Reviewerly/exHarmony huggingface dataset:
Fast Sequence Rejection for Multi-Goal Planning with Dubins Vehicle

Multi-goal curvature-constrained planning such as the Dubins Traveling Salesman Problem (DTSP) combines NP-hard combinatorial routing with continuous optimization to determine the optimal vehicle heading angle for each target location. The problem can be addressed as combinatorial routing using a finite set of heading samples at target locations. In such a case, optimal heading samples can be determined for a sequence of targets in polynomial time, and the DTSP can be solved as searching for a sequence with the minimal cost. However, the examination of sequences can be computationally demanding for high numbers of heading samples and target locations. A fast rejection schema is proposed to quickly examine unfavorable sequences using lower bound estimation of Dubins tour cost based on windowing technique that evaluates short subtours of the sequences. Furthermore, the computation using small problem instances can benefit from reusing stored results and thus speed up the search. The reported results indicate that the computational burden is decreased about two orders of magnitude, and the proposed approach supports finding high-quality solutions of routing problems with Dubins vehicle.
Developing a Smart PACS: CBIR System Using Deep Learning

With the growing number of digital medical imaging records, the need for an automatic procedure to retrieve only data of interest is of increasing importance. A Picture Archiving and Communication System (PACS) provides effective storage and retrieval based on TAGs but does not allow us for query by example. A possible solution is to use a Content-Based Image Retrieval (CBIR) system, namely a system able to retrieve images from a database based on the similarity to a given reference image. The features used to describe the images strongly affect both the performance and the applicability of CBIR to medical images, motivating for the finding of a suitable set of feature for realizing an effective CBIR based PACS. In recent years, Deep Learning (DL) approaches outperformed classical machine learning methods in many computer vision applications, thanks to their ability to learn compact hierarchical features of input data that well fit the specific task to solve. In this paper we introduce a simple yet effective modular architecture to implement a “Smart PACS”, namely a PACS exploiting a deep-based CBIR compatible with the classical Hospital Information System (HIS) infrastructure. The feature extraction relies on Convolutional Neural Networks, a DL approach commonly applied in image processing, while the image indexing and look-up are based on Apache Solr. As application case-study, we analysed the need for a physician to obtain all the images of past studies having similar traits with the patient under analysis.
Path Planning for Nonholonomic Multiple Mobile Robot System with Applications to Robotic Autonomous Luggage Trolley Collection at Airports

In this paper, we propose a novel path planning algorithm for the nonholonomic multiple mobile robot system with applications to a robotic autonomous luggage trolley collection system at airports. We consider this path planning algorithm as a Multiple Traveling Salesman Problem (MTSP). Our path planning algorithm consists of three parts. First, we use the Minimum Spanning Tree (MSP) algorithm to divide the MTSP into a number of independent TSPs, which achieves the task assignment for each mobile robot. Secondly, we implement a closed-loop forward control policy based on the kinematic model of the mobile robot to get a feasible and smooth path. The control cost of the path is used as the new metric in solving the TSPs. Finally, in order to adapt to our case, we modify the TSP as an Open Dynamic Traveling Salesman Problem with Fixed Start (ODTSP-FS) and implement an ant colony algorithm to achieve the path planning for each mobile robot. We evaluate our algorithm with simulation experiments and the experimental results demonstrate that our algorithm can quickly generate feasible and smooth paths for each robot while satisfying the nonholonomic constraints.
Towards General Infeasibility Proofs in Motion Planning

We present a general approach for constructing proofs of motion planning infeasibility. Effective high-dimensional motion planners, such as sampling-based methods, are limited to probabilistic completeness, so when no plan exists, these planners either do not terminate or can only run until a timeout. We address this completeness challenge by augmenting a sampling-based planner with a method to create an infeasibility proof in conjunction with building the search tree. An infeasibility proof is a closed polytope that separates the start and goal into disconnected components of the free configuration space. We identify possible facets of the polytope via a nonlinear optimization procedure using sampled points in the non-free configuration space. We identify the set of facets forming the separating polytope via a linear constraint satisfaction problem. This proof construction is valid for general (i.e., non-Cartesian) configuration spaces. We demonstrate this approach on the low-dimensional Jaco manipulator and discuss engineering approaches to scale to higher dimensional spaces.
Behavioral Science and Diversity in Software Engineering

The “Practioners' Digest” department in this issue of IEEE Software covers two topics: the behavioral science of software engineering and diversity in software engineering (this issue’s theme) and includes papers from the 42nd International Conference on Software Engineering (ICSE20), 2019 IEEE International Conference on Software Maintenance and Evolution (ICSME19), 13th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE20), Empirical Software Engineering and Measurement 2020 (ESEM20), and Association for Computing Machinery Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE20). Feedback or suggestions are welcome. In addition, if you try or adopt any of the practices included in this article, please send me and the authors of the paper(s) a note about your experiences.
Integrative Object and Pose to Task Detection for an Augmented-Reality-based Human Assistance System using Neural Networks

As a result of an increasingly automatized and digitized industry, processes are becoming more complex. Augmented Reality has shown considerable potential in assisting workers with complex tasks by enhancing user understanding and experience with spatial information. However, the acceptance and integration of AR into industrial processes is still limited due to the lack of established methods and tedious integration efforts. Meanwhile, deep neural networks have achieved remarkable results in computer vision tasks and bear great prospects to enrich Augmented Reality applications. In this paper, we propose an Augmented-Reality-based human assistance system to assist workers in complex manual tasks where we incorporate deep neural networks for computer vision tasks. More specifically, we combine Augmented Reality with object and action detectors to make workflows more intuitive and flexible. To evaluate our system in terms of user acceptance and efficiency, we conducted several user studies. We found a significant reduction in time to task completion in untrained workers and a decrease in error rate. Furthermore, we investigated the users learning curve with our assistance system.
Asynchronous Co-Dining: Enhancing the Intimacy in Remote Co-Dining Experience Through Audio Recordings

Co-dining benefits in closeness and connectedness between all family members. However, it is hard for those in different locations to enjoy a meal together due to the geographical barriers and inconsistent life schedule. The widely applied video technology caused excessive visual interference when solving problems, while the effect of sound in a meal on intimacy has not been studied. In this paper, we explored how can recorded audio create an asynchronous co-dining experience and support intimacy. We conducted a field study with 24 participants, and the result shows the system potentially enhance the intimacy in remote coding experience. In the follow-up workshop with 6 participants, we further discussed the future landscape of designing a sound interface for remote meals.
Applying MDD in the content management system domain

Abstract Content management systems (CMSs) such as Joomla and WordPress dominate today’s web. Enabled by standardized extensions, administrators can build powerful web applications for diverse customer demands. However, developing CMS extensions requires sophisticated technical knowledge, and the complex code structure of an extension gives rise to errors during typical development and migration scenarios. Model-driven development (MDD) seems to be a promising paradigm to address these challenges; however, it has not found adoption in the CMS domain yet. Systematic evidence of the benefit of applying MDD in this domain could facilitate its adoption; however, an empirical investigation of this benefit is currently lacking. In this paper, we present a mixed-method empirical investigation of applying MDD in the CMS domain, based on an interview suite, a controlled experiment, a field experiment, and case studies. During the experiments, we used JooMDD, an MDD infrastructure instantiation for CMS extensions. This infrastructure, which is also presented in this work, consists of a DSL with model editors, code generators, and reverse engineering facilities. We consider three scenarios of developing new (both independent and dependent) CMS extensions and of migrating existing ones to a new major platform version. The experienced developers in our interviews acknowledge the relevance of these scenarios and report on experiences that render them suitable candidates for a successful application of MDD. We found a particularly high relevance of the migration scenario. Our experiments largely confirm the potentials and limits of MDD as identified for other domains. In particular, we found a productivity increase up to factor 11.7 and a quality increase up to factor 2.4 during the development of CMS extensions. Furthermore, our observations highlight the importance of good tooling that seamlessly integrates with already used tool environments and processes.
WITHDRAWN: SMO-RF:A machine learning approach by random forest for predicting class imbalancing followed by SMOTE

In different fields, such as machine learning and data mining, class imbalances have been one of the most complex issues for the past few decades. The unique condition of an imbalanced dataset that distributes each class of a particular dataset unevenly. The positive class is slightly smaller than the negative one. Many standard classification algorithms in this case do not classify instances related to the positive class. Typically the main goal of the classification task is a positive class. To deal with this problem, several approaches were proposed, for example sampling dependent over-sampling, undersampling, classification level enhancements, or the combination of two or more classifiers. The major problem however is that most solutions have a negative class, a computational cost, a storage problem, or a long training period. Data upsampling or downsampling may resolve a possible solution to the issue of skewness of data. In this paper, a hybrid technique is presented, followed by a random forest algorithm (SMO-RF), to categorized binary imbalanced data using the Technique of Synthetic Minority Oversampling. We have tested our model with four standard imbalanced datasets and obtained a higher F-measure, G-mean as well as ROC values for all data sets.
ExerTiles: A Tangible Interactive Physiotherapy Toolkit for Balance Training with Older Adults

This study investigates the potential value of tangible interactive technology as a tool for physiotherapists in their work with older adults. The starting point for the investigation was the observation that physiotherapists are bricoleurs—that is, practitioners who are skillful in creatively using the available materials in their environment. We conducted co-design workshops with physiotherapists that informed the development of a prototype toolkit, the ExerTiles, comprising a set of interactive tiles with control components. The toolkit was assessed by seven physiotherapists who created balance training exercises for two older adults each. Post-test interviews revealed that all the physiotherapists were highly positive toward using the ExerTiles as part of their professional work. The following five factors contributed toward the acceptance of ExerTiles by the physiotherapists: tailorability, versatile training, creativity, fun, and portability. Our research indicates that tangible interactive technology has a strong potential to integrate with and add value to physiotherapy practice.
A New Sentence Ordering Method using BERT Pretrained Model

Building systems with capability of natural language understanding (NLU) has been one of the oldest areas of AI. An essential component of NLU is to detect logical succession of events contained in a text. The task of sentence ordering is proposed to learn succession of events with applications in AI tasks. The performance of previous works employing statistical methods is poor, while the neural networks-based approaches are in serious need of large corpora for model learning. In this paper, we propose a method for sentence ordering which does not need a training phase and consequently a large corpus for learning. To this end, we generate sentence embedding using BERT pre-trained model and measure sentence similarity using cosine similarity score. We suggest this score as an indicator of sequential events' level of coherence. We finally sort the sentences through brute-force search to maximize overall similarities of the sequenced sentences. Our proposed method outperformed other baselines on ROCStories, a corpus of 5-sentence human-made stories. The method is specifically more efficient than neural network-based methods when no huge corpus is available. Among other advantages of this method are its interpretability and needlessness to linguistic knowledge.
Exploration of unknown environments with a tethered mobile robot

This paper presents a tangle-free frontier based exploration algorithm for planar mobile robots equipped with limited length and anchored tethers. After planning a path to the closest point in the frontier between free and unknown space, the robot computes an estimate of the future length of its tether and decides, by comparing the anticipated length with the minimum possible tether length, whether the path should be followed or not. If the anticipated tether is longer than the minimum tether by a function of the expected radius of the obstacles, a path planner with homotopic constraints is used to plan a path that brings the robot tether to the same homotopy class of the shortest tether. This behavior will not only limit the tether length but also will prevent tether entangling on the obstacles of the environment. We evaluate our method in different simulated environments and illustrate the approach with an actual tethered robot.
Semi-Supervised Deep Learning Based Wireless Interference Identification for IIoT Networks

Accurate wireless interference identification (WII) is vital for wireless industrial internet of things (IIoT) network to coexist with other technologies in the crowded 2.4 GHz unlicensed band. Deep learning (DL) based methods have emerged as a promising candidate for such type of task. However, to achieve good accuracy, DL methods require large amount of labeled training data, which comes from tedious annotation work by domain expert. In contrast, unlabeled data is easier to obtain. In this paper we present a semi-supervised DL based WII algorithm which combines temporal ensembling technique with CNN network to exploit unlabeled data to improve the performance. The proposed algorithm is able to differentiate interference from multiple wireless standards accurately with reduced number of labels, such as IEEE 802.11, IEEE 802.15.4 and IEEE 802.15.1. Specifically, the proposed algorithm achieves 90% accuracy with less than 2% of labeled data with medium to high signal SNR. Extensive simulation results show that the proposed algorithm achieves a better classification accuracy than benchmark algorithms under various SNR conditions and with different number of labeled data.
Block Proposer Election Method Based on Verifiable Random Function in Consensus Mechanism

The consensus mechanism, as one of the most important technologies of the blockchain, always faces the balance of security, efficiency and consistency. The generation of block proposer in the existing consensus algorithm is predictive, and the target of malicious nodes is clear. In response to these problems, this paper introduces a non-interactive verifiable random node extraction method, using a verifiable random function (VRF) to randomly generate block proposer. This method makes it impossible to know the identity of key nodes before the proposal block is broadcast, and realizes the unpredictability and verifiability of the node identity. At the same time, the reputation model in this article is constructed to adjust node status dynamically, and to select nodes with higher credibility to form a consensus group. Analysis and experiments show that the optimized consensus algorithm has higher security and reliability, and the influence of malicious nodes on the system delay is negligible. Simultaneously, It can play better performance in systems where malicious nodes exist.
Prototyping "Color in Life" EduGame for Dichromatic Color Blind Awareness

The purpose of this research was to escalate players’ knowledge on color blindness by designing an educational video game which design was oriented to dichromatism color blind. The topic selection was based on the lack of players’ deeper knowledge on color blindness. The graphic and gameplay selection on this research was adjusted to the chosen color blind category. Research methods were conducted by analysis, development, and evaluation. Analysis was done by questionnaire. Development was done by game design document, UML, storyboard, and was implemented using Unity Game Engine. Evaluation on 35 players, which are 32 with normal eyes and 3 with color blindness, was done by two approaches, which are t-test and questionnaire. The result of t-test was t(34) = -7.704, p < 0.05 and Enjoyment score on CEGE is 0.763 for normal eyes and 0.651 for colorblind. To conclude, there was an improvement on knowledge from the video game and the design was enjoyable.
Geographical feature classification from text using (active) convolutional neural networks

Deep learning can discover intricate patterns hidden in big data, and has much better scalability than traditional machine learning when the volume of data increases dramatically. Thus, deep learning has gained many successes in various domains and applications such as image classification, text classification, and machine translation. In this paper, we use deep learning to classify geographical features (e.g., mountains, rivers, landmarks, and cities) from text, using geolocated Wikipedia entries as the case study application. We employ one of the most commonly used deep learning architectures, convolutional neural networks (CNNs) and its integration with active learning (creating what we call active CNNs), to train the geographical feature classifiers on the Wikipedia text data set obtained from GeoNames (which provides the feature type for each geolocated entity). We evaluate the performance of CNNs and active CNNs with multiple metrics (i.e., accuracy, F1 score, and confusion matrix). Our experiment results demonstrated that CNNs and active CNNs can effectively classify geo-referenced text entities into predefined geographical features. In addition, our experiment results show that active CNNs outperform CNNs for hard to distinguish classes. In our experiment, we also compared results for hierarchical multi-class classification and flat multiclass classification, and the results show that hierarchical multiclass classification significantly outperforms flat multi-class classification for the data set we used.
A Symptom Selection Algorithm Based on Classification Errors

In this paper the issues like preprocessing of ischemic heart disease data and optimization of feature space are discussed and solved. Here an algorithm for selection a set of information features based on classification error are proposed. Using this algorithm was obtain the set of most informative symptoms for three class of ischemic heart disease: “Progressive angina pectoris”, “Acute myocardial infarction” and “Arrhythmic form”.
Improving Hypergraph Attention and Hypergraph Convolution Networks

Graph Neural Networks (GNNs) are models that use the structure of graphs to better exploit the bilateral relationship between neighboring nodes. Some problems, however, require that we consider a more general relationship which involve not only two nodes but rather a group of nodes. This is the approach adopted in Hypergraph Convolution and Hypergraph Attention Networks (HGAT) [1]. In this paper, we first propose to incorporate a weight matrix into these networks which, as our experimentations show, can improve the performance of the models in question. The other novelty in our work is the introduction of self-loops in the graphs which again leads to slight improvements in the accuracy of our architecture(named iHGAN).
Permutation-based true discovery guarantee by sum tests

Sum-based global tests are highly popular in multiple hypothesis testing. In this paper we propose a general closed testing procedure for sum tests, which provides lower confidence bounds for the proportion of true discoveries (TDP), simultaneously over all subsets of hypotheses. These simultaneous inferences come for free, i.e., without any adjustment of the alpha-level, whenever a global test is used. Our method allows for an exploratory approach, as simultaneity ensures control of the TDP even when the subset of interest is selected post hoc. It adapts to the unknown joint distribution of the data through permutation testing. Any sum test may be employed, depending on the desired power properties. We present an iterative shortcut for the closed testing procedure, based on the branch and bound algorithm, which converges to the full closed testing results, often after few iterations; even if it is stopped early, it controls the TDP. We compare the properties of different choices for the sum test through simulations, then we illustrate the feasibility of the method for high dimensional data on brain imaging and genomics data.
Nonexchangeable random partition models for microclustering

Many popular random partition models, such as the Chinese restaurant process and its two-parameter extension, fall in the class of exchangeable random partitions, and have found wide applicability in various fields. While the exchangeability assumption is sensible in many cases, it implies that the size of the clusters necessarily grows linearly with the sample size, and such feature may be undesirable for some applications. We present here a flexible class of nonexchangeable random partition models, which are able to generate partitions whose cluster sizes grow sublinearly with the sample size, and where the growth rate is controlled by one parameter. Along with this result, we provide the asymptotic behaviour of the number of clusters of a given size, and show that the model can exhibit a power-law behaviour, controlled by another parameter. The construction is based on completely random measures and a Poisson embedding of the random partition, and inference is performed using a Sequential Monte Carlo algorithm. Experiments on real data sets emphasise the usefulness of the approach compared to a two-parameter Chinese restaurant process.