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Computer Science and Emerging Technologies (CSET), international conference on

作者： Sharaf Alzoubi Khalid Thaher Amayreh Mazen Mohammed Farea Yousef A. Baker El-Ebiary Syarilla Iryani Ahmad Saany Neeraj Bisht Information Technology department College of Computer Science and Informatics Amman Arab University (AAU) Jordan Business Administration Department Faculty of Finance Science and Business Irbid National University Jordan Faculty of Finance & Administrative Sciences Al-Madinah International University Malaysia Faculty of Informatics and Computing UniSZA Malaysia Birla Institute of Applied Sciences Bhimtal India

In today's highly competitive global business environment, effective supplier selection plays a crucial role in the success and sustainability of organizations. Decision Support Systems (DSS) have emerged as powerful tools to aid in this complex process by providing valuable insights and data-driven recommendations for selecting suppliers. This research paper presents a comprehensive review of the methodologies used to assess the effectiveness and efficiency of Decision Support Systems employed in supplier selection processes. The primary objective of this study is to evaluate and compare the various methodologies applied in Decision Support Systems, focusing on their effectiveness and efficiency in assisting decision-makers during supplier selection. The research critically analyzes a range of published literature, academic papers, and case studies to highlight the strengths and limitations of different DSS methodologies in this context. The paper commences by elucidating the fundamental concepts of supplier selection and the significance of Decision Support Systems in streamlining this multifaceted process. Subsequently, a systematic review of existing literature is conducted to identify the key methodologies employed in DSS frameworks. Furthermore, the research explores the novel integration of emerging technologies, such as Artificial Intelligence, Machine Learning, and Big data analytics, within Decision Support Systems for supplier selection. This investigation aims to discern how these advanced techniques contribute to the enhancement of decision-making precision and the overall efficiency of the selection process. By critically analyzing and comparing the effectiveness and efficiency of various Decision Support System methodologies, this research seeks to provide valuable insights for both academics and practitioners in supply chain management. The findings of this study will enable decision-makers to make informed choices regarding the adoption and customiz

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Optimal Fault-Tolerant data Fusion in Sensor Networks: Fundamental Limits and Efficient Algorithms

Optimal Fault-Tolerant Data Fusion in Sensor Networks: Funda...

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IEEE (first ITW conference of the year) Information Theory Workshop (ITW)

作者： Marian Temprana Alonso Farhad Shirani S. Sitharama Iyengar Knight Foundation School of Computing and Information Sciences Florida International University Miami Florida USA

Distributed estimation in the context of sensor networks is considered, where distributed agents are given a set of sensor measurements, and are tasked with estimating a target variable. A subset of sensors are assumed to be faulty. The objective is to minimize i) the mean squared estimation error at each node (accuracy objective), and ii) the mean squared distance between the estimates at each pair of nodes (consensus objective). It is shown that there is an inherent tradeoff between the former and latter objectives. Assuming a general stochastic model, the sensor fusion algorithm optimizing this tradeoff is characterized through a computable optimization problem, and a Cramér-Rao type lower bound for the achievable accuracy-consensus loss is obtained. Finding the optimal sensor fusion algorithm is computationally complex. To address this, a general class of low-complexity Brooks-Iyengar Algorithms are introduced, and their performance, in terms of accuracy and consensus objectives, is compared to that of optimal linear estimators through case study simulations of various scenarios.

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Expression of Concern for: Analyzing the Impact of Deep Learning Approaches on Real-Time data Analysis in Machine Learning

Expression of Concern for: Analyzing the Impact of Deep Lear...

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international conference on Power Energy, Environment and Intelligent Control (PEEIC)

作者： Deepti Mounika Nalluri Chinna babu Mupparaju Aruna Sri Rongali Sohong Dhar P. Ajitha Sloan School of Management Sloan Fellow Candidate 2023 MIT Masters in Computer Information Systems Murray State University USA Masters in Computer Science university of central Missouri warrensburg USA Graduate Doctoral Information Technology/Graduate (Information Technology Emphasis/Information Technology Ph.D.) University of The Cumberlands US Azure Microsoft library & information science Jadavpur University India School of Computing Sathyabama Institute of science and technology Chennai

ISBN: (数字)9798350357769

ISBN: (纸本)9798350357776

Deep learning is a powerful subset of synthetic intelligence, and it has the potential to significantly enhance the skills of machine getting to know. it's miles primarily based on the idea of artificial neural networks, which might be laptop fashions that are capable of gaining knowledge of from records and making decisions, just like the manner a human mind works. Deep mastering isn't the same as traditional system mastering algorithms in that it can technique complex inputs with multiple layers of abstraction, main to greater accurate evaluation. Deep studying algorithms are capable of unexpectedly examine raw facts and generate styles, making them perfect for actual-time statistics analysis. Deep studying is capable of as it should be predicting behaviors and trends, which may be used to optimize procedures and enhance operational efficiencies. Deep studying models can also be used for automated fault detection and predictive renovation, permitting businesses to count on and prevent capacity troubles. Additionally, deep mastering models frequently offer quicker consequences than other device studying or conventional techniques, as well as permitting an extra diploma of control over the resolution of the data being analyzed. This has widespread implications for real-time programs, such as medical diagnostics, independent automobiles, and manufacturing monitoring.

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Hyper-HMM: aligning human brains and semantic features in a common latent event space 23

Hyper-HMM: aligning human brains and semantic features in a ...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Caroline S. Lee Jane Han Ma Feilong Guo Jiahui James V. Haxby Christopher Baldassano Department of Psychology Columbia University New York NY Department of Psychological and Brain Sciences Dartmouth College Hanover NH Center for Cognitive Neuroscience Dartmouth College Hanover NH

Naturalistic stimuli evoke complex neural responses with spatial and temporal properties that differ across individuals. Current alignment methods focus on either spatial hyperalignment (assuming exact temporal correspondence) or temporal alignment (assuming exact spatial correspondence). Here, we propose a hybrid model, the Hyper-HMM, that simultaneously aligns both temporal and spatial features across brains. The model learns to linearly project voxels to a reduced-dimension latent space, in which timecourses are segmented into corresponding temporal events. This approach allows tracking of each individual's mental trajectory through an event sequence and for alignment with other feature spaces such as stimulus content. Using an fMRI dataset in which students watch videos of class lectures, we demonstrate that the Hyper-HMM can be used to map all participants and the semantic content of the videos into a common low-dimensional space, and that these mappings generalize to held-out data. Our model provides a new window into individual cognitive dynamics evoked by complex naturalistic stimuli.

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Bayesian nonparametric (non-)renewal processes for analyzing neural spike train variability 23

Bayesian nonparametric (non-)renewal processes for analyzing...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： David Liu Máté Lengyel Department of Engineering University of Cambridge Department of Engineering University of Cambridge and Department of Cognitive Science Central European University

Neural spiking activity is generally variable, non-stationary, and exhibits complex dependencies on covariates, such as sensory input or behavior. These dependencies have been proposed to be signatures of specific computations, and so characterizing them with quantitative rigor is critical for understanding neural computations. Approaches based on point processes provide a principled statistical framework for modeling neural spiking activity. However, currently, they only allow the instantaneous mean, but not the instantaneous variability, of responses to depend on covariates. To resolve this limitation, we propose a scalable Bayesian approach generalizing modulated renewal processes using sparse variational Gaussian processes. We leverage pathwise conditioning for computing nonparametric priors over conditional interspike interval distributions and rely on automatic relevance determination to detect lagging interspike interval dependencies beyond renewal order. After systematically validating our method on synthetic data, we apply it to two foundational datasets of animal navigation: head direction cells in freely moving mice and hippocampal place cells in rats running along a linear track. Our model exhibits competitive or better predictive power compared to state-of-the-art baselines, and outperforms them in terms of capturing interspike interval statistics. These results confirm the importance of modeling covariate-dependent spiking variability, and further analyses of our fitted models reveal rich patterns of variability modulation beyond the temporal resolution of flexible count-based approaches.

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k-median clustering via metric embedding: towards better initialization with differential privacy 23

k-median clustering via metric embedding: towards better ini...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Chenglin Fan Ping Li Xiaoyun Li Cognitive Computing Lab Baidu Research Bellevue WA

In clustering, the choice of initial centers is crucial for the convergence speed of the algorithms. We propose a new initialization scheme for the k-median problem in the general metric space (e.g., discrete space induced by graphs), based on the construction of metric embedding tree structure of the data. We propose a novel and efficient search algorithm which finds initial centers that can be used subsequently for the local search algorithm. The so-called HST initialization method can produce initial centers achieving lower error than those from another popular method k-median++, also with higher efficiency when k is not too small. Our HST initialization are then extended to the setting of differential privacy (DP) to generate private initial centers. We show that the error of applying DP local search followed by our private HST initialization improves prior results on the approximation error, and approaches the lower bound within a small factor. Experiments demonstrate the effectiveness of our proposed methods.

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Multimodal Biological Knowledge Graph Completion via Triple Co-Attention Mechanism

Multimodal Biological Knowledge Graph Completion via Triple ...

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international conference on data Engineering

作者： Derong Xu Jingbo Zhou Tong Xu Yuan Xia Ji Liu Enhong Chen Dejing Dou State Key Laboratory of Cognitive Intelligence University of Science and Technology of China Business Intelligence Lab Baidu Research Intelligent Healthcare Unit Baidu Big Data Lab Baidu Research BCG X

ISBN: (数字)9798350322279

ISBN: (纸本)9798350322286

Biological Knowledge Graphs (BKGs) can help to model complex biological systems in a structural way to support various tasks. Nevertheless, the incompleteness problem may limit the performance of existing BKGs, which still deserves new methods to reveal the missing relations. Though great efforts have been made to knowledge graph completion, existing methods are not easy to be adapted to the multimodal biological information such as molecular structures and textual descriptions. To this end, we propose a novel co-attention-based multimodal embedding framework, named CamE, for the multimodal BKG completion task. Specifically, we design a Triple Co-Attention (TCA) operator to capture and highlight the same semantic features among different modalities. Based on TCA, we further propose two components to handle multimodal fusion and multimodal entity-relation interaction, respectively. One is the multimodal TCA fusion module to achieve a multimodal joint representation for each entity in the BKG. It aims to project different modal information into a common space by capturing the same semantic features and overcoming the modality gap. The other is the relation-aware interactive TCA module to learn interactive representation by modelling the deep interaction between multimodal entities and relations. Extensive experiments on two real-world multimodal BKG datasets demonstrate that our method significantly outperforms several state-of-the-art baselines, including 10.3% and 16.2% improvement w.r.t MRR and Hits@1 metrics over its best competitors on public DRKG-MM dataset.

关键词： Measurement Knowledge engineering Adaptation models Fuses Biological system modeling Semantics Knowledge graphs

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computing a human-like reaction time metric from stable recurrent vision models 23

Computing a human-like reaction time metric from stable recu...

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Proceedings of the 37th international conference on Neural Information Processing Systems

作者： Lore Goetschalckx Lakshmi N. Govindarajan Alekh K. Ashok Aarit Ahuja David L. Sheinberg Thomas Serre Department of Cognitive Linguistic & Psychological Sciences Brown University Providence RI and Carney Institute for Brain Science Brown University Providence RI Integrative Computational Neuroscience (ICoN) Center MIT Cambridge MA Neuroscience Department Brown University Providence RI

The meteoric rise in the adoption of deep neural networks as computational models of vision has inspired efforts to "align" these models with humans. One dimension of interest for alignment includes behavioral choices, but moving beyond characterizing choice patterns to capturing temporal aspects of visual decisionmaking has been challenging. Here, we sketch a general-purpose methodology to construct computational accounts of reaction times from a stimulus-computable, task-optimized model. Specifically, we introduce a novel metric leveraging insights from subjective logic theory summarizing evidence accumulation in recurrent vision models. We demonstrate that our metric aligns with patterns of human reaction times for stimulus manipulations across four disparate visual decision-making tasks spanning perceptual grouping, mental simulation, and scene categorization. This work paves the way for exploring the temporal alignment of model and human visual strategies in the context of various other cognitive tasks toward generating testable hypotheses for neuroscience. Links to the code and data can be found on the project page: https://***/rnn_rts_site/.

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Graph representation learning method based on three-way partial order structure

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international JOURNAL OF APPROXIMATE REASONING 2024年 165卷

作者： Yan, Enliang Hao, Shikuan Zhang, Tao Hao, Tianyong Chen, Qiliang Yu, Jianping South China Normal Univ Sch Artificial Intelligence Foshan 528225 Guangdong Peoples R China South China Normal Univ Sch Comp Sci Guangzhou 510631 Guangdong Peoples R China Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Guangzhou Univ Chinese Med Sch Basic Med Guangzhou 510006 Guangdong Peoples R China Yanshan Univ Sch Foreign Studies Qinhuangdao 066004 Hebei Peoples R China

In the era of big data, handling massive datasets to extract valuable information has become increasingly critical. Knowledge representation emerges as a pivotal method to address this challenge. In the domain of knowledge representation, there exist two primary approaches: symbolic representation and vector representation. The integration of symbolic and vector representations to harness their respective strengths has become the cutting-edge approach to address challenges in the field of knowledge representation. This paper proposes a method that integrates a partial order formal structure analysis (POFSA) with graph representation learning. Specifically, we initially construct three-way partial order structure graphs, then create an attribute graph based on this structure, which can be processed by the graph representation learning methods. Finally, we utilize the graph representation learning to construct embeddings for three-way attribute partial order structure diagram (APOSD). We comprehensively assesse these embeddings across eight different datasets and present the results. The experiments indicate the feasibility of our proposed approach which is proven a novel approach of combining symbolic and vector representations for handling complex data and implicit knowledge.

关键词： Concept -cognitive learning Partial order formal structure analysis Three-way decision Granular computing Graph representation learning Graph neural networks

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A Critical Analysis of Object Tracking Using Radial Function Networks

A Critical Analysis of Object Tracking Using Radial Function...

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Smart Devices (ICSD), international conference on

作者： Amit Kumar Mishra Neeraj Kumar Pandey Manoj Diwakar Nilesh Bhaskarrao Bahadure Ankur Dumka Jagdish Chandra Patni Dept. of Computer Science & Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Dept. of CSE Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Computer Science & Engineering Women Institute of Technology Dehradun India Dept. of CSE Alliance School of Advance Computing Alliance University Bengaluru Karnataka India

ISBN: (数字)9798350394412

ISBN: (纸本)9798350394429

A important method in several fields, including driverless cars, medical imaging, human-computer interaction, and investigation systems, is object tracking. Because of their flexibility to changes in object presence and ambient factors, as well as their capacity to estimated complex, non-linear functions, radial networks have become a potent tool for object stalking in recent years. The article conditions for the analysis of the radical working networking where the algorithm is a tracking segment that aims in moving the object. Object recognition is one of the most basic and centralized factor analyses in determining outputs having relevant values and a strong research background. Radial functioning is a phenomenon that has gained rapid training and desirable properties for improving the presentation of networking sites. radial networks, a form of artificial neural network, utilize radial basis functions as initiation functions. Because of this characteristic, RBF networks are especially well-suited for applications like object tracking that call for high accuracy in function approximation and pattern recognition. We explore the design of RBF networks in detail, emphasizing the functions of the input layer, output layer, and hidden layer using radial basis functions. We examine the mathematical foundations that allow RBF networks to represent intricate connections in the data, emphasizing how well they can generalize and interpolate.

关键词： Training Accuracy Supervised learning Radial basis function networks Real-time systems Pattern recognition Object tracking Object recognition Smart devices Logistics

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