Journal articles on the topic 'Multiparty models'
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1
Glasgow, Garrett. "Mixed Logit Models for Multiparty Elections." Political Analysis 9, no. 2 (2001): 116–36. http://dx.doi.org/10.1093/oxfordjournals.pan.a004867.
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Mixed logit (MXL) is a general discrete choice model thus far unexamined in the study of multicandidate and multiparty elections. Mixed logit assumes that the unobserved portions of utility are a mixture of an IID extreme value term and another multivariate distribution selected by the researcher. This general specification allows MXL to avoid imposing the independence of irrelevant alternatives (IIA) property on the choice probabilities. Further, MXL is a flexible tool for examining heterogeneity in voter behavior through random-coefficients specifications. MXL is a more general discrete choice model than multinomial probit (MNP) in several respects, and can be applied to a wider variety of questions about voting behavior than MNP. An empirical example using data from the 1987 British General Election demonstrates the utility of MXL in the study of multicandidate and multiparty elections.
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Alvarez, R. Michael, and Jonathan Nagler. "When Politics and Models Collide: Estimating Models of Multiparty Elections." American Journal of Political Science 42, no. 1 (January 1998): 55. http://dx.doi.org/10.2307/2991747.
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Stoetzer, Lukas F., Marcel Neunhoeffer, Thomas Gschwend, Simon Munzert, and Sebastian Sternberg. "Forecasting Elections in Multiparty Systems: A Bayesian Approach Combining Polls and Fundamentals." Political Analysis 27, no. 2 (November 8, 2018): 255–62. http://dx.doi.org/10.1017/pan.2018.49.
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We offer a dynamic Bayesian forecasting model for multiparty elections. It combines data from published pre-election public opinion polls with information from fundamentals-based forecasting models. The model takes care of the multiparty nature of the setting and allows making statements about the probability of other quantities of interest, such as the probability of a plurality of votes for a party or the majority for certain coalitions in parliament. We present results from two ex ante forecasts of elections that took place in 2017 and are able to show that the model outperforms fundamentals-based forecasting models in terms of accuracy and the calibration of uncertainty. Provided that historical and current polling data are available, the model can be applied to any multiparty setting.
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KAMALI, KAIVAN, XIAOCONG FAN, and JOHN YEN. "TOWARDS A THEORY FOR MULTIPARTY PROACTIVE COMMUNICATION IN AGENT TEAMS." International Journal of Cooperative Information Systems 16, no. 02 (June 2007): 271–98. http://dx.doi.org/10.1142/s0218843007001640.
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Helping behavior in effective teams is achieved via some overlapping "shared mental models" that are developed and maintained by members of the team. In this paper, we take the perspective that multiparty "proactive" communication is critical for establishing and maintaining such a shared mental model among teammates, which is the basis for agents to offer proactive help and to achieve coherent teamwork. We first provide formal semantics for multiparty proactive performatives within a team setting. We then examine how such performatives result in updates to mental model of teammates, and how such updates can trigger helpful behaviors from other teammates. We also provide conversation policies for multiparty proactive performatives.
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Zheng, Boyuan, Patrick Xia, Mahsa Yarmohammadi, and Benjamin Van Durme. "Multilingual Coreference Resolution in Multiparty Dialogue." Transactions of the Association for Computational Linguistics 11 (2023): 922–40. http://dx.doi.org/10.1162/tacl_a_00581.
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Abstract Existing multiparty dialogue datasets for entity coreference resolution are nascent, and many challenges are still unaddressed. We create a large-scale dataset, Multilingual Multiparty Coref (MMC), for this task based on TV transcripts. Due to the availability of gold-quality subtitles in multiple languages, we propose reusing the annotations to create silver coreference resolution data in other languages (Chinese and Farsi) via annotation projection. On the gold (English) data, off-the-shelf models perform relatively poorly on MMC, suggesting that MMC has broader coverage of multiparty coreference than prior datasets. On the silver data, we find success both using it for data augmentation and training from scratch, which effectively simulates the zero-shot cross-lingual setting.
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SCHOFIELD, NORMAN, and ITAI SENED. "Multiparty Competition in Israel, 1988–96." British Journal of Political Science 35, no. 4 (August 22, 2005): 635–63. http://dx.doi.org/10.1017/s0007123405000335.
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Formal models of voting usually assume that political agents, whether parties or candidates, attempt to maximize expected vote shares. ‘Stochastic’ models typically derive the ‘mean voter theorem’ that each agent will adopt a ‘convergent’ policy strategy at the mean of the electoral distribution. In this article, it is argued that this conclusion is contradicted by empirical evidence. Estimates of vote intentions require ‘valence’ terms. The valence of each party derives from the average weight, given by members of the electorate, in judging the overall competence or ‘quality’ of the particular party leader. In empirical models, a party's valence is independent of current policy declarations and can be shown to be statistically significant in the estimation. It is shown here that the addition of valence gives a very strong Bayes factor over an electoral model without valence. The formal model is analysed and shown to be classified by a ‘convergence’ coefficient, defined in terms of the parameters of the empirical model. This coefficient gives necessary and sufficient conditions for convergence. When the necessary condition fails, as it does in these empirical studies with valence, then the convergent equilibrium fails to exist. The empirical evidence is consistent with a formal stochastic model of voting in which there are multiple local Nash equilibria to the vote-maximizing electoral game. Simulation techniques based on the parameters of the empirical model have been used to obtain these local equilibria, which are determined by the principal component of the electoral distribution. Low valence parties, in equilibrium, will tend to adopt positions at the electoral periphery. High valence parties will contest the electoral centre, but will not, in fact, position themselves at the electoral mean. Survey data from Israel for the elections of 1988, 1992 and 1996 are used to compute the parameters of the empirical model and to illustrate the dependence of equilibria on the electoral principal components. The vote maximizing equilibria do not perfectly coincide with the actual party positions. This divergence may be accounted for by more refined models that either (i) include activism or (ii) consider strategic party considerations over post-election coalition bargaining.
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Zhu, Xiao Ming. "Research on Privacy Preserving Data Mining Association Rules Protocol." Advanced Materials Research 756-759 (September 2013): 1661–64. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.1661.
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Privacy preserving in data mining is a significant direction. There has been growing interests in private concerns for future data mining research. Privacy preserving data mining concentrates on developing accurate models without sharing precise individual data records. A privacy preserving association rule mining algorithm was introduced. This algorithm preserved privacy of individual values by computing scalar product. Then, the data mining and secure multiparty computation are briefly introduced. And proposes an implementation for privacy preserving mining protocol based secure multiparty computation protocol.
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Atanasov, Ivaylo I., Evelina N. Pencheva, Denitsa L. Velkova, and Ivaylo P. Asenov. "Multiparty Call Control at the Network Edge." Elektronika ir Elektrotechnika 26, no. 5 (October 27, 2020): 39–49. http://dx.doi.org/10.5755/j01.eie.26.5.26007.
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Network programmability is a key feature of fifth generation (5G) system which, in combination with cloud-based services, can support many use cases, including mission critical and healthcare communications. Programmability enables flexibility in customization of service connectivity. Multi-access Edge Computing (MEC) services and applications are enablers for network programmability. In this paper, MEC capabilities for programmability of multiparty multimedia call control at the network edge are studied. Multiparty video calls are one of the key applications of 5G, and are efficient way to exchange ideas, knowledge, expertise, information, and so on. The paper presents an approach to design MEC Application Programming Interfaces (APIs) which enable third party applications to create multiparty multimedia sessions and dynamically manage session participations. The API functionality is described by required information and message flows. The paper specifies the proposed MEC API with data model. Feasibility study includes modelling and formal validation of multiparty session state models supported by the network and mobile edge application. The latency injected by the API is evaluated by emulation.
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Song, Hyunjin, Dominic Nyhuis, and Hajo Boomgaarden. "A Network Model of Negative Campaigning: The Structure and Determinants of Negative Campaigning in Multiparty Systems." Communication Research 46, no. 2 (June 13, 2017): 273–94. http://dx.doi.org/10.1177/0093650217712596.
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Scholarly attention to the nature and extent of negative campaigning in nonmajoritarian multiparty systems is steadily growing. While prior studies have made commendable progress in outlining the conditions and consequences of negative campaigning, they have typically disregarded the complex interdependencies of multiactor communication environments. The present study focuses on network-structural determinants of negative campaigning. It does so by relying on unique data from the 2013 Austrian federal election and using exponential random graph models to investigate patterns of mediated negative campaigning. We find that—above and beyond common determinants of negative campaigning—indicators of network structure are important predictors of campaign communication. This suggests that network models are crucial for accurately representing campaign communication patterns in multiparty systems.
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Ma, Xu, Cunmei Ji, Xiaoyu Zhang, Jianfeng Wang, Jin Li, Kuan-Ching Li, and Xiaofeng Chen. "Secure multiparty learning from the aggregation of locally trained models." Journal of Network and Computer Applications 167 (October 2020): 102754. http://dx.doi.org/10.1016/j.jnca.2020.102754.
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Deforth, Kevin, Marc Desgroseilliers, Nicolas Gama, Mariya Georgieva, Dimitar Jetchev, and Marius Vuille. "XORBoost: Tree Boosting in the Multiparty Computation Setting." Proceedings on Privacy Enhancing Technologies 2022, no. 4 (October 2022): 66–85. http://dx.doi.org/10.56553/popets-2022-0099.
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We present a novel protocol XORBoost for both training gradient boosted tree models and for using these models for inference in the multiparty computation (MPC) setting. Our protocol supports training for generically split datasets (vertical and horizontal splitting, or combination of those) while keeping all the information about features, thresholds, and evaluation paths private; only tree depth and the number of the binary trees are public parameters of the model. By using novel optimization techniques that reduce the number of oblivious permutation evaluations as well as sorting operations, we further speedup the algorithm. The protocol is agnostic to the underlying MPC framework or implementation.
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Cutler, Josh, Scott De Marchi, Max Gallop, Florian M. Hollenbach, Michael Laver, and Matthias Orlowski. "Cabinet Formation and Portfolio Distribution in European Multiparty Systems." British Journal of Political Science 46, no. 1 (June 9, 2014): 31–43. http://dx.doi.org/10.1017/s0007123414000180.
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Government formation in multiparty systems is of self-evident substantive importance, and the subject of an enormous theoretical literature. Empirical evaluations of models of government formation tend to separate government formation per se from the distribution of key government pay-offs, such as cabinet portfolios, between members of the resulting government. Models of government formation are necessarily specified ex ante, absent any knowledge of the government that forms. Models of the distribution of cabinet portfolios are typically, though not necessarily, specified ex post, taking into account knowledge of the identity of some government ‘formateur’ or even of the composition of the eventual cabinet. This disjunction lies at the heart of a notorious contradiction between predictions of the distribution of cabinet portfolios made by canonical models of legislative bargaining and the robust empirical regularity of proportional portfolio allocations – Gamson’s Law. This article resolves this contradiction by specifying and estimating a joint model of cabinet formation and portfolio distribution that, for example, predicts ex ante which parties will receive zero portfolios rather than taking this as given ex post. It concludes that canonical models of legislative bargaining do increase the ability to predict government membership, but that portfolio distribution between government members conforms robustly to a proportionality norm because portfolio distribution follows the much more difficult process of policy bargaining in the typical government formation process.
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Li, Liyuan, Qianli Xu, Gang S. Wang, Xinguo Yu, Yeow Kee Tan, and Haizhou Li. "Visual Perception Based Engagement Awareness for Multiparty Human–Robot Interaction." International Journal of Humanoid Robotics 12, no. 04 (November 27, 2015): 1550019. http://dx.doi.org/10.1142/s021984361550019x.
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Computational systems for human–robot interaction (HRI) could benefit from visual perceptions of social cues that are commonly employed in human–human interactions. However, existing systems focus on one or two cues for attention or intention estimation. This research investigates how social robots may exploit a wide spectrum of visual cues for multiparty interactions. It is proposed that the vision system for social cue perception should be supported by two dimensions of functionality, namely, vision functionality and cognitive functionality. A vision-based system is proposed for a robot receptionist to embrace both functionalities for multiparty interactions. The module of vision functionality consists of a suite of methods that computationally recognize potential visual cues related to social behavior understanding. The performance of the models is validated by the ground truth annotation dataset. The module of cognitive functionality consists of two computational models that (1) quantify users’ attention saliency and engagement intentions, and (2) facilitate engagement-aware behaviors for the robot to adjust its direction of attention and manage the conversational floor. The performance of the robot’s engagement-aware behaviors is evaluated in a multiparty dialog scenario. The results show that the robot’s engagement-aware behavior based on visual perceptions significantly improve the effectiveness of communication and positively affect user experience.
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Merrill, Samuel, and James Adams. "Computing Nash Equilibria in Probabilistic, Multiparty Spatial Models with Nonpolicy Components." Political Analysis 9, no. 4 (2001): 347–61. http://dx.doi.org/10.1093/oxfordjournals.pan.a004874.
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Although there exist extensive results concerning equilibria in spatial models of two-party elections with probabilistic voting, we know far less about equilibria in multiparty elections—i.e., under what conditions will equilibria exist, and what are the characteristics of equilibrium configurations? We derive conditions that guarantee the existence of a unique Nash equilibrium and develop an algorithm to compute that equilibrium inmultiparty elections with probabilistic voting, in which voters choose according to the behaviorists' fully specified multivariate vote model. Previously, such computations could only be approximated by laborious search methods. The algorithm, which assumes a conditional logit choice function, can be applied to spatial competition for a variety of party objectives including vote-maximization and margin-maximization, and can also encompass alternative voter policy metrics such as quadratic and linear loss functions. We show that our conditions for an equilibrium are plausible given the empirically-estimated parameters that behaviorists report for voting behavior in historical elections. We also show that parties' equilibrium positions depend not only on the distribution of voters' policy preferences but also on their nonpolicy-related attributes such as partisanship and sociodemographic variables. Empirical applications to data from a recent French election illustrate the use of the algorithm and suggest that a unique Nash equilibrium existed in that election.
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Zhou, Zhou, Youliang Tian, and Changgen Peng. "Privacy-Preserving Federated Learning Framework with General Aggregation and Multiparty Entity Matching." Wireless Communications and Mobile Computing 2021 (June 26, 2021): 1–14. http://dx.doi.org/10.1155/2021/6692061.
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The requirement for data sharing and privacy has brought increasing attention to federated learning. However, the existing aggregation models are too specialized and deal less with users’ withdrawal issue. Moreover, protocols for multiparty entity matching are rarely covered. Thus, there is no systematic framework to perform federated learning tasks. In this paper, we systematically propose a privacy-preserving federated learning framework (PFLF) where we first construct a general secure aggregation model in federated learning scenarios by combining the Shamir secret sharing with homomorphic cryptography to ensure that the aggregated value can be decrypted correctly only when the number of participants is greater than t . Furthermore, we propose a multiparty entity matching protocol by employing secure multiparty computing to solve the entity alignment problems and a logistic regression algorithm to achieve privacy-preserving model training and support the withdrawal of users in vertical federated learning (VFL) scenarios. Finally, the security analyses prove that PFLF preserves the data privacy in the honest-but-curious model, and the experimental evaluations show PFLF attains consistent accuracy with the original model and demonstrates the practical feasibility.
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Macdonald, Stuart Elaine, Ola Listhaug, and George Rabinowitz. "Issues and Party Support in Multiparty Systems." American Political Science Review 85, no. 4 (December 1991): 1107–31. http://dx.doi.org/10.2307/1963938.
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We investigate the relationship between party issue position and mass evaluation of political parties in multiparty systems. In so doing, we engage two competing theories of mass-elite linkage: the directional theory and the traditional spatial theory of elections. The alternate models are evaluated with survey data gathered in Norway in 1989. The data collection is unique in providing extensive information on the issue positions of all parties with potential for achieving representation in the parliament. Results suggest that the directional theory provides a better description of the relationship. Consistent with directional theory, we find that when parties occupy a centrist position on an issue they are not evaluated on the basis of that issue. Voters neither love nor hate a party in the middle. Thus, in order to build support on the basis of issues, parties must offer some strong stands.
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KERENIDIS, IORDANIS. "Quantum multiparty communication complexity and circuit lower bounds." Mathematical Structures in Computer Science 19, no. 1 (February 2009): 119–32. http://dx.doi.org/10.1017/s0960129508007263.
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We define a quantum model for multiparty communication complexity and prove a simulation theorem between the classical and quantum models. As a result, we show that if the quantum k-party communication complexity of a function f is Ω(n/2k), its classical k-party communication is Ω(n/2k/2). Finding such an f would allow us to prove strong classical lower bounds for k ≥ log n players and make progress towards solving a major open question about symmetric circuits.
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Wang, Qianlong, Yifan Guo, Xufei Wang, Tianxi Ji, Lixing Yu, and Pan Li. "AI at the Edge: Blockchain-Empowered Secure Multiparty Learning With Heterogeneous Models." IEEE Internet of Things Journal 7, no. 10 (October 2020): 9600–9610. http://dx.doi.org/10.1109/jiot.2020.2987843.
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Zhou, Ji, Zhusen Liu, Luyao Wang, Chuan Zhao, Zhe Liu, and Lu Zhou. "Practical and Malicious Multiparty Private Set Intersection for Small Sets." Electronics 12, no. 23 (November 30, 2023): 4851. http://dx.doi.org/10.3390/electronics12234851.
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Private set intersection (PSI) is a pivotal subject in the realm of privacy computation. Numerous research endeavors have concentrated on situations involving vast and imbalanced sets. Nevertheless, there is a scarcity of existing PSI protocols tailored for small sets. Those that exist are either restricted to interactions between two parties or necessitate resource-intensive homomorphic operations. To bring forth practical multiparty private set intersection solutions for small sets, we present two multiparty PSI protocols founded on the principles of Oblivious Key–Value Stores (OKVSs), polynomials, and gabled cuckoo tables. Our security analysis underscores the resilience of these protocols against malicious models and collision attacks. Through experimental evaluations, we establish that, in comparison to related endeavors, our protocols excel in small-set contexts, particularly in low-bandwidth wide area network (WAN) settings.
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Torra, Vicenç. "Random dictatorship for privacy-preserving social choice." International Journal of Information Security 19, no. 5 (October 16, 2019): 537–45. http://dx.doi.org/10.1007/s10207-019-00474-7.
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Abstract Social choice provides methods for collective decisions. They include methods for voting and for aggregating rankings. These methods are used in multiagent systems for similar purposes when decisions are to be made by agents. Votes and rankings are sensitive information. Because of that, privacy mechanisms are needed to avoid the disclosure of sensitive information. Cryptographic techniques can be applied in centralized environments to avoid the disclosure of sensitive information. A trusted third party can then compute the outcome. In distributed environments, we can use a secure multiparty computation approach for implementing a collective decision method. Other privacy models exist. Differential privacy and k-anonymity are two of them. They provide privacy guarantees that are complementary to multiparty computation approaches, and solutions that can be combined with the cryptographic ones, thus providing additional privacy guarantees, e.g., a differentially private multiparty computation model. In this paper, we propose the use of probabilistic social choice methods to achieve differential privacy. We use the method called random dictatorship and prove that under some circumstances differential privacy is satisfied and propose a variation that is always compliant with this privacy model. Our approach can be implemented using a centralized approach and also a decentralized approach. We briefly discuss these implementations.
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Enayet, Ayesha, and Gita Sukthankar. "Learning a Generalizable Model of Team Conflict from Multiparty Dialogues." International Journal of Semantic Computing 15, no. 04 (December 2021): 441–60. http://dx.doi.org/10.1142/s1793351x21400110.
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Good communication is indubitably the foundation of effective teamwork. Over time teams develop their own communication styles and often exhibit entrainment, a conversational phenomena in which humans synchronize their linguistic choices. Conversely, teams may experience conflict due to either personal incompatibility or differing viewpoints. We tackle the problem of predicting team conflict from embeddings learned from multiparty dialogues such that teams with similar post-task conflict scores lie close to one another in vector space. Embeddings were extracted from three types of features: (1) dialogue acts, (2) sentiment polarity, and (3) syntactic entrainment. Machine learning models often suffer domain shift; one advantage of encoding the semantic features is their adaptability across multiple domains. To provide intuition on the generalizability of different embeddings to other goal-oriented teamwork dialogues, we test the effectiveness of learned models trained on the Teams corpus on two other datasets. Unlike syntactic entrainment, both dialogue act and sentiment embeddings are effective for identifying team conflict. Our results show that dialogue act-based embeddings have the potential to generalize better than sentiment and entrainment-based embeddings. These findings have potential ramifications for the development of conversational agents that facilitate teaming.
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Zhang, Qiuyue, Xiao Zheng, and Xiujun Wang. "An Efficient Online Multiparty Interactive Medical Prediagnosis Scheme with Privacy Protection." Wireless Communications and Mobile Computing 2021 (December 15, 2021): 1–13. http://dx.doi.org/10.1155/2021/7746286.
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Medical prediagnosis systems are now available online to give users quick and preliminary diagnosis information. The need for such a system has become particularly evident in areas with insufficient health professionals. Due to the privacy of patient medical information and the sensitivity of cloud diagnosis models, it is necessary to protect the security of data, models, and communications. These existing diagnosis systems can hardly provide a satisfied diagnosis accuracy while ensuring comprehensive security and high efficiency. In order to solve these problems, we proposed Relief- k minimum Wasserstein distance (Relief- k MW) classification method, which combined data encryption and BLS signature to form a privacy-preserving efficient online multiparty interactive medical prediagnostic scheme (OMPD). Theoretical analysis shows our OMPD effectively provides high-precision prediagnosis services. Extensive experimental results demonstrate that OMPD not only greatly improves the diagnostic accuracy but also reduces the computational and communication overhead.
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Greenberg, Joseph, and Kenneth Shepsle. "The Effect of Electoral Rewards in Multiparty Competition with Entry." American Political Science Review 81, no. 2 (June 1987): 525–37. http://dx.doi.org/10.2307/1961965.
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The authors elaborate a model of electoral competition for a fixed number of seats in a legislature. The novel feature of this model is that candidates (or parties) not only choose spatial locations as platforms but also determine whether to enter the contest at all. In most previous spatial models, the set of candidates is specified exogenously. Here, however, the spatial positions and the set of candidates are determined endogenously. An equilibrium in this context is defined and results are proved, suggesting that entry may disrupt spatial equilibria. Finally, the authors compare their treatment of spatial competition with entry to that of Palfrey.
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Mishra, Utkarsh, Debraj Rakshit, R. Prabhu, Aditi Sen(De), and Ujjwal Sen. "Constructive interference between disordered couplings enhances multiparty entanglement in quantum Heisenberg spin glass models." New Journal of Physics 18, no. 8 (August 25, 2016): 083044. http://dx.doi.org/10.1088/1367-2630/18/8/083044.
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Jackson, John E. "A Seemingly Unrelated Regression Model for Analyzing Multiparty Elections." Political Analysis 10, no. 1 (2002): 49–65. http://dx.doi.org/10.1093/pan/10.1.49.
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This paper develops an estimator for models of election returns in multiparty elections. It shares the same functional formas the Katz—King estimator but is computationally simpler, can be used with any number of parties, and is based on more conventional distributional assumptions. Small sample properties of the estimator are derived, which makes it particularly useful in many of the applications where there are a relatively small number of voting districts. The distributional assumptions are contained in two elements. The first treats the observed votes as the outcomes resulting from sampling the voters in each district. The second stochastic element arises from the usual treatment of the stochastic term in a regression model, namely, the inability of the included variables and the linear form to match the underlying process perfectly. The model is then used to analyze the 1993 Polish parliamentary elections. The results from this analysis are used to develop Monte Carlo experiments comparing several different yet feasible estimators. The conclusion is that a number of accessible estimators, including the standard seemingly unrelated regression model and the Beck-Katz model with panel-corrected standard errors, are all good choices.
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Mukherjee, Bumba. "Political Parties and the Size of Government in Multiparty Legislatures." Comparative Political Studies 36, no. 6 (August 2003): 699–728. http://dx.doi.org/10.1177/0010414003254240.
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This article tests the effect of an increase in the number of represented political parties and the size of the majority party on the size of government—proxied by central government expenditure as a percentage of GDP—in multiparty legislatures. The author argues that an increase in the number of represented parties leads to higher central government expenditure. Conversely, as the size of the majority party grows from a bare-minimum majority to above the supermajority level, it has a nonlinear, specifically “cube” effect on central government expenditure. Panel data on central government expenditure from 110 countries are used to test these arguments. The results corroborate the theoretical claims and are robust in regression models where fixed-effects were introduced and endogeneity was corrected. Finally, an increase in the number of represented parties leads to higher government spending on subsidies and transfers but to lower spending on public goods.
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Honaker, James, Jonathan N. Katz, and Gary King. "A Fast, Easy, and Efficient Estimator for Multiparty Electoral Data." Political Analysis 10, no. 1 (2002): 84–100. http://dx.doi.org/10.1093/pan/10.1.84.
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Katz and King have previously developed a model for predicting or explaining aggregate electoral results in multiparty democracies. Their model is, in principle, analogous to what least-squares regression provides American political researchers in that two-party system. Katz and King applied their model to three-party elections in England and revealed a variety of new features of incumbency advantage and sources of party support. Although the mathematics of their statistical model covers any number of political parties, it is computationally demanding, and hence slow and numerically imprecise, with more than three parties. In this paper we produce an approximate method that works in practice with many parties without making too many theoretical compromises. Our approach is to treat the problem as one of missing data. This allows us to use a modification of the fast EMis algorithm of King, Honaker, Joseph, and Scheve and to provide easy-to-use software, while retaining the attractive features of the Katz and King model, such as thetdistribution and explicit models for uncontested seats.
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Swaab, Roderick I., Tom Postmes, Peter Neijens, Marius H. Kiers, and Adrie C. M. Dumay. "Multiparty Negotiation Support: The Role of Visualization’s Influence on the Development of Shared Mental Models." Journal of Management Information Systems 19, no. 1 (July 2002): 129–50. http://dx.doi.org/10.1080/07421222.2002.11045708.
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Nihei and Nakano. "Exploring Methods for Predicting Important Utterances Contributing to Meeting Summarization." Multimodal Technologies and Interaction 3, no. 3 (July 6, 2019): 50. http://dx.doi.org/10.3390/mti3030050.
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Meeting minutes are useful, but creating meeting summaries are a time consuming task. Aiming at supporting such task, this paper proposes prediction models for important utterances that should be included in the meeting summary by using multimodal and multiparty features. We will tackle this issue from two approaches: Handcrafted feature models and deep neural network models. The best handcrafted feature model achieved 0.707 in F-measure, and the best deep-learning based verbal and nonverbal model (V-NV model) achieved 0.827 in F-measure. Based on the V-NV model, we implemented a meeting browser, and conducted a user study. The results showed that the proposed meeting browser better contributes to the understanding of the content of the discussion and the participant roles in the discussion than the conventional text-based browser.
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Shi, Minyu, Yongting Zhang, Huanhuan Wang, Junfeng Hu, and Xiang Wu. "A Clonal Selection Optimization System for Multiparty Secure Computing." Complexity 2021 (July 9, 2021): 1–14. http://dx.doi.org/10.1155/2021/7638394.
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The innovation of the deep learning modeling scheme plays an important role in promoting the research of complex problems handled with artificial intelligence in smart cities and the development of the next generation of information technology. With the widespread use of smart interactive devices and systems, the exponential growth of data volume and the complex modeling requirements increase the difficulty of deep learning modeling, and the classical centralized deep learning modeling scheme has encountered bottlenecks in the improvement of model performance and the diversification of smart application scenarios. The parallel processing system in deep learning links the virtual information space with the physical world, although the distributed deep learning research has become a crucial concern with its unique advantages in training efficiency, and improving the availability of trained models and preventing privacy disclosure are still the main challenges faced by related research. To address these above issues in distributed deep learning, this research developed a clonal selective optimization system based on the federated learning framework for the model training process involving large-scale data. This system adopts the heuristic clonal selective strategy in local model optimization and optimizes the effect of federated training. First of all, this process enhances the adaptability and robustness of the federated learning scheme and improves the modeling performance and training efficiency. Furthermore, this research attempts to improve the privacy security defense capability of the federated learning scheme for big data through differential privacy preprocessing. The simulation results show that the proposed clonal selection optimization system based on federated learning has significant optimization ability on model basic performance, stability, and privacy.
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Thurner, Paul W. "The empirical application of the spatial theory of voting in multiparty systems with random utility models." Electoral Studies 19, no. 4 (December 2000): 493–517. http://dx.doi.org/10.1016/s0261-3794(99)00025-6.
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De La O, Ana L., and Jonathan A. Rodden. "Does Religion Distract the Poor?" Comparative Political Studies 41, no. 4-5 (April 2008): 437–76. http://dx.doi.org/10.1177/0010414007313114.
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This article asks whether religion undermines the negative relationship between income and left voting that is assumed in standard political economy models of democracy. Analysis of cross-country survey data reveals that this correlation disappears among religious individuals in countries that use proportional representation. This is the case in large part because there is a moral values dimension that has a correlation with income that is equal in magnitude but has the opposite sign as the economic dimension, and the votes of the religious are better explained by their positions on moral than economic issues, especially in countries with multiparty systems. The authors conclude by discussing implications for theories of redistribution.
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Radenkovic, Milena. "A Framework for Building and Deploying the Multiparty Audio Service for Collaborative Environments." Presence: Teleoperators and Virtual Environments 13, no. 6 (December 2004): 708–25. http://dx.doi.org/10.1162/1054746043280628.
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Distributed partial mixing (DPM) is an approach to creating a distributed audio service that supports optimization of bandwidth utilization across multiple related audio streams (e.g., from concurrently active audio sources) while maintaining fairness to TCP traffic in best-effort networks. Rate adaptation of streamed audio is difficult because of its rate sensitivity, the relatively limited range of encoding bandwidths available, and the potential impact on the end user of rate-adaptation artifacts (such as changes of encoding). This paper describes and demonstrates how our design combines TCP-fairness with the stability that is desirable for streaming audio and other rate-sensitive media. In particular, our design combines: a distributed multi-stream management/mixing architecture, loss-event and round-trip time monitoring, rate limiting based on a TCP rate equation, tuned increase and decrease strategies, and a loss-driven network-probing mode. Experimental validation is performed against TCP and independent DPM traffic. In particular, we summarize and discuss the two contrasting models for deploying DPM within the context of large dynamic environments that we introduced in Radenkovic and Greenhalgh (2002), Proceedings of ACM VRST 2002, 179–185. We argue that the DPM paradigm remains feasible and desirable in such environments.
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Kleinschmit, Stephen. "Addressing procedural bias in municipal planning governance: A case for incorporating citizen participation within technical advisory committees." International Journal of Organization Theory & Behavior 18, no. 1 (March 1, 2015): 1–20. http://dx.doi.org/10.1108/ijotb-18-01-2015-b001.
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This essay presents models of multiparty negotiation as a means to compare the conventional public meetings format of planning to a preliminary process, the technical advisory committee. A metric of market concentration, the Herfindahl-Hirschman Index, is used to quantify the structural advantages in each, and presented within the context of municipal planning processes. In doing so, this work advances several propositions: First, open meetings expand power differentials between parties, which lead to outcomes that reflect the political efficacy of participants over the regulatory purpose of government. Second, such meetings create substantial transaction costs for the public, creating a barrier to the expression of community values. Finally, preliminary processes constitute a more effective forum for citizen participation than open meetings.
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Jiang, Hang, Xianzhe Zhang, and Jinho D. Choi. "Automatic Text-Based Personality Recognition on Monologues and Multiparty Dialogues Using Attentive Networks and Contextual Embeddings (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (April 3, 2020): 13821–22. http://dx.doi.org/10.1609/aaai.v34i10.7182.
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Previous works related to automatic personality recognition focus on using traditional classification models with linguistic features. However, attentive neural networks with contextual embeddings, which have achieved huge success in text classification, are rarely explored for this task. In this project, we have two major contributions. First, we create the first dialogue-based personality dataset, FriendsPersona , by annotating 5 personality traits of speakers from Friends TV Show through crowdsourcing. Second, we present a novel approach to automatic personality recognition using pre-trained contextual embeddings (BERT and RoBERTa) and attentive neural networks. Our models largely improve the state-of-art results on the monologue Essays dataset by 2.49%, and establish a solid benchmark on our FriendsPersona. By comparing results in two datasets, we demonstrate the challenges of modeling personality in multi-party dialogue.
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Froelicher, David, Juan R. Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Sa Sousa, Jean-Philippe Bossuat, and Jean-Pierre Hubaux. "Scalable Privacy-Preserving Distributed Learning." Proceedings on Privacy Enhancing Technologies 2021, no. 2 (January 29, 2021): 323–47. http://dx.doi.org/10.2478/popets-2021-0030.
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Abstract In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. We design spindle (Scalable Privacy-preservINg Distributed LEarning), the first distributed and privacy-preserving system that covers the complete ML workflow by enabling the execution of a cooperative gradient-descent and the evaluation of the obtained model and by preserving data and model confidentiality in a passive-adversary model with up to N −1 colluding parties. spindle uses multiparty homomorphic encryption to execute parallel high-depth computations on encrypted data without significant overhead. We instantiate spindle for the training and evaluation of generalized linear models on distributed datasets and show that it is able to accurately (on par with non-secure centrally-trained models) and efficiently (due to a multi-level parallelization of the computations) train models that require a high number of iterations on large input data with thousands of features, distributed among hundreds of data providers. For instance, it trains a logistic-regression model on a dataset of one million samples with 32 features distributed among 160 data providers in less than three minutes.
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Wang, Yuyan, Zhaoqing Yu, Liang Shen, Runjie Fan, and Rongyun Tang. "Decisions and Coordination in E-Commerce Supply Chain under Logistics Outsourcing and Altruistic Preferences." Mathematics 9, no. 3 (January 27, 2021): 253. http://dx.doi.org/10.3390/math9030253.
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Considering the peculiarities of logistics in the electronic commerce (e-commerce) supply chain (ESC) and e-commerce platform’s altruistic preferences, a model including an e-commerce platform, third-party logistics service provider, and manufacturer is constructed. Based on this, three decision models are proposed and equilibrium solutions are obtained by the Stackelberg game. Then, an “altruistic preference joint fixed-cost” contract is proposed to maximize system efficiency. Finally, numerical analysis is used to validate the findings of the paper. The article not only analyzes and compares the optimal decisions under different ESC models, but also explores the intrinsic factors affecting the decisions. This paper finds that the conclusions of dual-channel supply chains or traditional supply chains do not necessarily apply to ESC, and that the effect of altruistic behavior under ESC is influenced by consumer preferences. Moreover, there is a multiparty win–win state for ESC, and this state can be achieved through the “altruistic preference joint fixed-cost” contract. Therefore, the findings of this paper contribute to the development of an e-commerce market and the cooperation of ESC members.
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Ellemers, Naomi, Susan T. Fiske, Andrea E. Abele, Alex Koch, and Vincent Yzerbyt. "Adversarial alignment enables competing models to engage in cooperative theory building toward cumulative science." Proceedings of the National Academy of Sciences 117, no. 14 (March 13, 2020): 7561–67. http://dx.doi.org/10.1073/pnas.1906720117.
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Crises in science concern not only methods, statistics, and results but also, theory development. Beyond the indispensable refinement of tools and procedures, resolving crises would also benefit from a deeper understanding of the concepts and processes guiding research. Usually, theories compete, and some lose, incentivizing destruction of seemingly opposing views. This does not necessarily contribute to accumulating insights, and it may incur collateral damage (e.g., impairing cognitive processes and collegial relations). To develop a more constructive model, we built on adversarial collaboration, which integrates incompatible results into agreed-on new empirical research to test competing hypotheses [D. Kahneman,Am. Psychol.58, 723–730 (2003)]. Applying theory and evidence from the behavioral sciences, we address the group dynamic complexities of adversarial interactions between scientists. We illustrate the added value of considering these in an “adversarial alignment” that addressed competing conceptual frameworks from five different theories of social evaluation. Negotiating a joint framework required two preconditions and several guidelines. First, we reframed our interactions from competitive rivalry to cooperative pursuit of a joint goal, and second, we assumed scientific competence and good intentions, enabling cooperation toward that goal. Then, we applied five rules for successful multiparty negotiations: 1) leveling the playing field, 2) capitalizing on curiosity, 3) producing measurable progress, 4) working toward mutual gain, and 5) being aware of the downside alternative. Together, these guidelines can encourage others to create conditions that allow for theoretical alignments and develop cumulative science.
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Verdugo, Sergio, and Marcela Prieto. "The dual aversion of Chile’s constitution-making process." International Journal of Constitutional Law 19, no. 1 (January 1, 2021): 149–68. http://dx.doi.org/10.1093/icon/moab011.
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Abstract Chile initiated a constitution-making process in late 2019, after the major political parties signed an agreement to respond to the massive demonstrations that took over the streets in October of 2019. Dominant trends in Chile and Latin America’s constitutional thought typically examine this type of process through the lenses of the constituent power or transformative constitutionalism. The authors of this essay offer a different view. They argue that Chile’s constitution-making process, as designed by the multiparty agreement, manifests a double aversion: to avoid the Bolivarian way of constitution-making—including its associated constituent power narrative—and to put an end to the institutional and symbolic legacy of the Pinochet regime. In attempting to stay clear of these two negative models, the authors argue that the rules of the constitution-making process have adopted the main features of the post-sovereign model of constitution-making.
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Patel, Unnati K., and Feon Jaison. "Diabesta Faction Security in Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 954–61. http://dx.doi.org/10.22214/ijraset.2022.40780.
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Abstract: Diabetes is a disease that could impact high levels of glucose in the human body. It should not be ignored until proper treatment is administered with proper precautions, sometimes due to irresponsibility assumed by patients, leading to heart problems, kidney problems, blood pressure, lesions eyes and may affect other organs of the human body. If precautions are taken from the beginning, it can be cured. In the proposed work machine learning classification and defined techniques on a dataset to predict diabetes is being done. Such as Logistic Regression (LR), Decision Tree (DT), Support Vector Machine (SVM) and Random Forest (RF). The end result shows that Random Forest achieved higher accuracy than other machine learning techniques. The importance of privacy in deep learning applications is directly related to the emergence of distributed and multiparty models. Keywords: Machine learning, prediction, security, differential privacy, random forest, logistic regression, super vector machine.
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Fedotov, I. A., A. S. Khritankov, and M. D. Obidare. "Automated Verification of Multi-Party Agreements and Scheduling of Sending Messages in Distributed Ledger Systems." Programmnaya Ingeneria 13, no. 4 (April 20, 2022): 200–208. http://dx.doi.org/10.17587/prin.13.200-208.
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One can use a multi-party agreement in distributed ledger systems and blockchain networks to reach an agreement on changes of the state of the system. If one of the network members proposes а transaction, then certain network participants shall confirm it. After that the whole network can consider transaction as a valid one. A multiparty agreement or consensus determines the composition of these participants. Based on the historical data set, one can calculate the probability of confirming a transaction for each of the participants. In this paper, we use a statistical model checking approach to determine the likelihood that the network accepts a transaction. Sending confirmation requests may require an additional fee. We calculate the probability, and the mathematical expectation of the number of messages before reaching a consensus. Further, consensus models are built in the form of a Markov chain with various strategies for sending messages. Based on the proposed methods, we design a tool that automatically builds models for various strategies of sending messages and verifies the model using a statistical model verification approach. After choosing the optimal model, one can send confirmation messages using the scheduler module of developed tool.
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Fedotov, I. A., A. S. Khritankov, and M. D. Obidare. "Automated Verification of Multi-Party Agreements and Scheduling of Sending Messages in Distributed Ledger Systems." Programmnaya Ingeneria 13, no. 4 (April 20, 2022): 200–208. http://dx.doi.org/10.17587/prin.13.200-208.
Full textAbstract:
One can use a multi-party agreement in distributed ledger systems and blockchain networks to reach an agreement on changes of the state of the system. If one of the network members proposes а transaction, then certain network participants shall confirm it. After that the whole network can consider transaction as a valid one. A multiparty agreement or consensus determines the composition of these participants. Based on the historical data set, one can calculate the probability of confirming a transaction for each of the participants. In this paper, we use a statistical model checking approach to determine the likelihood that the network accepts a transaction. Sending confirmation requests may require an additional fee. We calculate the probability, and the mathematical expectation of the number of messages before reaching a consensus. Further, consensus models are built in the form of a Markov chain with various strategies for sending messages. Based on the proposed methods, we design a tool that automatically builds models for various strategies of sending messages and verifies the model using a statistical model verification approach. After choosing the optimal model, one can send confirmation messages using the scheduler module of developed tool.
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Tožička, Jan, Michal Štolba, and Antonín Komenda. "The Limits of Strong Privacy Preserving Multi-Agent Planning." Proceedings of the International Conference on Automated Planning and Scheduling 27 (June 5, 2017): 297–305. http://dx.doi.org/10.1609/icaps.v27i1.13828.
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Multi-agent planning using MA-STRIPS-related models is often motivated by the preservation of private information. Such motivation is not only natural for multi-agent systems, but it is one of the main reasons, why multi-agent planning (MAP) problems cannot be solved centrally. In this paper, we analyze privacy-preserving multi-agent planning (PP-MAP) from the perspective of secure multiparty computation (MPC). We discuss the concept of strong privacy and its implications and present two variants of a novel planner, provably strong privacy-preserving in general. As the main contribution, we formulate the limits of strong privacy-preserving planning in the terms of privacy, completeness and efficiency and show that, for a wide class of planning algorithms, all three properties are not achievable at once. Moreover, we provide a restricted variant of strong privacy based on equivalence classes of planning problems and show that an efficient, complete and strong privacy-preserving planner exists for such restriction.
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Jofré, Hugo, and Patricio Navia. "O impacto da participação em primárias presidenciais simultâneas opcionais na porcentagem de votos para o candidato da coalizão na eleição geral: evidências do sistema de dois turnos do Chile." Colombia Internacional, no. 118 (April 11, 2024): 59–84. http://dx.doi.org/10.7440/colombiaint118.2024.03.
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Objective/context: We assess the effect of turnout in multiparty-coalition presidential primaries on the electoral support for the primary winner in two-round presidential elections. Does holding presidential primaries have a positive impact on the vote share received by the primary winner and/or political party in the corresponding presidential election? Methodology: We use municipal-level data in the three election cycles (2013-2021) since adopting optional presidential primaries in Chile to estimate ordinary least squares (OLS) models and assess the effect of turnout in the primaries on vote share in the general election. Conclusions: We identify a positive association between turnout in the primaries and vote share for the coalition candidate in the presidential election, with a higher impact on the runoff than in the first round. Originality: As primaries mobilize more ideological voters, the effect of primary turnout is stronger in the runoff when voters are more likely to align along clearly defined ideological lines than in the first round when primary voters normally have more than one option that matches their ideological preferences.
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Gupta, Gauri, Krithika Ramesh, Anwesh Bhattacharya, Divya Gupta, Rahul Sharma, Nishanth Chandran, and Rijurekha Sen. "End-to-end Privacy Preserving Training and Inference for Air Pollution Forecasting with Data from Rival Fleets." Proceedings on Privacy Enhancing Technologies 2023, no. 4 (October 2023): 436–51. http://dx.doi.org/10.56553/popets-2023-0118.
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Privacy-preserving machine learning (PPML) promises to train machine learning (ML) models by combining data spread across multiple data silos. Theoretically, secure multiparty computation (MPC) allows multiple data owners to train models on their joint data without revealing the data to each other. However, the prior implementations of this secure training using MPC have three limitations: they have only been evaluated on CNNs, and LSTMs have been ignored; fixed point approximations have affected training accuracies compared to training in floating point; and due to significant latency overheads of secure training via MPC, its relevance for practical tasks with streaming data remains unclear. The motivation of this work is to report our experience of addressing the practical problem of secure training and inference of models for urban sensing problems, e.g., traffic congestion estimation, or air pollution monitoring in large cities, where data can be contributed by rival fleet companies while balancing the privacy-accuracy trade-offs using MPC-based techniques.Our first contribution is to design a custom ML model for this task that can be efficiently trained with MPC within a desirable latency. In particular, we design a GCN-LSTM and securely train it on time-series sensor data for accurate forecasting, within 7 minutes per epoch. As our second contribution, we build an end-to-end system of private training and inference that provably matches the training accuracy of cleartext ML training. This work is the first to securely train a model with LSTM cells. Third, this trained model is kept secret-shared between the fleet companies and allows clients to make sensitive queries to this model while carefully handling potentially invalid queries. Our custom protocols allow clients to query predictions from privately trained models in milliseconds, all the while maintaining accuracy and cryptographic security.
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Jiménez, Ernesto, José Luis López-Presa, and Marta Patiño-Martínez. "Consensus in anonymous asynchronous systems with crash-recovery and omission failures." Computing 103, no. 12 (October 8, 2021): 2811–37. http://dx.doi.org/10.1007/s00607-021-01023-8.
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AbstractIn anonymous distributed systems, processes are indistinguishable because they have no identity and execute the same algorithm. Currently, anonymous systems are receiving a lot of attention mainly because they preserve privacy, which is an important property when we want to avoid impersonation attacks. On the other hand, Consensus is a fundamental problem in distributed computing. It is well-known that Consensus cannot be deterministically solved in pure asynchronous anonymous systems if processes can crash (the so-called crash-stop failure model). This impossibility holds even if message losses never occur in transmission. Failure detectors are an elegant and powerful abstraction for achieving deterministic Consensus in asynchronous distributed systems. A failure detector is a distributed object that gives the processes information about crashed processes. Failure detectors have attracted so much attention in the crash-stop failure model because they provide a totally independent abstraction. $$\varOmega $$ Ω is the weakest failure detector to solve Consensus in classic asynchronous systems when a majority of processes never crash, and $$A\varOmega '$$ A Ω ′ is its implementable version for anonymous systems. As far as we know, there is a lack of works in the literature which tackle Consensus in anonymous asynchronous systems where crashed process can recover (the so-called crash-recovery failure model) and also assuming errors in transmission operations (the so-called omission failure model). Extending failure models in the system allows us to design more realistic systems and solve more practical security problems (i.e., fair exchange and the secure multiparty computation). We present, in this paper, an algorithm to solve Consensus using $$A\varOmega '$$ A Ω ′ in anonymous asynchronous systems under the crash-recovery and omission failure models. Another important contribution of this paper is a communication-efficient and latency-efficient implementation of $$A\varOmega '$$ A Ω ′ for these new failure models.
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Bao, Ergute, Yizheng Zhu, Xiaokui Xiao, Yin Yang, Beng Chin Ooi, Benjamin Hong Meng Tan, and Khin Mi Mi Aung. "Skellam mixture mechanism." Proceedings of the VLDB Endowment 15, no. 11 (July 2022): 2348–60. http://dx.doi.org/10.14778/3551793.3551798.
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Deep neural networks have strong capabilities of memorizing the underlying training data, which can be a serious privacy concern. An effective solution to this problem is to train models with differential privacy ( DP ), which provides rigorous privacy guarantees by injecting random noise to the gradients. This paper focuses on the scenario where sensitive data are distributed among multiple participants, who jointly train a model through federated learning , using both secure multiparty computation ( MPC ) to ensure the confidentiality of each gradient update, and differential privacy to avoid data leakage in the resulting model. A major challenge in this setting is that common mechanisms for enforcing DP in deep learning, which inject real-valued noise , are fundamentally incompatible with MPC, which exchanges finite-field integers among the participants. Consequently, most existing DP mechanisms require rather high noise levels, leading to poor model utility. Motivated by this, we propose Skellam mixture mechanism (SMM), a novel approach to enforcing DP on models built via federated learning. Compared to existing methods, SMM eliminates the assumption that the input gradients must be integer-valued, and, thus, reduces the amount of noise injected to preserve DP. The theoretical analysis of SMM is highly non-trivial, especially considering (i) the complicated math of DP deep learning in general and (ii) the fact that the mixture of two Skellam distributions is rather complex. Extensive experiments on various practical settings demonstrate that SMM consistently and significantly outperforms existing solutions in terms of the utility of the resulting model.
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Filali, Hajar, Jamal Riffi, Chafik Boulealam, Mohamed Adnane Mahraz, and Hamid Tairi. "Multimodal Emotional Classification Based on Meaningful Learning." Big Data and Cognitive Computing 6, no. 3 (September 8, 2022): 95. http://dx.doi.org/10.3390/bdcc6030095.
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Emotion recognition has become one of the most researched subjects in the scientific community, especially in the human–computer interface field. Decades of scientific research have been conducted on unimodal emotion analysis, whereas recent contributions concentrate on multimodal emotion recognition. These efforts have achieved great success in terms of accuracy in diverse areas of Deep Learning applications. To achieve better performance for multimodal emotion recognition systems, we exploit Meaningful Neural Network Effectiveness to enable emotion prediction during a conversation. Using the text and the audio modalities, we proposed feature extraction methods based on Deep Learning. Then, the bimodal modality that is created following the fusion of the text and audio features is used. The feature vectors from these three modalities are assigned to feed a Meaningful Neural Network to separately learn each characteristic. Its architecture consists of a set of neurons for each component of the input vector before combining them all together in the last layer. Our model was evaluated on a multimodal and multiparty dataset for emotion recognition in conversation MELD. The proposed approach reached an accuracy of 86.69%, which significantly outperforms all current multimodal systems. To sum up, several evaluation techniques applied to our work demonstrate the robustness and superiority of our model over other state-of-the-art MELD models.
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Mullan, Leanne, Karen Wynter, Andrea Driscoll, and Bodil Rasmussen. "Implementation strategies to overcome barriers to diabetes-related footcare delivery in primary care: a qualitative study." Australian Journal of Primary Health 27, no. 4 (2021): 328. http://dx.doi.org/10.1071/py20241.
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The aim of this study is to identify, from the perspectives of key health policy decision-makers, strategies that address barriers to diabetes-related footcare delivery in primary care, and outline key elements required to support implementation into clinical practice. The study utilised a qualitative design with inductive analysis approach. Seven key health policy decisions-makers within Australia were interviewed. Practical strategies identified to support provision and delivery of foot care in primary care were: (a) building on current incentivisation structures through quality improvement projects; (b) enhancing education and community awareness; (c) greater utilisation and provision of resources and support systems; and (d) development of collaborative models of care and referral pathways. Key elements reported to support effective implementation of footcare strategies included developing and implementing strategies based on co-design, consultation, collaboration, consolidation and co-commissioning. To the authors’ knowledge, this is the first Australian study to obtain information from key health policy decision-makers, identifying strategies to support footcare delivery in primary care. Implementation of preventative diabetes-related footcare strategies into ‘routine’ primary care clinical practice requires multiparty co-design, consultation, consolidation, collaboration and co-commissioning. The basis of strategy development will influence implementation success and thus improve outcomes for people living with diabetes.
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Alaa Mahmoud Ibrahim, Mohmaed Farouk, and Mohamed Waleed Fakhr. "Privacy Preserving Image Retrieval Using Multi-Key Random Projection Encryption and Machine Learning Decryption." Journal of Advanced Research in Applied Sciences and Engineering Technology 42, no. 2 (April 3, 2024): 155–74. http://dx.doi.org/10.37934/araset.42.2.155174.
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Homomorphic Encryption (HE), Multiparty Computation (MPC), Differential Privacy (DP) and Random Projection (RP) have been used in privacy preserving computing. The main benefit of the random projection approach is the lighter time and space complexity compared to the other available techniques. However, RP is typically used in a symmetric encryption mode, with one random projection matrix single key, making it vulnerable to attacks. An enhanced multi-key RP approach is proposed in this paper where a set of N random matrices are used as projection keys. Moreover, a randomly chosen one is used for each new query. Machine learning models are trained to perform specific vector operations on the randomly projected vectors and produce another randomly projected results vector. Another machine learning model is trained to decrypt the final result at the user’s side. The proposed system is shown to offer privacy against known plaintext and cipher-only attacks while preserving Euclidean distance calculations accuracy in the randomly projected domain which are demonstrated on the COREL 1K image retrieval task. Results show that the cyphertext space took sixteen times less than the ciphertext done with homomorphic encryption, and the computation of distance using random projection was 8 times faster than homomorphic encryption distance calculation.