The rapid development of social media platforms has accelerated the generation and spread of fake news. News on different platforms varies significantly in content and audience. It makes most existing fake news detection models, which rely on single-source datasets, struggle to perform well on news from other sources. Many social platforms also lack high-quality annotated data. To address this problem, we propose a novel cross-source multi-modal fake news detection model named CMFNThinker. CMFNThinker simulates human thinking patterns. It detects fake news across platforms in three stages: summarizing the news content, retrieving similar news posts and reasoning the truthfulness of the news. We conducted extensive experiments on multi-source datasets. The results show that our model outperforms state-of-the-art baseline models by at least 11.3% in macro F1 for cross-source fake news detection.

The proliferation of misinformation on the internet and social media platforms poses a significant challenge to public discourse integrity. Traditional Fact Verification methods rely on costly, domain-specific annotated datasets, limiting their adaptability. While Large Language Models (LLM) offer new possibilities, existing LLM-based techniques face critical limitations: (1) Dependence on simplistic pipelines: Lacking robustness to handle contradictory evidence or ambiguous claims. (2) Underutilization of multi-agent interactions: Restricting thorough claim assessments from multiple perspectives. To address these challenges, we propose a novel Fact - based Debate (FBD) framework, which combines retrieval-augmented generation (RAG) with iterative argumentation using structured multi-agent debate. On the one hand, the FBD framework is introduced to enhance the robustness and reliability of fact - checking. On the other hand, the FBD is designed with a three - stage pipeline for knowledge retrieval, acquisition, and refinement based on authoritative sources. Extensive experiments on five real - world datasets demonstrate that our proposed FBD outperforms existing methods, achieving an average relative improvement of approximately 2.36% compared to the optimal baseline.

ABSTRACT Edge computing extends computing resources from the data center to the edge of the network to better handle latency‐sensitive tasks. However, with the rise of the Internet of Things, edge devices with limited processing capabilities face difficulties in executing requests with fluctuating request peaks. In order to meet the deadline constraints of latency‐sensitive tasks, a feasible solution is to offload some latency‐sensitive tasks to other nearby edge devices. This article studies the problem of request migration in edge computing systems and minimizes the request deadline violation rate based on actual online arrival patterns, performance interference phenomena, and deadline constraints. Since a request contains multiple services and request migration will lead to changes in server resource competition pressure, we split the problem into three sub‐problems, dividing the request deadline to determine the maximum response time of the service, determining the performance of the service under different resource pressures and the request migration strategies. To this end, we propose two deadline splitting methods, a performance interference model under multi‐resource pressure, and two heuristic request migration strategies. Since this article considers online edge scenarios, the number and type of requests are black boxes. We conduct simulation experiments and find that our method has only one‐third the number of request violations of other methods.

Nowadays, cloud computing has become a suitable platform for hosting and executing workflow applications. As the diversity and scale of these applications continue to increase, single-cloud environments are becoming insufficient to meet users’ requirements. Instead, multi-cloud environments have emerged as an ideal solution. However, the complexity of workflow scheduling in multi-cloud environments increases significantly due to the diversified billing mechanisms, heightened reliability demands, and the requirements for reducing energy consumption. To address these challenges, this paper proposes a multi-objective evolutionary algorithm called ECRWSM for workflow scheduling on multi-cloud systems. First, ECRWSM utilizes the population initialization strategy to generate a population with excellent uniformity and sufficient randomness. Then, the diversification strategy is employed to thoroughly explore the solution space. Next, the individual enhancement strategy is used to further improve the solutions. Additionally, an external archive is maintained to store non-dominated solutions throughout the evolutionary process. Comprehensive experiments are conducted to validate the performance of ECRWSM. The experimental results demonstrate that our proposed algorithm ECRWSM outperforms both classical and recent scheduling algorithms.

With the widespread application of microservices, data centers use co-location to improve server resource utilization. However, co-location causes performance interference between tasks, which poses challenges to service quality levels. Most of the existing co-location research does not consider the characteristics of microservices and does not take energy consumption as the optimization target of co-location systems. This paper introduces a new indicator to represent the Relative fluctuation of response Time and Energy consumption (RTE). This paper proposes a processor energy optimization mechanism and an offline task scheduling method to solve this problem. This method reduces overall response time by providing a good operating environment for frequently called microservices. The energy consumption optimization mechanism reduces energy consumption by dynamically controlling voltage and frequency. Simulation experiments in practical applications show that our proposed algorithm has the best response time and RTE compared with other algorithms.

Summary As a burgeoning paradigm, collaborative mobile edge computing (C‐MEC) can cater to growing computation demand of mobile devices (MDs). However, there are great challenges for joint task offloading and resource allocation. In addition, failures on both MDs and edge servers greatly affect reliable task execution. This paper investigates the joint optimization problem of offloading decision, power allocation, and computation resource allocation in the multi‐user C‐MEC system, where nondivisible tasks may be executed locally, offloaded to nearby collaborative devices, or processed by the edge server. We aim at minimizing the total energy consumption of MDs while satisfying the reliability and delay constraints, and the replication technique is adopted to enhance task execution reliability. To tackle this problem, we first transform it into a bi‐level optimization problem, and then propose an iterative algorithm named JOC. Specifically, the offloading decision is constructed in the upper level on the basis of ant colony system (ACS), and the allocation of working power, transmission power, and computation resources is optimized in the lower level by using the monotonic optimization approach. Simulation experimental results reveal that the proposed algorithm saves more energy and achieves a higher task success rate in comparison with baseline schemes.

Virtualized Network Function (VNF) and Service Function Chain (SFC) are the fundamental components in Network Functions Virtualization (NFV) infrastructure, which supports the evolution of modern 5G networks. For online Internet of Things (IoT) applications, characterized by dynamic and diverse requirements, achieving optimal quality of service hinges on a resource-efficient yet performant SFC caching strategy, which is a critical challenge. Besides, despite the performance boost and flexibility brought by modern cloud-native technology, it brings the cold-start problem due to the requirement for runtime image transmission and booting-up, resulting in a non-negligible launch latency. To tackle these challenges, this paper proposes CPSC (Cloud-Native Parallel SFC Caching framework), a novel approach to address the cloud-native parallel SFC caching problem in edge-cloud networks leveraging Deep Reinforcement Learning (DRL), seeking an efficient resource utilization of the edge-cloud network with consideration of SFC processing performance and cold-start suppressing. Graph Convolutional Network (GCN) -based embeddings are adopted for topology-aware feature extraction of the substrate edge-cloud network as well as the incoming SFC caching requests. Then, a Pointer Network (PN) is utilized for contextual information-aware caching decision-making. Benefiting from the online capability of DRL, CPSC makes caching decisions in an online manner with no prior knowledge requirement on future incoming requests. Extensive simulations show that CPSC manages to outperform the state-of-the-art approaches in edge network acceptance ratio and launch latency, with minimal overhead on the SFC processing performance and decision-making duration.

Hybrid cloud is an emerging computing cloud solution that leverages the power of the public cloud, without abandoning the computation resources of existing on-premises data-centers. Further, the wide adoption of cloud-native technology, like containers, brings the capability of rapid horizontal and vertical scaling to task workloads. However, the heterogeneity and flexibility can bring more complexity to task processing performance optimization, especially with constrained on-premises energy consumption and public cloud renting cost quota. In this paper, we seek to optimize the task processing performance under long-term on-premises energy consumption and public cloud renting cost constraints via dynamic task offloading and elastic scaling. We formulate the problem as a two-stage mixed integer non-linear programming (MINLP) problem, and propose an online approach named ETHC (elastic task offloading and resource allocation handler over hybrid cloud). For the first stage, we introduce a Lyapunov optimization-assisted Deep Reinforcement Learning (DRL) agent to decompose the long-term optimization problem into per-time-segment sub-problems on making task offloading decisions. In the second stage, based on the M/M/k queuing model, we prove the container instance number configuration and per-instance resource allocation problem as a convex MINLP problem. An efficient bi-section-based algorithm is introduced to obtain the optimal configurations. Extensive simulations show that ETHC manages to stabilize the task processing queue and satisfy the long-term constraints under various environments and parameters setup, with slight overhead on the convergence speed. Besides, optimal resource configuration and instance number can be obtained at each time-segment with low time complexity.

Numerous benchmarks have been established to assess the performance of foundation models on open-ended question answering, which serves as a comprehensive test of a model's ability to understand and generate language in a manner similar to humans.Most of these works focus on proposing new datasets, however, we see two main issues within previous benchmarking pipelines, namely testing leakage and evaluation automation. In this paper, we propose a novel benchmarking framework, Language-Model-as-an-Examiner, where the LM serves as a knowledgeable examiner that formulates questions based on its knowledge and evaluates responses in a reference-free manner. Our framework allows for effortless extensibility as various LMs can be adopted as the examiner, and the questions can be constantly updated given more diverse trigger topics. For a more comprehensive and equitable evaluation, we devise three strategies: (1) We instruct the LM examiner to generate questions across a multitude of domains to probe for a broad acquisition, and raise follow-up questions to engage in a more in-depth assessment. (2) Upon evaluation, the examiner combines both scoring and ranking measurements, providing a reliable result as it aligns closely with human annotations. (3) We additionally propose a decentralized Peer-examination method to address the biases in a single examiner. Our data and benchmarking results are available at: http://lmexam.xlore.cn.

Summary The elasticity and pay‐as‐you‐go features of cloud computing are popular with customers, and more and more workflow applications are migrating to cloud platforms. Many workflow scheduling algorithms aim to obtain minimal rental costs. However, most of the existing research assumes that task execution time is deterministic. In fact, due to the performance fluctuations of VMs, the task execution time is uncertain before scheduling. Furthermore, many works ignore the cost savings given by multi‐cloud systems. To this end, this paper provides a scheduling framework for real‐time workflows. The framework includes four main components: the workflow analyzer, task pool, task allocation controller, and resource manager. Then based on the framework, we propose the RWSMC heuristic algorithm. The algorithm's goal is to minimize the total rental cost while satisfying the deadline constraints and ensuring the reliability of task execution. The RWSMC algorithm reduces the cost by selecting the appropriate billing mechanism based on the task's execution time and mitigates the impact of uncertain execution time by scheduling the task to the VM with the shortest predicted start time. Simulation experiments demonstrate that our proposed algorithm outperforms three recent state‐of‐the‐art scheduling algorithms in the total rental cost, deadline violation rate, and VM resource utilization.

Microservices are becoming increasingly popular in the construction of cloud applications. On the basis of containers, microservice instances can be implemented with high scalability and maintainability. Due to the need of ensuring various quality of service (QoS) requirements and the two-layer resource structure of containers and virtual machines (VMs), microservice workflow scheduling in clouds is a challenging problem to address. This paper proposes a heuristic algorithm GSMS to minimize execution cost of a microservice-based workflow application while satisfying deadline and reliability constraints. GSMS adopts a greedy fault-tolerant scheduling strategy for replicas of each task to select appropriate resources that meet the sub-deadline and minimize the cost until the sub-reliability is guaranteed. Furthermore, a resource adjustment strategy is incorporated into GSMS to further improve resource utilization. By conducting extensive experiments with several realistic workflow applications, in comparison with existing algorithms, the effectiveness and efficiency of GSMS in achieving lower execution cost and meeting deadline and reliability requirements are validated.

Massive Open Online Courses (MOOCs) have opened new educational possibilities for learners around the world. Most of the research and spotlight has been concentrated on a handful of global, English-language providers, but there are a growing number of regional providers of MOOCS in languages other than English. In this work, we have partnered with thirteen MOOC providers from around the world. We apply a multi-platform approach generating a joint and comparable analysis with data from millions of learners. This allows us to examine learning analytics trends at a macro level across various MOOC providers, with a goal of understanding which MOOC trends are globally universal and which of them are context-dependent. The analysis reports preliminary results on the differences and similarities of trends based on the country of origin, level of education, gender and age of their learners across global and regional MOOC providers. This study exemplifies the potential of macro learning analytics in MOOCs to understand the ecosystem and inform the whole community, while calling for more large scale studies in learning analytics through partnerships among researchers and institutions.

In this paper two schemes of distributed antenna selection, Pathloss-based Antenna Selection (P-AS) and Energy-efficient Antenna Selection (E-AS), are studied for the purpose of improving energy efficiency in Distributed Antenna Systems (DAS). Path loss information is exploited in P-AS, whilst the energy consumption of antenna nodes is taken into account in E-AS for the Pre-coding Matrix Index (PMI) selection and resource scheduling. The performance of the two algorithms is evaluated by means of numerical simulations based on two different DAS topology models of node layouts; a homogeneous topology model where distributed directional antennas are deployed at equidistant positions on the cell edges, and a heterogeneous topology model where omni-directional antennas are positioned at the cell centers. Simulation results show that both P-AS and E-AS outperform the Single Frequency Network (SFN) where all the transmission antennas transmit the same signal continuously in terms of energy efficiency. When compared to SFN, energy efficiency is improved by more than 50% for both schemes and for both homogeneous and heterogeneous layouts. In the case of the homogeneous topology model, P-AS scheme improves the energy efficiency by 54% at a rather low throughput loss of 7%, whilst E-AS improves energy efficiency considerably by nearly 70% without any loss of throughput.

Cellular networks are evolving towards a mixture of macro cells and small cells, thereby increasing the number of cell sites to serve traffic hotspots. This trend poses challenges for managing inter-cell interference and traffic offloading, since a multitude of transmitters of different power classes creates imbalance between the downlink and uplink coverage. Conventional network planning and interference randomization techniques offer limited protection in such environments. Recently standardized semi-static inter-cell coordination strategies provide some capability for interference avoidance. This paper presents a scheme for dynamic coordination of the interference using cooperative multi-point transmission techniques in heterogeneous networks. The scheme relies on an efficient feedback technique coupled with a scheduling algorithm which dynamically handles inter-cell interference and traffic offloading between cell layers.

Space-frequency block codes (SFBC) combined with frequency switched transmit diversity (FSTD) is used in the downlink of 3GPP LTE system, where all transmit antennas are collocated at eNodeBs. However, all transmit antennas serving a cell are distributed at the cell edge in edge-excited distributed antenna systems. The performance of SFBC is greatly affected by the different large scale fading from different remote antenna units. In this paper, we investigate the tradeoff between the coding gain and average receive SNR of SFBC in DAS layout and propose to set apart the two antennas of SFBC transmission as far as possible. The optimal antenna pairing schemes in the edge-excited DAS cells compatible with 3GPP LTE system are those with the maximized average distance of SFBC transmission. According to the system level simulations in multicell environment, the 5% outage spectrum efficiency and average throughput per cell of SFBC transmission are improved by as much as 21.8% and 11.6% respectively in DAS3 cells,compared with those in LTE system. The DAS6 cells achieve even high gains in outage spectrum efficiency by 123.6% and average throughput per cell by 22.5%.

Multiuser diversity is achieved by maximum C/I scheduler in both fast and slow fading scenarios. However, fairness among multiple users is not guaranteed in slow fading channel because time and frequency resource are always occupied by the user with largest signal to interference and noise ratio(SINR). Opportunistic beamforming using dumb antennas is a multiple antennas transmit technique to increase the fluctuation rate and dynamic range of effective channel coefficients in slow fading environment. In this paper, we propose a method to improve the fairness of maximum C/I scheduler with the technique of dumb antennas. The theoretical analysis of users' scheduling probability shows that the fairness of maximum C/I scheduler can be greatly improved by dumb antennas in slow fading channels without significant loss in cell spectrum efficiency. The engineering issues of its application in the downlink transmission of 3GPP LTE system are discussed. The numerical results of simulation with practical LTE configurations and assumptions also verify our proposal.

To obtain the receive diversity of timing acquisition, the cooperative processing of multiple distributed received antennas is considered in this paper. In the flat Rayleigh channels, we assume two remote antennas at the base station for receiving the signal transmitted from the mobile station with a single antenna. By utilizing the coverage range of each remote antenna, a constraint is exploited to associate the time-delays from the mobile station to the two remote antennas. With this constraint, we propose a maximum likelihood-based cooperative timing acquisition method to improve the probability of correct acquisition for each remote antenna. Also, a lower bound of the probability of correct acquisition is derived to evaluate the performance of the proposed method. Compared to the independent timing acquisition, the analysis and simulations show that the probability of correct acquisition for each remote antenna can be improved by the cooperation of the two remote antennas.

Utilization of distributed antennas provides an additional dimension for performance enhancement in 4G and beyond cellular networks. Intelligent remote antenna allocation and practical transmission scheme are two most important aspects for the design of DAS. In this study, a sectorized distributed antenna structure is introduced and correspondingly, power adjusted beam switching as a practical transmission scheme is proposed with periodic beam switching and dynamic power allocation. Simulation results show that the proposed DAS structure and transmission scheme improve the capacity in all SINR regions especially with optimal power allocation.

Using equiprobable M-PAM/QAM constellation instead of Gaussian-distributed signal suffers performance loss, and the ultimate performance loss in high SNR regime is 1.53 dB. In this paper, we propose a new shaping method to optimize equiprobable constellations. The obtained PSK-PAM constellations can achieve performance gain in sense of mutual information by up to 0.3 dB and 0.57 dB over 16 QAM and 64 QAM, respectively. The new constellations are then applied to bit-interleaved coded modulation with iterative decoding (BICM-ID) and the performance gain over M-QAM is verified.