Multi-Frequency Radar Micro-Doppler Based Classification of Micro-Drone Payload Weight

The use of drones for recreational, commercial and military purposes has seen a rapid increase in recent years. The ability of counter-drone detection systems to sense whether a drone is carrying a payload is of strategic importance as this can help determine the potential threat level posed by a detected drone. This paper presents the use of micro-Doppler signatures collected using radar systems operating at three different frequency bands for the classification of carried payload of two different micro-drones performing two different motions. Use of a KNN classifier with six features extracted from micro-Doppler signatures enabled mean payload classification accuracies of 80.95, 72.50 and 86.05%, for data collected at S-band, C-band and W-band, respectively, when the drone type and motion type are unknown. The impact on classification performance of different amounts of situational information is also evaluated in this paper.

Video Scene Information Detection Based on Entity Recognition

Video situational information detection is widely used in the fields of video query, character anomaly detection, surveillance analysis, and so on. However, most of the existing researches pay much attention to the subject or video backgrounds, but little attention to the recognition of situational information. What is more, because there is no strong relation between the pixel information and the scene information of video data, it is difficult for computers to obtain corresponding high-level scene information through the low-level pixel information of video data. Video scene information detection is mainly to detect and analyze the multiple features in the video and mark the scenes in the video. It is aimed at automatically extracting video scene information from all kinds of original video data and realizing the recognition of scene information through “comprehensive consideration of pixel information and spatiotemporal continuity.” In order to solve the problem of transforming pixel information into scene information, this paper proposes a video scene information detection method based on entity recognition. This model integrates the spatiotemporal relationship between the video subject and object on the basis of entity recognition, so as to realize the recognition of scene information by establishing mapping relation. The effectiveness and accuracy of the model are verified by simulation experiments with the TV series as experimental data. The accuracy of this model in the simulation experiment can reach more than 85%.

The role of ethnic origin and situational information in teachers’ reactions to social exclusion among students

The current study investigated pre- and in-service teachers' reactions to interethnic exclusion in Germany. Using hypothetical scenarios, we examined a sample of 482 teachers (84 males, 398 females; 59% pre-service teachers, 41% in-service teachers) as observers of exclusion among students. In these scenarios, we varied the ethnic origin of the excluded student (German vs. Turkish) and the background information, providing participants either with no additional background information or with information specifying that the excluded student had shown prior norm-violating behavior (insult of another student). We assessed the teachers’ evaluations of the scenarios and their anticipated reactions. The aim of the study was to replicate and extend previous research on teachers’ reactions to social exclusion. As expected, the analyses revealed a strong effect of the background information on teachers’ evaluations and reactions. The teachers evaluated exclusion as much more acceptable and were less likely to intervene in the scenarios with negative background information compared to those without additional information. Teachers seem to view exclusion in these situations as an understandable consequence of norm-violating behavior. However, in contrast to our expectations, the ethnic origin of the excluded student in the scenarios had no impact on teachers’ reactions. That is, situational information seems to be much more important for teachers’ reactions to social exclusion than the ethnic origin of an excluded student.

Psychophysiological processes, ensuring the achievement of sports results

Objective: to investigate the role of psychophysiological processes in achieving sports results.Materials and methods: the driving force of sports behavior was measured in free­divers, basketball players and athletes, which consisted of motivation to achieve sports results, emotional stress, situational information, hypoxic stability, and physical endurance. Then the sportsmen rotated the pedals of the bicycle ergometer with simultaneous intermittent breath holdings from 20 to 60 s.Results: it has been established that all components of the driving force of behavior are necessary for the sports activity of all groups of sportsmen. For the driving force of free­divers’ behavior, hypoxic stability is of greatest importance (r = 0.59), for basketball players — emotional stress (r = 0.6) and for athletes — emotional stress and situational information (r = 0.71 and 0.58). In addition, hypoxic stability and physical endurance directly affect the final sports result (r = 0.7 and 0.65) in conjunction with the driving force of behavior (r = 0.53).Conclusion: sports result is provided by the driving force behind the behavior of sportsmen. For free­divers, hypoxic stability is of primary importance, for basketball players — physical endurance and emotional stress, and for athletes — the totality of all components of the driving force of behavior.

Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

AUTOMATIC OBJECT SEGMENTATION TO SUPPORT CRISIS MANAGEMENT OF LARGE-SCALE EVENTS

The management of large-scale events with a widely distributed camping area is a special challenge for organisers and security forces and requires both comprehensive preparation and attentive monitoring to ensure the safety of the participants. Crucial to this is the availability of up-to-date situational information, e.g. from remote sensing data. In particular, information on the number and distribution of people is important in the event of a crisis in order to be able to react quickly and effectively manage the corresponding rescue and supply logistics. One way to estimate the number of persons especially at night is to classify the type and size of objects such as tents and vehicles on site and to distinguish between objects with and without a sleeping function. In order to make this information available in a timely manner, an automated situation assessment is required. In this work, we have prepared the first high-quality dataset in order to address the aforementioned challenge which contains aerial images over a large-scale festival of different dates. We investigate the feasibility of this task using Convolutional Neural Networks for instance-wise semantic segmentation and carry out several experiments using the Mask-RCNN algorithm and evaluate the results. Results are promising and indicate the possibility of function-based tent classification as a proof-of-concept. The results and thereof discussions can pave the way for future developments and investigations.

Intangible features extraction in the processing of abstract concepts: Evidence from picture-word priming

Over the last decade, hypotheses ranging from linguistic symbol processing to embodiment have been formulated to account for the content and mechanisms responsible for the representation of abstract concepts. Results of recent studies have suggested that abstract concepts, just like concrete ones, can benefit from knowledge of real-world situational context, but that they can also be processed based on abstract pictures devoid of such situational features. This paper presents two semantic priming experiments to explore such mechanisms further. The first experiment replicates Kuipers, Jones, and Thierry (2018) in a cross-linguistic setting which shows that abstract concepts can be processed from abstract pictures devoid of tangible features. In the second experiment, we studied extraction mechanisms that come into play when participants are presented with abstract and concrete pictures that provide situational information to illustrate target abstract concepts. We expected this facilitatory effect to be limited to concrete picture primes. Our data analysed with both Bayesian and Frequentist tests showed however that even when presented with tangible situational information, the extraction of features still occurred for abstract pictures. We discuss the implications of this with respect to future avenues for studying the processing of abstract concepts.

Adults, but not preschoolers or toddlers integrate situational constraints in their action anticipations: a developmental study on the flexibility of anticipatory gaze

Recent theories stress the role of situational information in understanding others’ behaviour. For example, the predictive coding framework assumes that people take contextual information into account when anticipating other’s actions. Likewise, the teleological stance theory assumes an early developing ability to consider situational constraints in action prediction. The current study investigates, over a wide age range, whether humans flexibly integrate situational constraints in their action anticipations. By means of an eye-tracking experiment, 2-year-olds, 5-year-olds, younger and older adults (together N = 181) observed an agent repeatedly taking one of two paths to reach a goal. Then, this path became blocked, and for test trials only the other path was passable. Results demonstrated that in test trials younger and older adults anticipated that the agent would take the continuous path, indicating that they took the situational constraints into account. In contrast, 2- and 5-year-olds anticipated that the agent would take the blocked path, indicating that they still relied on the agent’s previous observed behaviour and—contrary to claims by the teleological stance theory—did not take the situational constraints into account. The results highlight developmental changes in human’s ability to include situational constraints in their visual anticipations. Overall, the study contributes to theories on predictive coding and the development of action understanding.