Extensive possibilities of applications have made emotion recognition ineluctable and challenging in the field of computer science. The use of non-verbal cues such as gestures, body movement, and ...facial expressions convey the feeling and the feedback to the user. This discipline of Human-Computer Interaction places reliance on the algorithmic robustness and the sensitivity of the sensor to ameliorate the recognition. Sensors play a significant role in accurate detection by providing a very high-quality input, hence increasing the efficiency and the reliability of the system. Automatic recognition of human emotions would help in teaching social intelligence in the machines. This paper presents a brief study of the various approaches and the techniques of emotion recognition. The survey covers a succinct review of the databases that are considered as data sets for algorithms detecting the emotions by facial expressions. Later, mixed reality device Microsoft HoloLens (MHL) is introduced for observing emotion recognition in Augmented Reality (AR). A brief introduction of its sensors, their application in emotion recognition and some preliminary results of emotion recognition using MHL are presented. The paper then concludes by comparing results of emotion recognition by the MHL and a regular webcam.
Over the past two decades, automatic facial emotion recognition has received enormous attention. This is due to the increase in the need for behavioral biometric systems and human-machine interaction ...where the facial emotion recognition and the intensity of emotion play vital roles. The existing works usually do not encode the intensity of the observed facial emotion and even less involve modeling the multi-class facial behavior data jointly. Our work involves recognizing the emotion along with the respective intensities of those emotions. The algorithms used in this comparative study are Gabor filters, a Histogram of Oriented Gradients (HOG), and Local Binary Pattern (LBP) for feature extraction. For classification, we have used Support Vector Machine (SVM), Random Forest (RF), and Nearest Neighbor Algorithm (kNN). This attains emotion recognition and intensity estimation of each recognized emotion. This is a comparative study of classifiers used for facial emotion recognition along with the intensity estimation of those emotions for databases. The results verified that the comparative study could be further used in real-time behavioral facial emotion and intensity of emotion recognition.
Game theory is being used in cybersecurity to observe different attacks as it can provide a mathematical representation of the interactions between system admins, hackers, and users. The ...game-theoretic solution determines the favorable parameters (strategies), predicts the player’s behavior, and suggests the best settings for minimizing the attack’s effect. To this end, our paper attempts to study the usefulness of game-theoretic applications for the prevention of Distributed Denial of Service (DDoS) attacks on a drone by deriving the information from conventional game solutions and augmenting that with the bounded rationality concept called Quantal response equilibrium (QRE). In this process, we identify feasible strategies for each player through simulations and formulate five non-cooperative game scenarios for two variants of DDoS attacks. In these games, the traditional game-theoretic solution or Nash Equilibrium (NashE) provides information about the drone’s recommended settings, the hacker’s preferred strategy, and the game-theoretic threshold assuming that all participants are highly intelligent. We augment this information by considering the participant’s tendency to make errors and the evolution in their behavioral pattern from zero to high-values of rationality using QRE. The information coupled from NashE and QRE provides better clarity to a drone operator, thus improving the drone’s security by two levels and allowing the drone operator to take timely precautions. Inspired by this multilevel process, we propose an equivalent real-world framework for protecting Unmanned Aerial Vehicle (UAV) nodes against a DDoS attack.
Over the past two decades, Unmanned Aerial Vehicles (UAVs), more commonly known as drones, have gained a lot of attention, and are rapidly becoming ubiquitous because of their diverse applications ...such as surveillance, disaster management, pollution monitoring, film-making, and military reconnaissance. However, incidents such as fatal system failures, malicious attacks, and disastrous misuses have raised concerns in the recent past. Security and viability concerns in drone-based applications are growing at an alarming rate. Besides, UAV networks (UAVNets) are distinctive from other ad-hoc networks. Therefore, it is necessary to address these issues to ensure proper functioning of these UAVs while keeping their uniqueness in mind. Furthermore, adequate security and functionality require the consideration of many parameters that may include an accurate cognizance of the working mechanism of vehicles, geographical and weather conditions, and UAVNet communication. This is achievable by creating a simulator that includes these aspects. A performance evaluation through relevant drone simulator becomes indispensable procedure to test features, configurations, and designs to demonstrate superiority to comparative schemes and suitability. Thus, it becomes of paramount importance to establish the credibility of simulation results by investigating the merits and limitations of each simulator prior to selection. Based on this motivation, we present a comprehensive survey of current drone simulators. In addition, open research issues and research challenges are discussed and presented.
Under partial shading conditions (PSC), a photovoltaic (PV) system may produce multiple local maximum power points (LMPPs). Traditional maximum power point tracking (MPPT) techniques are not able to ...distinguish between LMPPs and the global maximum power point (GMPP), leading to sub-optimal PV array power outputs. This article proposes a two-level algorithm for tracking the GMPP. The first level is a discretized global search, allowing the system to hone in on the neighborhood containing the GMPP. In the second level, the well-known ripple correlation control (RCC) technique is used to swiftly converge to the GMPP. Using the proposed two-level algorithm, it can be guaranteed that the GMPP is successfully found and tracked. A benchmark analysis involving other state of the art algorithm reveals that the proposed method is the most superior algorithm in terms of accurately and swiftly tracking the global maximum power point in dynamic partial shading conditions. The algorithm is implemented with a simple inexpensive microcontroller, and therefore can be readily adopted in a myriad of dynamic partial shading applications.Therefore, this work allows the penetration of future photovoltaic power conversion systems to be more efficient.
The exigency of emotion recognition is pushing the envelope for meticulous strategies of discerning actual emotions through the use of superior multimodal techniques. This work presents a multimodal ...automatic emotion recognition (AER) framework capable of differentiating between expressed emotions with high accuracy. The contribution involves implementing an ensemble-based approach for the AER through the fusion of visible images and infrared (IR) images with speech. The framework is implemented in two layers, where the first layer detects emotions using single modalities while the second layer combines the modalities and classifies emotions. Convolutional Neural Networks (CNN) have been used for feature extraction and classification. A hybrid fusion approach comprising early (feature-level) and late (decision-level) fusion, was applied to combine the features and the decisions at different stages. The output of the CNN trained with voice samples of the RAVDESS database was combined with the image classifier’s output using decision-level fusion to obtain the final decision. An accuracy of 86.36% and similar recall (0.86), precision (0.88), and f-measure (0.87) scores were obtained. A comparison with contemporary work endorsed the competitiveness of the framework with the rationale for exclusivity in attaining this accuracy in wild backgrounds and light-invariant conditions.
In this paper, an explainable intelligence model that gives the logic behind the decisions unmanned aerial vehicle (UAV) makes when it is on a predefined mission and chooses to deviate from its ...designated path is developed. The explainable model is on a visual platform in the format of if-then rules derived from the Sugeno-type fuzzy inference model. The model is tested using the data recorded from three different missions. In each mission, adverse weather, conditions and enemy locations are introduced at random locations along the path of the mission. There are two phases to the model development. In the first phase, the Mamdani fuzzy model is used to create rules to steer the UAV along the designated mission and the rules of engagement when it encounters weather and enemy locations along and near its chosen mission. The data are gathered as UAV traverses on each mission. In the second phase, the data gathered from these missions are used to create a reverse model using a Sugeno-type fuzzy inference system based on the subtractive clustering in the data. The model has seven inputs (time, x-coordinate, y-coordinate, heading direction, engage in attack, continue mission, and steer UAV) and two outputs (weather conditions and distance from the enemy). This model predicts the outputs regarding the weather conditions and enemy positions whenever UAV deviates from the predefined path. The model is optimized with respect to the number of rules and prediction accuracy by adjusting subtractive clustering parameters. The model is then fine-tuned with ANFIS. The final model has six rules and root mean square error value that is less than 0.05. Furthermore, to check the robustness of the model, the Gaussian random noise is added to a UAV path, and the prediction accuracy is validated.
Simulation test beds serve as a cost-effective and safe way to evaluate the system performance of various real world systems. With the rising concerns in the security of sensitive and critical ...systems, such as unmanned aerial vehicles (UAVs), a software simulation test bed is clearly the best choice given that the environment and various system components are modeled correctly. This becomes critical given the recently developed autonomous aerial vehicles. We therefore evaluate several available simulation test beds and use the most promising test bed to analyze the impact of Global Positioning System (GPS)-related attacks on navigation of UAVs. We also introduce a novel technique for GPS spoofing, attack detection, and mitigation. This paper presents GPS spoofing and GPS jamming attacks with their anatomy, design, and impact evaluation on navigation as well as a novel GPS spoofing mitigation and detection technique for UAVs, using UAVSim: an open source simulation test bed.
Multimodal human–computer interaction (HCI) systems pledge a more human–human-like interaction between machines and humans. Their prowess in emanating an unambiguous information exchange between the ...two makes these systems more reliable, efficient, less error prone, and capable of solving complex tasks. Emotion recognition is a realm of HCI that follows multimodality to achieve accurate and natural results. The prodigious use of affective identification in e-learning, marketing, security, health sciences, etc., has increased demand for high-precision emotion recognition systems. Machine learning (ML) is getting its feet wet to ameliorate the process by tweaking the architectures or wielding high-quality databases (DB). This paper presents a survey of such DBs that are being used to develop multimodal emotion recognition (MER) systems. The survey illustrates the DBs that contain multi-channel data, such as facial expressions, speech, physiological signals, body movements, gestures, and lexical features. Few unimodal DBs are also discussed that work in conjunction with other DBs for affect recognition. Further, VIRI, a new DB of visible and infrared (IR) images of subjects expressing five emotions in an uncontrolled, real-world environment, is presented. A rationale for the superiority of the presented corpus over the existing ones is instituted.
A significant amount of work is invested in human-machine teaming (HMT) across multiple fields. Accurately and effectively measuring system performance of an HMT is crucial for moving the design of ...these systems forward. Metrics are the enabling tools to devise a benchmark in any system and serve as an evaluation platform for assessing the performance, along with the verification and validation, of a system. Currently, there is no agreed-upon set of benchmark metrics for developing HMT systems. Therefore, identification and classification of common metrics are imperative to create a benchmark in the HMT field. The key focus of this review is to conduct a detailed survey aimed at identification of metrics employed in different segments of HMT and to determine the common metrics that can be used in the future to benchmark HMTs. We have organized this review as follows: identification of metrics used in HMTs until now, and classification based on functionality and measuring techniques. Additionally, we have also attempted to analyze all the identified metrics in detail while classifying them as theoretical, applied, real-time, non-real-time, measurable, and observable metrics. We conclude this review with a detailed analysis of the identified common metrics along with their usage to benchmark HMTs.
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