Reviewerly/exHarmony huggingface dataset:
MagicHand: Interact with IoT Devices in Augmented Reality Environment

We present an Augmented Reality (AR) visualization and interaction tool for users to control Internet of Things (IoT) devices with hand gestures. Today, smart IoT devices are becoming increasingly ubiquitous with diverse forms and functions, yet most user controls over them are still limited to mobile devices and web interfaces. Recently, AR has been developed rapidly, and provided immersive solutions to enhance user experience of applications in many fields. Its capability to create immersive interactions allows AR to improve the way smart devices are controlled via more direct visual feedback. In this paper, we create a functional prototype of one such system, enabling seamless interactions with sound and lighting systems through the use of augmented hand-controlled interaction panels. To interpret users' intentions, we implement a standard 2D convolution neural network (CNN) for real-time hand gesture recognition and deploy it within our system. Our prototype is also equipped with a simple but effective object detector which can identify target devices within a proper range by analyzing geometric features. We evaluate the performance of our system qualitatively and quantitatively and demonstrate it on two smart devices.
Accessibility and emotionality of online assessment feedback: Using emoticons to enhance student perceptions of marker competence and warmth

Assessment feedback is one of the most powerful learning tools, and in higher education this feedback is increasingly being provided online. The current study investigated the inclusion of emoticons as a method through which to enhance student perceptions of the accessibility and emotionality of written online assessment feedback. Undergraduate students (N = 241) were presented with an online faux essay along with associated written feedback typical of the comments students would receive in the higher education context. The feedback was identical except for the inclusion of emoticons. Using a between-groups design, emoticons were manipulated in two ways: frequency (none, 1, 3, or 6) and valence (happy, sad, or confused). The use of happy emoticons produced significantly higher perceptions of marker warmth when compared to no emoticons, or when negatively valenced emoticons were included. Furthermore, marker competence was significantly higher when 3 happy face emoticons were presented in the feedback than when 3 sad or confused faces were included. Student perceptions of feedback quality and marker professionalism were not affected by emoticon use. Thus, the results suggest that instructors can use positively valenced emoticons to inject some fun, warmth, and emotionality in written online assessment feedback without sacrificing feedback quality or professional integrity.
Classifying non-functional requirements using RNN variants for quality software development

Non-Functional Requirements (NFR), a set of quality attributes, required for software architectural design. Which are usually scattered in SRS and must be extracted for quality software development to meet user expectations. Researchers show that functional and non-functional requirements are mixed together within the same SRS, which requires a mammoth effort for distinguishing them. Automatic NFR classification would be a feasible way to characterize those requirements, where several techniques have been recommended e.g. IR, linguistic knowledge, etc. However, conventional supervised machine learning methods suffered for word representation problem and usually required hand-crafted features, which will be overcome by proposed research using RNN variants to categories NFR. The NFR are interrelated and one task happens after another, which is the ideal situation for RNN. In this approach, requirements are processed to eliminate unnecessary contents, which are used to extract features using word2vec to fed as input of RNN variants LSTM and GRU. Performance has been evaluated using PROMISE dataset considering several statistical analyses. Among those models, precision, recall, and f1-score of LSTM validation are 0.973, 0.967 and 0.966 respectively, which is higher over CNN and GRU models. LSTM also correctly classified minimum 60% and maximum 80% unseen requirements. In addition, classification accuracy of LSTM is 6.1% better than GRU, which concluded that RNN variants can lead to better classification results, and LSTM is more suitable for NFR classification from textual requirements.
Generation of Synchronized Configuration Space Trajectories of Multi-Robot Systems

We pose the problem of path-constrained trajectory generation for the synchronous motion of multi-robot systems as a non-linear optimization problem. Our method determines appropriate parametric representation for the configuration variables, generates an approximate solution as a starting point for the optimization method, and uses successive refinement techniques to solve the problem in a computationally efficient manner. We have demonstrated the effectiveness of the proposed method on challenging simulation and physical experiments with high degrees of freedom robotic systems.
Semiparametrical Gaussian Processes Learning of Forward Dynamical Models for Navigating in a Circular Maze

This paper presents a problem of model learning for the purpose of learning how to navigate a ball to a goal state in a circular maze environment with two degrees of freedom. The motion of the ball in the maze environment is influenced by several non-linear effects such as dry friction and contacts, which are difficult to model physically. We propose a semiparametric model to estimate the motion dynamics of the ball based on Gaussian Process Regression equipped with basis functions obtained from physics first principles. The accuracy of this semiparametric model is shown not only in estimation but also in prediction at n-steps ahead and its compared with standard algorithms for model learning. The learned model is then used in a trajectory optimization algorithm to compute ball trajectories. We propose the system presented in the paper as a benchmark problem for reinforcement and robot learning, for its interesting and challenging dynamics and its relative ease of reproducibility.
Optimized Block-Based Algorithms to Label Connected Components on GPUs

Connected Components Labeling (CCL) is a crucial step of several image processing and computer vision pipelines. Many efficient sequential strategies exist, among which one of the most effective is the use of a block-based mask to drastically cut the number of memory accesses. In the last decade, aided by the fast development of Graphics Processing Units (GPUs), a lot of data parallel CCL algorithms have been proposed along with sequential ones. Applications that entirely run in GPU can benefit from parallel implementations of CCL that allow to avoid expensive memory transfers between host and device. In this paper, two new eight-connectivity CCL algorithms are proposed, namely Block-based Union Find (BUF) and Block-based Komura Equivalence (BKE). These algorithms optimize existing GPU solutions introducing a block-based approach. Extensions for three-dimensional datasets are also discussed. In order to produce a fair comparison with previously proposed alternatives, YACCLAB, a public CCL benchmarking framework, has been extended and made suitable for evaluating also GPU algorithms. Moreover, three-dimensional datasets have been added to its collection. Experimental results on real cases and synthetically generated datasets demonstrate the superiority of the new proposals with respect to state-of-the-art, both on 2D and 3D scenarios.
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments

Autonomous navigation through unknown environments is a challenging task that entails real-time localization, perception, planning, and control. UAVs with this capability have begun to emerge in the literature with advances in lightweight sensing and computing. Although the planning methodologies vary from platform to platform, many algorithms adopt a hierarchical planning architecture where a slow, low-fidelity global planner guides a fast, high-fidelity local planner. However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan. This work proposes a planning framework in which multi-fidelity models are used to reduce the discrepancy between the local and global planner. Our approach uses high-, medium-, and low-fidelity models to compose a path that captures higher-order dynamics while remaining computationally tractable. In addition, we address the interaction between a fast planner and a slower mapper by considering the sensor data not yet fused into the map during the collision check. This novel mapping and planning framework for agile flights is validated in simulation and hardware experiments, showing replanning times of 5-40 ms in cluttered environments.
Collaborative Filtering Based on Gaussian Mixture Model and Improved Jaccard Similarity

The recommender systems play an important role in our lives, since it can quickly help users find what they are interested in.Collaborative filtering has become one of the most widely used algorithms in recommender systems due to its simplicity and efficiency.However, when the user's rating data is sparse, the accuracy of the collaborative filtering algorithm for predictive rating is badly reduced.In addition, the similarity calculation method is another important factor that affects the accuracy of the collaborative filtering algorithm recommendation.Faced with these problems, we propose a new collaborative filtering algorithm which based on Gaussian mixture model and improved Jaccard similarity.The proposed model uses Gaussian mixture model to cluster users and items respectively and extracts new features to build a new interaction matrix, which effectively solves the impact of rating data sparsity on collaborative filtering algorithms.Meanwhile, a new similarity calculation method is proposed, which is combined by triangle similarity and Jaccard similarity.Compare our proposed model with four models based on collaborative filtering algorithms on three public datasets.The experimental results show that the proposed model not only mitigates the sparseness of the data, but also improves the accuracy of the rating prediction.
Robust Standards in Cognitive Science

Recent discussions within the mathematical psychology community have focused on how Open Science practices may apply to cognitive modelling. Lee et al. (2019) sketched an initial approach for adapting Open Science practices that have been developed for experimental psychology research to the unique needs of cognitive modelling. While we welcome the general proposal of Lee et al. (2019), we believe a more fine-grained view is necessary to accommodate the adoption of Open Science practices in the diverse areas of cognitive modelling. Firstly, we suggest a categorization for the diverse types of cognitive modelling, which we argue will allow researchers to more clearly adapt Open Science practices to different types of cognitive modelling. Secondly, we consider the feasibility and usefulness of preregistration and lab notebooks for each of these categories and address potential objections to preregistration in cognitive modelling. Finally, we separate several cognitive modelling concepts that we believe Lee et al. (2019) conflated, which should allow for greater consistency and transparency in the modelling process. At a general level, we propose a framework that emphasizes local consistency in approaches while allowing for global diversity in modelling practices.
500,000 Images Closer to Eyelid and Pupil Segmentation

Human gaze behavior is not the only important aspect about eye tracking. The eyelids reveal additional important information; such as fatigue as well as the pupil size holds indications of the workload. The current state-of-the-art datasets focus on challenges in pupil center detection, whereas other aspects, such as the lid closure and pupil size, are neglected. Therefore, we propose a fully convolutional neural network for pupil and eyelid segmentation as well as eyelid landmark and pupil ellipsis regression. The network is jointly trained using the Log loss for segmentation and L1 loss for landmark and ellipsis regression. The application of the proposed network is the offline processing and creation of datasets. Which can be used to train resource-saving and real-time machine learning algorithms such as random forests. In addition, we will provide the worlds largest eye images dataset with more than 500,000 images DOWNLOAD .
The Governance of Blockchain Dispute Resolution

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Learning With Unsure Data for Medical Image Diagnosis

In image-based disease prediction, it can be hard to give certain cases a deterministic “disease/normal” label due to lack of enough information, e.g., at its early stage. We call such cases “unsure” data. Labeling such data as unsure suggests follow-up examinations so as to avoid irreversible medical accident/loss in contrast to incautious prediction. This is a common practice in clinical diagnosis, however, mostly neglected by existing methods. Learning with unsure data also inter-weaves with two other practical issues: (i) data imbalance issue that may incur model-bias towards the majority class, and (ii) conservative/aggressive strategy consideration, i.e., the negative (normal) samples and positive (disease) samples should NOT be treated equally - the former should be detected with a high precision (conservativeness) and the latter should be detected with a high recall (aggression) to avoid missing opportunity for treatment. Mixed with these issues, learning with unsure data becomes particularly challenging. In this paper, we raise “learning with unsure data” problem and formulate it as an ordinal regression and propose a unified end-to-end learning framework, which also considers the aforementioned two issues: (i) incorporate cost-sensitive parameters to alleviate the data imbalance problem, and (ii) execute the conservative and aggressive strategies by introducing two parameters in the training procedure. The benefits of learning with unsure data and validity of our models are demonstrated on the prediction of Alzheimer's Disease and lung nodules.
Mini-Batch Normalized Mutual Information: A Hybrid Feature Selection Method

Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to increase the efficacy of the predictor by finding the optimal set of features. The feature subset should be such that it enhances the classification accuracy by the removal of redundant features. We propose a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods. Our hybrid model is based on a two phase process where we rank the features and then choose the best subset of features based on the ranking. We validated our model with various datasets, using multiple evaluation metrics. Furthermore, we have also compared and analyzed our results with previous works. The proposed model outperformed many existent algorithms and has given us good results.
Software architecture design in global software development: An empirical study

In Global Software Development (GSD), the additional complexity caused by global distance requires processes to ease collaboration difficulties, reduce communication overhead, and improve control. How development tasks are broken down, shared and prioritized is key to project success. While the related literature provides some support for architects involved in GSD, guidelines are far from complete. This paper presents a GSD Architectural Practice Framework reflecting the views of software architects, all of whom are working in a distributed setting. In-depth interviews with architects from seven different GSD organizations revealed a complex set of challenges and practices. We found that designing software for distributed teams requires careful selection of practices that support understanding and adherence to defined architectural plans across sites. Teams used Scrum which aided communication, and Continuous Integration which helped solve synchronization issues. However, teams deviated from the design, causing conflicts. Furthermore, there needs to be a balance between the self-organizing Scrum team methodology and the need to impose architectural design decisions across distributed sites. The research presented provides an enhanced understanding of architectural practices in GSD companies. Our GSD Architectural Practice Framework gives practitioners a cohesive set of warnings, which for the most part, are matched by recommendations.
Development of an Eye Tracking-Based Human-Computer Interface for Real-Time Applications

In this paper, the development of an eye-tracking-based human-computer interface for real-time applications is presented. To identify the most appropriate pupil detection algorithm for the proposed interface, we analyzed the performance of eight algorithms, six of which we developed based on the most representative pupil center detection techniques. The accuracy of each algorithm was evaluated for different eye images from four representative databases and for video eye images using a new testing protocol for a scene image. For all video recordings, we determined the detection rate within a circular target 50-pixel area placed in different positions in the scene image, cursor controllability and stability on the user screen, and running time. The experimental results for a set of 30 subjects show a detection rate over 84% at 50 pixels for all proposed algorithms, and the best result (91.39%) was obtained with the circular Hough transform approach. Finally, this algorithm was implemented in the proposed interface to develop an eye typing application based on a virtual keyboard. The mean typing speed of the subjects who tested the system was higher than 20 characters per minute.
Multimodal Age and Gender Classification Using Ear and Profile Face Images

In this paper, we present multimodal deep neural network frameworks for age and gender classification, which take input a profile face image as well as an ear image. Our main objective is to enhance the accuracy of soft biometric trait extraction from profile face images by additionally utilizing a promising biometric modality: ear appearance. For this purpose, we provided end-to-end multimodal deep learning frameworks. We explored different multimodal strategies by employing data, feature, and score level fusion. To increase representation and discrimination capability of the deep neural networks, we benefited from domain adaptation and employed center loss besides softmax loss. We conducted extensive experiments on the UND-F, UND-J2, and FERET datasets. Experimental results indicated that profile face images contain a rich source of information for age and gender classification. We found that the presented multimodal system achieves very high age and gender classification accuracies. Moreover, we attained superior results compared to the state-of-the-art profile face image or ear image-based age and gender classification methods.
Supporting Both Range Queries and Frequency Estimation with Local Differential Privacy

Local Differential Privacy (LDP)provides provable privacy protection for data collection without the assumption of the trusted data server. Existing mechanisms that satisfy LDP or its variants either only consider aggregate queries from a group of users (e.g., frequency estimation)or individual queries for a single user (e.g., range queries). However, in complex real-world analytics applications, it is desirable to support both types of queries at the same time. In this paper, we tackle the challenge of privately answering range queries and providing frequency estimation at the same time with high utility. We develop a data perturbation mechanism, which is proved to satisfy local d-privacy (a generalized version of LDP with distance metric)and have optimal utility for the co-location query (a specific type of range query). Then, we utilize an inversion approach for frequency estimation using the perturbed data. We analyze the theoretical Mean Square Error (MSE)of this estimation method and show the relationship to another existing estimation method under LDP. The results on both synthetic and real-world location datasets validate the correctness of our theoretical analysis and show that the proposed mechanism has better utility for both range queries and frequency estimation than the state-of-the-art mechanisms.
Esnet: Edge-Based Segmentation Network for Real-Time Semantic Segmentation in Traffic Scenes

Semantic segmentation is widely used in the industry recently, especially in the field of scene understanding, surveillance and autonomous driving. However, majority of current state-of-the-art algorithms run accompany with high consumption of computation resources. Thus, our work focuses on real-time semantic segmentation which could reduce a large proportion of computation. Traditional methods to speed up segmentation process tend to down sample image. However, down sampling would cause the loss of information. Hence, we propose a real-time edge-based segmentation network (ESNet) that incorporate high-resolution global edge information with low-resolution classification-level semantic information. Our network performs real-time inference on single GPU card on high-resolution Cityscapes dataset.
Ship Detection Based on Deep Learning

China has abundant marine resources and a vast sea area. In military and civilian applications, the detection of targets for marine ships has important research significance. Complex and variable sea conditions make ship detection more difficult. In order to more accurately detect the ship's target, this paper proposes an improved YoloV3 algorithm to realize the end-to-end ship target detection system. Our algorithm achieves high-accuracy fast ship identification by introducing CFE modules, improving loss functions, and augmenting data for small targets. The experimental results show that compared with the traditional machine learning target detection algorithm, our method has greatly improved the detection accuracy and detection rate. It achieves an accuracy of 74.8% and a detection rate of 29.8 frames. The ship detection system of this paper is excellent in detection accuracy and speed.
Joint-task self-supervised learning for temporal correspondence

This paper proposes to learn reliable dense correspondence from videos in a self-supervised manner. Our learning process integrates two highly related tasks: tracking large image regions and establishing fine-grained pixel-level associations between consecutive video frames. We exploit the synergy between both tasks through a shared inter-frame affinity matrix, which simultaneously models transitions between video frames at both the region- and pixel-levels. While region-level localization helps reduce ambiguities in fine-grained matching by narrowing down search regions; fine-grained matching provides bottom-up features to facilitate region-level localization. Our method outperforms the state-of-the-art self-supervised methods on a variety of visual correspondence tasks, including video-object and part-segmentation propagation, keypoint tracking, and object tracking. Our self-supervised method even surpasses the fully-supervised affinity feature representation obtained from a ResNet-18 pre-trained on the ImageNet.