Reviewerly/exHarmony huggingface dataset:
The Application of 3D Morphable Model (3DMM) for Real-Time Visualization of Acupoints on a Smartphone

Acupuncture therapy is one of the main modalities of treatment in Traditional Chinese Medicine (TCM). Based on different symptoms of the patient, needling or massaging is applied to the corresponding acupuncture points to relieve the symptoms. However, given the large number of acupuncture points and the complexity of their specifities, it is difficult for one to remember the location and function of each acupuncture point without extensive training. In this work, through the use of augmented reality (AR), the acupuncture points can be displayed directly on the image of human body. Compared to existing acupoint probe devices that work by measuring the skin conductivity, our solution does not require any additional hardware and is purely software-based. In this paper, we propose a novel approach for acupoint localization by leveraging the landmark points and 3D morphable model (3DMM). The localization error of our system is about 2.4mm which outperforms the existing work on acupoint localization by 170 A prototype system is implemented on the Android phone. In the case of mild symptoms (e.g. headache, sleep disorder), with the aid of our proposed system, the patient can quickly locate the corresponding acupuncture points for the application of massage to relieve his/her symptoms without the help from TCM physicians.
Efficacy of Inheritance Aspect in Software Fault Prediction—A Survey Paper

Software fault prediction (SFP) is a research area that helps development and testing process deliver software of good quality. Software metrics are of various types and are used in SFP for measurements. Inheritance is a prominent feature, which measures the depth, breadth, and complexity of object-oriented software. A few studies exclusively addressed the efficacy of inheritance in SFP. This provokes the need to identify the potential ingredients associated with inheritance, which can be helpful in SFP. In this paper, our aim is to collecting, organizing, categorizing, and investigating published fault prediction studies. Findings include identification of 54 inheritance metrics, 78 public datasets with various combinations of 10 inheritance metrics, 60% use of method level & use of private datasets, an increased number of studies using machine learning approaches. This study will facilitate scholars to studying previous literature on software fault prediction having software metrics, with their methods, public data sets, performance evaluation of machine learning algorithms, and findings of experimental results in a comfortable, and efficient way, emphasizing the inherited aspect specifically.
Defining geologic domains using cluster analysis and indicator correlograms: a phosphate-titanium case study

One of the first decisions to be made when building a mineral resource model is the definition of geological/geostatistical domains. Cluster analysis is a set of techniques in machine learning that can be especially suited for this matter. In order to compare different approaches, two clustering algorithms were investigated in this study: k-means and the dual-space clustering algorithm. Choosing the most appropriate method and the number of clusters can be challenging and some metrics are needed to support these decisions, including the validation of the spatial distribution of the clusters, which is not always appropriately discussed in the literature. We introduce the use of correlograms of the indicators for that matter. Although clustering techniques can be robust for an application in resource modelling, expert knowledge is still necessary when applying cluster analysis to resource modeling, since final decisions should not be based solely on statistical indexes, but also on experience. In this paper, the proposed methodology was tested in a three-dimensional dataset related to a phosphate/titanium deposit.
Data classification by quantum radial-basis-function networks

Radial-basis-function (RBF) networks are third-layered neural networks that are widely used in function approximation and data classification. Here we propose a quantum model for RBF networks. As in the classical case, we use radial basis functions as activation functions; quantum linear algebraic techniques and coherent states can be applied to implement these functions. We define the state of the weight as a tensor product of single-qubit states. This gives a simple approach to implement the quantum RBF network in quantum circuits. In this model we prove that training is almost quadratically faster than in the classical case. We demonstrate numerically that the quantum RBF network can solve binary classification problems as effectively as the classical RBF network, while the time and number of iterations used for training are much smaller.
A discrete cosine transform-based query efficient attack on black-box object detectors

Deep learning models are being widely used in almost every field of computing and information processing. The advantages offered by these models are unparalleled, however, similar to any other computing discipline, they are also vulnerable to security threats. A compromised deep neural network can significantly impact its robustness and accuracy. In this work, we present a novel targeted attack method against state-of-the-art object detection models YOLO v3 and AWS Rekognition in a black-box environment. We present an improved attack method using Discrete Cosine Transform based on boundary attack plus plus mechanism, and apply it on attacking object detectors offline and online. By querying the victim detection models along with transforming the images from the spatial domain into the frequency domain, we ensure that any specified object in an image can be successfully recognized as any other desired class by YOLO v3 and AWS Rekognition. The results prove that our method has significant boosting effects on boundary attacks in offline and online object detection systems.
FPSiamRPN: Feature Pyramid Siamese Network With Region Proposal Network for Target Tracking

Target tracking based on Siamese network has reached the state-of-the-art performance.However is still limited in semantic feature extraction.In this paper, we propose a novel method to distinguish positive and negative samples.Taking deep neural network as the backbone, we fuse the feature maps from different layers and feed it to RPN (Region Proposal Network).In addition, we use a loss term for loss function to achieve self-adjusting and learn more discriminative embedding features of target objects with similar semantics.In the tracking stage, one-shot detection is used as the reference, fix the first frame as the weight of tracking to track the subsequent frames.Our method has achieved outstanding performance on several benchmark data set, such as: OTB2015, VOT2016, VOT2018, and VOT2019 et al.
Multi-purpose Syntax Definition with SDF3

Abstract SDF3 is a syntax definition formalism that extends plain context-free grammars with features such as constructor declarations, declarative disambiguation rules, character-level grammars, permissive syntax, layout constraints, formatting templates, placeholder syntax, and modular composition. These features support the multi-purpose interpretation of syntax definitions, including derivation of type schemas for abstract syntax tree representations, scannerless generalized parsing of the full class of context-free grammars, error recovery, layout-sensitive parsing, parenthesization and formatting, and syntactic completion. This paper gives a high level overview of SDF3 by means of examples and provides a guide to the literature for further details.
Experimental studies of the reinforcement percentage effect on the modulus of soil deformation fixed by cementation

Abstract The article presents the experimental studies’ results on the subsidence soils reinforcement percentage influence during cementation on the deformation characteristics of a soil base after fixing. A nonlinear dependence of the fixed soil deformation modulus on the reinforcement percentage and the initial (natural) deformation modulus is established. High convergence of the obtained theoretical and experimental data is proved.
Program Comprehension in Virtual Reality

Virtual reality is an emerging technology for various domains such as medicine, psychotherapy, architecture, and gaming. Recently, software engineering researchers have started to explore virtual reality as a tool for programmers. However, few studies examine source code comprehension in a virtual reality (VR) environment. In this paper, we explore the human experience of comprehending source code in VR. We conducted a study with 26 graduate students. We found that the programmers experienced more challenges when reading and comprehending code in VR. We found no statistically significant difference in the programmers' perceived productivity between our VR and traditional comprehension studies.
Continuous Support Vector Regression for Nonstationary Streaming Data

Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression.
Architecture-Centric Support for Integrating Security Tools in a Security Orchestration Platform

Security Operation Centers (SOC) leverage a number of tools to detect, thwart and deal with security attacks. One of the key challenges of SOC is to quickly integrate security tools and operational activities. To address this challenge, an increasing number of organizations are using Security Orchestration, Automation and Response (SOAR) platforms, whose design needs suitable architectural support. This paper presents our work on architecture-centric support for designing a SOAR platform. Our approach consists of a conceptual map of SOAR platform and the key dimensions of an architecture design space. We have demonstrated the use of the approach in designing and implementing a Proof of Concept (PoC) SOAR platform for (i) automated integration of security tools and (ii) automated interpretation of activities to execute incident response processes. We also report a preliminary evaluation of the proposed architectural support for improving a SOAR's design.
Semantic segmentation and colorization of grayscale aerial imagery with <scp>W‐Net</scp> models

Abstract The semantic segmentation of remotely sensed aerial imagery is nowadays an extensively explored task, concerned with determining, for each pixel in an input image, the most likely class label from a finite set of possible labels. Most previous work in the area has addressed the analysis of high‐resolution modern images, although the semantic segmentation of historical grayscale aerial photos can also have important applications. Examples include supporting the development of historical road maps, or the development of dasymetric disaggregation approaches leveraging historical building footprints. Following recent work in the area related to the use of fully‐convolutional neural networks for semantic segmentation, and specifically envisioning the segmentation of grayscale aerial imagery, we evaluated the performance of an adapted version of the W‐Net architecture, which has achieved very good results on other types of image segmentation tasks. Our W‐Net model is trained to simultaneously segment images and reconstruct, or predict, the colour of the input images from intermediate representations. Through experiments with distinct data sets frequently used in previous studies, we show that the proposed W‐Net architecture is quite effective in colouring and segmenting the input images. The proposed approach outperforms a baseline corresponding to the U‐Net model for the segmentation of both coloured and grayscale imagery, and it also outperforms some of the other recently proposed approaches when considering coloured imagery.
Competing Models: Inferring Exploration Patterns and Information Relevance via Bayesian Model Selection

Analyzing interaction data provides an opportunity to learn about users, uncover their underlying goals, and create intelligent visualization systems. The first step for intelligent response in visualizations is to enable computers to infer user goals and strategies through observing their interactions with a system. Researchers have proposed multiple techniques to model users, however, their frameworks often depend on the visualization design, interaction space, and dataset. Due to these dependencies, many techniques do not provide a general algorithmic solution to user exploration modeling. In this paper, we construct a series of models based on the dataset and pose user exploration modeling as a Bayesian model selection problem where we maintain a belief over numerous competing models that could explain user interactions. Each of these competing models represent an exploration strategy the user could adopt during a session. The goal of our technique is to make high-level and in-depth inferences about the user by observing their low-level interactions. Although our proposed idea is applicable to various probabilistic model spaces, we demonstrate a specific instance of encoding exploration patterns as competing models to infer information relevance. We validate our technique's ability to infer exploration bias, predict future interactions, and summarize an analytic session using user study datasets. Our results indicate that depending on the application, our method outperforms established baselines for bias detection and future interaction prediction. Finally, we discuss future research directions based on our proposed modeling paradigm and suggest how practitioners can use this method to build intelligent visualization systems that understand users' goals and adapt to improve the exploration process.
On Threat Analysis of IoT-Based Systems: A Survey

In this paper, we provide a survey on threat analysis of systems based on Internet of Things (IoT), and how the blockchain technology can help mitigate the threats. Although the topic of IoT security (and previously the security of wireless sensor networks) has been reviewed extensively, we believe that the topic deserves a more in-depth examination towards a more systematic threat analysis, which is not only necessary to better understand the threats against IoT-based systems, but also paves the way to effectively improve the security of IoT-based systems by integrating with the blockchain technology. The major research contributions of this paper include a new taxonomy of the threats against IoT-based systems, identify what threats can be effectively mitigated by integrating IoT with with blockchain technology, the challenges faced by the blockchain-enabled IoT-based systems, and the likely approaches to overcoming these challenges.
An Initial Study on the Association Between Architectural Smells and Degradation

It is generally assumed that architectural smells are associated with software architectural degradation. Treating smells could hence help treating degradation. This article investigates the association between three types of architectural smells and the existence of architecture-violating dependencies as manifestation of architectural degradation in software. We gathered data about architectural smells and violations from a single system with a validated prescriptive architecture. The data was analysed to identify and characterise associations between architectural smells and violations. Statistically relevant associations were identified for two of three smells, namely cyclic dependencies and unstable dependencies. Effect sizes were small for both though. These results provide evidence for cyclic and unstable dependencies having a larger risk of including architectural violations. The small effect sizes indicate that the presence of architectural smells cannot explain architectural degradation alone. This shows that complementing methods and techniques are required for exhaustive treatment of both phenomena.
A Randomized Kinodynamic Planner for Closed-Chain Robotic Systems

Kinodynamic rapidly-exploring random tree (RRT) planners are effective tools for finding feasible trajectories in many classes of robotic systems. However, they are hard to apply to systems with closed-kinematic chains, like parallel robots, collaborative arms manipulating an object, or legged robots keeping their feet in contact with the environment. The state space of such systems is an implicitly-defined manifold that complicates the design of the sampling and steering procedures, and leads to trajectories that drift from the manifold if standard integration methods are used. To address these issues, this article presents a kinodynamic RRT planner that constructs an atlas of the state space incrementally, and uses this atlas to generate random states, and to dynamically steer the system toward such states. The steering method exploits the atlas charts to compute locally optimal controls based on linear quadratic regulators. The atlas also allows the integration of the equations of motion using local coordinates, which eliminates any drift from the state space manifold and results in accurate trajectories. To the best of our knowledge, this is the first kinodynamic planner that explicitly takes closed kinematic chains into account. In this article, we illustrate the planner performance in significantly complex tasks involving planar and spatial robots that have to lift or throw a load using torque-limited actuators.
Performance-Oriented Neural Architecture Search

Hardware-Software Co-Design is a highly successful strategy for improving performance of domain-specific computing systems. We argue for the application of the same methodology to deep learning; specifically, we propose to extend neural architecture search with information about the hardware to ensure that the model designs produced are highly efficient in addition to the typical criteria around accuracy. Using the task of keyword spotting in audio on edge computing devices, we demonstrate that our approach results in neural architecture that is not only highly accurate, but also efficiently mapped to the computing platform which will perform the inference. Using our modified neural architecture search, we demonstrate 0.88% increase in TOP-1 accuracy with 1.85× reduction in latency for keyword spotting in audio on an embedded SoC, and 1.59× on a high-end GPU.
A Camouflaged Object Detection Model Based on Deep Learning

Camouflaged object detection is a hard assignment due to their textures are similar to the background. The main intention of this paper is probe into a problem about the camouflaged object detection, that is, detecting its camouflaged object for a given image. This problem has not been well studied in spite of a large area of potential applications such as camouflage military targets detection and wildlife protection. To address this problem, a camouflage object detection method based on deep learning is proposed. The suggested method can detect camouflaged object which can extract deep features automatically. It can also provide detection probability which reflect camouflage efficiency. Experimental results show that the deep learning measure can effectively detect different scene, representing the camouflage level of low, medium and high respectively.
Collecting and analyzing data jointly from multiple services under local differential privacy

Users' sensitive data can be collected and analyzed under local differential privacy (LDP) without the need to trust the data collector. Most previous work on LDP can be applied when each user's data is generated and collected from a single service or data source. In a more general and practical setting, sensitive data of each user needs to be collected under LDP from multiple services independently and can be joined on, e.g. , user id. In this paper, we address two challenges in this setting: first, how to prevent the privacy guarantee from being weakened during the joint data collection; second, how to analyze perturbed data jointly from different services. We introduce the notation of user-level LDP to formalize and protect the privacy of a user when her joined data tuples are released. We propose mechanisms and estimation methods to process multidimensional analytical queries, each with sensitive attributes (in its aggregation and predicates) collected and perturbed independently in multiple services. We also introduce an online utility optimization technique for multi-dimensional range predicates, based on consistency in domain hierarchy. We conduct extensive evaluations to verify our theoretical results using synthetic and real datasets.
Detection Violent Behaviors: A Survey

Violence detection behavior is a particular problem regarding the great problem action recognition. In recent years, the detection and recognition of violence has been studied for several applications, namely in surveillance. In this paper, we conducted a recent systematic review of the literature on this subject, covering a selection of various researched papers. The selected works were classified into three main approaches for violence detection: video, audio, and multimodal audio and video. Our analysis provides a roadmap to guide future research to design automatic violence detection systems. Techniques related to the extraction and description of resources to represent behavior are also reviewed. Classification methods and structures for behavior modelling are also provided .
A Model to Detect Readability Improvements in Incremental Changes

Identifying source code that has poor readability allows developers to focus maintenance efforts on problematic code. Therefore, the effort to develop models that can quantify the readability of a piece of source code has been an area of interest for software engineering researchers for several years. However, recent research questions the usefulness of these readability models in practice. When applying these models to readability improvements that are made in practice, i.e., commits, they are unable to capture these incremental improvements, despite a clear perceived improvement by the developers. This results in a discrepancy between the models we have built to measure readability, and the actual perception of readability in practice.