Relevant bibliographies by topics / Intent

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Intent'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

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Journal articles on the topic "Intent"

1

Zhao, Sen, Wei Wei, Ding Zou, and Xianling Mao. "Multi-View Intent Disentangle Graph Networks for Bundle Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 4379–87. http://dx.doi.org/10.1609/aaai.v36i4.20359.

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Bundle recommendation aims to recommend the user a bundle of items as a whole. Previous models capture user’s preferences on both items and the association of items. Nevertheless, they usually neglect the diversity of user’s intents on adopting items and fail to disentangle user’s intents in representations. In the real scenario of bundle recommendation, a user’s intent may be naturally distributed in the different bundles of that user (Global view). And a bundle may contain multiple intents of a user (Local view). Each view has its advantages for intent disentangling: 1) In the global view, more items are involved to present each intent, which can demonstrate the user’s preference under each intent more clearly. 2) The local view can reveal the association between items under each intent since the items within the same bundle are highly correlated to each other. To this end, in this paper we propose a novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which is capable of precisely and comprehensively capturing the diversity of user intent and items’ associations at the finer granularity. Specifically, MIDGN disentangles user’s intents from two different perspectives, respectively: 1) taking the Global view, MIDGN disentangles the user’s intent coupled with inter-bundle items; 2) taking the Local view, MIDGN disentangles the user’s intent coupled with items within each bundle. Meanwhile, we compare user’s intents disentangled from different views by a contrast method to improve the learned intents. Extensive experiments are conducted on two benchmark datasets and MIDGN outperforms the state-of-the-art methods by over 10.7% and 26.8%, respectively.
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Zhu, Nengjun, Jian Cao, Xinjiang Lu, and Hui Xiong. "Learning a Hierarchical Intent Model for Next-Item Recommendation." ACM Transactions on Information Systems 40, no. 2 (April 30, 2022): 1–28. http://dx.doi.org/10.1145/3473972.

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A session-based recommender system (SBRS) captures users’ evolving behaviors and recommends the next item by profiling users in terms of items in a session. User intent and user preference are two factors affecting his (her) decisions. Specifically, the former narrows the selection scope to some item types, while the latter helps to compare items of the same type. Most SBRSs assume one arbitrary user intent dominates a session when making a recommendation. However, this oversimplifies the reality that a session may involve multiple types of items conforming to different intents. In current SBRSs, items conforming to different user intents have cross-interference in profiling users for whom only one user intent is considered. Explicitly identifying and differentiating items conforming to various user intents can address this issue and model rich contextual information of a session. To this end, we design a framework modeling user intent and preference explicitly, which empowers the two factors to play their distinctive roles. Accordingly, we propose a key-array memory network (KA-MemNN) with a hierarchical intent tree to model coarse-to-fine user intents. The two-layer weighting unit (TLWU) in KA-MemNN detects user intents and generates intent-specific user profiles. Furthermore, the hierarchical semantic component (HSC) integrates multiple sets of intent-specific user profiles along with different user intent distributions to model a multi-intent user profile. The experimental results on real-world datasets demonstrate the superiority of KA-MemNN over selected state-of-the-art methods.
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Hsieh, Wan-Hua, Chien-Hsing Wang, and Tsung-Hsueh Lu. "Drowning mortality by intent: a population-based cross-sectional study of 32 OECD countries, 2012–2014." BMJ Open 8, no. 7 (July 2018): e021501. http://dx.doi.org/10.1136/bmjopen-2018-021501.

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ObjectiveTo compare the drowning mortality rates and proportion of deaths of each intent among all drowning deaths in Organisation for Economic Co-operation and Development (OECD) countries in 2012–2014.DesignA population-based cross-sectional study.Setting32 OECD countries.ParticipantsIndividuals in OECD countries who died from drowning.Main outcome measuresDrowning mortality rates (deaths per 100 000 population) and proportion (%) of deaths of each intent (ie, unintentional intent, intentional self-harm, assault, undetermined intent and all intents combined) among all drowning deaths.ResultsCountries with the highest drowning mortality rates (deaths per 100 000 population) were Estonia (3.53), Japan (3.49) and Greece (2.40) for unintentional intent; Ireland (0.96), Belgium (0.96) and Korea (0.89) for intentional self-harm; Austria (0.57), Korea (0.56) and Hungary (0.44) for undetermined intent and Japan (4.35), Estonia (3.70) and Korea (2.73) for all intents combined. Korea ranked 12th and 3rd for unintentional intent and all intents combined, respectively. By contrast, Belgium ranked 2nd and 15th for intentional self-harm and all intents combined, respectively. The proportion of deaths of each intent among all drowning deaths in each country varied greatly: from 26.2% in Belgium to 96.8% in Chile for unintentional intent; 0.7% in Mexico to 57.4% in Belgium for intentional self-harm; 0.0% in nine countries to 4.9% in Mexico for assault and 0.0% in Israel and Turkey to 38.3% in Austria for undetermined intent.ConclusionsA large variation in the practice of classifying undetermined intent in drowning deaths across countries was noted and this variation hinders valid international comparisons of intent-specific (unintentional and intentional self-harm) drowning mortality rates.
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Zhang, Hanlei, Hua Xu, Ting-En Lin, and Rui Lyu. "Discovering New Intents with Deep Aligned Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14365–73. http://dx.doi.org/10.1609/aaai.v35i16.17689.

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Discovering new intents is a crucial task in dialogue systems. Most existing methods are limited in transferring the prior knowledge from known intents to new intents. These methods also have difficulties in providing high-quality supervised signals to learn clustering-friendly features for grouping unlabeled intents. In this work, we propose an effective method (Deep Aligned Clustering) to discover new intents with the aid of limited known intent data. Firstly, we leverage a few labeled known intent samples as prior knowledge to pre-train the model. Then, we perform k-means to produce cluster assignments as pseudo-labels. Moreover, we propose an alignment strategy to tackle the label inconsistency problem during clustering assignments. Finally, we learn the intent representations under the supervision of the aligned pseudo-labels. With an unknown number of new intents, we predict the number of intent categories by eliminating low-confidence intent-wise clusters. Extensive experiments on two benchmark datasets show that our method is more robust and achieves substantial improvements over the state-of-the-art methods.
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Zhang, Hanlei, Hua Xu, and Ting-En Lin. "Deep Open Intent Classification with Adaptive Decision Boundary." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 16 (May 18, 2021): 14374–82. http://dx.doi.org/10.1609/aaai.v35i16.17690.

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Open intent classification is a challenging task in dialogue systems. On the one hand, it should ensure the quality of known intent identification. On the other hand, it needs to detect the open (unknown) intent without prior knowledge. Current models are limited in finding the appropriate decision boundary to balance the performances of both known intents and the open intent. In this paper, we propose a post-processing method to learn the adaptive decision boundary (ADB) for open intent classification. We first utilize the labeled known intent samples to pre-train the model. Then, we automatically learn the adaptive spherical decision boundary for each known class with the aid of well-trained features. Specifically, we propose a new loss function to balance both the empirical risk and the open space risk. Our method does not need open intent samples and is free from modifying the model architecture. Moreover, our approach is surprisingly insensitive with less labeled data and fewer known intents. Extensive experiments on three benchmark datasets show that our method yields significant improvements compared with the state-of-the-art methods.
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Liu, Xiaokang, Jianquan Li, Jingjing Mu, Min Yang, Ruifeng Xu, and Benyou Wang. "Effective Open Intent Classification with K-center Contrastive Learning and Adjustable Decision Boundary." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (June 26, 2023): 13291–99. http://dx.doi.org/10.1609/aaai.v37i11.26560.

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Open intent classification, which aims to correctly classify the known intents into their corresponding classes while identifying the new unknown (open) intents, is an essential but challenging task in dialogue systems. In this paper, we introduce novel K-center contrastive learning and adjustable decision boundary learning (CLAB) to improve the effectiveness of open intent classification. First, we pre-train a feature encoder on the labeled training instances, which transfers knowledge from known intents to unknown intents. Specifically, we devise a K-center contrastive learning algorithm to learn discriminative and balanced intent features, improving the generalization of the model for recognizing open intents. Second, we devise an adjustable decision boundary learning method with expanding and shrinking (ADBES) to determine the suitable decision conditions. Concretely, we learn a decision boundary for each known intent class, which consists of a decision center and the radius of the decision boundary. We then expand the radius of the decision boundary to accommodate more in-class instances if the out-of-class instances are far from the decision boundary; otherwise, we shrink the radius of the decision boundary. Extensive experiments on three benchmark datasets clearly demonstrate the effectiveness of our method for open intent classification.For reproducibility, we submit the code at: https://github.com/lxk00/CLAP
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Kumar, Prashant. "Intents of green advertisements." Asia Pacific Journal of Marketing and Logistics 29, no. 1 (January 9, 2017): 70–79. http://dx.doi.org/10.1108/apjml-03-2016-0044.

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Purpose The purpose of this paper is to explore intents of green advertisements. Design/methodology/approach Using NVivo, a convenient sample of 237 green print advertisements published between August 2010 and July 2015 in leading Indian newspapers and magazines were content analysed. Findings Four types of intents of green advertisements were identified: intent to communicate corporate environmental approaches; intent to develop believability towards environmental claims; intent to inform consumers; and intent to engage consumers. Research limitations/implications This study explored intents of green advertisements and elaborated upon strategic importance of content in green advertising. Practical implications The intent-based exploration of green advertisements indicates marketing managers of green products the importance of: expanding their advertising framework that incorporates sharing environmental vision and mission of their companies with consumers, and relating them with consumers’ needs and demands; inculcating functional, emotional and experiential elements in green advertisements that facilitate green product experience to the consumers; and active interactions between marketing managers and consumers for effectively capturing market-related information, and accordingly shaping their short- and long-term marketing and advertising decisions. Originality/value This study is unique to determine intents of green advertisements.
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Cheng, Xuxin, Zhihong Zhu, Hongxiang Li, Yaowei Li, Xianwei Zhuang, and Yuexian Zou. "Towards Multi-Intent Spoken Language Understanding via Hierarchical Attention and Optimal Transport." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 16 (March 24, 2024): 17844–52. http://dx.doi.org/10.1609/aaai.v38i16.29738.

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Multi-Intent spoken language understanding (SLU) can handle complicated utterances expressing multiple intents, which has attracted increasing attention from researchers. Although existing models have achieved promising performance, most of them still suffer from two leading problems: (1) each intent has its specific scope and the semantic information outside the scope might potentially hinder accurate predictions, i.e. scope barrier; (2) only the guidance from intent to slot is modeled but the guidance from slot to intent is often neglected, i.e. unidirectional guidance. In this paper, we propose a novel Multi-Intent SLU framework termed HAOT, which utilizes hierarchical attention to divide the scopes of each intent and applies optimal transport to achieve the mutual guidance between slot and intent. Experiments demonstrate that our model achieves state-of-the-art performance on two public Multi-Intent SLU datasets, obtaining the 3.4 improvement on MixATIS dataset compared to the previous best models in overall accuracy.
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Yin, Shangjian, Peijie Huang, and Yuhong Xu. "Uni-MIS: United Multiple Intent Spoken Language Understanding via Multi-View Intent-Slot Interaction." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 17 (March 24, 2024): 19395–403. http://dx.doi.org/10.1609/aaai.v38i17.29910.

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So far, multi-intent spoken language understanding (SLU) has become a research hotspot in the field of natural language processing (NLP) due to its ability to recognize and extract multiple intents expressed and annotate corresponding sequence slot tags within a single utterance. Previous research has primarily concentrated on the token-level intent-slot interaction to model joint intent detection and slot filling, which resulted in a failure to fully utilize anisotropic intent-guiding information during joint training. In this work, we present a novel architecture by modeling the multi-intent SLU as a multi-view intent-slot interaction. The architecture resolves the kernel bottleneck of unified multi-intent SLU by effectively modeling the intent-slot relations with utterance, chunk, and token-level interaction. We further develop a neural framework, namely Uni-MIS, in which the unified multi-intent SLU is modeled as a three-view intent-slot interaction fusion to better capture the interaction information after special encoding. A chunk-level intent detection decoder is used to sufficiently capture the multi-intent, and an adaptive intent-slot graph network is used to capture the fine-grained intent information to guide final slot filling. We perform extensive experiments on two widely used benchmark datasets for multi-intent SLU, where our model bets on all the current strong baselines, pushing the state-of-the-art performance of unified multi-intent SLU. Additionally, the ChatGPT benchmark that we have developed demonstrates that there is a considerable amount of potential research value in the field of multi-intent SLU.
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Hanna, Wael K., Aziza Saad Asem, and M. B. Senousy. "Dynamic Query Intent Prediction from a Search Log Stream." International Journal of Information Retrieval Research 6, no. 2 (April 2016): 66–85. http://dx.doi.org/10.4018/ijirr.2016040104.

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The users that used search engines are obligated to express their goals in few words (queries). Sometimes search queries are ambiguous. Moreover, the users' intents are dynamically evolving. This paper analyzes the user's query logs to classify the related queries, the related intent topic categories and the related intent types and use this classification to dynamically predict the users' future queries, its intent topic and its intent type. AOL Search Query Log is taken as an experimental data set. Then use evaluation metrics to evaluate the prediction results.
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Dissertations / Theses on the topic "Intent"

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Allister, Alexander Theodore. "Established Intent." Thesis, Boston College, 2009. http://hdl.handle.net/2345/680.

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Thesis advisor: Stephanie Greene
A Constitutional analysis of the American Recovery and Reinvestment Act of 2009 with respect to educational subsidies to religiously-affiliated universities; including a proposed framework for the adjudication of issues involving religion and the government
Thesis (BS) — Boston College, 2009
Submitted to: Boston College. Carroll School of Management
Discipline: Carroll School of Management Honors Program
Discipline: Business Law
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Nolan, Declan Cormac. "Defining simulation intent." Thesis, Queen's University Belfast, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.602714.

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The use of Simulation in the design and development process is rapidly increasing. Companies are simulating more processes and products, in an increasing number of ways. Also, there is a trend towards modelling much larger systems to more accurately capture complex physical interactions. One problem which remains unsolved is how these many types of analyses can be handled in an integrated and coherent manner alongside CAD technologies. In this work a concept termed 'Simulation Intent' is introduced which helps to alleviate the CAD/CAE integration problem. Simulation Intent captures the modelling and idealisation decisions required to create a fit-for-purpose analysis model. Simulation Intent is defined in a high-level manner, making it robust to significant changes in the base geometric model. Furthermore, multiple definitions of Simulation Intent can be applied to produce different analysis models which are all linked in a coherent manner to the base geometric model. Three technologies are integral to the concept; Cellular Modelling, Equivalencing and Virtual Topology. Given the novelty of the approach, multiple examples are provided to demonstrate its application. It is shown that by using Cellular Modelling, system interfaces can be easily extracted and used in analysis modelling. , These Cellular Models are created and stored through the use of Virtual Topology meaning the base topology of the original geometric model remains unperturbed. Equivalencing offers the ability to apply boundary conditions, loads and connections at multiple levels of detail and dimensionality. Some ideas for future work are presented, which may help to progress the concept to full industrial realisation. The application to Method's Engineering in large companies is highlighted as one significant operational benefit. Simulation Intent offers a way to directly apply the correct analysis methods in the correct situations.
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Silvander, Johan. "Towards Intent-Driven Systems." Licentiate thesis, Blekinge Tekniska Högskola, Institutionen för programvaruteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-15141.

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Context: Software supporting an enterprise’s business, also known as a business support system, needs to support the correlation of activities between actors as well as influence the activities based on knowledge about the value networks in which the enterprise acts. This can be supported with the help of intent-driven systems. The aim of intent-driven systems is to capture stakeholders’ intents and transform these into a form that enables computer processing of them. Only then are different machine actors able to negotiate with each other on behalf of their respective stakeholders and their intents, and suggest a mutually beneficial agreement. Objective: When building a business support system it is critical to separate the business model of the business support system itself from the business models used by the enterprise which is using the business support system. The core idea of intent-driven systems is the possibility to change behavior of the system itself, based on stakeholder intents. This requires a separation of concerns between the parts of the system used to execute the stakeholder business, and the parts which are used to design the business based on stakeholder intents. The business studio is a software that supports the realization of business models used by the enterprise by configuring the capabilities provided by the business support system. The aim is to find out how we can support the design of a business studio which is based on intent-driven systems. Method: We are using the design science framework as our research frame- work. During our design science study we have used the following research methods: systematic literature review, case study, quasi experiment, and action research. Results: We have produced two design artifacts as a start to be able to support the design of a business studio. These artifacts are the models and quasi-experiment in Chapter 3, and the action research in Chapter 4. The models found during the case study have proved to be a valuable artifact for the stakeholder. The results from the quasi-experiment and the action research are seen as new problem solving knowledge by the stakeholder. Conclusion: The synthesis shows a need for further research regarding semantic interchange of information, actor interaction in intent-driven systems, and the governance of intent-driven systems.
Professional Licentiate of Engineering Research School
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BOFFI, LAURA. "Cars with an Intent." Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2496471.

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In a near future autonomous cars will likely populate the urban environment together with traditional cars and other road users. While they will actually consist of urban- scale robots immersed in a socio-technical context, so far autonomous cars have been almost exclusively looked at from the perspective of safety and functionality and they have not been designed towards acting as social urban beings. ”Cars with an Intent” is a design- research driven PhD project which envisions cars beyond their core objectives of functionality and safety, and probes how positive and enriching car-to-human and human-to-human relationships can be prompted by embedding social intentions and behaviours in the car. After an initial exploration in the two different directions, the research delves into the specific concept called "Co-Drive", pursuing human-to human relationships. First, I describe the Co-Drive concept as an extended reality experience (XR); next I develop an early-stage prototyping methodology that allows me to test it with real people in their context without having the fully developed technology. Through three prototyping interventions, I draw the first conclusions on the social values of the "Co-Drive" concept and I suggest that the social intent of autonomous cars may emerge through i) teledriving, as a combined intent between the autonomous car and the human; and ii) the in-car interfaces, as a way to spot and board remote passengers and to embody them in the car.
In un prossimo futuro le automobili a guida autonoma popoleranno probabilmente l'ambiente urbano insieme alle auto tradizionali e ad altri utenti della strada. Sebbene siano a tutti gli effetti dei robot a scala urbana immersi in un contesto socio-tecnico, finora le auto autonome sono state considerate quasi esclusivamente dal punto di vista della sicurezza e della funzionalità di guida e non sono state progettate per agire come esseri urbani sociali. "Automobili con una Intenzionalità" è un progetto di dottorato che segue un processo di “ricerca attraverso il design” (design research) e che immagina le automobili oltre i loro obiettivi principali di funzionalità e sicurezza. Il progetto infatti esplora quali relazioni positive e arricchenti possano essere attivate tra automobili ed esseri umani e tra gli stessi esseri umani incorporando intenzionalità sociali e nuovi comportamenti nelle automobili e si sviluppa attraverso la prototipazione di nuove relazioni sociali nel contesto reale. Dopo una prima esplorazione nelle due diverse direzioni di ricerca, il progetto si concentra sulla specifica idea intitolata "Co-Drive", approfondendo le relazioni che possono emergere tra gli esseri umani attraverso le nuove capacità delle automobili. In primo luogo, descrivo il concetto di “Co-Drive” come un'esperienza di realtà estesa (XR); successivamente sviluppo una metodologia di prototipazione da applicarsi sin dalla fase iniziale del progetto, che mi consenta di sviluppare e testare l’idea con persone reali nel loro contesto, seppur non disponendo di una tecnologia completamente sviluppata e funzionante. Attraverso tre interventi di prototipazione, traggo le prime conclusioni sui valori sociali dell’idea "Co-Drive" e suggerisco che l’intenzionalità sociale delle automobili a guida autonoma può emergere attraverso i) la capacità di guida remota, come risultante dell’ intenzionalità dell’automobile autonoma e dell'essere umano, e ii) le interfacce all’interno dell’automobile, attraverso cui si possono, da un lato, localizzare e far salire a bordo i passeggeri remoti e, dall’altro, “incarnarli” in appendici robotiche dell’automobile (robotic embodiment).
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BOFFI, LAURA. "Cars with an Intent." Doctoral thesis, Università degli studi di Ferrara, 2022. http://hdl.handle.net/11392/2496470.

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In a near future autonomous cars will likely populate the urban environment together with traditional cars and other road users. While they will actually consist of urban- scale robots immersed in a socio-technical context, so far autonomous cars have been almost exclusively looked at from the perspective of safety and functionality and they have not been designed towards acting as social urban beings. ”Cars with an Intent” is a design- research driven PhD project which envisions cars beyond their core objectives of functionality and safety, and probes how positive and enriching car-to-human and human-to-human relationships can be prompted by embedding social intentions and behaviours in the car. After an initial exploration in the two different directions, the research delves into the specific concept called "Co-Drive", pursuing human-to human relationships. First, I describe the Co-Drive concept as an extended reality experience (XR); next I develop an early-stage prototyping methodology that allows me to test it with real people in their context without having the fully developed technology. Through three prototyping interventions, I draw the first conclusions on the social values of the "Co-Drive" concept and I suggest that the social intent of autonomous cars may emerge through i) teledriving, as a combined intent between the autonomous car and the human; and ii) the in-car interfaces, as a way to spot and board remote passengers and to embody them in the car.
In un prossimo futuro le automobili a guida autonoma popoleranno probabilmente l'ambiente urbano insieme alle auto tradizionali e ad altri utenti della strada. Sebbene siano a tutti gli effetti dei robot a scala urbana immersi in un contesto socio-tecnico, finora le auto autonome sono state considerate quasi esclusivamente dal punto di vista della sicurezza e della funzionalità di guida e non sono state progettate per agire come esseri urbani sociali. "Automobili con una Intenzionalità" è un progetto di dottorato che segue un processo di “ricerca attraverso il design” (design research) e che immagina le automobili oltre i loro obiettivi principali di funzionalità e sicurezza. Il progetto infatti esplora quali relazioni positive e arricchenti possano essere attivate tra automobili ed esseri umani e tra gli stessi esseri umani incorporando intenzionalità sociali e nuovi comportamenti nelle automobili e si sviluppa attraverso la prototipazione di nuove relazioni sociali nel contesto reale. Dopo una prima esplorazione nelle due diverse direzioni di ricerca, il progetto si concentra sulla specifica idea intitolata "Co-Drive", approfondendo le relazioni che possono emergere tra gli esseri umani attraverso le nuove capacità delle automobili. In primo luogo, descrivo il concetto di “Co-Drive” come un'esperienza di realtà estesa (XR); successivamente sviluppo una metodologia di prototipazione da applicarsi sin dalla fase iniziale del progetto, che mi consenta di sviluppare e testare l’idea con persone reali nel loro contesto, seppur non disponendo di una tecnologia completamente sviluppata e funzionante. Attraverso tre interventi di prototipazione, traggo le prime conclusioni sui valori sociali dell’idea "Co-Drive" e suggerisco che l’intenzionalità sociale delle automobili a guida autonoma può emergere attraverso i) la capacità di guida remota, come risultante dell’ intenzionalità dell’automobile autonoma e dell'essere umano, e ii) le interfacce all’interno dell’automobile, attraverso cui si possono, da un lato, localizzare e far salire a bordo i passeggeri remoti e, dall’altro, “incarnarli” in appendici robotiche dell’automobile (robotic embodiment).
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Van, der Merwe Maryke. "Discovering intent : the celebration of historical intent through the re-ordening of the landscape." Diss., University of Pretoria, 2016. http://hdl.handle.net/2263/78706.

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This dissertation focuses on how both the tangible, as well as the intangible heritage layering of Irene Dairy Farm can be exposed. First, the intangible heritage significance is identified and subsequently reintroduced to the site in the form of the vision and intent of the farm at its genesis. The farm will be reactivated as a productive landscape whilst acting as a didactic model through the public exhibition of innovative food production methods and the effect this has on the culinary experience of the user. The celebration of the heritage significance lies within the reintroduction of innovation through twenty-first century food cultivation methods and the integration thereof into new infrastructure, thereby reactivating the historic intent of the farm as a productive model and didactic establishment. Secondly, the tangible heritage significance was identified as being embedded within the landscape and is expressed as ruins consisting of low walls, storm water channels, forest remnants, tree boulevard remains and historic movement routes. The tectonic approach to the tangible heritage elements of the site links back to the conceptual approach of exposing the functionality and dismembering the structures in order to remember. The historic structures will thus be treated as fragmented limbs to be dismembered in order to reveal the skeleton and function through the subtle intersections of new material and infrastructure. The investigation aims to: Address the loss of heritage significance of the farm on a master plan and detail level; allow the opportunity for skills development in the formal agricultural sector through the introduction of the didactic program; and acknowledge the prospect of sustainable food cultivation methods which is a global and local issue.
Mini Dissertation (ML (Prof))--University of Pretoria, 2016.
Architecture
ML (Prof)
Unrestricted
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Davidson, Bradley Ross. "Poetic intent in architectural design." Thesis, Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/23392.

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Ekins, Richard. "The nature of legislative intent." Thesis, University of Oxford, 2009. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.530026.

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Jadhav, Ashutosh. "Knowledge Driven Search Intent Mining." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464464707.

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Miller, Forrest. "Intent in New Zealand competition policy." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29450.pdf.

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Books on the topic "Intent"

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Foley, Rae. Dark intent. Thorndike, Me., USA: G.K. Hall, 1996.

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Taylor, Bernard. Evil intent. US: Book Margins Inc., 1994.

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Egleton, Clive. Hostile intent. New York: St. Martin's Press, 1993.

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Bugeja, Michael J. Deadly intent. [Sharon, CT]: Grey Castle Press, 1988.

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Bork, Robert H. Donor intent. Indianapolis, Ind: Philanthropy Roundtable, 1993.

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Executive intent. New York: Harper, 2011.

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Cray, Jordan. Bad intent. New York: Aladdin Paperbacks, 1998.

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Robin, Cook. Harmful intent. New York: Berkley, 1991.

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Robin, Cook. Harmful intent. New York: Putnam, 1990.

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Bernhardt, William. Criminal intent. New York: Ballantine Books, 2003.

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Book chapters on the topic "Intent"

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Jackson, Wallace. "Understanding Intents and Intent Filters." In Android Apps for Absolute Beginners, 305–50. Berkeley, CA: Apress, 2012. http://dx.doi.org/10.1007/978-1-4302-4789-0_11.

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Jackson, Wallace. "Understanding Intents and Intent Filters." In Android Apps for Absolute Beginners, 255–95. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3447-0_11.

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Schwartz, Chad. "Intent." In Situate, Manipulate, Fabricate, 103–22. New York, NY: Routledge, 2020.: Routledge, 2020. http://dx.doi.org/10.4324/9780429057960-7.

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Levesque, Roger J. R. "Criminal Intent." In Encyclopedia of Adolescence, 571–72. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-1695-2_520.

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MacIntosh, Robert, and Donald MacLean. "Strategic Intent." In Strategic Management, 46–54. London: Macmillan Education UK, 2015. http://dx.doi.org/10.1007/978-1-137-03545-5_5.

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Rodriguez, Daniel B. "Legislative Intent." In The New Palgrave Dictionary of Economics and the Law, 1221–28. London: Palgrave Macmillan UK, 2002. http://dx.doi.org/10.1007/978-1-349-74173-1_231.

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Utgoff, Paul E., James Cussens, Stefan Kramer, Sanjay Jain, Frank Stephan, Luc De Raedt, Ljupčo Todorovski, et al. "Intent Recognition." In Encyclopedia of Machine Learning, 553. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_412.

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Tyler, Beverly. "Strategic Intent." In The Palgrave Encyclopedia of Strategic Management, 1621–23. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-137-00772-8_398.

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Pellegrinelli, Sergio. "Adaptive intent." In Thinking and Acting as a Great Programme Manager, 146–57. London: Palgrave Macmillan UK, 2008. http://dx.doi.org/10.1057/9780230583085_12.

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Tyler, Beverly. "Strategic Intent." In The Palgrave Encyclopedia of Strategic Management, 1–2. London: Palgrave Macmillan UK, 2016. http://dx.doi.org/10.1057/978-1-349-94848-2_398-1.

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Conference papers on the topic "Intent"

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Sasnauskas, Raimondas, and John Regehr. "Intent fuzzer: crafting intents of death." In the 2014 Joint International Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2632168.2632169.

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Li, Jiapeng, Ping Wei, Wenjuan Han, and Lifeng Fan. "IntentQA: Context-aware Video Intent Reasoning." In 2023 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2023. http://dx.doi.org/10.1109/iccv51070.2023.01099.

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Bezahaf, Mehdi, Eleanor Davies, Charalampos Rotsos, and Nicholas Race. "To All Intents and Purposes: Towards Flexible Intent Expression." In 2021 IEEE 7th International Conference on Network Softwarization (NetSoft). IEEE, 2021. http://dx.doi.org/10.1109/netsoft51509.2021.9492554.

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Si, Qingyi, Yuanxin Liu, Peng Fu, Zheng Lin, Jiangnan Li, and Weiping Wang. "Learning Class-Transductive Intent Representations for Zero-shot Intent Detection." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/540.

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Abstract:
Zero-shot intent detection (ZSID) aims to deal with the continuously emerging intents without annotated training data. However, existing ZSID systems suffer from two limitations: 1) They are not good at modeling the relationship between seen and unseen intents. 2) They cannot effectively recognize unseen intents under the generalized intent detection (GZSID) setting. A critical problem behind these limitations is that the representations of unseen intents cannot be learned in the training stage. To address this problem, we propose a novel framework that utilizes unseen class labels to learn Class-Transductive Intent Representations (CTIR). Specifically, we allow the model to predict unseen intents during training, with the corresponding label names serving as input utterances. On this basis, we introduce a multi-task learning objective, which encourages the model to learn the distinctions among intents, and a similarity scorer, which estimates the connections among intents more accurately. CTIR is easy to implement and can be integrated with existing ZSID and GZSID methods. Experiments on two real-world datasets show that CTIR brings considerable improvement to the baseline systems.
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Ng, Rachel S., Raghavendra Kandala, Sarah Marie-Foley, Dixon Lo, Molly Wright Steenson, and Austin S. Lee. "Expressing Intent." In TEI '16: Tenth International Conference on Tangible, Embedded, and Embodied Interaction. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2839462.2856526.

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Srivastava, Saurabh, and T. V. Prabhakar. "Intent Sets." In ICCAE 2020: 2020 12th International Conference on Computer and Automation Engineering. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3384613.3384639.

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Li, Tangyi, Ying Ouyang, Lulu Zhang, Yufei Bai, and Chungang Yang. "Autonomous Intent Detection for Intent-Driven Satellite Network." In 2023 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 2023. http://dx.doi.org/10.1109/iwcmc58020.2023.10183156.

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Li, Yanen, Bo-June Paul Hsu, and ChengXiang Zhai. "Unsupervised identification of synonymous query intent templates for attribute intents." In the 22nd ACM international conference. New York, New York, USA: ACM Press, 2013. http://dx.doi.org/10.1145/2505515.2505694.

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Li, Xianzhi, Will Aitken, Xiaodan Zhu, and Stephen W. Thomas. "Learning Better Intent Representations for Financial Open Intent Classification." In Proceedings of the Fourth Workshop on Financial Technology and Natural Language Processing (FinNLP). Stroudsburg, PA, USA: Association for Computational Linguistics, 2022. http://dx.doi.org/10.18653/v1/2022.finnlp-1.8.

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Mascarenhas, Manuel Duarte, and Rui Santos Cruz. "Int2IT: An Intent-based TOSCA IT Infrastructure Management Platform." In 2022 17th Iberian Conference on Information Systems and Technologies (CISTI). IEEE, 2022. http://dx.doi.org/10.23919/cisti54924.2022.9820004.

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Reports on the topic "Intent"

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Li, C., O. Havel, A. Olariu, P. Martinez-Julia, J. Nobre, and D. Lopez. Intent Classification. RFC Editor, October 2022. http://dx.doi.org/10.17487/rfc9316.

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Scace, Daniel R. The Operational Commander's Intent. Fort Belvoir, VA: Defense Technical Information Center, June 1996. http://dx.doi.org/10.21236/ada312228.

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Gilmore, Duane A., Lee S. Krause, Lynn A. Lehman, Jr Santos, Zhao Eugene, and Qunhua. Intent Driven Adversarial Modeling. Fort Belvoir, VA: Defense Technical Information Center, June 2005. http://dx.doi.org/10.21236/ada463701.

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Berger, E. L., V. Guarino, J. Repond, H. Weerts, L. Xia, J. Zhang, Q. Zhang, et al. SiD Letter of Intent. Edited by H. ,. Aihara, P. ,. Burrows, and M. ,. Oreglia. Office of Scientific and Technical Information (OSTI), April 2012. http://dx.doi.org/10.2172/1038429.

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Aihara, H., P. Burrows, and M. Oreglia. Silicon Detector Letter of Intent. Office of Scientific and Technical Information (OSTI), May 2010. http://dx.doi.org/10.2172/982086.

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Clemm, A., L. Ciavaglia, L. Z. Granville, and J. Tantsura. Intent-Based Networking - Concepts and Definitions. RFC Editor, October 2022. http://dx.doi.org/10.17487/rfc9315.

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Santos, Jr, and Eugene. Adversarial Intent Inference for Predictive Battlespace Awareness. Fort Belvoir, VA: Defense Technical Information Center, November 2005. http://dx.doi.org/10.21236/ada442035.

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Abe, Toshinori, /Tokyo U., Jason M. Abernathy, /Victoria U., Halina Abramowicz, /Tel Aviv U., Marek Adamus, et al. The International Large Detector: Letter of Intent. Office of Scientific and Technical Information (OSTI), February 2010. http://dx.doi.org/10.2172/975166.

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Sprankle, Kenneth A. Environmental Impact Assessments: Congressional Intent Versus Application. Fort Belvoir, VA: Defense Technical Information Center, April 1992. http://dx.doi.org/10.21236/ada262307.

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Claise, B., J. Quilbeuf, D. Lopez, D. Voyer, and T. Arumugam. Service Assurance for Intent-Based Networking Architecture. RFC Editor, July 2023. http://dx.doi.org/10.17487/rfc9417.

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