Reviewerly/exHarmony huggingface dataset:
Unsupervised Medical Image Alignment With Curriculum Learning

We explore different curriculum learning methods for training convolutional neural networks on the task of deformable pairwise 3D medical image registration. To the best of our knowledge, we are the first to attempt to improve performance by training medical image registration models using curriculum learning, starting from an easy training setup in the first training stages, and gradually increasing the complexity of the setup. On the one hand, we consider two existing curriculum learning approaches, namely curriculum dropout and curriculum by smoothing. On the other hand, we propose a novel and simple strategy to achieve curriculum, namely to use purposely blurred images at the beginning, then gradually transit to sharper images in the later training stages. Our experiments with an underlying state-of-the-art deep learning model show that curriculum learning can lead to superior results compared to conventional training. Additionally, we show that curriculum by input blur has the best accuracy versus speed trade-off among the compared curriculum learning approaches.
Faster Detection Method of Driver Smoking Based on Decomposed YOLOv5

Abstract Aiming at the problem of large computational resource consumption in the existing object detection network for driver smoking detection, a decomposing YOLOv5 network (Dec-YOLOv5) is proposed for optimization. This method uses singular value decomposition (SVD) to split the standard convolution in the pre-trained YOLOv5 network into two simpler convolution operations to reduce the computational cost. The optimized network generated after decomposition does not need to be retrained, which can reduce the number of parameters and calculations while maintaining the detection accuracy of the pre-trained model. The experimental results show that the detection time of Dec-YOLOv5 is only 80% of the original YOLOv5 when the overall detection accuracy reaches 93.5%. At the same time, compared with the current mainstream object detection model, the Dec-YOLOv5 network has a better ability to express the characteristics of the driver’s cigarettes, and the detection accuracy is higher.
Theoretical framework in graph embedding-based discriminant dimensionality reduction

Graph embedding-based discriminative dimensionality reduction has remained to be a popular research topic over the past few decades. The weight functions in adjacent graphs are the key to the performance of methods. In practice, the weight functions are always achieved experimentally. Thus far, the selection of effective weight functions has no any theoretical guidance. A theoretical framework considering hypothesis-margin is derived in this study to guide the selection of weight functions, whose truth is verified in a popular algorithm and a more effective supervised discriminant graph embedding-based dimensionality reduction method is introduced. Many experimental results demonstrate the truth of the proposed theoretical framework and the effectiveness of the introduced method. Importantly, the proposed framework can provide theoretical support for the selection of weight functions in the graph embedding-based dimensionality reduction.
Zero Watermarking for the TIN DEM Data Based on the Edge Length

How to keep the fidelity of the digital elevation model (DEM) data is a crucial problem in the current watermarking research, as the watermarked DEM data need to preserve their accuracy. We proposed a zero watermarking method for the triangulated irregular network (TIN) DEM data. It takes full advantage of the characteristics of the edge length in the TIN DEM data. First, the radio of the edge lengths is quantified to the watermark index, and then the comparison of the edge lengths is quantified to the watermark bit. Finally, the watermark is constructed by combing the watermark bits according to the watermark indices with the help of the majority voting mechanism. In the method, the TIN DEM data are only used to construct the watermark, not to be embedded by the watermark. Therefore, the data quality is preserved to the greatest extent. Experiments verify the theoretical achievements of this method and demonstrate the method is lossless to the TIN DEM data. Simulation results also show that the method has good robustness on translation, rotation, scaling, and cropping attacks.
Trace Link Recovery for Software Architecture Documentation

Software Architecture Documentation often consists of different artifacts. On the one hand, there is informal textual documentation. On the other hand, there are formal models of the system. Finding related information in multiple artifacts with different level of formality is often not easy. Therefore, trace links between these can help to understand the system. In this paper, we propose an extendable, agent-based framework for creating trace links between textual software architecture documentation and models. Our framework SWATTR offers different pipeline stages to extract text and model information, identify elements in text, and connect these elements to model elements. In each stage, multiple agents can be used to capture necessary information to automatically create trace links. We evaluate the performance of our approach with three case studies and compare our results to baseline approaches. The results for our approach are good to excellent with a weighted average \(F_1\)-Score of 0.72 over all case studies. Moreover, our approach outperforms the baseline approaches on non-weighted average by at least 0.24 (weighted 0.31).
The Covariate-Adjusted ROC Curve: The Concept and Its Importance, Review of Inferential Methods, and a New Bayesian Estimator

Accurate diagnosis of disease is of fundamental importance in clinical practice and medical research. Before a medical diagnostic test is routinely used in practice, its ability to distinguish between diseased and nondiseased states must be rigorously assessed. The receiver operating characteristic (ROC) curve is the most popular used tool for evaluating the diagnostic accuracy of continuous-outcome tests. It has been acknowledged that several factors (e.g., subject-specific characteristics such as age and/or gender) can affect the test outcomes and accuracy beyond disease status. Recently, the covariate-adjusted ROC curve has been proposed and successfully applied as a global summary measure of diagnostic accuracy that takes covariate information into account. The aim of this paper is three-fold. First, we motivate the importance of including covariate-information, whenever available, in ROC analysis and, in particular, how the covariate-adjusted ROC curve is an important tool in this context. Second, we review and provide insight on the existing approaches for estimating the covariate-adjusted ROC curve. Third, we develop a highly flexible Bayesian method, based on the combination of a Dirichlet process mixture of additive normal models and the Bayesian bootstrap, for conducting inference about the covariate-adjusted ROC curve. A simulation study is conducted to assess the performance of the different methods and it also demonstrates the ability of our proposed Bayesian model to successfully recover the true covariate-adjusted ROC curve and to produce valid inferences in a variety of complex scenarios. The methods are applied to an endocrine study where the goal is to assess the accuracy of the body mass index, adjusted for age and gender, for detecting clusters of cardiovascular disease risk factors.
Exploring the Key Factors for Preventing Public Health Crises Under Incomplete Information

The spread of COVID-19 has triggered one of the largest pandemics in modern human history. Humanity is still in the incomplete information period for this infectious disease, and how to effectively deal with such a major public crisis is a crucial problem. Although there are divergences in human natural semantics, the incomplete information increases it. Therefore, this study integrates the neutrosophic set and the Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods to explore the key factors which would prevent expansion of the epidemic in the face of incomplete knowledge. The neutrosophic set technique is an effective tool for the representation of the ambiguity of natural human semantic expression, for the analysis of incomplete, uncertain, and inconsistent information. DEMATEL is used to explore the causes and effects between factors and to generate an influential network relationship map. The results of analysis can help the government and relevant organizations to understand the cause and effect relationship between the factors and set appropriate prevention strategies. The results of this study show that the incorporation of neutrosophic set theory leads to a more meaningful evaluation under incomplete information. “Detect” is a key factor affecting the entire system. The results of this study contribute to the advancement and development of scientifically based decision-making by helping governments and relevant organizations to understand the causal relationships between factors, to set appropriate prevention strategies.
Dsrn: an Efficient Deep Network for Image Relighting

Custom and natural lighting conditions can be emulated in images of the scene during post-editing. Extraordinary capabilities of the deep learning framework can be utilized for such purpose. Deep image relighting allows automatic photo enhancement by illumination-specific retouching. Most of the state-of-the-art methods for relighting are run-time intensive and memory inefficient. In this paper, we propose an efficient, real-time framework Deep Stacked Relighting Network (DSRN) for image relighting by utilizing the aggregated features from input image at different scales. Our model is very lightweight with total size of about 42 MB and has an average inference time of about 0.0116s for image of resolution $1024\times 1024$ which is faster as compared to other multi-scale models. Proposed method is quite robust for translating image color-temperature from input image to target image. The method also performs moderately for light gradient generation with respect to the target image. Additionally, we demonstrate that the results further improve when images illuminated from opposite directions are utilized as input.
Do Larger (More Accurate) Deep Neural Network Models Help in Edge-assisted Augmented Reality?

Edge-assisted Augmented Reality (AR) which offloads compute-intensive Deep Neural Network (DNN)-based AR tasks to edge servers faces an important design challenge: how to pick the DNN model out of many choices proposed for each AR task for offloading. For each AR task, e.g., depth estimation, many DNN-based models have been proposed over time that vary in accuracy and complexity. In general, more accurate models are also more complex; they are larger and have longer inference time. Thus choosing a larger model in offloading can provide higher accuracy for the offloaded frames but also incur longer turnaround time, during which the AR app has to reuse the estimation result from the last offloaded frame, which can lead to lower average accuracy.
Physiological and Metabolic Requirements, and User-Perceived Exertion of Immersive Virtual Reality Exergaming Incorporating an Adaptive Cable Resistance System: An Exploratory Study

Objective: To measure metabolic and physiological demand, subjective fatigue, and enjoyment during a signature 30-minute immersive virtual reality (IVR) adaptive cable resistance exergaming session. Methods: Fourteen healthy college-aged individuals (seven females) were initially acquainted with the IVR equipment and gameplay dynamics. Participants then underwent a 30-minute IVR exergaming session performing six different cable resistance exercises. A portable metabolic gas exchange analyzer concurrently assessed energy expenditure (EE) through indirect calorimetry while a chest-worn monitor captured heart rate (HR). Participants subsequently completed questionnaires, including the Borg scale for rating of perceived exertion (RPE), Physical Activity Enjoyment Scale (PACES), and Simulator Sickness Questionnaire (SSQ). Results: The mean EE, mean metabolic equivalent, and average total calories expended during the 30-minute session were 14.7 (standard deviation [SD] 2.8) kcal/minute, 12.9 (SD 0.5), and 440 (SD 84) kcals respectively. The mean HR was 176 (SD 3.1) beats per minute (bpm) with a mean max HR of 188 SD (SD 2.9) bpm. The combined training volume among all participants was 16,102 kg (SD 4137). Participants classified the IVR training session to be "somewhat hard-to-hard" with a RPE score of 14 (SD 1) while indicating the session to be "enjoyable" with a PACES score of 4.31 (SD 0.36). The participants did not report any cybersickness symptoms, demonstrating an average total SSQ score of 24.04 (SD 24.13). Conclusions: IVR exergaming incorporating cable resistance training elicits high EE and physiological demand with high enjoyment scores while attenuating perceived fatigue. The potential for IVR to elicit these acute training effects over long-term training periods warrants further investigation into its contribution to fitness and health.
X-SBR: On the Use of the History of Refactorings for Explainable Search-Based Refactoring and Intelligent Change Operators

Refactoring is widely adopted nowadays in industry to restructure the code and meet high quality while preserving the external behavior. Many of the existing refactoring tools and research are based on search-based techniques to find relevant recommendations by finding trade-offs between different quality attributes. While these techniques show promising results on open-source and industry projects, they lack explanations of the recommended changes which can impact their trustworthiness when adopted in practice by developers. Furthermore, most of the adopted search-based techniques are based on random population generation and random change operators (e.g., crossover and mutation). However, it is critical to understand which good refactoring patterns may exist when applying change operators to either keep them or exchange with other solutions rather than destroying them with random changes. In this paper, we propose knowledge-informed change operators and an improved seeding mechanism that we integrated in a multi-objective genetic algorithm. We also provide explanations for refactoring solutions. First, we generate association rules using the Apriori algorithm to find relationships between applied refactorings in previous commits, their locations, and their rationale (quality improvements). Then, we use these rules to 1) initialize the population, 2) improve the change operators and seeding mechanisms of the multi-objective search in order to preserve and exchange good patterns in the refactoring solutions, and 3) explain how a sequence of refactorings collaborate in order to improve the quality of the system (e.g., fitness functions). The validation on large open-source systems shows that X-SBR provides refactoring solutions of a better quality than those given by the state-of-the-art techniques in terms of reducing the invalid refactorings, improving the quality, and increasing trustworthiness of the developers in the suggested refactorings via the provided explanations.
A study into the practice of reporting software engineering experiments

Abstract It has been argued that reporting software engineering experiments in a standardized way helps researchers find relevant information, understand how experiments were conducted and assess the validity of their results. Various guidelines have been proposed specifically for software engineering experiments. The benefits of such guidelines have often been emphasized, but the actual uptake and practice of reporting have not yet been investigated since the introduction of many of the more recent guidelines. In this research, we utilize a mixed-method study design including sequence analysis techniques for evaluating to which extent papers follow such guidelines. Our study focuses on the four most prominent software engineering journals and the time period from 2000 to 2020. Our results show that many experimental papers miss information suggested by guidelines, that no de facto standard sequence for reporting exists, and that many papers do not cite any guidelines. We discuss these findings and implications for the discipline of experimental software engineering focusing on the review process and the potential to refine and extend guidelines, among others, to account for theory explicitly.
AI &amp; multi-resolution Temporal Processing for Accurate Counting of Exercises Repetitions

Artificial intelligence has its importance in the field of objects' movement tracking, which is extensively applied in the fitness field. Applying machine learning technology in fitness field could introduce an auto judge by counting accurately the repetitions of any exercise. On the other hand, camera resolution depends heavily on number of frames per second (fps) which may affect the AI decision accuracy due to the possibility of losing some frames. In this paper a new algorithm relay on temporal processing for the combination of Computer Vision and deep machine learning techniques are introduced to analyze frames data accurately and report a feedback on the repetitions of exercises.
Blockchain – a new tehnology for the smart village

If there was a problem in transforming urban areas into smart cities, why not talk about smart villages. If we look at the rural area carefully, we find areas that do not even offer the minimum of conditions offered by the localities in the urban environment. The natural conditions existing here offer the possibility of developing areas that can be called smart villages. The question is to model these areas intelligently by using efficiently the IT technology but also the ecosystem in the area. If smart technology has played and continues to play an important role in urban areas, why not in rural areas as well. With its help we can offer better facilities in daily comfort in the field of education, healthcare, energy, water, job offers, tourism development, ease of materializing beneficially products made either in their own household, farm, lucrative associations, so in other words, offering the locals the possibility to be engaged in various forms of activity, regardless of the geographical area where the plain, hill or mountain village is located. If we analyze carefully the current situation - COVID-19 crisis but not only, both medically and economically we must not forget these areas, where the environment is favorable to us, and IT technology is available to us to we use it. It is proposed to create a platform, based on the use of blockchain and IoT technology, with the help of which the local leadership can manage and support the local community with its needs and desires.
Unsupervised Discriminative Embedding For Sub-Action Learning in Complex Activities

This paper proposes a novel approach for unsupervised sub-action learning in complex activities. The proposed method maps both visual and temporal representations to a latent space where the sub-actions are learnt discriminatively in an end-to-end fashion. To this end, we propose to learn sub-actions as latent concepts and a novel discriminative latent concept learning (DLCL) module aids in learning sub-actions. The proposed DLCL module lends on the idea of latent concepts to learn compact representations in the latent embedding space in an unsupervised way. The result is a set of latent vectors that can be interpreted as cluster centers in the embedding space. Our joint embedding learning with discriminative latent concept module is novel which eliminates the need for explicit clustering. We validate our approach on three benchmark datasets and show that the proposed combination of visual-temporal embedding and discriminative latent concepts allow to learn robust action representations in unsupervised setting.
Investigating User Perceptions Towards Wearable Mobile Electromyography

Wearables capture physiological user data, enabling novel user interfaces that can identify users, adapt to the user state, and contribute to the quantified self. At the same time, little is known about users’ perception of this new technology. In this paper, we present findings from a user study (N = 36) in which participants used an electromyography (EMG) wearable and a visualization of data collected from EMG wearables. We found that participants are highly unaware of what EMG data can reveal about them. Allowing them to explore their physiological data makes them more reluctant to share this data. We conclude with deriving guidelines, to help designers of physiological data-based user interfaces to (a) protect users’ privacy, (b) better inform them, and (c) ultimately support the uptake of this technology.
VLSI Implemetation of Euler Number Computation and Stereo Vision Concept for CORDIC based Image Registration

In the recent era of times, real-time image registration (IR) has been realized as a useful method mainly due to the wide applications. A real-time HR system is very expensive and would be a power-consuming system. The only solution which will be successful is to design on-board computer software and create the hardware to support it. A hardware design for disparity computation based on divide-and-conquer paradigm for stereovision is proposed with highly hardware optimization techniques. With an intention on making the imagined structure cost-effective, the proposed TME method is implemented through CORDIC technique which is universally recognized as frequency.
VR Book: A Tangible Interface for Smartphone-based Virtual Reality

In this work we present three prototypes of the tangible VR Book: a visual marker-based solution to tangible interaction for smartphone-based Virtual Reality (VR). Smartphone-based VR represents a low barrier to entry in VR experiences given that many people nowadays own a smartphone device and that VR headsets for these devices are affordable and quick to set up when compared to desktop-based VR. Tangible interaction in smartphone-based VR has not been much explored, in our opinion, despite the fact that it can result in easy to use and engaging experiences. In this work, we explore a marker-based solution to object tracking that allows tangibles to be created in an easy and cheap way, maintaining the overall system accessible. We describe a design space for visual marker-based tangible interaction and three prototypes of a tangible VR Book that explore different aspects of the design space. We also present user feedback on their expectations regarding the interaction with the VR Book.
Spline-Based Dense Medial Descriptors for Lossy Image Compression

Medial descriptors are of significant interest for image simplification, representation, manipulation, and compression. On the other hand, B-splines are well-known tools for specifying smooth curves in computer graphics and geometric design. In this paper, we integrate the two by modeling medial descriptors with stable and accurate B-splines for image compression. Representing medial descriptors with B-splines can not only greatly improve compression but is also an effective vector representation of raster images. A comprehensive evaluation shows that our Spline-based Dense Medial Descriptors (SDMD) method achieves much higher compression ratios at similar or even better quality to the well-known JPEG technique. We illustrate our approach with applications in generating super-resolution images and salient feature preserving image compression.