Miracle-dz/UniFAR-FTUs huggingface dataset:
An Exploratory Pilot Study on Human Emotions during Horror Game Playing

Enhancements in technology have allowed games to expand the variety of ways for new and better experiences. These experiences are often expressed by the players through their emotions either internally, externally, or in both manners. Nevertheless, it is crucial for developers to make sure that the players are not overwhelmed with emotions eventually ruining the experiences. With the introduction of immersive modalities, games can read the emotions of the players, allowing the developers ensure that the game does not overwhelm the players in any way. This is done by having the game adapt to the readings of the player’s emotions, allowing the players to cope and regulate their emotions frequently. The technologies can also allow players suffering from emotional illnesses to regulate their emotions and support studying methods of regaining control over their emotions. In this project, an immersive modality for emotional valence recognition is integrated into a horror game that includes the feature of adapting to the emotions of the players. It also aims to determine how well the readings from the modality are, by comparing them with the self-evaluation report of participants. The chosen modality for emotion recognition is facial emotion readings through a camera. To test the consistency and accuracy of the modality with the personal experiences of the players, the results from a self-evaluated questionnaire containing personal experiences answers are compared with the readings from the modality using the Pearson’s Correlation Coefficient formula indicating their correlation strength. Moreover, the research explores the temporal effect of induced emotional states in situations where the game play experience varies. The results show a moderately reliable consistency result of the facial emotion recognition modality and the self-evaluated emotional states. Finally, while initial results indicate that induced emotions may have a temporal effect, more data is needed to validate this observation.
Optimization of Sampling-Based Motion Planning in Dynamic Environments Using Neural Networks

Motion planning for autonomous vehicles is a challenging task, especially in dynamic environments. The motion of the vehicle itself needs to be considered while the vehicle needs to react to its surroundings at the same time. Sampling-based algorithms proved to be suitable to cope with these challenges. However, the performance of these algorithms is highly dependent on the sampling heuristics, which in turn are often hand crafted and thus need a large amount of tuning. Therefore, we developed two approaches based on deep learning to learn these heuristics for sampling-based motion planning in dynamic environments. The first approach predicts a discrete probability distribution for each point in time of the future trajectory, whereas the second approach directly predicts a variety of trajectories by using dropout sampling. Both approaches are based on an environment representation encoded as a grid-based tensor. The learned heuristics are integrated into an existing planning framework based on particle swarm optimization and are evaluated in several situations. This shows how to combine the strengths of machine learning based approaches and the traceability of rule- or model-based approaches. The evaluation demonstrates that, in total, we were able to improve on our current heuristics. However, none of the approaches performed consistently better in all scenarios evaluated.
Transfer Learning for Optical and SAR Data Correspondence Identification With Limited Training Labels

Recent advancements in sensor technology have reflected promise in collaborative utilization; specifically, multisource remote sensing data correspondence identification attracts increasing attention. In this article, a domain-transfer learning based generative correspondence analysis (DT-GCA) scheme is proposed, which enables identifying corresponding data in optical and synthetic aperture radar (SAR) images with small-sized reference data. In the proposed architecture, an adversarial domain-translator is investigated as general-purpose domain transference solution to learn cross domain features. The optical-aided implicit representation, which is regarded as the clone of SAR, is adopted to estimate the correlation with SAR images. Particularly, the designed GCA integrates optical-generated features with SAR tightly instead of treating them separately and eliminates the discrepancy influence of different sensors. Experiments on cross-domain remote sensing data are validated, and extensive results demonstrate that the proposed DT-GCA yields substantial improvements over some state-of-the-art techniques when only limited training samples are available.
A Two-Stage ϵ-Constraint Strategy-Based Heuristic for Bi-Objective Quadratic Multiple Knapsack Problems

In this paper, we propose a two-stage method for solving the Bi-Objective Quadratic Multiple Knapsack Problem (BO-QMKP). The method combines both a special local branching (the first stage) and the ϵ-constraint (the second stage) strategies, where the local branching tries to intensify the search process while a series of ϵ-constraints are added to tailor a diversification of the search space. The method is based on solving a series of mono-objective optimization problems: from the current problem, provided by combining the original problem and an ϵ-constraint (using a single objective function), an optimization phase is applied in order to generate a set of non-dominated solutions. A preliminary experimental part is given, where the performance of the proposed two-stage method is evaluated on a set of benchmark containing large-scale instances. Its provided results are compared to those achieved by one of the best method available in the literature. Encouraging results have been obtained.
Dynamic Control for Convective Apricots Dryer

The optimal control methodology is used to develop a control system for a thin-layer drying process. A multi-objective function is defined. It takes into account energy consumption, process duration and desired quality of the final products. The function is used in the process control in a laboratory dryer. It is necessary for the proposed control to determine the ambient air parameters, moisture content and quality assessment of the fruits and vegetables in the course of the process. The efficiency of the proposed control has been demonstrated for the drying of apricots. The proposed control system can be applied in the thin-layer drying of various fruits and vegetables.
Electrocaloric effect in the relaxor ferroelectric polymer composition P(VDF–TrFE–CFE)0.90–P(VDF–CTFE)0.10

The electrocaloric effect (ECE), i.e., the change of the temperature of the electrocaloric material due to the applied electrical field under adiabatic conditions, is a very attractive phenomenon used in a broad range of applications, for example in an electrical refrigerator. The first observations of a giant ECE were made by indirect measurements of the electrical polarization and not by direct measurements of the ECE itself. In our case, this effect was measured directly. Here a method and analysis of these direct measurements are presented and the obtained results are shown for P(VDF–TrFE–CFE) relaxor terpolymers blend with 10% composition of P(VDF–CTFE) copolymers.
Genetic Algorithm with Simulated Annealing for Resolving Job Shop Scheduling Problem

In order to solve the limitation of traditional genetic algorithm to solve the job shop scheduling problem, combined with the advantages of genetic algorithm (GA) and simulated annealing algorithm (SA), this paper proposes a kind of algorithm based on NSGA-II, which inserts simulated annealing algorithm during operation. A hybrid genetic algorithm simulated annealing algorithm (GASA) combining the advantages of the two algorithms is generated. The algorithm not only has the advantages of fast convergence speed of genetic algorithm and wide search area of simulated annealing algorithm, but also overcomes the problem of premature convergence of the former and slow convergence speed of the latter. In the operation details of the algorithm, adaptive function, non-dominated sorting and elite retention strategy are added to effectively improve the effectiveness of job shop scheduling.
Modeling Methodology of Driver-Vehicle-Environment System Dynamics in Mixed Driving Situation

The interactions between driver, vehicle and environment generate vehicle's behaviors, and the interactions of vehicle groups shape the traffic modes. Therefore, the conditionally or fully automated driving technologies should be developed and tested in the driver-vehicle-environment (DVE) system rather than being developed and tested individually. To build DVE system dynamics, firstly, we propose an architecture to cope with the complicated interactions in automated vehicles (AVs) and in mixed traffic situations. Then we summarize the driver behavior models and compare the differences of intelligence between human driver and automated vehicle. Finally, we summarize the feasible modeling approaches of DVE system into five categories. The primary distinctions are the modeling methods of human drivers' roles, which are realized by human driver per se, human driver's cognitive architecture, psychological motivation model, mechanism imitation, and specific mechanism transfer respectively. Taking the applications of human machine interface and AD strategy developments as examples, we analyze the benefits and drawbacks of these approaches.
Toward Hybrid Deep Convolutional Neural Network Architectures For Medical Image Processing

Artificial Intelligence (AI) has gained a great interest in improving systems and mechanisms. The talent of AI has been shown through its upgraded technologies including Deep Learning (DL), which has proven valuable performances in image processing. The enhancement of image treatment and analysis, particularly medical imaging, has became one of the most important steps toward the improvement of several systems in different applications such as medical treatment, analysis, and prognosis systems. Deep Convolutional Neural Network (CNN) presents one of the most applied DL approach in medical imaging. CNN covers more than one architecture which historically proved relevant performance results. In this paper, An analytical review of selective CNN architectures including ResNet, DenseNet, and wider networks, particularly Inception-V4, will be presented. Toward the need for a hybrid algorithm, a proposed architecture composed of two different CNNs will be defined by focusing mainly on the conveniences of both DenseNet and Inception-V4 networks.
Nuclei Segmentation in Hematoxylin and Eosin (H&E)-Stained Histopathological Images Using a Deep Neural Network

Breast cancer, being the second most frequently seen cancer globally, is the most common cancer among women. Early and accurate diagnosis is dependent on the correct identification of nuclei in histopathological images, which is a very tedious task when it is done manually. The proposed method is a deep learning model using U-Net architecture supplied with various pre- and post-processing techniques. The model was trained and tested on a unique dataset. Our model performed the nuclei segmentation task with a Dice score of 0.84, a true negative rate (TNR) of 0.95, a true positive rate (TPR) of 0.84, and an accuracy rate of 0.93. To enhance the performance of our model, our planned future work includes increasing the number of histopathological images in our dataset.
Sparse Causal Residual Neural Network for Linear and Nonlinear Concurrent Causal Inference and Root Cause Diagnosis

Reliable and effective fault diagnosis methods are necessary for complex industrial processes that consists of various units. After a process fault is detected, it remains a challenging task to locate the root cause unit and determine the propagation path of the fault. In this paper, a novel method, termed Sparse Causal Residual Neural Network (SCRNN), is proposed and applied for modern industrial root cause diagnosis. The advantage of SCRNN lies in that it can not only recognize linear and nonlinear causal relationships in parallel, but also automatically determine the causality lags and deduce the time delay of causal transmission. Besides, due to the specially designed sparse constraint and optimization algorithm, the SCRNN model can realize the function of key dependent variable selection, avoiding the high computational complexity and complicated procedure brought by pairwise comparison. The feasibility of the proposed method is illustrated through the benchmark TE process.
Towards Semantic Scene Segmentation for Autonomous Agricultural Vehicles

The development of autonomous agricultural vehicles depends on accurate visual interpretation of the environment. Semantic pixel-wise segmentation plays an important role in achieving a more detailed description of the scene and understanding of the spatial-relationship between different objects. In this paper, a deep architecture for road scene and indoor scene segmentation, SegNet, is applied in solving the scene recognition problem for the agricultural environment. The network is trained on an agriculture image dataset collected during field operation. The problem focuses on the segmentation of five classes of structures commonly found in the fields: vegetation, grass, ground, crop field and obstacles. We apply frequency balancing to address the challenge of class imbalance and transfer learning technique. The quantitative performance of the network is evaluated using pixel accuracy and intersection over union metrics. Moreover, we present an approach for an infield qualitative validation of the trained network.
Model-Less Location-Based Vehicle Behavior Prediction for Intelligent Vehicle

Predicting surrounding vehicle behavior plays an important role in an intelligent vehicle. Optimization of control strategy considering predicted future events could provide significant benefits by improving efficiency, comfort, and safety. However, realizing such prediction in an arbitrary environment is a challenging task as the real environment is highly diverse. In this paper, we propose a model-less location-based prediction method for a connected vehicle, which shares driving data through a cloud server. The shared data are stored in a relational database management system after associated with the location information. Surrounding vehicle behavior is then predicted with kernel density estimation by referring to nearby data, which implicitly reflect all location-dependent factors, such as road design, traffic rule, and region. Since this method does not rely on any pre-trained models, prediction performance is not affected by the overfitting issue. The performance of the proposed method has been evaluated by applying to optimization-based adaptive cruise control, which minimizes energy loss and a following error based on predicted future position of a preceding vehicle. The experimental result with urban driving data shows that the proposed method is more accurate and fuel efficient than several baseline models including kinematic model and neural networks.
Deep Learning Based Enhancement in Hyperspectral Object Detection

The interest in hyperspectral imaging has been increasing in recent years. Today, this technique is used in many fields for military and civil purposes. Hyperspectral images allow the detection of targets covering one or less pixels (sub-pixels), unlike panchromatic or color 3-band images. There are many algorithms for hyperspectral target detection in the literature. In this study, GLRT method was used for object detection and deep learning was used as the final processing. When the results were evaluated, it was seen that the final processing step contributed to the correct separation of the targets, especially spectrally similar to each other.
A time-delay SEAIR model for COVID-19 spread

A time delay-based SEAIR epidemic model is proposed to understand the COVID-19 spread and the numerical simulation is validated using the reported data from the Indian subcontinent. We perform a sensitivity analysis of the crucial input model-parameters for computing the threshold parameter, reproduction number. Further, we estimate the parameter which has a high sensitivity index. The global dynamics have been proved and demonstrated using numerical examples.
Robust Model Predictive Control for Markovian Jump Systems with All Unstable Modes

In this paper, robust model predictive control (RMPC) problem is investigated for a class of Markovian jump systems with unstable modes under polytopic uncertainties and hard constraints. The transition probability matrix and a mode-dependent control strategy in the framework of RMPC are co-designed. Moreover, in order to design a switching rule for the mean-square stability of the jump system, an off-line design scheme is first proposed to guarantee the on-line mode-dependent model predictive controller design. For the “on-line” part, a set of mode-dependent state feedback controllers is designed to minimize an upper bound of the worst-case infinite horizon cost function in terms of the solutions to a series of linear matrix inequalities. Finally, an simulation example regarding the economic system is implemented to verify the effectiveness of the proposed design scheme.
Region-based Segmentation of Terrain Fields in SAR Images

— Synthetic Aperture Radar (SAR) is widely used in a lot of areas in nowadays. Image segmentation is very difficult in SAR images due to image speckles and pixel similarities. Deep learning techniques that are used in recent years minimize to these difficulties that are in complexity images. Convolutional neural networks (CNN) which is a deep learning architecture are effectively used in region-based segmentation methods. In this study, an approach that shows to prediction, segmentation and an intelligent masking of terrains of the SAR images is proposed by being used to region-based segmentation method of Convolutional Neural Network. The proposed method shows that the terrains in the SAR images are classified when the region-based convolutional neural network is effectively used with high accuracy.
Multiple-Trial/Best-Move Simulated Annealing for Flexible Job Shop Scheduling with Scarce Setup-Operators

In this paper, we developed a multi-trial/best-move simulated annealing for a dual resource-constrained flexible job shop scheduling problem (DRC-FJSP). The algorithm creates many neighborhood solutions at each iteration by perturbing the leading solution. Then, the best solution generated is compared with the current leading solution for probabilistic acceptance or rejection. The algorithm outperforms the classical simulated annealing in terms of convergence speed while solving the considered DRC-FJSP. Unlike many papers in DRC-FJSP, which assumed machine operators as scarce resources, we consider skilled setup operators as a constraining resource instead. This is because machine operators are becoming less constraining due to increased adoption of automation and numerical controlled machines. As a result, machine operators become machine tenders who do not require the skills to perform the step-by-step production process. Thus, the assumption of those low skill labors as constraining resources can no longer be justified. Skilled setup operators, on the other hand, continue to become constraining resources as their skill cannot be easily automated, particularly with the increased complexity of automation in manufacturing.
Traditional Bulgarian Costume Classification in a Tourist Guide application

Bulgaria is a culturally rich country which has been kept unexplored for years. Modern information and communication technologies can shrink the gap between tourists and its rich historical heritage. A digital tourist guide has been in development that offers its users a tailored experience and guides them through their journey in Bulgaria. Users are able to take a picture of a traditional Bulgarian costume and using various machine learning algorithms the tourist guide is able to recognize it and present interesting information about the photographed object. The following article gives an overview of the tourist guide architecture and image classification process. The authors used a convolutional neural network to classify the uploaded images of traditional Bulgarian costumes. The lack of dataset for training the network is solved by collecting the user-uploaded images, classifying them and using them for improving the classification algorithm automatically. Through this process a lifelong learning has been achieved.
Energy Efficient Hardware Architecture for Matrix Multiplication

Matrix Multiplication features in many engineering and scientific problems, the reason which working on efficient algorithms and architectures to perform matrix multiplication is still relevant. In this work, we bring forward an efficient algorithm that is targeted towards the hardware implementation for generating a matrix multiplier targeted according to the input parameters of the user. The proposed architecture utilizes a carry-save adder tree multiplier for multiplication and carry-lookahead adder for performing addition at the final stage. When compared with state-of-the-art matrix multiplication algorithms like Strassen and Winograd, our architecture achieves a better energy efficiency of 59% and 69% respectively, and area delay product (cycle budget) of 58% and 73% respectively. The proposed architecture is well suited for power critical applications with a negligible impact on the delay.
UWB System Based UAV Swarm Testbed

Swarming of unmanned aerial vehicles (UAVs) has become a popular topic for its practicability in many scenarios. However an easy-to-use yet powerful swarm testbed is not widely available for most of the research labs globally. In this paper, a UAV swarm testbed is presented, which consist of an ultra-wide-band (UWB) positioning system, multiple UAVs and finite state machine based on Stateflow/Simulink. There are three important features that make this system standing out: the one is the tracking accuracy is the best as far as we know in the UWB positioning category; the second is that the system can resist magnetometer interference, i.e. the yaw angle is not effected by magnetic interferences. ; the last one is that the finite state machine is ready for the general swarm experiments. The system is easy to setup with auto calibration, not limited by the size of the testing space, and can be used for both indoor and outdoor. It is believed that the proposed testbed can be adopted by swarm research labs in different scenarios in multiple scales.
Experimental Study of the Characteristics of Ultrawideband TEM Horn with the Dielectric Filling

The article describes a fabricated TEM horn with an inhomogeneous dielectric filling. Experimental measurements were provided and important results were achieved. The gain in main direction of an ultrawideband TEM horn with an inhomogeneous dielectric filling of the space between its lobes was obtained using the substitution method. Also, the values of the VSWR of the studied antenna were found. A comparison of the experimental results with those obtained earlier in modeling shows their high similarity.