Reviewerly/exHarmony huggingface dataset:
AS-RIG: Adaptive Selection of Reconstructed Input by Generator or Interpolation for Person Re-Identification in Cross-Modality Visible and Thermal Images

Multimodal camera-based person re-identification (ReID) is important in the field of intelligent surveillance.Thermal cameras can solve the problem in that visible-light cameras cannot acquire the valid feature information of a person under poor illumination conditions.However, thermal cameras usually have lower frame resolution than visible-light cameras.To overcome this problem, we propose an adaptive selection of reconstructed input by generator or interpolation (AS-RIG) method, which can adaptively select the generative adversarial network (GAN), or an interpolation method (bi-linear or bi-cubic).AS-RIG automatically selects a resolution-model using the mean-squared error (MSE), feature distance (FD), and structural similarity (SSIM).To verify the performance of our proposed method, two open databases are used: the DBPerson-Recog-DB1 and Sun Yat-set University multiple modality Re-ID (SYSU-MM01).Infrared frames from both databases are resized to be smaller than the original ones for experimentation.Experimental results show that our generator outperforms traditional interpolation methods.In addition, the person ReID experimental results demonstrate that AS-RIG outperforms non-adaptive selection methods and state-of-the-art methods.
Evolving fuzzy reasoning approach using a novel nature-inspired optimization tool

In general, fuzzy reasoning tool with Mamdani approach has good readability, but low accuracy; whereas the same with Takagi and Sugeno’s approach ensures high accuracy but at the cost of readability. In the developed combined form of fuzzy reasoning, the merits of both the above two approaches are utilized to obtain both the high accuracy as well as good readability. The above combined form is evolved using a recently-developed nature-inspired technique, namely Bonobo Optimizer (BO). This optimization method mimics the fission-fusion social structure and reproductive schemes adopted by bonobos. In addition, controlling parameters of the BO are designed to be adaptive and self-adjusting to perform efficiently for a variety of problems. The performances of the developed models have been tested and compared with that of the combined fuzzy reasoning tool evolved using Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Grey-Wolf Optimizer (GWO) and Jaya Algorithm for three data sets. The novelty of this study lies with the ability of the recently proposed BO to evolve an efficient fuzzy reasoning approach.
Model-Free Deep Reinforcement Learning—Algorithms and Applications

This survey presents an overview of the current model-free deep reinforcement learning landscape. It provides a comparison of state-of-the-art on-policy and off-policy algorithms in the value-based and policy-based domain. Influences and possible drawbacks of different algorithmic approaches are analyzed and associated with new improvements in order to overcome previous problems. Further, the survey shows application scenarios for difficult domains, including the game of Go, Starcraft II, Dota 2, and the Rubik’s Cube.
Active Crowd Analysis for Pandemic Risk Mitigation for Blind or Visually Impaired Persons

During pandemics like COVID-19, social distancing is essential to combat the rise of infections. However, it is challenging for the visually impaired to practice social distancing as their low vision hinders them from maintaining a safe physical distance from other humans. In this paper, we propose a smartphone-based computationally-efficient deep neural network to detect crowds and relay the associated risks to the Blind or Visually Impaired (BVI) user through directional audio alerts. The system first detects humans and estimates their distances from the smartphone's monocular camera feed. Then, the system clusters humans into crowds to generate density and distance maps from the crowd centers. Finally, the system tracks detections in previous frames creating motion maps predicting the motion of crowds to generate an appropriate audio alert. Active Crowd Analysis is designed for real-time smartphone use, utilizing the phone's native hardware to ensure the BVI can safely maintain social distancing.
2D to 3D Medical Image Colorization

Colorization involves the synthesis of colors while preserving structural content as well as the semantics of the target image. This problem has been well studied for 2D photographs with many state-of-the-art solutions. We explore a new challenge in the field of colorization where we aim at colorizing multi-modal 3D medical data using 2D style exemplars. To the best of our knowledge, this work is the first of its kind and poses challenges related to the modality (medical MRI) and dimensionality (3D volumetric images) of the data. Our approach to colorization is motivated by modality conversion that highlights its robustness in handling multi-modal data.
An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models

Abstract Bayesian variable selection is an important method for discovering variables which are most useful for explaining the variation in a response. The widespread use of this method has been restricted by the challenging computational problem of sampling from the corresponding posterior distribution. Recently, the use of adaptive Monte Carlo methods has been shown to lead to performance improvement over traditionally used algorithms in linear regression models. This paper looks at applying one of these algorithms (the adaptively scaled independence sampler) to logistic regression and accelerated failure time models. We investigate the use of this algorithm with data augmentation, Laplace approximation and the correlated pseudo-marginal method. The performance of the algorithms is compared on several genomic data sets.
On the optimality of Concurrent Container Clusters Scheduling over heterogeneous smart environments

Smart environments rely on the cloud for most computation activities; however, leveraging the availability of resources at the edge could complement the cloud capabilities to provide a resources continuum spectrum that takes advantage of the benefits of both technologies, cloud and edge computing. In this scenario, applications are usually decomposed for execution in sets of tasks, which are in turn encapsulated into virtual components such as containers. Containers are lightweight implementations of virtual machines, improving efficiency and portability in distributed applications. Traditional containers scheduling in cloud is a well-known problem, but when the environment is heterogeneous, as it is in the scope of edge computing and edge-cloud systems, the problem becomes more challenging. In this paper we present the Concurrent Container Clusters Scheduling problem (C3S) aimed at optimizing the problem of placing containers in clusters of heterogeneous nodes satisfying a set of resource requirements, quality of service limitations, and considering additional stringent constraints in terms of applications execution in isolation for security guaranteeing. The C3S problem has been formulated using Integer Linear Programming with the dual objective of minimizing the number of applications rejected while minimizing the number of nodes used for computation. We have evaluated the optimality of this approach, analyzed the performance in terms of solving time and, finally, created a heuristic approach to solve the problem in realistic high demanding scenarios.
Loss of Control Detection for Commercial Transport Aircraft Using Conditional Variational Autoencoders

This work describes a detector for the loss of control condition of a commercial transport in flight. The detector has a belief state defined by the latent variable stochastic modeling of a conditional variational autoencoder (CVAE) constructed with bidirectional recurrent layers. In 2000, the Boeing Company and the NASA Langley Research Center jointly developed a quantitative set of metrics for defining loss-of-control (LOC) for a commercial transport. We use the thresholds for these quantitative metrics to define a condition vector for training the CVAE. First, we demonstrate through experimentation that reconstruction probability is an accurate indicator that the vehicle has shifted to an LOC state. Second, we introduce a technique for inferring that the vehicle is approaching a flight state change by measuring a shift in the sampling distributions of the CVAE latent space. The sampling distributions for flight observations that are approaching envelope limits are localized to external areas of the latent space. We provide an analysis of its applicability to flight data from NASA's dynamically-scaled generic transport model (GTM) aircraft.
A seq2seq Neural Network based Conversational Agent for Gulf Arabic Dialect

A Conversational Agent (CA), or dialogue system, is a computer system that has the ability to respond to humans automatically using natural language. CAs offer instant responses and can concurrently assist a potentially unlimited number of users. The modeling of CAs in Arabic has so far received less attention when compared with other languages due to the complexity of the Arabic language, the existence of several dialects, and a lack of data resources. The literature contends that modeling a CA in Arabic mostly done using pattern-matching and information retrieval, employing classification approaches with a closed-domain data source. There is extremely limited research so far on modeling an open-domain CA in the Arabic dialect. This research has utilized a deep-learning architecture, known as the Seq2Seq neural network, to build a CA in the Arabic Gulf dialect. We formulated the CA problem as a machine translation problem and, therefore, built our corpus from tweets, in the post-reply format, to train and evaluate the model. We investigated the effects of pretrained embeddings on the performance of the CA. For our evaluation, a Bilingual Evaluation Understudy (BLEU) score and human evaluators were used. The performance of the model was found to be comparable with existing deep learning models that have been trained on much larger corpora and in other languages. Our results present a promising first step towards building an open-domain CA in the Gulf Arabic dialect.
Reinforcement Learning for a Perched Landing in the Presence of Wind

Previous research by the University of Bristol's Flight Lab demonstrated the feasibility of using reinforcement learning to generate a controller to perform an agile perched landing flight manoeuvre. However, flight testing demonstrated the limits of the agent's ability to generalise to real-world flight conditions, in particular when encountering wind. This work builds on the previous project, adding simulated steady-state wind and turbulence during training. Improvements were made to the reinforcement learning process, such as the use of the more modern Proximal Policy Optimisation (PPO) algorithm, and refinement of the reward function. Using domain randomisation techniques, a series of models were trained in three simulated environments. The performance of each model was assessed in simulation by obtaining the mean reward across a range of conditions. The best performing models from each test case were deployed on the sweep-wing flight test vehicle. An improved flight testing system was developed to allow for a more repeatable testing process, with less variance in manoeuvre start conditions. Flight testing demonstrated that models trained with atmospheric disturbances perform better in the real world, achieving higher mean rewards than the baseline models that were trained without simulated wind. The testing also demonstrated areas of improvement to overcome performance discrepancies between simulation and reality, and improve real-world performance.
Sense–Assess–eXplain (SAX): Building Trust in Autonomous Vehicles in Challenging Real-World Driving Scenarios

This paper discusses ongoing work in demonstrating research in mobile autonomy in challenging driving scenarios. In our approach, we address fundamental technical issues to overcome critical barriers to assurance and regulation for large-scale deployments of autonomous systems. To this end, we present how we build robots that (1) can robustly sense and interpret their environment using traditional as well as unconventional sensors; (2) can assess their own capabilities; and (3), vitally in the purpose of assurance and trust, can provide causal explanations of their interpretations and assessments. As it is essential that robots are safe and trusted, we design, develop, and demonstrate fundamental technologies in real-world applications to overcome critical barriers which impede the current deployment of robots in economically and socially important areas. Finally, we describe ongoing work in the collection of an unusual, rare, and highly valuable dataset.
Performance Evaluation of Clustering-Based Classification Algorithms for Detection of Online Spam Reviews

The social Web contains enormous content, in which a higher range of online users believes the online reviews for their decision making before any online purchase or using services. The online reviews written are all not true, and some of them are Spam. Data mining classification helps in finding the review as spam or ham. Many text classification algorithms are existing, and it has been proved that these classifiers can be improved when clustering is used with classification to form features. This research work focuses on finding out the right classifier and improving it through clustering. Also, uncover the suitable clustering technique for improving the performance of the classifier. Three classifier algorithms such as Naive Bayes, support vector machine (SVM) and Decision Tree classifiers; clustering algorithms such as K-means, One-Pass and DBScan (Density-based spatial clustering of applications with noise) clustering algorithms are compared and found that the K-means clustering, when used with SVM classifier, outperforms than other combinations. Amazon and Yelp datasets are used for implementation, and the accuracy of SVM classifier has been improved from 89.02% to 90.02% for Amazon dataset and from 86.03% to 88.25% for the Yelp dataset when used with K-means clustering, which is significant than the other combinations compared.
Development of Image Stitching Using Feature Detection and Feature Matching Techniques

Image stitching is a process of merging two or more images of the scene into single image of high resolution which is also termed as panoramic image. Image stitching is used to assimilate information from several images by overlapping view fields to create a panoramic view without loss of any information. Stitching of images can be performed by two types of common approaches such as direct and indirect techniques. Direct techniques involve direct comparison of image pixel intensities that are combining. Indirect techniques are dependent on image features. These techniques incorporate feature detection and feature matching of the images to be stitched. In this proposed work, efficient feature detection and feature matching techniques such as Scale-Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST), Euclidean distance and Random sample consensus (RANSAC) are used for the process of image stitching. Images of a scene are captured with fifteen degree difference. These images are pre-processed if needed and then fed to feature detection and matching process. Using the matched feature points, images are stitched to get a 360 degree scene in a single image as outcome. The FAST technique gives good matching points of images using RANSAC which help in obtaining a better stitched image with integrating all the data of different images involved in stitching. The experiment is carried out for two datasets and average detected feature points and matched points for three algorithms are computed. The SIFT algorithm gives 36 matched points among 319 detected points, SURF gives 55 matched points among 334 detected feature points and FAST gives 102 matched points among 774 detected feature points. Image stitching finds application in various fields such as medical imaging, satellite imagery, automobile industries, image stabilization, document mosaicing and further image stitching can be extended to video stitching.
Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study

Bug prediction aims at locating defective source code components relying on machine learning models. Although some previous work showed that selecting the machine-learning classifier is crucial, the results are contrasting. Therefore, several ensemble techniques, i.e., approaches able to mix the output of different classifiers, have been proposed. In this paper, we present a benchmark study in which we compare the performance of seven ensemble techniques on 21 open-source software projects. Our aim is twofold. On the one hand, we aim at bridging the limitations of previous empirical studies that compared the accuracy of ensemble approaches in bug prediction. On the other hand, our goal is to verify how ensemble techniques perform in different settings such as cross- and local-project defect prediction. Our empirical experimentation results show that ensemble techniques are not a silver bullet for bug prediction. In within-project bug prediction, using ensemble techniques improves the prediction performance with respect to the best stand-alone classifier. We confirm that the models based on Validation and Voting achieve slightly better results. However, they are similar to those obtained by other ensemble techniques. Identifying buggy classes using external sources of information is still an open problem. In this setting, the use of ensemble techniques does not provide evident benefits with respect to stand-alone classifiers. The statistical analysis highlights that local and global models are mostly equivalent in terms of performance. Only one ensemble technique (i.e., ASCI) slightly exploits local learning to improve performance.
A Novel Neural Network Model Applied to Modeling of a Tandem-Wing Quadplane Drone

This research focuses on modeling one of the Quadplane flight phases: a hover state, similar to a regular Quadcopter hovering. The process is highly non-linear, and additionally, there are more phenomena to take into account - it is related to air turbulence around the wings and the fuselage. This work thoroughly studies the effectiveness of various types of neural networks to model the drone using the data recorded from real free-flight experiments. Finally, we introduce a novel type of neural-based model: the Feature-Sequence-To-Sequence (fseq2seq) Recurrent Neural Network Model. The new Model has interesting features: the input-data-driven initialization of RNN's internal states and a simplification of the input layer (significant reduction of used neurons' weights). We demonstrate that the new network outperforms all classic model types.
Dual Quaternion Visual Servo Control

This paper focuses on a dual quaternion-based estimation and control approach for position-based visual servoing (PBVS). The pose estimation of the camera is achieved using a dual quaternion-based Extended Kalman Filter (EKF), which estimates the position and orientation of the camera based on feature points acquired through a sequence of camera images. Based on the estimation, a dual quaternion control law is developed to regulate the camera to the desired pose. Leveraging the local exponential stability of the EKF and the global exponential stability of the designed controller, a nonlinear separation principle is used to prove the stability of the joint estimation and control for PBVS. The method is distinguished from other PBVS methods in the sense that a compact representation of dual quaternion is used to represent the pose, and a joint stability of estimator and controller for PBVS in dual quaternion space is presented. The proposed dual quaternion PBVS method is validated using a simulation.
Building Graduate Salary Grading Prediction Model Based on Deep Learning

Predicting salary trends of students after employment is vital for helping students to develop their career plans. Particularly, salary is not only considered employment information for students to pursue jobs, but also serves as an important indicator for measuring employability and competitiveness of graduates. This paper considers salary prediction as an ordinal regression problem and uses deep learning techniques to build a salary prediction model for determining the relative ordering between different salary grades. Specifically, to solve this problem, the model uses students’ personal information, grades, and family data as input features and employs a multi-output deep neural network to capture the correlation between the salary grades during training. Moreover, the model is pre-trained using a stacked denoising autoencoder and the corresponding weights after pre-training are used as the initial weights of the neural network. To improve the performance of model, dropout and bootstrap aggregation are used. The experimental results are very encouraging. With the predictive salary grades for graduates, school’s researchers can have a clear understanding of salary trends in order to promote student employability and competitiveness.
Day-ahead forecasting of regional photovoltaic production using deep learning

Power production based on solar energy is directly related to the state of the atmosphere. As the atmospheric state is undergone, this connection makes the solar energy a non-dispatchable source as opposed to controllable renewable sources such as hydroelectricity. In a context of growing photovoltaic generation, accurate forecast tools at regional scale are then increasingly important to grid operators. Indeed, forecasts allow getting information about future production over the next minutes, hours and days. Forecasting tools offer the possibility of a better grid management strategy specifically for transmission system operator (TSO) that are responsible for balancing renewable power production. High forecast accuracy could also lead to reduced costs for energy trading. In light of this situation, this study focuses on the development and analysis of a regional forecasting tool based on a deep learning approach. The selected model consists in a combination of a convolutional neural network (CNN) with a long short-term memory architecture (LSTM). The CNN layers allow extracting spatial features from Numerical Weather Prediction outputs while the LSTM part supports the temporal relationship. The day ahead regional forecast for Germany is chosen as a case study. The CNN-LSTM is compared to the classical Random Forest model known to be one of the reference techniques for this kind of problematic. Simpler deep learning models are also tested to validate the improvement brought by the CNN-LSTM architecture. All the comparisons are based on the classical root mean squared error (RMSE) metrics. The main result of this study shows that CNN-LSTM model can improve forecast accuracy when compared to state-of-the-art Random Forest. As expected, this improvement is strongly correlated to the amount of historical data which must cover several years according to the sensitivity study realized in this work.