The rapid growth of Electric Vehicles (EVs) has brought significant challenges in ensuring the privacy of sensitive data generated, particularly in Vehicle-to-Vehicle (V2V) energy trading systems. This study examines methods to balance data privacy preservation with the utility required for EV-related services. Existing privacy-preserving techniques often struggle to strike a balance between privacy and utility, particularly in dynamic environments where data sensitivity and usage patterns are constantly changing. In this paper, we propose an Adaptive Private Stable Matching (APSM) algorithm that incorporates a dynamic privacy budget algorithm for Differential Privacy (DP). APSM provides stable, privacy-preserving matches for EVs participating in V2V energy trading. The dynamic privacy budget mechanism adjusts allocation according to the number of EVs, offering enhanced privacy protection when necessary and increased utility when feasible. The proposed approach optimizes the utilization of the privacy budget, meeting both strict privacy requirements and ensuring efficient service delivery. Experimental results show that the technique outperforms static approaches in terms of privacy budget management, thereby enhancing privacy protection while maintaining high data utility. This combination renders APSM highly suitable for practical V2V energy trading scenarios, delivering robust privacy safeguards without compromising system performance.

Differential privacy (DP) is a leading paradigm for privacy preservation in statistical analysis and learning. Traditional DP mechanisms add noise independently of the original data, which yields inconsistent perturbations across groups and raises fairness concerns in downstream decision and learning tasks. Prior work often assesses fairness via bias and variance, while overlooking noise direction and the scale of the underlying query. We propose a novel Fairly Proportional Noise Mechanism (FPNM) that uniquely considers both the direction and magnitude of noise relative to raw query results. We define mathematical formulations for unfairness, factoring in weighting and temporal decay to allow nonlinear amplification of unfairness. The privacy analysis shows a negative correlation between unfairness and privacy strength that higher privacy levels lead to increased noise and thus greater unfairness. We then generalize to group-level assessment using the average unfairness and the Frobenius norm ($F$-Norm). We also prove that adaptive budget reallocation within a data independent feasible domain preserves the overall$(\epsilon ,\delta )$-DP guarantee. Experiments on both decision and learning tasks demonstrate consistent gains. In decision tasks, the proposed FPNM effectively reduces unfairness, achieving average reductions of 19.17% and 17.32% in$F$-Norm and average unfairness, respectively. In learning tasks, integrating FPNM with DP-SGD achieves fairness comparable to fairness-aware baselines and better accuracy. Besides, empirical privacy remains intact under membership inference attacks. These results highlight its effectiveness in improving utility while preserving privacy, offering a robust and comprehensive approach to enhancing fairness in DP.

Phishing emails increasingly embed complex social engineering (SE) tactics to manipulate recipients and increase success rates. However, existing organizational training simulations and detection systems seldom incorporate tactic complexity or reveal how such tactics are linguistically embedded. To address this, we develop methods for generating, annotating, and evaluating SE tactics across three complexity levels in phishing emails. A reliably annotated dataset is constructed via a generate–cross-verify–highlight pipeline, which ensures semantic alignment between labels and embedded SE tactics. These trigger segments are subsequently clustered and synthesized into fine-grained patterns that characterize how each SE tactic manifests at Level 1 (easily), Level 2 (moderately), and Level 3 (deeply). These patterns underpin a multi-level SE framework, validated through LLM-based detection experiments. Detection accuracy declines with increasing tactic complexity, confirming the framework's stratification capability and its utility in training, simulation, and tactic-aware detection design.

Accurately detecting out-of-scope queries is a challenging task in task-oriented dialog systems. Most existing research focus on adding an outlier detector after classification or designing an open world classification to identify unknown intents. There is still a major performance gap on achieving high efficiency and accuracy based on above methods. In our research, we tend to solve this problem by constructing an out-of-scope class in the classification. We propose an explainable centroid-based fine-tuning method including a modified decision metric (MDM) and a centroid-based cosine loss (CCL) on Pre-trained Transformer models for optimization. This loss function builds on Copernican structure and assigns the same margin to each in-scope class to resolve an ambiguous configuration on out-of-scope detection. Moreover, cosine similarity is utilized to remove radial variations of centroids. Experimental results show that our proposed method achieves improvement compared to other baseline methods.

The recent increase in Electric Vehicles (EVs) on the road has highlighted privacy concerns, particularly in the Vehicle-to-Vehicle (V2V) energy trading scenario. Ensuring location privacy in Vehicular Ad Hoc Networks (VANETs) is crucial for user confidentiality. Existing privacy techniques in the V2V paradigm protect the location coordinates of the EVs, but privacy risks persist after EVs are matched. In this paper, we introduce a dual-layer location privacy matching (DLLPM) technique to enhance the privacy of V2V matching. Our approach utilizes Laplace differential privacy and partial homomorphic encryption , ensuring that the EV’s private data remains inaccessible to both participants and adversaries. We introduce a noise addition and clipping algorithm to obfuscate EV coordinates within a defined radius. Encrypted distance-based preference lists are generated using partial homomorphic encryption to establish differentially private stable matches. DLLPM ensures EV location privacy throughout the matching process and mitigates the risk of location privacy leakage even after suppliers and demanders exchange location information . Theoretical analysis and experimental results confirm the efficiency of DLLPM, demonstrating robust privacy preservation with a computational complexity of O ( n 2 log n ⋅ ( C enc + C addHE + C subHE + C dec ) ) . We further evaluate computational performance using 128-bit and 256-bit encryption, showing that DLLPM achieves private and efficient matching in the V2V trading paradigm.

Notary cross-chain transaction technologies have obtained broad affirmation from industry and academia as they can avoid data islands and enhance chain interoperability. However, the increased privacy concern in data sharing makes the participants hesitate to upload sensitive information without the trust foundation of the external network. To address this issue, this paper proposes a differential private notary mechanism (DPNM) to preserve privacy in blockchain interoperations. It establishes a fully trusted notary organization to conduct data perturbation before replying query to the external blockchain network. In addition, the DPNM contains two built-in privacy budget allocation schemes: Efficiency priority scheme (EPS) and Privacy priority scheme (PPS). These schemes unify the privacy preferences among different nodes based on multi-node consensus in the decentralized environment. The EPS can generate noise linearly and work efficiently, and the PPS reflects better on nodes’ preferences. This paper utilizes several metrics including mechanism errors, elapsed time, latency, and gas consumption to evaluate the performance of DPNM compared to the traditional mechanisms. The experiment results indicate that the proposed mechanism can meet privacy preferences among different nodes and provide better utility with little extra cost.

The objective of differential privacy (DP) is to protect privacy by producing an output distribution that is indistinguishable between any two neighboring databases. However, traditional differentially private mechanisms tend to produce unbounded outputs in order to achieve maximum disturbance range, which is not always in line with real-world applications. Existing solutions attempt to address this issue by employing post-processing or truncation techniques to restrict the output results, but at the cost of introducing bias issues. In this paper, we propose a novel differentially private mechanism which uses a composite probability density function to generate bounded and unbiased outputs for any numerical input data. The composition consists of an activation function and a base function, providing users with the flexibility to define the functions according to the DP constraints. We also develop an optimization algorithm that enables the iterative search for the optimal hyper-parameter setting without the need for repeated experiments, which prevents additional privacy overhead. Furthermore, we evaluate the utility of the proposed mechanism by assessing the variance of the composite probability density function and introducing two alternative metrics that are simpler to compute than variance estimation. Our extensive evaluation on three benchmark datasets demonstrates consistent and significant improvement over the traditional Laplace and Gaussian mechanisms. The proposed bounded and unbiased composite differentially private mechanism will underpin the broader DP arsenal and foster future privacy-preserving studies.

Expanded complementary writing reflecting on the multi-output PAR project, including insights assembled and gleaned for a presentation for Storytellers and Machines (2024 conference at Manchester Metropolitan University) and the overall, 3-stage process of BOBBY....'s development (2019, 2021 and 2024).

Evolutionary multi-task optimization (EMTO) is a newly emerging research area which studies on how to solve multiple optimization problems simultaneously using evolutionary algorithms (EAs) so that useful knowledge (e.g. promising candidate solutions) obtained when solving one task can be transferred and reused to facilitate solving some other tasks. In EMTO, how to effectively transfer and reuse knowledge among multiple tasks is an important subject of study. Among existing EMTO techniques, DE-based EMTO algorithms deserve special attention because DE, as one of the most popular EAs, has achieved remarkable feats for solving challenging (single-task) optimization problems in the past although DE-based EMTO is not yet extensively studied. In this paper, we propose a general multitasking DE (MTDE) framework which aims to help more clearly understand the focuses and differences of existing and potential works on DE-based EMTO. We implement this framework via three commonly-used DE search schemes and perform a thorough empirical study on two DE-specific knowledge reuse strategies which are based upon base and differential vectors, respectively. Experiments on 18 MTO demonstrate that the base vector based knowledge reuse strategy outperforms the differential vector based one across all three tested DE search schemes.

Multi-task optimization (MTO) is a newly emerging research area in the field of optimization, studying on how to solve multiple optimization problems at the same time so that the processes of solving different but relevant problems could help each other via knowledge transfer to improve the overall performance of solving all problems. Evolutionary MTO (EMTO) employs evolutionary algorithms as the optimizer and treats the candidate solutions that perform commonly well on multiple tasks as the transferable knowledge between these tasks. In this work, we propose a multitasking multi-swarm optimization (MTMSO) algorithm which extends a popular dynamic multi-swarm optimization (DMS-PSO) algorithm into the multitasking scenario. In MTMSO, the whole swarm is randomly partitioned into multiple swarms (i.e., task groups) with each being responsible for solving a specific task, and each swarm is further partitioned into multiple sub-swarms. Within each task group, optimization is performed as per the mechanism of DMS-PSO for solving a specific task. Cross-task knowledge transfer is realized via probabilistic crossover of the personal bests of the particles from different task groups. Both task groups and each group's sub-swarms are periodically reformed to maintain search diversity. An adaptive local search process, featuring dynamic allocation of the computational resource for each task, is incorporated in the final stage of optimization to improve the quality of the best solution found for each task. The proposed MTMSO algorithm is compared with a single-task conventional PSO and a popular EMTO algorithm on two test suites composing of 9 simple and 10 complex single-objective MTO problems, respectively, which demonstrates its superiority.

Open multidimensional data from existing sources and social media often carries insightful information on social issues. With the increase of high volume data and the proliferation of visual analytics platforms, users can more easily interact with and pick out meaningful information from a large dataset. In this paper, we present VisCrime, a system that uses visual analytics to maps out crimes that have occurred in a region/neighbourhood. VisCrime is underpinned by a novel trajectory algorithm that is used to create trajectories from open data sources that reports incidents of crime and data gathered from social media. Our system can be accessed at http://viscrime.ml/deckmap

Abstract Spatial information is often expressed using qualitative terms such as natural language expressions instead of coordinates; reasoning over such terms has several practical applications, such as bus routes planning. Representing and reasoning on trajectories is a specific case of qualitative spatial reasoning that focuses on moving objects and their paths. In this work, we propose two versions of a trajectory calculus based on the allowed properties over trajectories, where trajectories are defined as a sequence of non-overlapping regions of a partitioned map. More specifically, if a given trajectory is allowed to start and finish at the same region, 6 base relations are defined (TC-6). If a given trajectory should have different start and finish regions but cycles are allowed within, 10 base relations are defined (TC-10). Both versions of the calculus are implemented as ASP programs; we propose several different encodings, including a generalised program capable of encoding any qualitative calculus in ASP. All proposed encodings are experimentally evaluated using a real-world dataset. Experiment results show that the best performing implementation can scale up to an input of 250 trajectories for TC-6 and 150 trajectories for TC-10 for the problem of discovering a consistent configuration, a significant improvement compared to previous ASP implementations for similar qualitative spatial and temporal calculi.

A wireless sensor network is a sensing system composed of a few or thousands of sensor nodes. These nodes, however, are powered by internal batteries, which cannot be recharged or replaced, and have a limited lifespan. Traditional two-tier networks with one sink node are thus vulnerable to communication gaps caused by nodes dying when their battery power is depleted. In such cases, some nodes are disconnected with the sink node because intermediary nodes on the transmission path are dead. Energy load balancing is a technique for extending the lifespan of node batteries, thus preventing communication gaps and extending the network lifespan. However, while energy conservation is important, strategies that make the best use of available energy are also important. To decrease transmission energy cost and prolong network lifespan, a three-tier wireless sensor network is proposed, in which the first level is the sink node and the third-level nodes communicate with the sink node via the service sites on the second level. Moreover, this study aims to minimize the number of service sites to decrease the construction cost. Statistical evaluation criteria are used as benchmarks to compare traditional methods and the proposed method in the simulations.

In this paper, a passive continuous authentication system based on both the hard and the soft biometrics is implemented. The passive continuous authentication system keeps verifying the user without interrupting the user concentrating on his work. It also provides the capacity for the machine to recognize who is in front of the terminal, reduces the potential security leak of the user is temporarily absent front the terminal, and denies the invader to login the system with the stolen account and password. Our system forces to logoff the user when the authentication result identifies the user leaves his seat, but the system won't logoff the user if the user only turns his face to some other directions. The reliability of the system is verified by 7 registered users. According to the experimental results, combining the soft and the hard biometrics in our theoretical framework achieves the goal of continuous authentication efficiently and correctly.

This study developed a mobile-memo system that supports the knowledge acquisition and posting-question to assist learners’ learning in a web-based learning environment. To understand the effectiveness of our proposed system, the data was collected from the use of the proposed system. The result showed that the mobile-memo was effective for learners to gather information in construction and reflection during the learning activities. In other words, the mobile-memo system could effectively support learners to acquire knowledge and post question relating to the learning course contents during learning activities.