Advanced manufacturing processes frequently require scheduling, optimization, and decision-making, areas where evolutionary multi-objective optimization (EMO) has demonstrated strong capabilities. The Pareto front estimation has gained significant attention due to its remarkable performance on EMO. Recent studies have proposed few Pareto front (PF) estimation strategies, which could guide the search direction according to the geometric characteristics of the approximated Pareto front. However, when handling the complicated MOPs with irregular Pareto fronts, these strategies encounter significant challenges. This paper proposes a local Pareto front shape estimation approach based on Minimum Manhattan distance, which estimates the geometric information at a fine-grained level. Then an adaptive fitness function based geometric information is used to guide the search direction. Experiments on benchmark problems show that the proposed algorithm is highly competitive compared with state-of-the-art multiobjective evolutionary algorithms.

Rumor detection models struggle with limited training samples and constrained generalization, making them less adaptable to evolving values, linguistic expressions, and factual variations in social media. To address this, we propose EKIM-RD, an External Knowledge-Guided Interaction-Aware Multimodal Rumor Detection approach that integrates text, vision, and propagation structure while incorporating knowledge graph and large language model (LLM) as external knowledge. Firstly, a Think Tank Decision mechanism processes textual modality, generating rumor scores that guide further text processing via a multi-scale convolutional network. Meanwhile, pre-trained models identify entities in both text and images, which are then mapped to a knowledge graph to extract image-text relational features. Secondly, the propagation structure is introduced as a complementary modality. Finally, an Adaptive Interaction-Aware Multimodal Fusion module integrates these modalities and feeds the results into a classifier for prediction. Experimental results show that EKIM-RD achieves 8.11% and 9.95% accuracy gains over all competing models on real-world datasets PHEME and Weibo, respectively, and surpassing the second-best model by 2.43% and 3.71%, demonstrating superior detection capability and adaptability.

The widespread of social media has led to the propagation of rumors, resulting in detrimental effects on society. Existing rumor detection methods rely on labeled data and face challenges in obtaining sufficient data, particularly in the early stages of rumor propagation. Active learning has been proposed as a solution to reduce labeling costs by selecting significant samples for training. However, applying active learning to rumor detection presents challenges, such as sensitivity to initial sets and performance loss due to sample reduction. Inspired by Active learning, Contrastive learning, and Prompt tuning, we propose a novel Rumor Detection framework named ACP-RD in this paper. ACP-RD addresses the limitations of active learning by leveraging the benefits of contrastive learning and prompt tuning. Our framework reduces labeling costs, improves rumor detection performance in zero-shot scenarios, aligns active query criteria with downstream tasks through unsupervised contrastive learning, and optimizes word embeddings using supervised contrastive learning. Experimental results demonstrate that ACP-RD achieves superior performance compared to fine-tuning with the whole labeled dataset, using only 10% of the labeled data. Overall, our proposed framework presents a novel and efficient approach to rumor detection, with potential for broader applications in natural language classification tasks.

Iteration-based non-autoregressive (INAR) language models make a trade-off between quality and latency by controlling the number of decoding iterations. Recently proposed diffusion language models, which explicitly model the diffusion process by splitting the intractable probability distribution into simpler distribution along the dimension of the time step, achieve comparable generation quality against the autoregressive (AR) model. However, diffusion models need many iterations to get the same generation quality as AR, which leads to slower generation speed. To reach higher performance with fewer steps, we propose a Conditional Masked Language Model with Time step Awareness (CMLMTA) that combines a discrete forward diffusion process with Conditional Masked Language Model (CMLM), one of leading INAR model. Explicit modeling time steps also allows us to employ curriculum learning strategy along the dimension of time step to make further improvement. Experiments on multiple machine translation tasks show that our model outperforms other diffusion models and CMLM by 1-3 BLEU.

Since open social platforms allow for a large and continuous flow of unverified information, rumors can emerge unexpectedly and spread quickly. However, existing rumor detection (RD) models often assume the same training and testing distributions and can not cope with the continuously changing social network environment. This paper proposed a Continual Prompt-Tuning RD (CPT-RD) framework, which avoids catastrophic forgetting (CF) of upstream tasks during sequential task learning and enables bidirectional knowledge transfer between domain tasks. Specifically, we propose the following strategies: (a) Our design explicitly decouples shared and domain-specific knowledge, thus reducing the interference among different domains during optimization; (b) Several technologies aim to transfer knowledge of upstream tasks to deal with emergencies; (c) A task-conditioned prompt-wise hypernetwork (TPHNet) is used to consolidate past domains. In addition, CPT-RD avoids CF without the necessity of a rehearsal buffer. Finally, CPT-RD is evaluated on English and Chinese RD datasets and is effective and efficient compared to prior state-of-the-art methods.

Data in many real-world applications such as social networks, users shopping behaviors, and inter-item relationships can be represented as graphs. Graph Neural Networks (GNNs) have shown great success in learning meaningful representations for graphs by inherently integrating node information and topological structure. Data in social recommendations can also be denotes as graph data in the form of user-user social graphs and user-item graphs. In addition, the relationships between items can be denoted as item-item graphs. GNNs provide an unprecedented opportunity to advance social recommendations. However, there are tremendous challenges in building GNNs-based social recommendations where (1) users (items) are simultaneously involved in the user-item graph and user-user social graph (item-item graph); (2) user-item graphs not only contain user-item interactions but also include users’ opinions on items; and (3) the nature of social relations are heterogeneous among users. In this paper, we propose a novel graph neural network framework (GraphRec+) for social recommendations, which is able to coherently model graph data in order to learn better user and item representations. Specifically, we introduce a principled approach for jointly capturing interactions and opinions in the user-item graph and also propose an attention mechanism to differentiate the heterogeneous strengths of social relations. Comprehensive experiments on three real-world datasets show the effectiveness of the proposed framework.

Aiming to solve the cluster lag consensus (CLC) problem of networked nonlinear agents with a switching topologies, this paper proposes a control algorithm based on the measurements of position and velocity. When the network has several clusters of agents, under the conditions that each cluster is a strongly connected digraph including isolated agents, and at least one agent in each cluster can communicate with the common leader each time, a sufficient condition is derived for reaching CLC. Finally, a numerical simulation is demonstrated to validate the proposed control algorithm and the developed conditions.

Social media analytics has drawn new quantitative insights of human activity patterns. Many applications of social media analytics, from pandemic prediction to earthquake response, require an in-depth understanding of how these patterns change when human encounter unfamiliar conditions. In this paper, we select two earthquakes in China as the social context in Sina-Weibo (or Weibo for short), the largest Chinese microblog site. After proposing a formalized Weibo information flow model to represent the information spread on Weibo, we study the information spread from three main perspectives: individual characteristics, the types of social relationships between interactive participants, and the topology of real interaction networks. The quantitative analyses draw the following conclusions. First, the shadow of Dunbar's number is evident in the "declared friends/followers" distributions, and the number of each participant's friends/followers who also participated in the earthquake information dissemination show the typical power-law distribution, indicating a rich-gets-richer phenomenon. Second, an individual's number of followers is the most critical factor in user influence. Strangers are very important forces for disseminating real-time news after an earthquake. Third, two types of real interaction networks share the scale-free and small-world property, but with a looser organizational structure. In addition, correlations between different influence groups indicate that when compared with other online social media, the discussion on Weibo is mainly dominated and influenced by verified users.

Securing cyberspaceCyberspace, the ubiquitous space that exists in relation to the Internet, is usually referred to as a dynamic broad domain ranging from Internet and its infrastructures to social networks.More research work in security has been extended from securing computers to securing Cyberspace, which includes the physical-level security, the network-level security, and the application-level security and addresses improvements in Cyberspace management.As a result, recent years have witnessed increasing research attention on securing Cyberspace, and many interesting methods have been proposed to locate suspicious IP, detect gossip content, prevent illegal information publication and distribution, manage social software and applications, and profile user behavior and opinion.This trend has provided the motivation to launch this special issue.Based on an open call in this area and invited best papers from The Fifth International Conference on Applications and Techniques for Information Security (ATIS 2014) and The first International Workshop on Curbing Cyber-Crimes (C 3 2014), five submissions have been accepted to best illustrate the main development and perspectives.The papers in this issue report a variety of methods used to tackle the security issues in cyberspace.They aim at improving security in applications ranging from RFID systems, location based service, discovery of software vulnerability to private medical records, and outsourcing in Multi-Cloud.The problems discussed in these papers are also related to disciplines including data mining, network security, digital forensics, and behavioral and psychological sciences.Here, we provide an integrative perspective of this special issue by summarizing each contribution contained therein.In [1], to address security and privacy issue in RFID systems, a new off-line reading orderindependent grouping-proof protocol is proposed to generate a proof that a group of tags have been scanned simultaneously in the range of a reader.The proposed protocol defines an ideal groupingproof functionality aiming at capturing the secure grouping-proof generation for a group of RFID tags in the UC framework.The new protocol maintains its security properties when composed concurrently with an unbounded number of instances of arbitrary protocol.In addition, the protocol conforms to the computational constraints of EPC Class-Gen-2 passive RFID tags.It is suitable for low-cost passive RFID tags, which are widely used in practical applications.In [2], the authors proposed an algorithm to address the problem of preserving privacy for individual users in location-aware applications.They define a novel distance measurement that combines the semantic and Euclidean distance to address the privacy-preserving issue.They conduct performance experiments on the proposed algorithm and distance metric, and results suggest that they can successfully retain the utility of the location services.In [3], to discover software vulnerability, an effective and efficient mechanism is proposed.The method also helps programmers to write secure code to avoid the existence of vulnerability at the early stage of software development.The proposed mechanism uses code clone verification to discover vulnerability in software programs and reduces the false positive of detection by combining the advantages of static and dynamic analysis.In addition, it also mitigates the path explosion problem in the testing process when verifying the existence of vulnerability.As a result, the proposed approach effectively improves the security of software systems, applications, and utilities in various areas of Cyberspace.In particular, it helps to create a reliable environment for the communications of all the social media participants.In [4], the authors analyze the security of Fair Remote Retrieval (FRR) model that is used to ensure the integrity of remote medical records.They show that FRR model fails to achieve its security goals, therefore present an improved protocol, called IFR 2, to fix the security minor faults

With the pervasion of social media, trust has been playing more of an important role in helping online users collect reliable information. In reality, user-specified trust relations are often very sparse; hence, inferring unknown trust relations has attracted increasing attention in recent years. Social status is one of the most important concepts in trust, and status theory is developed to help us understand the important role of social status in the formation of trust relations. In this paper, we investigate how to exploit social status in trust prediction by modeling status theory. We first vertify status theory in trust relations, then provide a principled way to model it mathematically, and propose a novel framework sTrust which incorporates status theory for trust prediction. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Futher experiments are conducted to understand the importance of status theory in trust prediction.

With the pervasion of social media, topic identification in short texts attracts increasing attention in recent years. However, in nature the texts of social media are short and noisy, and the structures are sparse and dynamic, resulting in difficulty to identify topic categories exactly from online social media. Inspired by social science findings that preference consistency and social contagion are observed in social media, we investigate topic identification in short and noisy texts by exploring social context from the perspective of social sciences. In particular, we present a mathematical optimization formulation that incorporates the preference consistency and social contagion theories into a supervised learning method, and conduct feature selection to tackle short and noisy texts in social media, which result in a Sociological framework for Topic Identification (STI). Experimental results on real-world datasets from Twitter and Citation Network demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of social context in topic identification.

Recently, user generated multimedia contents (e.g. text, image, speech and video) on social media are increasingly used to share their experiences and emotions, for example, a tweet usually contains both texts and images. Compared to sentiment analysis of texts and images separately, the combination of text and image may reveal tweet sentiment more adequately. Motivated by this rationale, we propose a method based on convolutional neural networks (CNN) for multimedia (tweets consist of text and image) sentiment analysis. Two individual CNN architectures are used for learning textual features and visual features, which can be combined as input of another CNN architecture for exploiting the internal relation between text and image. Experimental results on two real-world datasets demonstrate that the proposed method achieves effective performance on multimedia sentiment analysis by capturing the combined information of texts and images.

In recent years, microblogging platforms have become good places to spread various spams, making the problem of gauging information credibility on social networks receive considerable attention especially under an emergency situation. Unlike previous studies on detecting rumors using tweets' inherent attributes generally, in this work, we shift the premise and focus on identifying event rumors on Weibo by extracting features from crowd responses that are texts of retweets (reposting tweets) and comments under a certain social event. Firstly the paper proposes a method of collecting theme data, including a sample set of tweets which have been confirmed to be false rumors based on information from the official rumor-busting service provided by Weibo. Secondly clustering analysis of tweets are made to examine the text features extracted from retweets and comments, and a classifier is trained based on observed feature distribution to automatically judge rumors from a mixed set of valid news and false information. The experiments show that the new features we propose are indeed effective in the classification, and especially some stop words and punctuations which are treated as noises in previous works can play an important role in rumor detection. To the best of our knowledge, this work is the first to detect rumors in Chinese via crowd responses under an emergency situation.

Multi-label stream classification has not been fully explored for the unique properties of large data volumes, realtime, label dependencies, etc. Some methods try to take into account label dependencies, but they only focus on the existing frequent label combinations, leading to worse performance for multi-label classification. To deal with these problems, this paper proposes an algorithm which dynamically recognizes some new frequent label combinations and updates the trained classifier by class incremental learning strategy. Experimental results over both real-world and synthetic datasets demonstrate its better predictive performance.

Autonomic service-oriented computing system, which exposes much flexibility and intelligence, is taken as a promising paradigm to deal with drawbacks appearing in current complex large scale heterogeneous computing systems, such as difficulties in construction, administration and maintenance. However presently there are not suitable approaches to design and implement such kind of systems. In this paper, a lightweight approach to autonomic service-oriented computing system development is proposed. The approach consists of three parts: an architectural style which entails quite a few flexible and intelligent properties; a lightweight process to design computing system under the proposed architectural style; a high level executable modeling language to implement the designed system. A case study is given to illustrate the approach