visual-verbal signs, and drawing conclusions. The results of the study show that the character Kartini represents a Javanese noble figure who still holds fast to the values of manners despite having a critical view of customs that limit the role of women. The character Kartini does not fight tradition head-on, but chooses a dialogical path by continuing to carry out aristocratic ethics such as sembah, ndodhok, and timpuh. This attitude reflects that manners are a cultural awareness that plays a role in maintaining social harmony, not just a formality. Thus, the Kartini film emphasizes that tradition does not have to be a barrier to change, but can be a dialectical space between respect for cultural heritage and the courage to think progressively.

Xuancheng Xu, Yaning Li, Sisi You et al.

Customized video generation aims to produce videos that faithfully preserve the subject's appearance from reference images while maintaining temporally consistent motion from reference videos. Existing methods struggle to ensure both subject appearance similarity and motion pattern consistency due to the lack of object-level guidance for subject and motion. To address this, we propose SMRABooth, which leverages the self-supervised encoder and optical flow encoder to provide object-level subject and motion representations. These representations are aligned with the model during the LoRA fine-tuning process. Our approach is structured in three core stages: (1) We exploit subject representations via a self-supervised encoder to guide subject alignment, enabling the model to capture overall structure of subject and enhance high-level semantic consistency. (2) We utilize motion representations from an optical flow encoder to capture structurally coherent and object-level motion trajectories independent of appearance. (3) We propose a subject-motion association decoupling strategy that applies sparse LoRAs injection across both locations and timing, effectively reducing interference between subject and motion LoRAs. Extensive experiments show that SMRABooth excels in subject and motion customization, maintaining consistent subject appearance and motion patterns, proving its effectiveness in controllable text-to-video generation.

Jintao Rong, Xin Xie, Xinyi Yu et al.

Distilled video generation models offer fast and efficient synthesis but struggle with motion customization when guided by reference videos, especially under training-free settings. Existing training-free methods, originally designed for standard diffusion models, fail to generalize due to the accelerated generative process and large denoising steps in distilled models. To address this, we propose MotionEcho, a novel training-free test-time distillation framework that enables motion customization by leveraging diffusion teacher forcing. Our approach uses high-quality, slow teacher models to guide the inference of fast student models through endpoint prediction and interpolation. To maintain efficiency, we dynamically allocate computation across timesteps according to guidance needs. Extensive experiments across various distilled video generation models and benchmark datasets demonstrate that our method significantly improves motion fidelity and generation quality while preserving high efficiency. Project page: https://euminds.github.io/motionecho/

Chong Mou, Yanze Wu, Wenxu Wu et al.

Recently, extensive research on image customization (e.g., identity, subject, style, background, etc.) demonstrates strong customization capabilities in large-scale generative models. However, most approaches are designed for specific tasks, restricting their generalizability to combine different types of condition. Developing a unified framework for image customization remains an open challenge. In this paper, we present DreamO, an image customization framework designed to support a wide range of tasks while facilitating seamless integration of multiple conditions. Specifically, DreamO utilizes a diffusion transformer (DiT) framework to uniformly process input of different types. During training, we construct a large-scale training dataset that includes various customization tasks, and we introduce a feature routing constraint to facilitate the precise querying of relevant information from reference images. Additionally, we design a placeholder strategy that associates specific placeholders with conditions at particular positions, enabling control over the placement of conditions in the generated results. Moreover, we employ a progressive training strategy consisting of three stages: an initial stage focused on simple tasks with limited data to establish baseline consistency, a full-scale training stage to comprehensively enhance the customization capabilities, and a final quality alignment stage to correct quality biases introduced by low-quality data. Extensive experiments demonstrate that the proposed DreamO can effectively perform various image customization tasks with high quality and flexibly integrate different types of control conditions.

Riya Sultana, Khushboo Agarwal, Veeraruna Kavitha

Considering an M/M/1 queue with an additional lounge facility (LF), the quest of this paper is to understand the instances when LF is an attractive option, from customer perspective as well as from system perspective: will the customers choose to join the queue or prefer to detour briefly to lounge? In reality, customers do not perform complex computations for such tasks, but instead choose based on some heuristics. We further assume that the customers pessimistically anticipate the future congestion while making the choice. Our analysis reveals that the customers use the LF only when the queue is too crowded, and the lounge is relatively empty; however, strikingly, the customer choice is more inclined towards rejection for the LF in systems with higher traffic (load). We also explore an optimization problem where the system determines whether to implement an LF and what capacity it should have, while accounting for customers' behavioral responses. Under low load conditions, the system benefits from designing a high-capacity lounge, and the customers also prefer to use the LF actively. Surprisingly, neither the system prefers big LF, nor the customers prefer to use the LF profusely at high load conditions; optimal for either is to use the LF sparingly. Thus, importantly, the strategic system and the bounded-rational customers are not in a tug-of-war situation.

Jiawei Lin, Guanlong Jiao, Jianjin Xu

Multi-ID customization is an interesting topic in computer vision and attracts considerable attention recently. Given the ID images of multiple individuals, its purpose is to generate a customized image that seamlessly integrates them while preserving their respective identities. Compared to single-ID customization, multi-ID customization is much more difficult and poses two major challenges. First, since the multi-ID customization model is trained to reconstruct an image from the cropped person regions, it often encounters the copy-paste issue during inference, leading to lower quality. Second, the model also suffers from inferior text controllability. The generated result simply combines multiple persons into one image, regardless of whether it is aligned with the input text. In this work, we propose MultiID to tackle this challenging task in a training-free manner. Since the existing single-ID customization models have less copy-paste issue, our key idea is to adapt these models to achieve multi-ID customization. To this end, we present an ID-decoupled cross-attention mechanism, injecting distinct ID embeddings into the corresponding image regions and thus generating multi-ID outputs. To enhance the generation controllability, we introduce three critical strategies, namely the local prompt, depth-guided spatial control, and extended self-attention, making the results more consistent with the text prompts and ID images. We also carefully build a benchmark, called IDBench, for evaluation. The extensive qualitative and quantitative results demonstrate the effectiveness of MultiID in solving the aforementioned two challenges. Its performance is comparable or even better than the training-based multi-ID customization methods.

Cláudia Capela Ferreira

Este ensaio analisa a topografia de Ema de Vale Abraão, a rapariga do incisivo quebrado de “Um inverno frio” e Alfreda de A alma dos ricos, lendo tais personagens como excrescências similares ao tecido vegetal que as enquadra. A sua excentricidade e excesso fazem-nas marginais aos cenários interiores em que se movem, alcançando no exterior o cerne da sua realização. Reflete-se ainda sobre a construção narrativa dos espaços referentes a essa amostra de personagens em função daquilo que entendemos tratar-se de um princípio de mutabilidade das coordenadas espaciais, uma forma de deslocamento do espaço fixo. Esta impressão de movimento pode ser textualmente produzida pela prefiguração de horizontalidade e verticalidade dos espaços, tal como pela instauração de uma cadência mediada pela expansão e pausa na relação da personagem com o espaço, com fins mutáveis evolutivos. Para o demonstrar, recorre--se ao uso de instrumentos de leitura comparatista e topoanalítica, tomando-se de empréstimo e em cruzamento os conceitos de espaço e lugar de Yi-Fu Tuan (1983) e Marc Augé (2020), a noção de ambientede Ozíris Borges Filho (2020) e setting de Marie-Laure Ryan (2016), bem como la phénoménologie du rondde Gaston Bachelard (1994).

Pengxiang Cai, Zhiwei Liu, Guibo Zhu et al.

Pixel-level fine-grained image editing remains an open challenge. Previous works fail to achieve an ideal trade-off between control granularity and inference speed. They either fail to achieve pixel-level fine-grained control, or their inference speed requires optimization. To address this, this paper for the first time employs a regression-based network to learn the variation patterns of StyleGAN latent codes during the image dragging process. This method enables pixel-level precision in dragging editing with little time cost. Users can specify handle points and their corresponding target points on any GAN-generated images, and our method will move each handle point to its corresponding target point. Through experimental analysis, we discover that a short movement distance from handle points to target points yields a high-fidelity edited image, as the model only needs to predict the movement of a small portion of pixels. To achieve this, we decompose the entire movement process into multiple sub-processes. Specifically, we develop a transformer encoder-decoder based network named 'Latent Predictor' to predict the latent code motion trajectories from handle points to target points in an autoregressive manner. Moreover, to enhance the prediction stability, we introduce a component named 'Latent Regularizer', aimed at constraining the latent code motion within the distribution of natural images. Extensive experiments demonstrate that our method achieves state-of-the-art (SOTA) inference speed and image editing performance at the pixel-level granularity.

Yuan Pu, Zhuolun He, Tairu Qiu et al.

Retrieval augmented generation (RAG) enhances the accuracy and reliability of generative AI models by sourcing factual information from external databases, which is extensively employed in document-grounded question-answering (QA) tasks. Off-the-shelf RAG flows are well pretrained on general-purpose documents, yet they encounter significant challenges when being applied to knowledge-intensive vertical domains, such as electronic design automation (EDA). This paper addresses such issue by proposing a customized RAG framework along with three domain-specific techniques for EDA tool documentation QA, including a contrastive learning scheme for text embedding model fine-tuning, a reranker distilled from proprietary LLM, and a generative LLM fine-tuned with high-quality domain corpus. Furthermore, we have developed and released a documentation QA evaluation benchmark, ORD-QA, for OpenROAD, an advanced RTL-to-GDSII design platform. Experimental results demonstrate that our proposed RAG flow and techniques have achieved superior performance on ORD-QA as well as on a commercial tool, compared with state-of-the-arts. The ORD-QA benchmark and the training dataset for our customized RAG flow are open-source at https://github.com/lesliepy99/RAG-EDA.

Peng Xing, Ning Wang, Jianbo Ouyang et al.

The remarkable advancement in text-to-image generation models significantly boosts the research in ID customization generation. However, existing personalization methods cannot simultaneously satisfy high fidelity and high-efficiency requirements. Their main bottleneck lies in the prompt image encoder, which produces weak alignment signals with the text-to-image model and significantly increased model size. Towards this end, we propose a lightweight Inv-Adapter, which first extracts diffusion-domain representations of ID images utilizing a pre-trained text-to-image model via DDIM image inversion, without additional image encoder. Benefiting from the high alignment of the extracted ID prompt features and the intermediate features of the text-to-image model, we then embed them efficiently into the base text-to-image model by carefully designing a lightweight attention adapter. We conduct extensive experiments to assess ID fidelity, generation loyalty, speed, and training parameters, all of which show that the proposed Inv-Adapter is highly competitive in ID customization generation and model scale.

Ben Limpanukorn, Jiyuan Wang, Hong Jin Kang et al.

Compiler technologies in deep learning and domain-specific hardware acceleration are increasingly adopting extensible compiler frameworks such as Multi-Level Intermediate Representation (MLIR) to facilitate more efficient development. With MLIR, compiler developers can easily define their own custom IRs in the form of MLIR dialects. However, the diversity and rapid evolution of such custom IRs make it impractical to manually write a custom test generator for each dialect. To address this problem, we design a new test generator called SYNTHFUZZ that combines grammar-based fuzzing with custom mutation synthesis. The key essence of SYNTHFUZZ is two fold: (1) It automatically infers parameterized context-dependent custom mutations from existing test cases. (2) It then concretizes the mutation's content depending on the target context and reduces the chance of inserting invalid edits by performing k-ancestor and pre(post)fix matching. SYNTHFUZZ obviates the need to manually define custom mutation operators for each dialect. We compare SYNTHFUZZ to three baselines: Grammarinator, MLIRSmith, and NeuRI. We conduct this comprehensive comparison on four different MLIR projects. Each project defines a new set of MLIR dialects where manually writing a custom test generator would take weeks of effort. Our evaluation shows that SYNTHFUZZ on average improves MLIR dialect pair coverage by 1.75 times, which increases branch coverage by 1.22 times. Further, we show that our context dependent custom mutation increases the proportion of valid tests by up to 1.11 times, indicating that SYNTHFUZZ correctly concretizes its parameterized mutations with respect to the target context. Parameterization of the mutations reduces the fraction of tests violating the base MLIR constraints by 0.57 times, increasing the time spent fuzzing dialect-specific code.

Shwetha Rajaram, Nels Numan, Balasaravanan Thoravi Kumaravel et al.

Today's video-conferencing tools support a rich range of professional and social activities, but their generic meeting environments cannot be dynamically adapted to align with distributed collaborators' needs. To enable end-user customization, we developed BlendScape, a rendering and composition system for video-conferencing participants to tailor environments to their meeting context by leveraging AI image generation techniques. BlendScape supports flexible representations of task spaces by blending users' physical or digital backgrounds into unified environments and implements multimodal interaction techniques to steer the generation. Through an exploratory study with 15 end-users, we investigated whether and how they would find value in using generative AI to customize video-conferencing environments. Participants envisioned using a system like BlendScape to facilitate collaborative activities in the future, but required further controls to mitigate distracting or unrealistic visual elements. We implemented scenarios to demonstrate BlendScape's expressiveness for supporting environment design strategies from prior work and propose composition techniques to improve the quality of environments.

Onno Boxma, Offer Kella, Michel Mandjes

In this paper we consider an M/G/1-type queue fed by a finite customer-pool. In terms of transforms, we characterize the time-dependent distribution of the number of customers and the workload, as well as the associated waiting times.

Greig Cowan, Salvatore Mercuri, Raad Khraishi

Understanding customer lifetime value is key to nurturing long-term customer relationships, however, estimating it is far from straightforward. In the retail banking industry, commonly used approaches rely on simple heuristics and do not take advantage of the high predictive ability of modern machine learning techniques. We present a general framework for modelling customer lifetime value which may be applied to industries with long-lasting contractual and product-centric customer relationships, of which retail banking is an example. This framework is novel in facilitating CLV predictions over arbitrary time horizons and product-based propensity models. We also detail an implementation of this model which is currently in production at a large UK lender. In testing, we estimate an 43% improvement in out-of-time CLV prediction error relative to a popular baseline approach. Propensity models derived from our CLV model have been used to support customer contact marketing campaigns. In testing, we saw that the top 10% of customers ranked by their propensity to take up investment products were 3.2 times more likely to take up an investment product in the next year than a customer chosen at random.

Adi Putra Surya Wardhana, Fiqih Aisyatul Farokhah

Hal-hal yang berkaitan dengan seksualitas selalu menarik untuk dikaji meskipun diikat oleh tabu. Pada awal abad XX, naskah-naskah soal seksualitas cukup populer, apalagi sudah dicetak dalam bentuk buku yang diperjualbelikan di lapak-lapak buku. Salah satu naskah yang memuat seksualitas adalah Serat Kawruh Sanggama. Tujuan penelitian ini adalah untuk mengungkap bentuk, fungsi, dan makna politik tubuh dalam Serat Kawruh Sanggama. Metode yang digunakan adalah analisis data kualitatif-interpretatif dengan pendekatan teori politik tubuh. Hasil penelitian menunjukkan, Serat Kawruh Sanggama ditulis di Kediri dan disebarluaskan oleh penerbit Boekhandel Tan Khoen Swie Kediri. Bentuk politik tubuh berupa narasi tentang tata cara atau aturan bersenggama. Naskah ini mengandung politik tubuh yang berfungsi untuk menundukkan, mengontrol, dan mendominasi tubuh perempuan. Namun demikian, naskah ini dapat dimaknai sebagai upaya laki-laki untuk memahami misteri tubuh perempuan. Selain itu, naskah ini dimaknai pula sebagai daya perempuan, sehingga laki-laki harus berusaha untuk memahami seluk beluk tubuh perempuan. Despite a taboo subject amongst society, the matters related to sexuality are always interesting to study. In the early twentieth century, texts on sexuality were quite popular and had even been printed in the form of books that were sold in the book stalls. One of those was Serat Kawruh Sanggama. The purpose of this study was to analyze the form, the function, and the meaning of the politics of the body in the Serat Kawruh Sanggama. The method used in the research was the qualitative-interpretative data analysis combined with the approach of the Politics of the Body. The results of the study have shown that Serat Kawruh Sanggama was written in Kediri and then disseminated by the publisher of the Boekhandel Tan Khoen Swie Kediri. The elements of the Politics of the Body revealed in the text are in the form of narratives related to the procedures or rules of sexual intercourse. It is evident that the elements of the Politics of the Body found on the text served as an instrument of subjugating, controlling, and dominating the female body. This text can be interpreted as an attempt by men to understand the mystery of the female body. However, on the other hand, the text can also be interpreted as an attempt by men to understand the mystery of the female body. In addition, it also represented as a woman's power that encourages men to understand the ins and outs of the female body.

Nima Chitsazan, Samuel Sharpe, Dwipam Katariya et al.

As financial services (FS) companies have experienced drastic technology driven changes, the availability of new data streams provides the opportunity for more comprehensive customer understanding. We propose Dynamic Customer Embeddings (DCE), a framework that leverages customers' digital activity and a wide range of financial context to learn dense representations of customers in the FS industry. Our method examines customer actions and pageviews within a mobile or web digital session, the sequencing of the sessions themselves, and snapshots of common financial features across our organization at the time of login. We test our customer embeddings using real world data in three prediction problems: 1) the intent of a customer in their next digital session, 2) the probability of a customer calling the call centers after a session, and 3) the probability of a digital session to be fraudulent. DCE showed performance lift in all three downstream problems.

B Busro, Ai Yeni Yuliyanti, Abdul Syukur et al.

Indonesia is very famous for its rich culture. Cirebon as one of the districts in West Java is also very thick with its culture. This article discusses one of the cultures in Kedungsana Village Cirebon, the phenomenon of ritual slametan Memitu. The purpose of this study is to examine the practice of ritual slametan Memitu carried out by Kedungsana community together with its theological dimensions. The research subjects were the community of Kedungsana Village, Plumbon District, Cirebon Regency. The process of collecting data through direct observation and to get deep information in interviews, we use a purposive sampling technique. The results of the study found that the purpose of carrying out the ritual slametan Memitu was as a manifestation of gratitude for all the favors that had been given from the "Invisible Power" and also the hope of the smooth birth process. Express gratitude and the request is addressed to those considered to have the power to determine the smooth process of birth. In ritual slametan Memitu, there are theological dimensions that can be identified as belief in Invisible Substance and values for living in harmony together among residents of Kedungsana Village community. The theological dimensions in the earth alms ritual have been developed in such a way as to be in line with the development of social reality. Indonesia sangat terkenal dengan kekayan kebudayannya. Cirebon sebagai salah satu kabupaten di Jawa Barat juga sangat kental dengan budayanya. Artikel ini membahas salah satu budaya di Desa Kedungsana Cirebon yaitu fenomena tradisi ritual slametan Memitu.  Tujuan dari penelitian ini adalah untuk meneliti praktek ritual Memitu yang dilakukan oleh masyarakat Kedungsana bersama dengan dimensi-dimensi teologisnya. Subjek penelitian adalah komunitas masyarakat Desa Kedungsana Kecamatan Plumbon Kabupaten Cirebon. Proses pengumpulan data dilakukan dengan observasi langsung, untuk pendalaman dilakukan wawancara dengan teknik purposive sampling. Hasil penelitian ditemukan bahwa tujuan dilaksanakannya ritual slametan Memitu adalah sebagai manifestasi syukur atas segala nikmat yang telah diberikan dari “Kekuatan Tak Terlihat” dan juga pengharapan kelancaran proses kelahiran. Ungkapan rasa syukur dan permohonan tersebut ditujukan kepada yang diyakini memiliki kekuatan untuk menentukan kelancaran proses kelahiran. Dalam ritual slametan Memitu terdapat dimensi-dimensi teologi yang dapat diidentifikasi sebagai kepercayaan terhadap Zat Yang Gaib dan nilai-nilai untuk hidup rukun berdampingan antar-warga masyarakat Kelurahan Kedungsana.  Dimensi-dimensi teologis dalam ritual sedekah bumi ini telah dikembangkan sedemikian rupa agar sejalan dengan perkembangan realitas sosial.

Min Fu, Jiwei Guan, Xi Zheng et al.

Efficient and appropriate online customer service is essential to large e-commerce businesses. Existing solution recommendation methods for online customer service are unable to determine the best solutions at runtime, leading to poor satisfaction of end customers. This paper proposes a novel intelligent framework, called ICS-Assist, to recommend suitable customer service solutions for service staff at runtime. Specifically, we develop a generalizable two-stage machine learning model to identify customer service scenarios and determine customer service solutions based on a scenario-solution mapping table. We implement ICS-Assist and evaluate it using an over 6-month field study with Alibaba Group. In our experiment, over 12,000 customer service staff use ICS-Assist to serve for over 230,000 cases per day on average. The experimen-tal results show that ICS-Assist significantly outperforms the traditional manual method, and improves the solution acceptance rate, the solution coverage rate, the average service time, the customer satisfaction rate, and the business domain catering rate by up to 16%, 25%, 6%, 14% and 17% respectively, compared to the state-of-the-art methods.

Risa Nopianti, Selly Riawanti, Budi Rajab

Orang Tugu di Kelurahan Semper Barat merupakan sebuah komunitas keturunan Portugis yang  memiliki akar budaya dan sejarah yang cukup campuran sejak tahun 1661. Mereka berusaha untuk tetap bertahan dengan melestarikan aspek-aspek kebudayaan yang dimilikinya melalui beragam aktivitas dan tindakan-tindakan sosial sebagai upayanya untuk mendapatkan pengakuan akan identitas mereka sebagai Orang Tugu. Penelitian secara kualitatif dengan metode etnografi dan extended case method, digunakan sebagai alat untuk mengumpulkan dan menganalisis data. Paparan data menjelaskan bahwa interaksi sosial Orang Tugu dengan kelompok-kelompok lainnya dilakukan sebagai upaya mereka untuk mempertahankan identitasnya. Hal tersebut memunculkan dua kelompok utama yaitu, kelompok penting (significant others) hubungan di antara mereka didasari oleh adanya kepentingan-kepentingan tertentu yang sifatnya saling menguntungkan, yaitu salah satunya berkaitan dengan eksistensi musik keroncong. Ada pula kelompok umum lainnya (generalized others) hubungan mereka bersifat saling membutuhkan. Kelompok yang dikategorikan dalam hubungan saling menguntungkan adalah pemerintah daerah, komunitas pemerhati budaya dan sejarah, serta penanggap keroncong. Adapun kelompok-kelompok yang dibutuhkan oleh Orang Tugu dalam kehidupan sehari-hari adalah tetangga Betawi, dan jemaat gereja.    The Tugu people in Semper Barat Village are a community of Portuguese descent who has quite mixed cultural and historical roots since 1661. They try to stay afloat by preserving their cultural aspects through various activities and social actions as an effort to get recognition of their identity as Tugu People. Qualitative methods with ethnographic approaches and extended case method are used as tools to collect and analyze data. The results explain that the social interaction of Tugu People with important groups (significant others) is carried out because of the existence of certain interests which are mutually beneficial, but there are also those that are mutually needed, namely those in other general groups (generalized others). Groups that are categorized as mutually beneficial relationships are local governments, cultural and historical observer communities, and keroncong appreciators. The groups needed by Tugu People in their daily lives are neighbors from Betawi ethnic group, and church members.