Trust is a key concept in human–computer interaction, critical to the acceptance and use of generative artificial intelligence technologies. This study aimed to develop a trust scale tailored for human-generative AI interactions by adapting the original human–computer trust scale and evaluating its reliability and validity. An online survey was conducted with 310 valid responses. Results showed the scale consists of three dimensions—benevolence, competence, and reciprocity—comprising nine items, all exhibiting good discrimination. The scale’s overall Cronbach’s alpha was 0.891, with individual dimensions ranging from 0.806 to 0.810. Structural validity was confirmed (χ2/df = 2.32, TLI = 0.92, CFI = 0.95, RMSEA = 0.09, SRMR = 0.05), and criterion-related and convergent validity were well-supported. Based on the Human–Computer Trust Scale, the adapted Chinese version of the Human-Generative Artificial Intelligence Trust Scale exhibits strong reliability and validity, making it a valuable instrument for assessing trust in human-generative artificial intelligence interactions within a Chinese user context.

There are user privacy risks in cloud‐based vehicle dispatch platforms due to the unauthorized collection, use, and dissemination of data. However, existing data protection methods cannot balance privacy, usability, and efficiency well. To address this, we propose a local privacy‐preserving vehicle assignment strategy via spatial–temporal fusion (STF‐LPPVA). Specifically, the strategy allows the cloud platform to train and distribute a spatial–temporal representation model to the user side. Encoded by this model, drivers and passengers can privately fuze the spatial–temporal information of their trips and then transmit these fuzed vectors to the cloud platform. Based on the similarity of the vectors, the cloud platform can allocate vehicles using the Kuhn–Monkreth (KM) algorithm. In addition, we analyze the theoretical feasibility of the STF‐LPPVA strategy using entropy change and get good performance with a dataset from DiDi in Chengdu, China. The results show that the successful matching rate of the STF‐LPPVA strategy is very close to the original data matching with lower time overhead. Our approach can reduce the traveling distance by 66.5% and improve the matching success rate by 36.2% on average.

Ride-pooling significantly reduces traffic congestion by enhancing fleet utilization through effective ride-matching. Real-world ride-pooling systems are dynamic, with fluctuations in driver availability and demand throughout the day. This necessitates adaptive ride-matching strategies that can quickly adjust to changing proximities and identify new carpooling opportunities by recalculating driver-rider correlations. However, most current methods primarily focus on static demand-supply scenarios and short-term accessibility, falling short in dynamic environment. In this study, we introduce a dynamic heterogeneous network model that captures the evolving nature of ride-pooling systems, where new requests and carpooling arrangements continuously emerge. We propose an embedding model-based matching decision process that operates online, adjusting to changes in the network’s structure. This process involves constructing a dynamic heterogeneous ride-pooling network that encompasses diverse node attributes and driver-rider connections, updating these representations to reflect the network’s evolution, and quickly identifying and ranking candidate riders for efficient online matching. Our approach demonstrates improved performance in offline evaluations using datasets from Austin, TX (RideAustin) and Chengdu, China (DiDi Chuxing). We observe a reduction in the necessary fleet size as new orders are placed, and an improvement in drivers’ matching probability compared to existing methods (e.g., an increase of 5.4–31.1% in the assignment rate on DiDi dataset), showcasing the advantage of employing dynamic network embedding to cut down on matching time (e.g., a decrease of 3.7–228.8 seconds in running time on DiDi dataset). Furthermore, we develop a simulated ride-pooling system (SRPool) that mimics dynamic demand-supply fluctuations and supports vehicle routing, providing a robust platform for evaluating ride-matching strategies. Our strategy not only excels in the SRPool environment but also effectively minimizes the total trip distance and rider waiting times.

In the Vehicle Routing Problem with Time Windows (VRPTW), dealing with demand and travel time uncertainties poses significant challenges in terms of model efficacy, service expenditures, and customer satisfaction, notably in extensive operational scenarios. In this paper, we introduce VRPU as a solution to the robust VRPTW encompassing uncertain demands and travel time. VRPU adopts a robust optimization approach and, drawing on budget uncertainty theory, formulates uncertainty variables and reconceptualizes the arrival times at customers as a complex recursive function. The model is addressed through a two-stage Adaptive Large Neighborhood Search heuristic (ALNS), with the primary stage focused on minimizing the total number of utilized vehicles and the subsequent stage dedicated to minimizing total travel costs.Compared to the current state-of-the-art methods, the results of our two-stage Adaptive Large Neighborhood Search (ALNS) algorithm, tested on the Solomon and Gehring & Homberger benchmark datasets, demonstrate that, while incurring a certain computational overhead, it achieves more robust solutions. Additionally, when applied to the deterministic Vehicle Routing Problem with Time Windows (VRPTW), our algorithm successfully identifies the known optimal solutions within the current benchmark datasets.

MOTIVATION: The advancement of long-read RNA sequencing technologies leads to a bright future for transcriptome analysis, in which clustering long reads according to their gene family of origin is of great importance. However, existing de novo clustering algorithms require plenty of computing resources. RESULTS: We developed a new algorithm GeLuster for clustering long RNA-seq reads. Based on our tests on one simulated dataset and nine real datasets, GeLuster exhibited superior performance. On the tested Nanopore datasets it ran 2.9-17.5 times as fast as the second-fastest method with less than one-seventh of memory consumption, while achieving higher clustering accuracy. And on the PacBio data, GeLuster also had a similar performance. It sets the stage for large-scale transcriptome study in future. AVAILABILITY AND IMPLEMENTATION: GeLuster is freely available at https://github.com/yutingsdu/GeLuster.

The trend of progressive technology scaling makes the computing system more susceptible to soft errors. The most critical issue that soft error incurs is silent data corruption (SDC) since SDC occurs silently without any warnings to users. Estimating SDC probability of a program is the first and essential step towards designing protection mechanism. Prior work suffers from prediction inaccuracy since the proposed heuristic-based models fail to describe the semantic of fault propagation. We propose a novel approach SLOGAN which transfers the prediction of SDC probability into a graph regression task. A program is represented in the form of dynamic dependence graph. To capture the rich semantic of fault propagation, we apply structured graph attention network, which includes node-level, graph-level and layer-level self-attention. With the learned attention coefficients from node-level, graph-level, and layer-level self-attention, the importance of edges, nodes, and layers to the fault propagation can be fully considered. We generate the graph embedding by weighted aggregation of the embeddings of nodes and compute the SDC probability by the regression model. The experiment shows that SLOGAN achieves higher SDC accuracy than state-of-the-art methods with a low time cost.

The emergence of Third-Generation Sequencing (TGS) has revolutionized transcriptome sequencing, allowing the production of long reads that span multiple kilobases. This breakthrough has enabled the sequencing of entire transcript sequences. However, a major challenge is posed by the high error rates associated with TGS, making it difficult to accurately classify transcript sequences against reference sequences using traditional algorithms. Fortunately, deep learning-based embedding methods can be trained to overcome these errors. In this pioneering study, we introduce trxCNN, a deep learning model that exhibits remarkable accuracy in classifying erroneous transcript sequences compared to reference sequences. Specifically, evaluations of simulated data have revealed that trxCNN has an impressive classification accuracy of 87.1%. This accuracy exceeds that of the Minimap2 and magicBLAST aligners, both designed for TGS data, by 10.7% and 9.0%, respectively. Furthermore, we provide evidence that trxCNN is capable of accurately estimating the abundance of transcripts. These findings strongly suggest that deep learning methods have great potential to effectively process errors-affected sequencing data.

Soft errors can lead to silent data corruption (SDC), seriously compromising the reliability of a system. To detect SDC, a profiling of SDC-causing instructions is usually needed to decide which instructions to protect. Current approaches gain SDC-causing instructions by using machine learning algorithms. Most of existing algorithms suffer from a lack of accuracy. Researchers choose certain structural features as input based on their understanding of fault propagation. Such hand-tuned features prevent models from reproducing the reasoning of fault propagation and that, in turn, limits their ability to make good prediction decisions. We propose GATPS, which is a Graph Attention neTwork to Predict SDC-causing instructions. The task of SDC prediction is converted into node classification in a heterogenous graph, which applies different types of edges to represent different instruction relations. Low dimensional embedding of each node is computed by attending over its neighbors through multiple types of edges. The hidden structural features related to SDC propagation can be captured automatically. To quantify fault effects between instructions, attention mechanism is applied to assign different importance to nodes of a same neighborhood. Experimental results show that GATPS improves F1 score of SDC prediction by 11.0 %-21.7 % over most competitive machine learning methods.

In this paper, we aim to provide an optimal passenger matching solution by recommending ridesharing groups of passengers from GPS trajectories. Existing algorithms for rider grouping usually rely on matching pre-selected origin-destination coordinates. Unfortunately, the semantics in the spatial layout (e.g., social interactions and properties of the locations) are ignored, leading to inaccuracies in discovering the ridesharing groups. Meanwhile, the destinations manually entered by users impact the accuracy of matching, as these addresses are usually not available in a road network or are not optimal for passenger pickup. This is particularly true when a passenger travels in a less familiar place. Given a set of passengers and the distribution of their destination, our approach is to compute the ridesharing matching between passengers. The raw GPS trajectories can be characterized by a combination of time constraints, traffic environments, and social activities. We first developed a PrefixSpan-prediction using a partial matching (P-PPM) destination-prediction algorithm to mine the frequent movement patterns from the trajectory data and determine the confidence of the movement rules. Our method uses the total travel time as the matching objective. Our approach is superior to the baseline methods in terms of accuracy (increased from 46% to 80%). We have also achieved significant improvements on other metrics, such as users' saved travel distance. We demonstrated that using our proposed method, a group of passengers could save over 19% of total travel miles, which shows that the ridesharing scheme could be effective.

As process technology scales, electronic devices become more susceptible to soft error induced by radiation. The stack in the memory implements procedure calls and its behavior under soft error has not been studied yet. To analyze the effects of soft error on the stack behavior, we conduct a series of fault injection experiment in the IA-32 instruction set architecture. The injection targets are the EsSP register (used as the stack pointer) and the EBP register (used as the stack-frame base pointer). We obtain a few important observations from the fault injection experiment. Results show that injections on EsSP lead to silent data corruption (SDC) or benign only if the flipped EsSP points to another return address when executing the RET instruction, otherwise most of the injections cause crash. The injected bits of these SDC and benign cases are distributed in the particular bits (4–7) and the reason for the distribution is given. Moreover, flipped EBP may cause a series of infinite return operations, which is defined as return cycle. We describe the basic mechanism of return cycle and the essential condition for its occurrence.