The increase in phishing attacks through email and short message service (SMS) has shown no signs of deceleration. The first thing we need to do to combat the ever-increasing number of phishing attacks is to collect and characterize more phishing cases that reach end users. Without understanding these characteristics, anti-phishing countermeasures cannot evolve. In this study, we propose an approach using Twitter as a new observation point to immediately collect and characterize phishing cases via e-mail and SMS that evade countermeasures and reach users. Specifically, we propose CrowdCanary, a system capable of structurally and accurately extracting phishing information (e.g., URLs and domains) from tweets about phishing by users who have actually discovered or encountered it. In our three months of live operation, CrowdCanary identified 35,432 phishing URLs out of 38,935 phishing reports. We confirmed that 31,960 (90.2%) of these phishing URLs were later detected by the anti-virus engine, demonstrating that CrowdCanary is superior to existing systems in both accuracy and volume of threat extraction. We also analyzed users who shared phishing threats by utilizing the extracted phishing URLs and categorized them into two distinct groups - namely, experts and non-experts. As a result, we found that CrowdCanary could collect information that is specifically included in non-expert reports, such as information shared only by the company brand name in the tweet, information about phishing attacks that we find only in the image of the tweet, and information about the landing page before the redirect. Furthermore, we conducted a detailed analysis of the collected information on phishing sites and discovered that certain biases exist in the domain names and hosting servers of phishing sites, revealing new characteristics useful for unknown phishing site detection.

Action spotting is a key component in high-level video understanding. The large number of similar frames poses a challenge for recognizing actions in videos. In this paper we use frame saliency to represent the importance of frames for guiding the model to focus on keyframes. We propose the frame saliency weighting module to improve frame saliency and video representation at the same time. Our proposed model contains two encoders, for pre-action and post-action time windows, to encode video context. We validate our design choices and the generality of proposed method in extensive experiments. On the public SoccerNet-v2 dataset, the method achieves an average mAP of 57.3%, improving over the state of the art. Using embedding features obtained from multiple feature extractors, the average mAP further increases to 75%. We show that reducing the model size by over 90% does not significantly impact performance. Additionally, we use ablation studies to prove the effective of saliency weighting module. Further, we show that our frame saliency weighting strategy is applicable to existing methods on more general action datasets, such as SoccerNet-v1, ActivityNet v1.3, and UCF101.

Social relationships (e.g., couples, opponents) are the foundational part of society. Social relation atmosphere describes the overall interaction environment between social relationships. Discovering social relation atmosphere can help machines better comprehend human behaviors and improve the performance of social intelligent applications. Most existing research mainly focuses on investigating social relationships, while ignoring the social relation atmosphere. Due to the complexity of the expressions in video data and the uncertainty of the social relation atmosphere, it is even difficult to define and evaluate. In this paper, we innovatively analyze the social relation atmosphere in video data. We introduce a Relevant Visual Concept (RVC) from the social relationship recognition task to facilitate social relation atmosphere recognition, because social relationships contain useful information about human interactions and surrounding environments, which are crucial clues for social relation atmosphere recognition. Our approach consists of two main steps: (1) we first generate a group of visual concepts that preserve the inherent social relationship information by utilizing a 3D explanation module; (2) the extracted relevant visual concepts are used to supplement the social relation atmosphere recognition. In addition, we present a new dataset based on the existing Video Social Relation Dataset. Each video is annotated with four kinds of social relation atmosphere attributes and one social relationship. We evaluate the proposed method on our dataset. Experiments with various 3D ConvNets and fusion methods demonstrate that the proposed method can effectively improve recognition accuracy compared to end-to-end ConvNets. The visualization results also indicate that essential information in social relationships can be discovered and used to enhance social relation atmosphere recognition.

Online social networks have increased their impact on the real world, which motivates information senders to control the propagation process of information to promote particular actions of online users. However, the existing works on information provisioning seem to oversimplify the users' decision-making process that involves information reception, internal actions of social networks, and external actions of social networks. In particular, characterizing the best practices of information provisioning that promotes the users' external actions is a complex task due to the complexity of the propagation process in OSNs, even when the variation of information is limited. Therefore, we propose a new information diffusion model that distinguishes user behaviors inside and outside of OSNs, and formulate an optimization problem to maximize the number of users who take the external actions by providing information over multiple rounds. Also, we define a robust provisioning policy for the problem, which selects a message sequence to maximize the expected number of desired users under the probabilistic uncertainty of OSN settings. Our experiment results infer that there could exist an information provisioning policy that achieves nearly-optimal solutions in different types of OSNs. Furthermore, we empirically demonstrate that the proposed robust policy can be such a universally optimal solution.

Influential user detection is critical in supporting the human facilitator-based facilitation in the online forum. Traditional approaches to detect influential users in the online forum focus on the statistical activity information such as the number of posts. However, statistical activity information cannot fully reflect the influence that users bring to the online forum. In this paper, we propose to detect the influencers from the influence propagation perspective and focus on the influential maximization (IM) problem which aims at choosing a set of users that maximize the influence propagation from the entire social network. An online forum influence propagation network (OFIPN) is proposed to model the influence from an individual user perspective and influence propagation between users, and a heuristic algorithm that is proposed to find influential users in OFIPN. Experiments are conducted by simulations with a real-world social network. Our empirical results show the effectiveness of the proposed algorithm.

As the Internet has become prevalent, the popularity of net media has been growing, to a point that it has taken over conventional mass media. However, TWtrends, the Twitter trends visualization system operated by our research team since 2019, indicates that many topics on TV programs frequently appear on Twitter trendlines. This study investigates the relationship between Twitter and TV programs by collecting information on Twitter trends and TV programs simultaneously. Although this study provides a rough estimation of the volume of tweets that mention TV programs, the results show that several tweets mention TV programs at a constant rate, which tends to increase on the weekend. This tendency of TV-related tweets stems from the audience rating survey results. Considering the study outcome, and the fact that many TV programs introduce topics popular in social media, implies codependency between Internet media (social media) and mass media.

Electromagnetic scattering in a coaxial cable having two flanges and concentric grooves is studied. The associated Weber-Orr transform is used to represent electromagnetic fields in an infinitely long cavity, and the mode-matching method is used to enforce boundary continuity. S-parameters obtained by our approach are compared with the reference solutions, and the characteristics are discussed when geometric parameters are varied. The results show that the proposed model provides cost effective and accurate solutions to the problem.

Due to the limitations of cloud computing on latency, bandwidth and data confidentiality, edge computing has emerged as a novel location-aware paradigm to provide them with more processing capacity to improve the computing performance and quality of service (QoS) in several typical domains of human activity in smart society, such as social networks, medical diagnosis, telecommunications, recommendation systems, internal threat detection, transports, Internet of Things (IoT), etc. These application domains often handle a vast collection of entities with various relationships, which can be naturally represented by the graph data structure. Graph processing is a powerful tool to model and optimize complex problems in which the graph-based data is involved. In view of the relatively insufficient resource provisioning of the portable terminals, in this paper, for the first time to our knowledge, we propose an interactive and reductive graph processing library (GPL) for edge computing in smart society at low overhead. Experimental evaluation is conducted to indicate that the proposed GPL is more user-friendly and highly competitive compared with other established systems, such as igraph, NetworKit and NetworkX, based on different graph datasets over a variety of popular algorithms.

This study examined whether distance learning in a first-year PBL courses in the first unit of instruction improves the effectiveness of subsequent group work learning over face-to-face learning. The first-year PBL consisted of three units: an input unit, a group work unit and an outcomes presentation unit. In 2017/2018, the input unit was conducted in the classroom with face-to-face learning. In 2017, a workshop was held in addition to face-to-face learning in classroom. In 2020/2021, the input unit was conducted with distance learning. In the years, approximately 100 people completed the questionnaire. A preliminary check confirmed that the average score of students' self-assessment of their own social skills were not significantly different among the four years. Analysis showed that in 2018, the perceived efficacy in the group work unit depended on learners' high social skills. Alternatively, in 2017/2020/2021, the perceived efficacy in group work was not dependent on learners' social skills. This suggests that distance learning and face-to-face learning with workshop learning, instead of full face-to-face learning for the units placed before the group work unit facilitates the learning efficacy of the group work unit, even for students with social skill concerns.

Recently, multi-modal fusion methods based on remote sensing data and social sensing data have been widely used in the field of urban region function recognition. However, due to the high complexity of noise problem, most of the existing methods are not robust enough when applied in real-world scenes, which seriously affect their application value in urban planning and management. In addition, how to extract valuable periodic feature from social sensing data still needs to be further study. To this end, we propose a multi-modal fusion network guided by feature co-occurrence for urban region function recognition, which leverages the co-occurrence relationship between multi-modal features to identify abnormal noise feature, so as to guide the fusion network to suppress noise feature and focus on clean feature. Furthermore, we employ a graph convolutional network that incorporates node weighting layer and interactive update layer to effectively extract valuable periodic feature from social sensing data. Lastly, experimental results on public available datasets indicate that our proposed method yeilds promising improvements of both accuracy and robustness over several state-of-the-art methods.

The phenomenon known as social polarization, in which a social group splits into two or more groups, can cause division of the society by causing the radicalization of opinions and the spread of misinformation, is particularly significant in online communities. To develop technologies to mitigate the effects of polarization in online social networks, it is necessary to understand the mechanism driving its occurrence. There are some models of social polarization in which network structure and users' opinions change, based on the quantified opinions held by the users of online social networks. However, they are based on the interaction between users connected by online social networks. Current recommendation systems offer information from unknown users who are deemed to have similar interests. We can interpret this situation as being yielded non-local effects brought on by the network system, it is not based on local interactions between users. In this paper, based on the spectral graph theory, which can describe non-local effects in online social networks mathematically, we propose a model of polarization that user behavior and network structure change while influencing each other including non-local effects. We investigate the characteristics of the proposed model. Simultaneously, we propose an index to evaluate the degree of network polarization quantitatively, which is needed for our investigations.

This paper investigates the joint problem of user association and spectrum allocation in satellite-terrestrial integrated networks (STINs), where a low earth orbit (LEO) satellite access network cooperating with terrestrial networks constitutes a heterogeneous network, which is beneficial in terms of both providing seamless coverage as well as improving the backhaul capacity for the dense network scenario. However, the orbital movement of satellites results in the dynamic change of accessible satellites and the backhaul capacities. Moreover, spectrum sharing may be faced with severe co-channel interferences (CCIs) caused by overlapping coverage of multiple access points (APs). This paper aims to maximize the total sum rate considering the influences of the dynamic feature of STIN, backhaul capacity limitation and interference management. The optimization problem is then decomposed into two subproblems: resource allocation for terrestrial communications and satellite communications, which are both solved by matching algorithms. Finally, simulation results show the effectiveness of our proposed scheme in terms of STIN's sum rate and spectrum efficiency.

This letter studies a biobjective optimization problem in binary associative memories characterized by ternary connection parameters. First, we introduce a condition of parameters that guarantees storage of any desired memories and suppression of oscillatory behavior. Second, we define a biobjective problem based on two objectives that evaluate uniform stability of desired memories and sparsity of connection parameters. Performing precise numerical analysis for typical examples, we have clarified existence of a trade-off between the two objectives.

The integration of social networking concepts into the Internet of Things has led to the Social Internet of Things (SIoT) paradigm, and trust evaluation is essential to secure interaction in SIoT. In SIoT, when resource-constrained nodes respond to unexpected malicious services and malicious recommendations, the trust assessment is prone to be inaccurate, and the existing architecture has the risk of privacy leakage. An edge-cloud collaborative trust evaluation architecture in SIoT is proposed in this paper. Utilize the resource advantages of the cloud and the edge to complete the trust assessment task collaboratively. An evaluation algorithm of relationship closeness between nodes is designed to evaluate neighbor nodes' reliability in SIoT. A trust computing algorithm with enhanced sensitivity is proposed, considering the fluctuation of trust value and the conflict between trust indicators to enhance the sensitivity of identifying malicious behaviors. Simulation results show that compared with traditional methods, the proposed trust evaluation method can effectively improve the success rate of interaction and reduce the false detection rate when dealing with malicious services and malicious recommendations.

While social media is now used by many people and plays a role in distributing information, it has recently created an unexpected problem: the actual shrinkage of information sources. This is mainly due to the ease of connecting people with similar opinions and the recommendation system. Biased information distribution promotes polarization that divides people into multiple groups with opposing views. Also, people may receive only the seemingly positive information that they prefer, or may trigger them into holding onto their opinions more strongly when they encounter opposing views. This, combined with the characteristics of social media, is accelerating the polarization of opinions and eventually social division. In this paper, we propose a model of opinion formation on social media to simulate polarization. While based on the idea that opinion neutrality is only relative, this model provides new techniques for dealing with polarization.

Social Skills Training (SST) has been used for years to improve individuals' social skills toward building a better daily life. In SST carried out by humans, the social skills level is usually evaluated through a verbal interview conducted by the trainer. Although this evaluation is based on psychiatric knowledge and professional experience, its quality depends on the trainer's capabilities. Therefore, to standardize such evaluations, quantifiable metrics are required. To meet this need, the second edition of the Social Responsiveness Scale (SRS-2) offers a viable solution because it has been extensively tested and standardized by empirical research works. This paper describes the development of an automated method to evaluate a person's social skills level based on SRS-2. We use multimodal features, including BERT-based features, and perform score estimation with a 0.76 Pearson correlation coefficient while using feature selection. In addition, we examine the linguistic aspects of BERT-based features through subjective evaluations. Consequently, the BERT-based features show a strong negative correlation with human subjective scores of fluency, appropriate word choice, and understandable speech structure.

While online communities are important platforms for various social activities, many online communities fail to survive, which motivates researchers to investigate factors affecting the growth and survival of online communities. We comprehensively examine the effects of a wide variety of social network features on the growth and survival of communities in Reddit. We show that several social network features, including clique ratio, density, clustering coefficient, reciprocity and centralization, have significant effects on the survival of communities. In contrast, we also show that social network features examined in this paper only have weak effects on the growth of communities. Moreover, we conducted experiments predicting future growth and survival of online communities utilizing social network features as well as contents and activity features in the communities. The results show that prediction models utilizing social network features as well as contents and activity features achieve approximately 30% higher F1 measure, which evaluates the prediction accuracy, than the models only using contents and activity features. In contrast, it is also shown that social network features are not effective for predicting the growth of communities.

An efficient hardware implementation for Simultaneous Localization and Mapping (SLAM) methods is of necessity for mobile autonomous robots with limited computational resources. In this paper, we propose a resource-efficient FPGA implementation for accelerating scan matching computations, which typically cause a major bottleneck in 2D LiDAR SLAM methods. Scan matching is a process of correcting a robot pose by aligning the latest LiDAR measurements with an occupancy grid map, which encodes the information about the surrounding environment. We exploit an inherent parallelism in the Rao-Blackwellized Particle Filter (RBPF) based algorithm to perform scan matching computations for multiple particles in parallel. In the proposed design, several techniques are employed to reduce the resource utilization and to achieve the maximum throughput. Experimental results using the benchmark datasets show that the scan matching is accelerated by 5.31-8.75× and the overall throughput is improved by 3.72-5.10× without seriously degrading the quality of the final outputs. Furthermore, our proposed IP core requires only 44% of the total resources available in the TUL Pynq-Z2 FPGA board, thus facilitating the realization of SLAM applications on indoor mobile robots.

In this paper, we build a microscopic simulator of traffic flow in a three-modal transport society for pedestrians/slow vehicles/vehicles (P/SV/V) to evaluate a post P/V society. The simulator assumes that the SV includes bicycles and micro electric vehicles, whose speed is strictly and mechanically limited up to 30 km/h. In addition, this simulator adopts an SV overtaking model. Modal shifts caused by modal diversity requires new valuation indexes. The simulator has a significant feature of a traveler-based traffic demand simulation not a vehicle-based traffic demand simulation as well as new evaluation indexes. New assessment taking this situation into account is conducted and the results explain new aspects of traffic flow in a three-mode transport society.

With the spread of high-performance sensors and social network services (SNS) and the remarkable advances in machine learning technologies, fake media such as fake videos, spoofed voices, and fake reviews that are generated using high-quality learning data and are very close to the real thing are causing serious social problems. We launched a research project, the Media Clone (MC) project, to protect receivers of replicas of real media called media clones (MCs) skillfully fabricated by means of media processing technologies. Our aim is to achieve a communication system that can defend against MC attacks and help ensure safe and reliable communication. This paper describes the results of research in two of the five themes in the MC project: 1) verification of the capability of generating various types of media clones such as audio, visual, and text derived from fake information and 2) realization of a protection shield for media clones' attacks by recognizing them.