Reviewerly/exHarmony huggingface dataset:
Combining Yolov3-tiny Model with Dropblock for Tiny-face Detection

Face detection is one of the most basic tasks in many various face applications, which is gradually becoming the most acceptable biometric recognition method. However, tinyface detection is a complex and challenging pattern detection problem that encounters many difficulties in the application process. This paper uses the popular deep learning detection algorithm to complete tiny-face detection and combine the Yolov3-tiny model with Dropblock strategy. Dropblock is a regularization method used in the convolutional layer, in which the activation units are spatially interrelated. Neurons in the contiguous region of the feature map would be dropped by dropblock, which forces the neural network to learn novel features. Numerous experiments showed that Yolov3-tiny works better by combining with dropblock in tiny-face detection. We evaluate the performance of our proposed method on the two public datasets and increase 7.74% accuracy, compared with yolov3-tiny based model in the tinyface detection.
Generalized Shape Expansion-Based Motion Planning for UAVs in Three Dimensional Obstacle-cluttered Environment

The problem of motion planning for an unmanned aerial vehicle (UAV) in an environment cluttered with stationary obstacles has found growing attention. To this end, several algorithms, both deterministic and sampling-based, have been reported in the literature. Recently, a sampling-based motion planning algorithm, named 'GSE', has been presented for 2-D environment based on a novel sampling strategy - generalized shape expansion. Its effectiveness was found to be equivalent or even better in most aspects with respect to some existing seminal algorithms. Since for UAVs, 3-D environment is of more significance, this paper expands over the GSE algorithm and presents '3D-GSE' algorithm for generating shortest path and locally optimal trajectory from within the updated homotopy class using sequential convex programming in stationary obstacle-dense 3-D environments. The shape constructed by the 3D-GSE algorithm covers the available free space maximally, which is much more challenging than in case of 2-D environment. Thus, 3D-GSE exhibits its superiority in performance in terms of computational time efficiency when compared with well-established existing algorithms in extensive numerical simulation study. The trajectory costs obtained by the 3D-GSE algorithm is also found to be marginally better. With all these desired features, the 3D-GSE algorithm has a potential of real-time-implementation.
Technoperformances: using metaphors from the performance arts for a postphenomenology and posthermeneutics of technology use

Abstract Postphenomenology and posthermeneutics as initiated by Ihde have made important contributions to conceptualizing understanding human–technology relations. However, their focus on individual perception, artifacts, and static embodiment has its limitations when it comes to understanding the embodied use of technology as (1) involving bodily movement, (2) social, and (3) taking place within, and configuring, a temporal horizon. To account for these dimensions of experience, action, and existence with technology, this paper proposes to use a conceptual framework based on performance metaphors. Drawing on metaphors from three performance arts—dance, theatre, and music—and giving examples from social media and other technologies, it is shown that we can helpfully describe technology use and experience as performance involving movement, sociality, and temporality. Moreover, it is argued that these metaphors can also be used to reformulate the idea that in such uses and experiences, now understood as “technoperformances”, technology is not merely a tool but also takes on a stronger, often non-intended role: not so much as “mediator” but as choreographer, director, and conductor of what we experience and do. Performance metaphors thus allow us to recast the phenomenology and hermeneutics of technology use as moving, social, and temporal—indeed historical—affair in which technologies take on the role of organizer and structurer of our performances, and in which humans are not necessarily the ones who are fully in control of the meanings, experiences, and actions that emerge from our engagement with the world, with technology, and with each other. This promises to give us a more comprehensive view of what it means to live with technology and how our lives are increasingly organized by technology—especially by smart technologies. Finally, it is argued that this has normative implications for an ethics and politics of technology, now understood as an ethics and politics of technoperformances.
AR Food Changer using Deep Learning And Cross-Modal Effects

We propose an AR application that enables us to change the appearance of food without AR markers by applying machine learning and image processing. Modifying the appearance of real food is a difficult task because the shape of the food is atypical and deforms while eating. Therefore, we developed a real-time object region extraction method that combines two approaches in a complementary manner to extract food regions with high accuracy and stability. These approaches are based on color and edge information processing with a deep learning module trained with a small amount of data. Besides, we implemented some novel methods to improve the accuracy and reliability of the system. Then, we experimented and the results show that the taste and oral texture were affected by visual textures. Our application can change not only the appearance in real-time but also the taste and texture of actual real food. Therefore, in conclusion, our application can be virtually termed as an "AR food changer".
Ensemble cluster pruning via convex‐concave programming

Summary Ensemble learning is the process of aggregating the decisions of different learners/models. Fundamentally, the performance of the ensemble relies on the degree of accuracy in individual learner predictions and the degree of diversity among the learners. The trade‐off between accuracy and diversity within the ensemble needs to be optimized to provide the best grouping of learners as it relates to their performance. In this optimization theory article, we propose a novel ensemble selection algorithm which, focusing specifically on clustering problems, selects the optimal subset of the ensemble that has both accurate and diverse models. Those ensemble selection algorithms work for a given number of the best learners within the subset prior to their selection. The cardinality of a subset of the ensemble changes the prediction accuracy. The proposed algorithm in this study determines both the number of best learners and also the best ones. We compared our prediction results to recent ensemble clustering selection algorithms by the number of cardinalities and best predictions, finding better and approximated results to the optimum solutions.
How Understandable Are Pattern-based Behavioral Constraints for Novice Software Designers?

This article reports a controlled experiment with 116 participants on the understandability of representative graphical and textual pattern-based behavioral constraint representations from the viewpoint of novice software designers. Particularly, graphical and textual behavioral constraint patterns present in the declarative business process language Declare and textual behavioral constraints based on Property Specification Patterns are the subjects of this study. In addition to measuring the understandability construct, this study assesses subjective aspects such as perceived difficulties regarding learning and application of the tested approaches. An interesting finding of this study is the overall low achieved correctness in the experimental tasks, which seems to indicate that pattern-based behavioral constraint representations are hard to understand for novice software designers in the absence of additional supportive measures. The results of the descriptive statistics regarding achieved correctness are slightly in favor of the textual representations, but the inference statistics do not indicate any significant differences in terms of understandability between graphical and textual behavioral constraint representations.
Towards the Detection of UX Smells: The Support of Visualizations

Daily experiences in working with various types of computer systems show that, despite the offered functionalities, users have many difficulties, which affect their overall User eXperience (UX).The UX focus is on aesthetics, emotions and social involvement, but usability has a great influence on UX.Usability evaluation is acknowledged as a fundamental activity of the entire development process in software practices.Research in Human-Computer Interaction has proposed methods and tools to support usability evaluation.However, when performing an evaluation study, novice evaluators still have difficulties to identify usability problems and to understand their causes: they would need easier to use and possibly automated tools.This article describes four visualization techniques whose aim is to support the work of evaluators when performing usability tests to evaluate websites.Specifically, they help detect ''usability smells'', i.e. hints on web pages that might present usability problems, by visualizing the paths followed by the test participants when navigating in a website to perform a test task.A user study with 15 participants compared the four techniques and revealed that the proposed visualizations have the potential to be valuable tools for novice usability evaluators.These first results should push researchers towards the development of further tools that are capable to support the detection of other types of UX smells in the evaluation of computer systems and that can be translated into common industry practices.
Attributes Preserving Face De-Identification

In this paper, we propose a Face de-identification method to remove the identification information of a person while maintaining all the face attributes such as expression, age and gender. Motivated by the k-Same algorithm, our method consists of three steps: first, k face images are selected randomly. These k face images may contain same or different face attributes with the test face image. Secondly, ELEGANT model is employed to transfer attributes from the test face to the k selected faces. After attributes transferring, the k selected faces have the same attributes as the test face. Then we average the k selected faces as the de-identified image of the test face. Experimental results show that our method can de-identify a face image while preserving all of its attributes effectively.
Gesture-based Interface for Real-time Control of a Mitsubishi SCARA Robot Manipulator

A new approach to control robot manipulators using body gestures is presented. It implements the Microsoft Kinect sensor ability to track joint positions. The later has been used in the developed software application for gesture recognition, operating in Matlab programming environment, that maps the gestures into robot control commands. The goal is to control the movements of an industrial manipulator that shares its workspace with humans. Such a working environment is characterized with complexity and dynamism. In this way a more-effective interface with the controlled robot can been created. The experimental tests have been carried out with a Mitsubishi SCARA robot manipulator model RH-6CH7020-1D1-S15.
A Nozzle Path Planner for 3-D Printing Applications

Additive manufacturing technologies have been widely applied in both household and industrial applications. The fabrication time of a three-dimensional (3-D) printed object can be shortened by optimizing its segments printing order. The computational times required by existing nozzle path planning algorithms can increase rapidly with the number of printing segments. In this article, the nozzle path planning problem is formulated as an undirected rural postman problem and a computationally efficient heuristic search algorithm is proposed to find fast routes and mitigate overheads in printing processes. Both simulation and experimental results concur that the proposed algorithm can significantly speed up printing processes and outperform its counterparts in 3-D printing applications.
Improved Machine Reading Comprehension Using Data Validation for Weakly Labeled Data

Machine reading comprehension (MRC) is a natural language processing task wherein a given question is answered according to a holistic understanding of a given context. Recently, many researchers have shown interest in MRC, for which a considerable number of datasets are being released. Datasets for MRC, which are composed of the context-query-answer triple, are designed to answer a given query by referencing and understanding a readily-available, relevant context text. The TriviaQA dataset is a weakly labeled dataset, because it contains irrelevant context that forms no basis for answering the query. The existing syntactic data cleaning method struggles to deal with the contextual noise this irrelevancy creates. Therefore, a semantic data cleaning method using reasoning processes is necessary. To address this, we propose a new MRC model in which the TriviaQA dataset is validated and trained using a high-quality dataset. The data validation method in our MRC model improves the quality of the training dataset, and the answer extraction model learns with the validated training data, because of our validation method. Our proposed method showed a 4.33% improvement in performance for the TriviaQA Wiki, compared to the existing baseline model. Accordingly, our proposed method can address the limitation of irrelevant context in MRC better than the human supervision.
Metall: A Persistent Memory Allocator Enabling Graph Processing

We present Metall, a persistent memory allocator designed to provide developers with an API to allocate custom C++ data structures in both block-storage and byte-addressable persistent memories (e.g., NVMe and Intel Optane DC Persistent Memory). Metall incorporates state-of-the-art allocation algorithms in Supermalloc with the rich C++ interface developed by Boost.Interprocess, and provides persistent memory snapshoting (versioning) capabilities. We demonstrate Metall processing large graphs in a variety of conditions and data-structure configurations, indicating a bright future for data-analytics leveraging emerging persistent memory technologies.
Real-Time Traffic Status Detection from on-Line Images Using Generic Object Detection System with Deep Learning

In order to leverage the traffic burden and reduce traffic time, the Macao Government provides instant on-line traffic information for road users. In this paper, we propose a unified low-cost real-time traffic status detection system using free on-line images. Since the quality for vehicle detection from these on-line images can vary very differently, depending on the locations of cameras and weather conditions, we propose to use a generic object detection system with deep learning, e.g. YOLOv3, to detect real-time traffic status from these images. In order to determine the traffic status using the predicted bounding boxes and the number of detected vehicles output from the YOLOv3 detector, we propose to use the intersection over union (IOU) of the union of the bounding boxes for multiple objects predicted on images taken at different times, termed mIOU, and the corresponding estimated number of vehicles to estimate the multi-level traffic status. From the experimental results, it was found that the precision and recall rate can achieve 86% and 87%, respectively, depending on the locations of the cameras. It was also shown that the proposed system using YOLOv3 and mIOU can be used for traffic status estimation for low-resolution images, without requiring any pre-training of networks for different settings. The proposed traffic detection technique is applicable for a wide range of application scenarios.
A Robust Multi-Sensor Data Fusion Clustering Algorithm Based on Density Peaks

In this paper, a novel multi-sensor clustering algorithm, based on the density peaks clustering (DPC) algorithm, is proposed to address the multi-sensor data fusion (MSDF) problem. The MSDF problem is raised in the multi-sensor target detection (MSTD) context and corresponds to clustering observations of multiple sensors, without prior information on clutter. During the clustering process, the data points from the same sensor cannot be grouped into the same cluster, which is called the cannot link (CL) constraint; the size of each cluster should be within a certain range; and overlapping clusters (if any) must be divided into multiple clusters to satisfy the CL constraint. The simulation results confirm the validity and reliability of the proposed algorithm.
Basis Path Coverage Criteria for Smart Contract Application Testing

The widespread recognition of the smart contracts has established their importance in the landscape of next generation blockchain technology. However, writing a correct smart contract is notoriously difficult. Moreover, once a state-changing transaction is confirmed by the network, the result is immutable. For this reason, it is crucial to perform a thorough testing of a smart contract application before its deployment. This paper's focus is on the test coverage criteria for smart contracts, which are objective rules that measure test quality. We analyze the unique characteristics of the Ethereum smart contract program model as compared to the conventional program model. To capture essential control flow behaviors of smart contracts, we propose the notions of whole transaction basis path set and bounded transaction interaction. The former is a limited set of linearly independent inter-procedural paths from which the potentially infinite paths of Ethereum transactions can be constructed by linear combination, while the latter is the permutations of transactions within a certain bound. Based on these two notions, we define a family of path-based test coverage criteria. Algorithms are given to the generation of coverage requirements. A case study is conducted to compare the effectiveness of the proposed test coverage criteria with random testing and statement coverage testing.
VR/MR Supporting the Future of Defensive Cyber Operations

US Army C5ISR Center Cyber Security Service Provider (CSSP) is a 24/7 Defensive Cyber Operations (DCO) organization that defends US Department of Defense and US Army networks from hostile cyber activity, as well as develops technologies and capabilities for use by DCO operators within the DoD. In recent years, C5ISR Center CSSP has been researching various advanced data visualization concepts and strategies to enhance the speed and efficiency of cybersecurity analyst’s workflow. To achieve these goals Virtual and Mixed Reality (VR/MR) tools have been employed to investigate, whether these mediums would enable useful remote collaboration of DCO operators and whether stereoscopically perceivable 3D data visualizations would enable DCO operators to gain improved hindsight into their datasets. We’ll be giving overview of the capabilities being developed as aligned to our research and operational requirements, our expected outcomes of using VR/MR in training and operational cyber environments and our planned path to accomplish these goals.
Multiplicative noise removal via using nonconvex regularizers based on total variation and wavelet frame

Total variation based regularization and wavelet frame based regularization are two different methods for image restoration. Moreover, nonconvex regularization has advantages over convex regularization for preserving edges in images. In this paper, a variational model consisting of two nonconvex regularizers based respectively on total variation and wavelet frame is proposed for multiplicative noise removal. The proposed model combines the advantages of the nonconvex regularization and the hybrid regularization. So it can more effectively preserve edges and details in images than any single regularization can. The alternating minimization method is proposed to solve the novel model. Although the corresponding minimization problem is nonconvex, the convexity of all subproblems is guaranteed under some mild conditions. Therefore, we study the convergence property of the algorithm. Numerical experiments have proved that the proposed model with the corresponding algorithm outperforms some state-of-the-art models in terms of the peak signal-to-noise ratio and the relative error of the restored images.
Modified Echo State Network Enabled Dynamic Duty Cycle for Optimal Opportunistic Routing in EH-WSNs

Minimizing energy consumption is one of the major challenges in wireless sensor networks (WSNs) due to the limited size of batteries and the resource constrained tiny sensor nodes. Energy harvesting in wireless sensor networks (EH-WSNs) is one of the promising solutions to minimize the energy consumption in wireless sensor networks for prolonging the overall network lifetime. However, static energy harvesting in individual sensor nodes is normally limited and unbalanced among the network nodes. In this context, this paper proposes a modified echo state network (MESN) based dynamic duty cycle with optimal opportunistic routing (OOR) for EH-WSNs. The proposed model is used to act as a predictor for finding the expected energy consumption of the next slot in dynamic duty cycle. The model has adapted a whale optimization algorithm (WOA) for optimally selecting the weights of the neurons in the reservoir layer of the echo state network towards minimizing energy consumption at each node as well as at the network level. The adapted WOA enabled energy harvesting model provides stable output from the MESN relying on optimal weight selection in the reservoir layer. The dynamic duty cycle is updated based on energy consumption and optimal threshold energy for transmission and reception at bit level. The proposed OOR scheme uses multiple energy centric parameters for selecting the relay set oriented forwarding paths for each neighbor nodes. The performance analysis of the proposed model in realistic environments attests the benefits in terms of energy centric metrics such as energy consumption, network lifetime, delay, packet delivery ratio and throughput as compared to the state-of-the-art-techniques.
Integrating Interactive Clothing and Cyber-Physical Systems: A Humanistic Design Perspective

This study is aimed at bridging the gap from a transdisciplinary perspective between cyber-physical systems (CPS) architecture in the field of information science and emotional design in the field of humanistic science for interactive fashion innovation. Information related to a familiar feeling in the process of interactive clothing design is used to explain how the transformation could be realized from data. By creating the cyber-physical-clothing systems (CPCS), the architecture model in the hyper world and takes the development process of an interactive parent-child clothing as a case study for analyzing the transformation from the physical signal input to the social symbol recognition output. The experimental results, which from the perspective of clothing art design rather than information discipline, show that interactive parent-child clothing is not only suitable for the rehabilitation of autistic children but also recognized by most parents. The reasonable embedding of sensing technology can greatly enhance the added value of clothing products. This study provides a fruitful practical application reference for designers who are engaged in the field of art and design but not familiar with the relevant information technology. Furthermore, the application principle and the technical process of CPCS for further interactive clothing design is explained.