Reviewerly/exHarmony huggingface dataset:
Vehicle Image Classification Using Data Mining Techniques

This paper focuses on the application of different data mining techniques to classify images of vehicles into three classes using Weka. The dataset used for this study were collected from Google Image search engine and other dataset websites. In preprocessing the images, filters such as Color Layout, Edge Histogram and Pyramid Histogram of Oriented Gradients were explored to extract the image features from the dataset. Classification techniques such as Multilayer Perceptron, Sequential Minimal Optimization, Logistic Model Trees, Simple Logistic and Random Forest were used. Results of the study showed that the edge histogram features provided much information to the classifiers in order to correctly classify the images. The SMO classifier performs best with the highest accuracy of 82.37%.
Genetic improvement of data gives binary logarithm from sqrt

Automated search in the form of the Covariance Matrix Adaptation Evolution Strategy (CMA-ES), plus manual code changes, transforms 512 Newton-Raphson floating point start numbers from an open source GNU C library, glibc, table driven square root function to create a new bespoke custom mathematical implementation of double precision binary logarithm log2 for C in seconds.
Real-time contextual feedback for close-loop control of navigation

Close-loop control of brain and behavior will benefit from real-time detection of behavioral events to enable low-latency communication with peripheral devices. In animal experiments, this is typically achieved by using sparsely distributed (embedded) sensors that detect animal presence in select regions of interest. High-speed cameras provide high-density sampling across large arenas, capturing the richness of animal behavior, however, the image processing bottleneck prohibits real-time feedback in the context of rapidly evolving behaviors.Here we developed an open-source software, named PolyTouch, to track animal behavior in large arenas and provide rapid close-loop feedback in ~5.7 ms, ie. average latency from the detection of an event to analog stimulus delivery, e.g. auditory tone, TTL pulse, when tracking a single body. This stand-alone software is written in JAVA. The included wrapper for MATLAB provides experimental flexibility for data acquisition, analysis and visualization.As a proof-of-principle application we deployed the PolyTouch for place awareness training. A user-defined portion of the arena was used as a virtual target; visit (or approach) to the target triggered auditory feedback. We show that mice develop awareness to virtual spaces, tend to stay shorter and move faster when they reside in the virtual target zone if their visits are coupled to relatively high stimulus intensity (⩾49 dB). Thus, close-loop presentation of perceived aversive feedback is sufficient to condition mice to avoid virtual targets within the span of a single session (~20 min).Neuromodulation techniques now allow control of neural activity in a cell-type specific manner in spiking resolution. Using animal behavior to drive closed-loop control of neural activity would help to address the neural basis of behavioral state and environmental context-dependent information processing in the brain.
Cooperative Vehicle Tracking using Particle Filter Integrated with Interacting Multiple Models

Recently, targets tracking plays an important role in the rapidly expanding field of autonomous driving. With the presentation of vehicle-to-vehicle (V2V) communication standards and the improvement of Global Navigation Satellite System (GNSS) technology, the accuracy of cooperative vehicle tracking should be greatly improved. However, high-precision positioning suffers from a number of vulnerabilities and is unstable in clutter environment. In this paper, a modified approach using particle filter (PF) integrated with Interacting Multiple Models (IMM) algorithm is proposed to improve the accuracy of cooperative vehicle tracking in changing V2V environment. This paper focuses on a novel framework for cooperative vehicle tracking and implementation of data fusion originating from positioning information receiving from radars and connected vehicles. The simulations on scenarios of single vehicle and multi-vehicle tracking were implemented by utilizing Matlab. The performance of the proposed modified IMM-PF algorithm was evaluated. The results demonstrated that the proposed approach can adapt to a changing accuracy of positioning information and significantly reduce the tracking errors by approximately 50% compared with traditional methods. Consequently, the proposed method can effectively enhance the security and reliability of autonomous driver assistance systems, and its application has potential to support urban planning for smart cities.
Auto-encoder based Graph Convolutional Networks for Online Financial Anti-fraud

Many practical problems can be formulated as graph-based semi-supervised classification problems. For example, online finance anti-fraud. Recently, many researchers attempt using deep learning methods to solve such problems. In this paper, we propose a novel neural network architecture to perform semi-supervised classification on graph-structured data. We improve the graph convolutional network (GCN) by replacing the graph convolution matrix with auto-encoder module. The proposed neural network is trained by a multi-task objective function. Except the classification task, we train the auto-encoder module to reconstruct the graph convolution matrix. It can be seen as an adaptive spectral convolution on graph. It can increase the depth of neural network without causing over-smooth effect. Additionally, the introduction of reconstruction task can mitigate the cold-start problem. Even the graph topological structure is extreme sparse, our method can learn expressive latent features for vertices. The experimental results show that our method can achieve the state of art performance.
Catastrophic Child's Play: Easy to Perform, Hard to Defend Adversarial Attacks

The problem of adversarial CNN attacks is considered, with an emphasis on attacks that are trivial to perform but difficult to defend. A framework for the study of such attacks is proposed, using real world object manipulations. Unlike most works in the past, this framework supports the design of attacks based on both small and large image perturbations, implemented by camera shake and pose variation. A setup is proposed for the collection of such perturbations and determination of their perceptibility. It is argued that perceptibility depends on context, and a distinction is made between imperceptible and semantically imperceptible perturbations. While the former survives image comparisons, the latter are perceptible but have no impact on human object recognition. A procedure is proposed to determine the perceptibility of perturbations using Turk experiments, and a dataset of both perturbation classes which enables replicable studies of object manipulation attacks, is assembled. Experiments using defenses based on many datasets, CNN models, and algorithms from the literature elucidate the difficulty of defending these attacks -- in fact, none of the existing defenses is found effective against them. Better results are achieved with real world data augmentation, but even this is not foolproof. These results confirm the hypothesis that current CNNs are vulnerable to attacks implementable even by a child, and that such attacks may prove difficult to defend.
Recent advances in particle swarm optimization analysis and understanding

tutorial Share on Recent advances in particle swarm optimization analysis and understanding Authors: AP Engelbrecht Stellenbosch University, South Africa Stellenbosch University, South AfricaView Profile , CW Cleghorn University of Pretoria, South Africa University of Pretoria, South AfricaView Profile Authors Info & Claims GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference CompanionJuly 2019 Pages 923–950https://doi.org/10.1145/3319619.3323368Published:13 July 2019Publication History 4citation82DownloadsMetricsTotal Citations4Total Downloads82Last 12 Months2Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Design Principles for Wearable Enhanced Embodied Learning of Movement

Human Computer Interaction (HCI) has seen increased interest in designing embodied experiences and interactions. This also includes the field of Wearable Enhanced Learning which marks the transition from the desktop age through the mobile age to the age of wearable, ubiquitous computing. Wearable enhanced learning relates to learning in a state of physical mobility supported by body-worn devices and sensors. While computer-mediated communication has been observed to enhance conscious experiences without self-reference leading to the sense of disembodiment, wearable technologies have the potential to enhance embodied experience with a strong self-reference. In fact, the affordances of wearable technologies to support embodied learning make wearable enhanced learning unique compared to other technology enhanced learning approaches. The concept of embodiment is based on the assumption that thoughts, feelings, and behaviours are grounded in movement and bodily interactions. Wearable Enhanced Embodied Learning is enabled by transmitting bodily information gathered by wearable sensors onto dynamic displays and making bodily information accessible to learners, in this way extending the learning experience. This paper draws on literature review in HCI and Embodiment research and collates a set of principles to inform the design of wearable enhanced embodied learning of movement.
A Comparative Study of DNN-Based Models for Blind Image Quality Prediction

Recently, deep learning methods have gained substantial attention in the research community and have proven useful for blind image quality assessment (BIQA). Although previous study of deep neural networks (DNN) methods is presented, some novelty methods, which are recently proposed, are not summarized. In this paper, we provide a comparative study on the application of DNN methods for BIQA. First, we systematically analyze the existing DNN-based quality assessment methods. Then, we compare the predictive performance of various methods in synthetic and authentic databases, providing important information that can help understand the underlying properties between different methods. Finally, we describe some emerging challenges in designing and training DNN-based BIQA, along with few directions that are worth further investigations in the future.
Authoring Conversational Intelligent Tutoring Systems

Conversational Intelligent Tutoring Systems (ITSs) are expensive to develop. While simple online courseware could be easily authored by teachers, the authoring of conversational ITSs usually involves a team of experts with different expertise, including domain experts, linguists, instruction designers, programmers, artists, computer scientists, etc. Reducing the authoring cost has long been a problem in the use of ITSs. Using AutoTutor as example, this paper discusses the authoring process and possible solutions by automatizing some authoring processes in authoring conversational ITSs.
A Human-Robot Interaction for a Mecanum Wheeled Mobile Robot with Real-Time 3D Two-Hand Gesture Recognition

Abstract Human interaction with mobile robot becomes a popular research area and its applications are widely used in industrial, commercial and military fields. A two-hand gesture recognition method with depth camera is presented for real-time controlling the mecanum wheeled mobile robot. Seven different gestures could be recognized from one hand for mobile robot navigation and three gestures could be recognized from the other hand for controlling the gripper installed on the robot. Under the proposed control scheme, the mobile robot system can be navigated and can be operated at the same time for achieving missions by two different groups of hand gestures. The accuracy of the gesture recognition is about 94%. During mobile robot control experiment, the system works timely, accurately and stably for certain tasks such as directional movement, grasping and cleaning obstacles.
Designing an Artificial Intelligence Platform to Assist Undergraduate in Art and Design to Develop a Personal Learning Plans

This paper presents the initiative design research for developing a platform based on artificial intelligence technology. Though the platform, the students can be assisted to develop a personal learning plan and register for the courses they need. Follow the blooming of information technology; Artificial Intelligence begins to intervene deeply into different industries in China. Change people's life and work in considerable ways. In order to cultivate talents that adapt to the new type of social structure, the university continues to reform and innovative in the cultivation and teaching mode. In this paper, we put the emphasis on the introduction of personalized learning plan development and the learning information service system. The information service model will create new learning mode and opportunities to constructing students' knowledge structure. We design a learning information service platform for undergraduate. Though the platform, the student can be assisted to develop own learning plan based on own interest and career path. The AI system will recommend courses plan and schedule. In addition, the system can estimate the knowledge points required and time spent by different learning routes. The learning information service platform based on AI system and the new learning mode is showed as examples, and hope to provide a new framework for the further innovation in higher education model.
Combining bio-inspired meta-heuristics and novelty search for community detection over evolving graph streams

Finding communities of interrelated nodes is a learning task that often holds in problems that can be modeled as a graph. In any case, detecting an optimal partition in a graph is highly time-consuming and complex. For this reason, the implementation of search-based metaheuristics arises as an alternative for addressing these problems. This manuscript focuses on optimally partitioning dynamic network instances, in which the connections between vertices change dynamically along time. Specifically, the application of Novelty Search mechanism for solving the problem of finding communities in dynamic networks is studied in this paper. For this goal, this procedure has been embedded in the search process undertaken by three different bio-inspired meta-heuristic schemes: Bat Algorithm, Firefly Algorithm and Particle Swarm Optimization. All these methods have been properly adapted for dealing with this discrete and dynamic problem, using a reformulated expression of the modularity coefficient as its fitness function. A thorough experimentation has been conducted using a benchmark composed by 12 synthetically created instances, with the main objective of analyzing the performance of the proposed Novelty Search mechanism when facing this problem. In light of the outperforming behavior of our approach and its relevance dictated by two different statistical tests, we conclude that Novelty Search is a promising procedure for finding communities in evolving graph data.
Genuine multipartite correlations in Dicke superradiance

A thorough understanding of the structure of correlations in multipartite systems is essential for the success of most quantum information processing applications. The problem, however, quickly becomes non-trivial as the size of the multipartition increases. With this motivation in mind, in this paper, we put forth a detailed study of genuine multipartite correlations (GMCs) in the Dicke model of superradiance. We compute all genuine $k$-partite correlations for Dicke states with arbitrary excitations and use these results to characterize the evolution of multipartite correlations during the superradiant dynamics. Non-trivial effects in the way correlations in Dicke states are distributed between the multiple parts are found, showing strong finite-size effects. We also employ the concept of weaving to classify how multipartite correlations scale with the number of particles in the system.
Secure Opportunistic Contextual Logging for Wearable Healthcare Sensing Devices

Wearable technology is increasingly being used for medical applications such as continuous monitoring of chronically ill patients in homes and hospitals. The various stakeholders (patients, doctors, insurers) have an interest in ensuring not only that the data is untampered, but also that the context is verifiable (e.g., correct time and location can be associated with the data collected). Prior works have studied these aspects in isolation, typically using cryptographic techniques. In this paper, we develop a new solution that leverages the density of wireless devices in the vicinity of the transaction to create witness records ensuring data is tamper-protected and bound to its time and location context. Our first contribution is to develop a secure logging architecture that compacts witness records using Bloom filters and hash-chains them to bind them to the data, allowing fast and reliable forensic verification. Our second contribution is to identify the various configuration parameters influencing the performance of our scheme in terms of storage, processing, and transmission efficiency, and to quantify their effect on verification accuracy. Our third contribution implements and demonstrates the feasibility of our scheme, and quantifies its efficacy via simulation using real trace data from a multi-storey building representing a hospital environment.
Exploring Extended Reality as a Simulation Training Tool Through Naturalistic Interactions and Enhanced Immersion

Augmented reality (AR), mixed reality (MR), and virtual reality (VR) technologies – collectively extended reality (XR) – have been discussed in the context of training applications since their introduction to the market. While this discussion was initially founded on potential benefit, the rapid pace and advancements within the XR industry allow the technology to fulfill initial expectations in terms of training utility. As XR becomes an effective training tool, two open areas of research, among many, include integrating experiences between VR and AR/MR simulations, as XR training often obscures the trainer from the trainee's environment, and determining the optimal level of fidelity and overall usability of interaction within XR simulations for the training of fine- and gross-motor control tasks, as new peripherals and technologies enter the market supporting a variety of interaction fidelities, such as data glove controllers, skeletal motion capture suits, tracking systems, and haptic devices. XR may show more promise as a training platform the closer it can replicate real world and naturalistic training interactions and immersion. This paper discusses efforts in these two research areas, including a planned usability study on the impact of the fidelity of interactions on training of fine- and gross-motor control tasks in virtual environments.
A Bug-Inspired Algorithm for Obstacle Avoidance of a Nonholonomic Wheeled Mobile Robot with Constraints

Many bug-inspired algorithms are well-developed and can be easily simulated if the robot is assumed to be a small particle with perfect movements and self-positioning capability. The sensor is also presumed to be able to obtain complete obstacle information in order for the algorithm to work perfectly in the simulation. In practice, however, applications of the algorithms on mobile robots are not straightforward as the robots generally have limited mobility due to disturbances such as frictions as well as constraints on the actuators. The obstacle detection capability also depends on the type of sensors used which usually has a trade-off between accuracy and price. In this work, we propose a simple approach to design and develop an obstacle avoidance strategy based on one of bug-inspired algorithms for a nonholonomic wheeled mobile robot with constraints on its velocities and sensing range. The proposed method is validated via several experiments with different goal coordinates and number of obstacles, and we show that the robot is able to avoid collisions and reach the destinations with very small steady-state position errors.
State online voting and registration lookup tools: Participation, confidence, and ballot disposition

State online voting and registration lookup tools that allow voters to check their voter registration status, confirm their polling location, review a sample ballot, and check the status of an absentee or provisional ballot reduce the cost of information acquisition and service completion while providing voters with the information necessary to participate in the democratic process. The analysis evaluates the relationship between state voting and registration online lookup tools and voter turnout, voter confidence, and the disposition of provisional ballots. Findings suggest that voting and registration lookup tools are related to voter turnout and rejected provisional ballots.
Asymptotic Optimal Edge Resource Allocation for Video Streaming via User Preference Prediction

Mobile edge computing extends computing and storage resources to the proximity of mobile users, facilitating a number of innovative mobile applications. Particularly, video streaming is the most prevailing one that consumes substantial edge resources. In this paper, we investigate the multi-dimensional resource allocation for video service provisioning, with the objective of ensuring satisfied streaming experience at high resource utilization. Considering the diversified and constantly changing user preferences on the quality of video contents, the edge resource allocation process is modeled as a long-term utility maximization problem. To address this problem, we propose an online learning algorithm that actively estimates user preferences according to regression analysis on user feedback. This algorithm requires no training phase, and hence is adaptive to dynamic user interests and available edge resources. Both theoretical analysis and numerical results demonstrate that the performance of the proposed algorithm asymptotically approaches the hindsight optimal resource allocation strategy.
A memetic algorithm with distance-guided crossover

Data-intensive Web service composition (DWSC) combines numerous interoperating, distributed and reusable data-intensive services to accomplish a specific task. In this paper, we will propose a Memetic Algorithm (MA) with a novel crossover operator to solve the distributed DWSC problem. In particular, we define two heuristics which enable explicit use of two important sources of information in producing offspring solutions in the MA. 1) location information from the problem to quickly determine the suitability of any solution and 2) useful information embedded in sub-solutions (e.g. common parts of the solutions). Experiments have been performed on benchmark datasets and the results show that our method is more effective than a state-of-the-art baseline method.