Reviewerly/exHarmony huggingface dataset:
A computational phase-shifting interferometric method using a single-shot microinterferogram and its application

In this article, a fast computational phase-shifting interferometry (PSI) method for extracting the phase object from a single-shot two-beam microinterferogram has been proposed. This technique bases on utilizing the features of the two-beam microinterferometry to yield three daughter microinterferograms with accurately known phase shift between them from a recorded mother microinterferogram. The main idea of the proposed method relies on analyzing the recorded mother microinterferogram to compute the width of its bright and dark fringes which resulted equal in this study. Furthermore, we utilize the fact that the complete change from bright fringe to dark fringe or vice versa, in the two-beam microinterferogram, is identical to shift in phase by π radians. The proposed method is characterized by high ability to perform in-situ investigation for the objects on the contrary with the traditional PSI technique. The phase-shifting Pluta interference microscope is used for verifying this computational method for the polypropylene (PP) fiber. The calculated phase distribution values via this method show a good agreement with the obtained results by the phase-shifting Pluta interference microscope for the same fiber sample. Also, the proposed method is utilized to perform accurate in-situ investigation for the structural properties of the polypropylene fiber at different draw ratios. In addition, the proposed method is valid to investigate the fractures modes of fibers in short time comparing to the spatial carrier frequency algorithm and the spatial carrier-frequency phase-shifting (SCFPS) algorithm.
Mitigating Email Phishing Attacks using Convolutional Neural Networks

Phishing detection has gained huge attention from both academia and industry. Damages and data breaches affecting private and governmental entities caused by email phishing attacks needed an immediate solution. The diversity of attack patterns and mediums associated with phishing attacks made the development of an optimal solution challenging. Also, attackers usually make legitimate looking content using legitimate wording or legitimate looking URLs and websites. Many of the existing phishing solutions requires manual feature extraction that requires expert domain knowledge and thoughtful selection of valuable features to be efficient. Additionally, most effective phishing solutions suffered from large computational costs. In this paper, we propose CNNPD, an email phishing detection framework based on Convolutional Neural Network (CNN). CNNPD marks incoming emails into phishing or benign. Testing the framework on an email dataset shows promising performance in terms of accuracy, precision, and recall when compared to similar approaches.
Development of a solar radiation model considering the hourly sunshine duration for all-sky conditions – A case study for Beijing, China

The hourly values of solar radiation are more useful than daily values for some specific applications such as building thermal simulation, and photovoltaic system; however, the existing solar models for estimating hourly values are much less than those for estimating daily ones. Furthermore, most of the hourly solar models have been developed for clear-sky conditions only, not for all-sky conditions. In this study, two hourly solar models for all-sky conditions (Nimiya model and Zhang model) are tested and modified for Beijing, China. The results show that the modified Nimiya model using the three-hour temperature difference instead of the daily mean one has good performance with correlation coefficient of 0.948 and RMSE of 92.08 W/m2. With consideration of hourly sunshine duration which is rarely utilized in the current solar models, a new solar model is developed for all-sky conditions based on regression analysis. The RMSE of the new proposed model is 84.59 W/m2, which is reduced by 33.88% compared with the Zhang model for Beijing. The hourly sunshine duration is an effective parameter for hourly solar models. More locations are needed for testing the proposed model in the future study.
Votenet +: An Improved Deep Learning Label Fusion Method for Multi-Atlas Segmentation

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differ from the label of the target image. Furthermore, we show that JLF is more suitable for the VoteNet framework as a label fusion method than plurality voting. Lastly, we use Platt scaling to calibrate the probabilities of our new model. Results on LPBA40 3D MR brain images show that our proposed method can achieve better performance than VoteNet.
Heterogeneous data release for cluster analysis with differential privacy

Many models have been proposed to preserve data privacy for different data publishing scenarios. Among these models, ϵ-differential privacy has drawn increasing attention in recent years due to its rigorous privacy guarantees. While many existing solutions using ϵ-differential privacy deal with relational data and set-valued data separately, most of the real-life data, such as electronic health records, are in heterogeneous form. Privacy protection on heterogeneous data has not been widely studied. Furthermore, many existing works in privacy protection consider preserving the utility for the tasks of frequent itemset mining or classification analysis, but few works have focused on data publication for cluster analysis. In this paper, we propose the first differentially-private solution to release heterogeneous data for cluster analysis. The challenge facing us is how to mask raw data without any explicit guidance. Our approach addresses this challenge by converting a clustering problem to a classification problem, in which class labels can be used to encode the cluster structure of the raw data and assist the masking process. The approach generalizes the raw data probabilistically and adds noise to them for satisfying ϵ-differential privacy. Through extensive experiments on real-life datasets, we validate the performance of our approach.
OAC: Overlapping Office Activity Classification through IoT-Sensed Structural Vibration

Recognizing human activities in an office has great potential for productivity tracking and health monitoring applications. To do this, various sensing methods have been explored, including vision-based, RF-based, wearables, acoustic-based and sensor fusion. These methods have limitations such as limited range, installation requirements (such as line-of-sight, dense deployment, or wearing on the body), and privacy concerns (e.g. video recording). We present OAC, a room-level Internet of Things overlapping activity recognition system using ambient structural vibration. Our algorithm uses observation-based heuristics in the feature extraction process and classifies multiple activities simultaneously using multi-stage supervised learning. We divide activities into categories based on their overlap potential, allowing us to distinguish simultaneous activities. We evaluate on eight subjects in two locations, showing up to 97% classification accuracy for our first activity category, up to 90% accuracy for our second category of activities, and up to 90% accuracy for our combined overlapping activity combinations.
The Human Muscular Arm Avatar as an Interactive Visualization Tool in Learning Anatomy: Medical Students’ Perspectives

The perception of body ownership creates a sense of embodiment, which can be a powerful learning tool. Embodied learning can occur by watching an individual's body movement and also via human-computer interactions, such as virtual reality (VR) and augmented reality (AR). In this article, we designed and implemented a novel virtual body-ownership AR/VR tool for human anatomy-the human muscular arm avatar (HMAA). HMAA utilizes embodiment-based body ownership to explore the human hand/forearm musculature. The HMAA was trialed with medical students to explore the extent to which it could be used to aid student learning. The key findings of the usability study suggest that 98% (N = 100) of students found the tool extremely useful; 83% reported that the tool allowed them to engage with the learning materials, peers, and content effectively. Also, 10% of students mentioned that the HMAA fostered an embodied learning experience. This triggered an intentional exploration of instances suggesting embodiment in the data. HMAA is believed to have allowed individuals to visualize and conceptualize abstract ideas that would have been otherwise challenging using static models. The outcomes of this article indicate the significant potential of body-ownership-based self-learning tools for anatomy. However, further studies using learning outcomes are needed to investigate the potential advantages of body-ownership-based tools compared to current learning techniques.
Time Awareness in Deep Learning-Based Multimodal Fusion Across Smartphone Platforms

Many modern IoT applications integrate smartphones in their deployments to capture and interface with various sensors. Fusing across sensing modalities can often result in improved application performance. An essential yet often overlooked requirement in multimodal fusion is the synchronization across input data streams. Given the universality of the smartphone system clock, it is commonly used to timestamp sensing data. Through a systematic study of system clock accuracy on modern smartphones, we show that there are drastic timing errors ranging from milliseconds to multiple seconds. Thus, when fusing data derived from multiple smartphones, input data streams may have significant synchronization errors. Although deep learning classifiers for multimodal fusion can achieve state-of-the-art accuracy, the impact of timing errors has yet to be characterized. In this paper, we quantify the impact of timing errors on a multimodal fusion classifier for human activity recognition. Our results indicate that data sync errors of 600ms can degrade classifier accuracy by 3%, and the clock errors such as those observed in Android can result in accuracy drops of up to 25%. To mitigate these concerns, we suggest an application-level system clock replacement and introduce a novel data augmentation technique that improves the evaluated classifier resilience to timing errors.
Private and Hot-Pluggable Distributed Averaging

Some distributed optimization applications require privacy, meaning that the values of certain parameters local to a node should not be revealed to other nodes in the network during the joint optimization process. A special case is the problem of private distributed averaging, in which a network of nodes computes the global average of individual node reference values in a distributed manner while preserving the privacy of each reference. We present simple iterative methods that guarantee accuracy (i.e., the exact asymptotic computation of the global average) and privacy (i.e., no node can estimate another node's reference value). To achieve this, we require that the digraph modeling the communication between nodes satisfy certain topological conditions. Our method is hot-pluggable (meaning no reinitialization of the averaging process is required when the network changes or a node enters or leaves, when there is a communication or computation fault, or when a node's reference value changes); it does not require an initial scrambling phase; it does not inject noise or other masking signals into the distributed computation; it does not require random switching of edge weights; and it does not rely on homomorphic encryption.
Window frame obstacle edge detection based on improved Canny operator

For the edge detection problem of wall-mounted cleaning robots for window frame obstacles, based on the original canny operator, this paper proposes an algorithm based on improved canny obstacle edge detection. In order to suppress noise and extract image edge details, the improved canny operator increases the gradient template of 45° and 135°; for the traditional canny operator to set the limits of high and low thresholds, use the iterative method to find the best high and low thresholds. . Finally, through the MATLAB simulation, compared with the traditional canny edge detection algorithm, the improved canny operator has achieved good results in the edge detection of window frames.
Autonomy and metrics of autonomy

The quest for autonomy has been a pervasive theme in human culture through-out history. In this paper a general definition of autonomous systems is presented and discussed that leads naturally to the establishment of metrics to measure the level of autonomy of a system. This definition is based on the systems ability to achieve goals under uncertainties and it does not involve the means by which the goals are achieved, such as sensing and feedback. This paper takes the point of view that any autonomous system is a control system, and that to achieve higher levels of autonomy one may need to add methods traditionally developed in areas such as operations research and AI. The work presented here is based on earlier work by the author on functional architectures for autonomous spacecrafts.
Integrated data and knowledge driven methodology for human activity recognition

Human activity recognition has been a popular research area concerned with identifying the specific movement or action of a person based on variety of sensor data. Conventional human activity recognition approaches are mainly data driven, which are not working well for composite activity recognition due to the complexity and uncertainty of real scenarios. We propose in this paper a hierarchical structure-based framework and methodology for human activity recognition by an integration of data-driven approach and knowledge-based approach, which provides an interesting framework capable of bridging lower-level pattern recognition and higher-level knowledge for reasoning and explanation. More specifically, this approach constructs a hierarchical structure for representing the composite activity by a composition of lower-level actions and gestures according to its semantic meaning. This hierarchical structure is then transformed into formal syntactical logical formulas and rules, based on which the resolution based automated reasoning is applied to recognize the composite activity given the recognized lower-level actions by using data driven machine learning methods. The work is the validated using some open-source data about video based human activity recognition. The present work provides a promising framework and application illustration of integration of machine learning and symbolic reasoning.
Training Liver Vessel Segmentation Deep Neural Networks on Noisy Labels from Contrast CT Imaging

Liver vessel segmentation from contrast CT images is critical in liver surgical planning and navigation. Due to the complex vessel systems, manual segmentation of liver vessel is laborious and error-prone. In this work we propose liver vessel segmentation deep neural networks that are able to perform the segmentation automatically. Dense image labeling incurred by normal deep learning based segmentation makes it impractical for vessel segmentation. On the contrary, our method only requires a few of initial voxel-level labels, and thus tremendously reduces the workload of manual annotating. A small number of liver vessels are firstly annotated and used to train a sparse dictionary and logistic regressor to get the preliminary vessel predictions. Then the proposed deep neural network is trained based on bootstrapping technique where the training target is a convex combination of the model predictions and those preliminary vessel predictions. Followed by the post-processing step consisting of region connection and noisy removal, the liver vessel tree is finally built from model predictions. Experimental results of 3DIRCADb dataset show that our method can effectively extract liver vessel trees from contrast CT images with minimum annotations. The state-of-art results have been achieved on this small dataset with the average dice and sensitivity 0.687 ± 0.041 and 0.786 ± 0.105 respectively.
English text quality analysis based on recurrent neural network and semantic segmentation

In recent years, deep learning algorithm based on cyclic neural network and semantic segmentation has performed well in the field of image segmentation. The purpose of this paper is to realize the quality analysis of English text through recurrent neural network and semantic segmentation. This paper proposes an attention based English text quality analysis model based on recurrent neural network. Through the introduction of attention mechanism, the influence of semantics in the text is considered in the analysis of English text quality. The target relies on the quality of English text to determine the text quality of the sentence for a given target object. At present, most English text quality analysis methods are aimed at the traditional semantic analysis tasks. Based on rnn-attention model, a rnn-attention-t model is proposed, which introduces the information of the target object while modeling the text. In addition, considering that the influence of the top and bottom of the target object on the semantic trend is usually different, this paper proposes an rnn-attention-c model, which models the top and bottom of the target object respectively. The experimental data have shown that the quality analysis of English text based on recurrent neural network and semantic segmentation is faster than the traditional method. The experimental results have demonstrated that our method can effectively and quickly confirm the quality of English text, which is about 7% faster than the conventional method.
Scenario Co-Creation Cards

Values are an integral part of human identity and have a pervasive impact upon human behavior. This makes understanding them a central concern in the design of technology, as exemplified by approaches such as Value Sensitive Design ("VSD"). Identifying and concreting the values held by a given population can be a difficult endeavor, especially where there is a cultural barrier limiting an effective discussion of them, for example in societies where freedom of expression is discouraged. Addressing this concern requires an in-depth consideration of appropriate value elicitation methods, which responds to the fact that it is not possible to understand values detached from their cultural context. We introduce a novel implicit method, Scenario Co-Creation Cards, and show how it can be used to incorporate existing models of culture in the value elicitation process. We demonstrate this in a case study of Saudi women's visibility in the digital media.
Comparison of the Evaluation Metrics for Neural Grammatical Error Correction With Overcorrection

Grammar error correction (GEC) refers to the proper correction of grammatical errors in a given sentence.Important factors to consider in GEC are not only the grammatical correction of the sentence, but also the recognition of a correct sentence in which no changes are required.However, GEC approaches in which deep learning recently started being used consider only the former aspect, which leads to overcorrection, whereby changes are made to a correct sentence unnecessarily.Because this bias is also reflected in performance metrics, conventional performance metrics consider only part of the important factors in GEC.This study proposes a new metric to consider both important aspects in GEC and to provide a new viewpoint for the GEC task.To the best of the authors knowledge, this study is the first to deal with comprehensively considering the correction performance and overcorrection problem in GEC.The experimental results demonstrate that the model performance ranking was reversed when evaluating the performance with the proposed metric compared to the General Language Understanding Evaluation benchmark [21], which only considers the correction performance.This indicates that the high performance of the correction does not result in less problems with the overcorrection and that the overcorrection problem should also be considered when evaluating the model performance.Moreover, we found that the copy mechanism [14] helps to alleviate the problem of overcorrection.
A new unified method for detecting text from marathon runners and sports players in video (PR-D-19-01078R2)

Detecting text located on the torsos of marathon runners and sports players in video is a challenging issue due to poor quality and adverse effects caused by flexible/colorful clothing, and different structures of human bodies or actions. This paper presents a new unified method for tackling the above challenges. The proposed method fuses gradient magnitude and direction coherence of text pixels in a new way for detecting candidate regions. Candidate regions are used for determining the number of temporal frame clusters obtained by K-means clustering on frame differences. This process in turn detects key frames. The proposed method explores Bayesian probability for skin portions using color values at both pixel and component levels of temporal frames, which provides fused images with skin components. Based on skin information, the proposed method then detects faces and torsos by finding structural and spatial coherences between them. We further propose adaptive pixels linking a deep learning model for text detection from torso regions. The proposed method is tested on our own dataset collected from marathon/sports video and three standard datasets, namely, RBNR, MMM and R-ID of marathon images, to evaluate the performance. In addition, the proposed method is also tested on the standard natural scene datasets, namely, CTW1500 and MS-COCO text datasets, to show the objectiveness of the proposed method. A comparative study with the state-of-the-art methods on bib number/text detection of different datasets shows that the proposed method outperforms the existing methods.