Hasil untuk "Manners and customs (General)"

Arslan Ahmad, Ian Dobson

The impact of routine smaller outages on distribution system customers in terms of customer minutes interrupted can be tracked using conventional reliability indices. However, the customer minutes interrupted in large blackout events are extremely variable, and this makes it difficult to quantify the customer impact of these extreme events with resilience metrics. We solve this problem with the System Average Large Event Duration Index SALEDI that logarithmically transforms the customer minutes interrupted. We explain how this new resilience metric works, compare it with alternatives, quantify its statistical accuracy, and illustrate its practical use with standard outage data from five utilities.

Susana L. M. Antunes, Maria da Conceição Oliveira Guimarães

Carlos Silva, Paula Almeida Mendes

As personagens masculinas principais das duas narrativas do Auto dos Enfatriões, Feliseu e Anfatrião, demonstram, com os seus ditos e feitos, o funcionamento de duas importantes categorias da masculinidade: a sedução e a honra. Este estudo pretende explorar como a subjetividade masculina de Feliseu e Anfatrião se desenvolve em torno da tensão entre os seus desejos e vontades e a inevitabilidade dos ideais hegemónicos de “ser homem” no século XVI. Se o caso de Feliseu demonstra as complexidades da sedução, dotada até de um caráter homossocial, e desvenda os possíveis sofrimentos masculinos devido à definição patriarcal do matrimónio, a estória de Anfatrião revela como um sofrimento presente pode ser proveitoso no futuro, especialmente quando a recompensa é uma filiação a um ideal de masculinidade impossível de alcançar sem auxílio divino.

Mónica Ganhão

A moral social opressiva do século XIX determinava a marginalização de mulheres cujos comportamentos fossem considerados transgressores e ameaçadores da ordem social. Ser marginalizada significava ser apagada da memória da família e sofrer um processo de desapropriação de uma identidade enquanto mulher honrada, para passar a adoptar a caracterização de “mulher perdida”. Neste artigo, procura-se analisar de que forma se interligam culpa, identidade e memória nas figuras femininas de duas obras de autoras oitocentistas: “A Dama das Violetas” de Guiomar Torresão e La loi qui tue de Camille Delaville. Estas obras abordam o problema da calúnia contra mulheres e dos efeitos que esta tinha sobre a reputação feminina, sobretudo nas acusações de adultério. Ambos os romances procuram expôr as injustiças da sociedade oitocentista, na qual as mulheres se encontravam sempre num lugar de subalternidade, sujeitas à dominação masculina.

Peng Liu, Lian Cheng, Benjamin P. Omell et al.

Generation planning approaches face challenges in managing the incompatible mathematical structures between stochastic production simulations for reliability assessment and optimization models for generation planning, which hinders the integration of reliability constraints. This study proposes an approach to embedding reliability verification constraints into generation expansion planning by leveraging a weighted oblique decision tree (WODT) technique. For each planning year, a generation mix dataset, labeled with reliability assessment simulations, is generated. An WODT model is trained using this dataset. Reliability-feasible regions are extracted via depth-first search technique and formulated as disjunctive constraints. These constraints are then transformed into mixed-integer linear form using a convex hull modeling technique and embedded into a unit commitment-integrated generation expansion planning model. The proposed approach is validated through a long-term generation planning case study for the Electric Reliability Council of Texas (ERCOT) region, demonstrating its effectiveness in achieving reliable and optimal planning solutions.

Hei Yu Chan, Kuok Tou Ho, Chenglong Ma et al.

Many AI customer service systems use standard NLP pipelines or finetuned language models, which often fall short on ambiguous, multi-intent, or detail-specific queries. This case study evaluates recent techniques: query rewriting, RAG Fusion, keyword augmentation, intent recognition, and context reranking, for building a robust customer support system in the electric power domain. We compare vector-store and graph-based RAG frameworks, ultimately selecting the graph-based RAG for its superior performance in handling complex queries. We find that query rewriting improves retrieval for queries using non-standard terminology or requiring precise detail. RAG Fusion boosts performance on vague or multifaceted queries by merging multiple retrievals. Reranking reduces hallucinations by filtering irrelevant contexts. Intent recognition supports the decomposition of complex questions into more targeted sub-queries, increasing both relevance and efficiency. In contrast, keyword augmentation negatively impacts results due to biased keyword selection. Our final system combines intent recognition, RAG Fusion, and reranking to handle disambiguation and multi-source queries. Evaluated on both a GPT-4-generated dataset and a real-world electricity provider FAQ dataset, it achieves 97.9% and 89.6% accuracy respectively, substantially outperforming baseline RAG models.

Panwen Hu, Jiehui Huang, Qiang Sun et al.

Both zero-shot and tuning-based customized text-to-image (CT2I) generation have made significant progress for storytelling content creation. In contrast, research on customized text-to-video (CT2V) generation remains relatively limited. Existing zero-shot CT2V methods suffer from poor generalization, while another line of work directly combining tuning-based T2I models with temporal motion modules often leads to the loss of structural and texture information. To bridge this gap, we propose an autoregressive structure and texture propagation module (STPM), which extracts key structural and texture features from the reference subject and injects them autoregressively into each video frame to enhance consistency. Additionally, we introduce a test-time reward optimization (TTRO) method to further refine fine-grained details. Quantitative and qualitative experiments validate the effectiveness of STPM and TTRO, demonstrating improvements of 7.8 and 13.1 in CLIP-I and DINO consistency metrics over the baseline, respectively.

Dong Jing, Nanyi Fei, Zhiwu Lu

In the realm of Large Multi-modal Models (LMMs), the instruction quality during the visual instruction tuning stage significantly influences the performance of modality alignment. In this paper, we assess the instruction quality from a unique perspective termed \textbf{Writing Manner}, which encompasses the selection of vocabulary, grammar and sentence structure to convey specific semantics. We argue that there exists a substantial writing manner gap between the visual instructions and the base Large Language Models (LLMs) within LMMs. This gap forces the pre-trained base LLMs to deviate from their original writing styles, leading to capability degradation of both base LLMs and LMMs. To bridge the writing manner gap while preserving the original semantics, we propose directly leveraging the base LLM to align the writing manner of soft-format visual instructions with that of the base LLM itself, resulting in novel LLM-aligned instructions. The manual writing manner evaluation results demonstrate that our approach successfully minimizes the writing manner gap. By utilizing LLM-aligned instructions, the baseline models LLaVA-7B and QwenVL demonstrate enhanced resistance to hallucinations and non-trivial comprehensive improvements across all $15$ visual and language benchmarks.

Neda Bagherzadeh, Saeed Setayeshi, Samaneh Yazdani

Ontology is a general term used by researchers who want to share information in a specific domain. One of the hallmarks of the greatest success of a powerful manager of an organization is his ability to interpret unplanned and unrelated events. Tools to solve this problem are vital to business growth. Modern technology allows customers to be more informed and influential in their roles as patrons and critics. This can make or break a business. Research shows that businesses that employ a customer-first strategy and prioritize their customers can generate more revenue. Even though there are many different Ontologies offered to businesses, none of it is built from a cognitive perspective. The objective of this study is to address the concept of strategic business plans with a cognitive ontology approach as a basis for a new management tool. This research proposes to design a cognitive ontology model that links customer measurement with traditional business models, define relationships between components and verify the accuracy of the added financial value.

Shu-Ting Pi, Michael Yang, Qun Liu

Customers who reach out for customer service support may face a range of issues that vary in complexity. Routing high-complexity contacts to junior agents can lead to multiple transfers or repeated contacts, while directing low-complexity contacts to senior agents can strain their capacity to assist customers who need professional help. To tackle this, a machine learning model that accurately predicts the complexity of customer issues is highly desirable. However, defining the complexity of a contact is a difficult task as it is a highly abstract concept. While consensus-based data annotation by experienced agents is a possible solution, it is time-consuming and costly. To overcome these challenges, we have developed a novel machine learning approach to define contact complexity. Instead of relying on human annotation, we trained an AI expert model to mimic the behavior of agents and evaluate each contact's complexity based on how the AI expert responds. If the AI expert is uncertain or lacks the skills to comprehend the contact transcript, it is considered a high-complexity contact. Our method has proven to be reliable, scalable, and cost-effective based on the collected data.

Miyama Tamotsu, Okada Shogo

This paper describes a dialogue system developed for the Dialogue Robot Competition 2023 that achieves topic control for trip planning by inserting text into prompts using the ChatGPT-API. We built a system that is capable of generating compliments for the user based on recognition of the user's appearance and creating travel plans by extracting the knowledge about the user's preference from the history of the user's utterances. Complements and planning based on preference are the elements required to maintain the quality of customer service. A preliminary round was held at a travel agency's actual store, where real customers experienced and evaluated the system. This system was evaluated first in the preliminary round and participated in the final round. The results of the preliminary round showed the effectiveness of the proposed system.

Tuan Dinh Nguyen, Keisuke Hihara, Tung Cao Hoang et al.

Understanding customer behavior in retail stores plays a crucial role in improving customer satisfaction by adding personalized value to services. Behavior analysis reveals both general and detailed patterns in the interaction of customers with a store items and other people, providing store managers with insight into customer preferences. Several solutions aim to utilize this data by recognizing specific behaviors through statistical visualization. However, current approaches are limited to the analysis of small customer behavior sets, utilizing conventional methods to detect behaviors. They do not use deep learning techniques such as deep neural networks, which are powerful methods in the field of computer vision. Furthermore, these methods provide limited figures when visualizing the behavioral data acquired by the system. In this study, we propose a framework that includes three primary parts: mathematical modeling of customer behaviors, behavior analysis using an efficient deep learning based system, and individual and group behavior visualization. Each module and the entire system were validated using data from actual situations in a retail store.

Eduardo C. Garrido-Merchán, Jose L. Arroyo-Barrigüete, Francisco Borrás-Pala et al.

Large Language Models (LLMs), as the case of OpenAI ChatGPT-4 Turbo, are revolutionizing several industries, including higher education. In this context, LLMs can be personalized through a fine-tuning process to meet the student demands on every particular subject, like statistics. Recently, OpenAI has launched the possibility to fine-tune their model with a natural language web interface, enabling the possibility to create customized GPT version deliberately conditioned to meet the demands of a specific task. The objective of this research is to assess the potential of the customized GPTs that have recently been launched by OpenAI. After developing a Business Statistics Virtual Professor (BSVP), tailored for students at the Universidad Pontificia Comillas, its behavior was evaluated and compared with that of ChatGPT-4 Turbo. The results lead to several conclusions. Firstly, a substantial modification in the style of communication was observed. Following the instructions it was trained with, BSVP provided responses in a more relatable and friendly tone, even incorporating a few minor jokes. Secondly, and this is a matter of relevance, when explicitly asked for something like, "I would like to practice a programming exercise similar to those in R practice 4," BSVP was capable of providing a far superior response: having access to contextual documentation, it could fulfill the request, something beyond ChatGPT-4 Turbo's capabilities. On the downside, the response times were generally higher. Lastly, regarding overall performance, quality, depth, and alignment with the specific content of the course, no statistically significant differences were observed in the responses between BSVP and ChatGPT-4 Turbo. It appears that customized assistants trained with prompts present advantages as virtual aids for students, yet they do not constitute a substantial improvement over ChatGPT-4 Turbo.

Keqing He, Jingang Wang, Chaobo Sun et al.

Dialogue bots have been widely applied in customer service scenarios to provide timely and user-friendly experience. These bots must classify the appropriate domain of a dialogue, understand the intent of users, and generate proper responses. Existing dialogue pre-training models are designed only for several dialogue tasks and ignore weakly-supervised expert knowledge in customer service dialogues. In this paper, we propose a novel unified knowledge prompt pre-training framework, UFA (\textbf{U}nified Model \textbf{F}or \textbf{A}ll Tasks), for customer service dialogues. We formulate all the tasks of customer service dialogues as a unified text-to-text generation task and introduce a knowledge-driven prompt strategy to jointly learn from a mixture of distinct dialogue tasks. We pre-train UFA on a large-scale Chinese customer service corpus collected from practical scenarios and get significant improvements on both natural language understanding (NLU) and natural language generation (NLG) benchmarks.

Shuang Peng, Shuai Zhu, Minghui Yang et al.

With the development of online business, customer service agents gradually play a crucial role as an interface between the companies and their customers. Most companies spend a lot of time and effort on hiring and training customer service agents. To this end, we propose AdaCoach: A Virtual Coach for Training Customer Service Agents, to promote the ability of newly hired service agents before they get to work. AdaCoach is designed to simulate real customers who seek help and actively initiate the dialogue with the customer service agents. Besides, AdaCoach uses an automated dialogue evaluation model to score the performance of the customer agent in the training process, which can provide necessary assistance when the newly hired customer service agent encounters problems. We apply recent NLP technologies to ensure efficient run-time performance in the deployed system. To the best of our knowledge, this is the first system that trains the customer service agent through human-computer interaction. Until now, the system has already supported more than 500,000 simulation training and cultivated over 1000 qualified customer service agents.

Virgínia Maria Vasconcelos Leal

O artigo analisa as narrativas Todos nós adorávamos caubóis, de Carol Bensimon, e Amora, de Natália Borges Polesso, a partir das discussões a respeito das identidades e da lesbofobia. Fundamenta-se a discussão nas teorias de Sara Ahmed sobre os sentimentos queer e o binômio conforto/desconforto, bem como nas relações entre espaço e literatura teorizadas por Susan Stanford Friedman e Doreen Massey. O artigo pretende demonstrar como as obras ressignificam desconfortos em novas possibilidades amorosas e identitárias.

Mark Fackrell, Peter Taylor, Jiesen Wang

We investigate the behavior of equilibria in an $M/M/1$ feedback queue where price and time sensitive customers are homogeneous with respect to service valuation and cost per unit time of waiting. Upon arrival, customers can observe the number of customers in the system and then decide to join or to balk. Customers are served in order of arrival. After being served, each customer either successfully completes the service and departs the system with probability $q$, or the service fails and the customer immediately joins the end of the queue to wait to be served again until she successfully completes it. We analyse this decision problem as a noncooperative game among the customers. We show that there exists a unique symmetric Nash equilibrium threshold strategy. We then prove that the symmetric Nash equilibrium threshold strategy is evolutionarily stable. Moreover, if we relax the strategy restrictions by allowing customers to renege, in the new Nash equilibrium, customers have a greater incentive to join. However, this does not necessarily increase the equilibrium expected payoff, and for some parameter values, it decreases it.

Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder et al.

In a typical customer service chat scenario, customers contact a support center to ask for help or raise complaints, and human agents try to solve the issues. In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue. The goal of the present article is advancing the automation of this task. We introduce the first large scale, high quality, customer care dialog summarization dataset with close to 6500 human annotated summaries. The data is based on real-world customer support dialogs and includes both extractive and abstractive summaries. We also introduce a new unsupervised, extractive summarization method specific to dialogs.

Rismawidiawati Rusli

Artikel ini bertujuan untuk menulis sejarah pembentukan Kabupaten Luwu Utara dari perspektif aktor yang terlibat pada proses pembentukan tersebut. Tulisan ini menggunakan metode sejarah, dan hasil penelitian menunjukkan bahwa upaya pembentukan Kabupaten Luwu Utara bukan hanya sekali ini muncul, tapi keinginan tersebut sudah sejak lama diperjuangkan. Usaha tersebut dimulai sejak 1959, diulang kembali pada 1966, dan akhirnya pada 1999 Kabupaten Luwu Utara terbentuk. Pembentukan Luwu Utara adalah berkat perjuangan masyarakat Luwu Utara yang terdiri dari berbagai unsur, unsur mahasiswa yang tergabung pada Forum Komunikasi Mahasiswa Luwu Utara, unsur masyarakat biasa bahkan unsur pemerintah. Cepatnya proses pembentukan Luwu Utara pada 1999 ini berkat politik lobbying yang dilakukan oleh Ryass Rasyid yang memiliki kedekatan khusus dengan Lutfi A. Mutty (Dirjen PUOD). Alasan Utama pembentukan Kabupaten Luwu Utara tidak hanya dikarenakan pertimbangan desentralisasi, demokratisasi dan good governance, serta kalkulasi ekonomis namun juga karena kepentingan aktor-aktor dibaliknya. The study aims to reveal the history of the formation of the North Luwu District based on the perspective of the actors who were directly involved in the formation process. This research, which uses the historical method, shows the results of the research that the efforts to form the North Luwu District have been pursued for a long time. The effort, which had been started since 1959, was re-submitted in 1966, and finally in 1999 the North Luwu District was successfully formed. The formation of North Luwu District was the result of support from the North Luwu communities which consisted of various elements, such as elements of students who were members of the North Luwu Student Communication Forum, elements of societies, and even elements of the government. The process of formation the North Luwu District in 1999 proceeded rapidly because of political lobbying approached by Ryass Rasyid towards Director General for PUOD Lutfi A. Mutty. The main reasons behind the formation of the North Luwu District was not only due to the considerations of decentralization, the democratization and the good governance, as well as the economic calculations but also because of the interests of the actors behind it.

Julio Souto Salom

Angola Janga (D'Salete, 2017) narra a morte de Zumbi dos Palmares, líder do Quilombo mais importante da diáspora africana no Brasil, comunidade autônoma de africanos fugidos da escravidão. Esta saga é evocada em poemas, filmes, monumentos e memoriais, mas a história em quadrinhos providencia um meio com potencialidades específicas para costurar história e mitologia. D'Salete combina a pesquisa histórica e a poesia imagética para narrar uma história particular sobre esta mitologia viva. Para isso, são importantes: o enredo coral costurado por personagens não maniqueístas, o peso da morte na necropolítica colonial, a temporalidade não linear que faz durar o quilombismo até nossos dias, e a escrita do território com marcações cifradas. Com fins comparativos, comentamos as obras Cumbe e Encruzilhada (D'Salete, 2014; 2016), Zumbi dos Palmares (Moura; Moya, 1955) e Do inferno (Moore; Campbell, 2000), atentando especialmente para as ferramentas de costura entre história e mitologia na história em quadrinhos.