A Two-Leve Approach to Establishing a Marketing Strategy in the Electronic Marketplace

[[abstract]]This study concerns an analysis of what kinds of products are more suitable for distribution via the Internet than via traditional markets. The purpose of the analysis is to identify and fit marketing strategy to product in the virtual marketplace. The amount of product information needed by the consumer to reach a purchasing decision varies with the degree of consumer involvement with the purchase. We conjecture that two major factors affect the consumer's disposition toward online shopping: (1) the purchase involvement on the part of consumer, and (2) product information exposure provided by the Web. Therefore, in the virtual market products may be grouped into four categories. The implication of this is that online marketing functions at two levels. (1) Level-1: PNF (primitive network function), which is derived from the primitive network characteristics associated with the product's attributes fitting with the consumer's involvement. (2) Level-2: ANF (advanced network function), which is the marketing communication created by the virtual store to meet the demands of consumers purchasing online.[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20010103~20010106[[booktype]]紙本[[conferencelocation]]Hawaii, US

The System Simulation with Optimization Mechanism for Option Pricing

The Monte Carlo approach is a valuable and flexible computational tool in modern finance, and is one of numerical procedures used for solving option valuation problems. In recent years the complexity of numerical computation in financial theory and practice has increased and require more computational power and efficiency. Monte Carlo simulation is one of the numerical computation methods used for financial engineering problems. The drawback of Monte Carlo simulation is computationally intensive and time-consuming. In attempt to tackle such an issue, many recent applications of the Monte Carlo approach to security pricing problems have been discussed with emphasis on improvements in efficiency. This paper presents a novel approach combining system simulation with GA-based optimization to pricing options. This paper shows how the proposed approach can significantly resolve the option pricing problem

Balancing Market Share Growth and Customer Profitability: Budget Allocation for Customer Acquisition and Retention

This study adds to the knowledge of budget allocation for customer acquisition and retention spending in an inertia segment.  The results indicate that when retention spending surpassed the optimal budget allocation, increased spending did not grow the expected value of customer equity.  Since the inertia segment is comprised of loyal customers, an examination of brand equity and its role in customer loyalty and its influence on customer equity are discussed

PKE-RRT: Efficient Multi-Goal Path Finding Algorithm Driven by Multi-Task Learning Model

Multi-goal path finding (MGPF) aims to find a closed and collision-free path to visit a sequence of goals orderly. As a physical travelling salesman problem, an undirected complete graph with accurate weights is crucial for determining the visiting order. Lack of prior knowledge of local paths between vertices poses challenges in meeting the optimality and efficiency requirements of algorithms. In this study, a multi-task learning model designated Prior Knowledge Extraction (PKE), is designed to estimate the local path length between pairwise vertices as the weights of the graph. Simultaneously, a promising region and a guideline are predicted as heuristics for the path-finding process. Utilizing the outputs of the PKE model, a variant of Rapidly-exploring Random Tree (RRT) is proposed known as PKE-RRT. It effectively tackles the MGPF problem by a local planner incorporating a prioritized visiting order, which is obtained from the complete graph. Furthermore, the predicted region and guideline facilitate efficient exploration of the tree structure, enabling the algorithm to rapidly provide a sub-optimal solution. Extensive numerical experiments demonstrate the outstanding performance of the PKE-RRT for the MGPF problem with a different number of goals, in terms of calculation time, path cost, sample number, and success rate.Comment: 9 pages, 12 figure

Neural-Network-Driven Method for Optimal Path Planning via High-Accuracy Region Prediction

Sampling-based path planning algorithms suffer from heavy reliance on uniform sampling, which accounts for unreliable and time-consuming performance, especially in complex environments. Recently, neural-network-driven methods predict regions as sampling domains to realize a non-uniform sampling and reduce calculation time. However, the accuracy of region prediction hinders further improvement. We propose a sampling-based algorithm, abbreviated to Region Prediction Neural Network RRT* (RPNN-RRT*), to rapidly obtain the optimal path based on a high-accuracy region prediction. First, we implement a region prediction neural network (RPNN), to predict accurate regions for the RPNN-RRT*. A full-layer channel-wise attention module is employed to enhance the feature fusion in the concatenation between the encoder and decoder. Moreover, a three-level hierarchy loss is designed to learn the pixel-wise, map-wise, and patch-wise features. A dataset, named Complex Environment Motion Planning, is established to test the performance in complex environments. Ablation studies and test results show that a high accuracy of 89.13% is achieved by the RPNN for region prediction, compared with other region prediction models. In addition, the RPNN-RRT* performs in different complex scenarios, demonstrating significant and reliable superiority in terms of the calculation time, sampling efficiency, and success rate for optimal path planning.Comment: 9 pages, 8 figure

IANS: Intelligibility-aware Null-steering Beamforming for Dual-Microphone Arrays

Beamforming techniques are popular in speech-related applications due to their effective spatial filtering capabilities. Nonetheless, conventional beamforming techniques generally depend heavily on either the target's direction-of-arrival (DOA), relative transfer function (RTF) or covariance matrix. This paper presents a new approach, the intelligibility-aware null-steering (IANS) beamforming framework, which uses the STOI-Net intelligibility prediction model to improve speech intelligibility without prior knowledge of the speech signal parameters mentioned earlier. The IANS framework combines a null-steering beamformer (NSBF) to generate a set of beamformed outputs, and STOI-Net, to determine the optimal result. Experimental results indicate that IANS can produce intelligibility-enhanced signals using a small dual-microphone array. The results are comparable to those obtained by null-steering beamformers with given knowledge of DOAs.Comment: Preprint submitted to IEEE MLSP 202