Prompt optimization automatically refines prompting expressions, unlocking the full potential of LLMs in downstream tasks. However, current prompt optimization methods are costly to train and lack sufficient interpretability. This paper proposes enhancing LLMs' reasoning performance by eliciting their causal inference ability from prompting instructions to correct answers. Specifically, we introduce the Self-Causal Instruction Enhancement (SCIE) method, which enables LLMs to generate high-quality, low-quantity observational data, then estimates the causal effect based on these data, and ultimately generates instructions with the optimized causal effect. In SCIE, the instructions are treated as the treatment, and textual features are used to process natural language, establishing causal relationships through treatments between instructions and downstream tasks. Additionally, we propose applying Object-Relational (OR) principles, where the uncovered causal relationships are treated as the inheritable class across task objects, ensuring low-cost reusability. Extensive experiments demonstrate that our method effectively generates instructions that enhance reasoning performance with reduced training cost of prompts, leveraging interpretable textual features to provide actionable insights.

When solving expensive multi-objective optimization problems, traditional evolutionary algorithms rely on a large number of fitness evaluations, which leads to insufficient exploration of the solution space when the evaluation budget is limited. Meanwhile, purely machine learning methods typically require abundant high-quality data and are prone to modeling bias or getting stuck in local optima when training data is scarce or the objective function is a complex black-box. Therefore, in recent years, the combination of machine learning and evolutionary computation has become the primary approach to addressing expensive optimization problems. However, the integration of surrogate models into evolutionary algorithms often lacks depth, focusing primarily on superficial enhancements rather than comprehensive synergy based on a framework. To address these issues, this paper proposes a dynamic breathing surrogate-assisted particle swarm optimization algorithm (DB-SAPSO). Within the proposed unified framework, the core innovation of DB-SAPSO lies in its dynamic breathing mechanism, which iteratively expands and efficiently contracts the population and conducts expensive real evaluations on only a small number of high-potential solutions. This achieves better performance compared to existing algorithms, validating the potential of integrating machine learning and evolutionary computation within a unified framework.

Prompt engineering in Large Language Models (LLMs) has become pivotal for task adaptation, especially in complex reasoning tasks where traditional methods fall short. This paper introduces an innovative method to optimize prompt efficiency by constructing a decision tree of the Chain of Thought (CoT) for classification tasks, namely CoT-Tree. Initially, we demonstrate two advantages of using CoT as features for decision-tree prompts: task adaptability and semantic dissimilarity between features. These advantages theoretically ensure that the CoT-Tree prompting strategy achieves higher performance in classification tasks. Subsequently, we propose the CoT-Tree algorithm and apply it on three classic classification scenarios. By leveraging the CoT-Tree prompting strategy, we select distinctive and efficient prompts that maximize task relevance while minimizing redundancy and token consumption, allowing for a dynamic pruning mechanism, effectively controlling the depth and complexity of the prompts. The results indicate that our method achieves higher accuracy with fewer LLM interaction rounds. Additionally, a case study is conducted to further demonstrate the practicality of our method.

The fairness problem arouses attention in machine learning. One problem with traditional counterfactual fairness is the assumed causal models are constrained by prior knowledge. We propose a framework named Structural Causal Fairness Framework (SCFF) to achieve counterfactual fairness without assumptions like previous works. To correct observations adversely affected by the sensitive attributes, we follow the objectives of fair sampling and construct structural causal models based on causal discovery and causal inference. Experiments show our framework generates competitive results on both counterfactual fairness level and prediction accuracy compared with the other three baselines. More importantly, our framework is all based on data and has good generalization on machine learning problems.

Energy-efficient communication has become the center of attention from various interdisciplinary fields, such as industrial automation, healthcare, and transportation, among others. Besides, proliferation in the artificial intelligence (AI)-based sixth-generation (6G) technology for achieving the smart automation system has caught the attention of both academia and industry. Most of the intelligent automation systems are formed by IoT-based user terminal (UT) devices for multimedia (i.e., video, audio, image, and text) content delivery with high clarity and efficiency. Customer satisfaction/perception, i.e., Quality of Experience (QoE), is an essential factor to be analyzed because the Quality of Service (QoS) is not a suitable candidate to portray the feelings and expectations of users during multimedia transmission. Therefore, the energy-efficient, entropy-aware communication, and QoE analysis through IoT devices are the dire need. This article focuses on how the energy-efficient communication and user's QoE level can be captured through the UT device during multimedia transmission. Thus first, QoS-based joint energy and entropy optimization (QJEEO) algorithm is proposed. Second, the 6G-driven multimedia data structure model and framework are developed for modeling and evaluation of QoE with acquisition time. Third, the relationship between subjective test score (i.e., surveyed data) and objective performance metrics with mobility/speed of IoT-based devices for multimedia service is established. Fourth, the correlation model is proposed for integrating QoS parameters with estimated QoE perceptions. The experimental results indicate that QoE is modeled and evaluated with acquisition time and correlated with QoS parameter, i.e., packet loss ratio (PLR), and average transfer delay during energy-efficient multimedia transmission in 6G-based networks to improve the satisfaction level of customers.

Fifth generation (5G) technologies have become the center of attention in managing and monitoring high-speed transportation system effectively with the intelligent and self-adaptive sensing capabilities. Besides, the boom in portable devices has witnessed a huge breakthrough in the data driven vehicular platform. However, sensor-based Internet of Things (IoT) devices are playing the major role as edge nodes in the intelligent transportation system (ITS). Thus, due to high mobility/speed of vehicles and resource-constrained nature of edge nodes more data packets will be lost with high power drain and shorter battery life. Thus, this research significantly contributes in three ways. First, 5G-based self-adaptive green (i.e., energy efficient) algorithm is proposed. Second, a novel 5G-driven reliable algorithm is proposed. Proposed joint energy efficient and reliable approach contains four layers, i.e., application, physical, networks, and medium access control. Third, a novel joint energy efficient and reliable framework is proposed for ITS. Moreover, the energy and reliability in terms of received signal strength (RSSI) and hence packet loss ratio (PLR) optimization is performed under the constraint that all transmitted packets must utilize minimum transmission power with high reliability under particular active time slot. Experimental results reveal that the proposed approach (with Cross Layer) significantly obtains the green (55%) and reliable (41%) ITS platform unlike the Baseline (without Cross Layer) for aging society.

This paper presents Foggy, an anonymous communication system focusing on providing users with anonymous web browsing. Foggy provides a microservice-based proxy for web browsing and other low-latency network activities without exposing users' metadata and browsed content to adversaries. It is designed with decentralized information management, web caching, and configurable service selection. Although Foggy seems to be more centralized compared with Tor, it gains an advantage in manageability while retaining anonymity. Foggy can be deployed by several agencies to become more decentralized. We prototype Foggy and test its performance. Our experiments show Foggy's low latency and deployability, demonstrating its potential to be a commercial solution for real-world deployment.

One of the most significant problems in cluster analysis is to determine the number of clusters in unlabeled data, which is the input for most clustering algorithms. Some methods have been developed to address this problem. However, little attention has been paid on algorithms that are insensitive to the initialization of cluster centers and utilize variable weights to recover the number of clusters. To fill this gap, we extend the standard fuzzy k-means clustering algorithm. It can automatically determine the number of clusters by iteratively calculating the weights of all variables and the membership value of each object in all clusters. Two new steps are added to the fuzzy k-means clustering process. One of them is to introduce a penalty term to make the clustering process insensitive to the initial cluster centers. The other one is to utilize a formula for iterative updating of variable weights in each cluster based on the current partition of data. Experimental results on real-world and synthetic datasets have shown that the proposed algorithm effectively determined the correct number of clusters while initializing the different number of cluster centroids. We also tested the proposed algorithm on gene data to determine a subset of important genes.

Price information enables consumers to anticipate a price and to make purchasing decisions based on their price expectations, which are critical for agents with pricing decisions or price regulations. A company with pricing decisions can aim to optimise the short-term or the long-term revenue, each of which leads to different pricing strategies thereby different price expectations. Two key ingredients play important roles in the choosing of the short-term or the long-term optimisation objectives: the maximal revenue and the robustness of the chosen pricing strategy against market volatility. However the robustness is rarely identified in a volatile market. Here, we investigate the robustness of optimal pricing strategies with the short-term or long-term optimisation objectives through the analysis of nonlinear dynamics of price expectations. Bifurcation diagrams and period diagrams are introduced to compare the change in dynamics of the optomal pricing strategies. Our results highlight that period adding bifurcations occur during the dynamic pricing processes studied. These bifurcations would challenge the robustness of an optimal pricing strategy. The consideration of the long-term revenue allows a company to charge a higher price, which in turn increases the revenue. However, the consideration of the short-term revenue can reduce the occurrence of period adding bifurcations, contributing to a robust pricing strategy. For a company, this strategy is a robust guarantee of optimal revenue in a volatile market; for consumers, this strategy avoids rapid changes in price and reduce their dissatisfaction of price variations.

Today there are abounding collected data in cases of various diseases in medical sciences. Physicians can access new findings about diseases and procedures in dealing with them by probing these data. Clinical data is a collection of large and complex datasets that commonly appear in multidimensional data formats. It has been recognized as a big challenge in modern data analysis tasks. Therefore, there is an urgent need to find new and effective techniques to deal with such huge datasets. This paper presents an application of a new visual data mining platform for visual analysis of the stroke data for predicting the levels of risk to those people who have the similar characteristics of the stroke patients. The visualization platform uses a hierarchical clustering algorithm to aggregate the data and map coherent groups of data-points to the same visual elements - curved 'super-polylines' that significantly reduces the visual complexity of the visualization. On the other hand, to enable users to interactively manipulate data items (super-polylines) in the parallel coordinates geometry through the mouse rollover and clicking, we created many 'virtual nodes' along the multi-axis of the visualization based on the hierarchical structure of the value range of selected data attributes. The experimental result shows that we can easily verify research hypothesis and reach to the conclusion of research questions through human-data & human-algorithm interactions by using this visual platform with a fully transparency manner of data processing.

In Wireless Sensor Network (WSN), one of the primary issues is energy conservation for extending network lifetime. Communication protocols for WSN help reduce the energy consumption via adopting sleep scheduling in the network. Random sleep scheduling is a desirable mechanism for its simplicity and steady duty cycle. However, the low duty cycle sleeping of nodes may destroy the connectivity of network, which is the assumption taken by most traditional routing protocols. If certain delay is acceptable, partially connected routing can achieve successful packet forwarding in networks with the deficiency of connectivity. This paper presents a connectivity availability model to evaluate the end to end packet delivery ratio of multi-hop networks subjected to the impact of both partial connectivity and sleep latency. Deployment guidelines are given for WSN with random sleep scheduling policy satisfying reliability, timeliness and duty cycle requirements. The simulated and computed results validate the proposed approach.

Recent economic/financial crisis has posed great difficulties for SMEs in the business operations, making them more cautions in IT investment. In this paper we present an innovative service delivery mechanism by lowering the cost of ownership of IT functions provided by software vendors. For each SME in this service delivery mechanism, a virtual machine instance is dynamically maintained on the computer server to establish a SaaS environment. By designing usage control and system configuration modules, service delivery mechanism could supervise service flexibly and satisfy all kinds of environment requirements for software running. Extensive experiments based on the PRD waterway logistics operators demonstrate our method can simultaneously hosting about 100 instances on one server to serve users and fulfill a promise to cut down overhead expenses.

Warehouse financing has emerged as one of the most promising financing approaches for small and medium-sized enterprises (SME). Its basic working mechanism is to transfer the company's assets to collaterals which are more acceptable by the banks. In this paper, we take the position of illustrating how a logistics service provider, the 3rd Party Logistics (3PL), can transform into a financing service provider to coordinate and control the financing logistics processes. With the professional 3PL's help, it is much easier for SMEs to get loan from a bank. In the meantime, the 3PL's business opportunities will be increased by providing financing service in addition to their traditional logistics based functions. This paper first compares different available financing technologies and points out that warehouse financing is one of the most effective financing means for SMEs. After introducing the basic working mechanism of warehouse financing, the risk analysis framework is developed by applying SCOR reference model to identify the financing activities and associated risks. The implementation cost of this approach is reviewed. Future work will be devoted to product and service design for 3PLs based on the warehouse financing mechanism. An outlook for applying this approach for risk analysis in carbon trading and financing is also presented.

As an emerging technology for implementing Web services over the Internet, mobile agent model has several advantages over the traditional RFC model. However, with the popularity of distributed networks (e.g. Internet), Web service providers tend to rely on external resources to complete certain tasks. This definitely increases the difficulty in locating appropriate service providers according to clients' requirements in the new scenario. To address this issue, we propose a reinforcement learning process based on the mobile agent model, which makes agents more efficient and intelligent in selecting Web service providers. Finally, an implementation of our prototype is presented

Traditional capacity sizing of enterprise systems relies on benchmarking a system using benchmark clients that generate a workload pattern similar to real-world workload. When new functions are added to the system or when the workload pattern changes, benchmarking has to be performed again. This is a costly and time-consuming approach for capacity planning. Layered queueing models have been used to study the performance of software systems. The approach is able to identify major performance parameters of software systems. Given a workload pattern, the models can be solved analytically to predict the system performance quickly. This paper proposes a layered queueing model for predicting the performance of distributed enterprise applications built on Enterprise JavaBeans (EJB) technology. We show how such models can be applied for capacity sizing of distributed enterprise systems. We demonstrate this by using this methodology to predict the performance of a sample application built on an EJB-based business-to-business e-commerce platform. We compare deployment options and study the effect of different workload patterns on system capacity.