Reviewerly/exHarmony huggingface dataset:
The Effects of Immersion and Real-World Distractions on Virtual Social Interactions

This study explores the independent and joint effects of immersion and real-world distractions (a ringing cell phone) on cognitive (i.e., recognition and recall), affective valence, and interpersonal outcomes (i.e., interpersonal liking and communication satisfaction) as well as general feelings of presence (social presence and telepresence) during a virtual experience. Participants interacted with a virtual agent in an immersive virtual environment or nonimmersive virtual environment under three different levels of real-world distractions (i.e., no distraction, passively being exposed to the sound of a ringing cell phone, and actively responding to ringing cell phone). Increased immersion had a positive effect on telepresence, but a negative effect on recognition and recall; immersion did not have a significant effect on social presence. Real-world distractions had a negative effect on recognition, recall, and social presence, but did not affect telepresence or affective valence. Participants who were actively distracted performed more poorly on the recall measure and reported lower levels of social presence than their passively distracted counterparts. These findings suggest that (a) increased immersion will not uniformly improve social virtual reality experiences and (b) more research is needed on whether and how real-world events should be integrated into virtual environments.
Autonomous Obstacle Avoidance Vehicle Using LIDAR and an Embedded System

Autonomous miniature vehicles are widely used to test many types of algorithms and simulate the driving behavior as in the real world. In the last decade, several approaches have been proposed for obstacle avoidance in self-driving vehicles. In this paper, we present our autonomous vehicle implementation which gives a solid solution to this problem in an efficient manner, using a Raspberry Pi and a LIDAR module for indoor navigation. Our vehicle is capable of navigating in an unknown environment while avoiding obstacles. Unlike other implementations, our vehicle doesn't use Computer Vision (CV) techniques for obstacle detection but only a single LIDAR sensor and thus can safely navigate in low luminosity enviroments. We perfomed various experiments and verified the success of our implementation.
Robust High Dimensional Stream Classification with Novel Class Detection

A primary challenge in label prediction over a data stream is the emergence of instances belonging to unknown or novel class over time. Traditionally, studies addressing this problem aim to detect such instances using cluster-based mechanisms. They typically assume that instances from the same class are closer to each other than those belonging to different classes in observed feature space. Unfortunately, this may not hold true in higher-dimensional feature space such as images. In recent years, Convolutional neural network (CNN) have emerged as a leading system to be employed in many real-world application. Yet, based on the assumption of closed world dataset with a fixed number of categories, CNN lacks robustness for novel class detection, so it is unclear on how such models can be used to deal with novel class instances along a high-dimensional image stream. In this paper, we focus on addressing this challenge by proposing an effective learning framework called CNN-based Prototype Ensemble (CPE) for novel class detection and correction. Our framework includes a prototype ensemble loss (PE) to improve the intra-class compactness and expand inter-class separateness in the output feature representation, thereby enabling the robustness of novel class detection. Moreover, we provide an incremental learning strategy which maintains a constant amount of exemplars to update the network, making it more practical for real-world application. We empirically demonstrate the effectiveness of our framework by comparing its performance over multiple realworld image benchmark data streams with existing state-of-theart data stream detection techniques. The implementation of CPE is on: https://github.com/Vitvicky/Convolutional-Net-PrototypeEnsemble
Light Field Super-Resolution By Jointly Exploiting Internal and External Similarities

Light field images taken by plenoptic cameras often have a tradeoff between spatial and angular resolutions. In this paper, we propose a novel spatial super-resolution approach for light field images by jointly exploiting internal and external similarities. The internal similarity refers to the correlations across the angular dimensions of the 4D light field itself, while the external similarity refers to the cross-scale correlations learned from an external light field dataset. Specifically, we advance the classic projection-based method that exploits the internal similarity by introducing the intensity consistency checking criterion and a back-projection refinement, while the external correlation is learned by a CNN-based method which aggregates all warped high-resolution sub-aperture images upsampled from the low-resolution input using a single image super-resolution method. By analyzing the error distributions of the above two methods and investigating the upperbound of combining them, we find that the internal and external similarities are complementary to each other. Accordingly, we further propose a pixel-wise adaptive fusion network to take advantage of both their merits by learning a weighting matrix. Experimental results on both synthetic and real-world light field datasets validate the superior performance of the proposed approach over the state-of-the-arts.
Maximizing diversity by transformed ensemble learning

The diversity and the individual accuracies in an ensemble system are usually two opposite objects, which is ignored in most preliminary ensemble learning algorithms. To alleviate this issue, in this paper, a novel weighted ensemble learning is proposed by maximizing the diversity and the individual accuracy simultaneously. More specifically, in the proposed framework, the combination of multiple base learners is converted into a linear transformation of all these base learners, and the optimal weights are obtained by pursuing the optimal projective direction of the linear transformation. Then the derived objective function can be efficiently solved by the alternating directional multiplier method. Finally, the proposed method is verified on UCI datasets and face databases, and the experimental results illustrate that the proposed method effectively improves the performance compared with other ensemble methods.
Corporate Social Responsibility in Nigeria and Rural Youths in Sustainable Traditional Industries Livelihood in Oil Producing Communities

Abstract Since the first oil well was drilled in Nigeria, traditional economies have suffered neglect, and rural youths do not see a future for themselves in traditional industries livelihood (TIL). We examine the impact of corporate social responsibility of multinational oil companies on youths' participation in TIL. A total of 1200 youths were sampled across the rural Niger Delta. Results from the use of a logit model indicate a significant relationship between corporate social responsibility and TIL. The findings suggest increased general memorandum of understanding interventions in canoe carving, pottery making, cloth weaving, mat making and basket weaving to revive the traditional economic activities in Nigeria. © 2019 John Wiley &amp; Sons, Ltd.
ANFIS system for classification of brain signals

Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of br ain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational costs results.
Enabling Pedestrian Safety Using Computer Vision Techniques: A Case Study of the 2018 Uber Inc. Self-driving Car Crash

Human lives are important. The decision to allow self-driving vehicles operate on our roads carries great weight. This has been a hot topic of debate between policy-makers, technologists and public safety institutions. The recent Uber Inc. self-driving car crash, resulting in the death of a pedestrian, has strengthened the argument that autonomous vehicle technology is still not ready for deployment on public roads. In this work, we analyze the Uber car crash and shed light on the question, "Could the Uber Car Crash have been avoided?". We apply state-of-the-art Computer Vision models to this highly practical scenario. More generally, our experimental results are an evaluation of various image enhancement and object recognition techniques for enabling pedestrian safety in low-lighting conditions using the Uber crash as a case study.
A Deep Learning System for Recognizing Facial Expression in Real-Time

This article presents an image-based real-time facial expression recognition system that is able to recognize the facial expressions of several subjects on a webcam at the same time. Our proposed methodology combines a supervised transfer learning strategy and a joint supervision method with center loss, which is crucial for facial tasks. A newly proposed Convolutional Neural Network (CNN) model, MobileNet, which has both accuracy and speed, is deployed in both offline and in a real-time framework that enables fast and accurate real-time output. Evaluations towards two publicly available datasets, JAFFE and CK+, are carried out respectively. The JAFFE dataset reaches an accuracy of 95.24%, while an accuracy of 96.92% is achieved on the 6-class CK+ dataset, which contains only the last frames of image sequences. At last, the average run-time cost for the recognition of the real-time implementation is around 3.57ms/frame on a NVIDIA Quadro K4200 GPU.
Reusing the Past Difference Vectors in Differential Evolution—A Simple But Significant Improvement

Differential evolution (DE) has established itself as a simple but efficient population-based, nonconvex optimization algorithm for continuous search spaces. Unlike the conventional real-coded genetic algorithms (GAs) and evolution strategies (ESs), DE uses a mandatory self-referential mutation for its population members, each of which are perturbed with the scaled difference(s) of the individuals from the current generation (iteration). These difference vectors determine the direction of the search moves for the individuals. However, unlike the better individuals, they are not retained in the elitist evolution cycle of DE. In this paper, we show that by archiving the most promising difference vectors from past generations and then by reusing them for generating offspring in the subsequent generations, we can strikingly improve the performance of DE. This strategy can be integrated with any classical or advanced DE variant with no serious overhead in time or space complexity. We demonstrate that when combined with the DE-based winners of the IEEE Congress on Evolutionary Computation (CEC) 2013, 2014, and 2017 competitions on real parameter optimization, the simple reuse strategy leads to a statistically significant performance improvement in the majority of test cases. We further showcase the efficacy of our proposal on a practical optimization problem concerning the design of circular antenna arrays with a prespecified radiation pattern.
Assessing Heuristic Machine Learning Explanations with Model Counting

Machine Learning (ML) models are widely used in decision making procedures in finance, medicine, education, etc. In these areas, ML outcomes can directly affect humans, e.g. by deciding whether a person should get a loan or be released from prison. Therefore, we cannot blindly rely on black box ML models and need to explain the decisions made by them. This motivated the development of a variety of ML-explainer systems, including LIME and its successor $${\textsc {Anchor}}$$ . Due to the heuristic nature of explanations produced by existing tools, it is necessary to validate them. We propose a SAT-based method to assess the quality of explanations produced by $${\textsc {Anchor}}$$ . We encode a trained ML model and an explanation for a given prediction as a propositional formula. Then, by using a state-of-the-art approximate model counter, we estimate the quality of the provided explanation as the number of solutions supporting it.
Security Analysis of Processor Instruction Set Architecture for Enforcing Control-Flow Integrity

Intel has developed Control-flow Enforcement Technology (CET) [27] that provides CPU instruction set architecture (ISA) capabilities to defend against Return-oriented Programming (ROP) and call/jmp-oriented programming (COP/JOP) style control-flow subversion attacks. This attack methodology uses code sequences in authorized modules with at least one instruction in the sequence being a control transfer instruction that depends on attacker-controlled data either in the return stack or in a register/memory for the target address. Attackers stitch these sequences together by diverting the control flow instruction (e.g. RET, CALL, JMP) from its original target address to a new target (via modification in the data stack or in the register or memory used by these instructions). This paper describes CET security objectives, threat model and various architectural design choices to ensure that the design meets the security objectives. We conclude the paper with performance data and related work in this domain.
A Distributed Ledger Approach to Digital Twin Secure Data Sharing

The Digital Twin refers to a digital representation of any real-world counterpart allowing its management (from simple monitoring to autonomy). At the core of the concept lies the inclusion of the entire asset lifecycle. To enable all lifecycle parties to partake, the Digital Twin should provide a sharable data base. Thereby, integrity and confidentiality issues are pressing, turning security into a major requirement. However, given that the Digital Twin paradigm is still at an early stage, most works do not consider security yet. Distributed ledgers provide a novel technology for multi-party data sharing that emphasizes security features such as integrity. For this reason, we examine the applicability of distributed ledgers to secure Digital Twin data sharing. We contribute to current literature by identifying requirements for Digital Twin data sharing in order to overcome current infrastructural challenges. We furthermore propose a framework for secure Digital Twin data sharing based on Distributed Ledger Technology. A conclusive use case demonstrates requirements fulfillment and is followed by a critical discussion proposing avenues for future work.
Mobile Robot Path Planning in Dynamic Environment Using Voronoi Diagram and Computation Geometry Technique

This paper presents a novel path planning method for mobile robots in complex and dynamic environments using Voronoi diagram (VD) and computation geometry technique (CGT) termed, VD-CGT. An algorithm to categorize moving obstacles based on their positions, velocities, distances, and directions to ascertain their collision threat level and possible replanning decision is introduced. The initial path computation is done using morphological dilation, VD, A-star, and cubic spline algorithms. Instead of considering the entire map of the environment, CGT is used to compute a small rectangular region estimated to enclose a detected collision-threat obstacle and the current position of the robot. The roadmap is computed in the geometrical shape using VD and nodes are added to the initial roadmap nodes to compute a new path for replanning. To avoid increasing time and space requirements, these nodes are discarded before subsequent replanning is done. The results indicate better path replanning performance in complex and dynamic environments in terms of success path computation rate, path cost, time, and the number of replanning computations compared with other five popular related path planning approaches. The proposed method is efficient, and it computes safe and shortest replan path to goal with low computation time requirement. Unnecessary replanning computations are avoided which aid in reducing time and distance to get to the goal. With the performance results, the proposed method is a promising method for achieving safe, less path cost, and time in path replanning computations in complex and dynamic environments.
An improved artificial bee colony algorithm combined with extremal optimization and Boltzmann Selection probability

Artificial Bee Colony (ABC) algorithm is an optimization algorithm based on a particular intelligent behavior of honeybee swarms. The standard ABC has been utilized to deal with a lot of optimization problems in real world. However, there are still some defects of the standard ABC such as weak local-search capability and low solution precision. In order to improve the performance of ABC, in this paper, we propose two improved versions of ABC-EO and IABC-EO presented in our previous work, called ABC-EO II and IABC-EO II, where Extremal Optimization (EO) is introduced to ABC and IABC in different ways. There are some advanced characteristics of our proposed algorithms: (1) Compared with ABC-EO and IABC-EO, the improved versions have lower computational costs by introducing EO in different ways; (2) An easier-operated mutation method is introduced which can increase the diversity of new offspring and helps our algorithms jump out of local optima; (3) The selection pressure can be dynamically adjusted in evolutionary process by means of Boltzmann selection probability; (4) A novel selection probability is used to select the worse solutions for the mutation operation by EO mechanism. The experimental results on three groups of benchmark functions indicate that the performance of the proposed algorithms is as good as or superior to those of 15 state-of-the-art optimization algorithms in terms of solution accuracy, convergence speed, successful rate and statistical tests. Finally, in order to testify the feasibilities of the proposed methods for solving the real life problems, our algorithms are applied to solving two kinds of parameters identification of photovoltaic models and four well-recognized evolutionary algorithms are selected as the competitors. The simulation results indicate that the proposed IABC-EO II algorithm has superior performance in comparison with other five algorithms, while the proposed ABC-EO II outperforms at least competitive with other four algorithms in term of solution accuracy and statistical tests. As a result, our algorithms may be good alternatives for solving complex unconstrained continuous optimization problems.
Digital four-step phase-shifting technique from a single fringe pattern using Riesz transform

A digital four-step phase-shifting method for obtaining the optical phase distribution from a single fringe pattern is proposed in this Letter. By computing the first-, second-, and third-order Riesz transform components for a given fringe pattern, three π/2 phase-shifted fringe patterns are generated from the obtained Riesz components and, finally, the wrapped phase map is extracted. The validity of the proposed method is demonstrated on both the simulated and experimentally obtained fringe patterns. The performance of the proposed method is evaluated by using the image quality index and edge preservation index. Further, the performance of the proposed method is tested on speckled correlation fringes obtained from digital speckle pattern interferometry, and the resulting phase from the proposed method is compared with the phase obtained from three experimentally recorded phase-shifted fringe patterns. The obtained results reveal that the proposed method provides a simple and robust solution for optical phase extraction from a single fringe pattern with good accuracy and, therefore, make it suitable for real-time measurement applications.
Addressing the Overlapping Data Problem in Classification Using the One-vs-One Decomposition Strategy

Learning good-performing classifiers from data with easily separable classes is not usually a difficult task for most of the algorithms.However, problems affecting classifier performance may arise when samples from different classes share similar characteristics or are overlapped, since the boundaries of each class may not be clearly defined.In order to address this problem, the majority of existing works in the literature propose to either adapt well-known algorithms to reduce the negative impact of overlapping or modify the original data by introducing/removing features which decrease the overlapping region.However, these approaches may present some drawbacks: the changes in specific algorithms may not be useful for other methods and modifying the original data can produce variable results depending on data characteristics and the technique used later.An unexplored and interesting research line to deal with the overlapping phenomenon consists of decomposing the problem into several binary subproblems to reduce its complexity, diminishing the negative effects of overlapping.Based on this novel idea in the field of overlapping data, this paper proposes the usage of the One-vs-One (OVO) strategy to alleviate the presence of overlapping, without modifying existing algorithms or data conformations as suggested by previous works.To test the suitability of the OVO approach with overlapping data, and due to the lack of proposals in the specialized literature, this research also introduces a novel scheme to artificially induce overlapping in real-world datasets, which enables us to simulate different types and levels of overlapping among the classes.The results obtained show that the methods using the OVO achieve better performances when considering data with overlapped classes than those dealing with all classes at the same time.
Grid-Based Object Tracking With Nonlinear Dynamic State and Shape Estimation

Object tracking is crucial for planning safe maneuvers of mobile robots in dynamic environments, in particular for autonomous driving with surrounding traffic participants. Multi-stage processing of sensor measurement data is thereby required to obtain abstracted high-level objects, such as vehicles. This also includes sensor fusion, data association, and temporal filtering. Often, an early-stage object abstraction is performed, which, however, is critical, as it results in information loss regarding the subsequent processing steps. We present a new grid-based object tracking approach that, in contrast, is based on already fused measurement data. The input is thereby pre-processed, without abstracting objects, by the spatial grid cell discretization of a dynamic occupancy grid, which enables a generic multi-sensor detection of moving objects. On the basis of already associated occupied cells, presented in our previous work, this paper investigates the subsequent object state estimation. The object pose and shape estimation thereby benefit from the freespace information contained in the input grid, which is evaluated to determine the current visibility of extracted object parts. An integrated object classification concept further enhances the assumed object size. For a precise dynamic motion state estimation, radar Doppler velocity measurements are integrated into the input data and processed directly on the object-level. Our approach is evaluated with real sensor data in the context of autonomous driving in challenging urban scenarios.