This paper introduces an evaluation method for large language models (LLMs) based on multi-model factual cognition consistency. Traditional evaluation methods, especially in terms of factuality assessments, face challenges in constructing extensive domain-specific question sets and relying on specific model answers. These methods fall short in the face of dynamic and diverse model development. To overcome these limitations, the proposed approach does not depend on a fixed set of standard answers. Instead, it utilizes the responses of multiple models to construct a dynamic, relative evaluation benchmark. We first developed a framework to capture and compare the cognitive consistency of different models when addressing specific questions. Subsequently, a dynamic iterative algorithm was designed to evaluate models based on these sets of answers. Experiments across multiple domains demonstrated the effectiveness of this method. This innovative evaluation strategy not only provides a more comprehensive and flexible approach to understanding and assessing the performance of LLMs in various scenarios but also offers practical guidance for future model development and improvement.

Mobile Edge Computing (MEC) is one of the key enabling technologies for future 6G wireless networks that can provide lower latency service and more efficient resource utilization for future intelligent applications and the Internet of Things (IoT), while also reducing the energy consumption of end devices. In the intricate dynamic edge environment, the task offloading problem is entangled with several factors, such as the uncertainty of online tasks, the heterogeneity of edge servers, and the mobility of devices. In this paper, considering the randomness of online task arrivals, time-varying channels, and mobility of devices, a deep reinforcement learning-based online task offloading (DRL-OTO) algorithm is designed to minimize the energy consumption of all mobile devices. Specifically, by portraying the system model consisting of the communication model, energy consumption model, and node mobility model, the task offloading optimization problem is modeled as a mixed integer nonlinear programming (MINLP) problem. By decomposing this problem, each mobile device first determines the edge server to be offloaded, and then the DRL-OTO algorithm is designed by utilizing the DDPG method, in which each mobile device is able to determine the offloading rate. Simulation results show that the proposed DRL-OTO algorithm can achieve fast convergence and is able to reduce energy consumption, thus increasing the utility of all devices in the dynamic edge environment.

Node localization is one of the basic requirements in various Internet of Things applications. Among a wide range of localization schemes, the range-free localization algorithm is promising as a cost-effective technique. However, the localization accuracy of this technique is susceptible to various anisotropy factors, such as the existence of holes, nonuniform node distribution, and dynamic radio propagation pattern. To this end, an accurate range-free localization model using extreme learning machine (ELM) and ring-shaped salp swarm algorithm (SSA) is proposed for anisotropic wireless sensor networks. First, the integer hop count between two adjacent nodes is quantized as a real number according to the Jaccard coefficient of their shared neighbor nodes. Second, exploiting the strong generalization and fast learning speed of ELM, a distance mapping model based on the modified real hop count is developed for solving anisotropic signal attenuation. Third, the coordinate calculation of normal nodes is formulated as a minimum problem by taking into account the weighted squared error of estimated distance, and the bounding box method is utilized to initialize the possible location boundary area of normal nodes. Finally, the SSA based on the ring-shaped topology is designed to compute the coordinates of normal nodes. Extensive simulations on several network topologies are conducted with the effect of multiple anisotropic factors. Experimental results show that the proposed algorithm is superior to other developed ones not only in localization accuracy but also in robustness against network anisotropy.

Hop Count Matrix (HCM) contains rich connectivity information, which is very important for various Internet of Things (IoT) applications, especially for obtaining the locations of sensor nodes. However, some items of HCMs may be missing due to attacks by malicious nodes or unexpected termination of flooding operations. To solve this problem, two methods, called HCMR-AM and HCMR-DT, are proposed to recover the missing items. In HCMR-AM, the collected partial hop counts are employed to construct Adjacency Matrix (AM), and then the constructed AM is used to obtain the complete HCM. In HCMR-DT, the recovery of HCM is transformed into a classification problem, where multi-dimensional features are used for joint prediction to achieve more accurate recovery performance. Extensive experimental results demonstrate that compared to the original SVT and HCMR-NBC, our proposed algorithms have significant improvement in recovery performance and execution efficiency. In addition, the complete HCM is used for node localization, and experimental results show that the HCM recovered by the proposed methods can achieve the same localization performance as the HCM without missing value when the observation ratio of HCM is greater than 30%, which cannot be achieved by other recovery algorithms.

The detection and recognition of traffic signs is an important topic in intelligent transportation systems. The automatic detection and recognition of traffic signs during driving is the basis for realizing the unmanned driving. Therefore, the work on the detection and recognition of traffic signs has a potential value and application prospect. In the traditional detection and recognition methods, they often detect and recognize traffic signs image by image. In this case, only the information of the current image is used, and the relationship between the image sequences is not considered. To end this issue, we propose a novel model that can use the relationship in multi-images to detect and recognize traffic signs in a driving video sequence quickly and accurately. The model proposed in this paper is a fusion model based on YOLO-V3 and VGG19 network. Finally, we test this proposed model on a public dataset and compare it to the baseline method, and results show that this proposed model achieves accuracy over 90% and outperforms the baseline method for all types of traffic signs in different conditions. Thus, we can conclude this proposed model is efficient and accurate.

Given that the limitations of the manifold assumption that the low-resolution (LR) and high-resolution (HR) patch manifolds are locally isometric, the geometrical information of HR patch manifold, which is much more credible and discriminant than LR patch manifold, has been paid more attention to in the recent face super-resolution algorithms. In general, these algorithms first construct its initial HR patch using conventional face super-resolution methods and then update the K-nearest neighbors (K-NN) of the input patch as well as corresponding reconstruction weights based on the initial HR patch to generate the final HR patch. Whether or not we can effectively utilize the information of the HR manifold depends on the quality of the initial HR patch. In this paper, to capture the nonlinear similarity of face features, we apply kernel principal component analysis (KPCA) to the conventional face super-resolution method and achieve a better initial HR patch. Furthermore, we propose the concept “optimized reference patch” to deal with the variations in human facial features and find the best-matched neighbors of input patch. Experimental results show that the proposed method outperforms several state-of-the-art face super-resolution algorithms.

Finding pre-image is crucial for kernel principal component analysis (KPCA) based pattern de-noising. This paper proposes to learn the systematic error of some classical methods of pre-image finding, and to refine the obtained pre-image via error compensation. Experiments based on simulated data as well as real-world data demonstrate that the proposed approach can improve effectively the results from two classical pre-image methods: gradient decent and distance constraint.

Professionals and end users of computers often experience being constantly surrounded by modern technology. One side effect of modern technology is termed technostress, which refers to the “negative impact on attitudes, thoughts, behaviors, or body physiology that is caused either directly or indirectly by technology” (Well and Rosen, 1997). Based on social cognitive theory, this study developed a conceptual model in which computer-related technostress was studied as consequences of computer self-efficacy and technology dependence. Results show that (a) employees with higher level of computer self-efficacy have lower level of computer-related technostress, (b) employees with higher level of technology dependence have higher level of computer-related technostress, and (c) employees under different individual situations may perceive different levels of technostress. Contributions of this research and implications for theory and managerial practice are also discussed.

UNLABELLED: In the genomic era, researchers often want to know more information about a biological sequence by retrieving its related articles. However, there is no available tool yet to achieve conveniently this goal. Here we developed a new literature-mining tool MedBlast, which uses natural language processing techniques, to retrieve the related articles of a given sequence. An online server of this program is also provided. AVAILABILITY: Both online server and the program are available freely at http://medblast.sibsnet.org

Research and practice in the application of software architecture has reaffirmed the need to consider software systems from several distinct points of view. Previous work by P. Kruchten (1995) and C. Hofmeister et al. (2000) suggests that four or five points of view may be sufficient: the logical view (i.e., the domain object model), the (static) code view, the process/concurrency view, the deployment/execution view, plus scenarios and use-cases. We have found that some classes of software systems exhibit interesting and complex build-time properties that are not explicitly addressed by previous models. In this paper, we present the idea of build-time architectural views. We explain what they are, how to represent them, and how they fit into traditional models of software architecture. We present three case studies of software systems with interesting build-time architectural views, and show how modelling their build-time architectures can improve developer understanding of what the system is and how it is created. Finally, we introduce a new architectural style, the "code robot" that is often present in systems with interesting build-time views.

Most studies of software evolution have been performed on systems developed within a single company using traditional management techniques. With the widespread availability of several large software systems that have been developed using an "open source" development approach, we now have a chance to examine these systems in detail, and see if their evolutionary narratives are significantly different from commercially developed systems. The paper summarizes our preliminary investigations into the evolution of the best known open source system: the Linux operating system kernel. Because Linux is large (over two million lines of code in the most recent version) and because its development model is not as tightly planned and managed as most industrial software processes, we had expected to find that Linux was growing more slowly as it got bigger and more complex. Instead, we have found that Linux has been growing at a super-linear rate for several years. The authors explore the evolution of the Linux kernel both at the system level and within the major subsystems, and they discuss why they think Linux continues to exhibit such strong growth.