Reviewerly/exHarmony huggingface dataset:
Detection and Tracking of Small Objects in Sparse 3D Laser Range Data

Detection and tracking of dynamic objects is a key feature for autonomous behavior in a continuously changing environment. With the increasing popularity and capability of micro aerial vehicles (MAVs) efficient algorithms have to be utilized to enable multi object tracking on limited hardware and data provided by lightweight sensors. We present a novel segmentation approach based on a combination of median filters and an efficient pipeline for detection and tracking of small objects within sparse point clouds generated by a Velodyne VLP-16 sensor. We achieve real-time performance on a single core of our MAV hardware by exploiting the inherent structure of the data. Our approach is evaluated on simulated and real scans of in- and outdoor environments, obtaining results comparable to the state of the art. Additionally, we provide an application for filtering the dynamic and mapping the static part of the data, generating further insights into the performance of the pipeline on unlabeled data.
A Study on Knowledge Reuse Strategies in Multitasking Differential Evolution

Evolutionary multi-task optimization (EMTO) is a newly emerging research area which studies on how to solve multiple optimization problems simultaneously using evolutionary algorithms (EAs) so that useful knowledge (e.g. promising candidate solutions) obtained when solving one task can be transferred and reused to facilitate solving some other tasks. In EMTO, how to effectively transfer and reuse knowledge among multiple tasks is an important subject of study. Among existing EMTO techniques, DE-based EMTO algorithms deserve special attention because DE, as one of the most popular EAs, has achieved remarkable feats for solving challenging (single-task) optimization problems in the past although DE-based EMTO is not yet extensively studied. In this paper, we propose a general multitasking DE (MTDE) framework which aims to help more clearly understand the focuses and differences of existing and potential works on DE-based EMTO. We implement this framework via three commonly-used DE search schemes and perform a thorough empirical study on two DE-specific knowledge reuse strategies which are based upon base and differential vectors, respectively. Experiments on 18 MTO demonstrate that the base vector based knowledge reuse strategy outperforms the differential vector based one across all three tested DE search schemes.
Dentronics: Review, First Concepts and Pilot Study of a New Application Domain for Collaborative Robots in Dental Assistance

In this paper we introduce dentronics as a new emerging application domain for collaborative lightweight robots in the dental context backed up by a user survey supporting the clear need. Specifically, we developed a multi-modal interaction framework, applied this framework to a specific dental use-case, and conducted a preliminary user-study for evaluation. Our results demonstrate usability and feasibility beyond a controlled experimental setup. We conclude that dentronics is indeed within reach given today's technology and deserves further investigation towards clinical use.
Multi-Turn Response Selection for Chatbots With Hierarchical Aggregation Network of Multi-Representation

Matching an appropriate response with its multi-turn context is a crucial challenge in retrievalbased chatbots.Current studies construct multiple representations of context and response to facilitate response selection, but they use these representations in isolation and ignore the relationships among representations.To address these problems, we propose a hierarchical aggregation network of multirepresentation (HAMR) to leverage abundant representations sufficiently and enhance valuable information.First, we employ bidirectional recurrent neural networks (BiRNN) to extract syntactic and semantic representations of sentences and use a self-aggregation mechanism to combine these representations.Second, we design a matching aggregation mechanism for fusing different matching information between each utterance in context and response, which is generated by an attention mechanism.By considering the candidate response as the real part of the context, we try to integrate all of them in chronological order and then accumulate the vectors to calculate the final matching degree.An extensive empirical study on two multi-turn response selection data sets indicates that our proposed model achieves a new state-of-the-art result.
Visual analytics for spatiotemporal events

Crimes, forest fires, accidents, infectious diseases, or human interactions with mobile devices (e.g., tweets) are being logged as spatiotemporal events. For each event, its geographic location, time and related attributes are known with high levels of detail (LoDs). The LoD plays a crucial role when analyzing data, as it can highlight useful patterns or insights and enhance the user’ perception of phenomena. For this reason, modeling phenomena at different LoDs is needed to increase the analytical value of the data, as there is no exclusive LOD at which the data can be analyzed. Current practices work mainly on a single LoD of the phenomena, driven by the analysts’ perception, ignoring that identifying the suitable LoDs is a key issue for pointing relevant patterns. This article presents a Visual Analytics approach called VAST, that allows users to simultaneously inspect a phenomenon at different LoDs, helping them to see in what LoDs do interesting patterns emerge, or in what LoDs the perception of the phenomenon is different. In this way, the analysis of vast amounts of spatiotemporal events is assisted, guiding the user in this process. The use of several synthetic and real datasets supported the evaluation and validation of VAST, suggesting LoDs with different interesting spatiotemporal patterns and pointing the type of expected patterns.
A New Hybrid Adaptive Cuckoo Search-Squirrel Search Algorithm for Brain MR Image Analysis

This chapter presents a new hybrid adaptive cuckoo search-squirrel search (ACS-SS) algorithm for brain magnetic resonance (MR) image analysis. Thresholding is one of the popular methods utilized for brain image segmentation. Thresholding-based methods are easily implemented. In this context, we present an optimal multilevel thresholding technique for brain MR images using edge magnitude information. The edge magnitude is computed using the gray-level co-occurrence matrix (GLCM) of the brain image slice. The optimum thresholds are found by maximizing the edge magnitude. A new hybrid evolutionary computing technique, namely ACS-SS, is investigated to maximize the edge magnitudes. The proposed scheme is tested with T2-w brain MR images from Harvard medical education database. The results are compared with cuckoo search (CS), squirrel search (SS), and adaptive cuckoo search (ACS) algorithms. It is witnessed that the findings, using the proposed ACS-SS technique, are superior to the other techniques in terms of qualitative and quantitative measures. The advantages of the proposed technique are as follows: (i) The ACS-SS shows improved fitness function values; (ii) the ACS technique gives speed improvement.
Energy-Optimized Trajectory Planning for High Altitude Long Endurance (HALE) Aircraft

This paper outlines the energy-optimized trajectory planning problem for high altitude, long endurance (HALE) aircraft and explores both offline and online optimization techniques to address it. The goal is to find the optimal state and input trajectories for the solar-powered airplane, with input and nonlinear state constraints, which maximize net battery and gravitational potential energy storage. Solutions to the energy-optimal trajectory planning problem, using a six degree-of-freedom model of the nonlinear HALE aircraft dynamics, are computed using both an interior point optimization technique and a bounded nonlinear simplex search algorithm. The optimal trajectories, computed offline, are utilized to train an adaptive neuro-fuzzy inference system (ANFIS) which can be implemented on the flight control computer for online, in-flight trajectory planning. Simulation results show up to 15% more energy storage compared to a baseline parametrically-optimized trajectory.
Task Packing: Efficient task scheduling in unbalanced parallel programs to maximize CPU utilization

Load imbalance in parallel systems can be generated by external factors to the currently running applications like operating system noise or the underlying hardware like a heterogeneous cluster. HPC applications working on irregular data structures can also have difficulties to balance their computations across the parallel tasks. In this article we extend, improve and evaluate more deeply the Task Packing mechanism proposed in a previous work. The main idea of the mechanism is to concentrate the idle cycles of unbalanced applications in such a way that one or more CPUs are freed from execution. To achieve this, CPUs are stressed with just useful work of the parallel application tasks, provided performance is not degraded. The packing is solved by an algorithm based on the Knapsack problem, in a minimum number of CPUs and using oversubscription. We design and implement a more efficient version of such mechanism. To that end, we perform the Task Packing “in place”, taking advantage of idle cycles generated at synchronization points of unbalanced applications. Evaluations are carried out on a heterogeneous platform using FT and miniFE benchmarks. Results showed that our proposal generates low overhead. In addition the amount of freed CPUs are related to a load imbalance metric which can be used as a prediction for it.
Realistic Data Synthesis Using Enhanced Generative Adversarial Networks

Real data with privacy and confidentiality concerns are not often available or are too expensive to afford in respect of both time and money. In this situation, it is a good alternative to use synthetic data. The objective of this research is to generate realistic synthetic data so that people can use it freely. We propose a synthetic data generation model based on boundary-seeking generative adversarial networks (BGANs)-designated as medical BGAN or medBGAN and compare its performances with an existing method medical GAN (medGAN). We aim to perform the investigation on several datasets in two different domains: electronic health records (EHRs) in the medical domain and a crime dataset in the City of Los Angeles Police Department. Firstly, we train the models and generate synthetic data by using these trained models. We then analyze and compare the models' performance by applying some statistical methods (dimension-wise average and Kolmogorov-Smirnov test) and two machine learning tasks (association rule mining and prediction). The comprehensive analysis of this study shows that the proposed model is more efficient in generating realistic synthetic data than those generated using medGAN.
The Parameter Configuration Landscape: A Case Study on Particle Swarm Optimization

It is well known that tuning a meta-heuristic optimizer is a challenging, yet rewarding process. Despite the benefits of a properly tuned optimizer, there is very little that is understood about the actual tuning process - many automated parameter tuning methods use an assumption that parameter configurations near a promising configuration will also be promising. However, this assumption has not been verified, in general. While the field of fitness landscape analysis can provide insight into the difficulty of an optimization problem, these techniques have not yet been applied to the parameter tuning problem. This paper proposes a methodology to apply standard techniques from fitness landscape analysis to the parameter configuration landscape of an arbitrary optimizer. This allows the characterization of the parameter tuning problem for an arbitrary optimizer on an arbitrary optimization problem. The proposed methodology is then investigated for the particle swarm optimization (PSO) algorithm on 20 benchmark problems in both 10 and 30 dimensions. The results indicate that the parameter configuration landscape of the PSO algorithm is globally unimodal, yet not necessarily an easy landscape to search. Furthermore, it is found that the characteristics of the PSO parameter configuration landscape do not correlate with the characteristics of the target benchmark problems.
An Observation Capability Information Association Model for Multisensor Observation Integration Management: A Flood Observation Use Case in the Yangtze River Basin

Sensor planners find it difficult to choose the right sensor combination for observation tasks because current sensor discovery methods can only generate lists of individual sensors without considering the association between sensors that is conveyed by understanding their observation capability information. This limitation results in the failure to properly plan sensor observations and to obtain accurate observation information. The observation capability information association model (OCIAM) is proposed. An inherent state model, dynamic observation model, and correlation model are formulated in the framework of the OCIAM. The core of OCIAM is the correlation model in which four correlation modes (competitive, complementary, enhanced, and cooperative) are illustrated. A prototype system called MultisensorAssociation is developed for constructing an experiment for flood observation in the lower reaches of the Jinsha River Basin. The results of this case study verify that the OCIAM can be used for the selection of sensors or sensor combinations and has important theoretical and practical significance for multisensor observation integration management.
Achieving Flexible Scheduling of Heterogeneous Workflows in Cloud through a Genetic Programming Based Approach

Cloud computing enables enormous computational resources to be scheduled as parallel workflow applications. Most traditional heuristics can only solve one particular scheduling problem. For example, Heterogeneous Earliest Finish Time (HEFT) and Greedy algorithms allocate resources to given ordered list of tasks using a specific single heuristic, which only caters for a specific scheduling problem, e.g. the fixed number of tasks in a workflow and available resources. Many researchers considered the heterogeneous workflows and cloud resources in scheduling in order to minimize the cost and makespan, but the solutions provided are only for specific workflow pattern. In this paper, we demonstrate a workflow scheduling problem which considers the combination of heterogeneous workflows as well as heterogeneous computing resources. We proposed Flexible Scheduling using Genetic Programming (FSGP) approach to min-imize the total cost and makespan of heterogeneous workflows in the cloud. The performance of our proposed FSGP is regardless of the number of tasks in the workflow, available resources and workflow patterns. We evaluated our proposed approach using a benchmark dataset. Performance evaluation of some well-known algorithms such as HEFT and greedy algorithms exhibit that our FSGP approach perform better than other competing algorithms.
Improving SHADE with Center-based Mutation for Large-scale Optimization

Differential Evolution is a powerful and efficient approach for numerical optimization. A Success-History Based Parameter Adaptation (SHADE) is the recent variant of the adaptive DE that utilizes a historical performance of the successful control parameter. In this paper, we propose a center-based mutation for SHADE algorithm (CSHADE). In this mutation scheme, the base vector for SHADE's mutation is replaced with center-based sampled candidate solution using the normal distribution. The proposed method is evaluated on CEC-2010 and CEC-2013 LSGO benchmark functions with dimension 1000. The experimental results show that CSHADE outperforms SHADE algorithm over the majority of benchmark functions in terms of solution accuracy.
Crowdsourcing a self-evolving dialog graph

In this paper we present a crowdsourcing-based approach for collecting dialog data for a social chat dialog system, which gradually builds a dialog graph from actual user responses and crowd-sourced system answers, conditioned by a given persona and other instructions. This approach was tested during the second instalment of the Amazon Alexa Prize 2018 (AP2018), both for the data collection and to feed a simple dialog system which would use the graph to provide answers. As users interacted with the system, a graph which maintained the structure of the dialogs was built, identifying parts where more coverage was needed. In an offline evaluation, we have compared the corpus collected during the competition with other potential corpora for training chatbots, including movie subtitles, online chat forums and conversational data. The results show that the proposed methodology creates data that is more representative of actual user utterances, and leads to more coherent and engaging answers from the agent. An implementation of the proposed method is available as open-source code.
Efficient Feature Selection using Particle Swarm Optimization: A hybrid filters-wrapper Approach

In machine learning, feature selection can be used to reduce the computation time and improve the learning accuracy, especially when dealing with high-dimensional data sets. Particle Swarm Optimization (PSO) has attracted significant concerns to enhance the feature selection process due to its efficiency in solving problems. This paper introduces a new hybrid filters-wrapper approach that is used to enhance the feature selection process using PSO algorithm. Our proposed approach combines five filtration methods in with different weights to produce a new hybrid filters-wrapper algorithm using BPSO. The proposed approach has been evaluated by performing comparisons with other methods like wrapper alone and filter alone. The obtained results show that the proposed approach has achieved better performance than other approaches taking into account three parameters; The number of selected features, the classification accuracy, and the execution time. In addition, the new approach has been tested to ensure its stability in the feature selection and it has shown a high degree of stability.
Towards Generating Stylized Image Captions via Adversarial Training

While most image captioning aims to generate objective descriptions of images, the last few years have seen work on generating visually grounded image captions which have a specific style (e.g., incorporating positive or negative sentiment). However, because the stylistic component is typically the last part of training, current models usually pay more attention to the style at the expense of accurate content description. In addition, there is a lack of variability in terms of the stylistic aspects. To address these issues, we propose an image captioning model called ATTEND-GAN which has two core components: first, an attention-based caption generator to strongly correlate different parts of an image with different parts of a caption; and second, an adversarial training mechanism to assist the caption generator to add diverse stylistic components to the generated captions. Because of these components, ATTEND-GAN can generate correlated captions as well as more human-like variability of stylistic patterns. Our system outperforms the state-of-the-art as well as a collection of our baseline models. A linguistic analysis of the generated captions demonstrates that captions generated using ATTEND-GAN have a wider range of stylistic adjectives and adjective-noun pairs.
Mining Process Mining Practices: An Exploratory Characterization of Information Needs in Process Analytics

Many business process management activities benefit from the investigation of event data. Thus, research, foremost in the field of process mining, has focused on developing appropriate analysis techniques, visual idioms, methodologies, and tools. Despite the enormous effort, the analysis process itself can still be fragmented and inconvenient: analysts often apply various tools and ad-hoc scripts to satisfy information needs. Therefore, our goal is to better understand the specific information needs of process analysts. To this end, we characterize and examine domain problems, data, analysis methods, and visualization techniques associated with visual representations in 71 analysis reports. We focus on the representations, as they are of central importance for understanding and conveying information derived from event data. Our contribution lies in the explication of the current state of practice, enabling the evaluation of existing as well as the creation of new approaches and tools against the background of actual, practical needs.
A photogrammetry-based experimental modal analysis method by tracking visible laser spots

A photogrammetry-based experimental modal analysis (EMA) method is developed, where a single-point laser shines a laser beam to a surface of a test object and an exterior feature is added to the test object in the form of a high-contrast laser spot. A three-dimensional kinematic model is developed to derive the kinematic relation between the laser spot and its associated image target on an image plane of a camera. A point-tracking technique is proposed to determine two-dimensional pixel coordinates of the optical intensity centroid of the image target on a digital image. The derived kinematic relation and EMA method were experimentally investigated. In the experimental investigation, displacements of multiple points at a cantilever beam were measured by a digital camera. Natural frequencies and mode shapes of the cantilever beam were estimated using the EMA method, and they compared well with those from a finite element model of the cantilever beam.
A Novel Feature Selection Method for Video-Based Human Activity Recognition Systems

This paper introduces a new feature selection approach for human activity recognition systems to accurately recognize the human activities.We proposed normalized mutual information-based feature selection (NMIFS) method that will select good features extracted from numerous existing feature extraction techniques.The proposed method is an extension of the max-relevance and min-redundancy method.The ability of this method is to combine the strengths of different extraction techniques.However, the selection process might be influenced because of the inequality among the feature's classification power and the feature's redundancy.To escape this influenced selection, we normalize both terms by the proposed feature independent upper bound of the mutual information function.Moreover, we exploit the curvelet transform for feature extraction, and linear discriminant analysis for reduction of feature space.Moreover, we use the hidden Markov model for activity recognition based on our proposed method of feature selection.Finally, by using the benchmark datasets such as KTH and Weizmann datasets, we experimentally compare the proposed scheme with state-of-the-art.Simulation results show that the proposed scheme is not only more accurate for some datasets, but outperforms competing method by weighted average accuracy 98%.