Reviewerly/exHarmony huggingface dataset:
Nonrigid registration for tracking incompressible soft tissues with sliding motion

Purpose Respiration causes the deformation and sliding motion of the soft tissues, and affects the accuracy of the assessment of minimally invasive abdominal surgery. Nonrigid registration is used to eliminate the effects of respiration for the assessment. Because the soft tissues with high water content are volume preserving during deformation, incompressibility has to be considered when tracking soft tissues for nonrigid registration. The purpose of the study was to develop an incompressible nonrigid registration for tracking deformable soft tissues with sliding motion. Methods The nonrigid registration framework proposed in the present study includes two main steps: (a) The solution in the subspace of diffeomorphisms is searched and encoded to stationary velocity field in the log domain. (b) The divergence‐free component and harmonic remainder are extracted by Fourier‐based Helmholtz‐Hodge decomposition (FHHD) and further integrated by an adaptive weight to simultaneously retain the incompressibility of deformation and compensate the sliding motion. The method was evaluated on 11 groups of synthetic datasets and five groups of clinical images. Registration accuracy is evaluated by using four quantitative measures, including mean surface distance (MSD), Hausdorff distance (HD), mean corresponding distance (MCD), and Dice similarity coefficient (DSC). Incompressibility is evaluated by using two quantitative measures, including relative volume change (RVC) and Jacobian determinant (J). Results Compared with three state‐of‐the‐art nonrigid registration methods, the proposed method shows an advantage in handling the incompressible deformation of images with large sliding motion. The lowest (MSD, 0.631 mm), (HD, 6.000 mm), and (MCD, 3.555 mm) and the highest (DSC, 0.970) are obtained proving the high registration accuracy with sliding motion compensation of the proposed method. The (RVC, 0.006) and Jacobian determinant (J, 1.008 ± 0.070) are nearly close to 0 and 1, respectively, showing the strong incompressibility of the proposed method. The proposed method improves registration accuracy in nearly all cases, which maintains the incompressibility of tissue transformation while simultaneously compensating the sliding motion on clinical datasets. Conclusions The proposed method improves the registration accuracy of incompressible tissues when dealing with large sliding motion, and thus has the potential to improve current minimally invasive abdominal surgery.
Toward the Health Measure for Open Source Software Ecosystem Via Projection Pursuit and Real-Coded Accelerated Genetic

The benign development of Open-source Software Ecosystem (or OSSE) helps to fuse the wisdom of the community. It can facilitate the development and solve the urgent application needs of large-scale complex software systems. To guarantee that an OSSE is stable and effective for supporting the application development, health assessment for an OSSE has become a research hotspot. In this paper, starting from a new perspective, the OSSE is compared with the ecosystem in the natural world. An OSSE health measure method is proposed by integrating projection pursuit and real-coded accelerated genetic algorithm. First, according to the snowball sampling data collection method and the grounded theory, the data is collected and processed. Second, by designing evaluation indicators and utility functions, the projection pursuit classification model of the natural ecosystem is evaluated and combined with a real-coded accelerated genetic algorithm, thereby designing the health measure model. The experimental results suggest the effectiveness of the proposed approach.
Does UML Modeling Associate with Lower Defect Proneness?: A Preliminary Empirical Investigation

The benefits of modeling the design to improve the quality and maintainability of software systems have long been advocated and recognized. Yet, the empirical evidence on this remains scarce. In this paper, we fill this gap by reporting on an empirical study of the relationship between UML modeling and software defect proneness in a large sample of open-source GitHub projects. Using statistical modeling, and controlling for confounding variables, we show that projects containing traces of UML models in their repositories experience, on average, a statistically minorly different number of software defects (as mined from their issue trackers) than projects without traces of UML models.
Separable Reversible Data Hiding in Encrypted Images Based on Difference Histogram Modification

In this paper, an efficient reversible data hiding method for encrypted image based on neighborhood prediction is proposed, which includes image encryption, reversible data hiding in encrypted domain, and hidden data extraction. The cover image is first partitioned into non-overlapping blocks, and then the pixel value in each block is encrypted by modulo operation. Therefore, the linear prediction difference in the block that satisfies the specific condition is consistent before and after encryption, ensuring that data extraction is completely separable from image decryption. In addition, by using the linear weighting of three adjacent pixels in the block to predict the current pixel, the prediction accuracy can be improved. The data-hider, who does not know the original image content, may embed additional data based on prediction difference histogram modification. Data extraction and image recovery are free of any error. Experimental results demonstrate the feasibility and efficiency of the proposed scheme.
Moving Target Defense Mechanism for Side-Channel Attacks

In this paper, we present a side-channel resilient moving target defense mechanism against power-/electromagnetic-based side-channel attacks. Recent countermeasures use fresh rekeying after every encryption/decryption process; this causes major overhead in synchronizing the communicating parties. In contrast to previous work, our mechanism integrates fresh rekeying and masking techniques at an interval, where these techniques are driven by the maximum number of side-channel leakage traces required toward a successful embedded attack. Hence, the mechanism tracks the effect of attacks on the number of traces, and consequently applies rekeying at suitable intervals to reduce the computational/communication overhead, while increasing the attack cost. The mechanism scalability was evaluated against an advanced attack model based on machine learning methods that reduces significantly the number of traces required for a successful attack under masking implementation.
A Broad View of Wearables as Learning Technologies: Current and Emerging Applications

Wearables have increasingly been appearing in various forms as technologies that have the potential to support teaching and learning. This chapter surveys a number of recent and current wearable technology projects in the educational technology, learning sciences, and human-computer interaction literatures to illustrate the diversity of wearable technologies. It also identifies some regularities in how various forms of wearables are being deployed to support learning. These forms of support include using wearables to: promote personal expression; integrate digital information into social interactions; support educative role-play; provide just-in-time notification in a learning environment; and produce records of bodily experience for subsequent inspection, reflection, and interpretation. While examples of each support are provided, two more extended descriptions from the authors’ recent and ongoing work with wearables are given as examples of how human and other animal bodily experiences can be harnessed to support learning. Future directions and considerations for educational technologists are discussed.
Evaluation of diagnostician user interface aspects in a virtual reality-based tele-ultrasonography simulation

Ultrasonography (USG) is a common medical examination that can be performed remotely and that highly benefits from robotics. In this paper, we evaluate a novel interface for a telediagnosis system that features haptic feedback and a 2D/3D visualization of the remote site. A Virtual Environment (VE) representing the patient site was used for the validation to stress the interaction capabilities of the interface and to decouple expert's interface performance from the effects of the whole teleoperation loop of a telediagnosis system. The interface evaluation measures include the accuracy that doctors achieve when positioning the interface's end-effector in the VE and the success of experts to perform tasks related to cardiac USG that require force feedback. Twelve experienced sonographers performed four experiments in which visualization modality and VE navigation technique varied. Given the limitations of an evaluation in simulated settings, both quantitative results and experts' opinions suggest that the interface is suitable for integration in tele-USG systems. After a positive evaluation of latencies and rendered forces, we show how navigation condition influences the accuracy of the end-effector positioning as well as the covered distance and the elapsed time to accomplish the tasks. Similarly, we highlight the effect of visualization on accuracy.
Refactoring-Aware Code Review: A Systematic Mapping Study

Modern Code Review (MCR) demands enhancements in the way change logs are presented to reviewers. Their task benefits from higher-level descriptions about the intention behind commits; for instance, evolution tasks, such as refactorings, could be more effectively reviewed in the presence of the intended transformation - better if automatically detected by MCR tool support. This paper presents a systematic literature mapping (combining results from search strings, snowballing and a two-phase classification scheme) on refactoring-aware solutions to support MCR. We could observe that, since 2015, interest in tools and techniques for automatic detection of refactorings has been steadily growing. Most publications refer to new development methods or carry out characterisation studies. As a consequence of this overview, we point out a few potential research topics for the next years. In special, detection of multiple refactoring types in a mixed change log (in which refactorings are combined with other kinds of changes), or the need for case studies or experiments in applying refactoring detection in MCR, on distinct application domains and development environments.
Detecting Persuasive Arguments based on Author-Reader Personality Traits and their Interaction

Persuasion is one of the most frequent, albeit challenging, tasks in human interaction. In a textual argument, one party (author) aims to change the view of the other party (reader). In this paper, we propose to detect persuasive textual arguments while considering the parties personality traits. We find that we can substantially improve accuracy by introducing features that capture author-reader personality traits and their interaction. Our model improves performance of state-of-the-art baselines from 66% to 71% on a new dataset of more than 19K arguments we collected.
Procedural Synthesis of Remote Sensing Images for Robust Change Detection with Neural Networks

Data-driven methods such as convolutional neural networks (CNNs) are known to deliver state-of-the-art performance on image recognition tasks when the training data are abundant. However, in some instances, such as change detection in remote sensing images, annotated data cannot be obtained in sufficient quantities. In this work, we propose a simple and efficient method for creating realistic targeted synthetic datasets in the remote sensing domain, leveraging the opportunities offered by game development engines. We provide a description of the pipeline for procedural geometry generation and rendering as well as an evaluation of the efficiency of produced datasets in a change detection scenario. Our evaluations demonstrate that our pipeline helps to improve the performance and convergence of deep learning models when the amount of real-world data is severely limited.
Crowd Counting Method Based on Convolutional Neural Network With Global Density Feature

Crowd counting is an important research topic in the field of computer vision.The multi-column convolution neural network (MCNN) has been used in this field and achieved competitive performance.However, when the crowd distribution is uneven, the accuracy of crowd counting based on the MCNN still needs to be improved.In order to adapt to uneven crowd distributions, crowd global density feature is taken into account in this paper.The global density features are extracted and added to the MCNN through the cascaded learning method.Because some detailed features during the down-sampling process will be lost in the MCNN and it will affect the accuracy of the density map, an improved MCNN structure is proposed.In this paper, the max pooling is replaced by max-ave pooling to keep more detailed features and the deconvolutional layers are added to restore the lost details in the down-sampling process.The experimental results in the UCF_CC_50 dataset and the ShanghaiTech dataset show that the proposed method has higher accuracy and stability.
Adaptive Cybersecurity Framework for Healthcare Internet of Things

Connecting people, processes, devices and data, the Internet of Things brings new security challenges and may significantly increase the vulnerability of healthcare services. This paper investigates advanced adaptive security to anticipate and respond to dynamic and adaptive attacks on healthcare critical infrastructures. We propose the Adaptive Cybersecurity Framework that supports dynamic adaptation to cyber threats. Further, we simulate and evaluate the framework using evolutionary game theory, and outline the further steps for our future work.
Automatic Gauge Detection via Geometric Fitting for Safety Inspection

For safety considerations in electrical substations, the inspection robots are recently deployed to monitor important devices and instruments with the presence of skilled technicians in the high-voltage environments. The captured images are transmitted to a data station and are usually analyzed manually. Toward automatic analysis, a common task is to detect gauges from captured images. This paper proposes a gauge detection algorithm based on the methodology of geometric fitting. We first use the Sobel filters to extract edges which usually contain the shapes of gauges. Then, we propose to use line fitting under the framework of random sample consensus (RANSAC) to remove straight lines that do not belong to gauges. Finally, the RANSAC ellipse fitting is proposed to find most fitted ellipse from the remaining edge points. The experimental results on a real-world dataset captured by the GuoZi Robotics demonstrate that our algorithm provides more accurate gauge detection results than several existing methods.
Distributed MCMC Inference in Dirichlet Process Mixture Models Using Julia

Due to the increasing availability of large data sets, the need for general-purpose massively-parallel analysis tools become ever greater. In unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet-Process Mixture Model (DPMM), provide a principled Bayesian approach to adapt model complexity to the data. Despite their potential, however, DPMMs have yet to become a popular tool. This is partly due to the lack of friendly software tools that can handle large datasets efficiently. Here we show how, using Julia, one can achieve efficient and easily-modifiable implementation of distributed inference in DPMMs. Particularly, we show how a recent parallel MCMC inference algorithm - originally implemented in C++ for a single multi-core machine - can be distributed efficiently across multiple multi-core machines using a distributed-memory model. This leads to speedups, alleviates memory and storage limitations, and lets us learn DPMMs from significantly larger datasets and of higher dimensionality. It also turned out that even on a single machine the proposed Julia implementation handles higher dimensions more gracefully (at least for Gaussians) than the original C++ implementation. Finally, we use the proposed implementation to learn a model of image patches and apply the learned model for image denoising. While we speculate that a highly-optimized distributed implementation in, say, C++ could have been faster than the proposed implementation in Julia, from our perspective as machine-learning researchers (as opposed to HPC researchers), the latter also offers a practical and monetary value due to the ease of development and abstraction level. Our code is publicly available at https://github.com/dinarior/dpmm subclusters.jl
Comparison of Decision Trees and Deep Learning for Object Classification in Autonomous Driving

Road transportation is among the grand global challenges affecting human lives, health, society, and economy. Autonomous vehicles (AVs) are the latest among the radical solutions to address transportation challenges. AVs have become a reality although their penetration in real environments needs more time. The foremost challenge for AVs is to recognize objects in real driving environment with highest certainty. This paper is an extension of our earlier work where we developed a methodology to integrate supervised learning and decision fusion to enhance object classification accuracy in a driving environment, i.e., to enable an auto-pilot to take better driving decisions. This problem equates to pixel classification. Our study revealed that the C5.0 decision tree classifier performs similar to deep learning. This paper extends and investigates the topic further and provides an in-depth performance comparison of deep learning and C5.0 decision tree classifier for object classification in driving environments using a bigger dataset. We manually label images from a subset of KITTI road dataset by using free-form selection (polygon) rather than a box or rectangular selection enabling highly accurate pixel labeling. Our analysis reveals that C5.0 and deep learning provide similar accuracies while deep learning is over 30% faster than C5.0.
Segmentation of Scanned Documents Using Deep-Learning Approach

In the paper we present an approach to the automatic segmentation of interesting elements from paper documents i.e. stamps, logos, printed text blocks, signatures, and tables. Presented approach involves object detection by means of Convolutional Neural Network. Resulting regions are then subjected to integration based on confidence level and shape. Experiments performed on representative set of digitizsed paper documents proved usefulness and efficiency of the developed approach. The results were compared with the standard cascade-based detection and showed the superiority of the CNN-based approach.
DingNet: A Self-Adaptive Internet-of-Things Exemplar

Recent efforts have shown that research on selfadaptive systems can benefit from exemplar to evaluate and compare new methods, techniques and tools.One highly relevant application domain for self-adaptation is the Internet-of-Things (IoT).While some initial exemplars have been proposed for IoT, these applications are small in scale and hence limited to support research on larger IoT deployments, such as typically required in smart cities.To address this limitation, we introduce the DingNet exemplar, a reference implementation for research on self-adaptation in the domain of IoT.DingNet offers a simulator that maps directly to a physical IoT system that is deployed in the area of Leuven, Belgium.DingNet models a set of geographically distributed gateways that are connected to a user application deployed at a front-end server.The gateways can interact over a LoRaWAN network with local, possibly mobile motes that can be equipped with sensors and actuators.The exemplar comes with a set of scenarios for comparing the effectiveness of different self-adaptive solutions.We illustrate how the exemplar is used for a typical adaptation problem of IoT where mobile motes dynamically adapt their communication settings to ensure reliable and energy efficient communication.
Unsupervised Cross-Database Micro-Expression Recognition Using Target-Adapted Least-Squares Regression

Over the past several years, the research of micro-expression recognition (MER) has become an active topic in affective computing and computer vision because of its potential value in many application fields, e.g., lie detection. However, most previous works assumed an ideal scenario that both training and testing samples belong to the same micro-expression database, which is easily broken in practice. In this letter, we hence consider a more challenging scenario that the training and testing samples come from different micro-expression databases and investigated unsupervised cross-database MER in which the source database is labeled while the label information of target database is entirely unseen. To solve this interesting problem, we propose an effective method called target-adapted least-squares regression (TALSR). The basic idea of TALSR is to learn a regression coefficient matrix based on the source samples and their provided label information and also enable this learned regression coefficient matrix to suit the target micro-expression database. We are thus able to use the learned regression coefficient matrix to predict the micro-expression categories of the target micro-expression samples. Extensive experiments on CASME II and SMIC micro-expression databases are conducted to evaluate the proposed TALSR. The experimental results show that our TALSR has better performance than lots of recent well-performing domain adaptation methods in dealing with unsupervised cross-database MER tasks.
On the Efficiency of Privacy-Preserving Smart Contract Systems

Along with blockchain technology, smart contracts have found intense interest in lots of practical applications. A smart contract is a mechanism involving digital assets and some parties, where the parties deposit assets into the contract and the contract redistributes the assets among the parties based on provisions of the smart contract and inputs of the parties. Recently, several smart contract systems are constructed that use zk-SNARKs to provide privacy-preserving payments and interconnections in the contracts (e.g. Hawk [KMS+16] and Gyges [JKS16]). Efficiency of such systems severely are dominated by efficiency of the underlying UC-secure zk-SNARK that is achieved using C $$\emptyset $$ C $$\emptyset $$ framework [KZM+15] applied on a non-UC-secure zk-SNARK. In this paper, we show that recent progresses on zk-SNARKs, allow one to simplify the structure and also improve the efficiency of both systems with a UC-secure zk-SNARK that has simpler construction and better efficiency in comparison with the currently used ones. More precisely, with minimal changes, we present a variation of Groth and Maller's zk-SNARK from Crypto 2017, and show that it achieves UC-security and has better efficiency than the ones that currently are used in Hawk and Gyges. We believe, new variation can be of independent interest.