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The purpose of this paper is to address the problem of profiling human behaviour patterns captured in surveillance videos for the application of online normal behaviour recognition and anomaly detection.

A novel framework is developed for automatic behaviour profiling and online anomaly detection without any manual labeling of the training dataset.

Experimental results demonstrate the effectiveness and robustness of the authors' approach using noisy and sparse datasets collected from one real surveillance scenario.

To discover the topics, co‐clustering topic model not only captures the correlation between words, but also models the correlations between topics. The major difference between the conventional co‐clustering algorithms and the proposed CCMT is that CCMT shows a major improvement in terms of recall, i.e. interpretability.

Amid all the bad news of Wall Street wrongdoing, there is good news for financial services companies and compliance staff: A new generation of technology, called “behavior detection”, means that more and more financial abuses and violations will be detected in the early stages rather than long after they have severely damaged a company and its customers. In this article, we will explain: how behavior detection technology works, how it finds potential violations quickly, and how it meets the new and growing expectations and demands of regulators for comprehensive and more effective compliance systems.

Corporate misconduct carries significant social and economic costs, and therefore regulators and other stakeholders seek to deter it. Despite the significant costs and deterrence efforts, corporate misconduct is widespread and our understanding of it is limited. As argued in this chapter, one key reason for this is the lack of understanding of the benefits and penalties of misconduct for the companies and individuals involved, as well as the detection of such behavior. This chapter seeks to advance our understanding of corporate misconduct and builds on the rational choice model (RCM) – where the decision to engage in misconduct hinges on a calculation of the expected costs and benefit – and links it to research in organization theory and strategy. Specifically, it sets a research agenda at the intersection of organizational and strategic perspectives, to deepen our understanding of corporate misconduct and shed light on opportunities for empirical and theoretical research which can potentially aid in developing effective deterrence strategies.

Object detection models have gained considerable popularity as they aid in lot of applications, like monitoring, video surveillance, etc. Object detection through the video tracking faces lot of challenges, as most of the videos obtained as the real time stream are affected due to the environmental factors.

This research develops a system for crowd tracking and crowd behaviour recognition using hybrid tracking model. The input for the proposed crowd tracking system is high density crowd videos containing hundreds of people. The first step is to detect human through visual recognition algorithms. Here, a priori knowledge of location point is given as input to visual recognition algorithm. The visual recognition algorithm identifies the human through the constraints defined within Minimum Bounding Rectangle (MBR). Then, the spatial tracking model based tracks the path of the human object movement in the video frame, and the tracking is carried out by extraction of color histogram and texture features. Also, the temporal tracking model is applied based on NARX neural network model, which is effectively utilized to detect the location of moving objects. Once the path of the person is tracked, the behaviour of every human object is identified using the Optimal Support Vector Machine which is newly developed by combing SVM and optimization algorithm, namely MBSO. The proposed MBSO algorithm is developed through the integration of the existing techniques, like BSA and MBO.

The dataset for the object tracking is utilized from Tracking in high crowd density dataset. The proposed OSVM classifier has attained improved performance with the values of 0.95 for accuracy.

This paper presents a hybrid high density video tracking model, and the behaviour recognition model. The proposed hybrid tracking model tracks the path of the object in the video through the temporal tracking and spatial tracking. The features train the proposed OSVM classifier based on the weights selected by the proposed MBSO algorithm. The proposed MBSO algorithm can be regarded as the modified version of the BSO algorithm.

This paper aims to address the collision problem between robot and the external environment (including human) in an unstructured situation. A new collision detection and torque optimization control method is proposed.

Firstly, when the collision appears, a second-order Taylor observer is proposed to estimate the residual value. Secondly, the band-pass filter is used to reduce the high-frequency torque modeling dynamic uncertainty. With the estimate information and the torque value, a variable impedance control approach is then synthesized to guarantee that the collision is avoided or the collision will be terminated with different contact models and positions. However, in terms of adaptive linear force error, the variation of the thickness of the boundary layer is controlled by the new proximity function.

Finally, the experimental results show the better performance of the proposed control method, realizing the force control during the collision process.

Origin approach and origin experiment.

The consulting team intervenes in the integrated construction consulting (ICC) network structure centered on “client-contractor-consultant.” Team boundary-spanning behavior (TBB) driven by the network structure is crucial to project performance. This article investigated how to stimulate the consulting project performance (CPP) improvement by considering the interactive effect of network structure and TBB. To be specific, this paper explored the configuration between structural characteristics of project networks, the dimension of TBB, and project performance in ICC projects.

Network density and centrality were used to reflect network structure. This study collected 216 valid responses from construction professionals (including project managers, department managers, and project engineers) via a questionnaire survey and analyzed the data using fsQCA.

Combining with the corresponding typical project case and analysis, the results concluded four types of configurations for achieving high performance in the ICC projects. Meanwhile, network centrality, density, ambassadorial behavior, coordination behavior, and detection behavior significantly impact high consulting project performance. Matching ICC network characteristics with the TBB is important. There are also three low performance configurations for the ICC projects. Low performance state also occurs when network centrality or density and coordination behavior is simultaneously low. Only the right match between the network characteristics and TBB can produce high consulting project performance.

The network centrality and density, the implementation of TBB vary, and the paths to achieve high consulting project performance are different. Clients, ICC projects, and consulting teams should choose the appropriate development paths according to the actual situation. (1) Clients should commit to applying the ICC project model with high network centrality, density, and coordination behavior of ICC enterprises to promote project performance. (2) Consulting enterprises should carry out ICC business based on detecting behavior and coordinating behavior. (3) The market should cultivate head consulting enterprises with independence and integration, and bring into play the effectiveness of consulting team ambassadorial behavior.

Comparing the results of the four high CPP configurations, the network structure characteristics are essential, which means that in the Chinese consulting practice between the owner and the consulting firm pay attention to the use of appropriate ICC organizational structure model and arrange the degree of centralization of authorized responsibilities. Coordination behavior is necessary to achieve high CPP. Therefore, Chinese consulting firms should pay attention to effective communication and exchange with project contractors in order to get high CPP in conducting business; meanwhile, enabling behavior can achieve high CPP both in the presence and absence of configuration H1 and H4, which indicates that enabling behavior has substitution effect. Comparing the three low CPP configurations also contrarily confirms the indispensability of coordinating behavior. Comparing the results of high and low CPP configurations, the TBB is seriously missing and not properly applied in CPP enhancement. In detail, Chinese consulting firms have been regarded as independent third parties providing services, and less attention has been paid to the TBB of Chinese consulting firms in past practice, thus leading to the dilemma of inadequate empowerment of consulting firms due to their unclear status. To solve this dilemma, the findings of this paper offer a solution at the micro level to change the previous perception of consulting and demonstrate that Chinese consulting practice needs to pay attention to TBB with owners and contractors, and apply it well to enhance the reputation, management consulting level and capability, and experience and expertise of consulting firms to achieve high CPP.

The research results changed from the previous bilateral project governance to a new perspective of network embedding. It provided a theoretical basis for the improvement path of high consulting project performance, as well as providing ideas for clients on the organizational design of ICC projects. On the other hand, it provided a practical reference for TBB positioning of ICC enterprises for transformation and upgrading development.

A zero-day vulnerability is a complimentary ticket to the attackers for gaining entry into the network. Thus, there is necessity to device appropriate threat detection systems and establish an innovative and safe solution that prevents unauthorised intrusions for defending various components of cybersecurity. We present a survey of recent Intrusion Detection Systems (IDS) in detecting zero-day vulnerabilities based on the following dimensions: types of cyber-attacks, datasets used and kinds of network detection systems.

Purpose: The study focuses on presenting an exhaustive review on the effectiveness of the recent IDS with respect to zero-day vulnerabilities.

Methodology: Systematic exploration was done at the IEEE, Elsevier, Springer, RAID, ESCORICS, Google Scholar, and other relevant platforms of studies published in English between 2015 and 2021 using keywords and combinations of relevant terms.

Findings: It is possible to train IDS for zero-day attacks. The existing IDS have strengths that make them capable of effective detection against zero-day attacks. However, they display certain limitations that reduce their credibility. Novel strategies like deep learning, machine learning, fuzzing technique, runtime verification technique, and Hidden Markov Models can be used to design IDS to detect malicious traffic.

Implication: This paper explored and highlighted the advantages and limitations of existing IDS enabling the selection of best possible IDS to protect the system. Moreover, the comparison between signature-based and anomaly-based IDS exemplifies that one viable approach to accurately detect the zero-day vulnerabilities would be the integration of hybrid mechanism.

This paper is part II of a previous article of the same title: Intrusion detection. Part II is concerned with intrusion threats, attacks, defense, models, methods and systems.

The aim of the paper is to investigate the impact of pre‐existing audience mood on responses to health public service advertisements (PSAs). The paper also aims to show the practical and theoretical importance of mood as a variable in health communication.

Hypotheses regarding the impact of audience mood on the outcome of health PSAs were tested experimentally using health PSAs about vaccination and virus detection behaviors.

The influence of pre‐existing mood was mediated by the perceived risk of contracting the illness mentioned in the health advertisement. Personal estimations of risk mediated the impact of audience mood on behavioral intent and actual behavior. The more negative one's mood, the higher the perceived risk of contracting the disease mentioned in the message, and the more likely one was to adopt the precautionary behavior recommended by the PSA. Positive mood had opposite effects.

The findings suggest a novel media planning approach to maximizing the effectiveness of health risk messages. Due to the impact of context‐induced mood on perceptions of risk, messages could be more effective if placed in editorial contexts which induce negative mood (e.g. crime investigation reports) versus environments which induce positive mood (e.g. sitcoms), because negative mood makes people think they are more at risk and motivates them to act.

The mood‐and‐risk mediation hypothesis proposed here has never been examined in public health marketing. Findings call for further research on the impact of contextual affect on responses to public health communication. The paper suggests a new placement technique for media planners working in public health advertising.