Negotiation is a complex social interaction that encapsulates emotional encounters in human decision-making. Virtual agents that can negotiate with humans are useful in pedagogy and conversational AI. To advance the development of such agents, we explore the prediction of two important subjective goals in a negotiation - outcome satisfaction and partner perception. Specifically, we analyze the extent to which emotion attributes extracted from the negotiation help in the prediction, above and beyond the individual difference variables. We focus on a recent dataset in chat-based negotiations, grounded in a realistic camping scenario. We draw extensive qualitative and quantitative comparisons between three types of emotion variables - emoticons, as well as lexical and contextual}variables, by leveraging affective lexicons and a state-of-the-art deep learning architecture. To further validate the findings, we analyze the prediction of these subjective negotiation goals after controlling for the objective performance of the participants. We also study the temporal effects, understanding the contribution of emotion expressed in the initial and latter parts of the conversation. Finally, we discuss our insights, which will be helpful for designing adaptive negotiation agents that interact through realistic communication interfaces.

Implementation of a two Degrees of Freedom Pointing System model to enable ship-satellite communication with the aim of creating self driving cargo ships. The Pointing System is similar to a telescope in terms of Degrees of Freedom (Azimuth and Elevation) and the model kinematics definition was possible using MSC ADAMS software. The model was exported to a Simulink block in order to generate the control block diagram and than an ADAMS-Simulink Co-simulation. The acquisition and orbital propagation errors for the satellite and the acquisition errors of ship motions have been taken into consideration in the control system generation. The main goal is to evaluate the pointing motion to verify the satellite’s locking with the adopted optical communication technologies. In order to reduce pointing error, scanning strategies were considered and the scanning amplitude and frequency values used for the choice of piezoelectric actuators for the optical component.

Classification of mental tasks from electroencephalogram (EEG) signals play a crucial role in designing various brain-computer interface (BCI) applications. Most of the current techniques consider each channel as independent, neglecting the functional connectivity of the brain during mental activity and are primarily subject specific. This paper proposes a graph signal representation to classify a pair of mental tasks using multichannel EEG signals (MTMC-EEG) with cross subject classification within the database. Here, each channel of EEG signal corresponds to nodes of the task based graph whose EEG time series resides on the respective nodes. Functional connectivity of the brain between these nodes is obtained using smoothness constraint based Graph Signal Processing (GSP) technique. Graph spectral features namely, two-norm total variation of eigen vector (TNTV) corresponding to weighted adjacency matrix, graph Laplacian energy (GLE) using eigenvalues of Laplacian matrix and convex sum of TNTV and GLE in the form of joint total variation energy (JTVE) are proposed in this paper. The performance of the proposed methodology is evaluated on publicly available two different databases of MTMC EEG signals using benchmark classifiers and compared with the state of the art. Further, the superiority of the proposed metric obtained from the smoothened graph of GSP technique is validated by comparing it with Pearson correlation and Gaussian radial basis function (RBF) based functional connectivity in terms of accuracy, F-Score, and information transfer rate (ITR). The robustness of the proposed method is validated by adding white Gaussian noise (AWGN) to the EEG signals using different SNRs.

In this paper, we continue the analysis of electrooptical studies of the prebreakdown stage in water in the submicrosecond range. We consider a cathode streamer developing in a system of the spherical electrodes. Computer simulation of electric showed that at a constant e = 80, the field strength at the head of the cathode streamer should not exceed E = 1.9 MV/cm. Taking into account the nonlinearity of the permittivity e = e(E) made it possible to estimate this field strength as E ~ 2.4-3.1 MV/cm. It is shown that even with some excess of the strength over the threshold value (ES ~ 3 MV/cm), at which the state of dipole saturation is possible; its consideration does not lead to a noticeable change in the result. Thus, a significant decrease in the field strength near the cathode streamer compared to the anode streamer was confirmed.

We present a first detailed survey that focuses on the security challenges faced by the underwater and air-water (A-W) wireless communication networks (WCNs), as well as the countermeasures proposed to date. Specifically, we provide a detailed literature review of the various kinds of active and passive attacks which hamper the confidentiality, integrity, authentication and availability of both underwater and A-W WCNs. For clarity of exposition, this survey paper is mainly divided into two parts. The first part of the paper is essentially a primer on underwater and A-W WCNs whereby we outline the benefits and drawbacks of the three promising underwater and A-W candidate technologies: radio frequency (RF), acoustic, and optical, along with channel modelling. To this end, we also describe the indirect (relay-aided) and direct mechanisms for the A-W WCNs along with channel modelling. This sets the stage for the second part (and main contribution) of the paper whereby we provide a thorough comparative discussion of a vast set of works that have reported the security breaches (as well as viable countermeasures) for many diverse configurations of the underwater and A-W WCNs. Finally, we highlight some research gaps in the open literature and identify some open problems for the future work.

Robotic surgery technique has gained admissibility in recent years. To provide ergonomic postures for the surgeon along with dexterous workspace for the Sinaflex robotic telesurgery system, a new haptic interface is introduced at the surgeon side of the robot. To analyze its performance, in this paper, the forward and inverse kinematics of the master manipulator are derived using a new geometrical approach. The workspace of the robot is derived, and its dexterity is investigated. Finally, to show the operational workspace and dexterity, the data collected during a dogs’ vasectomy robotic surgery by Sinaflex are analyzed. According to the simulation results, the workspace of the end effector is as large as 600×571×560 mm3 which is 40 times greater than the ergonomic human hand workspace. The in-vivo experiment shows that the operational surgery workspace is about 2.6% and 3.4% of the whole workspace for the left and right master manipulator, respectively. The dexterity of the robot based on the Global Conditioning Index, during simulation and experiment, for the position is 0.4557 and 0.6565, respectively. A large workspace along with its high dexterity of the master manipulator of Sinaflex robotic telesurgery system make it a good choice to fulfill robotic surgery technique requirements.

One challenge in the deployment of a fog computing infrastructure is the support of demands which are variable in time and space as this can temporarily overload fog nodes. Mobile fog nodes can be promptly dispatched to support dynamic demands, helping to cope with overloaded terrestrial infrastructure. Specifically, unmanned aerial vehicles (UAVs) can be used as mobile fog nodes since they benefit from autonomous operation and maneuverability. However, UAVs are typically battery-constrained and can operate only for a limited time. This paper investigates how fixed-wing UAVs can fulfill the role of fog nodes. These UAVs have different aerodynamics and consume less energy than do rotary-wing UAVs. This paper proposes the Spatio-Temporal UAV Fog Node Location algorithm to determine the position of fog nodes mounted on UAVs. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible.

Rapid prototyping of Connected and Automated Vehicles (CAV) is challenging because of the physical distribution of vehicles. Furthermore, experiments with CAV may be subject to external influences which prevent reproducibility. This article presents an architecture for the experimental testing of CAVs with a focus on decision-making. Our architecture for experiments of CAV is strictly modular and hierarchical, and therefore it supports an easy and rapid exchange of every single controller as well as of optimization libraries. Additionally, the architecture synchronizes the whole network of sensors, computation devices, and actuators. Thus, it achieves deterministic and reproducible results, even for time-variant network topologies. Using this architecture, we are able to include active and passive vehicles and vehicles with heterogeneous dynamics in the experiments. The architecture also allows for handling communication uncertainties, e.g., data packet drop and time delay. The resulting architecture supports performing different in-the-loop tests and experiments. We demonstrate the architecture in the Cyber-Physical Mobility Lab (CPM Lab) using 20 vehicles in 1:18 scale. The architecture can be applied to other domains.

IoT devices constantly communicate with servers over the Internet, allowing an attacker to extract sensitive information by passively monitoring the network traffic. Recent research works have shown that a network attacker with a trained machine learning (ML) model can accurately fingerprint IoT devices learned from the (encrypted) traffic flows of the devices. Such fingerprinting attacks are capable of revealing the make and model of the devices, which can further be used to extract detailed user activities. In this work, we develop and propose iPET, a novel adversarial perturbation-based traffic modification system that defends against fingerprinting attacks. iPET design employs GAN (Generative Adversarial Networks) in a tuneable way, allowing users to specify the maximum bandwidth overhead they are willing to tolerate for the defense. A fundamental idea of iPET is to deliberately introduce stochasticity between model instances. This approach limits a counter attack, as it inhibits an attacker from recreating an identical perturbation model and using it for fingerprinting. We evaluate the effectiveness of our defense against state-of-the-art fingerprinting models and with three different attacker capabilities. Our evaluations on synthetic and real-world datasets demonstrate that iPET decreases the accuracy of even the potent attackers. We also show that the traffic perturbations generated by iPET generalize well to different fingerprinting schemes that an attacker may deploy.

A document by Ryman Hashem . Click on the document to view its contents.

A document by Marko Orescanin. Click on the document to view its contents.

Cooperative multi-agent manipulation systems allow to extend on the manipulative limitations of individual agents, increasing the complexity of the manipulation tasks the ensemble can handle. Controlling such a system requires meticulous planning of subsequent subtasks, queried to the individual agents, in order to execute the master task successfully. Real-time planning is essential to ensure the task can still be achieved when subtasks execution suffers from uncertainty or when the master task changes intermittently requiring real-time reconfiguration of the plan. In this work we develop a supervisory control architecture tailored to the cooperation of two robotic manipulators equipped with standard pick-and-place facilities in the plane. We consider a toy task description where we control the planar position and orientation of an object. A time-invariant policy function is trained using deep reinforcement learning, which can determine a finite sequence pick-and-place maneuvers to manipulate the object into its desired configuration. A comparison is made between two strategies, with the distinction made based on different treatments of the final step. The more information is given to the policy the easier it trains. In return, it becomes less adaptable and loses some of its generalizability.

Reconfigurable intelligent surfaces (RISs) are envisioned to transform the propagation space into a smart radio environment (SRE) to realize the diverse applications of sixth-generation (6G) wireless communication. By smartly tuning the massive number of elements via controller, an RIS can passively phase-shift the electromagnetic (EM) waves to enhance the system performance. The absence of radio-frequency (RF) chains makes RIS an energy-efficient and cost-effective solution for future wireless networks. In this paper, we explore the state-of-the-art research on different aspects of RIS-assisted communication. Specifically, we first introduce the fundamentals of RIS, including the RIS’s structure, operating principle, and deployment strategies. We then comprehensively discuss the emerging applications of RISs for 6G wireless networks. In addition, we elaborate on the crucial challenges for RIS-assisted networks, namely, RIS channel state information (CSI) acquisition and passive beamforming optimization. Furthermore, we present the recent research contributions leveraging the artificial intelligence (AI) based techniques for channel estimation, phase-shift optimization, and resource allocation in RIS-assisted networks. Finally, to provide effective guidance for future research, we highlight important research directions for realizing RIS-assisted network.

Intelligent reflecting surface (IRS) is a cost-efficient technique to improve power efficiency and spectral efficiency. However, IRS-aided multi-antenna transmission needs to jointly optimize the passive and active beamforming, imposing a high computational burden and high latency due to its iterative optimization process. Making use of hybrid analog-digital beamforming in high-frequency transmission systems, a novel technique, coined dual-beam IRS, is proposed in this paper. The key idea is to form a pair of beams towards the IRS and user, respectively. Then, the optimization of passive and active beamforming can be decoupled, resulting in a simplified system design. Simulation results corroborate that it achieves a good balance between the cell-edge and cell-center performance. Compared with the performance bound, the gap is moderate, but it remarkably outperforms other sub-optimal schemes.

A Computing System for Measuring the Relationship of Smell and Pain

Micro expression recognition (MER) is a very challenging area of research due to its intrinsic nature and finegrained changes. In the literature, the problem of MER has been solved through handcrafted/descriptor-based techniques. However, in recent times, deep learning (DL) based techniques have been adopted to gain higher performance for MER. Also, rich survey articles on MER are available by summarizing the datasets, experimental settings, conventional and deep learning methods. In contrast, these studies lack the ability to convey the impact of network design paradigms and experimental setting strategies for DL based MER. Therefore, this paper aims to provide a deep insight into the DL-based MER frameworks with a perspective on promises in network model designing, experimental strategies, challenges, and research needs. Also, the detailed categorization of available MER frameworks is presented in various aspects of model design and technical characteristics. Moreover, an empirical analysis of the experimental and validation protocols adopted by MER methods is presented. The challenges mentioned earlier and network design strategies may assist the affective computing research community in forge ahead in MER research. Finally, we point out the future directions, research needs and draw our conclusions.

A document by Adrian David Cheok . Click on the document to view its contents.

The following report was undertaken to investigate the detailed study for Organic Solar cells with the material review, benefits, and challenges to fulfill the energy demand and give flexibility for the future generation.