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This paper introduces some problems of analysis and design of error information detection software. These problems include the basic aim of the system, the overall design, detection requirement express standard, detecting functions design, and so on. In our experience, not only quality problems in a lot of operations have been detected, but also some problems in MIS analysis and design as well as the problem in organization systematic operation have been detected. The information in error state is hologram and the barometer of the state of organization system. Therefore, it is highly possible to discover problems existing in MIS through the inspection of the information in error state, deduce the state of systems' operation, predict systematic development, and organize and design development strategy.

Should the enforcers of rules inform potential violators about how likely violations will be detected? In practice, there is some mixture of revelation and secrecy‐police inform potential speeders about new detection technologies, but not about other dimensions of detection. We explain the mix of revelation and secrecy using games of asymmetric information in which the detection level is modeled exogenously. Our analysis applies to various legal and social conflict areas such as terrorism, speeding, and parenting.

The purpose of this paper is to improve photoelectric detection target (PDT) optical detection performance and detection view, by analyzing its influence factors and putting forward a new method to design its optical detection system.

Using rectangle linked photoelectric detector, with low noise and high response, to design optical detection system and gain faint projectile image information; bringing forward a deviating focusing technique to eliminate detection blind area of photoelectric detector; and designing adjustable slit diaphragm to weaken background light influence.

The results of experimentation in shooting range show that the new PDT has improved detection sensitivity and performance.

The paper presents a new design method in photoelectric detection target (PDT) optical detection system, which can provide a new method to design fire across measurement system and gain accurate projectile's coordinates data in the shooting range.

Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.

In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.

In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.

In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.

Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.

Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.

Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.

The rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.

The detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.

The paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.

The study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

The purpose of this paper is to accurately capture the risks which are caused by each road user in time.

The authors proposed a novel risk assessment approach based on the multi-sensor fusion algorithm in the real traffic environment. Firstly, they proposed a novel detection-level fusion approach for multi-object perception in dense traffic environment based on evidence theory. This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was accurately obtained. Then, they conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated. The prediction steering angle and trajectory were considered in the determination of traffic risk influence area.

The results of multi-object perception in the experiments showed that the proposed fusion approach achieved low false and missing tracking, and the road traffic risk was described as a field of equivalent force. The results extend the understanding of the traffic risk, which supported that the traffic risk from the front and back of the vehicle can be perceived in advance.

This approach integrated four states of track life into a generic fusion framework to improve the performance of multi-object perception. The information of object type, position and velocity was used to reduce erroneous data association between tracks and detections. Then, the authors conducted several experiments in real dense traffic environment on highways and urban roads, which enabled them to propose a novel road traffic risk modeling approach based on the dynamic analysis of vehicles in a variety of driving scenarios. By analyzing the generation process of traffic risks between vehicles and the road environment, the equivalent forces of vehicle–vehicle and vehicle–road were presented and theoretically calculated.

This paper aims to present a system that is fully capable of addressing the issue of detection, tracking and following pedestrians, which is a very challenging task, especially when it is considered for using in large outdoors infrastructures. Three modules, detection, tracking and following, are integrated and tested over long distances in semi-structured scenarios, where static or dynamic obstacles, including other pedestrians, can be found.

The detection is based on the probabilistic fusion of a laser scanner and a camera. The tracking module pairs observations with previously detected targets by using Kalman Filters and a Mahalanobis-distance. The following module allows to safely pursue the target by using a well-defined navigation scheme.

The system can track pedestrians from static position to 3.46 m/s (running). It handles occlusions, crossings or miss-detections, keeping track of the position even if the pedestrian is only detected in 55/per cent of the observations. Moreover, it autonomously selects and follows a target at a maximum speed of 1.46 m/s.

The main novelty of this study is the integration of the three algorithms in a fully operational system, tested in real outdoor scenarios. Furthermore, the addition of labelling to the detection algorithm allows using the full range of a single sensor while preserving the high performance of a combined detection. False-positives’ rate is reduced by handling the uncertainty level when pairing observations. The inclusion of pedestrian speed in the model speeds up and simplifies tracking process. Finally, the most suitable target is automatically selected by a scoring system.

This study aims to recognize the challenge of identifying deceptive information and provides a framework for thinking about how we as humans negotiate the current media environment filled with misinformation and disinformation.

This study reviews the influence of Wilson’s (2016) General Theory of Information Behavior (IB) in the field of information science (IS) before introducing Levine’s Truth-Default Theory (TDT) as a method of deception detection. By aligning Levine’s findings with published scholarship on IB, this study illustrates the fundamental similarities between TDT and existing research in IS.

This study introduces a modification of Wilson’s work which incorporates truth-default, translating terms to apply this theory to the broader area of IB rather than Levine’s original face-to-face deception detection.

False information, particularly online, continues to be an increasing problem for both individuals and society, yet existing IB models cannot not account for the necessary step of determining the truth or falsehood of consumed information. It is critical to integrate this crucial decision point in this study’s IB models (e.g. Wilson’s model) to acknowledge the human tendency to default to truth and thus providing a basis for studying the twin phenomena of misinformation and disinformation from an IS perspective. Moreover, this updated model for IB contributes the Truth Default Framework for studying how people approach the daunting task of determining truth, reliability and validity in the immense number of news items, social media posts and other sources of information they encounter daily. By understanding and recognizing our human default to truth/trust, we can start to understand more about our vulnerability to misinformation and disinformation and be more prepared to guard against it.

The purpose of this paper is to discuss the autonomous navigation and guidance scheme for future precise and safe planetary landing.

Autonomous navigation and guidance schemes are proposed based on inertial measurement unit (IMU) and optical navigation sensors for precise and safe landing of spacecrafts on the moon and planetary bodies. First, vision‐aided inertial navigation scheme is suggested to achieve precise relative navigation; second, two autonomous obstacle detection algorithms, based on grey image from optical navigation camera and digital elevation map form light detection and ranging sensor, respectively, are proposed; and third, flowchart of automatic obstacle avoidance maneuver is also given out.

This paper finds that the performance of the proposed scheme precedes the traditional planetary landing navigation and guidance mode based on IMU and deep space network.

The presented schemes need to be further validated by the mathematical simulations and hardware‐in‐loop simulations, and then they can be used in the real flight missions.

The presented schemes are applicable to both future planetary pin‐point landing missions and sample return missions with little modification.

This paper presents the new autonomous navigation and guidance scheme in order to achieve the precise and safe planetary landing.

The ability to detect disturbances quickly as they arise in a supply chain helps to manage them efficiently and effectively. The purpose of this paper is to demonstrate the feasibility of automatically and therefore quickly detecting a specific disturbance, which is constrained capacity at a supply chain echelon.

Different supply chain echelons of a simulated four echelon supply chain were individually capacity constrained to assess their impacts on the profiles of system variables, and to develop a signature that related the profiles to the echelon location of the capacity constraint. A review of disturbance detection techniques across various domains formed the basis for considering the signature-based technique.

The signature for detecting a capacity constrained echelon was found to be based on cluster profiles of shipping and net inventory variables for that echelon as well as other echelons in a supply chain, where the variables are represented as spectra.

Detection of disturbances in a supply chain including that of constrained capacity at an echelon has seen limited research where this study makes a contribution.