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.

We present a decentralized secure data release application built on Ethereum, named Timed Data Release (TDR). TDR allows users to encrypt sensitive data by enabling the decryption of the data only after a predetermined period of time has elapsed. We present the technical foundation for TDR and its implementation on Ethereum. Our demonstration features a microblogging application that enables scheduled publication of microblogs, showing a practical application of timed data release using smart contracts.

Timed Data Release (TDR) is a practical security mechanism that safeguards data until a prescribed time has elapsed. However, existing TDR frameworks do not focus on reliability guarantees and lack formal security analysis. To this end, we propose PR-TDR, a novel framework that supports privacy-preserving and reliable timed data release while providing provable security properties. PR-TDR includes two novel contributions: a formal privacy-preserving design for TDR, named P-TDR and a reliable lifetime secret key management built on top of P-TDR that systematically empowers P-TDR with reliability. P-TDR prevents adversaries from accessing the data prior to the prescribed release time. At the core of the design of P-TDR, a group of decentralized peers, which operates under an honest-majority assumption, collaboratively takes charge of managing the lifetime secret key. Each peer stores a key share of the secret key. The proposed reliability layer that empowers P-TDR with reliability guarantees incorporates two carefully designed protocols that operate before the prescribed release time, namely the lifetime secret key auditing protocol and the lifetime secret key share recovery protocol. The auditing protocol enables a semi-honest auditor to confirm the availability of the lifetime secret key with the peers while not gaining any knowledge about the secret key itself. The recovery protocol allows peers that have lost their respective shares of the lifetime secret key to recover them with the help of other peers, ensuring that the lifetime secret key remains private. We provide formal security proof to demonstrate that PR-TDR satisfies the desired security properties. We implement our framework using Ethereum and our performance evaluations confirm that PR-TDR not only embodies the desired security properties but also operates efficiently.

Timed data release refers to protecting sensitive data that can be accessed only after a pre-determined amount of time has passed. While blockchain-based solutions for timed data release provide a promising approach for decentralizing the process, designing an attack-resilient timed-release service that is resilient to malicious adversaries in a blockchain network is inherently challenging. A timed-release service on a blockchain network is inevitably exposed to the risk of post-facto attacks where adversaries may launch attacks after the data is released in the blockchain network. Existing incentive-based solutions for timed data release in Ethereum blockchains guarantee protection under the assumption of a fully rational adversarial environment in which every peer acts rationally. However, these schemes fail invariably when even a single participating peer node in the protocol starts acting maliciously and deviates from the rational behavior. In this paper, we propose a systematic solution for attack-resilient and practical blockchain-based timed data release in a mixed adversarial environment, where both malicious adversaries and rational adversaries exist. We first propose an effective uncertainty-aware reputation measure to capture the behaviors of the peer involved in timed data release activities in the network. In light of such a measure, we present the design of a basic protocol that consists of two critical ingredients, namely reputation-aware peer recruitment and verifiable enforcement protocols. The former, prior to the start of the enforcement protocols, performs peer recruitment based on the reputation measure to make the design probabilistically attack-resilient to the post-facto attacks. The latter is responsible for contractually guarding the recruited peers at runtime by transparently reporting observed adversarial behaviors. However, the basic recruitment design is only aware of the reputation of the peers and it does not consider the working time schedule of the participating peers and as a result, it results in lower attack-resilience. To enhance the attack resilience further without impacting the verifiable enforcement protocols, we propose a temporal graph-based reputation-aware peer recruitment algorithm that carefully determines the peer recruitment plan to make the service more attack-resilient. In our proposed approach, we formally capture the timed data release service as a temporal graph and we develop a novel maximal attack-resilient path-finding algorithm on the temporal graph for the participating peers. We implement a prototype of the proposed approach using Smart Contracts and deploy it on the Ethereum official test network, Rinkeby. For extensively evaluating the proposed techniques, we perform simulation experiments to validate the effectiveness of the reputation-aware timed data release protocols as well as our proposed temporal-graph-based improvements. The results demonstrate the effectiveness and strong attack resilience of the proposed mechanisms and our approach incurs only a modest gas cost.

Timed-release of information refers to releasing protected sensitive data at a future point of time while securely protecting the information until the release time. Blockchain-based self-emerging data infrastructures consist of a group of blockchain accounts that jointly take charge of protecting and transferring the data at the release time. Existing solutions have focused on fully rational adversarial environments in which all peer accounts are rational. However, such protection disregards scenarios in which malicious peer accounts also exist. In our work, we focus on protecting blockchain-based timed-release service in mixed adversarial environments in which both rational peer accounts and malicious peer accounts exist. We introduce our blockchain-based timed-release framework designed for mixed adversarial environments and illustrate two concrete attacks, namely drop attack and release-ahead attack, and discuss our reputation-based solution.

To enhance the intelligentization level of the high speed train (HST), wireless train communication network (WTCN) is a promising technology to improve the networking flexibility and situational awareness capabilities of HST. Different from traditional wireless network, not only the coverage quality inside of the HST should be guaranteed, but also the mutual interference between the adjacent HSTs should be avoided. In this paper, we investigate the indoor and outdoor radio propagation simulation model of HST. The three-dimensional (3D) structure model of HST are build, and then the ray-tracing based simulation method is adopted to investigate the effects of train body on radio propagation inside and outside of HST. The proposed propagation simulation model are optimized based on the measurements in the HST. Based on the simulation results, the radio propagation characteristics inside and outside of the HST are analyzed. The proposed simulation model can be used for WTCN deployment and optimization.

Advancements in distributed ledger technologies are driving the rise of blockchain-based social media platforms such as Steemit, where users interact with each other in similar ways as conventional social networks. These platforms are autonomously managed by users using decentralized consensus protocols in a cryptocurrency ecosystem. The deep integration of social networks and blockchains in these platforms provides potential for numerous cross-domain research studies that are of interest to both the research communities. However, it is challenging to process and analyze large volumes of raw Steemit data as it requires specialized skills in both software engineering and blockchain systems and involves substantial efforts in extracting and filtering various types of operations. To tackle this challenge, we collect over 38 million blocks generated in Steemit during a 45 month time period from 2016/03 to 2019/11 and extract ten key types of operations performed by the users. The results generate SteemOps, a new dataset that organizes more than 900 million operations from Steemit into three sub-datasets namely (i) social-network operation dataset (SOD), (ii) witness-election operation dataset (WOD) and (iii) value-transfer operation dataset (VOD). We describe the dataset schema and its usage in detail and outline possible future research studies using SteemOps. SteemOps is designed to facilitate future research aimed at providing deeper insights on emerging blockchain-based social media platforms.

Disk additions to an RAID-6 storage system can increase the I/O parallelism and expand the storage capacity simultaneously. To regain load balance among all disks including old and new, RAID-6 scaling requires moving certain data blocks onto newly added disks. Existing approaches to RAID-6 scaling, restricted by preserving a round-robin data distribution, require migrating all the data, which results in an expensive cost for RAID-6 scaling. In this paper, we propose RS6—a new approach to accelerating RDP RAID-6 scaling by reducing disk I/Os and XOR operations. First, RS6 minimizes the number of data blocks to be moved while maintaining a uniform data distribution across all data disks. Second, RS6 piggybacks parity updates during data migration to reduce the cost of maintaining consistent parities. Third, RS6 selects parameters of data migration so as to reduce disk I/Os for parity updates. Our mathematical analysis indicates that RS6 provides uniform data distribution, minimal data migration, and fast data addressing. We also conducted extensive simulation experiments to quantitatively characterize the properties of RS6. The results show that, compared with existing “moving-everything” Round-Robin approaches, RS6 reduces the number of blocks to be moved by 60.0%–88.9%, and saves the migration time by 40.27%–69.88%.