Journal articles: 'Intent'

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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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Ninio, Anat. "The relation of children's single word utterances to single word utterances in the input." Journal of Child Language 19, no. 1 (February 1992): 87–110. http://dx.doi.org/10.1017/s0305000900013647.

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ABSTRACTThe reported study investigated the relation between children's single-word utterances and maternal single-word utterances expressing similar communicative intents. Twenty-four Hebrew-speaking dyads (the children about 1;6) were videotaped for 30 min in an unstructured session. Single-word utterances were analysed for their communicative intent and the relationship of the expression to the underlying intent was defined in the form of realization rules. Of 17,471 child utterances, 97·0% were realizations that were also used by mothers for expressing the same communicative intent. The most frequently modelled rule for an intent had the highest chance of being adopted by children, and the probability sharply decreased for the relatively less frequently modelled rules. The results suggest that children's single-word utterances are similar to, and probably learned from, single-word utterances of caretakers expressing the same specific communicative intents.

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Liu, Hai, Yuanxia Liu, Leung-Pun Wong, Lap-Kei Lee, and Tianyong Hao. "A Hybrid Neural Network BERT-Cap Based on Pre-Trained Language Model and Capsule Network for User Intent Classification." Complexity 2020 (November 21, 2020): 1–11. http://dx.doi.org/10.1155/2020/8858852.

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User intent classification is a vital component of a question-answering system or a task-based dialogue system. In order to understand the goals of users’ questions or discourses, the system categorizes user text into a set of pre-defined user intent categories. User questions or discourses are usually short in length and lack sufficient context; thus, it is difficult to extract deep semantic information from these types of text and the accuracy of user intent classification may be affected. To better identify user intents, this paper proposes a BERT-Cap hybrid neural network model with focal loss for user intent classification to capture user intents in dialogue. The model uses multiple transformer encoder blocks to encode user utterances and initializes encoder parameters with a pre-trained BERT. Then, it extracts essential features using a capsule network with dynamic routing after utterances encoding. Experiment results on four publicly available datasets show that our model BERT-Cap achieves a F1 score of 0.967 and an accuracy of 0.967, outperforming a number of baseline methods, indicating its effectiveness in user intent classification.

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Huang, Qing, Yangrui Yang, Xudong Wang, Hongyan Wan, Rui Wang, and Guoqing Wu. "Query Expansion via Intent Predicting." International Journal of Software Engineering and Knowledge Engineering 27, no. 09n10 (November 2017): 1591–601. http://dx.doi.org/10.1142/s0218194017400137.

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To make the code search (CS) become more effective, a novel query expansion with intents (QEI) is proposed, in which the intent refers to the common subsequent modifications of the search results. The intent is extracted from the modification history. Within the intent scope, the CS is speeded up based on the semantic and structural matches. The precision of the search results is also increased by expanding the query with the intent. Compared with CodeHow and Google CS, QEI outperforms them by 28.5% with a precision score of 0.846. (i.e. 84.6% of the first results are accepted directly by users).

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Bower, Bruce. "Hostile Intent." Science News 161, no. 25 (June 22, 2002): 389. http://dx.doi.org/10.2307/4013302.

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Thiara, Balvinder. "Suicidal intent." Nursing Standard 29, no. 21 (January 21, 2015): 61. http://dx.doi.org/10.7748/ns.29.21.61.s51.

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Siddique, Irum, Sadaf Aijaz, M. Akhter Parvez, Imtiaz Ahmad Dogar, Moin Ansari, and Nighat Haider. "SUICIDE INTENT." Professional Medical Journal 23, no. 08 (August 10, 2016): 996–1000. http://dx.doi.org/10.29309/tpmj/2016.23.08.1676.

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Objectives: To see the Socio demographic profile of depressive patients whohas current suicide intent coming to the Psychiatry Departments of LUMS, SCJIP, Hyderabadand DHQ Hospital/ PMC Faisalabad. Design: Cross Sectional Study. Place & Duration ofStudy: The study was conducted in six months from 1st April 2014 to 30th September 2014 atLiaquat University of Medical & Health Sciences (LUMHS) & Sir Cowasjee institute of Psychiatryand Department of Psychiatry and Behavioral Sciences, Faisalabad. Subjects and Methods:A total of 117 depressive patients were included in this study having moderate to high SuicidalIntent. Results: Out of 117 depressed patients with suicidal intent, predominantly females (59%)approached psychiatry ward as compared with males (41%). Mean age of the participants wasbetween 31 -40 years. Among patients 56% were married, 28% were housewives and 28% werestudent. Most of them were educated. Suicide intent was found more in middle socioeconomicgroup and more in nuclear family system. 96 patients (82.1%) were taking treatment forDepression or Anxiety at the time of interview. Conclusion: Prevalence of depressed populationwith suicidal intent predominates in students, married females especially housewives belongingfrom middle socioeconomic background and nuclear family system. Most of patients useddrugs of abuse. Past psychiatric history, family psychiatric history, past history of suicide, familyhistory of suicide and suicidal Ideations were present in patients with current suicide Intent.

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Minkel, JR. "Discerning Intent." Scientific American 292, no. 5 (May 2005): 34. http://dx.doi.org/10.1038/scientificamerican0505-34b.

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Ockelford, Adam, and Angela Voyajolu. "With intent." Nursery World 2015, no. 21 (October 19, 2015): 18–19. http://dx.doi.org/10.12968/nuwa.2015.21.18.

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Engle, Robert L., Nikolay Dimitriadi, Jose V. Gavidia, Christopher Schlaegel, Servane Delanoe, Irene Alvarado, Xiaohong He, Samuel Buame, and Birgitta Wolff. "Entrepreneurial intent." International Journal of Entrepreneurial Behavior & Research 16, no. 1 (February 2, 2010): 35–57. http://dx.doi.org/10.1108/13552551011020063.

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Carello, Christopher D., and Richard J. Krauzlis. "Manipulating Intent." Neuron 43, no. 4 (August 2004): 575–83. http://dx.doi.org/10.1016/j.neuron.2004.07.026.

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Devereux, Paul. "Acoustic intent." New Scientist 207, no. 2778 (September 2010): 27. http://dx.doi.org/10.1016/s0262-4079(10)62273-7.

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Wambui, Hellen. "Suicidal intent." Nursing Standard 28, no. 34 (April 23, 2014): 61. http://dx.doi.org/10.7748/ns2014.04.28.34.61.s48.

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Jefferis, Danielle. "Carceral Intent." Michigan Journal of Race & Law, no. 27.2 (2022): 323. http://dx.doi.org/10.36643/mjrl.27.2.carceral.

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For decades, scholars across disciplines have examined the stark injustice of American carceralism. Among that body of work are analyses of the various intent requirements embedded in the constitutional doctrine that governs the state’s power to incarcerate. These intent requirements include the “deliberate indifference” standard of the Eighth Amendment, which regulates prison conditions, and the “punitive intent” standard of due process jurisprudence, which regulates the scope of confinement. This Article coins the term “carceral intent” to refer collectively to those legal intent requirements and examines critically the role of carceral intent in shaping and maintaining the deep-rooted structural racism and sweeping harms of America’s system of confinement. This Article begins by tracing the origins of American carceralism, focusing on the modern prison’s relationship to white supremacy and the post-Emancipation period in U.S. history. The Article then turns to the constitutional doctrine of incarceration, synthesizing and categorizing the law of carceral intent. Then, drawing upon critical race scholarship that examines anti-discrimination doctrine and the concept of “white innocence,” the Article compares the law’s reliance on carceral intent with the law’s reliance on discriminatory intent in equal protection jurisprudence. Critical race theorists have long critiqued the intent-focused antidiscrimination doctrine as incapable of remedying structural racism and inequities. The same can be said of the doctrine of incarceration. The law’s preoccupation with an alleged wrongdoer’s “bad intent” in challenges to the scope and conditions of incarceration makes it ill-suited to remedying the U.S. prison system’s profoundly unjust and harmful features. A curative approach, this Article asserts, is one in which the law focuses on carceral effect rather than carceral intent, as others have argued in the context of equal protection. While such an approach will not remedy the full scope of harms of U.S. incarceration, it would be a start.

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Adelson, Bernard H. "Harmful Intent." JAMA: The Journal of the American Medical Association 264, no. 2 (July 11, 1990): 266. http://dx.doi.org/10.1001/jama.1990.03450020118041.

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Sharma, Yogesh, Deval Bhamare, Andreas Kassler, and Javid Taheri. "Intent Negotiation Framework for Intent-Driven Service Management." IEEE Communications Magazine 61, no. 6 (June 2023): 73–79. http://dx.doi.org/10.1109/mcom.001.2200504.

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Zisser, Mackenzie R., Sheri L. Johnson, Michael A. Freeman, and Paige J. Staudenmaier. "The relationship between entrepreneurial intent, gender and personality." Gender in Management: An International Journal 34, no. 8 (October 25, 2019): 665–84. http://dx.doi.org/10.1108/gm-08-2018-0105.

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Purpose The purpose of this study is to examine gender differences in personality traits of people with and without entrepreneurial intent to assess whether women who intend to become entrepreneurs exhibit particular tendencies that can be fostered. Design/methodology/approach Participants completed an online battery of well-established questionnaires to cover a range of personality traits relevant to entrepreneurship and gender. Participants also answered items concerning intent to become an entrepreneur. A factor analysis of personality traits produced four factors (esteem and power, ambition, risk propensity and communal tendency, the latter reflecting openness and cooperation, without hubris). The authors constructed four parallel regression models to examine how gender, entrepreneurial intent and the interaction of gender with intent related to these four personality factor scores. Findings Participants who endorsed a desire to become an entrepreneur reported higher ambition. Women with entrepreneurial intentions endorsed higher levels of communal tendency than men with entrepreneurial intent. Those without entrepreneurial intent did not show gender differences in communal tendency. Research limitations/implications Current findings suggest that men and women who intend to become entrepreneurs share many traits, but women with entrepreneurial intent show unique elevations in communal tendencies. Thus, a worthwhile locus for intervention into the gender disparity in self-employment would be providing space and acknowledgement of prosocial motivation and goals as one highly successful route to entrepreneurship. Originality/value Given the underused economic potential of women entrepreneurs, there is a fundamental need for a rich array of research on factors that limit and promote women’s entry into entrepreneurship. Current findings indicate that personality may be one piece of this puzzle.

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Bhattacharyya, Rupam, and Shyamanta M. Hazarika. "Object Affordance Driven Inverse Reinforcement Learning Through Conceptual Abstraction and Advice." Paladyn, Journal of Behavioral Robotics 9, no. 1 (September 1, 2018): 277–94. http://dx.doi.org/10.1515/pjbr-2018-0021.

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Abstract Within human Intent Recognition (IR), a popular approach to learning from demonstration is Inverse Reinforcement Learning (IRL). IRL extracts an unknown reward function from samples of observed behaviour. Traditional IRL systems require large datasets to recover the underlying reward function. Object affordances have been used for IR. Existing literature on recognizing intents through object affordances fall short of utilizing its true potential. In this paper, we seek to develop an IRL system which drives human intent recognition along with the capability to handle high dimensional demonstrations exploiting the capability of object affordances. An architecture for recognizing human intent is presented which consists of an extended Maximum Likelihood Inverse Reinforcement Learning agent. Inclusion of Symbolic Conceptual Abstraction Engine (SCAE) along with an advisor allows the agent to work on Conceptually Abstracted Markov Decision Process. The agent recovers object affordance based reward function from high dimensional demonstrations. This function drives a Human Intent Recognizer through identification of probable intents. Performance of the resulting system on the standard CAD-120 dataset shows encouraging result.

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González-Arribas, Daniel, Manuel Soler, Javier López-Leonés, Enrique Casado, and Manuel Sanjurjo-Rivo. "Automated optimal flight planning based on the aircraft intent description language." Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering 233, no. 3 (January 30, 2018): 928–48. http://dx.doi.org/10.1177/0954410017751990.

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The future air traffic management system is to be built around the notion of trajectory-based operations. It will rely on automated tools related to trajectory prediction in order to define, share, revise, negotiate and update the trajectory of the aircraft before and during the flight, in some case, in near real time. This paper illustrates how existing standards on trajectory description such as the aircraft intent description language can be enhanced including optimisation capabilities based on numerical optimal control. The Aircraft Intent Description Language is a formal language that has been created in order to describe aircraft intent information in a rigorous, unambiguous and flexible manner. It has been implemented in a platform for a modular design of the trajectory generation process. A case study is presented to explore its effectiveness and identify the requirements and needs to generate optimised aircraft intents with higher automation and flexibility. Preliminary results show the suitability of numerical optimal control to design optimised aircraft intents based on the aircraft intent description language.

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Shams, Sana, and Muhammad Aslam. "Improving User Intent Detection in Urdu Web Queries with Capsule Net Architectures." Applied Sciences 12, no. 22 (November 21, 2022): 11861. http://dx.doi.org/10.3390/app122211861.

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Detecting the communicative intent behind user queries is critically required by search engines to understand a user’s search goal and retrieve the desired results. Due to increased web searching in local languages, there is an emerging need to support the language understanding for languages other than English. This article presents a distinctive, capsule neural network architecture for intent detection from search queries in Urdu, a widely spoken South Asian language. The proposed two-tiered capsule network utilizes LSTM cells and an iterative routing mechanism between the capsules to effectively discriminate diversely expressed search intents. Since no Urdu queries dataset is available, a benchmark intent-annotated dataset of 11,751 queries was developed, incorporating 11 query domains and annotated with Broder’s intent taxonomy (i.e., navigational, transactional and informational intents). Through rigorous experimentation, the proposed model attained the state of the art accuracy of 91.12%, significantly improving upon several alternate classification techniques and strong baselines. An error analysis revealed systematic error patterns owing to a class imbalance and large lexical variability in Urdu web queries.

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Bemmaor, Albert C. "Predicting Behavior from Intention-to-Buy Measures: The Parametric Case." Journal of Marketing Research 32, no. 2 (May 1995): 176–91. http://dx.doi.org/10.1177/002224379503200205.

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The author develops a probabilistic model that converts stated purchase intents into purchase probabilities. The model allows heterogeneity between nonintenders and intenders with respect to their probability to switch to a new “true” purchase intent after the survey, thereby capturing the typical discrepancy between overall mean purchase intent and subsequent proportion of buyers (bias). When the probability to switch of intenders is larger (smaller) than that of nonintenders, the overall mean purchase intent overestimates (underestimates) the proportion of buyers. As special cases, the author derives upper and lower bounds on proportions of buyers from purchase intents data and shows the consistency of those bounds with observed behavior, except in predictable cases such as new products and business markets. However, a straightforward modification of the model deals with new product purchase forecasts.

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Dong, Yichen, Zhen Wang, and Tianjun Wu. "An open intent detection model optimized for datasets based on the Bert large model." Applied and Computational Engineering 47, no. 1 (March 15, 2024): 225–31. http://dx.doi.org/10.54254/2755-2721/47/20241381.

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Within current task-oriented dialogue systems, the focus of intent detection predominantly centers on closed domains. Nevertheless, in real-world usage scenarios, a substantial proportion of interactions fall into the open-domain category. User intentions frequently transcend predefined boundaries, giving rise to a multitude of out-of-domain intents, which pose a formidable challenge to existing models, ultimately leading to diminished recognition rates and accuracy. The demand for open intent detection models is increasing in today's society to address this issue effectively. This paper proposes a method to optimize datasets, thereby enhancing the training accuracy of open intent detection models. Specifically, this paper employs the Adaptive Decision Boundary Learning algorithm, which is currently popular in open intent detection. Leveraging this algorithm, this paper suggests using the K-means clustering algorithm to refine the intent labels within the dataset. This process helps identify and remove outliers in the dataset, making the distinction between known domain and open-domain intent labels more precise. Experimental results on two datasets, banking77 and stackoverflow, demonstrate the effectiveness of our approach in significantly improving model accuracy.

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Yichiet, Aun, Jasmina Khaw Yen Min, Gan Ming Lee, and Low Jun Sheng. "Intent-Based Network Policy to Solution Architecting Recommendations." International Journal of Business Data Communications and Networking 17, no. 1 (January 2021): 55–74. http://dx.doi.org/10.4018/ijbdcn.2021010104.

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The semantic diversity of policies written by people with different IT literacy to achieve certain network security or performance goals created ambiguity to otherwise straightforward solution implementations. In this project, an intent-aware solution recommender is designed to decode semantic cues in network policies written by various demographics for robust solution recommendations. A novel policy analyzer is designed to extract the intrinsic networking intents from ICT policies to provide context-specific solution recommendations. A custom network-aware intent recognizer is trained on a small keywords-to-intents dataset annotated by domain experts using NLP algorithms in AWS comprehend. The bin-of-words model is then used to classify sentences in the policies into predicted ‘intent' class. A collaborative filtering recommendation system using crowd-sourced ground-truth is designed to suggest optimal architecting solutions to achieve the requirements outlined in ICT policies.

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Wan, Xue, Wensheng Zhang, Mengxing Huang, Siling Feng, and Yuanyuan Wu. "A Unified Approach to Nested and Non-Nested Slots for Spoken Language Understanding." Electronics 12, no. 7 (April 6, 2023): 1748. http://dx.doi.org/10.3390/electronics12071748.

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As chatbots become more popular, multi-intent spoken language understanding (SLU) has received unprecedented attention. Multi-intent SLU, which primarily comprises the two subtasks of multiple intent detection (ID) and slot filling (SF), has the potential for widespread implementation. The two primary issues with the current approaches are as follows: (1) They cannot solve the problem of slot nesting; (2) The performance and inference rate of the model are not high enough. To address these issues, we suggest a multi-intent joint model based on global pointers to handle nested and non-nested slots. Firstly, we constructed a multi-dimensional type-slot label interaction network (MTLN) for subsequent intent decoding to enhance the implicit correlation between intents and slots, which allows for more adequate information about each other. Secondly, the global pointer network (GP) was introduced, which not only deals with nested and non-nested slots and slot incoherence but also has a faster inference rate and better performance than the baseline model. On two multi-intent datasets, the proposed model achieves state-of-the-art results on MixATIS with 1.6% improvement of intent Acc, 0.1% improvement of slot F1 values, 3.1% improvement of sentence Acc values, and 1.2%, 1.1% and 4.5% performance improvements on MixSNIPS, respectively. Meanwhile, the inference rate is also improved.

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Zhang, Jinghan, Yuxiao Ye, Yue Zhang, Likun Qiu, Bin Fu, Yang Li, Zhenglu Yang, and Jian Sun. "Multi-Point Semantic Representation for Intent Classification." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9531–38. http://dx.doi.org/10.1609/aaai.v34i05.6498.

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Detecting user intents from utterances is the basis of natural language understanding (NLU) task. To understand the meaning of utterances, some work focuses on fully representing utterances via semantic parsing in which annotation cost is labor-intentsive. While some researchers simply view this as intent classification or frequently asked questions (FAQs) retrieval, they do not leverage the shared utterances among different intents. We propose a simple and novel multi-point semantic representation framework with relatively low annotation cost to leverage the fine-grained factor information, decomposing queries into four factors, i.e., topic, predicate, object/condition, query type. Besides, we propose a compositional intent bi-attention model under multi-task learning with three kinds of attention mechanisms among queries, labels and factors, which jointly combines coarse-grained intent and fine-grained factor information. Extensive experiments show that our framework and model significantly outperform several state-of-the-art approaches with an improvement of 1.35%-2.47% in terms of accuracy.

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Cho, Seonghee, Misty M. Johanson, and Priyanko Guchait. "Employees intent to leave: A comparison of determinants of intent to leave versus intent to stay." International Journal of Hospitality Management 28, no. 3 (September 2009): 374–81. http://dx.doi.org/10.1016/j.ijhm.2008.10.007.

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Gadhave, Kiran, Jochen Görtler, Zach Cutler, Carolina Nobre, Oliver Deussen, Miriah Meyer, Jeff M. Phillips, and Alexander Lex. "Predicting intent behind selections in scatterplot visualizations." Information Visualization 20, no. 4 (August 18, 2021): 207–28. http://dx.doi.org/10.1177/14738716211038604.

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Predicting and capturing an analyst’s intent behind a selection in a data visualization is valuable in two scenarios: First, a successful prediction of a pattern an analyst intended to select can be used to auto-complete a partial selection which, in turn, can improve the correctness of the selection. Second, knowing the intent behind a selection can be used to improve recall and reproducibility. In this paper, we introduce methods to infer analyst’s intents behind selections in data visualizations, such as scatterplots. We describe intents based on patterns in the data, and identify algorithms that can capture these patterns. Upon an interactive selection, we compare the selected items with the results of a large set of computed patterns, and use various ranking approaches to identify the best pattern for an analyst’s selection. We store annotations and the metadata to reconstruct a selection, such as the type of algorithm and its parameterization, in a provenance graph. We present a prototype system that implements these methods for tabular data and scatterplots. Analysts can select a prediction to auto-complete partial selections and to seamlessly log their intents. We discuss implications of our approach for reproducibility and reuse of analysis workflows. We evaluate our approach in a crowd-sourced study, where we show that auto-completing selection improves accuracy, and that we can accurately capture pattern-based intent.

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Parajuli, Srijana, and Subarna Shakya. "Malware Detection and Classification Using Latent Semantic Indexing." Journal of Advanced College of Engineering and Management 4 (December 31, 2018): 153–61. http://dx.doi.org/10.3126/jacem.v4i0.23205.

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The increasing popularity of smart phones has led to the dramatic growth in mobile malware especially in Android platform. Many aspects of android permission has been studied for malware detection but sufficient attention has not been given to intent. This research work proposes using Latent Semantic Indexing for malware detection and classification with permissions and intents based features. This method analyses the Manifest file of an android application by understanding the risk level of permission and intents and assigning weight score based on their sensitivity. In an experiment conducted using a dataset containing 400 malware samples and 400 normal/benign samples, the results show accuracy of 83.5% using Android Intent against 79.1 % using Android permission. Additionally, experiment on combination of both features results in accuracy of 89.7%. It can be concluded from this research work that dataset with intent based features is able to detect malwares more when compared to permissions based features.

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Jung, Shing-Yun, Ting-Han Lin, Chia-Hung Liao, Shyan-Ming Yuan, and Chuen-Tsai Sun. "Intent-Controllable Citation Text Generation." Mathematics 10, no. 10 (May 21, 2022): 1763. http://dx.doi.org/10.3390/math10101763.

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We study the problem of controllable citation text generation by introducing a new concept to generate citation texts. Citation text generation, as an assistive writing approach, has drawn a number of researchers’ attention. However, current research related to citation text generation rarely addresses how to generate the citation texts that satisfy the specified citation intents by the paper’s authors, especially at the beginning of paper writing. We propose a controllable citation text generation model that extends a pre-trained sequence to sequence models, namely, BART and T5, by using the citation intent as the control code to generate the citation text, meeting the paper authors’ citation intent. Experimental results demonstrate that our model can generate citation texts semantically similar to the reference citation texts and satisfy the given citation intent. Additionally, the results from human evaluation also indicate that incorporating the citation intent may enable the models to generate relevant citation texts almost as scientific paper authors do, even when only a little information from the citing paper is available.

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Hidayatulloh, Muhammad Kris Yuan, and Hilyah Ashoumi. "Creativity and entrepreneur knowledge to increase entrepreneurial intent among vocational school students." Journal of Education and Learning (EduLearn) 16, no. 4 (November 1, 2022): 434–39. http://dx.doi.org/10.11591/edulearn.v16i4.19771.

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One way to overcome the problem of unemployment is to change the mindset that in meeting the needs of life, one does not have to work as an employee or employee, but is able to play a role as a business pioneer. This study aims to find out the contribution level of entrepreneurship education and creativity taking into account factors that support entrepreneurs’ intents. This type of investigation was ex post facto with a quota sampling technique of 76 vocational high school students. The data collection was done through tests to measure entrepreneurial knowledge and instruments to measure entrepreneurial intentions. The data analysis technique employed a regression test to determine the relationship and the magnitude of the influence between variables. The study has found that there is a positive correlation between entrepreneurial education and students’ intent in entrepreneurship. The contribution of change in entrepreneurship education partly to intent in entrepreneurship was 32.60%. There is a positive contribution to creativity in students’ intents in entrepreneurship. The contribution to allow for creative change in entrepreneurial intent is partially 18.40%. There is a positive contribution between entrepreneurship education and creativity in students’ intent in entrepreneurship. Other supporting factors are self-efficacy and locus of control.

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Fernando, A. J. "Commonality Breeds Intent." Physiotherapy Theory and Practice 8, no. 1 (January 1992): 1. http://dx.doi.org/10.3109/09593989209108073.

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Percival, Jennifer. "Desire vs intent." Nursing Standard 19, no. 7 (October 27, 2004): 27. http://dx.doi.org/10.7748/ns.19.7.27.s40.

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Williams, Ruth. "Intent on success." Nursing Standard 21, no. 40 (June 13, 2007): 62–63. http://dx.doi.org/10.7748/ns.21.40.62.s59.

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Domhnaill, Aisling Ó., Eamon Grennan, Norman Dugdale, Gerald Dawe, and Pat Boran. "Not Intent Enough." Books Ireland, no. 153 (1991): 200. http://dx.doi.org/10.2307/20626486.

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Lawson, Euan. "Loitering with Intent." British Journal of General Practice 72, no. 714 (December 31, 2021): 3. http://dx.doi.org/10.3399/bjgp22x718001.

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Caswell, Jo. "Intent, implementation, impact." Early Years Educator 23, no. 1 (August 2, 2021): 18–19. http://dx.doi.org/10.12968/eyed.2021.23.1.18.

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Settings are assessed on the quality of education they provide, which is their curriculum. This article explains what is meant by ‘curriculum’ and how leaders can be instrumental in designing a framework of learning tailored specifically for the children in their setting.

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James, Clive. "DECLARATION OF INTENT." Yale Review 105, no. 2 (2017): 16–17. http://dx.doi.org/10.1353/tyr.2017.0070.

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Feminist Network of Hungary. "Declaration of Intent." Feminist Review, no. 39 (1991): 171. http://dx.doi.org/10.2307/1395455.

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Rozendaal, Marco. "Objects with intent." Interactions 23, no. 3 (April 26, 2016): 62–65. http://dx.doi.org/10.1145/2911330.

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Whall, Ann L. "ASSESSING SUICIDAL INTENT." Journal of Gerontological Nursing 13, no. 8 (August 1, 1987): 36–37. http://dx.doi.org/10.3928/0098-9134-19870801-13.

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Ruotsalo, Tuukka, Giulio Jacucci, Petri Myllymäki, and Samuel Kaski. "Interactive intent modeling." Communications of the ACM 58, no. 1 (January 2015): 86–92. http://dx.doi.org/10.1145/2656334.

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Journal articles on the topic 'Intent'

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