Reviewerly/exHarmony huggingface dataset:
Risk-Based Obstacle Avoidance in Unknown Environments using Scenario-Based Predictive Control for an Inspection Drone Equipped with Range Finding Sensors

This paper develops an obstacle avoidance strategy for inspection drones equipped with simple range finding sensors, such as radar or sonar. The obstacle avoidance strategy uses scenario-based model predictive control where the predicted outcomes of a set of possible control actions are evaluated. The action with the best predicted outcome amongst the safe options is chosen. The resulting behaviour is deemed safe if the probability of collision at each time-step in the prediction is lower than a given maximal accepted probability. The probability of collision is calculated by combining a probability density function of the position of the drone with an obstacle probability map generated by the range finding sensors. This constraint is checked at each step over the prediction horizon thus ensuring that the control action will give rise to safe behaviour. The algorithm is implemented in a 2D case and tested with a simple model for the range finding sensors. Simulations show that the drone is able to avoid obstacles and that the drone will change speed or take detours to avoid flying in potentially dangerous areas to mitigate risk. The algorithm is designed for avoiding obstacles along a pre-planned path. The pre-planned path is assumed to be generally good, but might be unsafe or not take some unknown obstacles into account. If the pre-planned path goes through a larger convex area or does not take a large obstacle into account, then this algorithm might not find a way around the obstacle and the drone will stop at a safe distance. The path of the drone must then be re-planned taking these obstacles into account. The resulting obstacle avoidance strategy guarantees safe behaviour which enables higher level controllers or human planners to plan a path based on lacking obstacle information without taking the safety of the drone into consideration.
A Mixed Presence Collaborative Mixed Reality System

Research has shown that Mixed Presence Groupware (MPG) systems are a valuable collaboration tool. However research into MPG systems is limited to a handful of tabletop and Virtual Reality (VR) systems with no exploration of Head-Mounted Display (HMD) based Augmented Reality (AR) solutions. We present a new system with two local users and one remote user using HMD based AR interfaces. Our system provides tools allowing users to layout a room with the help of a remote user. The remote user has access to a marker and pointer tools to assist in directing the local users. Feedback collected from several groups of users showed that our system is easy to learn but could have increased accuracy and consistency.
A grouping method based on improved PSO for task allocation in rescue environment

The environment after disaster is complicated and it is difficult to rescue the survivors timely and effectively. Robots can complete the rescue work instead of rescuers with high-efficiency and without limits of the disaster area. Based on this, a novel task allocation method for multi-robot in an environment after disaster is presented. Firstly, according to the locations and time constraints of the tasks, a new grouping method of the tasks is proposed to reduce the computational complexity. Following that, a new initial solution generation method is used to speed up the evolution. Finally, an improved particle swarm algorithm with the adaptive inertia weight and velocity update is developed to solve the grouping-based task allocation. The experimental results indicate that the proposed method can increase the success rate of rescue and speed up the rescue effectively.
Information Fusion via Multimodal Hashing with Discriminant Canonical Correlation Maximization

In this paper, we introduce an effective information fusion method using multimodal hashing with discriminant canonical correlation maximization. As an effective computation method of similarity between different inputs, multimodal hashing technique has attracted increasing attentions in fast similarity search. In this paper, the proposed approach not only finds the minimum of the semantic similarity across different modalities by multimodal hashing, but also is capable of extracting the discriminant representations, which minimize the between-class correlation and maximize the within-class correlation simultaneously for information fusion. Benefiting from the combination of semantic similarity across different modalities and the discriminant representation strategy, the proposed algorithm can achieve improved performance. A prototype of the proposed method is implemented to demonstrate its performance in audio emotion recognition and cross-modal (text-image) fusion. Experimental results show that the proposed approach outperforms the related methods, in terms of accuracy.
Simple User Assistance by Data Posting

Big Data deeply changed both researchers and industries approaches to data management. This impressive amount of data contains a lot of information regarding user suggestions and searches. In this paper, we focus on the effect of user suggestions on its social environment. Suggestions are provided to user by means of simple rules that are defined on the basis of the data being analyzed. The above mentioned activity is crucial to perform efficient identification of users able to spread their influence across the network (influencer), which is a key activity in several scenarios like tourism promotion, personalized marketing, and entertainment suggestion. In this paper we address the reaction of users in a competitive environment when they were invited to judge each other choices.
On Learning Weight Distribution of Tai Chi Chuan Using Pressure Sensing Insoles and MR-HMD

Tai Chi Chuan (TCC) is a famous Chinese martial art. In addition to reading instruction books and watching demonstration videos, the traditional way of learning TCC for most people is to observe and mimic the movements of the coach. However, it is not easy for a novice to determine the weight distribution of the coach, and it is also difficult for a novice to strike a pose with the correct weight distribution. To help people to learn correct weight distribution when practicing TCC, we proposed a TCC augmented reality (AR)/mixed reality (MR) learning system that consists of a mixed reality head-mounted display (MR-HMD) and a pair of pressure sensing insoles. We have designed three kinds of visual hints to help people strike poses with correct weight distributions. Two user studies have shown that our TCC learning system is helpful for people to learn the weight distribution correctly and helpful for people to improve their proprioceptive sensitivity on weight distribution.
Collaboration Analysis in Global Software Development

In global software development distributed teams are often challenged by global distances, such as cultural diversity and linguistic barriers. This stimulates the search for groups of people that can work together and collaborate in a positive way, even though they are working in a distributed environment. In addition, the demand for high quality software motivates the search for experienced developers who are able to solve complex tasks or to help others with their own tasks. This work aims to detect experts and to identify collaborative groups with experienced members in some topics of software development. To achieve these goals the StackOverflow forum was used and modeled as a complex network. The presented method uses NetSCAN algorithm to detect overlapping communities, expert developers and their topics of expertise in social networks. Through a temporal analysis over the social network multidisciplinary developers were found as well as people who are changing their interests over time. In a first evaluation a statistic test showed that experts (indicated by the proposed method) have a higher performance than common users. A second evaluation showed that these experts are suitable for recommendation systems in question-answer forums.
Head Pose Estimation via Multi-Task Cascade CNN

In our daily life, many face applications need to complete three tasks: face detection, facial landmark localization and head pose estimation. Currently, most methods accomplish these three tasks separately. Multi-task cascade convolution neural network(MTCNN) adpots the idea of casecading that combines face detection and face alignment. Inspired by MTCNN, we combine the three tasks of face detection, head pose estimation and key points detection under a cascade framework. Simultaneously, we increased the number of key points detected by MTCNN from 5 to 21. By training and testing our model on the WIDER and Umdfaces datasets, we explored the inherent correlation between these three facial tasks and demonstrated the excellent results of the model tested in an unconstrained environment.
Research on face detection method based on improved MTCNN network

Face detection is one of the important topics in computer vision research and is the basis of many applications. A face detection algorithm based on improved Multi-Task Convolution Neural Network (MTCNN) is proposed in this paper. To increase the accuracy of eye location in complex situations, this method improves the network structure of MTCNN, builds a neural network model based on MTCNN using TensorFlow, and cascades an eye regression network. The Face-Net neural network model was used for training, and the obtained training model was used for detection. Experiments have shown that the accuracy on the LFW dataset is 0.9963 and the accuracy on the YouTube Faces DB dataset is 0.9512.
Fast Stochastic Functional Path Planning in Occupancy Maps

Path planners are generally categorised as either trajectory optimisers or sampling-based planners. The latter is the predominant planning paradigm for occupancy maps. Most trajectory optimisers require a fully defined artificial potential field for planning and cannot incorporate updates from a partially observed model such as an occupancy map. A stochastic trajectory optimiser capable of planning over occupancy map was presented in [1]. However, its scalability is limited by the cubic complexity of the Gaussian process path representation. In this work, we introduce a novel highly expressive path representation based on kernel approximation to perform trajectory optimisation over occupancy maps. This approach reduces the computational complexity to a fixed cost that only depends on the number of features. We show that stochastic sampling is crucial for planning in occupancy maps and present comparisons to other state-of-the-art planning methods, using simulated and real occupancy data. These experiments demonstrate the significant reduction in runtime, resulting in performance comparable to or better than sampling-based methods.
Visualizing image content to explain novel image discovery

The initial analysis of any large data set can be divided into two phases: (1) the identification of common trends or patterns and (2) the identification of anomalies or outliers that deviate from those trends. We focus on the goal of detecting observations with novel content, which can alert us to artifacts in the data set or, potentially, the discovery of previously unknown phenomena. To aid in interpreting and diagnosing the novel aspect of these selected observations, we recommend the use of novelty detection methods that generate explanations. In the context of large image data sets, these explanations should highlight what aspect of a given image is new (color, shape, texture, content) in a human-comprehensible form. We propose DEMUD-VIS, the first method for providing visual explanations of novel image content by employing a convolutional neural network (CNN) to extract image features, a method that uses reconstruction error to detect novel content, and an up-convolutional network to convert CNN feature representations back into image space. We demonstrate this approach on diverse images from ImageNet, freshwater streams, and the surface of Mars.
Shoehorning in the name of science

This provocation paper calls for a deeper understanding of what spoken human-computer interaction is, and what it can be. Its given structure by a story of humanlikeness and fraudulent spoken dialogue systems - specifically systems that deliberately attempts to mislead their interlocutors into believing that they are speaking to a human. Against this backdrop, a plea that conversational user interfaces are viewed from the perspective of conversation and spoken interaction first, and from the perspective of GUIs and interface design second, lest we impose the limitations of one field onto the possibilities of another, rather than the other way around.
Discovering and Visualizing Efficient Patterns in Cost/Utility Sequences

Many algorithms have been proposed to discover interesting patterns in sequences of events or symbols, to support decision-making or understand the data. In sequential pattern mining, patterns are selected based on criteria such as the occurrence frequency, periodicity, or utility (eg. profit). Although this has many applications, it does not consider the effort or resources consumed to apply these patterns. To address this issue, this paper proposes to discover patterns in cost/utility sequences, in which each event/symbol is annotated with a cost, and where a utility value indicates the benefit obtained by performing each sequence. Such sequences are found in many fields such as in e-learning, where learners do various sequences of learning activities having different cost (time), and obtain different utility (grades). To find patterns that provide a good trade-off between cost and benefits, two algorithms are presented named CEPDO and CEPHU. They integrate many optimizations to find patterns efficiently. Moreover, a visualization module is implemented to let users browse patterns by their skyline and visualize their properties. A case study with e-learning data has shown that insightful patterns are found and that the designed algorithms have excellent performance.
A Fuzzy Paradigmatic Clustering Algorithm

Clustering algorithms resume the datasets into few number of data points such as centroids or medoids, which explain the entire dataset briefly. In the domain of data-driven machine learning, the more precision with the clustering rule leads directly to more precise classification, prediction, and recognition. We propose an efficient clustering method, which applies the paradigms - mainly 3D Gaussian model - to estimate the optimum cluster number, cluster border, and congestion coordinates to model the datasets of the natural distributions. This approach considers both qualitative and quantitative features of the dataset and calculates the best scale to analyze it. We used fuzzy logic to compare the models with data, to generate and rank the hypotheses, and finally to reject or accept the assumptions. The proposed approach which is called Fuzzy Gaussian Paradigmatic Clustering (FGPC) algorithm is used as the basis of a fast (with the complexity order of O(n)) and robust algorithm for identifying fuzzy models.
Multilingual Patent Text Retrieval Evaluation: CLEF–IP

The CLEF–IP evaluation lab ran between 2009 and 2013 with a two-fold expressed purpose: (a) to encourage research in the area of patent retrieval with a focus on cross language retrieval, and (b) to provide a large and clean data set of patent related data, in the three main European languages, for experimentation. In its first year, CLEF–IP organized one task only, a text retrieval task that modelled the "Search for Prior Art" done by experts at patent offices. In the following years the types of CLEF–IP tasks broadened to include patent text classification, patent image retrieval and classification, and (formal) structure recognition. With each task, the test collection was extended to accommodate for the additional tasks. In this chapter we overview the evaluation tasks dealing with the textual content of the patents. The Intellectual Property (IP) domain is one where specific expertise is critical, implementing Information Retrieval (IR) approaches to support some of its tasks cannot be done without the use of this domain know-how. Even when such know-how is at hand, retrieval results, in general, do not come close to the expectations of patent experts.
Toward Detection of Child Exploitation Material: A Forensic Approach

With continual advances in Internet capability, in addition to its global and decentralized nature, the Internet along with different social networking sites are experiencing a boom in demand and supply. Recent study found that the social networking sites like Facebook, Twitter, and MySpace are providing a forum for paedophiles to share child pornography. With the advent of sophisticated digital technology, Law Enforcement Agency (LEAs) around the world dealing with child pornography facing real challenge to combat with the technologically-savvy paedophiles. The major challenge in child pornography lies in authentic detection of children face in an image. The main objective of this research is to present a novel framework for a dedicated child face detection tool, where we will use child’s face specific contextual contexts and visual cues that are based on new knowledge in terms of features or contexts representatives of child’s skin and face. The proposed technique can estimate age categorically – adult or child based on a new hybrid feature descriptor, called Luminance Invariant & Geometrical Relation based Descriptor (LIGRD). LIGRD is composed of some low and high-level features, which are found to be effective in characterizing the local appearance in terms of chromaticity, texture, and geometric relational information of few facial visual cues simultaneously. Comparison of our experimental results with that of another recently published work reveals our proposed approach yields the highest precision and recall, and overall accuracy in recognition.
UAV Aided Dynamic Routing of Resources in a Flood Scenario

This paper addresses the problem of dynamic routing of resources in a fast-changing flood scenario using land vehicles aided by Unmanned Aerial Vehicles (UAVs). The road network of the area is modelled as a graph network with travel time and road capacities and vehicles containing the resources are routed through the roads from the relief centres, or source nodes, to the disaster affected areas, or destination nodes, by considering the goods as a dynamic flow in the road network. The quickest flow algorithm is initially implemented to solve this basic transportation problem. A team of UAVs survey the flooded area to check the safety of the chosen routes and monitor the status of the vehicles over time. Changes in the routing parameters, which can hinder the transportation process, are communicated and the goods are re-routed by considering the current position of the vehicles spread on the road network as individual sources in the problem and then by applying the earliest arrival flows algorithm for a multiple source problem. Simulations are performed on virtual and real street environments considering various scenarios which show the effectiveness of the methodology.
Analyzing Variability in 25 Years of Industrial Legacy Software

In certain domains, safety-critical software systems may remain operational for decades. To comply with changing requirements, new system variants are commonly created by copying and modifying existing ones. Typically denoted clone-and-own, software quality and overall maintainability are adversely affected in the long-run. With safety being pivotal, a fault in one variant may require the entire portfolio to be assessed. Thus, engineers need to maintain legacy systems dating back decades, implemented in programming languages such as Pascal. Software product lines (SPLs) can be a remedy but migrating legacy systems requires their prior analysis and comparison. For industrial software systems, this remains a challenge.
A graph-based framework for analysing the design of smart contracts

Used as a platform for executing smart contracts, Blockchain technology has yielded new programming languages. We propose a graph-based framework for computing software design metrics for the Solidity programming language, and use this framework in a preliminary study on 505 smart contracts mined from GitHub. The results show that most of the smart contracts are rather straightforward from an objected-oriented point of view and that new design metrics specific to smart contracts should be developed.
Mutational Robustness and Structural Complexity in Grammatical Evolution

A recent study in Artificial Life found that the need for mutational robustness can give rise to simpler structures in an evolving population. This begs the question, do we observe a similar phenomenon in Genetic Programming? Genetic Programming requires the search of structural space of solutions, usually requiring code growth to find fitter solutions. Typically Genetic Programming algorithms then suffer from code bloat, which is code growth in the absence of fitness gains. In this study we ask a simple question. Would the necessity for mutational robustness under selection pressure drive the evolution of less complex solution structures in Genetic Programming, with the potential to counteract code bloat?
Dewarping of Single-Folded Camera Captured Bangla Document Images

The document images captured through mobile camera get warped due to the curved surfaces like an open thick book page, notices posted on a cylindrical lamppost or a paper hanging from a notice board attached at top-middle. The very recent state of the art techniques for dewarping, work mostly for document images containing alphabetic script like English; these cannot handle all the variety of warping. This paper proposed a method for dewarping of single folded warped documentGarai, Arpan images containing alpha-syllabary script like Bangla. Here the information of top line and baseline increase the robustness of the algorithm. Unlike other related methods, this work has not used any additional information like focal length, distance from the cameraBiswas, Samit or view angle. Experimental evaluation with the collected dataset shows promising results.
Ambient Intelligence for Patient-Centric Healthcare Delivery: Technologies, Framework, and Applications

Current technological changes have implemented advanced solutions for enhancing the quality of human life. Ambient intelligence (AmI) is a powerful paradigm which is characterized by various computing technologies that enable the machines to interact with humans. Such developments made it possible to evolve different real-time solutions especially in the healthcare domain. This chapter describes how AmI infrastructures have emerged in the field of healthcare for providing effective solutions. Also, this chapter discusses the role of sensors and wearable devices in developing an ambient healthcare system. This chapter proposes a secured cloud-oriented architecture and algorithm for secure data transmission from wireless body area network (WBAN) to cloud storage. The proposed theoretical framework focuses on case-based and context-aware reasoning facility which satisfies the various use cases and requirements of ambient-based healthcare systems. Finally, an illustration case describes how the ambient care works in a hospital environment, and it also elaborates a few contexts, events, and rules using Unified Modeling Language (UML) class diagram.