Real-time rumor detection methods that do not rely on propagation features have emerged as an effective strategy to curb the spread of misinformation. To address the pressing challenge of enhancing semantic understanding of short texts and extracting latent user features in real-time rumor detection, this article proposes a novel approach that integrates B ackground K nowledge and U ser F eatures (BKUF). First, relevant background knowledge is extracted from an external knowledge graph through knowledge distillation. To accommodate different granularities of knowledge, we design two fusion strategies: one based on graph attention networks and the other on co-attention mechanisms, effectively enriching the semantic representation of the text. In addition to traditional user features, we further introduce two novel latent user attributes—rationality and professionalism—which are inferred from users’ historical posts. Finally, the enhanced semantic and user features are adaptively integrated and passed into a multi-layer perceptron for classification. Experiments conducted on four widely used public rumor datasets—Weibo, PHEME, Twitter15, and Twitter16—show that our method achieves accuracies of 92.8%, 84.9%, 81.5%, and 82.7%, respectively, outperforming state-of-the-art baselines.

Coordinating multiple agents with diverse tasks and changing goals without interference is a challenge. Multi-Agent Reinforcement Learning (MARL) aims to develop effective communication and joint policies using group learning. Some of the previous approaches required each agent to maintain a set of networks independently, resulting in no consideration of interactions. Joint communication work causes agents receiving information unrelated to their own tasks. Currently, agents with different task divisions are often grouped by action tendency, but this can lead to poor dynamic grouping. This paper presents a two-phase solution for multiple agents, addressing these issues. The first phase develops heterogeneous agent communication joint policies using a Group Communication MARL framework (GCMA). The framework employs a periodic grouping strategy, reducing exploration and communication redundancy by dynamically assigning agent group hidden features through hyper-network and graph communication. The scheme efficiently utilizes resources for adapting to multiple similar tasks. In the second phase, each agent's policy network is distilled into a generalized simple network, adapting to similar tasks with varying quantities and sizes. GCMA is tested in complex environments like StarCraft II and UAV take-off, showing its well-performing for large-scale, coordinated tasks. It shows GCMA's effectiveness for solid generalization in multi-task tests with simulated pedestrians.

Even though the traffic systems in developed countries have been analyzed rigorously and operated efficiently, the same does not generally hold for developing countries due to inadequate planning, design, and operations of their transportation systems. Because of inherent differences between internal infrastructures, the strategies deployed in developed countries may not be amenable to developing ones. Besides, developing countries’ traffic systems are not well-studied in the literature to the best of our knowledge. For example, it is yet to explore how a developed country’s lane-based traffic flow would perform in the context of a developing country, which generally experiences non-lane-based traffic. As such, by using our newly developed traffic simulator ‘RoadBird,’ we investigate outcomes of both lane-based and non-lane-based traffic from the contexts of both developing and developed countries. To do so, we run simulations over real road topologies (extracted from the GIS maps of major cities such as Dhaka, Miami, and Riyadh), considering different scenarios such as lane-based or non-lane-based flows, homogeneous or heterogeneous traffic, with or without pedestrians, etc. We also incorporate various car-following and lane-changing models to mimic traffic behaviors and investigate their performances. While the lane changing dilemma remains an open research question, our experimental evidence indicates: 1) lane-based approaches will not necessarily perform better in the case of currently-adopted non-lane-based scenarios, and 2) non-lane-based strategies may benefit system performance in lane-based scenarios while having heavy mixed traffic. Nonetheless, we reveal several new insights for on-road experiences both in developing and developed countries.

Building a human-computer conversational system that can communicate with humans is a research hotspot in the field of artificial intelligence. Traditional dialogue systems tend to produce irrelevant and non-information responses, which reduce people’s interest in engaging in a conversation. This often leads to boring conversations. To alleviate this problem, many researchers use external knowledge to assist conversation generation. The accuracy of knowledge selection is the prerequisite to ensure the quality of knowledge conversation. This approach has worked positively to a certain extent, but generally only searches knowledge information based on entity words themselves, without considering the specific conversation context. Therefore, if irrelevant knowledge is retrieved, the quality of conversation generation will be reduced. Motivated by this, we propose a novel neural knowledge-based conversation generation model, namedSiamese Network based Posterior Knowledge Selection Model for Knowledge Driven Conversation Generation (SPK-CG). We have designed a novel knowledge selection mechanism to obtain knowledge information that is highly relevant to the context of the conversation. Specifically, the posterior knowledge distribution is used as a soft label to make the prior distribution consistent with the posterior distribution in the training process. At the same time, in order to narrow the gap between prior and posterior distributions and improve the accuracy of knowledge selection, we leverage siamese network and design multi-granularity matching module for knowledge selection. Compared with previous knowledge-based models, our method can select more appropriate knowledge and use the selected knowledge to generate responses that are more relevant to the conversation context. Extensive automatic and human evaluations demonstrate that our model has advantages over previous baselines.

In the era of social networks, the rapid growth of data mining in information retrieval and natural language processing makes automatic text summarization necessary. Currently, pretrained word embedding and sequence to sequence models can be effectively adapted in social network summarization to extract significant information with strong encoding capability. However, how to tackle the long text dependence and utilize the latent topic mapping has become an increasingly crucial challenge for these models. In this article, we propose a topic-aware extractive and abstractive summarization model named T-BERTSum, based on Bidirectional Encoder Representations from Transformers (BERTs). This is an improvement over previous models, in which the proposed approach can simultaneously infer topics and generate summarization from social texts. First, the encoded latent topic representation, through the neural topic model (NTM), is matched with the embedded representation of BERT, to guide the generation with the topic. Second, the long-term dependencies are learned through the transformer network to jointly explore topic inference and text summarization in an end-to-end manner. Third, the long short-term memory (LSTM) network layers are stacked on the extractive model to capture sequence timing information, and the effective information is further filtered on the abstractive model through a gated network. In addition, a two-stage extractive–abstractive model is constructed to share the information. Compared with the previous work, the proposed model T-BERTSum focuses on pretrained external knowledge and topic mining to capture more accurate contextual representations. Experimental results on the CNN/Daily mail and XSum datasets demonstrate that our proposed model achieves new state-of-the-art results while generating consistent topics compared with the most advanced method.

With the fast development of various computing paradigms, the amount of data is rapidly increasing that brings the huge storage overhead. However, the existing data deduplication techniques do not make full use of similarity detection to improve the storage efficiency and data transmission rate. In this paper, we study the problem of utilising the duplicate and resemblance detection techniques to further compress data. We first present a framework of FIDCS-ERD, a feature-based intelligent deduplication compression system with extreme resemblance detection. We also introduce the main components and the detailed workflow of our compression system. We propose a content-defined chunking algorithm for duplicate detection and a Bloom filter-based resemblance detection algorithm. FIDCS-ERD implements the intelligent file chunking and the fast duplicate and resemblance detection. By extensive experiments over the real datasets, we demonstrate that FIDCS-ERD has better compression effect and more accurate resemblance detection compared to the existing approaches.

The Internet of Medical Things (IoMT) is a brand new technology of combining medical devices and other wireless devices to access to the healthcare management systems. This article has sought the possibilities of aiding the current Corona Virus Disease 2019 (COVID-19) pandemic by implementing machine learning algorithms while offering emotional treatment suggestion to the doctors and patients. The cognitive model with respect to IoMT is best suited to this pandemic as every person is to be connected and monitored through a cognitive network. However, this COVID-19 pandemic still remain some challenges about emotional solicitude for infants and young children, elderly, and mentally ill persons during pandemic. Confronting these challenges, this article proposes an emotion-aware and intelligent IoMT system, which contains information sharing, information supervision, patients tracking, data gathering and analysis, healthcare, etc. Intelligent IoMT devices are connected to collect multimodal data of patients in a surveillance environments. The latest data and inputs from official websites and reports are tested for further investigation and analysis of the emotion analysis. The proposed novel IoMT platform enables remote health monitoring and decision-making about the emotion, therefore greatly contribute convenient and continuous emotion-aware healthcare services during COVID-19 pandemic. Experimental results on some emotion data indicate that the proposed framework achieves significant advantage when compared with the some mainstream models. The proposed cognition-based dynamic technology is an effective solution way for accommodating a big number of devices and this COVID-19 pandemic application. The controversy and future development trend are also discussed.

Cloud computing is widely used for its powerful and accessible computing and storage capacity. However, with the development trend of Internet of Things (IoTs), the distance between cloud and terminal devices can no longer meet the new requirements of low latency and real-time interaction of IoTs. Fog has been proposed as a complement to the cloud which moves servers to the edge of the network, making it possible to process service requests of terminal devices locally. Despite the fact that fog computing solves many obstacles for the development of IoT, there are still many problems to be solved for its immature technology. In this paper, the concepts and characteristics of cloud and fog computing are introduced, followed by the comparison and collaboration between them. We summarize main challenges IoT faces in new application requirements (e.g., low latency, network bandwidth constraints, resource constraints of devices, stability of service, and security) and analyze fog-based solutions. The remaining challenges and research directions of fog after integrating into IoT system are discussed. In addition, the key role that fog computing based on 5G may play in the field of intelligent driving and tactile robots is prospected.

Blockchain is an innovated and revolutionized technology, which has attracted wide attention from academia and industry. At present, blockchain has been widely used in certificate management and credential delivery in network access authentication. In a large-scale multidomain Internet of Things (IoT) environment, one of the important issues is cross-domain key sharing and secure data exchange between different IoT. In this paper, aiming at the multidomain authentication requirements of the IoT, this paper introduces the blockchain cross-chain technology into the cross-domain authentication process of the IoT and proposes an effective cross-domain authentication scheme of the IoT based on the improved PBFT algorithm. First, an architecture of blockchain-based cross-domain authentication is proposed. Then, the block data structure is designed in order to enhance the function of access authentication. Third, the authentication process is realized by intelligent contract. The authentication information is encrypted and distributed by a key sharing method to ensure the security of authentication data. Simulation results show that the proposed scheme has significant advantages in security and availability.

In this article, first a comprehensive study of the impact of term weighting schemes on the topic modeling performance (i.e., LDA and DMM) on Arabic long and short texts is presented. We investigate six term weighting methods including Word count method (standard topic models), TFIDF, PMI, BDC, CLPB, and CEW. Moreover, we propose a novel combination term weighting scheme, namely, CmTLB. We utilize the mTFIDF that takes into account the missing terms and the number of the documents in which the term appears when calculating the term weight. For further robust term weight, we combine mTFIDF with two weighting methods. We evaluate CmTLB against the studied weighting schemes by the quality of the learned topics (topic visualization and topic coherence), classification, and clustering tasks. We applied weighting schemes to Latent Dirichlet allocation (LDA) and Dirichlet multinomial mixture (DMM) on eight Arabic long and short document datasets, respectively. The experiment results outline that appropriate weighting schemes can effectively improve topic modeling performance on Arabic texts. More importantly, our proposed CmTLB significantly outperforms the other weighting schemes. Secondly, we investigate whether the Arabic stemming process can improve topic modeling performance. We study the three approaches of Arabic stemming including root-based, stem-based, and statistical approaches. We also train topic models with weighting schemes on documents after applying four stemmers related to different stemming approaches. The results outline that applying the stemming process not only reduces the dimensionality of term-document matrix leading to fast estimation process, but also show enhancement of topic modeling performance both on short and long Arabic documents. Moreover, Farasa stemmer achieves the highest performance in most cases, since it prevents the ambiguity that may happen because of the blind removal of the affixes such as in root-based or stem-based stemmers.

Future Wireless Sensor Networks (WSNs) will be composed of a large number of densely deployed sensors. A key feature of such networks is that their nodes are untethered and unattended. Distributed techniques are expected in WSNs. Connected Dominating Sets (CDSs) have been widely used for virtual backbone construction in WSNs to control topology, facilitate routing, and extend network lifetime. This paper proposes a new distributed algorithm for CDS construction in WSNs. The algorithm represents an extension for our previously proposed centralized algorithm. The algorithm is intended to construct a CDS with the smallest ratio when compared to its centralized version. Simulation shows that our distributed approach has a maximum ratio of 1.53 to the centralized approach in term of CDS size, and it satisfies all of the geometrical properties of its canalized version. Based on this ratio, this distributed algorithm has an approximation factor of 7.65 to the optimal CDS. This approximation outperforms the existing distributed CDS construction algorithms.

Wireless sensor networks (WSNs) are gaining more interest in variety of applications. Of their different characteristics and challenges, network lifetime and efficiency are the most considered issues in WSN-based systems. The scarcest WSN's resource is energy, and one of the most energy-expensive operations is route discovery and data transmission. This paper presents a novel design of a cooperative nodes learning scheme for cooperative energy-efficient routing (CEERA) in wireless sensor networks. In CEERA, nodes perform a cooperative learning in delivering data to the base station. The retransmission of packets is controlled through an address-based timer. CEERA achieves overhead reduction and energy conservation by controlling various parameters that affect the overall network efficiency. Performance results are evaluated using NS2 simulator and our own implemented event-driven simulation. The simulation results show that our algorithm minimizes the overall energy consumption of the WSN, extends network operational lifetime, and improves network efficiency and throughput.